Robust and energy efficient routing for wireless sensor networks

ROBUST AND ENERGY EFFICIENT ROUTING FOR WIRELESS SENSOR NETWORKS WANG HAIGUANG (Master of Science) NUS A THESIS SUBMITTED FOR THE DEGREE OF DOCTOR OF PHILOSOPHY Department of Electrical and Computer Engineering National University of Singapore October 2009 ABSTRACT In this thesis, we propose a robust and energy efficient routing scheme for wireless sensor networks. There are two main contributions. Firstly, we improve the existing neighbor list management method by shortening the time required for neighbor join and detection of link breakage. Neighbor join here refers to the process of neighbor selection for data forwarding. Neighbor join and link breakage detection are two important features for the mission critical wireless sensor networks which are deployed for military purposes. Secondly, for sensor networks to operate efficiently, we propose that sensor nodes in the network maintain information about their two next-hops, namely the primary and backup next hops. Different strategies have been designed to utilize the information about the two next-hops in order to reduce the transmission cost or increasing the end-to-end reliability. The strategies are based on Markov Decision Process or Bayes rule. They have been implemented in the NS-2 simulator. Simulation results show that the end-to-end reliability is improved and the transmission cost is also reduced with the proposed routing scheme. ACKNOWLEDGMENTS This thesis is the end of my long journey in obtaining my Ph.D degree in the Computer Engineering. During this period, I have received a lot of encouragement and help from several important persons. Without their help, the research work would be much tougher. First I would like to give my very special thanks to Dr. Winston Khoon Guan Seah. Dr. Seah gave me the confidence and support to begin my Ph.D program in Computer Engineering. He allows me to choose the topic of research according to my own interest. Without his guidance, I would not have finished my dissertation. I also acknowledge the support from some of my friends and colleagues in the Institute for Infocomm Research. They are Chan Kwang Mien, Ge Yu, Dr. Sun Peng, Dr. Su Wen, He Dajiang, Dr. Kong Peng-Yong, Dr. Yin Qinghe, Dr. J. Shankar, Dr Ngoh Lek Heng, and Assoc. Prof. Tham Chen-Kong. My sincere thanks to them for their immense encouragement and friendship. Finally, I would like to thank my wife, Wang Meizhen. She provides me a cozy home after each hard-working day. Contents Table of Contents iv List of Figures Introduction 1.1 Features of the Sensor Node 1.2 Potential Applications . . . 1.3 Research Challenges . . . . 1.4 Objective and Motivation . 1.5 Contribution . . . . . . . . . 1.6 Organization of the Thesis . vii . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 10 Background 2.1 Hardware/Software Platforms and Implications . . . . . . . . . 2.2 Design Space of Routing Protocols for Wireless Sensor Networks 2.2.1 Communication Scenarios . . . . . . . . . . . . . . . . . 2.2.2 Design of Routing Protocol . . . . . . . . . . . . . . . . 2.3 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.1 Routing Structure . . . . . . . . . . . . . . . . . . . . . . 2.3.2 Path Selection . . . . . . . . . . . . . . . . . . . . . . . . 2.3.3 Robustness . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.4 Reliability . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.5 Energy Efficiency . . . . . . . . . . . . . . . . . . . . . . 2.3.6 Characteristics of Wireless Channel . . . . . . . . . . . . 2.4 Detailed roadmap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 12 14 14 15 17 17 17 20 21 22 23 26 Neighbor List Management 3.1 Link Quality Measurement . . 3.2 Fast Neighbor Join . . . . . . 3.3 Fast Link Breakage Detection 3.4 Performance Results . . . . . 3.4.1 Simulation Setup . . . 3.4.2 Fast Neighbor Join . . . . . . . . . . . . . . . . . . . . 29 30 32 36 39 39 41 . . . . . . . . . . . . iv . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CONTENTS 3.5 v 3.4.3 Fast Link Breakage Detection . . . . . . . . . . . . . . . . . . Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Routing with Backup Path: Initial Study 4.1 An empirical Study of Lower Power Wireless 4.2 Routing with Redundant Path . . . . . . . . 4.2.1 Primary and Backup path Selections 4.2.2 Dynamic Link Switch . . . . . . . . . 4.3 Performance Results . . . . . . . . . . . . . 4.3.1 Simulation of the Channel . . . . . . 4.3.2 String Topology . . . . . . . . . . . . 4.3.3 Grid Topology . . . . . . . . . . . . . 4.3.4 Random Topology . . . . . . . . . . 