Parallel Meta-Heuristic Approaches for Deployment of Heterogenous Sensing Devices

Genetic and simulated annealing algorithms have been used to solve many combinatorial problems. Their results are proven to be efficient in solving such problems. The running time of these techniques is generally less than the run time needed to find the optimal solution. However, in very large-scale problems such as sensor deployment, their running time is extremely slow. In this paper, we introduce several parallelization methods to speedup the genetic and simulated annealing heuristics used for the deployment of sensing devices on a field with differential security/surveillance requirements. The parallelization methods can be classified into two categories: slave based, and master based, we implemented these two categories using iteration division, and chromosome division. A large number of experiments were conducted on a PVM cluster with 14 nodes to show the speed up and efficiency of these parallelization techniques. Keywords—Genetic Algorithm, simulated annealing, sensor networks, parallelization.