Controlled sink mobility for prolonging wireless sensor networks lifetime

This paper demonstrates the advantages of using controlled mobility in wireless sensor networks (WSNs) for increasing their lifetime, i.e., the period of time the network is able to provide its intended functionalities. More specifically, for WSNs that comprise a large number of statically placed sensor nodes transmitting data to a collection point (the sink), we show that by controlling the sink movements we can obtain remarkable lifetime improvements. In order to determine sink movements, we first define a Mixed Integer Linear Programming (MILP) analytical model whose solution determines those sink routes that maximize network lifetime. Our contribution expands further by defining the first heuristics for controlled sink movements that are fully distributed and localized. Our Greedy Maximum Residual Energy (GMRE) heuristic moves the sink from its current location to a new site as if drawn toward the area where nodes have the highest residual energy. We also introduce a simple distributed mobility scheme (Random Movement or RM) according to which the sink moves uncontrolled and randomly throughout the network. The different mobility schemes are compared through extensive ns2-based simulations in networks with different nodes deployment, data routing protocols, and constraints on the sink movements. In all considered scenarios, we observe that moving the sink always increases network lifetime. In particular, our experiments show that controlling the mobility of the sink leads to remarkable improvements, which are as high as sixfold compared to having the sink statically (and optimally) placed, and as high as twofold compared to uncontrolled mobility. Stefano Basagni holds a Ph.D. in electrical engineering from the University of Texas at Dallas (December 2001) and a Ph.D. in computer science from the University of Milano, Italy (May 1998). He received his B.Sc. degree in computer science from the University of Pisa, Italy, in 1991. Since Winter 2002 he is on faculty at the Department of Electrical and Computer Engineering at Northeastern University, in Boston, MA. From August 2000 to January 2002 he was professor of computer science at the Department of Computer Science of the Erik Jonsson School of Engineering and Computer Science, The University of Texas at Dallas. Dr. Basagni’s current research interests concern research and implementation aspects of mobile networks and wireless communications systems, Bluetooth and sensor networking, definition and performance evaluation of network protocols and theoretical and practical aspects of distributed algorithms. Dr. Basagni has published over four dozens of referred technical papers and book chapters. He is also co-editor of two books. Dr. Basagni served as a guest editor of the special issue of the Journal on Special Topics in Mobile Networking and Applications (MONET) on Multipoint Communication in Wireless Mobile Networks, of the special issue on mobile ad hoc networks of the Wiley’s Interscience’s Wireless Communications & Mobile Networks journal, and of the Elsevier’s journal Algorithmica on algorithmic aspects of mobile computing and communications. Dr. Basagni serves as a member of the editorial board and of the technical program committee of ACM and IEEE journals and international conferences. He is a senior member of the ACM (including the ACM SIGMOBILE), senior member of the IEEE (Computer and Communication societies), and member of ASEE (American Society for Engineering Education). Alessio Carosi received the M.S. degree “summa cum laude” in Computer Science in 2004 from Rome University “La Sapienza.” He is currently a Ph.D. candidate in Computer Science at Rome University “La Sapienza.” His research interests include protocols for ad hoc and sensor networks, underwater systems and delay tolerant networking. Emanuel Melachrinoudis received the Ph.D. degree in industrial engineering and operations research from the University of Massachusetts, Amherst, MA. He is currently the Director of Industrial Engineering and Associate Chairman of the Department of Mechanical and Industrial Engineering at Northeastern University, Boston, MA. His research interests are in the areas of network optimization and multiple criteria optimization with applications to telecommunication networks, distribution networks, location and routing. He is a member of the Editorial Board of the International Journal of Operational Research. He has published in journals such as Management Science, Transportation Science, Networks, European Journal of Operational Research, Naval Research Logistics and IIE Transactions. Chiara Petrioli received the Laurea degree “summa cum laude” in computer science in 1993, and the Ph.D. degree in computer engineering in 1998, both from Rome University “La Sapienza,” Italy. She is currently Associate Professor with the Computer Science Department at Rome University “La Sapienza.” Her current work focuses on ad hoc and sensor networks, Delay Tolerant Networks, Personal Area Networks, Energy-conserving protocols, QoS in IP networks and Content Delivery Networks where she contributed around sixty papers published in prominent international journals and conferences. Prior to Rome University she was research associate at Politecnico di Milano and was working with the Italian Space agency (ASI) and Alenia Spazio. Dr. Petrioli was guest editor of the special issue on “Energy-conserving protocols in wireless Networks” of the ACM/Kluwer Journal on Special Topics in Mobile Networking and Applications (ACM MONET) and is associate editor of IEEE Transactions on Vehicular Technology, the ACM/Kluwer Wireless Networks journal, the Wiley InterScience Wireless Communications & Mobile Computing journal and the Elsevier Ad Hoc Networks journal. She has served in the organizing committee and technical program committee of several leading conferences in the area of networking and mobile computing including ACM Mobicom, ACM Mobihoc, IEEE ICC,IEEE Globecom. She is member of the steering committee of ACM Sensys and of the international conference on Mobile and Ubiquitous Systems: Networking and Services (Mobiquitous) and serves as member of the ACM SIGMOBILE executive committee. Dr. Petrioli was a Fulbright scholar. She is a senior member of IEEE and a member of ACM. Z. Maria Wang received her Bachelor degree in Electrical Engineering with the highest honor from Beijing Institute of Light Industry in China, her M.S. degree in Industrial Engineering/Operations Research from Dalhousie University, Canada and her Ph.D. in Industrial Engineering/Operations Research from Northeastern University, Boston. She served as a R&D Analyst for General Dynamics. Currently MS. Wang serves as an Optimization Analyst with Nomis Solutions, Inc.     

