Abstract
We have employed evolutionary computation to solve the optimization problem of sensor deployment in battlefield environments. A genetic algorithm has the advantage of delivering results of a higher quality than simple computational algorithms, but it has the drawback of requiring too much computing time. This study aimed not only to shorten the computing time to as close to real-time as possible by using the Compute Unified Device Architecture (CUDA) but also to maintain a solution quality that is as good as or better than the case when the proposed algorithm is not used. In the proposed genetic algorithm, parallelization was applied to speed up the fitness evaluation requiring heavy computation time. The proposed CUDA-based design approach for complex and various sensor deployments is validated by means of simulation. We parallelized two parts in Monte Carlo simulation for the fitness evaluation: moving lots of tested vehicles and calculating the probability of detection (POD) for each vehicle. The experiment was divided into CPU and GPU experiments depending on arithmetic unit types. In the GPU experiment, the results showed similar levels for the detection probability by GPU and CPU, and the computing time decreased by approximately 55-56 times.
Highlights
A number of studies have been conducted on wireless sensor networks (WSNs), which have become an essential component in the Internet of Things (IoT) in recent years [1]
The contributions of this paper include the following: (i) based on real environments, we used two types of sensors and three scenarios with different terrains and varied the number of sensors from 15 to 200 for comparison between CPU and GPU experiments; (ii) we shortened the computing time to as close to real-time as possible by using the Compute Unified Device Architecture (CUDA) and maintained solution qualities that are as good as the results shown in the CPU test; (iii) we took an elaborated parallelized approach based on CUDA for complex and various sensor deployments
This paper describes the use of a generational genetic algorithm (GA) and probabilistic sensing models in WSN environments to discuss issues relating to barrier coverage
Summary
A number of studies have been conducted on wireless sensor networks (WSNs), which have become an essential component in the Internet of Things (IoT) in recent years [1]. Unlike commonly used wired and wireless networks, WSNs have many limitations to consider, such as battery life, computation capability, and communications. WSNs are very much application oriented; they require a customizable design according to application environments, and they require cross-layer optimization in the communication protocol stack. For this reason, WSNs require a wide range of research in multiple fields, including MAC, data routing, and transport protocols. In the case of planned deployment, the location where sensors are deployed is determined beforehand to aim for maximum coverage, minimum power consumption, and strengthening
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