A study on maximizing the lifetime of wireless sensor networks

國立高雄應用科技大學 電子工程系博士班 博士論文 最大化無線感測網路壽命之研究 A Study on Maximizing the Lifetime of Wireless Sensor Networks 研 究 生：范文忠 (Van-Trung Pham) 指導教授：劉炳宏 (Bing-Hong Liu) 中 華 民 國 一百零四 年 七 月 最大化無線感測網路壽命之研究 A Study on Maximizing the Lifetime of Wireless Sensor Networks 研 究 生：范文忠 (Van-Trung Pham) 指導教授：劉炳宏 (Bing-Hong Liu) 國立高雄應用科技大學 電子工程系博士班 博士論文 A Thesis submitted to Institute of Electronic Engineering National Kaohsiung University of Applied Sciences in Partial Fulfillment of the Requirements for the Degree of PhD of Engineering in Electronic Engineering July 2015 Kaohsiung, Taiwan, Republic of China 中 華 民 國 一百零四 年 七 月 Declaration of Authorship I, Van-Trung Pham, declare that this thesis titled, ‘A Study on Maximizing the Lifetime of Wireless Sensor Networks’ and the work presented in it are my own I confirm that: This work was done for a research degree at KUAS by me under the guidance of my supervisor This work has not been submitted to any other Institute for any degree or diploma I have consulted the published work of others, this is always clearly attributed I have acknowledged all main sources The thesis is based on work done by myself jointly with others, I have made clear exactly what was done by others and what I have contributed myself Signed: Date: Van-Trung i 最大化無線感測網路壽命之研究 指導教授：劉炳宏 博士 研 究 生: 范文忠 國立高雄應用科技大學電子工程系博士班 摘要 無線感測網路是由許多佈置在廣大區域的無線感測器所組成，無線感 測網路能夠用於收集、處理和儲存環境資訊。由於許多需要被監控的環境 或是目標物較難接近，像是災區、火山、戰爭的區域。如果對這些區域內 的感測器進行充電或是重新部署，成本是相當高的，因此，如何延長無線 感測網路壽命是相當重要的議題。 本論文主要著重在研究最大化無線感測網路壽命方法，為了能在這個 問題上取得進展，本論文針對兩個議題提出研究。在第一個議題中，我 們研究在不同時間下使用不同的資料匯集樹以延長網路壽命，在此網路 中，資料可以透過資料匯集函式來匯集，如 MAX， MIN， COUNT， SUM。在此提出應用於無線感測網路的最大化網路壽命資料匯集樹排程 方法，此方法為根據距離匯集點 k-hop 的區域資訊來建造適用於不同時間 的資料匯集樹，在建造資料匯集樹時，在匯集點 k-hop 內的樹路徑可以根 據網路壽命和感測器所收集到的資料進行重建。 在另一個議題中，我們著重在使用骨幹結構來傳送資料到匯集點以最 大化雙頻無線感測網路壽命，在雙頻無線感測網路中，每個感測器可以使 用雙頻無線電傳送資料，即小範圍無線電傳送及大範圍無線電傳送。我們 在此提出應用於雙頻無線感測網路的骨幹網路建構演算法，在骨幹網路中 的節點使用小範圍無線電或大範圍無線電傳送來維護骨幹網路，其餘非骨 幹節點則使用小範圍無線電以連結骨幹網路，此外，我們提出虛擬骨幹網 路排程方法，可以讓不同的骨幹網路交替使用以延長網路壽命。 關鍵詞: 無線感測網路, 虛擬骨幹, 網路壽命, 資料匯集, NP困難 A Study on Maximizing the Lifetime of Wireless Sensor Networks Author: Van-Trung Pham Supervisor： Dr Bing-Hong Liu Institute of Electronic Engineering National Kaohsiung University of Applied Sciences ABSTRACT Wireless sensor networks are composed of many wireless sensors deployed in a wide range of areas to collect, process, and store environmental information Because many environments or objects that need to be monitored are difficult to approach, such as disaster areas, volcanos, and battle fields, charging batteries of sensors or redeploying sensors is costly Therefore, prolonging the lifetime of wireless sensor networks is an important issue This thesis focuses on the proposal of solutions for maximizing the lifetime of wireless sensor networks For making progress on these issues, there are two novel issues proposed in this thesis In the first issue, we study the problem of constructing data aggregation trees for different time to maximize the network lifetime In which, the data is aggregated to the nodes in a tree by some aggregated data functions, such as MAX, MIN, COUNT, and SUM In addition, the scheduling of data aggregation trees for maximizing the lifetime of wireless sensor networks is proposed In which, the k-hop local information to the sink is used to construct data aggregation trees for different time While constructing a data aggregation tree, the tree topology within the k-hop of the sink is reconstructed by the lifetime and the collected data of sensors iv In the other issue, we focus on maximizing the lifetime of dual-radio wireless sensor network by using a backbone structure that is used to transmit aggregated data to sink In the dual-radio wireless sensor network, every sensor is assumed to have dual radios, that is, small-range radio and largerange radio We then propose an algorithm to construct a backbone in dualradio wireless sensor networks, where the backbone nodes use small-range radio or large-range radio to maintain the backbone, and the rest nodes use small-range radio to connect to the backbone In addition, a scheduling for constructing virtual backbones is proposed such that the constructed backbones can work sequentially to prolong the network lifetime Keywords: Wireless sensor network, virtual backbone, network