4.4 Summary . . . . . . . . . . . . . . . . . . . Channel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Reducing Transmission Cost with Help of Backup Path 5.1 Overall Design . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Channel Model . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Transmission Cost Calculation . . . . . . . . . . . . . . . . 5.4 The Route Selection Method . . . . . . . . . . . . . . . . . 5.5 Channel State Estimation . . . . . . . . . . . . . . . . . . 5.5.1 Channel State Estimation before a Transmission . . 5.5.2 Channel State Estimation after a Transmission . . . 5.6 Performance Results . . . . . . . . . . . . . . . . . . . . . 5.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . Improve End-to-End Reliability with Backup Path 6.1 Objective of Optimization . . . . . . . . . . . . . . . 6.2 Introduction to Markov Decision Process . . . . . . . 6.3 An Abstract Decision Process . . . . . . . . . . . . . 6.4 Random Loss Channel . . . . . . . . . . . . . . . . . 6.4.1 The Decision Process . . . . . . . . . . . . . . 6.4.2 Simple Methods to Derive the Optimal Policy 6.5 Decision Model for the Markov Channel . . . . . . . 6.5.1 The Decision Model . . . . . . . . . . . . . . 6.5.2 Mean Reliability of a Node . . . . . . . . . . . 6.6 Reliability and Scheduling Policy . . . . . . . . . . . 6.6.1 Random Loss Channel . . . . . . . . . . . . . 6.6.2 Markov Channel . . . . . . . . . . . . . . . . 6.7 Performance Results . . . . . . . . . . . . . . . . . . 6.7.1 Simulation Configuration . . . . . . . . . . . . 6.7.2 Random Loss Channel . . . . . . . . . . . . . 6.7.3 Markov Channel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 50 . . . . . . . . . . 51 52 55 58 60 62 63 66 69 70 72 . . . . . . . . . 74 75 75 77 79 81 82 83 85 92 . . . . . . . . . . . . . . . . 93 94 95 96 101 101 104 109 110 115 116 117 119 121 122 125 127 CONTENTS 6.8 6.9 vi Comparison of Different Routing Schemes . . . . . . . . . . . . . . . 129 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 Conclusion 135 7.1 Thesis Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 7.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138 Bibliography 139 List of Figures 1.1 1.2 1.3 An example of wireless sensor networks . . . . . . . . . . . . . . . . . Typical components of a sensor node . . . . . . . . . . . . . . . . . . The Example Sensor Platforms . . . . . . . . . . . . . . . . . . . . . 3 2.1 The MicaZ Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 Probability for P (M (0.75, L) > x) . . . . . . . . . . . . . The distribution of link quality vs. distance . . . . . . . Fast Neighbor Join: String Topology . . . . . . . . . . . Fast Neighbor Join: Grid Topology . . . . . . . . . . . . The distribution of link quality for the random topology Fast Neighbor Join: Random Topology . . . . . . . . . . The grid topology for fast link breakage detection . . . . The performance of fast link breakage detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 41 43 45 45 46 48 49 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 4.10 4.11 4.12 4.13 Environment for the Measurement . . . . . . . . . . . . . . . . Variation of RSS . . . . . . . . . . . . . . . . . . . . . . . . . Impact of Disturbance over Mean RSS . . . . . . . . . . . . . Impact of Disturbance over PRR . . . . . . . . . . . . . . . . Routing with Backup . . . . . . . . . . . . . . . . . . . . . . . The interaction of transmission between routing and link layer Routing State Transition . . . . . . . . . . . . . . . . . . . . . The Channel Model . . . . . . . . . . . . . . . . . . . . . . . . The Routing Path for String Topology . . . . . . . . . . . . . The Packet Reception Ratio for String Topology . . . . . . . . The Mean Cost for String Topology . . . . . . . . . . . . . . . The Routing Path for the Grid Topology . . . . . . . . . . . . The Routing Path for the Random Topology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 54 54 55 56 57 61 63 67 68 69 70 72 5.1 5.2 5.3 5.4 5.5 The The The The The . . . . . . . . . . . . . . . . . . . . 76 86 87 88 89 TSMC Channel Model . . Variation of Link Quality . Grid Topology . . . . . . . state estimation with Bayes Random Topology . . . . . . . . . . . . . . rules . . . vii . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . LIST OF FIGURES viii 5.6 5.7 5.8 The ETX and the ATX . . . . . . . . . . . . . . . . . . . . . . . . . . The reliability achieved with or without backup link . . . . . . . . . . The end-to-end delay achieved with or without backup link . . . . . . 