Research On Node Deployment Optimization Of Heterogeneous Wireless Sensor Networks Based On Swarm Intelligence Algorithm

In recent years,the advanced VLSI and RF technologies have accelerated the development and application of wireless sensor networks.Wireless sensor networks have the advantages of low energy consumption and strong distributed self-organization ability,and play an increasingly important role in medical,outer space exploration,battlefield monitoring,and emergency response.The quality of service of the network depends largely on the node deployment in the network.Therefore,the problem of node deployment is the basic problem that the wireless sensor network needs to solve.At present,the deployment strategy of wireless sensor networks is divided into static deployment strategy and dynamic deployment strategy according to whether the sensors in the network have mobile devices and can be self-deployed after the nodes are scattered.This paper focuses on the deterministic deployment of static network,the dynamic deployment of limited node energy and node deplo yment with multiobjective optimization.The goal of static deployment is to maximize area coverage with a limited number of sensor nodes.Aiming at the problem of maximum coverage,this paper proposes a static network node deployment method based on adaptive bacterial foraging algorithm.Firstly,the method selects the chemotaxis direction adaptively by simulated annealing criterion and uses the regional centralized search strategy to control the chemotaxis step size,which overcomes the shortcoming of the original bacteria foraging algorithm.Secondly,the proposed ASBFO algorithm enhances the directivity of the original BFO algorithm,improves the accuracy of understanding,and can effectively find the optimal deployment plan of the region.Simulation results show that the proposed algorithm not only maximizes the coverage area of the working area,but also minimizes the overlap area between the nodes,and has good stability.Sensor nodes in the dynamic deployment network can be self-deployed in the work area through the mobility of their own devices after being scattered.However,the energy stored by the sensor itself is limited,and the node movement consumes a lot of energy.In order to solve the problem of energy limitation of nodes in dynamic deployment network,a dynamic deployment optimization method with energy limitation based on vfa-pso algorithm is proposed in this paper.This method firstly calculates the virtual force on the nodes,adjusts the position of the nodes,reduces the repeated coverage between the nodes,and effectively solves the problem of premature maturity on the basis of balancing the global search ability and local search ability,so as to realize the maximum coverage of the network.Secondly,considering the impact of energy consumption on the overall network coverage performance,the paper limits the moving distance of nodes in the virtual force-based particle swarm optimization algorithm.Setting the maximum threshold of node movement determines whether the node continues to move,thus reducing physical movement.The energy loss achieves the purpose of improving the energy efficiency of the network.Finally,the simulation experiment shows that the proposed algorithm can guarantee the regional coverage and ensure the total moving distance and energy consumption of the node.When deploying wireless sensor network nodes,many factors are often considered,such as area coverage,total network energy consumption,network life cycle,network connectivity and so on.But modeling all the factors as a single optimization problem is complex,especially when there are conflicts between multiple goals.In this paper,the overall coverage rate,network energy consumption and network life cycle are optimized simultaneously,and the node deployment problem is constructed into a multi-objective optimization problem.Therefore,for the problem of multi-objective node deployment,this paper proposes a multi-objective optimization node deployment method based on the VFA-MOPSO algorithm.Firstly,the virtual force operator is used to adjust the position of the particles in the population to improve the convergence speed of the algorithm and make the sensor nodes evenly distributed in the region.Secondly,mutation operator is introduced after position update to provide a mutation probability for each particle and recalculate the optimal solution of individual history,so as to avoid the algorithm falling into local optimization and improve the accuracy of the algorithm.Finally,VFA-MOPSO algorithm is validated by computer simulation in the multi-objective optimization of node deployment issues get conform to the requirements of the Pareto optimal solution,and the node number solution under the condition of different concentration solution of network life cycle were greater than the average NSGA-II algorithm,network total energy consumption is less than the average of the NSGA-II algorithm,element distribution more uniform,and has strong advantages and reliability.