lifetime, data aggregation, NP-Hard Acknowledgements First at all, I wish to express my sincere thanks to my advisor, Dr Bing-Hong Liu, Associate Professor, National Kaohsiung University of Applied Sciences, Taiwan, for his earnest guidance and support throughout the course of this research He give me the moral support, persistent encouragement and perpetual ideas His depth of knowledge and enthusiasm for research has inspired me a lot during years in KUAS I would like to thank all members of Wireless Networking and Distributed Computing Lab for their helping to finish this work More generally, the opportunity to work with the members of Wireless Networking and Distributed Computing Lab has helped me increase my knowledge of the Wireless Sensor Network field I would like to thank National Kaohsiung University of Applied Sciences and Institute of Electronic Engineering, for providing me with a precious scholarship and resources for me to concentrate on pursuing a PhD In additon, I would thank all the professors of Department of Electronic Engineering for taking a course with them and gain knowledge and gaining a style of teaching from them during the period of my researching in KUAS I would like to thank Pham Van Dong University and Faculty of Information Technology, for believing in me and giving me the support to come to KUAS to finish my doctor degree In addition, I would like to thank my friends, who are studying in KUAS, for their encouragement and help me quite a lot in sharing the problems occurred in my life during four years in KUAS Finally, I would like to thank my parents and the members in my family, for everything that they have given me too much Especially, I would like to thank my wife for understanding, sacrifices and take care our babies while I studied in KUAS v Contents Declaration of Authorship i 摘要 ii Abstract iii Acknowledgements v Contents vi List of Figures viii Abbreviations x Introduction 1.1 Literature Survey and Motivation 1.2 Contributions of the thesis 1.2.1 Constructing Virtual Data Aggregation Trees Scheduling 1.2.2 Construct Virtual Backbone Scheduling 1.3 Organization of the thesis 7 8 Background 2.1 An overview of Wireless Sensor network 2.2 Specific Issues in Wireless Sensor Network Systems 2.2.1 The problem of Duty cycle scheduling 2.2.2 The problem of Data aggregation 2.2.3 Maximum lifetime problem in WSNs 2.2.4 Coverage problem in WSNs 2.3 Summary 10 10 14 14 15 16 16 17 On Maximizing the Lifetime for Data Aggregation in Wireless Sensor Network Using Virtual Data Aggregation Trees 18 3.1 Problem Definition and Its Hardness 20 3.1.1 Network Model 21 vi Chapter Constructing Virtual Backbone Scheduling for Maximizing the Lifetime of Dual-Radio WSNs 72 backbone nodes using a large-range radio is β times of that using a smallrange radio, where β ranges from 1.25 to 2.75 Because the results of evaluating the network lifetime in the networks with nodes having varying initial energy are similar to those of the networks with nodes having the same initial energy, as shown in Fig 4.4, we show only the results when the nodes in the networks have the same initial energy Fig 4.5 shows the simulation results for the network lifetime versus β when the initial energy of each sensor in the networks is 400, and the number of nodes in the networks is 500 It is clear that the DSBA has a longer network lifetime than that of the STG r and STG R, as demonstrated in Fig 4.4 In addition, the STG R has a longer network lifetime than that of the STG r when β ≤ 1.5 This is because when β ≤ 1.5, the STG R can select more nodes than STG r to be backbone nodes whose energy consumption for one working cycle is, at most, 1.5, which is close to the energy consumption for each backbone node in the STG r Furthermore, note that when β increases, the network lifetime in the DSBA (or STG R) decreases This is because more energy is consumed when backbone nodes use large-range radios Also, note that there are almost no changes in the STG r as β increases because no backbone nodes in the STG r use a large-range radio 4.4.2 Size of Virtual Backbone Fig 4.6(a) (or 4.6(b)) shows the simulation results concerning the average nodes of virtual backbones in the networks when the initial energy of each sensor is 400 (or randomly chosen from the interval [0, 400]) Because the empirical data in