6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9 6.10 6.11 6.12 6.13 6.14 6.15 6.16 6.17 The Abstract Decision Process . . . . . . . . . . . . . . . . . . . . The Decision Process for Random Loss Channel . . . . . . . . . . The State Transition Diagram for a Specific State . . . . . . . . . A typical network topology . . . . . . . . . . . . . . . . . . . . . . Reliability Achieved with Optimal Policy: Random Loss Channel The Percentage of Improvement: Random Loss Channel . . . . . Reliability Achieved with Optimal Policy: Markov Channel . . . . The Percentage of Improvement: Markov Channel . . . . . . . . . Topology of the Network . . . . . . . . . . . . . . . . . . . . . . . Random Loss Channel: the link quality . . . . . . . . . . . . . . . Random Loss Channel: the end-to-end reliability vs. hop count . Random Loss Channel: the end-to-end delay vs. hop count . . . . Markov Channel: the End-to-end reliability vs. hop count . . . . Markov Channel: the End-to-end Delay vs. hop count . . . . . . . Comparison of reliability . . . . . . . . . . . . . . . . . . . . . . . Comparison of delay . . . . . . . . . . . . . . . . . . . . . . . . . Comparison of Transmission Cost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 91 91 99 102 114 116 117 118 120 121 123 124 126 127 128 129 131 132 133 List of Tables 1.1 Features of the Example Sensor Platform . . . . . . . . . . . . . . . . 3.1 3.2 3.3 The fast join threshold value . . . . . . . . . . . . . . . . . . . . . . . Definition of Variables . . . . . . . . . . . . . . . . . . . . . . . . . . Simulation Configuration Parameters . . . . . . . . . . . . . . . . . . 35 37 40 4.1 4.2 4.3 Parameters for the Markov Channel . . . . . . . . . . . . . . . . . . . Simulation Configuration Parameters . . . . . . . . . . . . . . . . . . The PDR and cost of Grid Topology . . . . . . . . . . . . . . . . . . 64 65 70 5.1 State Transition Parameters for TSMC Channel . . . . . . . . . . . . 86 6.1 6.2 States and Action transition probabilities . . . . . . . . . . . . . . . . 112 State Transition Parameters . . . . . . . . . . . . . . . . . . . . . . . 119 ix Chapter Introduction With the advance of technology, computers can be built in small size while still maintaining the capability of data processing and communication. A good example is the wireless sensor platform. A typical sensor node usually has a size close to a coin or even smaller, including the battery. It integrates the computing system, the radio component and the sensing units together on a single tiny platform. The cost is kept relatively low by jointly applying the Complementary Mental-Oxide Semiconductor(CMOS) and micro electro-mechanical structures (MEMS) technologies in the manufacturing. The wireless sensor nodes can be deployed to form a wireless network automatically to collect data from a faraway place and send back to the sink via multiple hops. These features allow wireless sensor networks to have great potential in various applications such as environment monitoring, surveillance and target tracking, etc. It has been identified as one of the 21 key technologies of the 21st century by Business Week 1999 [8]. Figure 1.1 shows a typical configuration of wireless sensor networks. A sink is usually connected to the Internet and is the interface between the user and the sensor network. Sensor nodes themselves are not connected to the Internet directly. 6.9 Summary 134 show that performance can be improved significantly with the help of Markov Decision Process. Currently, the proposed scheme may not be suitable for implementation in real wireless sensor networks due to the complexity in channel characteristics learning and limited on-board memory (4 KB) to MDP-based decision processing. However, in the future, with the progress of technology, sensor nodes can be equipped with more powerful CPU and more on-board memory, thus enabling the proposed method in this chapter to be put into use. Chapter Conclusion 7.1 Thesis Contribution The objective of this thesis is to design a robust and energy efficient routing protocol for the wireless sensor networks. The robust aspect refers to fact that the performance of routing is stable in the presence of link dynamics. We achieved our objectives by improving the agility of neighbor list management and using backup next-hops to overcome the temporary link degradation caused by environmental disturbance. We summarize our achievement as follows: • Firstly, from our observation of the METX-routing proposed in [24, 72], we found that the neighbor list management is important for the ETX based routing. By using the existing link quality estimator, the WMEWMA estimator, we found that the neighbor join and link breakage detection was rather slow. As a result, the network performance is affected when new sensor nodes are added in the network or sensor nodes being disconnected from the network due to the changing of environment dynamic or loss of power. To