Wireless sensor network (WSN) works in a complex environment. To interdict the malicious nodes which attacks the safety of network, such as interrupt attacks and selective forwarding attacks, based on TS-BRS reputation model, a model for malicious node identification based on MNRT-OEP&RS algorithm is constructed. Using the linear regression of machine learning and combining the energy of nodes, data volume, number of adjacent nodes, the node sparsity and other deterministic parameters can solve environmental parameters. Then the similarity of between the benchmark reputation sequence and cycle reputation sequence sets the dynamic reputation double threshold are calculated in order to identify the malicious nodes by dynamically considering the information forwarding behavior. The simulated results show that the improved algorithm can guarantee the security of wireless sensor networks in complex environments effectively with above 90% recognition of malicious nodes and below 8% false positive rate.

In the wireless sensor network (WSN), nodes show a low forwarding rate under a poor-quality links environment and a resource-constrained state. The malicious nodes imitate this forwarding behavior, which can selectively forward date, eavesdropping, or discarding important dates. The traditional reputation model is challenging to identify with this kind of sub-attack nodes. To address these problems, a malicious node identification strategy based on time reputation model and environmental parameters optimization (TRM-EPO) is proposed in the WSN. First of all, the comprehensive reputation is calculated according to the direct reputation and the recommended indirect reputation. The environmental parameters matrix is based on nodes' running state, taking into account nodes' energy, data volume, number of adjacent nodes, and node sparsity. Besides, according to the environmental parameters matrix, and the recorded comprehensive reputation matrix, the next cycle's trust can be predicted. Finally, a similarity of the actual reputation and predicted trust matrix is proposed to compare with an adaptive threshold to identify malicious nodes. The experimental results demonstrate that the proposed strategy improves sensor nodes' security and reliability in a complex environment. Moreover, compared to comparison algorithms, the TRM-EPO improves the recognition rate above 1% and reduces the false-positive rate by more than 1%.

为了有效降低信道占用对节点信誉评价的影响,提高信誉评价模型的准确性,针对数据中断攻击和选择性转发攻击,结合信道状态对网络的影响,引入节点行为时间序列和信道状态时间序列,提出了基于时序信息分析的TS-BRS信誉模型.采用时序分析法,对两条时间序列匹配分析,降低信道冲突对信誉评价模型的干扰,提高模型识别的准确性;并在信誉值更新中引入适应性维护函数μ,加重现阶段节点行为对信誉值的影响,提高评价模型的适应性.仿真实验表明,新的信誉评价模型能有效提升模型的检测率和检测速度.引入维护函数,网络中被捕获的恶意节点的信誉值可以更快收敛.

The existing grey wolf optimization algorithm has some disadvantages, such as slow convergence speed, low precision and so on. So this paper proposes a grey wolf optimization algorithm combined with particle swarm optimization (PSO_GWO). In this new algorithm, the Tent chaotic sequence is used to initiate the individuals’ position, which can increase the diversity of the wolf pack. And the nonlinear control parameter is used to balance the global search and local search ability of the algorithm and improve the convergence speed of the algorithm. At the same time, the idea of PSO is introduced, which utilize the best value of the individual and the best value of the wolf pack to update the position information of each grey wolf. This method preserves the best position information of the individual and avoids the algorithm falling into a local optimum. To verify the performance of this algorithm, the proposed method is tested on 18 benchmark functions and compared with some other improved algorithms. The simulation results show that the proposed algorithm can better search global optimal solution and better robustness than other algorithm.

The coverage problem is one basic problem in the wireless sensor networks (WSN). In one limited region, how to reasonably arrange the sensor nodes to achieve the best coverage is the key to improve the performance of the whole networks. So this paper proposes an improved particle swarm optimization algorithm based on dynamic acceleration factor (PSO DAC). It adopts decreasing inertia weight coefficients and introduces dynamic acceleration coefficients. The experimental results show that the algorithm has improved the coverage ratio by 34.6% than that of the standard particle swarm algorithm (SPSO), which is 29.3% higher than the particle swarm algorithm based on the decreasing inertia weight coefficient (LDWPSO). It is proved that the PSO-DAC algorithm can effectively improve the convergence speed and improve the coverage rate of nodes, so as to improve the coverage effect of the whole network and prolong the network lifetime.

In this paper, game theory is applied to the WSN power control to reduce the energy consumption of the point transmission and prolong the lifetime of the network. The utility function is constructed by considering the residual energy and the transmitted power of the node. The remaining energy larger node is as the next hop node, which connects other nodes and takes more transmission tasks. In the communication process, the nodes take power control game algorithm as strategy and constantly adjust their strategies to improve the accuracy of information transmission. Nash equilibrium is proved to exist in the algorithm. Simulation results show that this algorithm can realize the purpose of reducing the energy consumption of wireless sensor network through the control of node transmission power, calculating the optimal transmit power faster, and getting the higher signal interference ratio compared with traditional distributed power control algorithm.

针对移动传感器网络节点部署易出现分布不均和能量消耗过高等问题,在传统虚拟力节点部署算法的基础上,提出一种基于密集度的虚拟力节点部署算法,通过对节点所受合力进行分析,推导出具有一定适应性的虚拟力引力参数和斥力参数,同时引入节点密集度的概念,利用节点自身密集度来选择虚拟力模型中最优距离阈值,从而改进传统的虚拟力模型,最终实现网络节点的部署优化.仿真结果表明,在随机部署的情况下,本文提出的算法能够更有效地提高网络覆盖率,减少覆盖漏洞并延长网络的生命周期.

As particle swarm optimization algorithm in the optimization of wireless sensor networks is easy to fall into local optimal solution and slow late convergence as well as other shortcomings,an improved particle swarm optimization algorithm based on dynamic acceleration factor (PSO-DAC)is proposed.It adopts decreasing inertia weight coefficients and introduces dynamic acceleration coefficients.The experimental results show that the algorithm has improved the coverage ratio by 34.6%than that of the basic particle swarm algorithm,which is 2 9.3% higher than that of the particle swarm algorithm based on decreasing inertia weight coefficient.It is proved that the PSO-DAC algorithm can effectively increase the convergence speed and improve the coverage rate of nodes,so as to improve the coverage effect of the whole network and prolong the network lifetime.

For fixed acceleration factors in the particle swarm optimization cause the function optimization accuracy poorly ,easy to fall into local optimal solution and slow late convergence ,this paper presents an improved particle swarm optimization base on dynamic acceleration coefficients (PSO-DAC ) .Adopting decreasing inertia weight coefficients improve the ability of weigh local search and global search capability .At the same time ,introducing dynamic acceleration coefficients raise the convergence speed and accuracy of the particle swarm algorithm .By four commonly used benchmark functions ,the improved particle swarm algorithm contrasts with the standard particle swarm optimization through simulation experiments .The experimental results show ,the improved algorithm comparing with the standard particle swarm optimization has higher accuracy and reduce the number of iterations over 51 .28% .The optimal solution can be found more quickly ,especially in multimodal function .