An Efficient Large-Scale Sensor Deployment Using a Parallel Genetic Algorithm Based on CUDA

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.

Designing a Surveillance Sensor Network with Information Clearinghouse for Advanced Air Mobility

“…(1) open, (2) water, (3) neighborhood or residential area, (4) hill, and ( 5) busy commercial area or downtown. The Google Maps platform is utilized to observe and determine the terrain type of each block z ∈ Z. T z is obtained by reference to the terrain classification of the z-th block, as recorded in the list T. The probability values in the ω s T matrix, as given in Table 5, are approximated based on the approach reported in [79][80][81]. By analyzing the values presented in the table, it is evident that the detection probabilities of the sensors tend to decrease as the terrain type changes, which aligns with the discussion in Section 3.1.2.…”

On Coverage of 3D Terrains by Wireless Sensor Networks

“…This choice is based on one of the following justifications. (i) Some events are detectable even if they occur in locations invisible to the SNs [21]; (ii) The terrain is assumed to be sufficiently convex, so that the visibility between a SN and any point within its sensing range is always possible [3], [1], [22]; (iii) The impact of the RoI topography, is already taken into account during the parameterization of C(p i , s j ) depending on d(p i , s j ) [6], [7].…”

“…Moreover, some formulations adopt clearly unrealistic assumptions, such as the deterministic impact of the various factors on the coverage quality [3], [1], as well as the omnidirectional sensing capability of the SNs [22], [21]. Furthermore, most of the formulations [5], [6] do not consider the constraints imposed by the RoI, which limits the possible positions of the SNs.…”

On Efficient Deployment of Wireless Sensors for Coverage and Connectivity in Constrained 3D Space

“…Sensor deployment for coverage and connectivity in 2D space has been investigated in-depth in the literature. Many efficient techniques have been proposed, including several recent efforts based on integer linear programming (ILP) models and artificial intelligence such as randomization-based genetic algorithm [ 6 , 7 ]…”