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Analysis of Radio Model Performance for
Sensors & Transducers Volume 128, Issue 5, May 2011 www.sensorsportal.com ISSN 1726-5479 Editors-in-Chief: professor Sergey Y. Yurish, tel.: +34 696067716, e-mail: [email protected] Editors for Western Europe Meijer, Gerard C.M., Delft University of Technology, The Netherlands Ferrari, Vittorio, Universitá di Brescia, Italy Editor South America Costa-Felix, Rodrigo, Inmetro, Brazil Editors for North America Datskos, Panos G., Oak Ridge National Laboratory, USA Fabien, J. Josse, Marquette University, USA Katz, Evgeny, Clarkson University, USA Editor for Asia Ohyama, Shinji, Tokyo Institute of Technology, Japan Editor for Eastern Europe Sachenko, Anatoly, Ternopil State Economic University, Ukraine 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 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 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 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 Jaime Calvo-Galleg, Jaime, Universidad de Salamanca, Spain 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 Kratz, Henrik, Uppsala University, Sweden Kockar, Hakan, Balikesir University, Turkey Kong, Ing, RMIT University, Australia Kumar, Arun, University of South Florida, 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, 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 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 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 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 Sandacci, Serghei, Sensor Technology Ltd., UK Sapozhnikova, Ksenia, D.I.Mendeleyev Institute for Metrology, Russia 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 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 Srinivas, Talabattula, Indian Institute of Science, Bangalore, India Srivastava, Arvind K., NanoSonix Inc., USA Stefan-van Staden, Raluca-Ioana, University of Pretoria, South Africa 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 © 2011 by International Frequency Sensor Association. All rights reserved. Sensors & Transducers Journal Contents Volume 128 Issue 5 May 2011 www.sensorsportal.com ISSN 1726-5479 Research Articles Handbook of Laboratory Measurements and Instrumentation: Editorial Review Sergey Y. Yurish................................................................................................................................. I Designing Fuzzy Adaptive Nonlinear Filter for Land Vehicle Ultra-Tightly Coupled Integrated Navigation Sensor Fusion Chien-Hao Tseng, Dah-Jing Jwo ....................................................................................................... 1 Condition Monitoring of a Process Filter Applying Wireless Vibration Analysis Pekka Koskela, Marko Paavola, Jukka Karjanlahti and Kauko Leiviskä............................................ 17 Analysis of Radio Model Performance for Clustering Sensor Networks H. Bello-Salau, A. F. Salami, F. Anwar, Md. Rafiqul Islam ................................................................ 27 Wireless Crankarm Dynamometer for Cycling Caetano Decian Lazzari, Alexandre Balbinot..................................................................................... 39 Prediction of the Surface Oxidation Process of AlCuFe Quasicrystals by Using Artificial Neural Network Techniques Moh'd Sami S. Ashhab, Abdulla N. Oimat and Nabeel Abo Shaban. ................................................ 55 Online Remote Recording and Monitoring of Sensor Data Using DTMF Technology Niladri Sekhar Tripathy, Sagarika Pal ................................................................................................ 66 Detection of VX Simulants Using Piezoresistive Microcantilever Sensors Timothy Porter, Richard Venedam, Kevin Kyle and Gus Williams..................................................... 73 Study of a Modified Displacement Transducer of a Piston in a Power Cylinder S. C. Bera and Madan Bhowmick....................................................................................................... 81 Development of an Eletromiograph and Load Cell for the Silent Period Generation and Measurement in Myoelectric Signals of the Masseter and Temporal Muscles Alexandre Balbinot, Jeferson Figueró Feijó ....................................................................................... 89 New Method for Fabrication of Co3O4 Thin Film Sensors: Structural, Morphological and Optoelectronic Properties Vikas Patil, Shailesh Pawar, Manik Chougule, Bharat Raut, Ramesh Mulik, Shashwati Sen ........... 100 Langmuir-Schäfer Film of Calix[4]pyrrole Exhibiting Sensing Properties with Gas Anesthetic Agent Sevoflurane Salvatore Petralia ............................................................................................................................... 