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SSeennssoorrss  &&  TTrraannssdduucceerrss  

  

Volume 14-2, Special Issue 
March 2012 

wwwwww..sseennssoorrssppoorrttaall..ccoomm  ISSN 1726-5479 

 

Editors-in-Chief: Sergey Y. Yurish, tel.: +34 93 413 7941, e-mail: editor@sensorsportal.com 
 

Editors for Western Europe 
Meijer, Gerard C.M., Delft University of Technology, The Netherlands 
Ferrari, Vittorio, Universitá di Brescia, Italy 
 

Editor for Eastern Europe 
Sachenko, Anatoly, Ternopil State Economic University, Ukraine 
 

Editors for North America 
Datskos, Panos G., Oak Ridge National Laboratory, USA 
Fabien, J. Josse, Marquette University, USA 
Katz, Evgeny, Clarkson University, USA 

Editor South America 
Costa-Felix, Rodrigo, Inmetro, Brazil 
 

Editor for Africa 
Maki K.Habib, American University in Cairo, Egypt 
 
Editor for Asia 
Ohyama, Shinji, Tokyo Institute of Technology, Japan 
 

Editor for Asia-Pacific 
Mukhopadhyay, Subhas, Massey University, New Zealand 

 
Editorial Advisory Board 

 
Abdul Rahim, Ruzairi, Universiti Teknologi, Malaysia 
Ahmad, Mohd Noor, Nothern University of Engineering, Malaysia 
Annamalai, Karthigeyan, National Institute of Advanced Industrial Science 

and Technology, Japan 
Arcega, Francisco, University of Zaragoza, Spain 
Arguel, Philippe, CNRS, France 
Ahn, Jae-Pyoung, Korea Institute of Science and Technology, Korea 
Arndt, Michael, Robert Bosch GmbH, Germany 
Ascoli, Giorgio, George Mason University, USA 
Atalay, Selcuk, Inonu University, Turkey 
Atghiaee, Ahmad, University of Tehran, Iran 
Augutis, Vygantas, Kaunas University of Technology, Lithuania 
Avachit, Patil Lalchand, North Maharashtra University, India 
Ayesh, Aladdin, De Montfort University, UK 
Azamimi, Azian binti Abdullah, Universiti Malaysia Perlis, Malaysia 
Bahreyni, Behraad, University of Manitoba, Canada 
Baliga, Shankar, B., General Monitors Transnational, USA 
Baoxian, Ye, Zhengzhou University, China 
Barford, Lee, Agilent Laboratories, USA 
Barlingay, Ravindra, RF Arrays Systems, India 
Basu, Sukumar, Jadavpur University, India 
Beck, Stephen, University of Sheffield, UK 
Ben Bouzid, Sihem, Institut National de Recherche Scientifique, Tunisia 
Benachaiba, Chellali, Universitaire de Bechar, Algeria 
Binnie, T. David, Napier University, UK 
Bischoff, Gerlinde, Inst. Analytical Chemistry, Germany 
Bodas, Dhananjay, IMTEK, Germany 
Borges Carval, Nuno, Universidade de Aveiro, Portugal 
Bouchikhi, Benachir, University Moulay Ismail, Morocco  
Bousbia-Salah, Mounir, University of Annaba, Algeria 
Bouvet, Marcel, CNRS – UPMC, France 
Brudzewski, Kazimierz, Warsaw University of Technology, Poland 
Cai, Chenxin, Nanjing Normal University, China 
Cai, Qingyun, Hunan University, China 
Calvo-Gallego, Jaime, Universidad de Salamanca, Spain 
Campanella, Luigi, University La Sapienza, Italy 
Carvalho, Vitor, Minho University, Portugal 
Cecelja, Franjo, Brunel University, London, UK 
Cerda Belmonte, Judith, Imperial College London, UK 
Chakrabarty, Chandan Kumar, Universiti Tenaga Nasional, Malaysia 
Chakravorty, Dipankar, Association for the Cultivation of Science, India 
Changhai, Ru, Harbin Engineering University, China 
Chaudhari, Gajanan, Shri Shivaji Science College, India 
Chavali, Murthy, N.I. Center for Higher Education, (N.I. University), India 
Chen, Jiming, Zhejiang University, China 
Chen, Rongshun, National Tsing Hua University, Taiwan 
Cheng, Kuo-Sheng, National Cheng Kung University, Taiwan 
Chiang, Jeffrey (Cheng-Ta), Industrial Technol. Research Institute, Taiwan 
Chiriac, Horia, National Institute of Research and Development, Romania 
Chowdhuri, Arijit, University of Delhi, India 
Chung, Wen-Yaw, Chung Yuan Christian University, Taiwan 
Corres, Jesus, Universidad Publica de Navarra, Spain 
Cortes, Camilo A., Universidad Nacional de Colombia, Colombia 
Courtois, Christian, Universite de Valenciennes, France 
Cusano, Andrea, University of Sannio, Italy 
D'Amico, Arnaldo, Università di Tor Vergata, Italy 
De Stefano, Luca, Institute for Microelectronics and Microsystem, Italy 
Deshmukh, Kiran, Shri Shivaji Mahavidyalaya, Barshi, India 
Dickert, Franz L., Vienna University, Austria 
Dieguez, Angel, University of Barcelona, Spain 
Dighavkar, C. G., M.G. Vidyamandir’s L. V.H. College, India 
Dimitropoulos, Panos, University of Thessaly, Greece 

Ding, Jianning, Jiangsu Polytechnic University, China 
Djordjevich, Alexandar, City University of Hong Kong, Hong Kong 
Donato, Nicola, University of Messina, Italy 
Donato, Patricio, Universidad de Mar del Plata, Argentina 
Dong, Feng, Tianjin University, China 
Drljaca, Predrag, Instersema Sensoric SA, Switzerland 
Dubey, Venketesh, Bournemouth University, UK 
Enderle, Stefan, Univ.of Ulm and KTB Mechatronics GmbH, Germany 
Erdem, Gursan K. Arzum, Ege University, Turkey 
Erkmen, Aydan M., Middle East Technical University, Turkey 
Estelle, Patrice, Insa Rennes, France 
Estrada, Horacio, University of North Carolina, USA 
Faiz, Adil, INSA Lyon, France 
Fericean, Sorin, Balluff GmbH, Germany 
Fernandes, Joana M., University of Porto, Portugal 
Francioso, Luca, CNR-IMM Institute for Microelectronics and Microsystems, Italy 
Francis, Laurent, University Catholique de Louvain, Belgium 
Fu, Weiling, South-Western Hospital, Chongqing, China 
Gaura, Elena, Coventry University, UK 
Geng, Yanfeng, China University of Petroleum, China 
Gole, James, Georgia Institute of Technology, USA 
Gong, Hao, National University of Singapore, Singapore 
Gonzalez de la Rosa, Juan Jose, University of Cadiz, Spain 
Granel, Annette, Goteborg University, Sweden 
Graff, Mason, The University of Texas at Arlington, USA 
Guan, Shan, Eastman Kodak, USA 
Guillet, Bruno, University of Caen, France 
Guo, Zhen, New Jersey Institute of Technology, USA 
Gupta, Narendra Kumar, Napier University, UK 
Hadjiloucas, Sillas, The University of Reading, UK 
Haider, Mohammad R., Sonoma State University, USA 
Hashsham, Syed, Michigan State University, USA 
Hasni, Abdelhafid, Bechar University, Algeria 
Hernandez, Alvaro, University of Alcala, Spain 
Hernandez, Wilmar, Universidad Politecnica de Madrid, Spain 
Homentcovschi, Dorel, SUNY Binghamton, USA 
Horstman, Tom, U.S. Automation Group, LLC, USA 
Hsiai, Tzung (John), University of Southern California, USA 
Huang, Jeng-Sheng, Chung Yuan Christian University, Taiwan 
Huang, Star, National Tsing Hua University, Taiwan 
Huang, Wei, PSG Design Center, USA 
Hui, David, University of New Orleans, USA 
Jaffrezic-Renault, Nicole, Ecole Centrale de Lyon, France 
James, Daniel, Griffith University, Australia 
Janting, Jakob, DELTA Danish Electronics, Denmark 
Jiang, Liudi, University of Southampton, UK 
Jiang, Wei, University of Virginia, USA 
Jiao, Zheng, Shanghai University, China 
John, Joachim, IMEC, Belgium 
Kalach, Andrew, Voronezh Institute of Ministry of Interior, Russia 
Kang, Moonho, Sunmoon University, Korea South 
Kaniusas, Eugenijus, Vienna University of Technology, Austria 
Katake, Anup, Texas A&M University, USA 
Kausel, Wilfried, University of Music, Vienna, Austria 
Kavasoglu, Nese, Mugla University, Turkey 
Ke, Cathy, Tyndall National Institute, Ireland 
Khelfaoui, Rachid, Université de Bechar, Algeria 
Khan, Asif, Aligarh Muslim University, Aligarh, India 
Kim, Min Young, Kyungpook National University, Korea South 
Ko, Sang Choon, Electronics. and Telecom. Research Inst., Korea South 
Kotulska, Malgorzata, Wroclaw University of Technology, Poland 
Kockar, Hakan, Balikesir University, Turkey 

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Kong, Ing, RMIT University, Australia 
Kratz, Henrik, Uppsala University, Sweden 
Krishnamoorthy, Ganesh, University of Texas at Austin, USA 
Kumar, Arun,  University of Delaware, Newark, USA 
Kumar, Subodh, National Physical Laboratory, India 
Kung, Chih-Hsien, Chang-Jung Christian University, Taiwan 
Lacnjevac, Caslav, University of Belgrade, Serbia 
Lay-Ekuakille, Aime, University of Lecce, Italy 
Lee, Jang Myung, Pusan National University, Korea South 
Lee, Jun Su, Amkor Technology, Inc. South Korea 
Lei, Hua, National Starch and Chemical Company, USA 
Li, Fengyuan (Thomas), Purdue University, USA 
Li, Genxi, Nanjing University, China 
Li, Hui, Shanghai Jiaotong University, China 
Li, Xian-Fang, Central South University, China 
Li, Yuefa, Wayne State University, USA 
Liang, Yuanchang, University of Washington, USA 
Liawruangrath, Saisunee, Chiang Mai University, Thailand 
Liew, Kim Meow, City University of Hong Kong, Hong Kong 
Lin, Hermann, National Kaohsiung University, Taiwan 
Lin, Paul, Cleveland State University, USA 
Linderholm, Pontus, EPFL - Microsystems Laboratory, Switzerland 
Liu, Aihua, University of Oklahoma, USA 
Liu Changgeng, Louisiana State University, USA 
Liu, Cheng-Hsien, National Tsing Hua University, Taiwan 
Liu, Songqin, Southeast University, China 
Lodeiro, Carlos, University of Vigo, Spain 
Lorenzo, Maria Encarnacio, Universidad Autonoma de Madrid, Spain 
Lukaszewicz, Jerzy Pawel, Nicholas Copernicus University, Poland 
Ma, Zhanfang, Northeast Normal University, China 
Majstorovic, Vidosav, University of Belgrade, Serbia 
Malyshev, V.V., National Research Centre ‘Kurchatov Institute’, Russia 
Marquez, Alfredo, Centro de Investigacion en Materiales Avanzados, Mexico 
Matay, Ladislav, Slovak Academy of Sciences, Slovakia 
Mathur, Prafull, National Physical Laboratory, India 
Maurya, D.K., Institute of Materials Research and Engineering, Singapore 
Mekid, Samir, University of Manchester, UK 
Melnyk, Ivan, Photon Control Inc., Canada  
Mendes, Paulo, University of Minho, Portugal 
Mennell, Julie, Northumbria University, UK 
Mi, Bin, Boston Scientific Corporation, USA 
Minas, Graca, University of Minho, Portugal 
Moghavvemi, Mahmoud, University of Malaya, Malaysia 
Mohammadi, Mohammad-Reza, University of Cambridge, UK 
Molina Flores, Esteban, Benemérita Universidad Autónoma de Puebla, 

