Energy-efficient mechanism based on ACO for the coverage problem in sensor networks

An energy-efficient heuristic mechanism is presented to obtain the optimal solution for the coverage problem in sensor networks. The mechanism can ensure that all targets are fully covered corresponding to their levels of importance at minimum cost, and the ant colony optimization algorithm （ACO） is adopted to achieve the above metrics. Based on the novel design of heuristic factors, artificial ants can adaptively detect the energy status and coverage ability of sensor networks via local information. By introducing the evaluation function to global pheromone updating rule, the pheromone trail on the best solution is greatly enhanced, so that the convergence process of the algorithm is speed up. Finally, the optimal solution with a higher coverage- efficiency and a longer lifetime is obtained.

ENERGY-EFFICIENT HEURISTIC METRIC FOR SCP IN SENSOR NETWORKS

A heuristic metric is presented to achieve the optimal connected set covering problem （SCP） in sensor networks. The coverage solution with the energy efficiency can guarantee that all targets are fully covered. Among targets, the crucial ones are redundantly covered to ensure more reliable monitors. And the information collected by the above coverage solution can be transmitted to Sink by the connected data-gathering structure. A novel ant colony optimization （ACO） algorithm--improved-MMAS-ACS-hybrid algorithm （IMAH） is adopted to achieve the above metric. Based on the design of the heuristic factor, artificial ants can adaptively detect the coverage and energy status of sensor networks and find the low-energy-cost paths to keep the communication connectivity to Sink. By introducing the pheromone-judgment-factor and the evaluation function to the pheromone updating rule, the pheromone trail on the global-best solution is enhanced, while avoiding the premature stagnation. Finally, the energy efficiency set can be obtained with high coverage-efficiency to all targets and reliable connectivity to Sink and the lifetime of the connected coverage set is prolonged.

An immune-swarm intelligence based algorithm for deterministic coverage problems of wireless sensor networks

A novel immune-swarm intelligence (ISI) based algorithm for solving the deterministic coverage problems of wireless sensor networks was presented.It makes full use of information sharing and retains diversity from the principle of particle swarm optimization (PSO) and artificial immune system (AIS).The algorithm was analyzed in detail and proper swarm size,evolving generations,gene-exchange individual order,and gene-exchange proportion in molecule were obtained for better algorithm performances.According to the test results,the appropriate parameters are about 50 swarm individuals,over 3 000 evolving generations,20%-25% gene-exchange proportion in molecule with gene-exchange taking place between better fitness affinity individuals.The algorithm is practical and effective in maximizing the coverage probability with given number of sensors and minimizing sensor numbers with required coverage probability in sensor placement.It can reach a better result quickly,especially with the proper calculation parameters.