solve these problems, we provided a method that can estimate whether the link is eligible for rout135 7.1 Thesis Contribution 136 ing without waiting for the WMEWMA estimator to reach a certain threshold value. We also provided a method for the link breakage detection by checking observed consecutive hello message loss. A threshold value is calculated based on the minimum desired link quality and constraints on false alarm. Using this information, a node can safely conclude that the link has broken. Given that the rate of application traffic is known, we also provide a method for the derivation of the threshold value so that the link breakage can be detected based on the application traffic. This method can detect the link breakage faster than using hello messages. Performance results show that our method improves the neighbor join speed by an average of three to five times compared to the original WMEWMA estimator. • Secondly, we found that the disturbance such as the movement of human, animals and vehicles could significantly affect the data transmission of the low power wireless channel. Such disturbance can be common for the wireless sensor networks deployed for environment monitoring and target tracking. To make the routing more robust, we proposed that each node in the network maintained two next hops towards the sink. The node switches to a different next hop when one of them is broken. It is a challenging issue to decide which of the two next hops to use in data forwarding when disturbance happens. We first provided a simple method based on the feedback from the link layer in the retransmission of a packet. Given that maximally K retransmission are allowed at the link layer, a node switches to the backup next hop when K − of them fail. It uses the backup next hop for a while and then switch back to the primary next hop when there is no disturbance. Simulation results showed that the end-to-end reliability had been improved significantly under various topologies. We then investigated the proposed routing scheme in reducing the transmission 7.1 Thesis Contribution 137 cost. Assuming the characteristics of the channel, which is modeled by a Two State Markov Chain, is known, we provided new method for transmission cost calculation and a strategy in selecting the next hop for data forwarding. Simulation results showed that the transmission cost has been successfully reduced with the help of backup path compared to the single path METX-based routing. At the same time, the end-to-end reliability is also improved. We have also explored the potential of backup next hop in maximizing the end-to-end reliability. The objective is to enable the routing to make a decision on each transmission at the link layer based on the information from the physical, link and routing layers so that the end-to-end reliability can be maximized with the selected paths. With the help of Markov Decision Process, we provided an abstract decision framework for the proposed routing scheme that is independent of the channel characteristics. We then extended the framework to the random loss and Markov channels. With the decision process, the backward induction algorithm can be used to search the optimal decision policy. Besides that, we also provide a new method that can find the optimal policy for the random loss channel quickly. Simulation results showed that the reliability was improved for networks with both types of channels. • The energy efficiency in data transmission is always considered in routing design. We use transmission cost as path selection criteria and avoid using the channel when it is in a bad state. This reduces the number of transmissions. We also proposed a more accurate method to calculate the transmission cost when the backup route is used. From our performance results showed in this thesis, we can conclude that our objective of designing a robust and energy efficient routing scheme for wireless sensor 7.2 Future Work 138 network has been achieved. 7.2 Future Work The proposed routing scheme in which one node maintains two next hops toward the sink and the strategies in using them are creative solutions to solve the problems encountered in the wireless sensor network in a harsh environment. However, due to the versatile nature of the disturbance and their impact on data transmission, it is impossible to give a complete solution just in one thesis. In fact, the solution proposed in Chapter and can be implemented in a real network as they depend little on the information from the low layer. On the other hand, for the strategies proposed in Chapter and 6, they depend on the accurate channel characteristics information. 