On structure less and everlasting data collection in wireless sensor networks

ON STRUCTURE-LESS AND EVERLASTING DATA COLLECTION IN WIRELESS SENSOR NETWORKS DISSERTATION Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University By Kai-Wei Fan, B.S., M.S ***** The Ohio State University 2008 Dissertation Committee: Approved by Prasun Sinha, Adviser Anish Arora David Lee Adviser Graduate Program in Computer Science & Engineering c Copyright by Kai-Wei Fan 2008 ABSTRACT Computing and maintaining network structures for efficient data aggregation incurs high overhead for dynamic events where the set of nodes sensing an event changes with time Prior works on data aggregation protocols have focused on tree-based or cluster-based structured approaches Although structured approaches are suited for data gathering applications, they incur high maintenance overhead in dynamic scenarios for event-based applications The goal of this dissertation is to design techniques and protocols that lead to efficient data aggregation without explicit maintenance of a structure We propose the first structure-free data aggregation technique that achieves high efficiency Based on this technique, we propose two semi-structured approaches to support scalability We conduct large scale simulations and real experiments on a testbed to validate our design The results show that our protocols can perform similar to an optimum structured approach which has global knowledge of the event and the network In addition to conserving energy through efficient data aggregation, renewable energy sources are required for sensor networks to support everlasting monitoring services Due to low recharging rates and the dynamics of renewable energy such as solar and wind power, providing data services without interruptions caused by battery runouts is non-trivial Moreover, most environment monitoring applications require ii data collection from all nodes at a steady rate The objective is to design a solution for fair and high throughput data extraction from all nodes in the network in presence of renewable energy sources Specifically, we seek to compute the lexicographically maximum data collection rate for each node in the network, such that no node will ever run out of energy We propose a centralized algorithm and an asynchronous distributed algorithm that can compute the optimal lexicographic rate assignment for all nodes The centralized algorithm jointly computes the optimal data collection rate for all nodes along with the flows on each link, while the distributed algorithm computes the optimal rate when the routes are pre-determined We prove the optimality for both the centralized and the distributed algorithms, and use a testbed with 158 sensor nodes to validate the distributed algorithm iii To my family iv ACKNOWLEDGMENTS First and foremost, I would like to express my sincerest gratitude to my Adviser, Dr Prasun Sinha, for the guidance and support in the last four years This work would have never reached completion without all the discussions and brainstorming with him His advice and patience make this work possible I am fortunate to have him as my adviser I am also thankful to my research committee members, Dr Anish Arora and Dr David Lee for their invaluable inputs and comments to make this work complete I would also like to express my gratitude to my colleagues in our research group, Sha Liu, Ren-Shiou Liu, and Zizhan Zheng, and my friends Ming-Feng Hsieh, YenChen Lu, and Yi-Wen Kuo, for numerous collaborations and discussions I would also like to thank Chi-Hsien Yao, Yu-Neng Li, Xu Wang, for being such wonderful friends Finally, I would like to thank all my family members for their unconditional love and support To my parents for giving my such a wonderful family, to my sister for looking after me, and to my brother for being so supportive v VITA May 25, 1975 Born - Hsinchu, Taiwan 1997 B.S Computer Science & Information Engineering, National Chiao Tung University, Taiwan 1999 M.S Computer Science & Information Engineering, National Chiao Tung University, Taiwan 1999-2004 Software Engineer & Project Manager, Formosoft Inc., Taiwan 2007 M.S Computer Science & Engineering, The Ohio State University 2004-present Graduate Teaching & Research Associate, The Ohio State University PUBLICATIONS Research Publications Kai-Wei Fan, Sha Liu, and Prasun Sinha “Dynamic Forwarding over Tree-on-DAG for Scalable Data Aggregation in Sensor Networks” IEEE Transactions on Mobile Computing (TMC), preprint, Apr 2008, doi:10.1109/TMC.2008.55 Ren-Shiou Liu, Kai-Wei Fan, and Prasun Sinha “ClearBurst: Burst Scheduling for Contention-Free Transmissions in Sensor Networks” IEEE Wireless Communications and Networking Conference (WCNC), pages 1899-1904, March 2008 Sha Liu, Kai-Wei Fan, and Prasun Sinha “CMAC: An Energy Efficient MAC Layer Protocol Using Convergent Packet Forwarding for Wireless Sensor Networks” Fourth vi Annual IEEE Communications Society Conference on Sensor, Mesh, and