Abstract
Coverage problem is a critical issue in wireless sensor networks for security applications. The k-barrier coverage is an effective measure to ensure robustness. In this paper, we formulate the k-barrier coverage problem as a constrained optimization problem and introduce the energy constraint of sensor node to prolong the lifetime of the k-barrier coverage. A novel hybrid particle swarm optimization and gravitational search algorithm (PGSA) is proposed to solve this problem. The proposed PGSA adopts a k-barrier coverage generation strategy based on probability and integrates the exploitation ability in particle swarm optimization to update the velocity and enhance the global search capability and introduce the boundary mutation strategy of an agent to increase the population diversity and search accuracy. Extensive simulations are conducted to demonstrate the effectiveness of our proposed algorithm.
Highlights
Interest in wireless sensor networks (WSNs) in numerous applications has increased considerably
The connection of physical things to the Internet makes it possible to access remote sensor data and to control the physical world from a distance [1]. This capability is a critical issue for security applications, such as border surveillance, forest fires monitoring, and intruding enemy planes detection [2,3,4,5,6]
(iii) We propose the newly modified Gravitational search algorithm (GSA) (PGSA), which adjusts the velocity updating by integrating the ability to exploit in particle swarm optimization (PSO) to enhance the global search capability and introduce agent boundary mutation strategy to increase population diversity and search accuracy
Summary
Interest in wireless sensor networks (WSNs) in numerous applications has increased considerably. We formulate the k-barrier coverage problem as a constrained optimization problem in large-scale WSNs. For large-scale constrained optimization problems, the classical optimization algorithms cannot provide a suitable solution because the search space is increasing exponentially with problem size. GSA has been proven to have high-quality performance in solving different optimization problems [37,38,39] It can speed up the solution process by adjusting the accuracy of the search with gravitational constant. (ii) We formulate the k-barrier coverage into constraint optimization problem and first propose the energy constraint of sensor node to prolong the lifetime of the k-barrier coverage. (iii) We propose the newly modified GSA (PGSA), which adjusts the velocity updating by integrating the ability to exploit in PSO to enhance the global search capability and introduce agent boundary mutation strategy to increase population diversity and search accuracy.
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