Mexico 
Moradi, Majid, University of Kerman, Iran 
Morello, Rosario, University "Mediterranea" of Reggio Calabria, Italy 
Mounir, Ben Ali, University of Sousse, Tunisia 
Mrad, Nezih, Defence R&D, Canada 
Mulla, Imtiaz Sirajuddin, National Chemical Laboratory, Pune, India 
Nabok, Aleksey, Sheffield Hallam University, UK 
Neelamegam, Periasamy, Sastra Deemed University, India 
Neshkova, Milka, Bulgarian Academy of Sciences, Bulgaria 
Oberhammer, Joachim, Royal Institute of Technology, Sweden 
Ould Lahoucine, Cherif, University of Guelma, Algeria 
Pamidighanta, Sayanu, Bharat Electronics Limited (BEL), India 
Pan, Jisheng, Institute of Materials Research & Engineering, Singapore 
Park, Joon-Shik, Korea Electronics Technology Institute, Korea South 
Penza, Michele, ENEA C.R., Italy 
Pereira, Jose Miguel, Instituto Politecnico de Setebal, Portugal 
Petsev, Dimiter, University of New Mexico, USA 
Pogacnik, Lea, University of Ljubljana, Slovenia 
Post, Michael, National Research Council, Canada 
Prance, Robert, University of Sussex, UK 
Prasad, Ambika, Gulbarga University, India 
Prateepasen, Asa, Kingmoungut's University of Technology, Thailand 
Pugno, Nicola M., Politecnico di Torino, Italy 
Pullini, Daniele, Centro Ricerche FIAT, Italy 
Pumera, Martin, National Institute for Materials Science, Japan 
Radhakrishnan, S. National Chemical Laboratory, Pune, India 
Rajanna, K., Indian Institute of Science, India 
Ramadan, Qasem, Institute of Microelectronics, Singapore 
Rao, Basuthkar, Tata Inst. of Fundamental Research, India 
Raoof, Kosai, Joseph Fourier University of Grenoble, France 
Rastogi Shiva, K. University of Idaho, USA 
Reig, Candid, University of Valencia, Spain 
Restivo, Maria Teresa, University of Porto, Portugal 
Robert, Michel, University Henri Poincare, France 
Rezazadeh, Ghader, Urmia University, Iran 
Royo, Santiago, Universitat Politecnica de Catalunya, Spain 
Rodriguez, Angel, Universidad Politecnica de Cataluna, Spain 
Rothberg, Steve, Loughborough University, UK 
Sadana, Ajit, University of Mississippi, USA 
Sadeghian Marnani, Hamed, TU Delft, The Netherlands 
Sapozhnikova, Ksenia, D.I.Mendeleyev Institute for Metrology, Russia 
Sandacci, Serghei, Sensor Technology Ltd., UK 

Saxena, Vibha, Bhbha Atomic Research Centre, Mumbai, India 
Schneider, John K., Ultra-Scan Corporation, USA 
Sengupta, Deepak, Advance Bio-Photonics, India 
Seif, Selemani, Alabama A & M University, USA 
Seifter, Achim, Los Alamos National Laboratory, USA 
Shah, Kriyang, La Trobe University, Australia 
Sankarraj, Anand, Detector Electronics Corp., USA 
Silva Girao, Pedro, Technical University of Lisbon, Portugal 
Singh, V. R., National Physical Laboratory, India 
Slomovitz, Daniel, UTE, Uruguay 
Smith, Martin, Open University, UK 
Soleymanpour, Ahmad, Damghan Basic Science University, Iran 
Somani, Prakash R., Centre for Materials for Electronics Technol., India 
Sridharan, M., Sastra University, India 
Srinivas, Talabattula, Indian Institute of Science, Bangalore, India 
Srivastava, Arvind K., NanoSonix Inc., USA 
Stefan-van Staden, Raluca-Ioana, University of Pretoria, South Africa 
Stefanescu, Dan Mihai, Romanian Measurement Society, Romania 
Sumriddetchka, Sarun, National Electronics and Computer Technology Center, 

Thailand 
Sun, Chengliang, Polytechnic University, Hong-Kong 
Sun, Dongming, Jilin University, China 
Sun, Junhua, Beijing University of Aeronautics and Astronautics, China 
Sun, Zhiqiang, Central South University, China 
Suri, C. Raman, Institute of Microbial Technology, India 
Sysoev, Victor, Saratov State Technical University, Russia 
Szewczyk, Roman, Industrial Research Inst. for Automation and Measurement, 

Poland 
Tan, Ooi Kiang, Nanyang Technological University, Singapore, 
Tang, Dianping, Southwest University, China 
Tang, Jaw-Luen, National Chung Cheng University, Taiwan 
Teker, Kasif, Frostburg State University, USA 
Thirunavukkarasu, I., Manipal University Karnataka, India 
Thumbavanam Pad, Kartik, Carnegie Mellon University, USA 
Tian, Gui Yun, University of Newcastle, UK 
Tsiantos, Vassilios, Technological Educational Institute of Kaval, Greece 
Tsigara, Anna, National Hellenic Research Foundation, Greece 
Twomey, Karen, University College Cork, Ireland 
Valente, Antonio, University, Vila Real, - U.T.A.D., Portugal 
Vanga, Raghav Rao, Summit Technology Services, Inc., USA 
Vaseashta, Ashok, Marshall University, USA 
Vazquez, Carmen, Carlos III University in Madrid, Spain 
Vieira, Manuela, Instituto Superior de Engenharia de Lisboa, Portugal 
Vigna, Benedetto, STMicroelectronics, Italy 
Vrba, Radimir, Brno University of Technology, Czech Republic 
Wandelt, Barbara, Technical University of Lodz, Poland 
Wang, Jiangping, Xi'an Shiyou University, China 
Wang, Kedong, Beihang University, China 
Wang, Liang, Pacific Northwest National Laboratory, USA 
Wang, Mi, University of Leeds, UK 
Wang, Shinn-Fwu, Ching Yun University, Taiwan 
Wang, Wei-Chih, University of Washington, USA 
Wang, Wensheng, University of Pennsylvania, USA 
Watson, Steven, Center for NanoSpace Technologies Inc., USA 
Weiping, Yan, Dalian University of Technology, China 
Wells, Stephen, Southern Company Services, USA 
Wolkenberg, Andrzej, Institute of Electron Technology, Poland 
Woods, R. Clive, Louisiana State University, USA 
Wu, DerHo, National Pingtung Univ. of Science and Technology, Taiwan 
Wu, Zhaoyang, Hunan University, China 
Xiu Tao, Ge, Chuzhou University, China 
Xu, Lisheng, The Chinese University of Hong Kong, Hong Kong 
Xu, Sen, Drexel University, USA 
Xu, Tao, University of California, Irvine, USA 
Yang, Dongfang, National Research Council, Canada 
Yang, Shuang-Hua, Loughborough University, UK  
Yang, Wuqiang, The University of Manchester, UK 
Yang, Xiaoling, University of Georgia, Athens, GA, USA 
Yaping Dan, Harvard University, USA 
Ymeti, Aurel, University of Twente, Netherland 
Yong Zhao, Northeastern University, China 
Yu, Haihu, Wuhan University of Technology, China 
Yuan, Yong, Massey University, New Zealand 
Yufera Garcia, Alberto, Seville University, Spain 
Zakaria, Zulkarnay, University Malaysia Perlis, Malaysia 
Zagnoni, Michele, University of Southampton, UK 
Zamani, Cyrus, Universitat de Barcelona, Spain 
Zeni, Luigi, Second University of Naples, Italy 
Zhang, Minglong, Shanghai University, China 
Zhang, Qintao, University of California at Berkeley, USA 
Zhang, Weiping, Shanghai Jiao Tong University, China 
Zhang, Wenming, Shanghai Jiao Tong University, China 
Zhang, Xueji, World Precision Instruments, Inc., USA 
Zhong, Haoxiang, Henan Normal University, China 
Zhu, Qing, Fujifilm Dimatix, Inc., USA 
Zorzano, Luis, Universidad de La Rioja, Spain 
Zourob, Mohammed, University of Cambridge, UK 

 
Sensors & Transducers Journal (ISSN 1726-5479) is a peer review international journal published monthly online by International Frequency Sensor Association (IFSA). 

Available in electronic and on CD. Copyright © 2012 by International Frequency Sensor Association. All rights reserved. 



  SSeennssoorrss  &&  TTrraannssdduucceerrss  JJoouurrnnaall  
 
  

  

CCoonntteennttss  
  

Volume 14-2 
Special Issue 
March 2012  

www.sensorsportal.com  ISSN 1726-5479 

 
 

Research Articles 
 

Information Extraction from Wireless Sensor Networks: System and Approaches 
Tariq Alsboui, Abdelrahman Abuarqoub, Mohammad Hammoudeh, Zuhair Bandar, Andy Nisbet ...  1 
  
Assessment of Software Modeling Techniques for Wireless Sensor Networks: A Survey 
John Khalil Jacoub, Ramiro Liscano, Jeremy S. Bradbury ................................................................  18 
  
Effective Management and Energy Efficiency in Management of Very Large Scale Sensor 
Network 
Moran Feldman, Sharoni Feldman.....................................................................................................  47 
  
Energy Efficient in-Sensor Data Cleaning for Mining Frequent Itemsets 
Jacques M. Bahi, Abdallah Makhoul, Maguy Medlej..........................................................................   64 
  
IPv6 Routing Protocol for Low Power and Lossy Sensor Networks Simulation Studies 
Leila Ben Saad, Cedric Chauvenet, Bernard Tourancheau ...............................................................  79 
  
Self-Powered Intelligent Sensor Node Concept for Monitoring of Road and Traffic 
Conditions 
Sebastian Strache, Ralf Wunderlich and Stefan Heinen....................................................................  93 
  
Variable Step Size LMS Algorithm for Data Prediction in Wireless Sensor Networks 
Biljana Risteska Stojkoska, Dimitar Solev, Danco Davcev ................................................................  111 
  
A Framework for Secure Data Delivery in Wireless Sensor Networks 
Leonidas Perlepes, Alexandros Zaharis, George Stamoulis and Panagiotis Kikiras.........................  125 
  
An Approach for Designing and Implementing Middleware in Wireless Sensor Networks 
Ronald Beaubrun, Jhon-Fredy Llano-Ruiz, Alejandro Quintero.........................................................  150 
  
Mobility Model for Self-Organizing and Cooperative MSN and MANET Systems 
Andrzej Sikora and Ewa Niewiadomska-Szynkiewicz........................................................................  164 
  
Evaluation of Hybrid Distributed Least Squares for Improved Localization via Algorithm 
Fusion in Wireless Sensor Networks 
Ralf Behnke, Jakob Salzmann, Philipp Gorski, Dirk Timmermann ....................................................  179 
  
An Effective Approach for Handling both Open and Closed Voids in Wireless Sensor 
Networks 
Mohamed Aissani, Sofiane Bouznad, Abdelmalek Hariza and Salah-Eddine Allia ...........................  196 
  
