Algorithm design for a class of base station location problems in sensor networks

Base station placement has significant impact on sensor network performance. Despite its significance, results on this problem remain limited, particularly theoretical results that can provide performance guarantee. This paper proposes a set of procedure to design (1− ε) approximation algorithms for base station placement problems under any desired small error bound ε > 0. It offers a general framework to transform infinite search space to a finite-element search space with performance guarantee. We apply this procedure to solve two practical problems. In the first problem where the objective is to maximize network lifetime, an approximation algorithm designed through this procedure offers 1/ε² complexity reduction when compared to a state-of-the-art algorithm. This represents the best known result to this problem. In the second problem, we apply the design procedure to address base station placement problem when the optimization objective is to maximize network capacity. Our (1− ε) approximation algorithm is the first theoretical result on this problem. Yi Shi received his B.S. degree from University of Science and Technology of China, Hefei, China, in 1998, a M.S. degree from Institute of Software, Chinese Academy of Science, Beijing, China, in 2001, and a second M.S. degree from Virginia Tech, Blacksburg, VA, in 2003, all in computer science. He is currently working toward his Ph.D. degree in electrical and computer engineering at Virginia Tech. While in undergraduate, he was a recipient of Meritorious Award in International Mathematical Contest in Modeling and 1997 and 1998, respectively. His current research focuses on algorithms and optimizations for wireless sensor networks, wireless ad hoc networks, UWB-based networks, and SDR-based networks. His work has appeared in journals and highly selective international conferences (ACM Mobicom, ACM Mobihoc, and IEEE Infocom). Y. Thomas Hou received the B.E. degree from the City College of New York in 1991, the M.S. degree from Columbia University in 1993, and the Ph.D. degree from Polytechnic University, Brooklyn, New York, in 1998, all in Electrical Engineering. Since Fall 2002, he has been an Assistant Professor at Virginia Tech, the Bradley Department of Electrical and Computer Engineering, Blacksburg, VA. His current research interests are radio resource (spectrum) management and networking for software-defined radio wireless networks, optimization and algorithm design for wireless ad hoc and sensor networks, and video communications over dynamic ad hoc networks. From 1997 to 2002, Dr. Hou was a Researcher at Fujitsu Laboratories of America, Sunnyvale, CA, where he worked on scalable architectures, protocols, and implementations for differentiated services Internet, service overlay networking, video streaming, and network bandwidth allocation policies and distributed flow control algorithms. Prof. Hou is a recipient of an Office of Naval Research (ONR) Young Investigator Award (2003) and a National Science Foundation (NSF) CAREER Award (2004). He is a Co-Chair of Technical Program Committee of the Second International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CROWNCOM 2007), Orlando, FL, August 1–3, 2007. He also was the Chair of the First IEEE Workshop on Networking Technologies for Software Defined Radio Networks, September 25, 2006, Reston, VA. Prof. Hou holds two U.S. patents and has three more pending. Alon Efrat earned his Bachelor in Applied Mathematics from the Technion (Israel’s Institute of Technology) in 1991, his Master in Computer Science from the Technion in 1993, and his Ph.D in Computer Science from Tel-Aviv University in 1998. During 1998–2000 he was a Post Doctorate Research Associate at the Computer Science Department of Stanford University, and at IBM Almaden Research Center. Since 2000, he is an assistant professor at the Computer Science Department of the University of Arizona. His main research areas are Computational Geometry, and its applications to sensor networks and medical imaging.