115 Authors are encouraged to submit article in MS Word (doc) and Acrobat (pdf) formats by e-mail: [email protected] Please visit journal’s webpage with preparation instructions: http://www.sensorsportal.com/HTML/DIGEST/Submition.htm International Frequency Sensor Association (IFSA). Sensors & Transducers Journal, Vol. 128, Issue 5, May 2011, pp. 27-38 Sensors & Transducers ISSN 1726-5479 © 2011 by IFSA http://www.sensorsportal.com Analysis of Radio Model Performance for Clustering Sensor Networks H. BELLO-SALAU, A. F. SALAMI, F. ANWAR, Md. RAFIQUL ISLAM Department of Electrical and Computer Engineering International Islamic University Malaysia (IIUM) P. O. Box 10, 53100, Gombak, Malaysia E-mail: [email protected] Received: 15 February 2011 /Accepted: 20 May 2011 /Published: 28 May 2011 Abstract: Contemporary advancements in the field of wireless technology and sensor instrumentation have led to the proliferation of novel protocols which are specially designed for low-power networks such as wireless sensor networks. In the field of wireless sensor networks, researchers are currently interested in tackling the challenge of designing an optimum routing protocol that will satisfy the application and network requirements with minimum consumption of energy. Cluster-based routing protocols have been found to be more advantageous to other types of routing techniques when there is a relatively huge number of a sensor node to be deployed. This paper is an investigation of the performance of selected cluster-based routing protocols with special emphasis on the effectiveness of their radio propagation models. Copyright © 2011 IFSA. Keywords: Clustering routing techniques, Radio models, Sensor networks, Propagation methods. 1. Introduction Recent advances in signal processing, digital and microelectronics technology and wireless communications have tremendously led to the development of smart and miniaturized sensor devices. As a result of this technological advancement, the design and implementation of wireless sensor networks was made possible by creating opportunities for using sensor nodes for monitoring remote scenarios or events [1-4]. Applications of sensor networks include wildlife migration tracking, wild fire monitoring, reconnaissance and tactical surveillance, weather monitoring and ubiquitous computing [1-3, 5]. In a wireless sensor network, a number of nodes collaboratively work together in order to achieved a global task by forwarding the desired information to the sink. The nature of data 27 Sensors & Transducers Journal, Vol. 128, Issue 5, May 2011, pp. 27-38 transmission to the sink depends on cogent factors such as the amount of residual energy, timecriticality of data and other constraints due to the inherent limited processing and communication capabilities of the sensor nodes [1, 3, 4]. Customarily, sensor nodes are designed to be deployed in places where it can be very difficult to replace dead nodes or even recharge their in-built batteries. Therefore, the major objective of designing wireless sensor networks is to maximize the network lifetime by minimizing energy consumption during the transmission and reception of data. Low duty cycling which is the strategic turning on and off of sensor radio based on sensing demands is one of the solutions proffered for minimizing energy consumption. Other energy conservation techniques are data filtering, data compression, data aggregation and data fusion [2-4]. A number of routing algorithms have been proposed for wireless sensor networks [5, 6]. However, the task of developing energy-efficient routing protocols is a challenge due to the inherent limitations of the sensor nodes. Researchers in this field are currently exploring the concept of cluster-based routing which promises a better solution to the problem of energy consumption when compared to other types of routing [3, 7-12]. Furthermore, it has been claimed in the literature that despite the fact that clustering introduces overheads due to network management operations, they possess better energy conservation ability when compared to flat-based routing protocols [3, 4]. This paper is an investigation of the performance of selected cluster-based routing protocols with special emphasis on the effectiveness of their radio propagation models. The aim of this research work is to foster better understanding of clustering sensor networks and highlight possible areas of improvement for further investigation. The remainder of the paper is organized as follows: Section 2 presents a survey of selected clustering routing protocols. Section 3 summarizes the key findings of the investigated protocols and Section 4 concludes this paper. 2. Clustering Routing Techniques In this section, a survey of selected clustering routing protocols and their radio model for wireless sensor networks are reviewed. 2.1. Low-Energy Adaptive Clustering Hierarchy The Energy consumption in wireless sensor network [2, 7] is reduced by an adaptive and selforganizing protocol called the Low-Energy Adaptive Clustering Hierarchy (LEACH), the use of randomized rotation of cluster heads so that energy dissipation is shared evenly among all participating sensor nodes is the underlying idea behind LEACH.