Fig 4.6(b) are similar to those in Fig 4.6(a), we discuss only the results shown in Fig 4.6(a) In Fig 4.6(a), it is clear that the Chapter Constructing Virtual Backbone Scheduling for Maximizing the Lifetime of Dual-Radio WSNs 73 9000 Network lifetime 8000 7000 6000 5000 4000 3000 1.25 STG_r STG_R DSBA 1.5 1.75 β 2.25 2.5 2.75 Figure 4.5: The network lifetime versus β, where the backbone nodes using a large-range radio (or small-range radio) consume β (or 1) energy power for one working cycle, the network has 500 nodes and the initial energy of nodes in the networks is 400 STG R uses fewer backbone nodes than the DSBA and the STG r because a large-range radio is used for each backbone node in the STG R Although the STG R has the lowest average number of backbones nodes, the backbone nodes in the STG R consume much energy power, and, thus, the STG R often has a shorter network lifetime than the others, as shown in Figs 4.44.5 In addition, note that the more nodes in the networks, a bigger average size of virtual backbones in the DSBA, STG r, or STG R is required when the number of nodes is less than or equal to 300 This is because more nodes must be dominated, and, thus, more backbone nodes are required Furthermore, when the number of nodes is greater than 300, the empirical data in the DSBA, STG r, or STG R are stable and have few differences This is because the average number of backbone nodes in the DSBA, STG r, or STG R is enough to dominate all nodes in the networks 4.4.3 Residual Energy Fig 4.7(a) (or 4.7(b)) shows the simulation results in terms of the average residual energy of all nodes in the networks when the initial energy of each sensor is 400 (or randomly chosen from the interval [0, 400]) Because the 22 Number of nodes per backbone Number of nodes per backbone Chapter Constructing Virtual Backbone Scheduling for Maximizing the Lifetime of Dual-Radio WSNs STG_r STG_R DSBA 20 18 16 14 12 10 50 100 150 200 250 300 350 400 450 500 74 22 STG_r STG_R DSBA 20 18 16 14 12 10 50 100 150 200 Number of nodes 250 300 350 400 450 500 Number of nodes (a) (b) Figure 4.6: The average size of virtual backbones versus the number of network nodes ranging from 50 to 500 The initial energies of nodes in the networks are 400 in (a) and randomly chosen from the interval [0, 400] in (b), respectively 160 STG_r STG_R DSBA 250 200 150 100 50 50 STG_r STG_R DSBA 140 Residual energy Residual energy 300 120 100 80 60 100 150 200 250 300 350 400 450 500 40 50 100 150 200 Number of nodes (a) 250 300 350 400 450 500 Number of nodes (b) Figure 4.7: The average residual energy of all nodes in the networks versus the number of network nodes ranging from 50 to 500 The initial energies of nodes in the networks are 400 in (a) and randomly chosen from the interval [0, 400] in (b), respectively empirical data in Fig 4.7(b) are similar to those in Fig 4.7(a), we discuss only the results shown in Fig 4.7(a) In Fig 4.7(a), it is clear that the DSBA has lower average residual energy of all nodes in the networks than STG r and STG R This is because more nodes are selected to be the backbone nodes whose energies are fully utilized to prolong the network lifetime In addition, when the number of nodes in the networks increases, the average residual energy of all nodes in the network in the DSBA, STG r, or STG R appears to decrease This is because more nodes can be selected to be backbone nodes in the DSBA, STG r, or STG R, and, thus, the network lifetime is prolonged, as shown in Fig 4.4 Chapter Constructing Virtual Backbone Scheduling for Maximizing the Lifetime of Dual-Radio WSNs 4.5 75 Summary In this chapter, while considering sensors each equipped with two radio interfaces, including small-range and large-range radios, a new problem, called the Maximum Lifetime Backbone Scheduling for Dual-Radio Wireless Sensor Network problem, is introduced for finding virtual backbones to prolong the network lifetime The Maximum Lifetime Backbone Scheduling for Dual-Radio Wireless Sensor Network problem proved to be NP-complete Moreover, because centralized algorithms are not feasible in a wide range of wireless sensor networks, we propose a distributed algorithm, called the Dominating-Set-Based Algorithm (DSBA), to find a backbone when a new backbone is required for the network In the DSBA, three steps are required In