改进白骨顶鸡优化算法的WSN网络覆盖

Improved WSN network coverage with coot optimization algorithm

为解决二维无线传感器网络随机部署产生的节点分布不均、覆盖率低的问题,提出一种融合元启发式算法的网络部署方案。该方案以节点部署空间作为约束条件、网络覆盖范围作为目标函数对二维网络覆盖模型进行数学建模。针对白骨顶鸡优化算法全局探索能力不强且在迭代后期容易陷入局部最优的缺点,该方案引入复合突变策略和随机反向策略对原算法进行改进。在二维网络覆盖模型进行的仿真测试结果表明:部署改进白骨顶鸡优化算法的二维无线传感器网络不仅网络覆盖率更高,节点也更加均匀,验证了改进白骨顶鸡优化算法解决节点部署问题的有效性和实用性。

In order to solve the problem of uneven node distribution and low coverage caused by random deployment of two-dimensional wireless sensor network,a network deployment scheme of fusion meta heuristic algorithm was proposed.Firstly,the node deployment space was used as the constraint and the network coverage was used as the objective function to mathematically model the two-dimensional network coverage model.Secondly,an improved coot optimization algorithm(COA)was proposed,which introduced a composite mutation strategy and a stochastic inverse strategy to improve the original algorithm in view of such shortcomings that the original algorithm’s global exploration ability is not strong and it is easy to fall into local optimization in the later iteration.Finally,simulation tests in the 2D network coverage model show that the WSN deployed by IOVA not only has higher network coverage,but also has more uniform nodes,which verifies the effectiveness and utility of ICAO in solving this problem.