[2-4, 6]. Mechanism of Operation: the set up phase and the steady phase are the two phase’s category of operation of LEACH. A random number in the range of 0 and 1 is selected by a sensor node in the setup phase, if the number is greater than a specified threshold, the sensor node will be elected as a cluster head. The newly-elected cluster head will advertise to other nodes after selecting it as the cluster heads, each node will determine the cluster to belong to based on the signal strength of the advertisements received, minimum communication energy is required for node nearer to the cluster head due to the presence of strong signal strength. Afterwards, the nearest cluster heads to the nodes is notified of their interest in becoming a cluster member. The Time Division Multiple Access (TDMA) is the principle used by the cluster heads to allocate the time for sending data after cluster formation. Subsequently, 28 Sensors & Transducers Journal, Vol. 128, Issue 5, May 2011, pp. 27-38 the nodes start sensing and sending data to cluster heads. Prior to sending data finally to the sink, data aggregation is performed by the cluster heads. After successfully communicating the data to the sink, a new cluster heads is selected because the network reconfigures itself. Lastly, a single-hop communication is used by LEACH [3, 5-7]. Analysis of Performance: the data aggregation performs help enhanced the network lifetime and transmission of redundant data is reduced, the randomized rotation of the cluster head minimized the energy consumption. The intra-cluster and inter-cluster collisions are reduced by the use of TDMA MAC scheme and negotiation. However, the use of single-hop communication which is ineffective and energy consuming for long distance communications cause the scalability problem facing the use of LEACH in a dense network scenario. The conserved energy is diminished by the extra overhead introduce such as cluster head advertisements by the use of dynamic clustering. The periodic data transmissions can rapidly drain the limited amount of energy since Data collection is centralized and periodic. There is the possibility of cluster heads being unfairly concentrated in a network segment due to non-uniform distribution of cluster heads. Hence, some nodes will suffer by not having cluster heads in their locality. Radio model: In LEACH radio propagation model assumes that the device is placed one meter above the ground. This can degrades performance and scalability of wireless simulators. More so, a misleading result may be gotten due to an inappropriate configuration of model parameters. 2.2. Threshold-Sensitive Energy-Efficient Sensor Network Protocol For time-critical applications, TEEN (Threshold-Sensitive Energy-Efficient Sensor Network) and APTEEN (Adaptive Periodic Threshold-Sensitive Energy-Efficient Sensor Network) were proposed in [8] and [9] respectively. A protocol developed to respond to abrupt changes in the sensed attributes is called TEEN [3-5]. Mechanism of Operation: The cluster formation is done by grouping nodes that are nearer to each other as clusters at the start. Higher priority is assigned to cluster heads of clusters nearer to the sink while lower priority is assigned to cluster heads of clusters farther from the sink [1, 3, 5-6, 8]. Hard and soft threshold are the two threshold disseminated by cluster heads to cluster members after cluster formation. The minimum possible value of the sensed attribute that will trigger nodes to turn on their radio for transmitting data to their cluster heads is the hard threshold. The transmission of the data by the node will begin if the following conditions are true: (1) The present value of the sensed attribute’s is greater than the hard threshold (2) The present value sensed attribute’s differs from the previous sensed value by an amount equal to or greater than the soft threshold specified. Consequently, soft threshold helps in reducing transmissions when there are no considerable changes in the sensed attributes [1, 3-6, 8]. An extension of TEEN called APTEEN, aims at reacting to time-critical events and capturing periodic data collections. The user demands and application type serve as the base use by APTEEN in changing the threshold values used in TEEN. Cluster formation is made by the base station and elected cluster heads distribute these parameters, (1) Thresholds, (2) Attributes, (3) Schedule and (4) Count Time. In APTEEN, the conditions for data transmission are just like TEEN. Data aggregation is performed by cluster heads to save energy [1, 3-6, 9]. 29 Sensors & Transducers Journal, Vol. 128, Issue 5, May 2011, pp. 27-38 Analysis of Performance: Redundant data transmission is prevented by reducing the number of transmission by the soft and hard threshold which leads to energy conservation. APTEEN provide a wide range of flexibility by enabling users to set the count time interval and minimize energy consumption. However, the inability to communicate if the thresholds are lost is a drawback of TEEN. The inapplicability for networks where periodic readings need to be delivered to the sink is one of the scalability problems of TEEN. In such a situation, there is the possibility that useful data will not be transmitted because the attributes’ values might not reach the threshold at all. Also, another limitation is that message can get lost if cluster heads are not in each other’s transmission radius. The complexity and overhead related to (1) cluster formation and (2) threshold management and query handling are the common weakness of both TEEN and APTEEN. Radio model: In TEEN and APTEEN, the radio model assumes that the distance between the sensors is very short hence there is limited radio range. This is in accordance with the first order radio model proposed for LEACH. This can degrades scalability and performance of wireless simulators. More so, a misleading result may be gotten due to an unsuitable configuration of model parameters. 