Abstract
Sensor networks are ubiquitously used for detection and tracking and, as a result, covering is one of the main tasks of such networks. We study the problem of maximizing the coverage lifetime of a barrier by mobile sensors with limited battery power, where the coverage lifetime is the time until there is a breakdown in coverage due to the death of a sensor. Sensors are first deployed and then coverage commences. Energy is consumed in proportion to the distance traveled for mobility, while for coverage, energy is consumed in direct proportion to the radius of the sensor raised to a constant exponent. We study two variants which are distinguished by whether the sensing radii are given as part of the input or can be optimized: the fixed radii problem and the variable radii problem. We design parametric search algorithms for both problems for the case where the final order of the sensors is predetermined (e.g., sensors cannot swap locations and the initial order must be preserved) and for the case where sensors are initially located at barrier endpoints. In contrast, we show that the variable radii problem is strongly NP-hard and provide hardness of approximation results for fixed radii for the case where all the sensors are initially colocated at an internal point of the barrier.
Original language | English |
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Pages (from-to) | 573-596 |
Number of pages | 24 |
Journal | SIAM Journal on Discrete Mathematics |
Volume | 31 |
Issue number | 1 |
DOIs | |
State | Published - 2017 |
Bibliographical note
Publisher Copyright:© 2017 Society for Industrial and Applied Mathematics.
Funding
The first author was partially supported by the CUNY Institute for Computer Simulation, Stochastic Modeling and Optimization (CoSSMO). The second author was partially supported by the Israel Science Foundation (grant 497/14).
Funders | Funder number |
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CUNY Institute for Computer Simulation | |
Israel Science Foundation | 497/14 |
Keywords
- Approximation algorithms
- Barrier coverage
- Mobile sensors
- Network lifetime
- Sensor deployment
- Sensor networks