A Fast Iteration Algorithm for Coverage Optimization of Circular Sector Sensor Networks In this paper, the coverage optimization of a circular sector sensor network is considered. To achieve the fast iteration and higher coverage rate tasks in industry, a novel fast iteration algorithm is proposed in this paper to replace the gradient ascent based algorithms in some exiting works. The key issue of the proposed algorithm is that the control gains are set to be timevarying and nonuniform for the mobile sensors. Moreover, some distributed conditions that only address the overlapped coverage rates of the sensors are proposed to trigger the control gains changing. The timevarying gains together with the distributed triggered conditions guarantee the less iteration steps and higher coverage rate of the sensor network. At last, three simulation examples are provided to illustrate the effectiveness of the proposed method via comparing the algorithm in this paper and some existing works.

An EnergyEfficient Coverage Enhancement Strategy for Wireless Sensor Networks Based on a Dynamic Partition Algorithm for Cellular Grids and an Improved Vampire Bat Optimizer Sensor nodes perform missions based on the effectual invariable coverage of events, and it is commonly guaranteed by the determinate deployment for sensor nodes who deviate from the optimum site frequently. To reach the optimal coverage effect with the lowest costs is a primary goal of wireless sensor networks. In this paper, by splicing the sensing area optimally with cellular grids, the best deployment location for sensors and the required minimum number of them are revealed. The optimization problem of coverage rate and energy consumption is converted into a task assignment problem, and a dynamic partition algorithm for cellular grids is also proposed to improve the coverage effect when the number of sensors is variable. Furthermore, on the basis of solving the multiobjective problem of reducing and balancing the energy cost of sensors, the vampire bat optimizer is improved by introducing virtual bats and virtual preys, and finally solves the asymmetric assignment problem once the number of cellular grids is not equal to that of sensors. Simulation results indicate that the residual energy of sensors during redeployment is balanced notably by our strategy when compared to three other popular coverageenhancement algorithms. Additionally, the total energy cost of sensor nodes and coverage rate can be optimized, and it also has a superior robustness when the number of nodes changes.