Embedded Wireless System for Pedestrian Localization in Indoor Environments 
Nicolas Fourty, Yoann Charlon, Eric Campo......................................................................................  211 
  
Neighbourtables – A Cross-layer Solution for Wireless CiNet Network Analysis and 
Diagnostics 
Ismo Hakala and Timo Hongell ..........................................................................................................  228 

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A Column Generation based Heuristic to extend Lifetime in Wireless Sensor Network 
Karine Deschinkel...............................................................................................................................  242 
  
Adapting OLSR for WSNs (iOLSR) Using Locally Increasing Intervals 
Erlend Larsen, Joakim Flathagen, Vinh Pham, Lars Landmark.........................................................  254 
  
Risk Assessment along Supply Chain: A RFID and Wireless Sensor Network Integration 
Approach 
Laurent Gomez, Maryline Laurent, Ethmane El Moustaine ...............................................................  269 
  
Structure Crack Identification Based on Surface-mounted Active Sensor Network with 
Time-Domain Feature Extraction and Neural Network 
Chunling Du, Jianqiang Mou, L. Martua, Shudong Liu, Bingjin Chen, Jingliang Zhang, F. L. Lewis .  283 
  
Efficient Gatherings in Wireless Sensor Networks Using Distributed Computation of 
Connected Dominating Sets 
Vincent Boudet, Sylvain Durand, László Gönczy, Jérôme Mathieu and Jérôme Palaysi ..................  297 
  
Secure Packet Transfer in Wireless Sensor Networks 
Yenumula B. Reddy............................................................................................................................  308 
  

 
 

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Sensors & Transducers Journal, Vol. 14-2, Special Issue, February 2012, pp. 1-17 

 1

   
SSSeeennnsssooorrrsss   &&&   TTTrrraaannnsssddduuuccceeerrrsss  

ISSN 1726-5479
© 2012 by IFSA

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Information Extraction from Wireless Sensor Networks:  
System and Approaches 

 
Tariq ALSBOUI, Abdelrahman ABUARQOUB, Mohammad HAMMOUDEH, 

Zuhair BANDAR, Andy NISBET 
School of Computing, Mathematics & Digital Technology, Manchester Metropolitan University, 

Manchester, UK 
E-mail: {tariq.al-sboui, abdelrahman.m.abuarqoub}@stu.mmu.ac.uk,  

{m.hammoudeh, z.bandar, a.nisbet}@mmu.ac.uk 
 
 

Received: 27 November 2011   /Accepted: 20 December 2011   /Published: 12 March 2012 
 
 
Abstract: Recent advances in wireless communication have made it possible to develop low-cost, and 
low power Wireless Sensor Networks (WSN). The WSN can be used for several application areas 
(e.g., habitat monitoring, forest fire detection, and health care). WSN Information Extraction (IE) 
techniques can be classified into four categories depending on the factors that drive data acquisition: 
event-driven, time-driven, query-based, and hybrid. This paper presents a survey of the state-of-the-art 
IE techniques in WSNs. The benefits and shortcomings of different IE approaches are presented as 
motivation for future work into automatic hybridization and adaptation of IE mechanisms.  
Copyright © 2012 IFSA. 
 
Keywords: Wireless sensor networks, Information extraction, Event-driven, Time-driven,  
Query-based. 
 
 
 
1. Introduction 
 
The main purpose of a WSN is to provide users with access to the information of interest from data 
collected by spatially distributed sensors. In real-world applications, sensors are often deployed in high 
numbers to ensure a full exposure of the monitored physical environment. Consequently, such 
networks are expected to generate enormous amount of data. The desire to locate and obtain 
information makes the success of WSNs applications, largely, determined by the quality of the 
extracted information. The principal concerns when extracting information include the timeliness, 

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Sensors & Transducers Journal, Vol. 14-2, Special Issue, February 2012, pp. 1-17 

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accuracy, cost, and reliability of the extracted information and the methods used for its extraction. The 
process of IE enables unstructured data to be retrieved and filtered from sensor nodes using 
sophisticated techniques to discover specific patterns [1, 2]. Practical constraints on sensor nodes, such 
as power consumption, computational capability, and maximum memory storage, make IE a 
challenging distributed processing task. 
 
In terms of data delivery required by an application, IE in WSNs can be classified into four broad 
categories: event-driven, time-driven, query-based, and hybrid. In event-driven, data is only generated 
when an event of interest occurs, while, in the time-driven, data is periodically sent to a sink every 
constant interval of time. With query-based, the data is collected according to end user’s demand. 
Finally, the hybrid approach is a combination of one or more of the above. 
 
The rest of this paper is organized as follows: Section 2 identifies what types of information need to be 
reported to end users. Section 3 looks at event-driven IE approaches and presents sample 
developments. Section 4, describes time-driven IE and recent successful deployments. Section 5, 
describes query-based IE and present some of the recent approaches. Section 6, describes and identify 
recent advances in hybrid IE methods. Section 7, describes recent approaches to IE from mobile 
WSNs. In section 8, a summary on future research direction for IE is discussed. Section 9, presents a 
model for accommodating and integrating the diverse IE approaches and mechanisms. Section 10 
concludes this paper. 
 
 
2. What Needs to Be Reported? 
 
IE is one of the most vital efforts to utilize the ever burgeoning amount of data returned by WSNs for 
achieving detailed, often costly task of finding, analyzing and identifying needed information. The 
process of IE involves the classification of data based on the type of information they hold, and is 
concerned with identifying the portion of information related to a specific fact. In the context of 
WSNs, the notion of fact can be defined as a property or characteristic of the monitored phenomenon 
at a certain point in time or during a time interval. Fact can also refer to an event or action. An event is 
a pattern or exceptional change that occasionally appears in the observed environment [3]. Events have 
some distinct features that can be used as thresholds, e.g. temperature > 50, to make a distinction 
between usual and unusual environmental parameters. 
 
An event may arise in many other forms. It can be a continuous, gradually occurs over time  
(e.g. temperature does not change instantly), and has obvious limit with normal environment 
parameters. In [4] complex events are defined as sequences of sensor measurements over a period of 
time indicating an unusual activity in the monitored environment. In WSNs, the network owners may 
be unaware in advance what type of events may occur. This is because one of the ultimate goals of 
such networks is to discover new events and interesting information about the monitored phenomenon. 
For this reason, threshold-based event detection methods are not always efficient to identify and 
extract event-based facts. From this deficiency arise the need for periodic, query-based, and hybrid IE 
approaches. 
 
Events can be further classified into two categories: system events and environmental events. System 
events are concerned with architectural or topological changes, e.g. a mobile node entered a cluster 
area. Environmental events are concerned with the occurrences of unusual changes across the 
monitored environment, e.g. spotting a moving target [4]. 
 
Nodes organization plays an important role in IE because it defines, among other factors, the cost 
(amount of energy required to collect raw data), accuracy (level of coverage), reliability  
(e.g. timeliness) of extracted information. The organization of nodes can be either centralized or 



Sensors & Transducers Journal, Vol. 14-2, Special Issue, February 2012, pp. 1-17 

 3

hierarchical. In the centralized approach, data collected by all nodes are sent towards a sink node using 
single or multi-hop communication [5]. However, this approach does not provide scalability, which is 
a main design factor for WSN. Also, it causes communication bottlenecks and transmission delays due 
to congestions especially in areas around the sink [6]. To overcome the problems in the centralized 
approaches, hierarchical techniques have been proposed as an effective solution for achieving longer 
network lifetime and better scalability. 
 
Since the number of existing IE approaches is significantly large, it will not be feasible to provide a 
detailed description of each approach. Instead, we have selected recent approaches that particularly 
represent directions of future research without focusing on the details of these approaches. However, 
characteristics of various approaches that are common for the approach they apply will be presented. 
Table 1 lists the reviewed approaches and some older approaches for the more interested readers. In 
Sections 3 to 6 the approaches are presented based on the categorization so as related sub-categories 
are discussed in the common context. To make the analysis of different approaches more logical and to 
set up a common base for their comparison and connection we consider some qualitative criteria. 
 
 

Table 1. Overview of the selected approaches to IE. 
 

Event-driven [2-3, 7-15] 
Time-driven [6, 13-19] 
Query-based [21-34] 
Hybrid [38-42] 

 
 
3. Event-driven IE 
 
3.1. Description and Operation 
 
In event-driven approaches to IE, the initiative is with the sensor node and the end user is in the 
position of an observer, waiting for incoming information. Any node may generate a report when a 
significant event (e.g., a change of state) or an unusual event (e.g., fire) occurs. Event-driven is a 
valuable tool for detecting events as soon as they occur. In the simplest form, sensor nodes are 
preconfigured with threshold values that when exceeded indicate an event. 
 
Event-driven approaches incur low power consumption and require low maintenance. Among the 
benefits of this class of approaches are: they reduce the amount of communication overhead by 
applying local filtering on collected data to determine whether to send new data or not; they implement 
local mechanisms to prevent multiple nodes reporting the same event; they exploit redundancy to 
reduce the number of false alarms; they allow timely responses to detected events; they are easy to 
implement and configure; they allow distributed processing at the node level or within a group of node 
to collaboratively detect an event; and they are suitable for time critical applications, e.g. forest fire 
monitoring or intrusion detection. 
 
However, there are a number of limitations to the event-driven IE. First, it is difficult to capture events 
of spatio-temporal characteristics. Second, detecting complex event may require non trivial distributed 
algorithms, which require the involvement of multiple sensor nodes [7]. Third, due to the fact that 
events occur randomly, some nodes generate higher rates of data than other nodes. This will lead to 
unbalanced workloads among sensor nodes. Fourth, it is not suitable for continuous monitoring 
applications, where sensed measurements change gradually and continuously. Finally, due to sensors 
measurement inaccuracies, event-driven approaches may potentially generate false alarms. 
 



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3.2. Event-driven Approaches 
 
In earlier studies, events were detected with a user-defined threshold values [8, 9]. In such approaches, 
sensor nodes are preconfigured with a static threshold value. When the sensor node reading deviates 
from the pre-defined thresholds, this indicates an event that triggers the node to convey its data back to 
the sink. To overcome some of the inherent problems in the threshold-based event-detection, [3] have 
adopted the infrequent pattern discovery technique and developed a function for detecting events of 
interest in the monitored environment. The function is split into two phases, learning phase and event 
detection phase. In the learning phase, the function will learn the frequent changes from the 
measurement series. In the event detection phase, a new pattern is built using the incoming 
measurement values in which a decision is made on whether the new pattern is frequent (e.g., system 
events) or infrequent (e.g., event). Then the infrequent changes will be reported to the fusion centre as 
potential events. The proposed approach reduces the number of transmission, which extends the 
network life time. It is also characterized by low computational and time complexities. Due to its 
distributed nature the approach is also scalable. However, high false alarms will be generated due to 
sensor measurement inaccuracies. Also, it is incapable of capturing complex events, because there is a 
spatial and temporal correlation in these types of events that requires more complex rules. Finally, it 
does not have any mechanism to distinguish between errors and events. 
 
