Energy Efficient MAC for Wireless Sensor Networks

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Oct 26, 2010 - Corres, Jesus, Universidad Publica de Navarra, Spain. Cortes, Camilo .... Sysoev, Victor, Saratov State Technical University, Russia. Szewczyk ...

Sensors & Transducers Volume 121, Issue 10, September 2010

www.sensorsportal.com

ISSN 1726-5479

Editors-in-Chief: professor Sergey Y. Yurish, tel.: +34 696067716, fax: +34 93 4011989, 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 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 Kockar, Hakan, Balikesir University, Turkey Kotulska, Malgorzata, Wroclaw University of Technology, Poland Kratz, Henrik, Uppsala University, Sweden 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 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 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 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 Schneider, John K., Ultra-Scan Corporation, USA Sengupta, Deepak, Advance Bio-Photonics, India Shah, Kriyang, La Trobe University, Australia Sapozhnikova, Ksenia, D.I.Mendeleyev Institute for Metrology, Russia Saxena, Vibha, Bhbha Atomic Research Centre, Mumbai, India

Seif, Selemani, Alabama A & M University, USA Seifter, Achim, Los Alamos National Laboratory, 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 Srinivas, Talabattula, Indian Institute of Science, Bangalore, India Srivastava, Arvind K., Northwestern University, 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, 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 © 2010 by International Frequency Sensor Association. All rights reserved.

Sensors & Transducers Journal

Contents Volume 121 Issue 10 October 2010

www.sensorsportal.com

ISSN 1726-5479

Research Articles Computational Sensor Network: Book Review Sergey Y. Yurish.................................................................................................................................

I

ANN Modeling of a Chemical Humidity Sensing Mechanism Souhil Kouda, Zohir Dibi, Fayçal Meddour, Abdelghani Dendouga and Samir Barra........................

1

Design of Artificial Neural Network-Based pH Estimator Shebel A. Alsabbah, Maazouz A. Salahat and Mohammad K. Abuzalata.........................................

10

Improved RBF Neural Network Based Soft Sensor: Application to the Optimal Robust Calibration of a Six Degrees of Freedom Parallel Kinematics Manipulator Dan Zhang and Zhen Gao..................................................................................................................

18

Real Time Interfacing of a Transducer with a Non-Linear Process using Simulated Annealing S. M. GirirajKumar, K. Ramkumar, Bodla Rakesh, Sanjay Sarma O. V. and Deepak Jayaraj ..........

29

Visible and Near Infrared (VIS-NIR) Spectroscopy: Measurement and Prediction of Soluble Solid Content of Apple Herlina Abdul Rahim, Kim Seng Chia and Ruzairi Abdul Rahim. ......................................................

42

Control System Design for Cylindrical Tank Process Using Neural Model Predictive Control Technique M. Sridevi, P. Madhavasarma, S. Sundaram .....................................................................................

50

Application of Genetic Algorithm for Tuning of a PID Controller for a Real Time Industrial Process S. M. Giri Rajkumar, Atal. A. Kumar, N. Anantharaman. ...................................................................

56

Modeling and Control of Multivariable Process Using Intelligent Techniques Subathra Balasubramanian, Radhakrishnan T. K..............................................................................

68

Limitations of Feedback, Feedforward and IMC Controller for a First Order Non-Linear Process with Dead Time Maruthai Suresh and Ranganathan Rani Hemamalini .......................................................................

77

Embedded Based DC Motor Speed Control System Chandrasekhar T., Nagabhushan Raju K., V. V. Ramana C. H., Nagabhushana KATTE and Mani Kumar C..............................................................................................................................................

94

Real Time Implementation of a DC Motor Speed Control by Fuzzy Logic Controller and PI Controller Using FPGA G. Sakthivel, T. S. Anandhi, S. P. Natarajan......................................................................................

106

IDC Based Battery-free Wireless Pressure Sensor Jose G. Villalobos, Zhen Xu, and Yi Jia .............................................................................................

