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ENERGY BALANCED TOPOLOGY FOR WIRELESS SENSOR NETWORKS By Hamzeh Jamil Aljawawdeh Supervisor Dr. Iman Musa Almomani This Thesis was Submitted in Partial Fulfillment of the Requirements for the Master s
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ENERGY BALANCED TOPOLOGY FOR WIRELESS SENSOR NETWORKS By Hamzeh Jamil Aljawawdeh Supervisor Dr. Iman Musa Almomani This Thesis was Submitted in Partial Fulfillment of the Requirements for the Master s Degree in Computer Science Faculty of Graduate Studies University of Jordan May, 2013 ii COMMITTEE DECISION This Thesis (Energy Balanced Topology for Wireless Sensor Networks) was Successfully Defended and Approved on 9 th May 2013 Examination Committee Signature Dr. Iman Musa Almomani (Supervisor) Associate Professor of Wireless Networks and Security Dr. Wesam Almobaideen Associate Professor of Computer Networks Dr. Basel Ali Mahafzah Associate Professor of Parallel, Distributed Computing & Interconnection Networks Dr. Wail Mardini Associate Professor of Computer Science Jordan University of Science and Technology iii DEDICATION I lovingly dedicate this thesis to my family and my friends who offered me unconditional love and support each step of the way. In particular, the patience and understanding shown by my parents and my wife is greatly appreciated. Thank you all, without you this degree would remain only a dream. iv ACKNOLEDGMENT First of all thank ALLAH for the wisdom and perseverance that he has been bestowed upon me during this research project, and indeed, throughout my life I would like to express my sincere gratitude to my advisor Dr. Iman Almomani for the continuous support of my master's study and research, for her patience, motivation, enthusiasm, and immense knowledge. Her guidance helped me in all the time of research and writing of this thesis. I could not have imagined having a better advisor for my study. Besides my advisor, I would like to thank Dr. Wesam Al Mobaideen, for his guidance and with whom I have first started learning the computer networks. I would like also to thank all the professors at the University of Jordan for their support and for everything I have learned from them. I would like to thank my colleagues for encouraging and supporting me all the time. Last, but not least, I would like to thank my wife for her understanding and love during the past few years. Her support and encouragement was in the end what made this dissertation possible. My parents receive my deepest gratitude and love for their dedication and the many years of support during my undergraduate studies that provided the foundation for this work. v Table of Contents COMMITTEE DECISION... ii DEDICATION... iii ACKNOLEDGMENT... iv Table of Contents... v List of Figures... vii List of Tables... viii List of Abbreviations... ix Abstract x CHAPTER 1:Introduction Overview Wireless Sensor Network Applications of WSNs Current Challenges of WSNs Problem Statement Research Contribution Thesis Organization CHAPTER 2:Literature Review Flat Based Networks Location Based Routing Protocols Hierarchical Based Routing Protocols Load Balancing Techniques in WSNs Achieve Load Balancing by Constructing a Balanced Tree Achieve Load Balancing by Calculating Next Hop Weight Achieve Load Balancing by Data Compression Load Balancing Techniques Inspired from Animals Behavior Cluster Based Techniques to Achieve Load Balancing Location Based Techniques to Achieve Load Balancing CHAPTER 3:Dynamic Load Balancing Protocol for WSNs Overview Tree Construction Phase Dynamic Load Balancing Phase Link Maintenance and Termination CHAPTER 4:Results and Evaluation Network Scalability... 50 vi 4.2 Network Throughput Routing Overhead Routing Protocol Computations Network Lifetime Summary CHAPTER 5:Conclusions and Recommendations Conclusions Summary for the contributions Future Directions and Recommendations REFERENCES Abstract in Arabic... 70 vii List of Figures FIGURE 1: A TYPICAL SENSOR NODE STRUCTURE... 2 FIGURE 2: SMALL SENSOR NODES... 2 FIGURE 3: VOLCANO ALERT SYSTEM... 