Ad Hoc Communications and Networks (SECON), pages 11-20, June 2007 Kai-Wei Fan, Sha Liu, and Prasun Sinha “Structure-free Data Aggregation in Sensor Networks” IEEE Transactions on Mobile Computing (TMC), August 2007 Kai-Wei Fan, Sha Liu, and Prasun Sinha “Scalable Data Aggregation for Dynamic Events in Sensor Networks” 4th ACM Conference on Embedded Networked Sensor Systems (SenSys), pages 181-194, November 2006 Kai-Wei Fan, Sha Liu, and Prasun Sinha “On the Potential of Structure-free Data Aggregation in Sensor Networks” IEEE INFOCOM, pages 1-12, April 2006 Sha Liu, Kai-Wei Fan, and Prasun Sinha “Dynamic Sleep Scheduling using Online Experimentation for Wireless Sensor Networks” in Proceedings of SenMetrics, July 2005 Wen-Her Yang, Kai-Wei Fan, and Shiuh-Pyng Shieh “A Secure Multicast Protocol for The Internet’s Multicast Backbone” ACM/PH International Journal of Network Management, March/April 2001 Wen-Her Yang, Kai-Wei Fan, and Shiuh-Pyng Shieh “A Scalable and Secure Multicast Protocol on MBone Environments” Information Security Conference, Taiwan, May 2000 Instructional Publications Sha Liu, Kai-Wei Fan, and Prasun Sinha “Protocols for Data Aggregation in Sensor Networks, chapter in book titled Wireless Sensor Networks and Applications” Springer Verlag’s book series Network Theory and Applications, 2005 Kai-Wei Fan, Sha Liu and Prasun Sinha “Ad-hoc Routing Protocols, chapter in book titled Algorithms and Protocols for Wireless and Mobile Networks” CRC/Hall Publisher, 2004 vii FIELDS OF STUDY Major Field: Computer Science and Engineering Studies in: Computer Networking Database System Operations Research Prof Prof Prof Prof Prof Prasun Sinha Anish Arora David Lee Hakan Ferhatosmanoglu Marc E Posner viii TABLE OF CONTENTS Page Abstract ii Dedication iv Acknowledgments v Vita vi List of Tables xii List of Figures xiii Chapters: Introduction 1.1 1.2 10 11 Structure-Free Data Aggregation 12 2.1 2.2 2.3 2.4 12 14 22 24 25 1.3 1.4 1.5 Background Data Aggregation in Wireless Sensor Networks 1.2.1 Cluster-Based Approaches 1.2.2 Tree-Based Approaches Rate Allocation in Rechargeable Sensor Networks Contributions Organization of the Dissertation Objective Spatial Convergence for Data Aggregation Temporal Convergence for Data Aggregation Analysis 2.4.1 Expected Number of Transmissions ix Fig 5.14 shows the total number of packets received at the sink and the percentage of nodes running out of energy during the 24 hours of simulation From the figure we can see clearly that NAVG can hardly receive any packets for about 2.5 hours of the time, from 5:50 to 8:15 due to a lot of nodes running out of energy during that period DLEX-A is better, however its throughput is also affected severely during the same period because the nodes that run out of energy are usually those nodes close to the sink When they run out of energy, they can not forward packets for other nodes, thus resulting in severe drop of the throughput DLEX is affected only for a small portion of time because it has fewest nodes running out of energy for very short duration Again, if we store finer grained recharging profile on sensor nodes, we can maintain stable throughput for entire simulation for all nodes This shows that DLEX performs better in terms of uniformly collecting data across time NAVG 100 DLEX-A 40 20 100 DLEX 40 20 0:00 Percentage (%) Througput (K pkts) 100 50 100 6:00 12:00 Time 18:00 24:00 Figure 5.14: Total number of packets received (top) and ratio of nodes out of energy 147 5.4.5 Topology In this experiment we vary the transmission power to create networks with different sizes and densities With lower transmission power, nodes have few choices for selecting the routing paths in shortest path tree, and the diameter, i.e., the maximum hop count of the network, will increase too Fig 5.15 shows the results of three different transmission powers We can see that with higher transmission power, we can get better lexicographic rate assignment This is due to the higher density of the network In high density network, there will be more nodes that are one hop away from the sink The size of subtrees rooted at these one hop nodes will be smaller, compared with the subtrees of one hop nodes in low density network which has fewer number of one hop nodes Since the first hop nodes are usually the bottleneck nodes, fewer nodes in the subtree implies higher share of the available energy thus resulting in higher achievable rate 12 -10 dBm -5 dBm dBm 10 Rate(pkt/s) 0 20 40 60 80 100 120 140 Figure 5.15: Rate assignment in different network topology 148 5.5 Summary In this chapter we study the rate assignment problem for rechargeable sensor networks We propose a centralized algorithm and a distributed algorithm for optimal lexicographic rate assignment The centralized algorithm computes the optimal rate for each node along with determining the amount of flow on all links, while the distributed algorithm computes the optimal rate when the routing tree is