In [10], the authors presented a data fusion tool to increase the resilience of event detection techniques. 
They introduced two levels for event detection: at the first level, each sensor node will individually 
decide on detecting event using a classifier (naive bayes). At the second level, fusion technique is 
placed at higher level (e.g., cluster head) and used to distinguish between outliers. Outliers are 
measurements that differ from the normal pattern of sensed data occurring at individual nodes and 
events that more nodes agree upon [11]. This approach reduces the number of false alarms, since 
cluster heads are able to distinguish between anomalies and event. However, processing data at the 
cluster head introduces delays in reporting an event. Moreover, the efficiency of the approach depends 
on the efficiency of cluster formation methods. For instance, many clustering algorithms result in 
energy-unbalanced clusters. 
 
Another threshold-based approach proposed in [12] introduced a double decision mechanism. A sensor 
may decide about the presence of an event either directly or by asking for additional data from nearby 
nodes. This approach minimizes the energy consumption since the latter step is activated only when it 
is needed. There is no need for fusion centre to process the data as a fixed number of nodes will take 
the responsibility to make decisions about the occurrences of an event. However, it is always difficult 
to determine node’s neighbours. Although these approaches can reduce communication overhead and 
report events promptly, however, it is difficult to define the optimal threshold values. 
 
More advanced approaches, such as SAF [13] and Ken [14], exploit the fact that physical 
environments frequently exhibit predictable stable and strong attribute correlations to improve 
compression of the data communicated to the sink node. The basic idea is to use replicated dynamic 
models to reflect the state of the environment being monitored. This is done by maintaining a pair of 
dynamic probabilistic models over the WSN attributes with one copy distributed in the network and 
the other at the sink. The sink computes the expected values of the WSN attributes according to the 
defined prediction model and uses it to extract information. When the sensor nodes detect anomalous 
data that was not predicted by the model within the required certainty level, they route the data back to 
the sink. This approach is subject to failure as basic suppression. It does not have any mechanism to 
distinguish between node failure and the case that the data is always within the error bound. Ken is not 
robust to message loss; it relies on the Markovian nature of the prediction models to presume that any 
failures will eventually be corrected with model updates, and the approximation certainty will not be 
affected by the missed updates. They propose periodic updates to ensure models can not be incorrect 



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indefinitely. This approach is not suitable for raw value reconstruction; for any time-step where the 
model has suffered from failures and is incorrect, the corresponding raw value samples will be wrong. 
Finally, as the approach presented in [15], SAF and Ken can only handle static network models. 
 
A decentralized, lightweight, and accurate event detection technique is proposed in [16]. The technique 
uses decision trees for distributed event detection and a reputation-based voting method for 
aggregating the detection results of each node. Each sensor node perform event detection using it is 
own decision tree-based classifier. The classification results, i.e. detected events, from several nodes 
are aggregated by a higher node, e.g., a cluster head. Each node sends it is detected events, called 
detection value, to all other nodes in it is neighbourhood. The detection value will be stored in a table. 
Finally, tables are sent to the voter (e.g. cluster head), which in turns decides to make a final decision 
among different opinion. The decision tree approach provides accurate event detection and 
characterized by low computational and time complexities. However, similar to [9], the processing of 
data at the cluster head will introduce further delays in reporting an event. 
 
 
4. Time-driven IE 
 
4.1. Description and Operation 
 
In time-driven approaches to IE, a sensor node periodically generates a report from the physical 
environment to give the end-user its current status. The reporting period may be preconfigured or set 
by the end-user depending on the nature of the monitored environment and applications requirements. 
 
Time-driven approaches have the ability to enable arbitrary data analysis, they provide continuous 
monitoring of the sensor network to reflect environmental changes, they scale to handle millions of 
nodes (through aggregation), they extend network life time by sending nodes to sleep between 
transmissions, they can reduce congestion and improve system reliability by scheduling nodes to 
transmit at different times, they explicitly incorporate resource capacity, and highlights unused 
resources. However, there are a number of limitations to the time-driven approaches. First, they are 
limited to specific set of applications where consistent changes occur across the network, e.g. 
agricultural applications. Second, a large portion of the returned data might be redundant and not 
useful for the end-user thereby resulting in wastage of resources. Third, nodes have to maintain global 
clock and deal with synchronization issues. Finally, it is extremely difficult to define optimal time 
intervals. 
 
 
4.2. Time-driven Approaches to IE 
 
In time-driven IE, most of the published work in the literature is based on probabilistic models that 
attempt to predict the next value that the sensor is expected to acquire. For example, Ken's [14] model 
exploits the spatio-temporal data correlations while guaranteeing correctness. It involves placing a 
dynamic probabilistic model on the sensor node and on the sink, and these models are always kept in 
synchronization for periodic updates. An approach similar to Ken was proposed in [17]. In contrast to 
Ken, the approach exploits only the temporal correlation of sensed data and is based on the Auto 
Regressive Integrated Moving Average prediction model. It places the model on the sensor nodes and 
the aggregator nodes (cluster head) to predict the next values. These models are always kept in 
synchronization for periodical updates. The approach is energy efficient since the number of 
transmitted massages is reduced. However, the forecasting can be badly distorted by outliers leading to 
wrong prediction. 
 



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Similar approaches to [14, 17] have been suggested in [7, 18]. These approach use dynamically 
changing subset of the nodes as samplers where the sensor readings of the sampler nodes are directly 
collected, while, the values of non sampler nodes are predicted through probabilistic models that are 
locally and periodically constructed. All approaches in [7, 14, 17, 18] save energy by reducing the 
number of transmitted messages. However, the additional cost to maintain models synchronized is not 
negligible. 
 
Another approach called Cascading Data Collection (CDC) is presented in [19]. In CDC only a subset 
of sensor nodes are selected randomly to periodically transfer data back to the sink node. The 
mechanism is distributed and only utilizes local information of sensor node. The CDC reduces energy 
consumption by choosing a subset of sensor nodes to periodically transmit readings back to the sink. 
However, the CDC uses packet aggregation at an intermediate node, which introduces undesirable 
communication delays. The work presented in [20] takes CDC one step further by enabling each node 
to use its local and neighbourhood state information to adapt its routing and MAC layer behaviour. 
 
 
5. Query-based IE 
 
5.1. Description and Operation 
 
Query-based approaches to IE, typically involve request-response interactions between the end-user or 
application components and sensor nodes. End users issue queries in an appropriate language, and then 
each query is disseminated to the network to retrieve the desired data from the sensors based on the 
description in the query. 
 
Query based approaches provide a high level interface that hides the network topology as well as radio 
communication from end users. Queries can be sent on demand or at fixed intervals. They provide a 
solution if the data needs to be retrieved from the entire network. 
 
However, there are a number of limitations to the query-based approaches. First, most of existing 
query languages do not provide suitable constructs to easily articulate spatio-temporal sense data 
characteristics. Second, it is difficult to formulate queries using current languages that represent higher 
level behaviour, or specify a subset of nodes that have significant effect on the query answer. This may 
result in generating large amount of data of which big portion is not useful for the end user. Third, to 
the best of our knowledge, there is no published work that fully exploits all the potentials of different 
heterogeneous resources in WSN applications in a context-aware manner. Forth, approaches that take a 
database view of the network are inclined more towards the extraction of the reactive behaviour of the 
WSN and suggestions were made that the active database should be viewed as two end-points of the 
range of rule-based languages in databases [21]. Finally, though declarative languages came into view 
in WSNs settings, the trigger that are the fundamental means for specifying the reactive behaviour in a 
database have not yet been maturely developed. 
 
 
5.2. Query-based Approaches to IE 
 
Query-based approaches, applies techniques used in traditional database systems to implement IE in 
WSNs. A query is sent to the network and data is collected according to the description in the query. 
COUGAR [22] was the first project that attempted to introduce the concept of WSN as a distributed 
database. It allows the end user to issue a declarative query (SQL) for retrieving information. The 
authors introduced a query layer between the application layer and the network layer. The query layer 
comprises a query proxy, which is placed on each sensor node to interact with both the application 
layer and the networking layer. The goal of the query proxy is to perform in-network processing. In-



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network processing increases efficiency by reduces the amount of data that needs to be sent to the 
gateway node. The user does not need to have knowledge about the network, or how the data is 
retrieved or processed. However, COUGAR is incapable of capturing complex events, e.g. of spatio-
temporal nature, or a produce queries that targets only a subset of the network [23]. 
 
A similar approach to COUGAR is proposed in [24]. TinyDB is a query processing system, which 
extracts information from the data collected by the WSN using the TinyOS operating system. TinyDB 
maintains a virtual database table called SENSORS. It disseminates the queries throughout the network 
by maintaining a routing tree (spanning tree) rooted at the end point (usually the user's physical 
location). Every sensor node has its own query processor that processes and aggregates the sensor data 
and maintains the routing information. TinyDB is extensible and complete framework with effective 
declarative queries. In-network processing reduces the amount of data that is required to be sent to the 
sink, thus, energy consumption is reduced. However, data does not include a geo-referencing of sensor 
nodes for spatial quires and it imposes tight correlation among routing and queries. 
 
In [25], a new IE algorithm is proposed with the aim of reducing energy consumption by focusing on 
selective aggregate queries. The proposed algorithm, named Pocket Driven Trajectories (PDT), deals 
with queries that aggregate data only from a subset of all network nodes. PDT is based on the logical 
assumption that spatial correlation in sensor values coupled with query selectivity gives rise to a subset 
of participating nodes formed by one or more geographically clustered sets (pockets). The algorithm 
starts by discovering the set of pockets for a given query. Then, the aggregation tree to the spatially 
optimal path connecting these pockets is aligned. Targeting only nodes with interesting data makes 
PDT energy efficient and suitable for large scale WSNs. However, PDT introduces a delay in reporting 
data to a sink, because data is processed at an intermediate node. Moreover, the formation of pockets is 
not a trivial task. 
 
The authors in [26], proposed a query processing algorithm, that allows the user to specify a value and 
time accuracy constraints based on an optimized query plan. Using these optimization constraints, the 
algorithm can find an optimal sensing and transmission of attribute readings to sink node. Rather than 
sending sensors readings directly to the sink, the proposed algorithm report only updates. This results 
in considerable reduction in communication costs. However, the algorithm does not support dynamic 
adjustment of accuracy constraints. 
 
More recently in [27], the authors designed and implemented a distributed in-network query 
processing, called Corona. Corona is composed of three components: the query engine that is executed 
on the sensors; a host system on the client's PC that is connected to the sink; and GUI that is connected 
to the host system via TCP/IP. The Corona query processing provides multi-tasking capabilities by 
running multiple queries concurrently, which in turns reduces processing delays and communications 
cost by applying data aggregation. However, the language can not easily capture spatio-temporal 
events even though most of the processing is done centrally at the client PC. 
 
 
5.3. Macroprogramming 
 
Macroprogramming is an application development model for WSNs, where the programmer specifies 
the global behaviour of the system instead of specifying the behaviour of its individual nodes. Over the 
past few years, a number of macroprogramming systems have been proposed in the literature [28-34]. 
We refer the interested reader to the recent survey [35] and the references therein for a comprehensive 
review of older macroprogramming approaches. A number of abstraction levels have been proposed in 
the literature ranging from a database or node-level, group, or semantic data streams. The data within 
the network is accessed through these different abstractions that offer access through logical groups of 
nodes. 



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Regiment [33] is one of the early macroprogramming systems where the network is represented as 
time varying signals. Signals might represent one node sensor readings, the computational state of that 
node, or aggregate values based on multiple source signals. Streams of data can be grouped into 
regions. A region is defined as a collection of spatially distributed signals, e.g. the collection of nodes 
in a geographic area with a specific range of sensor readings. Therefore, Regiment is capable of 
representing groups of nodes with topological, geographical, and logical relationships. In all cases, the 
region abstracts away the details of data collection, storage, and dissemination from the programmer. 
 