the first step, the distributed dominating set algorithm (DDSA) is proposed to construct a dominating set for a given dual radio wireless sensor network such that the nodes in the set have enough energy power to be the backbone nodes and can dominate all nodes in the network In the second step, the distributed backbone algorithm (DBA) is proposed to establish a backbone according to the dominating set constructed by the DDSA In the third step, the refinement for the generated backbone is proposed to minimize the size of the generated backbone or reduce the energy consumption of backbone nodes In the simulations, we compared our proposed method with the STG [26] that is a centralized algorithm and is used to find the virtual backbones in the network with sensors each having a single radio The STG that used a small-range radio (or large-range radio) to construct virtual backbones was adopted and denoted by STG r (or, STG R) The performance was Chapter Constructing Virtual Backbone Scheduling for Maximizing the Lifetime of Dual-Radio WSNs 76 evaluated in terms of the network lifetime, the average size of the constructed virtual backbones, and the average residual energy of sensors Simulation results showed that our proposed method had a significantly longer network lifetime and the lower average residual energy of sensors than STG r and STG R Chapter Conclusions The work presented in this thesis comprises for the techniques for lifetime problem in Wireless Sensor Network In which, two problem are addressed in the thesis: Maximum Lifetime Data Aggregation Tree Scheduling Problem and Maximum Lifetime Backbone Scheduling for Dual-Radio Wireless Sensor Network problem These problems are NP-Hard, therefore all the solutions that have been presented here are the distributed algorithms In this chapter, we summary the results of this research work for the specific problems considered in this thesis 5.1 Contributions Maximum Lifetime Data Aggregation Tree Scheduling Problem In this work, we consider the problem of constructing a data aggregation tree in WSNs to collect the sensing data to sink, where each node is WSNs can generate a number of units data to send to sink In addition, we present a maximum lifetime data aggregation tree scheduling problem to increase the number of working rounds for the data aggregation in the network, thereby 77 Chapter Conclusions 78 prolonging the network lifetime The first of this work propose an algorithm to reduce the collected data of the bottleneck nodes in the data aggregation tree to prolong the lifetime of the bottleneck nodes and then prolong the lifetime of data aggregation tree The bottleneck node is defined as the first node in the data aggregation tree running out its energy We then present a maximum lifetime data aggregation tree scheduling problem, namely MLTS, and a schedule in MLTS is a set of virtual data aggregation trees working sequentially in each working round The lifetime of schedule for the data aggregation tree is denoted the lifetime of network Simulation results showed that our proposed method had a significantly longer network lifetime and the lower average residual energy of sensors than existing methods Maximum Lifetime Backbone Scheduling for Dual-Radio Wireless Sensor Network problem In this work, we consider the problem of constructing a backbone in Dual-Radio Wireless Sensor Network In which, the nodes in the backbone can use small-range radio or large-range radio to maintain the backbone, the rest nodes use small-range radio to connect to the backbone In addition, we present a maximum lifetime backbone scheduling for Dual-Radio Wireless Sensor Network problem to increase the number of working rounds for the backbone in the network, thereby prolonging the network lifetime In this manner, we propose a distributed algorithm, termed Dominating-Set-Based Algorithm (DSBA), to find a backbone when a new backbone is required, that is, the network is initialized or one node in the backbone is going to run out of energy The simulations reveal a improvement in reducing the energy consumption of the nodes and prolong the lifetime of network Chapter Conclusions 5.2 79 Future directions In the future, our research will include research into efficient data aggregation in a three-dimension wireless sensor network and in dynamic wireless