4 FIGURE 4: FIRE ALERT SYSTEM IN FOREST USING WSN... 5 FIGURE 5: RANODM NODES DISTRIBUTION FIGURE 6: DLBP ACCUMULATIVE LINK COST FIGURE 7: EXAMPLE OF PATH CHOOSING BASED ON LINKS COST FIGURE 8: DLBP ALGORITHM FIGURE 9: CHILDREN LINK SHARE FIGURE 10: CONSUMED ENERGY VS. NUMBER OF NODES FIGURE 11: NODES REMAINING ENERGY PERCENTAGE VS. NUMBER OF NODES FIGURE 12: NUMBER OF RECEIVED PACKETS VS. NUMBER OF NODES FIGURE 13: NUMBER OF FAILED PACKETS VS. NUMBER OF NODES FIGURE 14: RECEIVED PACKETS RATIO VS. NUMBER OF NODES FIGURE 15: FAILED PACKETS RATIO VS. NETWOK SIZE FIGURE 16: ROUTING OVERHEAD VS. NUMBER OF NODES FIGURE 17: NETWORK LIFETIME. NODE REMAINING ENERGY VS. SIMULATION HOURS... 61 viii List of Tables TABLE 1: DLBP TREE CONSTRUCTION CONTROL MESSAGES TABLE 2: DATA PACKET HEADER TABLE 3: SIMULATION PARAMETERS TABLE 4: NUMBER OF SENT CONTROL PACKETS PER NODE TABLE 5: AVERAGE NUMBER OF COMPUTATION TIMES..60 TABLE 6: DLBP ENHANCEMENTS COMPARING TO XUE ALGORITHM... 63 ix List of Abbreviations ACK ADC ADV ANT-B ANT-F BAN BLLCT BS CH DLBP EBRA EOFS FEAR GEAR GPS ID LEACH MAC REQ SPIN TDMA TEEN TX WSN Acknowledge Message Analog to Digital Converter Advertise Message Ant Backward Ant Forward Body Area Networks Balanced Low-Latency Converge-Cast Tree Base Station Cluster Head Dynamic Load Balancing Protocol Energy Balanced Routing Algorithm Environment and Observation Forecasting System Fuzzy-Based Energy Aware Routing Protocol Geographic and Energy Aware Routing Geographic Positioning System Identifier Low Energy Adaptive Clustering Hierarchy Medium Access Control Request Message Sensor Protocols for Information via Negotiation Time Division Multiple Access Threshold sensitive Energy Efficient sensor Network protocol Transmit Wireless Sensor Network x ENERGY BALANCED TOPOLOGY FOR WIRELESS SENSOR NETWORKS By Hamzeh Jamil Aljawawdeh Supervisor Dr. Iman Musa Almomani ABSTRACT Wireless Sensor Networks (WSN) is currently one of the hot topics for researchers. The limited resources of sensor nodes, such as battery, memory and processor, create a big challenge to researchers. One of the challenges when working on sensor nodes is to overcome the limited resources to create a routing protocol that saves energy and prolong the WSN lifetime. This research presents a Dynamic Load Balancing Protocol (DLBP) for WSN. The presented technique was inspired from Game Theory. It works dynamically to balance the load on all WSN nodes and exploits the network nodes to distribute the load fairly on every available sensor node. Xue algorithm is a dynamic load balancing technique (Xue, et al., 2011). Xue algorithm main idea is to calculate the weights of all neighbor nodes then find the possibility for each link. The data packets will be sent through the link with the highest possibility. A set of simulation experiments were conducted to evaluate the presented protocol through different metrics. Network scalability was studied, and it was found that DLBP saved energy with a ratio reached 20% comparing to Xue algorithm. The success ratio reached 97%, which is 16% better than Xue algorithm. Moreover, the routing overhead decreased by 72%, and the complexity of calculations decreased by %. The network lifetime also increased by 20%. 1 CHAPTER 1 Introduction 1.1 Overview A WSN is a network of hundreds or thousands of wireless sensor nodes. Each node is a small input device that gathers data by sensing the desired environmental parameters such as heat, humidity and movement. Sensors then send the collected data to a more powerful machine called the sink. The sensor node usually measures a physical quantity and converts it into a signal which can be read by an observer or by an instrument. Sensors are hardware devices that produce measurable response to a change in a physical condition like temperature and pressure. Sensors are used to sense or measure physical data of the area to be monitored (Almomani, et al., 2011, a). Figure 1 shows a typical sensor node structure; it shows the sensor main parts which are the transceiver, microcontroller, analog to digital converter (ADC), power unit, and external memory. The transceiver is the part that handles sending and receiving data, it consists of a transmitter and a receiver. The second part is the microcontroller which processes data, performs tasks and controls other parts of the sensor node. Analog to Digital Converter (ADC) is that part which converts continuous analog signals to digital signals or numbers (Wikipedia, 2013). 