Name
Abstract | ||
|---|---|---|
In this paper, we consider the problem of maximizing the lifetime of a target-covering sensor network in which each sensor can adjust its sensing range. The network model consists of a large number of sensors with adjustable sensing ranges being deployed to monitor a set of targets. Since more than one sensor can cover a target, in order to be energy efficient, one can activate successive subsets of sensors that cover all targets. This paper addresses the problem of maximizing the total lifetime of such an activation schedule. In contrast to the approach taken by Cardei et al. [4], our formulation directly maximizes the network lifetime rather than maximizing the number of sensor covers. We give a mathematical model of this problem using a linear program with exponential number of variables and solve this linear program using the approximation algorithm of Garg-Könemann [8]. Our experimental results on simulated data show a 4x increase in lifetime when compared with the previous approach taken by Cardei et al. [4]. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/SNPD-SAWN.2006.46 | SNPD |
Keywords | Field | DocType |
adjustable sensing range,exponential number,network model,activation schedule,linear program,sensor networks,mathematical model,large number,maximum lifetime,target-covering sensor network,network lifetime,total lifetime,previous approach,linear programming,energy efficiency,energy efficient,scattering,approximation theory,wireless sensor networks,scheduling,approximation algorithm,sensor network,approximation algorithms | Approximation algorithm,Mathematical optimization,Exponential function,Computer science,Efficient energy use,Approximation theory,Linear programming,Artificial intelligence,Wireless sensor network,Network model,Machine learning | Conference |
ISBN | Citations | PageRank |
0-7695-2611-X | 47 | 1.91 |
References | Authors | |
6 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Akshaye Dhawan | 1 | 81 | 7.13 |
| C. T. Vu | 2 | 47 | 1.91 |
| A. Zelikovsky | 3 | 289 | 38.30 |
| Yingshu Li | 4 | 373 | 23.07 |
| S. K. Prasad | 5 | 47 | 1.91 |