Research On Node Location Technology In Wireless Sensor Networks Based On Intelligent Optimization

Wireless sensor network is composed of a large number of sensor nodes that are freely organized and communicate with each other.The sensor nodes send the data they perceive to the base station for further processing.For most practical applications of WSNs,such as target tracking,forest monitoring,environmental data collection,etc.,the usability of the data collected by the nodes in these scenarios is greatly reduced if this information is not combined with location information.Therefore,accurate knowledge of the physical location of nodes is key to WSN applications,and the localisation of sensor nodes is an important technology for WSNs.Node localisation algorithms for wireless sensor networks are usually divided into two main categories:range-based node localisation algorithms and node localisation algorithms that do not require range.The latter does not require additional redundant hardware devices compared to the former,and is less expensive to implement,making the nodes more flexible.This paper focuses on positioning algorithms that do not require ranging,and the main work includes.(1)DV-Hop location algorithm is one of the most common classical node location algorithms,but it is greatly affected by the network topology and every step of the algorithm itself will produce errors,so a DV-Hop algorithm based on neighborhood search particle swarm optimization algorithm is proposed on the basis of the original algorithm.Particle swarm optimization algorithm has been widely used due to its advantages such as small computation amount,easy implementation,few parameters,fast convergence rate,etc.,but also because it is easy to fall into local optimal in application,this paper introduces neighborhood search strategy on the basis of particle swarm optimization algorithm.In the process of neighborhood search,particles will first use the obtained optimal solution as the initial solution.Continue to search for other candidate solutions in the neighborhood of the initial solution,and compare and optimize the current optimal solution with other candidate solutions according to certain specific rules,and re-determine the current state of the new optimal solution.Repeat the above operations until the conditions set in advance are met,the algorithm terminates,and the optimal solution is obtained.At the same time,the minimum number of half-hops is refined,the weighted average jump distance is calculated,and the location of the unknown node is obtained by using the neighborhood search particle swarm optimization algorithm instead of the least square method.The simulation results show that the location error of DV-Hop algorithm based on neighborhood search particle swarm optimization is obviously smaller than that of traditional DV-Hop algorithm.(2)To further improve the positioning accuracy of the algorithm,a DV-Hop localization algorithm based on improved simulated annealing particle swarm optimization is proposed.The simulated annealing algorithm has good global searching ability and can accept slightly worse solutions with certain probability and jump out of the local optimal in this way.The Metropolis criterion of simulated annealing algorithm helps particle swarm jump out of local optimal,makes up the disadvantage of particle swarm optimization easily falling into local optimal,and enhances the global search ability.The simulation results show that the positioning accuracy of DV-Hop algorithm based on improved simulated annealing particle swarm optimization has been significantly improved.