121

Energy Efficient MAC for Wireless Sensor Networks Pekka Koskela, Mikko Valta and Tapio Frantti................................................................................... 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).

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Sensors & Transducers ISSN 1726-5479 © 2010 by IFSA http://www.sensorsportal.com

Energy Efficient MAC for Wireless Sensor Networks Pekka KOSKELA, Mikko VALTA and Tapio FRANTTI Network Technologies, VTT, Kaitoväylä 1, P.O. Box 1100, Finland Tel.: +358 40 751 3902, fax: +358 20 722 2320 E-mail: [email protected]

Received: 10 August 2010 /Accepted: 18 October 2010 /Published: 26 October 2010

Abstract: This paper considers an overlay solution for asynchronous Medium Access Control (MAC) protocols in a duty-cycled wireless sensor network (WSN). The solution extends sleeping times and corrects time drift when the sampling rate is low. The sleeping time is adjusted according to the requisite data sampling rate and the delay requirements of the prevailing application. This and the time drift correction considerably reduced idle listening and thus also decreased power consumption. When the power consumption is reduced, the life of wireless sensor nodes extends. Copyright © 2010 IFSA. Keywords: Energy efficiency, Medium access control, Wireless sensor network.

1. Introduction A wireless sensor network (WSN) consists of spatially distributed autonomous wireless devices that use sensors to monitor physical or environmental conditions such as temperature, sound, vibration, pressure, motion or pollutants at different locations. Wireless sensor nodes are typically batterypowered devices with very limited energy resources, memory and computational capabilities, thus placing special requirements on sensor networks. Due to the limited energy resources, one of the most important constraints of sensor networks is energy efficiency. Most of the energy is consumed for communication (transmission and reception consume almost equal amounts of energy) between devices. Energy consumption during communication can be more than a thousand times higher than energy consumption during the sleeping time. Medium Access Control (MAC) protocols control communication and directly affect the duration times of duty cycles and the sleeping times of wireless sensor nodes. Therefore, this paper considers an energy-efficient overlay Medium Access Control (MAC) protocol for wireless sensor networks to extend the life of battery-powered sensor nodes. The 133

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developed revive MAC (R-MAC) protocol shortens the duty cycle and extends the sleeping time. The sleeping time is adjusted according to the required data-sampling rate and the delay requirements of the prevailing application. The rest of the paper is organized as follows. The section 2 presents a literature review of related work. Section 3 describes the developed R-MAC protocol. In section 4, a test environment is introduced. Section 5 covers performance analysis and results. Finally, discussions and conclusions are presented in sections 6 and 7, respectively.

2. Literature Review Energy saving at the MAC level can be mainly achieved through two complementary controls: topology control and duty-cycle control.

2.1. Topology Control Protocols Topology-control protocols like GAF [1] and ASCENT [2] define a minimum set of nodes that can maintain connectivity in networks. The nodes that do not participate in communication can be asleep. In GAF, scheme nodes utilize geographic location information to associate themselves with a “virtual grid”. In a grid square, all nodes are equivalent with respect to forwarding packets. Nodes in the same grid decide which one will sleep and which will stay awake and for how long. ASCENT [2] is an adaptive scheme in which the number of active nodes depends on communication performance. Initially only some nodes are active. If there is a very high packet loss when the source node is transmitting, the sink node starts to request help from neighboring nodes. When the neighboring nodes receive the help request, they can decide to change from passive to active mode and inform the other neighboring nodes. The other way to take advantage of topology is to assume a topology- (traffic pattern) specific form as in DMAC [3]. This is designed to allow packets to be forwarded continuously through WSN from node to sink. This protocol assumes that data are formed in trees that remain stable for a reasonable period of time. Because the data gather in trees, it is possible to stagger the wakeup scheme so that packets flow continuously from sensor to sink. The protocol also assumes fixed-length packets. To send multiple packets, more data flags are used to inform other nodes that there will be more packets. The disadvantage of this protocol is that it can only be used in fairly stable networks in which the gathered trees are fairly stable. Nodes also need some local time synchronization, which causes extra control traffic.

