Recently, the wireless sensor networks (WSNs) found its extensive application in surveillance and target tracking. For these two WSN applications, connectivity and coverage play a major role most particularly for target tracking. A large number of available sensor nodes track targets, during which a massive redundant data gets generated, which may minimize the system performance. Most particularly during the sensor node failure, the major intention of coverage and connectivity optimization model is to select less number of sensor nodes with maximum direct sensor node connectivity. But existing algorithms fail to achieve minimal node selection, therefore to mitigate the barriers of the traditional coverage algorithms, this paper proposed the hybrid Gravitational Search algorithm with social ski-driver (GSA-SSD) based model. This hybrid approach in target based WSN optimizes the coverage and connectivity requirement. By adapting the dynamic behaviour of SSD algorithm, the performance of GSA gets improved. Finally, the relative performance of the proposed hybrid GSA-SSD based optimization model is validated and compared with other optimization algorithms. On the basis of uncovered area rate and a number of sensor nodes the performance is evaluated. The results are implemented in the MATLAB simulation tool. Further, the performance enhancement in terms of uncovered area rate, number of selected active sensors, energy consumption, connectivity and network lifetime is achieved with randomly deployed nodes.

最近，无线传感器网络（WSN）在监视和目标跟踪中发现了其广泛的应用。对于这两个WSN应用程序，连接性和覆盖范围尤其在目标跟踪中起着主要作用。大量可用的传感器节点跟踪目标，在此期间会生成大量冗余数据，这可能会使系统性能降到最低。最特别地，在传感器节点故障期间，覆盖范围和连接性优化模型的主要目的是选择具有最大直接传感器节点连接性的较少数量的传感器节点。但是现有算法无法实现最小的节点选择，因此为减轻传统覆盖算法的障碍，本文提出了基于社交滑雪驾驶员（GSA-SSD）的混合重力搜索算法模型。基于目标的WSN中的这种混合方法优化了覆盖范围和连接性要求。通过适应SSD算法的动态行为，GSA的性能得以提高。最后，验证了所提出的基于混合GSA-SSD的优化模型的相对性能，并将其与其他优化算法进行了比较。基于未发现的面积率和多个传感器节点，可以评估性能。结果在MATLAB仿真工具中实现。此外，通过随机部署的节点可以实现未覆盖面积率，所选有源传感器数量，能耗，连接性和网络寿命方面的性能增强。验证了所提出的基于混合GSA-SSD的优化模型的相对性能，并将其与其他优化算法进行了比较。基于未发现的面积率和多个传感器节点，可以评估性能。结果在MATLAB仿真工具中实现。此外，通过随机部署的节点可以实现未覆盖面积率，所选有源传感器数量，能耗，连接性和网络寿命方面的性能增强。验证了所提出的基于混合GSA-SSD的优化模型的相对性能，并将其与其他优化算法进行了比较。基于未发现的面积率和多个传感器节点，可以评估性能。结果在MATLAB仿真工具中实现。此外，通过随机部署的节点可以实现未覆盖面积率，所选有源传感器数量，能耗，连接性和网络寿命方面的性能增强。