Similar to Regiment, Kairos [36] is another high level abstraction that provides abstractions for 
manipulating global behaviour. Kairos provide programmers with three levels of abstractions: Node 
abstraction, where programmers use nodes integer-based identifier to control single nodes or lists of 
nodes; List of direct neighbours, this is similar to the region abstraction and express the natural 
construct of radio neighbourhood; Remote data access, gives programmers direct access to variables at 
a specific node. 
 
Unlike Regiment, Kairos is language independent, which makes it possible to be deployed as a plug-in 
to existing declarative programming languages. However, the distributed computation in Kairos covers 
the whole network but uses a centralized method. In attempt to reduce communication overhead, 
Kairos utilizes inefficient consistency model called "eventual consistency". Compared to Regiment, 
Kairos provides a small number of constructs for its three levels of abstractions, whereas, Regiments 
offers a bigger set of operations and data types. These systems are tailored for rather narrow range rang 
of target applications, e.g. target tracking or composing in-network data-processing services. 
 
Logical Neighborhood [34] is a virtual node programming abstraction. A node is characterized by 
several exportable attributes that can be static or dynamic. These attributes are used to replace the 
notion of physical neighbourhood (defined by wireless broadcast) with the notion of logical 
neighbourhood (defined by application conditions). The span of a logical neighbourhood is defined 
declaratively based on the state of the node and communications costs. These logical neighbourhoods 
are implemented using SPIDEY programming language and are supported by a special routing strategy 
enabled by SPIDEY programming constructs. The authors of Logical Neighbourhood assure that their 
approach can be on top of any routing protocol. However, they confirm that the utilization of other 
routing mechanisms to support their abstractions will result with various performance drawbacks. 
Another limitation of this approach is that the user is expected to manually declaratively specify using 
the SPIDEY language which nodes to consider as neighbours. This is not always easy to achieve; the 
configuration of various parameters requires the developers to have deep understanding of the nature 
of data being collected and good knowledge of the network topology which might change frequently. 
Therefore, an approach that places the intelligence of defining the span of communication is desired. 
 
Pathak et al. [29] presented a data-driven macroprogramming framework to solve the problem of 
initial placement of high-level task graph representation onto the nodes of the target network. Their 
framework adopts the view of data-flow to model this task-mapping function in the context of WSNs 
macroprogramming. Srijan [31] is another graphical toolkit designed to aid application developers in 
the compilation of the macroprogram into individual customized runtimes for each constituent node of 
the target network. These frameworks are centralized in nature because the sink knows the location 
and initial energy levels of all sensor nodes. Also, adaptation via code updates decisions is made by the 
developer at a centralized gateway. Moreover, the dissemination of the code from the gateway to the 
WSN nodes is performed via the gateway, which is an energy-intensive process. 
 
Hnat et al. [30] implemented a macroprogramming compiler for MacroLab [32]. Their compiler 
separates function decompositions from low-level compiler optimizations, which makes it expandable 
and modular. MacroLab programming abstractions are based on the notion of macro vector where one 



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dimension in the vector is indexed by node identifiers. The system-wide or global computation scope 
provided by MacroLab limits the efficiency of the new compiler. However, a logical segmentation of 
nodes can be used to partition the large macro vector to carry only data of logically-neighboring nodes. 
 
The Watershed Segmentation approach [28] groups nodes sharing some common group state into 
segments. A segment is described as a collection of spatially distributed nodes with an example being 
the set of nodes in a geographic area with sensor readings in a specific range. The logical segments of 
nodes generated by the Watershed algorithm replace the tight physical neighbourhood provided by 
wireless broadcast with a higher level, application defined abstractions. Segments are created such that 
the span of a logical neighbourhood is specified dynamically and declaratively based on the attributes 
of nodes, along with requirements about communication costs (specifically the diameter of the 
segment). A network segment formed with a logical notion of proximity determined by applicative 
information is, therefore, capable to return specified information with high confidence. Differently 
from [30, 31, 34], segments memberships are dynamically updated in a distributed manner without 
developers intervention, attaining grater robustness and higher efficiency. The advantages of 
Watershed Segmentation approach include: dynamic construction and updating of segments; no need 
for compilers or new programming constructs; it cater for node level communication; localized 
computation; segment setup considers the physical topology provided by wireless broadcast; and the 
spam of the segment improves response accuracy while reducing the processing cost. 
 
 
6. Hybrid-based IE 
 
6.1. Description and Operation 
 
A hybrid IE approach is an approach that combines two or more algorithms from different IE 
categories. Hybrid approaches aim to overcome some of the disadvantages of individual IE categories 
described above. 
 
 
6.2. Hybrid Approaches to IE 
 
Many hybrid approaches to IE have been recently proposed in the literature. In [37], the authors 
proposed a hybrid protocol that adaptively switches between time-driven and event-driven data 
collection. A sensor node is triggered to detect an event of interest, and from the point when an event 
detected to the point when the event becomes no longer valid, the protocol switches to behave as a 
time-driven protocol. During this period sensor nodes continuously report data to the sink. This 
protocol reduces unnecessary data transmission and minimizes event notification time. However, it is 
not guaranteed to work well for all applications. 
 
More recently, in [38], the authors proposed a hybrid framework, similar to [7, 14], which deploys 
both event-driven and query-based approaches to IE. The idea is to process continuous "group-by" 
aggregate queries, and to allow each sensor node to check whether sensor readings satisfy local 
predicates based on predefined thresholds. Then, nodes send only data that satisfy local predicates to 
their cluster heads, which in turns process the data to answer the query as accurate as possible. The 
proposed hybrid framework is able to target a subset of the network by using the group-by clause. It 
reduces communication cost by using one dimensional Haar wavelets. However, it introduces a delay 
in reporting events since the data is processed at the cluster head. Moreover, this approach requires the 
extension of existing query languages to include the group-by clause and the associated language 
parser. 
 



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The authors in [39] proposed a hybrid framework that deploys both event-driven and time-driven 
approaches. The idea is to use Complex Event Processing (CEP) to find meaningful events from raw 
data. As soon as a node detects an event, local data is continuously transferred towards the sink for 
further processing. This approach provides continues monitoring of the environment, which enables 
arbitrary data analysis. However, using CEP, data has to be sent to a central location for integration 
with data from other sources. It does not support in-network processing, which has been identified as 
an efficient technique for resource utilization [40]. 
 
 
7. IE from Mobile WSNs 
 
Recent applications of WSNs (e.g. in medical care) make use of mobile sensor nodes to improve their 
performance. However, mobility poses new challenges to IE researchers including: increased data loss 
and delivery delay due to intermittent connectivity; lower throughput due to low channel utilization; 
frequent topological changes; amongst others. Few IE solutions have been proposed to deal with some 
of these challenges. These solutions can be categorized according to the type of the mobile entity as 
follows: 
 
7.1. Mobile Nodes 
 
Researchers developed approaches specifically designed to extract information from WSNs where 
sensor nodes are mobile. These approaches can be classified according to the purpose of mobility as 
follows: 
 
a) Coverage: Some approaches, e.g. [41-43], move nodes to provide better coverage by filling in 
holes in sensing coverage. They relocate redundant nodes to areas where node density is low to 
improve the accuracy of extracted information. However, this type of algorithms needs complex 
relocating models to calculate the moving nodes’ trajectories and their new locations. 
 
b) Mobile environments: Approaches such as [44-46] move mobile nodes to monitor moving objects, 
e.g. wildlife monitoring or offline monitoring of vehicle fleets. Mobile nodes are mounted on the 
monitored objects to log the sensed information on their memories for later analysis. When the mobile 
nodes move within radio range of the sink, they upload the logged information to it. However, these 
algorithms have several drawbacks. They are designed for specific types of applications that are not 
time critical. Moreover, in most situations animals or vehicles move in groups, the nodes density will 
not be disseminated in an effective way. Consequently, sinks located in dense areas will be overloaded, 
leading to increase latency and data loss. 
 
c) Relay nodes: In some approaches, mobile nodes are used as relay nodes besides their sensing 
duties. Mobile nodes can be used to carry information from the sensing field and deliver it to a fixed 
sink. In these algorithms, mobile nodes send data over a short range communication (from a sensor to 
the relay node) that necessitates less transmission power. In [47], the authors proposed energy-efficient 
hybrid data collection approach. Its aim is to enhance the network performance and reduce the total 
energy consumption by introducing mobile node entities. A mobile node is moving through the 
network deployment region to collect data from the static nodes over a single hop radio links. The 
mobile node visits the sink periodically to drop off the collected data. The proposed solution reduces 
energy consumption and communication overhead by moving the sink node near to the nodes to 
collect data. Mobile nodes can move randomly, as in [48-50], on fixed trajectories, as in [51-54] or 
based on occurrence of an event of interest, as in [55, 56]. However, these approaches introduce 
considerable delays on data delivery and may potentially miss some important information in case of 
frequently changing phenomena. To overcome some of these issues, some recent researches have 
proposed moving the base station itself rather than moving relay nodes. 



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7.2. Mobile Data-sinks 
 
The largest set of IE approaches that has been proposed in the literature suggests using a mobile sink 
for data collection and analysis. The mobile sink moves towards isolated nodes according to a 
particular trajectory to collect their data. Based on the nature of the trajectory, the sink mobility 
approaches to IE can be further classified into three classes: 
 
a) Fixed trajectory, e.g. [57-60] assumes that the trajectory is fixed such as in roads. In these 
approaches data or information is conveyed to rendezvous nodes, which are closer to the trajectory, 
which is then cached until the mobile sink passes by and picks it up. The mobile sink can perform 
further processing on the received information or data. Hence, IE is achieved periodically. 
 
b) Dynamic trajectory, e.g. [61], assumes that the trajectory is dynamic, different algorithms are used 
to calculate the trajectory. In these approaches IE is performed according to a pre-computed schedule, 
e.g. [62], or according to event occurrence, e.g. [63]. Approaches such as [63], propose mobility 
models that moves the sink node according to the evolution of the current events. 
 
c) Random trajectories, e.g. [64-66] assume that mobile sink moves randomly in the sensor field. 
Mobile sinks are mounted on people, vehicles, or animals moving chaotically to collect information of 
interest around the network. 
 
Although mobile sink strategies are desirable due to their simplicity, they suffer from some drawbacks. 
As the sink moves through the sensor field, it causes high control overhead to find a route to the sink 
and send packets to it. This may possibly dissipate the energy saved by using the mobile sink strategy. 
Moreover, constantly relocating the sink introduces significant delays on data or information delivery. 
Despite the extended coverage, these approaches lack scalability; as the network grows in size, the 
nodes located close to the mobile sink’s trajectory get overloaded leading to energy depletion in the 
network, disconnections, and bandwidth bottlenecks. Finally, the trajectory calculation is a complex 
problem. 
 
 
8. Discussion and Possible Future Directions 
 
Before concluding this paper, this section provides a discussion about research issues, and future 
directions in the area of IE in WSNs. This survey revealed that most of the existing approaches to IE 
suffer from inherent problems that limit their applications including: they are application specific; 
characterized by poor spatio-temporal IE capabilities; consume high power; many approaches trade the 
amount and quality of returned information by energy consumption; they lack appropriate high-level 
interfaces that allow the user to set thresholds and issue queries; and the tight coupling between IE 
algorithms, applications, and hardware stacks leads to lack of code reuse. The lack of development 
frameworks means each new application has to be tackled from the ground up. These issues limit the 
usefulness of the developed IE approaches, making it hard to use them on anything other than the 
application it was designed for. 
 