2.3. Geographic Adaptive Fidelity Protocol Geographic Adaptive Fidelity (GAF) is a protocol initially developed for mobile ad hoc networks (MANETs) but realized to be useful for sensor networks [3, 5, 10]. In each grid area, a node serves as a leader to convey data to other nodes which is the basic idea behind GAF, unlike other cluster routing protocols, these leader nodes do not perform data aggregation [4, 6, 10]. Mechanism of Operation: A Global Positioning System (GPS) is use by nodes to associate themselves with a location in the virtual grid after the protocol has commences by forming a virtual grid over the deployed area. Clusters are form by nodes associated with the same location known as equivalent nodes [3-5, 10]. The discovery, active and sleeping states are the three states GAF is made up of. In the discovery state, other neighboring nodes in the same grid are discovered by nodes, by exchanging messages for specified time duration. Afterwards, routing is performed during the allotted active time and radios of active nodes are turned off for a specified sleeping period. Allowing nodes to change from sleeping to active state will ensure Load balancing. Ensuring that each node in the grid calculates it’s leaving time and send it to its neighbors support mobility. Before the leaving time of the active node expires, one of the sleeping nodes wakes and becomes active. GAF is designed both for non-mobility (GAF-basic) and mobility (GAF-mobility adaptation) [3-6, 10]. Analysis of Performance: This protocol conserves energy by discovering corresponding nodes and turning off idle nodes. As a result, GAF can considerably increase network life span as the number of sensor nodes increases. However, the use of GPS technology which is too energy-demanding and costly for a huge and dynamic network is one of the scalability problems of this algorithm. Aside this, this algorithm determines travel time in order to support mobility. This might be difficult or nearly impossible to estimate in larger networks with unfavorable environmental conditions when nodes are deployed in such area. Radio model: In GAF a two ray ground model is used which accounts for the effect of multipathing 30 Sensors & Transducers Journal, Vol. 128, Issue 5, May 2011, pp. 27-38 and fading. Compared to other LEACH-type radio schemes, this provides better performance and it is more suitable for large scale sensor networks. However, there is a need for a more accurate radio model that will yield practical results that describe the real behavior of the network. 2.4. Periodic, Event-driven and Query-based Routing Protocol Periodic, event-driven and query-based (PEQ) protocol is intended for networks which are used as surveillance systems operating under critical conditions. The use of hop level of nodes to minimize redundant data transmission is the basic idea behind PEQ [3, 4, 11]. Mechanism of Operation: The shortest distance from each sensor node to the sink is find which is the base for configuring the entire network by the protocol. Initiation of this configuration process is done by the sink via broadcasting the hop value, time-stamp, and source address to nearest neighbors. Afterwards, nodes will store, increment and send the hop level to the next neighboring nodes. The hop value for each node is compares with the one in the packet. Update is carried out and retransmission is done if the hop value is greater. This process continues until the whole network is configured [3, 4, 11]. Just as in the configuration process, the sink broadcasts subscription message over the network. If an event matching the sink's interest is detected by the node, the event packet will be send to its neighboring node by the node. When data reaches the sink, the event notification and data delivery process ends. An ACK-based repair mechanism is also implemented by this protocol [3, 4, 11]. Analysis of Performance: In a large network scenario, PEQ uses multi-hop communication which is simple and effective for long distance communication. Energy consumption is reduced and low latency is ensured by using optimal path routing. An ACK-based repair mechanism is used to maintained reliability. However, the flooding and broadcasting of configuration and subscription messages is a major concern. When the network grows larger and becomes more dynamic it becomes a major problem. In such scenario, there will be mismanagement of the scarce energy resources due to redundant transmission and reception of data. Radio model: In PEQ, radio propagation model assumes that the distance between the sensors is too short hence there is limited radio range. This is in accordance with the first order radio model proposed for LEACH. This can degrades performance and scalability of wireless simulators. More so, a deceptive result may be gotten due to an inappropriate configuration of model parameters. 