2.2. Duty-cycle Control Protocols Duty-cycle control protocols can be classified to on demand, asynchronous, and synchronous or mixing, i.e., a hybrid protocols.

2.3. On-demand Protocols On-demand protocols are based on technology in which nodes can wake each other up on demand. These systems comprise different kinds of multi-radio solutions such as Chunlong et al. [4], Nosovic et al. [5], and Doorn et al. [6], in which nodes have two radios: one low-power radio for waking up and one radio for actual communication. The advantage of this kind of system is that it can easily be scaled to different kinds of sampling rates and methods (event, interval, and request) and can achieve very low power consumption with low latency. In terms of cost, the disadvantage is that it needs two radio chips. 134

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2.4. Asynchronous Protocols In asynchronous protocols, such as B-MAC [7] and X-MAC [8], nodes follow their own constant sleep/awake cycle and transmit data whenever they need to do so. B-MAC is a CSMA/CA-based protocol that employs a preamble sampling scheme to reduce the duty cycle and minimize idle listing time. If a node wishes to transmit, it precedes the data packet with a preamble that is slightly longer than the sleep period of the receiver. During the awake period, a node samples the medium and, if a preamble is detected, it remains awake to receive the data. The B-MAC protocol provides an interface by which an application can adapt a suitable sampling scheme to reduce energy consumption. One shortcoming of B-MAC is the long preamble, which consumes transmitter energy and causes overhearing. X-MAC [8] improves energy consumption compared with B-MAC through shorter preambles. In order to send a packet, a node broadcasts a train of short strobe packets. The strobe packet train is long enough to allow all nearby devices to be switched on at least once. After receiving a strobe packet, a node checks the address information of the strobe. If it is the node's address, it sends a short acknowledgment packet and prepares to receive a full packet. Otherwise the node goes back to sleep.

2.5. Synchronous Protocols Synchronous, i.e., TDMA-based approaches like WiseMAC [9], S-MAC [10], T-MAC [11] and NanoMAC [12] are scheduled systems that create schedules in a centralized or distributed way. S-MAC sets up local synchronization between neighboring nodes, and the nodes can select their own fixed listen/sleep schedules. The schedule information is exchanged by broadcasting an SYNC packet between nodes located right next to each other. In order to maintain synchronization, nodes periodically listen to a whole synchronization period. The shortcoming of the S-MAC is the fixed active time period, which prevents it from being adapted to a variable load. Other disadvantages are the required control traffic to maintain synchronization and the memory space needed for that information. As the size of the network increases, S-MAC has to maintain a greater number of neighbor schedules, which incurs an additional overhead through the repeated rounds of resynchronization. The TDMA-based protocols have a built-in duty-cycle and can avoid collisions and reduce idle listening time efficiently. The disadvantage of these protocols is that they require coordination and synchronization to allocate TDMA slots. Ad hoc network maintenance of slot synchronization, in particular, causes extra control traffic overhead and makes TDMA schemes complex and resource-hungry. Timeout-MAC (T-MAC) [11] is an S-MAC successor that has been proposed to improve the operation of S-MAC under variable traffic load. T-MAC changes fixed active time to adaptive active time. Adaptivity is provided by observing the activation events (like data reception and transmission) of a node. If no activation event has occurred after the specified time, the node goes to sleep. All the traffic must therefore be buffered between activity periods and sent in bursts at the beginning of the next active period. Under variable traffic load, T-MAC improves the throughput but increases the delays and decreases the scalability. NanoMAC [12] is a CSMA/CA-based protocol that is specifically designed for high-density wireless networks. In NanoMAC, a node may operate in four sleeping groups that are assigned by applications. These groups use different wake-up cycle periods but are synchronized with one another, enabling them to communicate. Compared with T-MAC, NanoMAC decreases the amount of control traffic, but its sleeping schedules are not as flexible as those of T-MAC. According to its developers, NanoMAC also outperforms the B-MAC protocol in congested networks. 135

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The WiseMAC [9] protocol improves communication in the downlink direction in infrastructure networks in which sampling is done regularly. Access points learn the sampling schedules for all sensor nodes and start the transmission at the right time. Sensor nodes have independent schedules and are not synchronized with each other. Hence, wake-up preambles are used for synchronization between the sender and the receiver and to correct the clock drifts between them.