The problems and limitations presented above are the opportunities we intend to follow in our future 
work. Possible solutions that we are currently investigating for the integration of the three IE 
approaches will be achieved through the use of coordination rules [67] and mobile agents [68]. 
 
Coordination rules are a set of modelling primitives, design principles, and patterns that deal with 
enabling and controlling the collaboration among a group of software distributed agents performing a 
common task. If each algorithm in each IE category is viewed as a service, then the composition of 
these services will result in a complete IE framework. Service composition provides new services by 



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combining existing services. The coordination rules specifies the order in which services are invoked 
and the conditions under which a certain service may or may not be invoked. 
 
The mobile agent paradigm will be adopted to facilitate cooperation among services on different 
nodes. Mobile agent is a piece of software that performs data processing autonomously while 
migrating from node to node [68]. The agent can collect local data and perform any necessary data 
aggregation. Mobile agents can make decision autonomously without user input. They provide 
flexibility in terms of decision making, and reliability in terms of node failure [69]. 
 
Fig. 1 shows an illustration of the described hybrid framework. It shows how services on one node are 
connected and how a service can access other services on remote node. 
 
 

 
 

Fig. 1. An integrated IE framework for WSNs. 
 
 
9. IE Framework: An Application Scenario 
 
This section presents an application scenario that illustrates how different IE services can be 
coordinated. The coordination is to provide a means of integrating two of the IE services together by 
interfacing with each service to form a single service that can execute on a distributed WSN system. 
We consider events that are caused by multiple elements targets, e.g. a herd of animals in a habitat 
monitoring application. In this WSN system, an event-driven service is used to inform the time-driven 
service to increase the extracted information accuracy. Nodes send their sensor readings periodically to 
the end user to indicate the location of the herd. Besides periodic transmission of information, some 
nodes run an event-driven service. When a node detects an event in the form of movement in the 
environment, it modifies its time-driven service to increase information transmission frequency. The 
node will then notify its neighbours about the event in their area to adjust their time-driven services 
accordingly. 
 
The Coordination Language Facility (CLF) [70] is used in this scenario as a coordination layer on top 
of the WSN system infrastructure. In CLF, the use of rules to coordinate services is based on a 
proactive system. A CLF system actively seeks to influence its environment, rather than just 
responding to external stimuli. This feature respects the autonomy of each participant sensor node. A 



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 13

sensor node waits for events to occur and generates new ones depending on the state of its rules 
engine. 
 
waitUntilNext @ reading(Motion) <>− submit(Motion) @ check activity(Motion) 
activity(normal) @ interval(normal) <>− #b 
activity(normal) @ interval(high) <>− change_interval(normal) 
activity(high) <>− change_interval(fast) @ notify_neighbour(Motion, 
Location, Interval) 
received(Motion, Location, Interval) @ interval(normal) <>− reading(Motion) 
@ submit(Motion) @ change_interval(fast) 
received(Motion, Location, Interval) @interval(high) <>− #b 
 
In implementing the scenario described above, the rules are distributed amongst and maintained by the 
nodes themselves so multiple nodes can collaborate independently, i.e. these rules apply to individual 
nodes. The waitUntilNext token is read-only and returns a value at the beginning of each sensing 
interval; it acts as a trigger to start the search for an instance of the rule. The reading token holds the 
current motion measurement from the motion sensor. The submit token takes the sensor 
measurement and sends it to the higher level nodes (e.g. cluster head or sink). The 
check_activity token checks the level of animal activity or movement within the node's coverage 
area. When the level of activity, activity(normal), is normal and the transmission interval is 
also normal, interval(normal), then the node will do nothing. When the node's current level of 
activity is normal and the transmission interval is high, interval(high), then the node will 
change it to normal to save its energy, change_interval(normal). If the node senses a high 
activity within its area of coverage, then it will change its time-driven service interval to fast and notify 
its neighbours about the event in their area notify_neighbour(Motion, Location, 
Interval). The node will include in its notification message its sensor measurement, location, and a 
new interval value. When a node receives the notification message, received(), it changes its time-
driven service interval to fast and it submits its current sensor measurement. 
 
This example shows that coordination can provide an intuitive method for combining services 
belonging to a mixture of IE categories and manage the resources required for implementing them. 
What precisely is being coordinated, how the coordination is accomplished, and what are the relevant 
metaphors that must be implemented and used, are all questions that we intend to tackle in the future. 
 
 
10. Conclusion 
 
The main objective of this paper is to provide an understanding of the current issues in this area for 
better future academic research and industrial practice of WSNs IE. We have presented a review of the 
state of the art for IE approaches in WSNs. We discussed various approaches to IE. We also discussed 
the challenges as well as future research directions in developing a complete integrated WSNs IE 
framework. Finally, an application scenario that demonstrates how coordination can be used to deal 
with the cooperation among very large number of nodes with several active services that comprise a 
single IE system. 
 
 
References 
 
[1]. T. Alsbouí, M. Hammoudeh, Z. Bandar, and A. Nisbet, An Overview and Classification of Approaches to 

Information Extraction in Wireless Sensor Networks, in Proceedings of the 5th International Conference on 
Sensor Technologies and Applications( SENSORCOMM' 2011), France, 2011. 

[2]. S. Sarawagi, Information Extraction, Found. Trends databases, Vol. 1, March 2008, pp. 261-377. 



Sensors & Transducers Journal, Vol. 14-2, Special Issue, February 2012, pp. 1-17 

 14

[3]. C. Zhang, C. Wang, D. Li, X. Zhou, and C. Gao, Unspecific Event Detection in Wireless Sensor Networks, 
in Proceedings of the International Conference on Communication Software and Networks, Vo l. 0, 2009, 
pp. 243-246. 

[4]. R. Bhargavi, V. Vaidehi, P. T. V. Bhuvaneswari, P. Balamurali, and G. Chandra, Complex Event 
Processing for Object Tracking in Wireless Sensor Networks, Web Intelligence and Intelligent Agent 
Technology (WI-IAT), 2010 IEEE/WIC/ACM International Conference, Vol. 3, 2010, pp. 211 -214. 

[5]. A. Iranli, M. Maleki, and M. Pedram, Energy efficient strategies for deployment of a two-level wireless 
sensor network, in Proceedings of the 2005 International Symposium on Low Power Electronics and 
Design, 2005, pp. 233-238. 

[6]. K. Akkaya and M. Younis, A survey on routing protocols for wireless sensor networks, Ad Hoc Networks, 
Vol. 3, 2005, pp. 325 - 349. 

[7]. B. Gedik, L. Liu, and P. S. Yu, ASAP: An Adaptive Sampling Approach to Data Collection in Sensor 
Networks, IEEE Transactions on Parallel and Distributed Systems, Vol. 18, pp. 1766-1783, 2007. 

[8]. M. L. Segal, F. P. Antonio, S. Elam, J. Erlenbach, and K. R. De Paolo, Method and apparatus for automatic 
event detection in a wireless communication system, United States Patent No. 6124810, 
September 2000. 

[9]. G. Werner-Allen, K. Lorincz, M. Welsh, O. Marcillo, J. Johnson, M. Ruiz, and J. Lees, Deploying a 
Wireless Sensor Network on an Active Volcano, IEEE Internet Computing, Vol. 10, 2006, pp. 18-25. 

[10]. M. Bahrepour, M. Meratnia, and N. Havinga, Sensor Fusion-based Event Detection in Wireless Sensor 
Networks, in Proceedings of the 6th Annual International Conference on  Mobile and Ubiquitous Systems: 
Networking Services (MobiQuitous' 09), 2009, pp. 1-8. 

[11]. Y. Zhang, N. Meratnia, and P. Havinga, Outlier Detection Techniques for Wireless Sensor Networks: A 
Survey, Communications Surveys Tutorials, IEEE, Vol. 12, 2010, pp. 159 -170. 

[12]. S. Sardellitti, S. Barbarossa, and L. Pezzolo, Distributed double threshold spatial detection algorithms in 
wireless sensor networks, in Proceedings of the 10th IEEE Workshop on Signal Processing Advances in 
Wireless Communications (SPAWC '09,) 2009, pp. 51 -55. 

[13]. D. Tulone and S. Madden, An energy-efficient querying framework in sensor networks for detecting node 
similarities, in Proceedings of the 9th ACM International Symposium on Modeling Analysis and Simulation 
of Wireless and Mobile Systems, 2006 pp. 191-300. 

[14]. D. Chu, A. Deshpande, J. M. Hellerstein, and W. Hong, Approximate Data Collection in Sensor Networks 
using Probabilistic Models, in Proceedings of the 22nd International Conference on Data Engineering, 
2006, pp. 48. 

[15]. A. Deshpande, C. Guestrin, S. R. Madden, J. M. Hellerstein, and W. Hong, Model-driven data acquisition 
in sensor networks, in Proceedings of the Thirtieth International Conference on Very Large Data Bases,  
Vol. 30, 2004, pp. 588-599. 

[16]. M. Bahrepour, N. Meratnia, M. Poel, Z. Taghikhaki, and P. J. M. Havinga, Distributed Event Detection in 
Wireless Sensor Networks for Disaster Management, in Proceedings of the International Conference on 
Intelligent Networking and Collaborative Systems (INCoS' 2010), November 2010, pp. 507-512. 

[17]. Y. W. Guorui Li, An Efficient Data Aggregation Scheme Leveraging Time Series Prediction in Wireless 
Sensor Networks, International Journal of Machine Learning and Computing, Vol. 1, October 2011,  
pp. 372-377. 

[18]. G.-S. Ahn, Information-driven Tracking and Access Control in Wireless Ad hoc and Sensor Networks, 
Columbia University, 2009. 

[19]. H. Li, H. Yu, L. Li, and A. Liu, On the Cascading Data Collection Mechanism in Wireless Sensor 
Networks, in Proceedings of the International Conference on Wireless Communications, Networking and 
Mobile Computing, Shanghai, China, 2007, pp. 2479-2482. 

[20]. R. Jurdak, P. Baldi, and C. Videira Lopes, Adaptive Low Power Listening for Wireless Sensor Networks, 
IEEE Transactions on Mobile Computing, Vol. 6, 2007, pp. 988-1004. 

[21]. G. Trajcevski and P. Scheuermann, (Reactive /+ Proactive Behavior) Situation Awareness in Sensor 
Networks, in Proceedings of the Workshop on Research Directions in Situational-aware Self-managed 
Proactive Computing in Wireless Adhoc Networks, 2009. 

[22]. Y. Yao and J. Gehrke, The cougar approach to in-network query processing in sensor networks, SIGMOD 
Rec., Vol. 31, 2002, pp. 9-18. 

[23]. M. Bestehorn, K. Bohm, E. Buchmann, and S. Kessler, Energy-efficient processing of spatio-temporal 
queries in wireless sensor networks, in Proceedings of the 18th SIGSPATIAL International Conference on 
Advances in Geographic Information Systems, 2010, pp. 340-349. 



Sensors & Transducers Journal, Vol. 14-2, Special Issue, February 2012, pp. 1-17 

 15

[24]. S. R. Madden, M. J. Franklin, J. M. Hellerstein, and W. Hong, TinyDB: an acquisitional query processing 
system for sensor networks, ACM Trans. Database Syst., Vol. 30, 2005, pp. 122-173. 

