Research On Energy-efficient Hierarchical Routing Algorithms For Wireless Sensor Networks

The popularity of wireless sensor network(WSN)applications has gradually revealed the limitations of existing hierarchical routing algorithms in wireless sensor networks.The limited battery capacity of sensors limits the duration of network monitoring,and the quality of network services and the stability and persistence of network applications are particularly important in the application scenario of large-scale monitoring.Based on this,this paper applies metaheuristic algorithms and reinforcement learning to the field of wireless sensor routing algorithms,mainly from the mechanism of node clustering,communication routing and network scheme solving methods,etc.,to conduct in-depth research to establish efficient and low-energy wireless sensor network routing algorithms and extend the survival time of the network.The main work of this paper is as follows.(1)To address the problems of high energy priority and insufficient consideration of parameters in the traditional node clustering mechanism,this article proposes a clustering mechanism based on two-dimensional parameters for the election of cluster heads by lowenergy nodes.In order to ensure that the elected cluster head is suitable for the surrounding nodes and coordinate with the overall network planning,this article allows nodes to elect their own cluster head,and give each node two-dimensional Competitive Parameters(CP),i.e.,energy and routing advantages parameters,to participate in the election,so as to select a reasonable cluster head.The experimental results show that the election mechanism of low energy nodes to elect cluster heads has a superior performance compared with the existing election mechanism.To address the problem that the two phases of cluster division and routing are too fragmented,this article proposes the Integrated cluster-routing solution(ICRS),in which ICRS solves the core problem of two different but objectively related phases of the routing algorithm at the cluster and routing levels in one ICRS solves the core problem of two different but objectively linked clustering and routing algorithms at one time,so as to obtain a more coordinated and tighter communication solution between clustering and routing.To address the problem that the current node clustering approach is too crude and still sticks to joining clusters nearby,this article proposes a pruning-based clustering rule(CPR)to adapt the nodes in the network for clustering in order to avoid the early death of the cluster head node due to excessive load.Simulation results show that both ICRS as well as CPR can improve the performance of the routing algorithm.(2)To address the problem of irreconcilable conflicts between nodes and the overall network caused by the current communication modes of single-hop and multi-hop,a multipath Alternating Multi-hop(MAM)communication scheme is proposed,and an energy maximization formula for alternate multi-hop(Energy The energy balance life maximization routing problem for alternate multi-hop(EB-FALM)is proposed.The multi-path alternate multi-hop communication scheme aims to extend the lifetime of the network by selecting a reasonable next-hop node after weighing its own energy consumption and the energy consumption of the network,while ensuring the life of the node itself.Simulation results show that EB-FALM reconciles the conflict between nodes and network well by maximizing the nodes with minimum lifetime while reducing the network energy consumption of WSN.For the multi-hop communication,the relay nodes need to forward the data of the rest of the cluster,and repeatedly sending and receiving the same data reduces the energy utilization efficiency of the whole network,this article proposes the Spanning Multi-hop(SPMH)communication mode,which can reduce the energy waste caused by repeatedly sending and receiving the same data by the nodes in the network and thus prolong the network The SPMH can reduce the energy waste caused by repeatedly sending and receiving the same data in the network,thus extending the network survival time.Simulation results show that SPMH can significantly extend the network lifetime compared to existing routing protocols.(3)To address the problem of limited and unstable merit-seeking ability of a single Metaheuristic Algorithm(MA),this article introduces Reinforcement Learning(RL)into the routing algorithm and propose a cooperative algorithm based on reinforcement learning and metaheuristic algorithm(CRLM)to solve the problem of metaheuristic algorithms falling into local optimality and precocity.In this model,this article uses CRLM to let multiple metaheuristic algorithms deconstruct the current optimal or worst solution they can find and learn from this solution set,so that the metaheuristic algorithms can learn the best solution to jump out of the local optimum and learn the worst solution features to improve the stability of the algorithm.Finally,this article compares the CRLM with the native algorithm and the existing algorithms,and the simulation results show that the CRLM has better optimal finding ability as well as stability compared with the existing algorithms.