To increase the node coverage of wireless sensor networks (WSN) more effectively, in this paper, we propose a hybrid-strategy-improved butterfly optimization algorithm (H-BOA). First, we introduce Kent chaotic map to ...
详细信息
To increase the node coverage of wireless sensor networks (WSN) more effectively, in this paper, we propose a hybrid-strategy-improved butterfly optimization algorithm (H-BOA). First, we introduce Kent chaotic map to initialize the population to ensure a more uniform search space. Second, a new inertial weight modified from the Sigmoid function is introduced to balance the global and local search capacities. Third, we comprehensively use elite-fusion and elite-oriented local mutation strategies to raise the population diversity. Then, we introduce a perturbation based on the standard normal distribution to reduce the possibility of the algorithm falling into premature. Finally, the simulated annealing process is introduced to evaluate the solution's quality and improve the algorithm's ability, which is helpful to jump out of the local optimal value. Through numerous experiments of the international benchmark functions, the results show the performance of H-BOA has been significantly raised. We apply it to the WSN nodes coverage problem. The results show that H-BOA improves the WSN maximum coverage and it is far more than other optimization algorithms.
With the help of fog computing theory, this paper proposes Cluster Routing optimized Algorithm of Nonlinear Event Migration Strategy, CR-NEMS. First, the fog node is used for high computing power and control ability t...
详细信息
With the help of fog computing theory, this paper proposes Cluster Routing optimized Algorithm of Nonlinear Event Migration Strategy, CR-NEMS. First, the fog node is used for high computing power and control ability to match and schedule sensor nodes to make them evenly distributed to achieve the purpose of network energy balance. Secondly, the intelligent algorithm is adopted to optimize the data transmission link to reduce network delays and improve transmission efficiency. Thirdly, the routing optimization is achieved through the iterative change and update strategy of controllable parameters to improve the global traversal capability of the entire network. Finally, the simulation experiment shows that the algorithm is compatible with other algorithms under the conditions of data transmission in the entire network. Compared with the network delay, network energy and network lifetime, the proposed strategy reduces by 23.49%, 13.22% and 12.17% respectively. It verifies that the algorithm in this paper effectively balances the network energy while solving the routing optimization problem and resource allocation problem in the target area.
暂无评论