[25]. M. Umer, L. Kulik, and E. Tanin, Optimizing query processing using selectivity-awareness in Wireless 
Sensor Networks, Computers, Environment and Urban Systems, Vol. 33, 2009, pp. 79 - 89. 

[26]. J.-Z. Sun, An Energy-Efficient Query Processing Algorithm for Wireless Sensor Networks, in Ubiquitous 
Intelligence and Computing. Vol. 5061, 2008, pp. 373-385. 

[27]. R. Khoury, T. Dawborn, B. Gafurov, G. Pink, E. Tse, Q. Tse, M. Gaber, U. Rohm, and B. Scholz, Corona: 
Energy-Efficient Multi-query Processing in Wireless Sensor Networks, in Database Systems for Advanced 
Applications. Vol. 5982, 2010, pp. 416-419. 

[28]. M. Hammoudeh and T. Alsboui, Building Programming Abstractions for Wireless Sensor Networks Using 
Watershed Segmentation, in Smart Spaces and Next Generation Wired/Wireless Networking. Vol. 6869: 
Springer Berlin / Heidelberg, 2011, pp. 587-597. 

[29]. A. Pathak and V. K. Prasanna, Energy-Efficient Task Mapping for Data-Driven Sensor Network 
Macroprogramming, IEEE Trans. Comput., Vol. 59, July 2010, pp. 955-968. 

[30]. T. W. Hnat, T. I. Sookoor, P. Hooimeijer, W. Weimer, and K. Whitehouse, A modular and extensible 
macroprogramming compiler, in Proceedings of the Workshop on Software Engineering for Sensor 
Network Applications (ICSE' 10), 2010, pp. 49-54. 

[31]. A. Pathak and M. K. Gowda, Srijan: a graphical toolkit for sensor network macroprogramming, in 
Proceedings of the 7th Joint Meeting of the European Software Engineering Conference and the ACM 
SIGSOFT Symposium on The Foundations of Software Engineering, 2009, pp. 301-302. 

[32]. T. I. Sookoor, T. W. Hnat, and K. Whitehouse, Programming cyber-physical systems with MacroLab, in 
Proceedings of the 6th ACM Conference on Embedded Network Sensor Systems, 2008, pp. 363-364. 

[33]. R. Newton, G. Morrisett, and M. Welsh, The regiment macroprogramming system, in Proceedings of the 
6th International Conference on Information Processing in Sensor Networks, 2007, pp. 489-498. 

[34]. L. Mottola and G. P. Picco, Using logical neighborhoods to enable scoping in wireless sensor networks, in 
Proceedings of the 3rd International Middleware Doctoral Symposium, 2006, pp. 6. 

[35]. L. Mottola and G. P. Picco, Programming wireless sensor networks: Fundamental concepts and state of the 
art, ACM Comput. Surv., Vol. 43, April 2011, pp. 19:1-19:51. 

[36]. R. Gummadi, N. Kothari, R. Govindan, and T. Millstein, Kairos: a macro-programming system for wireless 
sensor networks, in Proceedings of the Twentieth ACM Symposium on Operating Systems Principles, 2005,  
pp. 1-2. 

[37]. B.-D. Lee, Adaptive Data Dissemination Protocol for Wireless Sensor Networks, in Security-Enriched 
Urban Computing and Smart Grid. Vol. 78, 2010, pp. 188-195. 

[38]. C.-H. Lee, C.-W. Chung, and S.-J. Chun, Effective processing of continuous group-by aggregate queries in 
sensor networks, J. Syst. Softw., Vol. 83, pp. 2627-2641, December 2010. 

[39]. R. Bhargavi, V. Vaidehi, P. T. V. Bhuvaneswari, P. Balamuralidhar, and M. G. Chandra, Complex Event 
Processing for object tracking and intrusion detection in Wireless Sensor Networks, in Proceedings of the 
11th International Conference on Control Automation Robotics Vision (ICARCV' 2010), December 2010, 
pp. 848 -853. 

[40]. S. Madden, M. J. Franklin, J. M. Hellerstein, and W. Hong, The design of an acquisitional query processor 
for sensor networks, in Proceedings of the International Conference on Management of Data  
(ACM SIGMOD' 03), 2003, pp. 491-502. 

[41]. V. Coskun, Relocating Sensor Nodes to Maximize Cumulative Connected Coverage in Wireless Sensor 
Networks, Sensors, Vol. 8, 2008, pp. 2792-2817. 

[42]. G. Wang, G. Cao, and T. L. Porta, Movement-assisted sensor deployment, IEEE Transactions on Mobile 
Computing, Vol. 5, 2006, pp. 640 -652. 

[43]. P. Guo, G. Zhu, and L. Fang, An Adaptive Coverage Algorithm for Large-Scale Mobile Sensor Networks, 
in Ubiquitous Intelligence and Computing, Vol. 4159, Springer Berlin / Heidelberg, 2006, pp. 468-477. 

[44]. N. Gajaweera and D. Dias, FAMA/TDMA Hybrid MAC for Wireless Sensor Networks, in Proceedings of 
the Information and Automation for Sustainability (ICIAFS' 08), 2008, pp. 67-72. 

[45]. S. Sivaramakrishnan and A. Al-Anbuky, Analysis of network connectivity: Wildlife and Sensor Network, 
Telecommunication Networks and Applications Conference (ATNAC), 2009 Australasian, 2009, pp. 1-6. 

[46]. T. Wark, P. Corke, P. Sikka, L. Klingbeil, Y. Guo, C. Crossman, P. Valencia, D. Swain, and G. Bishop-
Hurley, Transforming Agriculture through Pervasive Wireless Sensor Networks, Pervasive Computing, 
IEEE, Vol. 6, 2007, pp. 50 -57. 



Sensors & Transducers Journal, Vol. 14-2, Special Issue, February 2012, pp. 1-17 

 16

[47]. A. Rasheed and R. Mahapatra, An Energy-Efficient Hybrid Data Collection Scheme in Wireless Sensor 
Networks, in Proceedings of the 3rd International Conference on Intelligent Sensors, Sensor Networks and 
Information (ISSNIP'  07), 2007, pp. 703 -708. 

[48]. S. Jain, R. C. Shah, W. Brunette, G. Borriello, and S. Roy, Exploiting mobility for energy efficient data 
collection in wireless sensor networks, Mob. Netw. Appl., Vol. 11, June 2006, pp. 327-339. 

[49]. R. C. Shah, S. Roy, S. Jain, and W. Brunette, Data MULEs: modeling a three-tier architecture for sparse 
sensor networks, in Proceedings of the 1st IEEE International Workshop on Sensor Network Protocols and 
Applications, 2003, pp. 30 - 41. 

[50]. D. N. Jayasingha, N. P. M. L. Jayawardhane, P. N. Karunanayake, G. D. Karunarathne, and D. Dias, 
Wireless Sensor Network Testbed for Mobile Data Communication, in Proceedings of the 4th International 
Conference on Information and Automation for Sustainability (ICIAFS' 08), 2008, pp. 97 -103. 

[51]. G. Anastasi, M. Conti, and M. Di Francesco, Data collection in sensor networks with data mules: An 
integrated simulation analysis, in Proceedings of the IEEE Symposium on Computers and Communications, 
(ISCC' 08), July 2008, pp. 1096 -1102. 

[52]. A. Chakrabarti, A. Sabharwal, and B. Aazhang, Communication power optimization in a sensor network 
with a path-constrained mobile observer, ACM Trans. Sen. Netw., Vol. 2, August 2006, pp. 297-324. 

[53]. W. Alsalih, H. Hassanein, and S. Akl, Routing to a Mobile Data Collector on a Predefined Trajectory, in 
Proceedings of the IEEE International Conference on Communications (ICC '09), June 2009, pp. 1 -5. 

[54]. D. Jea, A. Somasundara, and M. Srivastava, Multiple controlled mobile elements (data mules) for data 
collection in sensor networks, In DCOSS, 2005, pp. 244-257. 

[55]. M. Xi, K. Wu, Y. Qi, J. Zhao, Y. Liu, and M. Li, Run to Potential: Sweep Coverage in Wireless Sensor 
Networks, in Proceedings of the International Conference on Parallel Processing (ICPP '09), September 
2009, pp. 50-57. 

[56]. L. He, Y. Zhuang, J. Pan, and J. Xu, Evaluating On-Demand Data Collection with Mobile Elements in 
Wireless Sensor Networks, in Proceedings of the IEEE 72nd Conference on Vehicular Technology (VTC 
2010-Fall), September 2010, pp. 1-5. 

[57]. G. Pantziou, A. Mpitziopoulos, D. Gavalas, C. Konstantopoulos, and B. Mamalis, Mobile Sinks for 
Information Retrieval from Cluster-Based WSN Islands, in Proceedings of the 8th International Conference 
on Ad-Hoc, Mobile and Wireless Networks, 2009, pp. 213-226. 

[58]. S. Gao, H. Zhang, and S. Das, Efficient data collection in wireless sensor networks with path-constrained 
mobile sinks, in Proceedings of the IEEE International Symposium on World of Wireless, Mobile and 
Multimedia Networks Workshops (WoWMoM' 09), June 2009, pp. 1-9. 

[59]. L. Song and D. Hatzinakos, Architecture of Wireless Sensor Networks With Mobile Sinks: Sparsely 
Deployed Sensors, IEEE Transactions on Vehicular Technology, Vol. 56, July 2007, pp. 1826-1836. 

[60]. L. Cheng, Y. Chen, C. Chen, and J. Ma, Query-based data collection in wireless sensor networks with 
mobile sinks, in Proceedings of the 2009 International Conference on Wireless Communications and 
Mobile Computing: Connecting the World Wirelessly, 2009, pp. 1157-1162. 

[61]. H. Lee, M. Wicke, B. Kusy, O. Gnawali, and L. Guibas, Data stashing: energy-efficient information 
delivery to mobile sinks through trajectory prediction, in Proceedings of the 9th ACM/IEEE International 
Conference on Information Processing in Sensor Networks, 2010, pp. 291-302. 

[62]. G. Xing, T. Wang, W. Jia, and M. Li, Rendezvous design algorithms for wireless sensor networks with a 
mobile base station, in Proceedings of the 9th ACM international symposium on Mobile ad hoc networking 
and computing, 2008, pp. 231-240. 

[63]. H. Sabbineni and K. Chakrabarty, Datacollection in Event-Driven Wireless Sensor Networks with Mobile 
Sinks, International Journal of Distributed Sensor Networks, Vol. 2010. 

[64]. L. Tzevelekas and I. Stavrakakis, Sink mobility schemes for data extraction in large scale WSNs under 
single or zero hop data forwarding, in Proceedings of the European Wireless Conference (EW' 2010), April 
2010, pp. 896-902. 

[65]. I. Chatzigiannakis, A. Kinalis, S. Nikoletseas, and J. Rolim, Fast and energy efficient sensor data collection 
by multiple mobile sinks, in Proceedings of the 5th ACM international workshop on Mobility Management 
and Wireless Access, 2007, pp. 25-32. 

[66]. A. Viana, A. Ziviani, and R. Friedman, Decoupling data dissemination from mobile sink's trajectory in 
wireless sensor networks, Communications Letters, IEEE, Vol. 13, March 2009, pp. 178 -180. 

[67]. J. Abreu and J. Fiadeiro, A Coordination Model for Service-Oriented Interactions, in Coordination Models 
and Languages. Vol. 5052, 2008, pp. 1-16. 