pre-determined We prove the optimality of both centralized and distributed algorithms To evaluate the proposed distributed algorithm, we conduct experiments using a testbed with 158 sensor nodes under various scenarios How to jointly compute the rates for all nodes and the flows on each link distributedly is still an open problem 149 CHAPTER CONCLUSIONS 6.1 The Thesis In this dissertation we propose techniques and structures that not incur control overhead for data aggregation in event-triggered networks We first propose DataAware Anycast (DAA), which is the first structure-free data aggregation protocol It achieves efficient data aggregation without explicit control messages by improving spatial convergence and temporal convergence The spatial convergence is achieved by MAC layer anycast which forwards packets to one of the neighbors that have packets for aggregation The temporal convergence is achieved by Randomized Waiting (RW) at the application layer at sources The structure-free approach makes the design and implementation simple since it does not maintain any structure, and performs close to the optimal structure without incurring any structure control overhead To improve the scalability of the structure-free data aggregation, we propose ToD, which guarantees that packets will be aggregated close to the sources if the maximum event size is known In ToD, two trees, F-Tree and S-Tree, are constructed such that for any event smaller than the size of the maximum event, it will be fully covered by the F-Tree or S-Tree Based on where packets originate from, nodes can 150 forward packets on one of the trees to achieve further aggregation AFT further extends ToD by eliminating the requirement of maximum event size and still guarantees early aggregation AFT creates multi-level, overlapping clusters with exponentially increasing cluster size at each level Packets are forwarded to parent clusters based on where they originate from AFT guarantees that the distance between the sources and where the packets are aggregated will be bounded by a constant factor of event √ diameter, which is 2(1 + 13) ToD and AFT eliminate high structure maintenance overhead by using structurefree data aggregation to avoid involving all nodes in the structure, thereby reducing control messages This shows that semi-structured approaches that only maintain a structure for a small set of nodes can actually reduce the overall control overhead while assuring scalability and performing close to the optimal structure approach In addition to conserving energy through data aggregation, we also study system optimization for rechargeable sensor networks in data collection applications We design fair and high throughput rate assignment algorithms for rechargeable sensor networks We propose a centralized algorithm and a distributed algorithm for optimal lexicographic rate assignment The centralized algorithm computes the optimal rate for each node along with determining the amount of flow on all links, while the distributed algorithm computes the optimal rate when the routing tree is predetermined We prove the optimality of both centralized and distributed algorithms To evaluate the proposed distributed algorithm, we conduct experiments on a sensor network testbed under various scenarios From the results of the experiments we know that the rates computed by the distributed algorithm are highly dependent on the routing tree If a data collection tree that considers load balance is used, 151 the rates computed by the distributed algorithm can achieve higher throughput and better fairness 6.2 Future Work Besides the works that have been done in this dissertation, here are two open problems in related areas: • Distributed Joint Computation of Routing and Rate Assignment: Though the distributed algorithm we proposed computes the optimal lexicographic rate assignment for fixed routes and unsplittable flows, jointly computation of the optimal rates for the nodes and the amount of flows on each link in distributed fashion is still an open problem • Optimal Rate Assignment in Presence of Storage: With the advances in NAND technology, the energy consumption for storing data in flash disk is much smaller than the energy consumption for radio transmission Therefore storage can be exploited to boost the data collection rate when the battery level and the energy recharging rate are low How to optimize the rate assignment in presence of storage is a quest worth investigating 152 BIBLIOGRAPHY [1] Center for Embedded Networked Sensing at UCLA http://www.cens.ucla.edu [2] ExScal http://www.cast.cse.ohio-state.edu/exscal/ [3] Networked Infomechanical Systems http://www.cens.ucla.edu [4] Stargate http://platformx.sourceforge.net/home.html [5] TinyOS http://www.tinyos.net [6] Noga Alon, Richard M Karp, David Peleg, and Douglas West A graph theoretic game and its application to the k-server problem In SIAM Journal of Computing, 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