sensor networks Other future research will include research in routing in multi-radio mobile wireless sensor networks and in three-dimensional multiradio wireless sensor networks Bibliography [1] You-Chiun Wang, Fang-Jing Wu, and Yu-Chee Tseng Mobility management algorithms and applications for mobile sensor networks Wireless Communications and Mobile Computing, 12(1):7–21, 2012 ISSN 1530-8677 [2] Shiow-Fen Hwang, Kun-Hsien Lu, Yi-Yu Su, Chi-Sen Hsien, and ChyiRen Dow Hierarchical multicast in wireless sensor networks with mobile sinks Wireless Communications and Mobile Computing, 12(1): 71–84, 2012 [3] Wenfeng Li, Philippe Martins, and Lianfeng Shen Determination method of optimal number of clusters for clustered wireless sensor networks Wireless Communications and Mobile Computing, 12(2):158– 168, 2012 [4] R.R Rout and S.K Ghosh Enhancement of lifetime using duty cycle and network coding in wireless sensor networks Wireless Communications, IEEE Transactions on, 12(2):656–667, February 2013 [5] Kiran Maraiya, Kamal Kant, and Nitin Gupta Wireless sensor network: a review on data aggregation ISSN, 2011 80 Bibliography 81 [6] A Giridhar and P.R Kumar Computing and communicating functions over sensor networks Selected Areas in Communications, IEEE Journal on, 23(4):755–764, April 2005 [7] S Madden, R Szewczyk, M.J Franklin, and D Culler Supporting aggregate queries over ad-hoc wireless sensor networks In Mobile Computing Systems and Applications, 2002 Proceedings Fourth IEEE Workshop on, pages 49–58, 2002 doi: 10.1109/MCSA.2002.1017485 [8] Konstantinos Kalpakis, Koustuv Dasgupta, and Parag Namjoshi Efficient algorithms for maximum lifetime data gathering and aggregation in wireless sensor networks Computer Networks, 42(6):697 – 716, 2003 doi: http://dx.doi.org/10.1016/S1389-1286(03)00212-3 [9] Bhaskar Krishnamachari, Deborah Estrin, and Stephen Wicker Modelling data-centric routing in wireless sensor networks In Proceedings INFOCOM, 2002 [10] Yan Wu, Sonia Fahmy, and Ness B Shroff On the construction of a maximum-lifetime data gathering tree in sensor networks: Npcompleteness and approximation algorithm In INFOCOM, 2008 Proceedings IEEE, 2008 [11] C Liu and G Cao Distributed monitoring and aggregation in wireless sensor networks In INFOCOM, 2010 Proceedings IEEE, page 2097–2105, 2010 [12] Tung-Wei Kuo and Ming-Jer Tsai On the construction of data aggregation tree with minimum energy cost in wireless sensor networks: Np-completeness and approximation algorithms In INFO- COM, 2012 Proceedings IEEE, pages 2591–2595, March 2012 doi: Bibliography 82 10.1109/INFCOM.2012.6195659 [13] Bing-Hong Liu and Jyun-Yu Jhang Efficient distributed data scheduling algorithm for data aggregation in wireless sensor networks Computer Networks, 65(0):73 – 83, 2014 doi: http://dx.doi.org/10.1016/j comnet.2014.03.003 [14] Hao Chen and Seapahn Megerian Cluster sizing and head selection for efficient data aggregation and routing in sensor networks In WCNC, 2006 Proceedings IEEE, 2006 [15] Ying Liang and Hongwei Gao An energy-efficient clustering algorithm for data gathering and aggregation in sensor networks In ICIEA, 2009 Proceedings IEEE, 2009 [16] K Dasgupta, K Kalpakis, and P Namjoshi An efficient clusteringbased heuristic for data gathering and aggregation in sensor networks In WCNC, 2003 Proceedings IEEE, 2003 [17] M Lee and V.W.S Wong Lpt for data aggregation in wireless sensor networks In GLOBECOM, 2005 Proceedings IEEE, pages 2969–2974, 2005 [18] Dijun Luo, Xiaojun Zhu, Xiaobing Wu, and Guihai Chen Maximizing lifetime for the shortest path aggregation tree in wireless sensor networks In INFOCOM, 2011 Proceedings IEEE, pages 1566–1574, 2011 [19] Koustuv Dasgupta, Konstantinos Kalpakis, and Parag Namjoshi Improving the lifetime of sensor networks via intelligent selection of data aggregation trees In CNDS, 2003 Bibliography 83 [20] Y Wu, Z Mao, S Fahmy, and N.B Shroff Constructing maximumlifetime data-gathering forests in sensor networks In Networking, IEEE/ACM Transactions on, pages 1571–1584, 2010 [21] X Cheng, M Ding, D H Du, and X Jia Virtual backbone construction in multihop ad hoc wireless networks Proceedings of IEEE Wireless Communications and Mobile Computing, 6:183–190, 2006 [22] Yu Wang, Weizhao Wang, and Mo Li Efficient distributed low-cost backbone formation for wireless networks Parallel