2.5. Clustering Periodic, Event Driven and Query based Routing Protocol In Clustering Periodic, Event-driven and Query-based (CPEQ) protocol, a sensor node with more energy is selected as cluster heads. Cluster heads form clusters and cluster members communicate with their respective cluster heads [3, 4, 11]. Mechanism of Operation: Just like in the PEQ protocol, this protocol starts with network configuration. The propagation of an additional field to specify the percentage of nodes that can become cluster heads is the only difference. The cluster head selection process is based on LEACH [3, 4, 11]. 31 Sensors & Transducers Journal, Vol. 128, Issue 5, May 2011, pp. 27-38 The next stage after selecting the cluster heads is the cluster configuration stage, where broadcasting notifications is used by cluster heads to form their clusters. This is the same process as in the configuration phase of PEQ. Whenever an event is sensed by a node, they relay it to their respective cluster heads. This data routing scheme is also similar to the one used in PEQ. Furthermore, just like in the PEQ algorithm CPEQ also employs an ACK-based path repair mechanism. [3, 4, 11]. The cluster heads performed data aggregation on the incoming data to reduce redundancy. Afterward, the aggregated data will be transmitted by the cluster heads to the sink via the shortest path. The event and data delivery process is similar to the one employed in PEQ [3, 4, 11]. Analysis of Performance: All the strengths of PEQ is also possessed by this algorithm, specifically; low energy consumption, support for low latency; support for reliability and simplicity. The aggregation of data which saves energy by reducing repetitive data transmission is another merit of this algorithm. However, the redundant transmission and reception of packets in the configuration process is a major concern. High amount of energy will be wasted in the transmission of and listening to unwanted or unnecessary packets in a highly-dense network scenario. Radio model: In CPEQ, radio propagation model assumes that the distance between the sensors is very short hence limited radio range device is used. Hence there is limited radio communication range; this is similar to the first order radio model proposed by LEACH. This can degrades performance and scalability of wireless simulators. More so, due to an inappropriate configuration of model parameters, a misleading result may be gotten. 2.6. Energy Efficient Inter-cluster Communication based Routing Protocol A protocol designed for periodic, event-driven and query-based networks is the Energy Efficient Intercluster Communication based (ICE) algorithm. Cluster heads and nodes nearest to each other within two adjacent clusters help to accomplished message routing. As a result, data transmission is carried out via short transmissions [3, 4, 12]. Mechanism of Operation: The setup phase begins this protocol where the network is configured just like in the PEQ and CPEQ protocol. The cluster head selection is based on LEACH [3, 4, 12]. The cluster configuration process where cluster heads form clusters by broadcasting notifications to neighboring nodes is similar to that of the CPEQ algorithm. The discovery of free nodes which do not belong to any cluster is a unique property of this protocol. Notification messages are sent to adjacent nodes by free nodes. These neighboring nodes forward their requests to their cluster heads [3, 4, 12]. An improved version of the ACK-based path repair mechanism employed in PEQ and CPEQ is used in this algorithm. One of the adjacent clusters is selected to help relay the data, whenever a cluster member has data to send to the sink. A node belonging to an adjacent cluster will receive the transmitted data and that node will send the message to its cluster head. The data is finally delivered to the sink by following this sequence [3, 4, 12]. Analysis of Performance: This protocol has the merits of CPEQ and PEQ, namely; data aggregation, support for reliability, simplicity and support for low latency. Using nearest neighbors, energy is conserved as a result of short-range transmissions. The use of multipath routing ensures load balancing, network longevity and fault tolerance. Quality of Service (QoS) is provided using the leastcost path and Notifications are prioritized. 32 Sensors & Transducers Journal, Vol. 128, Issue 5, May 2011, pp. 27-38 However, the inability to form a logical line for clustering is a drawback. This implies that no nearest neighbors will be discovered and data transmission will be negatively affected. Redundant transmission and reception of packets are highly likely to occur. In a scenario where the network size is increasingly growing and becoming more dynamic, the network management can be costly and difficult. Radio model: In ICE, radio propagation model assumes that the distance between the sensors is very short hence there is limited radio communication range. This is similar to the first order radio model proposed for LEACH. This can degrades performance and scalability of wireless simulators. More so, a misleading result may be gotten due to an inappropriate configuration of model parameters. 