2 Figure 1: A typical sensor node structure (Wikipedia, 2013) Sensors could be found anywhere as their size could be small and tinny, and this feature increased the number of applications that depend on the sensor networks. Figure 2 shows some sensor types that we may see every day in our lives. Figure 2 shows some small and tinny sensor devices that could be used in some applications (Harvard, 2013). Figure 2: Small Sensor Nodes (Harvard, 2013) (Singularityhub, 2013) Wireless Sensor Network A WSN almost consists of two main things: the first one is the base station (BS) or sometimes it is called the Sink; which is a powerful device that controls and receives signals from wireless sensors in the network (Almomani, et al., 2011, a). The sink also can make some computations and evaluation on the accumulated data (AlKaraki and Kamal, 2004). The second thing in the WSN is the sensors; the WSN consists of hundreds or thousands of sensor nodes that are used to gather data and communicate directly with the sink or among other sensors to deliver the needed data (Ning, 2003). 3 Even though the sensor signal can reach the sink but most of the designers of WSNs prefer to make the communication between sink and sensors among other sensors such that a less energy would be consumed in the communication process (AlKaraki and Kamal, 2004). When the WSN contains more sensors it could be extended to cover a wide area, and the large number of sensors in the WSN should extend the network life such that it could live more and send much more messages to the sink (Ning, 2003) Applications of WSNs WSNs are widely used in many applications such as monitoring applications, military applications, medical care, Environment Observation and Forecasting Systems (EOFS) (AlKaraki and Kamal, 2004). Some of these applications could be very critical or sensitive such that any fault in the WSN may endanger the lives of some people or animals or put them under risk. Therefore any mistakes or weaknesses in the WSN topology or routing algorithm could be very expensive (Ning, 2003). WSNs are commonly used in civil or military applications, and these applications could be classified into data collection and surveillance that includes object tracking as a special case. Another classification is to classify applications into event driven and periodically data collecting applications (Singh, et al., 2010). For data collection hundreds or thousands of sensors may be spread in some field or area to gather some information about the environmental changes. These sensors collect data periodically. In some systems the collected data is aggregated and 4 analyzed to help in keeping the condition under control. As an example of using WSNs in data collecting is spreading sensors in a field to watch humidity, heat, pollution and many other environmental parameters (Jawhar, et al. 2011) (Swain, et al., 2010). Volcano alert system is another example on using WSN on the volcano. WSN could be used to give an early alert if there is an active volcano by sensing the earthquakes and temperature in the volcano location (Jawhar, et al. 2011). Figure 3: Volcano alert system (MCSL, 2013) Monitoring and surveillance applications require spreading or planting sensors in fixed locations such that we can know exactly the place of the event. Data sensing in these application depends on some event so we can call these systems event driven systems. Applications of this type could be used indoor or outdoor. For example they could be used inside a building to monitor the movements or track a person who is walking through the building rooms (Swain, et al., 2010). Forest fires alert system is another example of using WSN. It was found that early alerts could help to stop fires 67 Imad Jawhar, Nader Mohamed, Dharma P. Agrawal, Linear wireless sensor networks: Classification and applications, Journal of Network and Computer Applications, Volume 34 Issue 5, September, 2011, pp W. Jinghua, G. Tingting, H. Huan, and C. Yuanyuan, An energy and load-based routing algorithm in wireless sensor network in Computational Intelligence and Desig. ISCID 09. Second International Symposium, vol. 1, Dec 2009, Changsha, China, pp N.P. Karthickraja, V.Sumathy, A Study of Routing Protocols and A Hybrid Routing Protocol Based on Rapid Spanning Tree and Cluster Head Routing in Wireless Sensor Networks, Wireless Communication and Sensor Computing, ICWCSC International Conference, 2-4 Jan 2010, Chennai, India, pp J. Kulik, W. R. Heinzelman, and H. 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