and Distributed Systems, IEEE Transactions on, 17(7):681–693, July 2006 [23] Ziyi Zhang, Xin Wang, and Qin Xin A new performance metric for construction of robust and efficient wireless backbone network Computers, IEEE Transactions on, 61(10):1495–1506, Oct 2012 [24] Shun-Yu Chuang and Chien Chen Smartbone: An energy-efficient smart backbone construction in wireless sensor networks In Wireless Communications and Networking Conference, 2007.WCNC 2007 IEEE, pages 3394–3399, March 2007 [25] Javad Akbari Torkestani Backbone formation in wireless sensor networks Sensors and Actuators A: Physical, 185(0):117 – 126, 2012 [26] Yaxiong Zhao, Jie Wu, Feng Li, and Sanglu Lu On maximizing the lifetime of wireless sensor networks using virtual backbone scheduling Parallel and Distributed Systems, IEEE Transactions on, 23(8):1528– 1535, Aug 2012 Bibliography 84 [27] Thomas Kunz and Benjamin Tatham Localization in wireless sensor networks and anchor placement Sensor and Actuator Networks, 1: 36–58, June 2012 [28] Vikram P Munishwar and Nael B Abu-Ghazaleh Coverage algorithms for visual sensor networks ACM Trans Sen Netw., 9(4):45:1–45:36, July 2013 [29] Silicon Laboratories The evolution of wireless sensor networks URL http://www.silabs.com/SupportDocuments/TechnicalDocs/ evolution-of-wireless-sensor-networks.pdf [30] John A Stankovic When sensor and actuator networks cover the world ETRI Journal, 30(5):627–633, 2008 ISSN 1424-8220 [31] Brent N Clark, Charles J Colbourn, and David S Johnson Unit disk graphs Discrete Mathematics, 86(1–3):165 – 177, 1990 [32] Oded Goldreich Computational Complexity: A Conceptual Perspective Cambridge University Press, New York, NY, USA, 2008 [33] Kenneth H Rosen Discrete mathematics and its applications (Seventh Edition) McGraw-Hill Higher Education, 2012 [34] C Intanagonwiwat, R Govindan, and D Estrin Directed diffusion: A scalable and robust communication paradigm for sensor networks In ACM MobiCom, 2000 [35] Soo-Hoon Moon and Seung-Jae Han Benefits of dual-radio wireless sensor networks In Consumer Communications and Networking Conference (CCNC), 2010 7th IEEE, pages 1–2, Jan 2010 Bibliography 85 [36] T.P Sharma, R.C Joshi, and M Misra Dual radio based cooperative caching for wireless sensor networks In Networks, 2008 ICON 2008 16th IEEE International Conference on, pages 1–7, Dec 2008 [37] Jongwon Kim, Yeonsik Jeong, and Seung-Jae Han Experimental analysis of interference in dual-radio wireless sensor networks In Consumer Communications and Networking Conference (CCNC), 2010 7th IEEE, pages 1–2, Jan 2010 [38] Chaiporn Jaikaeo and Chien-Chung Shen Adaptive backbone multicast routing for mobile ad hoc networks Wireless Communications and Mobile Computing, 2008 [39] Xianlong Jiao, Wei Lou, Junchao Ma, Jiannong Cao, Xiaodong Wang, and Xingming Zhou Minimum latency broadcast scheduling in dutycycled multihop wireless networks Parallel and Distributed Systems, IEEE Transactions on, 23(1):110–117, Jan 2012 [40] Shuguang Xiong, Jianzhong Li, Mo Li, Jiliang Wang, and Yunhao Liu Multiple task scheduling for low-duty-cycled wireless sensor networks In INFOCOM, 2011 Proceedings IEEE, pages 1323–1331, April 2011 [41] Dimitrios Lymberopoulos, Nissanka B Priyantha, Michel Goraczko, and Feng Zhao Towards energy efficient design of multi-radio platforms for wireless sensor networks In Proceedings of the 7th International Conference on Information Processing in Sensor Networks, IPSN ’08, pages 257–268, Washington, DC, USA, 2008 IEEE Computer Society ISBN 978-0-7695-3157-1 [42] Carlene E.-A Campbell, Shafiullah Khan, Dhananjay Singh, and KokKeong Loo Multi-channel multi-radio using 802.11 based media access for sink nodes in wireless sensor networks Sensors, 11(5):4917–4942, 2011 ISSN 1424-8220 [43] Lujun Jia, Rajmohan Rajaraman, and Torsten Suel An efficient distributed algorithm for constructing small dominating sets Distrib Comput., 15(4):193–205, December 2002 [44] Oliver Schaudt and Rainer Schrader The complexity of connected dominating sets and total dominating sets with specified induced subgraphs Information Processing Letters, 112(24):953 – 957, 2012 [45] Jiguo Yu, Nannan Wang, Guanghui Wang, and Dongxiao Yu Connected dominating sets in wireless ad hoc and sensor networks – a comprehensive survey Computer Communications, 36(2):121 – 134, 2013 [46] Donghyun Kim, Yiwei Wu, Yingshu Li, Feng Zou, and Ding-Zhu Du Constructing minimum connected dominating sets with bounded diameters in wireless networks Parallel and Distributed Systems, IEEE Transactions on, 20(2):147–157, Feb 2009 [...]