3. Radio Propagation Models in Wireless Sensor Networks 3.1. Free Space Path-Loss Radio Model Free space path-loss radio propagation model used in wireless sensor network is popularly known as the first order radio model in LEACH and many other cluster-based routing protocols [7, 9]. It assumes that there is only one clear line-of-sight path in the ideal propagation condition between the transmitter and receiver. The communication range is basically represented as a circle around the transmitter in the free space model. All the packets are received from the transmitter if a receiver is within the circular range else, it loses all packets [7, 8, 13]. The received signal power in free space at distance d from the transmitter can be calculated by using the equation below: (1) , where, Pt is the transmitted signal power. Gt and Gr are the antenna gains of the transmitter and the receiver respectively. L (L≥1) is the system loss and is the wavelength [7, 13]. 3.2. Two-Ray Ground Reflection Model This is also known as the second-order model and it is used in GAF which considers both the direct path and the ground reflection path. A single line-of-sight path between two nodes is often the only means of propagation [10]. This model gives a more accurate prediction at a long distance than the free space path-loss radio model [14]. However, due to the oscillation caused by the constructive and destructive combination of the two rays, a misleading result may be obtained for short distance [13]. The predicted received power at distance d is obtained by the equation below , (2) where, ht and hr are the heights of the transmitting and receiving antennas respectively. This model predicts the received power as a deterministic function of distance by representing the communication range as an ideal circle [10, 13]. 33 Sensors & Transducers Journal, Vol. 128, Issue 5, May 2011, pp. 27-38 3.3. Shadowing Model This model was recently adopted by researchers in sensor network as the third order model [14], the model was able to overcome the effect of multipath propagation also called fading effect which results in the random variable obtained for the received power at certain distance [13, 14]. Nodes can only probabilistically communicate when near the edge of the communication range thus extending the ideal circle model to a richer statistic model [13]. The model consists of two parts namely the path loss model and the log-normal random variable. The first part known as the path loss model uses a close-in distance d0 as reference in predicting the mean received power at distance d [13], denoted by Pr(d) which is computed relative to Pr(d0) [13, 14] as shown below: (3) , where is usually empirically determined by field measurement and is called the path loss exponent. The variation of the received power at certain distance is being reflected by the second part of the shadowing model which is a log-normal random variable and of Gaussian distribution if measured in dB [13]. The complete log-normal shadowing model is represented below: (4) , where, XdB is a Gaussian random variable with zero mean and standard deviation shadowing deviation [13]. dB known as the 4. Numerical Results The performance metrics of the radio model on energy consumption and network lifetime for the clustering sensor network is examined; a clustering wireless sensor network is simulated in a field of dimensions 50 m by 50 m using MATLAB and OMNET++. The total number of sensors node deployed is n=100. The initial energy of a lower energy node is set to E0 = 0.5 Joules. The energy dissipated by the transceiver, cluster head and amplifier are 50 nJ/bit, 5 nJ/bit/report and 10 pJ/bit/m2 respectively. The message packet size that sensor nodes forward to cluster-head as well as the message packet size that cluster-head forward to the sink is 2000 bits. The performance metrics used in this work are energy consumption and network lifetime. Two of the clustering routing protocols are used for comparison thus the Low-Energy Adaptive Clustering Hierarchy (LEACH) is selected because it uses the First-Order radio model just like other clustering routing protocols discussed above except the Geographic Adaptive Fidelity Protocol (GAF) which uses the second-order radio model. Hence LEACH and GAF are being compared. 