ENERGY-EFFICIENT HEURISTIC METRIC FOR SCP IN SENSOR NETWORKS

应用于传感器网络覆盖问题的能量有效性启发式机制(英文)

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.

提出了一种解决无线传感器网络覆盖问题的分布式启发式机制。该机制在节能前提下,得到优化的目标覆盖集合,以实现对目标监控区域的完全覆盖,并通过对其中重点目标集合的冗余覆盖来满足对重点目标集的可靠监控。同时,该目标覆盖集合与数据汇集点在通信结构上保持连通性。本文采用了改进的蚁群优化算法(最大最小蚁群混合算法)来实现上述启发式机制。通过构造新颖的启发式因子,人工蚂蚁能够由局域信息感知传感器网络的能量状况和覆盖能力,从而自适应地建立具备通信连通性的数据汇集路径。此外,将信息素浓度调节因子和评价函数引入了信息素更新规则的设计,使得蚁群在扩大搜索范围的基础上,提高了解的质量,且避免了求解过程陷入局部最优。算法的输出为能量有效的优化解集,具备较长生命周期,能够在保证与数据汇集点可靠连通的同时实现对目标区域的有效覆盖。