基于蚁群优化算法的传感器网络能量有效性覆盖机制

提出了一种解决无线传感器网络覆盖问题的能量有效性启发式机制.该机制在节能的前提下,实现了对目标监控区域的完全覆盖,且覆盖精度与目标的重要性级别成正比关系.机制的实现运用了蚁群优化算法,算法的设计过程采用了新颖的启发式因子构造方法和基于评价函数的全局信息素更新规则,由此,人工蚂蚁被赋予了对目标监控区域的覆盖状况和对传感器网络区域能量状况的自适应感知能力,并通过增加优化解集中节点上的信息素量,加速求取最优解的收敛过程.最后,蚁群在迭代优化的基础上构建出解决无线传感器网络覆盖问题的健壮优化解,该优化解能够在能量有效性的基础上具备良好的覆盖有效性和较长的生命周期.

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.