For the optimal performance of wireless sensor networks in different areas of applications needs to maximize the coverage area of sensor nodes. The coverage of sensor nodes in monitoring region can be improved by using efficient node deployment algorithms. In this paper node deployment based on bat algorithm (BA) is proposed to enhance the coverage rate of nodes. Each bat describes solution for deployment of sensor nodes individually. In bat algorithm based node deployment grid points covered by one sensor node are excluded for remaining sensor nodes. The benefit of eliminating the grid points is that the load on remaining nodes is decreased and there is no chance of overlapping i.e. grid point is covered by only one sensor node. The simulations of node deployment based on BA and fruit fly optimization algorithm (FOA) are also demonstrated. In this paper to further increase the coverage rate of sensor nodes the performance of various parameters of bat algorithm such as loudness, pulse emission rate, maximum frequency, grid points and sensing radius has been optimized. The simulation results of node deployment based on optimized bat algorithm are also compared with BA and FOA based node deployment in terms of mean coverage rate, computation time and standard deviation. The coverage rate curve for various numbers of iterations and sensor nodes are also presented for optimized bat algorithm, BA and FOA. The results demonstrate the effectiveness of optimized bat algorithm as it achieved more coverage rate than BA and FOA.

为了使无线传感器网络在不同应用领域具有最佳性能，需要最大化传感器节点的覆盖范围。通过使用有效的节点部署算法，可以提高监视区域中传感器节点的覆盖范围。本文提出了一种基于蝙蝠算法的节点部署方法，以提高节点的覆盖率。每个蝙蝠都描述了分别部署传感器节点的解决方案。在基于蝙蝠算法的节点部署中，一个传感器节点覆盖的网格点被排除在其余的传感器节点之外。消除网格点的好处是减少了其余节点上的负载，并且没有重叠的机会，即网格点仅被一个传感器节点覆盖。还演示了基于BA和果蝇优化算法（FOA）的节点部署仿真。为了进一步提高传感器节点的覆盖率，对蝙蝠算法的响度，脉冲发射率，最大频率，网格点和感应半径等参数进行了优化。在平均覆盖率，计算时间和标准差方面，还将基于优化蝙蝠算法的节点部署仿真结果与基于BA和FOA的节点部署进行了比较。还针对优化的蝙蝠算法，BA和FOA给出了各种迭代次数和传感器节点的覆盖率曲线。结果证明了优化的蝙蝠算法的有效性，因为它实现了比BA和FOA更高的覆盖率。最大频率，网格点和感应半径已优化。在平均覆盖率，计算时间和标准差方面，还将基于优化蝙蝠算法的节点部署仿真结果与基于BA和FOA的节点部署进行了比较。还针对优化的蝙蝠算法，BA和FOA给出了各种迭代次数和传感器节点的覆盖率曲线。结果证明了优化的蝙蝠算法的有效性，因为它实现了比BA和FOA更高的覆盖率。最大频率，网格点和感应半径已优化。在平均覆盖率，计算时间和标准差方面，还将基于优化蝙蝠算法的节点部署仿真结果与基于BA和FOA的节点部署进行了比较。还针对优化的蝙蝠算法，BA和FOA给出了各种迭代次数和传感器节点的覆盖率曲线。结果证明了优化的蝙蝠算法的有效性，因为它实现了比BA和FOA更高的覆盖率。还针对优化的蝙蝠算法，BA和FOA给出了各种迭代次数和传感器节点的覆盖率曲线。结果证明了优化的蝙蝠算法的有效性，因为它实现了比BA和FOA更高的覆盖率。还针对优化的蝙蝠算法，BA和FOA给出了各种迭代次数和传感器节点的覆盖率曲线。结果证明了优化的蝙蝠算法的有效性，因为它实现了比BA和FOA更高的覆盖率。