2.6. Hybrid Protocols The idea of hybrid methods is to combine the best properties of different methods while reducing their shortcomings. There are several combinations of synchronous and asynchronous methods. TRAMA [13] is a TDMA-based algorithm proposed to improve energy efficiency. The idea of energy saving is to allow nodes to determine time slots in which they do not have to participate in communication so that they can switch to idle mode. The question of which nodes should transmit and receive during each time slot is based on a two-hop neighborhood and on an information exchange that covers the transmission schedule in order to select the transmitter and receiver. To enable broadcasting signaling time, space is divided into random access and schedule access periods. Random access is used for control traffic and schedule access for data traffic. The disadvantage of the protocol is that it is more complicated than in basic TDMA and needs extra control traffic, processing and memory to update the duty slots. PDMA (Probabilistic TDMA) [14] combines features of the ordinary time division and the random access (RA) schemes. The proposed access method can vary from one extreme (TDMA) to the other (RA) by adjusting the value of a single parameter in a formula used to define medium time division. The advantage of the scheme is that it provides a seamless transition for TDMA to random access and thus improves channel utilization compared with pure TDMA. The disadvantage is that, e.g. from the point of view of ad hoc networks, it has the same scalability and synchronous problems as conventional TDMA. Z-MAC [15] combines TDMA and CSMA, in which nodes maintain the local slot assignment. The slot space is further divided into dedicated slots, in which the owner and one-hop neighbor nodes have priority for sending as well as free slots, which can use any node to compete with the CSMA method. The advantage of Z-MAC compared with conventional TDMA is that small changes in network topology can be handled locally and improve the utilization of free slots. The disadvantages are the initial configuration cost and the fact that it is more complex than conventional TDMA. Our solution differs from the previous approaches being an overlay solution which improves energy efficiency of base asynchronous MAC. The solution operates like a hybrid protocol that attempts to couple advantages of both asynchronous and synchronous approaches at a minimal cost. The presented solution improves the asynchronous base MAC operation and energy efficiency, especially in dutycycled WSNs that have very low sampling rates.

3. R-MAC Protocol Design The design goals of the R-MAC protocol for asynchronous MAC in duty-cycled WSNs are: o To make use of the advantages of both the asynchronous and synchronous approaches with minimum trade off; o To improve the performance of the asynchronous MAC protocol, especially energy efficiency, in cases where the sampling rate is very low; 136

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o To introduce an overlay solution that can be integrated without unreasonable effort to the asynchronous MAC protocol in WSN; o To ensure that latency is not increased compared to a base MAC solution; o To ensure a minimal overhead cost of synchronization between the sample rate and the wake-up cycling. As described in the previous section, the asynchronous protocols follow constant sleep/awake cycles and transmit data whenever needed. That kind of operation is simple and appropriate when the exact time of transmission is not known or the regular sampling interval is short. When the sampling rate decreases, the inefficiency in the power consumption increases: the asynchronous protocols follow their constant sleep/awake rhythm even though the need for wake-ups is reduced. These causes increasingly idle listening, thus wasting energy. When designing the R-MAC, the goal was to find a solution that improved performance, especially idle listening, of an asynchronous base MAC during regular transmissions with a low sampling rate. In order to avoid idle listening, the receiver node should know when it can stay asleep and when it has to wake up in order to receive data packets. This means that synchronization has to be based on the activity of the node and the sampling rate. There are several methods to synchronize nodes in terms of time, such as GPS. The shortcoming of those methods is that they remarkably increase the amount of control traffic. In addition to the time synchronization, the system needs to synchronize the sampling rates and active periods. This is the drawback of the time synchronization protocols. In our approach, additional time synchronization packets were avoided by piggybacking tiny synchronization information in preamble ACK (acknowledgment) packets that are sent in the base MAC protocol before each data transmission. The active cycle synchronization was based on centrally collected sampling rate information. When synchronization is set up, it allows the node to stay asleep until needed.