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Maximizing lifetime of large-scale wireless sensor networks using multi-objective whale optimization algorithm

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无线传感器网络的神经网络模型

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Energy centroid clustering algorithm to enhance the network lifetime of wireless sensor networks

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An exact algorithm for maximum lifetime data gathering tree without aggregation in wireless sensor networks

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On MAC optimization for large-scale wireless sensor network

The media access control (MAC) performance of a large-scale wireless sensor network (L-WSN) determines the efficiency of the wireless communication channel. A good MAC protocol could reduce network energy consumption and network delay, which are two problems to be solved urgently in L-WSN. In this paper, we proposed a multi-level integrated MAC protocol (MI-MAC) to solve the overall performance optimization problem of L-WSN. Compared with other protocols, MI-MAC has two mainly improved performances: (1) It improved binary exponential backoff algorithm by twice back off strategy; (2) It designed a sending and receiving algorithm based on the threshold value to recognize control frames (small frames), which effectively avoids the collision probability of data frame. The simulation results show that the MI-MAC protocol improves network throughput and delay performance, significantly reduces energy consumption, and obtains overall network optimization.     

Multilevel heterogeneous network model for wireless sensor networks

The lifetime of a network can be increased by increasing the network energy. The network energy can be increased either increasing the number of sensors or increasing the initial energy of some sensors without increasing their numbers. Increasing network energy by deploying extra sensors is about ten times costlier than that using some sensors of high energy. Increasing the initial energy of some sensors leads to heterogeneous nodes in the network. In this paper, we propose a multilevel heterogeneous network model that is characterized by two types of parameters: primary parameter and secondary parameters. The primary parameter decides the level of heterogeneity by assuming the values of secondary parameters. This model can describe a network up to nth level of heterogeneity (n is a finite number). We evaluate the network performance by applying the HEED, a clustering protocol, on this model, naming it as MLHEED (Multi Level HEED) protocol. For n level of heterogeneity, this protocol is denoted by MLHEED-n. The numbers of nodes of each type in any level of heterogeneity are determined by the secondary model parameter. The MLHEED protocol (for all level heterogeneity) considers two variables, i.e., residual energy and node density, for deciding the cluster heads. We also consider fuzzy implementation of the MLHEED in which four variables are used to decide the cluster heads: residual energy, node density, average energy, and distance between base station and the sensor nodes. In this work, we illustrate the network model up to seven levels (\(1\le n\le 7\)). Experimentally, as the level of heterogeneity increases, the rate of energy dissipation decreases and hence the nodes stay alive for longer time. The MLHEED-m, \(m=2,3,4,5,6,7\), increase the network lifetime by \(73.05, 143.40, 213.17, 267.90, 348.60, 419.10\,\%\), respectively, by increasing the network energy as \(40, 57, 68.5, 78, 84, 92.5\,\%\) with respect to the original HEED protocol. In case of fuzzy implementation, the MLHEEDFL-m, \(m=2,3,4,5,6,7,\) increases the network lifetime by \(282.7, 378.5, 435.78, 498.50, 582.63, 629.79\,\%\), respectively, corresponding to the same increase in the network energy as that of the MLHEED (all levels) with respect to the original HEED. The fuzzy implementation of the HEED, MLHEEDFL-1, increases the network lifetime by \(176.6\,\%\) with respect to the original HEED with no increase in the network energy.     

Robust data authentication for unattended wireless sensor networks

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