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Routing is a critical issue for multi-hop wireless networks and in the past few years, much effort has been put into research in route discovery and path selection for multihop wireless networks such as Moible Ad Hoc Networks (MANET) and wireless sensor networks Routing protocols for MANET have been designed to handle fast topology changes On the other hand, in wireless sensor networks, nodes are typically... the routing problem due to the dynamics of wireless links and therefore focus on designing robust and energy efficient routing protocols for wireless sensor networks With our scheme, each node maintains two next hops towards the sink for data forwarding We focus on the routing path selection and the strategies of using these paths to achieve different objectives such as reducing the transmission cost and. .. design Wireless sensor networks are different from the Internet and MANETs From the perspective of topology, wireless sensor networks are multi-hop wireless networks like MANETs Each node must play two roles, i.e data source and relay Hence, routing protocols designed for the MANETs may be adopted by wireless sensor networks On the other hand, compared to MANETs, the topology change of wireless sensor network... A routing path can be setup between any node on the Internet or inside the MANET as long as they are connected However, for wireless sensor networks, the many-to-few communication is common for many applications For such networks, they usually consist of two types of nodes, sensor nodes and sinks Compared to the common sensor nodes, sinks 2.2 Design Space of Routing Protocols for Wireless Sensor Networks. .. slow In many applications, the physical topologies of wireless sensor networks are static Therefore, the routing protocols designed for MANET are not optimal for wireless sensor networks because mobility is not the core issue anymore Instead, wireless sensor networks require energy efficient data delivery protocols that can sustain sensor nodes work for a few years with limited power supplies In view of... and security 1.3 Research Challenges 7 for wireless sensor networks Routing As the wireless sensor nodes usually use batteries as the power supply, the radio transmission range is rather short Sensors that are beyond the immediate communication range of the sink must send their data to the sink using multi-hop relaying which means wireless sensor networks are naturally multi-hop wireless networks Routing. .. of Routing Protocols for Wireless Sensor Networks 16 Internet is not suitable for MANETs and new routing protocols have to be designed for them In MANETs, all the nodes have to learn the network topology Since the topology changes from time to time, the routing protocol must be adaptive to the rapid changes of the topology Therefore, handling topology is one of the core issues for routing design Wireless. .. processes and stores the data It is the core of the sensor node and is responsible for the management of the whole platform The communication unit transmits and receives data to and from the network The power unit provides the energy for other units Batteries are the most common power sources for the sensor platform Many sensor platforms have been developed in recent years Figure 1.3(a) shows some sensor. .. traffic lights, and monitor vehicle speed Many of these sensors networked together can provide a clearer picture of the traffic situation Military usage Sensor networks have been used for military purposes for a long time They are used for surveillance and target tracking For example, in the Vietnam War in 1970s, wireless sensor networks were deployed by the US military in the forest and used for tracking... the routing protocols will address the issues of path selection and data forwarding with the wireless channel characteristics in consideration The objective is to design a routing protocol that is robust, reliable and energy efficient in data forwarding 2.3 Related Work 2.3 17 Related Work In recent years, many routing protocols have been designed for wireless sensor networks They addressed the routing . multi- hop wireless networks such as Moible Ad Hoc Networks (MANET) and wireless sensor networks. Routing protocols for MANET have been designed to handle fast topology changes. On the other hand, in wireless. attempts to solve the routing problem due to the dynamics of wireless links and therefore focus on designing robust and energy efficient routing protocols for wireless sensor networks. With our scheme,. ROBUST AND ENERGY EFFICIENT ROUTING FOR WIRELESS SENSOR NETWORKS WANG HAIGUANG (Master of Science) NUS A THESIS SUBMITTED FOR THE DEGREE OF DOCTOR OF PHILOSOPHY Department of Electrical and