3.1. Time Synchronization and Drift Correction An oscillator is one of the biggest power drains when low-capacity devices are on standby. By selecting a less accurate oscillator, energy can be saved but the trade off is increased clock drift. At the same time, when sleeping time is extended from seconds to days, the correction of clock drift has to be taken in consideration. Ideally, the sender and receiver will wake up at the same time but because of the drift, the time lag between the sender and the receiver will increase. In our approach, the time lag was reduced by including its information (length 16 bits) in the ACK packet which the receiver sends to the sender before each data transmission. When the sender receives the ACK packet, it reads the time lag information and also calculates how many preambles were sent and how much time it consumed before the ACK was received. The total time lag is a sum of the lag information. According to the total time lag, the sender schedules the active periods, as shown in Fig. 1. 3.2. Sampling Rate Synchronization The main aim of the developed R-MAC protocol is to shorten the duty cycle and extend the sleeping time, especially in cases where a regular sampling rate is low. The sleeping time can be extended by adjusting the radio on and off periods according to the requisite sampling rate and the requirements of the applications. In our approach, this information was collected in a centralized way with a suitable device such as a gateway or a database. The system selects the main minimal sampling rate that satisfies all the requested sampling rates and delay requirements based on the sampling rate information. The main sampling rate is distributed by broadcasting it over the network during the system configuration. 137

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Fig. 1. The developed overlay R-MAC protocol.

If some of the requested sampling rates differ greatly from the main sampling rate, a different sampling rate can also be set for specific routes. In order to have a connection between all the nodes, the sampling rates must be a multiple of the main sampling rate. This way, there are also connections between faster and slower sampling rate nodes. Unused individual routes expire after a specified time and are reset to the main sampling rate. The fast sampling route is set by sending an uncast message with sampling rate information along the route. The routes which will be selected as individual routes depend on the WSN policy used.

3.3. Operation of Protocol The protocol has two main states: sleep and active. The sleep cycle can follow the X-MAC (chosen as base MAC, see chapter 4) scheme or the much longer sleep period of R-MAC. After each long sleep period, the node wakes up to operate in the X-MAC sleep/awake cycle, see Fig. 1. In the active mode, the node listens for any preambles sent to it. If the preamble is received, the node sends ACK and waits to receive data. After the data is received, the node keeps on the X-MAC mode for a couple of 138

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sleep/awake cycles before it changes back to the R-MAC sleep cycle. If no preamble is received, the node also stays in a couple of X-MAC sleep/awake cycles before it goes back to the longer sleep period. This way, new nodes have possibility to join the network.

4. Evaluation The target was to improve performance of asynchronous protocols in duty-cycled WSNs. The authors of B-MAC showed that their protocol surpasses existing protocols in terms of throughput, latency and, in most cases, energy consumption. According to the findings of the X-MAC developers, the use of a short preamble train instead of B-MAC's long preamble further improves power efficiency. For that reason, we selected X-MAC for inspection, where we compared X-MAC with and without our overlay R-MAC solution.

4.1. Experimental Setup The test environment consists of wireless sensor nodes, i.e., Tmote devices. These devices have a Texas Instruments MSP430 8 MHz microcontroller unit (MCU) [16] with Chipcon's CC2402 radio [17]. The MCU has 10 kB Random Access Memory (RAM) and 48 kB of flash memory. The radio is IEEE 802.15.4 compliant, yielding a data rate of 250 kbps in the 2.4 GHz ISM (Industrial, Scientific and Medical) band. The original operating system TinyOS [18] was replaced with ContikiOS [19, 20]. One reason for the change was that ContikiOS has energy-estimation software and it uses native C as a programming language. The Tmote devices have a USB interface, which can be connected to a PC. This connection was utilized for test control and monitoring purposes, such as comprehensive data logging.