... collected data at source node as bottleneck nodes to prolong its lifetime and then to prolong the lifetime of the data aggregation tree We then present a maximum lifetime data aggregation tree scheduling problem to increase the number of working rounds for the data aggregation in the network The lifetime of schedule for the data aggregation tree is denoted the lifetime of network Based on the simulation,... In the active group, the sensors always turn on their radio to maintain the activation of the network Otherwise, the sensor nodes in the inactive group turn off their radio to save energy In this thesis, the duty cycle scheduling problem for the wireless sensor network will be examined based on the backbone structure and data aggregation tree structure of the network 2.2.2 The problem of Data aggregation... aggregation trees to maximize the network lifetime when a fixed number of data are allowed to be aggregated into one packet, termed the Maximum Lifetime Data Aggregation Tree Scheduling (MLDATS) problem The remaining sections of this chapter are organized as follows The definition and the hardness of the MLDATS are formally illustrated in Section 3.1 In addition, a local-tree-reconstruction-based scheduling... aggregation The most importance operation in these applications in WSNs is data aggregation, to collect sensing data from the sensor nodes and report to a sink, at each time unit The collection data process is repeated until the data packet of all nodes reaches to sink Data aggregation is a well-know technique for data collection to reduce the energy consumption of reporting data in WSNs In which, the collected... like a relay node to help relay data Fig 3.2 (a) shows an example of the connected graph representing a WSN, where the number of units of raw data generated by a node is shown as the Chapter 3 On Maximizing the Lifetime for Data Aggregation in WSNs 22 right number in parentheses Note that node s is the sink, three nodes a, e and g are relay nodes, and the rest of the nodes are source nodes 3.1.2 Data Aggregation... is generally associated with a small storage unit and it can manage the procedures that make the sensor node collaborate with the other nodes to carry out the assigned sensing tasks The transceiver unit can connect the other node to transmit the sensed data together within the fixed radio range In addition, the power unit is one of the most important components of a sensor node This unit can be supported... the data gathering mechanism must be carefully designed to save the energy consumption of sensors and to reduce the traffic data of the nodes in WSNs • Storage, search and retrieval: Since the sensed data in WSNs is continuous and needs to be processed in real time, the traditional Chapter 2 Background 14 database are not suitable In addition, the sensor nodes have storage constraints, processing and... [9], Krishnamachari et al use the data aggregation tree to model data-centric routing to yield energy-efficient dissemination In [10], Wu et al study the construction of a data-gathering tree to maximize the network lifetime In addition, Many Chapter 1 Introduction 4 researchers have studied efficiently gathering data in WSNs when a fixed number of data are allowed to be aggregated into one packet [11–13]... data is generated by some aggregation functions [5, 6], such as, max, min, sum, etc., and would be aggregated into a data packets with a fix constant size before they are transmitted In this thesis, the data aggregation problem for the wireless sensor network will be examined based on the data aggregation tree structure of the network Chapter 2 Background 2.2.3 16 Maximum lifetime problem in WSNs Maximizing. .. data from the source sensor to the destination via radio transmissions Each sensor node has only limited communication range compared with the size of the monitored area Two sensors are called neighbors if they are within each other’s communication range The sensor nodes and the communication links between each pair of neighbors build the network topology, which is required to be connected by the connectivity