[68]. M. Chen, T. Kwon, Y. Yuan, and V. Leung, Mobile Agent Based Wireless Sensor Networks, Journal of 
Computers, Vol. 1, 2006. 



Sensors & Transducers Journal, Vol. 14-2, Special Issue, February 2012, pp. 1-17 

 17

[69]. D. Massaguer, C.-L. Fok, N. Venkatasubramanian, G.-C. Roman, and C. Lu, Exploring sensor networks 
using mobile agents, in Proceedings of the 5th International Joint Conference on Autonomous Agents and 
Multiagent Systems, 2006, pp. 323-325. 

[70]. J.-M. Andreoli, S. Freeman, and R. Pareschi, The Coordination Language Facility: coordination of 
distributed objects, Journal of Theory and Practice of Object Systems (TAPOS), Vol. 2, 1996, pp. 1-18. 

 
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SSSeeennnsssooorrrsss   &&&   TTTrrraaannnsssddduuuccceeerrrsss  

ISSN 1726-5479
© 2012 by IFSA

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Assessment of Software Modeling Techniques for Wireless 
Sensor Networks: A Survey 

 
John Khalil Jacoub, Ramiro Liscano, Jeremy S. Bradbury 

University of Ontario Institute of Technology 
Oshawa, Ontario, Canada 

E-mail: {john.khalil, ramiro.liscano, jeremy.bradbury}@uoit.ca 
 
 

Received: 3 December 2011   /Accepted: 20 December 2011   /Published: 12 March 2012 
 
 
Abstract: Wireless Sensor Networks (WSNs) monitor environment phenomena and in some cases 
react in response to the observed phenomena. The distributed nature of WSNs and the interaction 
between software and hardware components makes it difficult to correctly design and develop WSN 
systems. One solution to the WSN design challenges is system modeling. In this paper we present a 
survey of 9 WSN modeling techniques and show how each technique models different parts of the 
system such as sensor behavior, sensor data and hardware. Furthermore, we consider how each 
modeling technique represents the network behavior and network topology. We also consider the 
available supporting tools for each of the modeling techniques. Based on the survey, we classify the 
modeling techniques and derive examples of the surveyed modeling techniques by using SensIV 
system. Copyright © 2012 IFSA. 
 
Keywords: Wireless sensor networks (WSN), Modeling, Node, Software design, Hardware design. 
 
 
 
1. Introduction 
 
A Wireless Sensor Network (WSN) consists of small wireless units called motes, which are attached to 
specific type of sensors. The sensors measure an environment phenomenon (e.g., humidity, 
temperature, or soil moisture) and the measured value is expressed as an analog signal generated by 
the sensors. According to Akyildiz et al. [1], WSN applications can be classified into 2 types; those 
that have mobile sensors and those that have static sensors. This classification is too broad because 
within the static WSN category we one can also identify 3 distinct style of static WSNs; Collector 
WSNs, aggregation WSNs, and actuation WSNs. 
 

http://www.sensorsportal.com/


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Collector WSNs consist of a number of sensors that gather data and this data is collected at one or 
more nodes that typically are a gateway to the Internet. These WSNs typically take advantage of tree 
style of routing. Aggregation WSNs perform some data aggregation and filtering within the network 
nodes and publish this filtered information for other collector nodes to acquire. Actuator WSNs not 
only sense the phenomenon, but also react in response to the sensed data. These networks are typically 
referred to as Wireless Sensor Actuator Networks (WSANs) or at times Wireless Sensor Actor 
Networks (WSANs) and typically have more stringent real time constraints on the delivery of the data. 
 
WSN systems can be complex and many different challenges can arise during the design of a WSN 
such as: (i) the distribution of nodes and sensors in a physical environment that may result in lost and 
delayed data; (ii) the inclusion of real-time behavior within a distributed WSAN; (iii) memory 
management within the sensor nodes (the small size of the nodes leads to physical limitations that 
restrict the available memory and therefore memory management is often required;) (iv) operational 
reliability (WSNs often consist of self-powered nodes in environmentally challenging domains 
imposing strong reliability requirements;) and (v) improving the network performance, i.e. reduce 
network delays, packet loss, while increasing throughput (network performance improvements may 
involve the use of concurrency and event driven communication, which can add additional complexity 
to the system.) 
 
The design of WSN systems usually occurs at the implementation level and does not involve design at 
higher levels of abstraction. This leads to a decrease in code portability and to platform-specific 
implementations [2]. A WSN system produced using this approach is prone to both design and 
implementation errors and very challenging code debugging (user interfaces to sensor nodes are very 
limited so even simple text output is challenging). If errors are not detected during the implementation 
and verification stages of development then they may appear once the system is deployed and is 
operational. The nodes of an operational WSN application are generally difficult to access once they 
are deployed in their working locations. 
 
The challenges of developing WSN systems can be mitigated by leveraging higher system level-design 
and analysis. The use of modeling languages and techniques can drive the design through different 
abstraction layers and analysis tools can help refine the model. In this paper we survey 9 modeling 
techniques for WSNs. For each technique we examine how a WSN is modeled at the node and system-
level. We also describe a WSN system called Sensor Infrastructure for Viticulture introduce (SensIV) 
that we use as a case study to assess how each modeling approach would be used to develop the 
SensIV system. SensIV is considered a collector WSN that was designed and built by our research 
team to monitor the temperature in a vineyard field. The deployed nodes are static there is no direct 
action taken in response to the sensed data. 
 
The rest of the paper is organized into 8 sections. Section 2 describes the SensIV case study. Section 3 
gives an overview of the modeling techniques reviewed in this survey. Section 4 discusses the 
modeling of WSN elements including sensors, nodes and hardware. Section 5 presents WSN modeling 
at the system level. Section 6 discusses the supporting tools and the importance of each tool for WSN 
design. Section 7 presents related work (i.e other sensor modeling surveys.) Finally section 8 presents 
the conclusion and future work. 
 
 
2. Case Study 
 
In this section we present SensIV [3] project as a case study for the modeling techniques surveyed. 
Using a case study helps in understanding how the modeling approaches presented in this paper could 
be applied to help develop and analyze the software for a system that we are fairly familiar with. 
 



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2.1. System Overview 
 
The aim of the SensIV project is to monitor the temperature of a vineyard field. For this survey 
understanding the vineyard application is not crucial and therefore SensIV can be considered as simply 
a collector WSN consisting of a set of sensor nodes that collect temperature data across a field and 
transmit this data to a collection point using a wireless sensor network. Each sensor node supports 4 
temperature sensors that measure the gradient in the vertical direction at one spot in the field. The 
motes communicate with each other wirelessly until the data reaches the gateway where the 
temperature values are stored in a database for further analysis. 
 
 
2.2. Sensor Physical Layer 
 
Each node consists of the following hardware: 
 
- Four temperature sensors: The sensors used are thermal sensors of type LM135 [4] The sensor 

accuracy is  1 oC and can measure a temperature range in between -55 oC to 150 oC. 
- An MDA300CA acquisition board: The MDA300CA data acquisition board from Crossbow has 

expansion connectors for 7 single-ended and 4 differential ADC channels. 
- A Wireless Iris mode: The Iris wireless module uses the Atmel processor with 128 KB program 

memory and 8 KB RAM. In terms of radio communication, it uses 2.4GHz (IEEE802.15.4) with a 
range of 500 meters. This type of mote also supports a low-power mode of operation for the micro-
processor, radio, and logger. 

- A solar cell is used to recharge the 2 AA alkaline batteries that power the sensor node. The system 
was designed for continuous operation with sunlight conditions encountered in Southern Ontario. 

 
 
2.3. Routing Protocol 
 
The protocol used for routing a data packet to the collector is the Collector Tree Protocol (CTP) [5] 
CTP is an address free protocol that maintains a tree routing topology among the sensor network to the 
collector. The version of CTP used is the one available in the TinyOS deployment that is fairly 
reliable. The parent of a node is chosen based on the link estimation value that is continuously updated 
as data is being sent through the network. 
 
 
2.4. Sensor Software 
 
The sensor software leverages the TinyOS 2.1 [6] operating system which provides many useful 
services for sensor networks some of these which are: a task scheduler, boot sequence, timers, memory 
storage, serial and radio communication, radio management, and multi-hop routing protocols. 
 
There are 2 distinct sensor software packages. One package is for the collector and another package is 
for the sensor nodes in the field. For the study case we will focus primarily on the software for that 
resides on the sensor nodes on the field. We also have designed a software component on sensor node 
that captures “control messages” sent from the collector that are transmitted using a dissemination 
protocol that is also provided by the TinyOS community. For the purpose of the case study we focus 
only on the software modules that collect data and transmit this data to the collector and do not present 
any logic or models that relate to the capture of the “control messages”. 
 



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In TinyOS there is a task execution process coordinated by a time scheduler. If there is no task in the 
task queue, the timer scheduler will put the processor into sleep mode. The processor is woken up by 
either a message arriving signal from the dissemination protocol or the timer firing event used to 
collect data (the system was designed for a 5 minute data acquisition period.) 
 
 
2.5. Location of the Sensor Nodes 
 
The sensor nodes were deployed in a field at the University of Ontario Institute of Technology in 
Oshawa, Canada. We have captured the coordinates of the nodes and these are shown in a Google map 
image in Fig. 1. The approximate average distance between the sensor nodes is about 32 m. The terrain 
slopes downwards from nodes 4, 9, 2, 8, 7, and 3 towards 5, 1, 6, and 10. The collector node (marked 
as Base) is located about 4 m of the ground in a 2nd floor office of a building. From a system modeling 
perspective the location of the nodes is important for the performance analysis of the sensor network 
system. 
 
 

 
 

Fig. 1. Nodes Physical Location. 
 
 
3. Overview of the Software Modeling Techniques for Sensor Networks  
 
In this section, we provide an overview of each of the software modeling techniques for sensor 
networks included in our survey. Some of those modeling techniques have been used in the application 
development process, while others take WSNs as a case study for their modeling approach. Some use 
standard software modeling notations, such as UML, while others use their own custom notation. We 
also attempt for each approach to describe how the SensIV system would be modeled using that 
technique. 
 
The techniques use different basic elements, such as channels, processes, modules, and components, to 
express a WSN as a model. A channel is used to represent the communication between two elements 
of a WSN. For example, channels can represent the characteristics of sensor-node communication, 
node-node communication, and node-gateway communication. Processes, modules, and components 
are used to represent the sensors and nodes of a WSN. We will describe details of the modeling 
elements later when we discuss the individual modeling techniques. 
 
The modeling techniques surveyed also vary in terms of the scope of modeling. For example, some 
techniques are intended to model a single node or communication between a pair of nodes while others 
are intended to model the entire WSN. 



Sensors & Transducers Journal, Vol. 14-2, Special Issue, February 2012, pp. 18-46 

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3.1. HL-SDL 
 
HL-SDL [7] is a modeling language that uses the Specification and Description Language (SDL) [8], 
which is normally used to model and simulate communication protocols. SDL has been adapted by 
Dietterle, et al., to model TinyOS components using SDL processes (i.e., extended finite state 
machines). The system is modeled as a collection of channels and processes. The model can be used to 
generate nesC source code, which is commonly used in WSNs based on the TinyOS environment. 
While generating nesC code, each process (which is the smallest unit of the model) represents a 
component in TinyOS In their work, Dietterle et al. used manual optimization to enhance the generate 
code 
 
Their approach proposes that a TinyOS component be considered as the minimal entity to encapsulate 
the behavior of an SDL process by implementing the process state machine. Communication between 
SDL pro