5. R-MAC Performance 5.1. Energy Efficiency The energy consumption was calculated with energy-estimation software implemented in ContikiOS [21]. The software determines how long different hardware parts are active. When the times are known, the total power consumption in each sleep/awake cycle can be calculated by multiplying time by the component’s power consumption. Momentary power consumption of the Tmote device was measured with an oscilloscope using a shunt resistor (see Fig. 2).

Fig. 2. Energy consumption measurement. 139

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The results were compared with the values provided by the component supplier, Table 1. Because ContikiOS was utilized to turn off respective hardware components, there were differences between the values measured and those provided. For example, in the MCU standby measurement, some parts of the Tmote, such as the radio chip, the voltage regulator, the crystal oscillator and the analogue light sensor might be in a different power state than in the reference measurements. In addition, we were not able to set MCU to deep “sleep” because the system cannot recover after that.

Table 1. Power consumption of the tmote device. Current consumption MCU on, RX on MCU on, TX on MCU on, Radio off MCU idle, Radio off MCU standby

NOM 21.8 19.5 1.8 54.4 5.1

MAX 23 21 2.4 1200 21

Measured 23 25 5 400 30

Unit mA mA mA A A

Fig. 3. compares energy consumption between the original X-MAC and the X-MAC integrated with R-MAC. The figure clearly shows the advantage of using R-MAC when the sampling rate decreases. When the sampling interval is longer than 180 s, the power consumption of R-MAC solution decreases by 10 % compared to the original X-MAC. From the figure, it can also be seen that after 300 seconds, the improvement achieved with R-MAC saturates to a minimum of 6.5 %. This is due to the fact that when the sampling rate decreases, the MCU (and not the radio) is the main power drain, as shown on Fig. 4. The reason that the MCU becomes the main power drain is that during the sleeping period, the other hardware parts can be shutdown but the MCU that contains an oscillator needs to be on standby. The figure also clearly shows that our solution reduces the useless listening of X-MAC. At 5 minute sampling rate the average power consumption for the X-MAC with the developed overlay R-MAC is 55 A and for the X-MAC, 686 A, as seen on Table 2. It can be calculated that with a standard AA cell (2450 mAh [22]) the lifetime of node is 5 months with the X-MAC and correspondingly 62 months with the overlay R-MAC.

Power consumption: X-MAC & R-MAC / X-MAC

Power Consumption: X-MAC with R-MAC / X-MAC Operating voltage 3V 45 % 40 % 35 % 30 % 25 % 20 % 15 % 10 % 5% 0% 0

100

200

300

400

500

600

700

800

900 1000

Sampling rate (Seconds)

Fig. 3. Software based online energy consumption estimate. 140

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Average power consumption of one sleep/awake cycle 800 700 600 500 400 300 200 100 0

60 50 40 30 20 10 0 X-MAC

R-MAC

Average current ( μA ) / cycle

Average Current ( μA ) / cycle

Cycle periode 5 minutes; Operating voltage 3.0 V

RADIO, listen RADIO, transmit RADIO, idle CPU, active CPU, LPM

R-MAC

Fig. 4. Power consumption between original X-MAC and X-MAC integrated with R-MAC, when the cycle period is 5 minutes.

Table 2. Power consumption of the Tmote node (base MAC is X-MAC) with and without overlay R-MAC at 5 minute sampling rate and correspondingly lifetime with standard AA cell battery.

X-MAC X-MAC with R-MAC

Current consumption at 5 minute sampling rate [A] 686 55

Lifetime of node [months] 5 62

5.2. Latency X-MAC follows a constant sleep/awake cycle. When the node has something to transmit, it starts to send a short preamble train until it receives ACK from the receiver. Because every node independently follows its own sleep/awake rhythm, the duration of the sender's preamble time is random, where the maximum time is close to the time between sequential wake ups. In our approach, the receiver and sender wake ups are synchronized with one other, thus reducing the average amount of preambles to ten whereas for X-MAC, the amount is typically 120 in the worst case and 60 in the average case. Because in most of the cases, our approach has the shorter preamble duration, the latency is thus also smaller than in X-MAC. Our approach has always some tiny extra latency because wake-up synchronization leaves tolerance between the receiver and sender wake-up times. The sender should wake up a moment before the receiver does to make sure that the sender does not miss the receiver's listening period. After the system configuration, the total latency will be about 8 ms. If the sender misses the period, it will operate like X-MAC in the worst case scenario.