4.1 Energy Consumption Analysis This metric is measured as the total amount of energy exhausted in the entire sensor network from the start of the network operation till the last alive node is dead. In Fig. 1 and Fig. 2 (below), it is observed that the energy available for the sensor network decreases gradually as the time round increases, however it is seen in Fig. 1 (LEACH) that energy is expended faster than that of Fig. 2 (GAF) thus 34 Sensors & Transducers Journal, Vol. 128, Issue 5, May 2011, pp. 27-38 shortening the lifespan of the network. one of the factor which account for the longevity of the network lifespan of Fig. 2 below (GAF) is the used of the second-order radio model and other enhancements in the protocol which cater for a better allocation and management of energy among sensor nodes than the first-order radio model employed in LEACH and thus it exhibits a better performance on average when compared to that of LEACH. Fig. 1. Total Network Energy for LEACH. Fig. 2. Total Network Energy for GAF. 35 Sensors & Transducers Journal, Vol. 128, Issue 5, May 2011, pp. 27-38 4.2. Network Lifetime Analysis This metric is measured as the time interval from the commencement of operation by the sensor nodes until the last alive node is dead. As shown in Fig. 3 (LEACH) and Fig. 4 (GAF) below. It is observed that the network lifetime for LEACH is shorter than that of GAF for it is seen from Fig. 3 that after about 1,512 time rounds about 60 % of the sensor nodes were already dead and thus terminating the lifespan of the sensor network while in Fig. 4 it is seen that before the dead of the 60 % of sensor node which lead to the termination of the sensor network, it was after about 2,375 time rounds. Thus we can deduce that the network lifetime of GAF in Fig. 4 is longer due to the employment of the second-order radio model and other enhancements in the protocol which provide a better allocation and management of energy among sensor nodes compared to that of LEACH in Fig. 3 that uses the first-order radio model. Fig. 3. Network Lifetime. Fig. 4. Network Lifetime. 36 Sensors & Transducers Journal, Vol. 128, Issue 5, May 2011, pp. 27-38 5. Summary of Features In this section, a summary of the key features of the investigated clustering routing protocols and their radio propagation model is presented in the table below for easy and quick comparison. Table 1. Summary of Features. Radio Radio Propagation Model Model Simplicity FIRSTORDER HIGH LEACH FIRSTORDER HIGH TEEN FIRSTORDER HIGH APTEEN SECONDORDER LOW GAF FIRSTORDER HIGH PEQ FIRSTORDER HIGH CPEQ FIRSTORDER HIGH ICE Radio Radio Model Radio Query- Energy Optimal Fault Model EnergyModel Multipath based Awareness Route Tolerance Accuracy Efficiency Sensitivity LOW LOW LOW NO NO YES NO NO LOW LOW LOW NO NO YES NO NO LOW LOW LOW NO NO YES NO NO MEDIUM MEDIUM MEDIUM NO NO YES NO NO LOW LOW LOW YES YES YES YES YES LOW LOW LOW YES YES YES YES YES LOW LOW LOW YES YES YES YES YES 6. Conclusion Radio modeling has attracted considerable attention of researchers in the field of wireless sensor networks. Sensor nodes are organized in clustering sensor networks such that propagation of message is achieved with minimal energy to the sink via cluster heads. Recently, researchers have shown that an improved radio propagation model will assist in conserving energy and improving network lifetime. This paper investigated some selected clustering routing protocols, outlined their key features and analysis of their radio model performance. LEACH is ineffective for large-scale sensor networks due to the assumption of limited radio communication range and short inter-node distance. Other routing techniques address this challenging issue by considering the performance of multi-hop communication. Most of the routing techniques use a LEACH-type first order radio model except GAF which uses a two-ray ground model which accounts for the effects of fading and multipathing on designing an accurate radio model. The key issues that need further investigation are energy-efficient radio propagation modeling, designing a realistic radio model that accurately describes practical scenarios, tradeoff between simplicity and accuracy and finding optimal radio model, network reliability and fault tolerance. References [1]. Mohammad Ilyas (Editor) and Imad Mahgoub, Handbook of Sensor Networks: Compact Wireless and Wired Sensing Systems, CRC Press, LLC, 2005, pp. 120-138. [2]. I. F. Akyildiz, W. Su, Y. Sankarasubramaniam and E. Cayirci, Wireless Sensor Networks: A Survey, Elsevier Computer Networks Journal, Vol. 38, Issue 4, 15 March 2002, pp. 393-422. [3]. A. F. Salami, F. Anwar and A. U. Priantoro, An Investigation into Clustering Routing Protocols for Wireless Sensor Networks, Sensors & Transducers, Vol. 105, Issue 6, June 2009. [4]. A. F. Salami, S. M. S. Bari, F. Anwar and S. Khan, Feasibility Analysis of Clustering Routing Protocols for Multipurpose Sensor Networking, in Proceedings of the 2nd International Conference on Multimedia and Computational Intelligence (ICMCI 2010), 29-30 September, 2010. [5]. A. F. Salami, F. Anwar, A. M. Aibinu and A. H. Abdalla, Investigative Analysis of Clustering Routing 37 Sensors & Transducers Journal, Vol. 128, Issue 5, May 2011, pp. 27-38 Protocols for Scalable Sensor Networks, Submitted to the 4th International Conference on Mechatronics, Kuala Lumpur, Malaysia, 17-19 May, 2011. [6]. Jamal N. Al-Karaki and Ahmed E. Kamal, Routing Techniques in Wireless Sensor Networks: A Survey, IEEE Wireless Communications Journal, Vol. 11, Issue 6, December 2004, pp. 6-28. [7]. W. R. Heinzelman, A. Chandrakasan and H. 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