5.3. Throughput Field of applications in which data transmission intervals are a several dozen minutes, hours, days or even months, throughput is rarely a problem. In any case, our approach performs as well as X-MAC (i.e. base MAC) in terms of throughput. If a high throughput is needed, the system can decrease a “long” sleeping period to zero, which means that the system operates just like X-MAC. In case R-MAX is used on top of other asynchronous MACs such as B-MAC, the situation is the same. 141

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5.4. Integration To correct time lags, R-MAC needs information on the existing time lag from the base MAC. If the base MAC uses ACK messages before the original data transmission, then the implementation is quite straightforward as time lag information can be transformed to R-MAC. To set up a longer sleep period, R-MAC needs information about the sampling rate requirements and to do so, a central device is needed to collect the requirements and decide the main and individual sampling rates. After the decision is made, the main sampling rate is distributed by broadcasting to WSN and individual sampling rates are unicasted to specific routes. The implementation of overlay R-MAC in a node requires 722 bytes of extra code, when base MAC is X-MAC. It is thus logical to assume that in corresponding cases (such as base MAC is B-MAC), the cost of implementation would be similar.

6. Discussion Integrating the developed overlay R-MAC to asynchronous MAC saves a considerable amount of energy in low sampling rate duty-cycled WSNs. If the base MAC has an ACK mechanism, then synchronization information can be integrated to the ACK message, thus avoiding extra control packages and keeping the cost of synchronization low. Generally, the proposed solution reduces delay because wake ups are synchronized. The exception is when the packets are lost. If retransmissions are delayed for some reason and the next nodes have time to go to sleep for long periods, resending has to be delayed until the next active period, thus producing a considerable delay in transmission. The main duty cycle is a compromise between the application requirements and therefore, it is not always optimal. In the case of multiple hops, it will take several data transmission cycles until the system is configured. The reason for this is that every transmitter adjusts its time reference to the next receiver's time and drift correction takes place step by step starting from the sink end. If the sampling rate information is not available in a centralized way, a distributed method should be used. In the distributed method, the system learns the sampling rates. The distributed method has weak scalability, however, and it needs a lot of control traffic.

7. Conclusions This paper considered an overlay solution, R-MAC, for asynchronous Medium Access Control (MAC) protocols in duty-cycled wireless sensor network (WSN). The developed overlay R-MAC protocol saves energy and extends the lifetime of wireless sensor nodes by shortening the duty cycle and extending the sleeping time. The result proved that with a low sampling rate (>5 seconds), energy consumption can be significantly reduced by increasing the sleeping time. With up-to-date technology devices, however, there is a threshold (about 15 min, when the base MAC is X-MAC) after which the energy consumption of the radio is negligible and other sources like MCU and the reading of sensors dominate the energy consumption. Thus, the final power improvement was 6.5 % compared to the consumption of the base MAC (i.e. X-MAC). Suitable main sleeping intervals are determined and set in a centralized way, thus requiring less traffic than distributed methods. The trade offs resulting from overlay R-MAC integration are additional control information (16 bit/ACK) and more code (~ 0.7 kB, when the base MAC is X-MAC) in nodes,.

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References [1]. X. Ya, H. John, and E. Deborah, Geography-informed energy conservation for ad hoc routing, in Proceedings of the 7th Annual International Conference on Mobile Computing and Networking (MobiCom ’01), New York, NY, USA, 2001, pp. 70–84. [2]. A. Cerpa and D. Estrin, Ascent: Adaptive self-configuring sensor networks topologies, in Proceedings of the ACM/IEEE INFOCOM, New York 2002, June 2002. [3]. G. Lu, B. Krishnamachari, and C. S. Raghavendra, An adaptive energy efficient and low-latency MAC for data gathering in wireless sensor networks, in Proc. of the International Symposium on Parallel and Distributed Processing, Vol. 13, 2004, p. 224a. [4]. J. Chunlong Guo, Lizhi Charlie Zhong, Rabaey, Low power distributed MAC for ad hoc sensor radio networks, in Proc. of the IEEE Global Telecommunications Conference (GLOBECOM ’01).Vol. 5, 2001, pp. 2944–2948. [5]. T. Nosovic, W. Todd, Scheduled rendezvous and RFID wakeup in embedded wireless networks, in Proc. of the IEEE International Conference on Communications (ICC’ 02), Vol. 5, No. 2002, pp. 3325–3329. [6]. L. K. Doorn B., Kavelaars W., A prototype low-cost wakeup radio for the 868 MHz band, International Journal of Sensor Networks Archive, Vol. 5. [7]. J. Polastre, J. Hill, and D. Culler, Versatile low power media access for wireless sensor networks, in Proc. of The 2nd ACM Conference on Embedded Networked Sensor Systems (SenSys), 2004, pp. 95–107. [8]. E. A. M. Buettner, G. V. Yee and R. Han, X-MAC: a short preamble MAC protocol for duty-cycled wireless sensor networks, in Proceedings of the 4th International Conference on Embedded Networked Sensor Systems, 2006, pp. 307–320. [9]. A. El-Hoiyi, J.-D. Decotignie, and J. Hernandez, Low power MAC protocols for infrastructure wireless sensor networks, in Proceedings of the 5th European Wireless Conference, 2004. [10].W. Ye, J. Heidemann, and D. Estrin, An energy-efficient MAC protocol for wireless sensor networks, in Proc. of the 21st IEEE Conference of the Computer and Communications Societies (INFOCOM), 2002, pp. 1567–1576. [11].V. Dam and K. Langendoen, An adaptive energy-efficient MAC protocol for wireless sensor networks, in Proc. of the 1st ACM Conf. on Embedded Networked Sensor Systems (SenSys 2003), 2003, pp. 171–180. [12].J. Haapola, Mac energy performance in duty cycle constrained sensor network and effect of sleep, in Proc. of The International Workshop on Wireless Ad-Hoc Networks, 2004, 2004, pp. 320–324. [13].O. K. Rajendran V. and G.-L.-A. J. J., Energy-efficient, collision-free medium access control for wireless sensor networks, Journal Wireless Networks. [14].A. Ephremides and O. A. Mowafi, Analysis of a hybrid access scheme for buffered users-probabilistic time division, IEEE Trans. Softw. Eng., Vol. 8, No. 1, 1982, pp. 52–61. [15].I. Rhee, A. Warrier, M. Aia, J. Min, and M. L. Sichitiu, Z-mac: a hybrid MAC for wireless sensor networks, IEEE/ACM Trans. Netw., Vol. 16, No. 3, 2008, pp. 511–524. [16].Texas instrument’s ultra low power MSP430-series microcontroller, (http://www.ti.com/msp430). [17].Chipcon corporation, IEEE 802.15.4 compliant low-cost transceiver (cc2420), (http://www.chipcon.com/files/CC2420 Data Sheet 1 3.pdf). [18].TinyOS, (http://www.tinyos.net/) [19].ContikiOS, (http://www.sics.se/contiki/) [20].B. G. A. Dunkels and T. Voigt, Contiki - a lightweight and flexible operating system for tiny networked sensors, in Proceedings of the 1st IEEE Workshop on Embedded Networked Sensors, 2004. [21].N. T. A. Dunkels, F. Osterlind and Z. He, Software-based online energy estimation for sensor nodes, in Proceedings of the 4th IEEE Workshop on Embedded Networked Sensors (Emnets IV), 2007. [22].Batteryholders, (http://www.batteryholders.org/aa.pdf). ___________________

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