Research On Charging Nodes Deployment Optimization In Wireless Rechargeable Sensor Networks

In recent years,wireless sensor networks have developed rapidly and have been widely used in military,commercial,environmental,and medical fields.However,due to the small size of the sensor,the limited carrying capacity,and the difficulty in replacing the battery in some application scenarios,the energy limitation problem is a key factor that restricts the development of the current sensor network.The wireless chargeable method has a good effect on solving this problem,and has made good research progress,but there are still problems such as difficulty in solving the charging model,dynamic charging node,and insufficient flexibility of static deployment.Therefore,it's one of the research hotspots of today that how to properly design the charging deployment scheme.In this paper,the wireless charging node deployment problem in the wireless rechargeable sensor network is studied.The optimization model is built for the static deployment of the charging node and the dynamic deployment of the charging vehicle.Two improved firefly algorithms are proposed for the construction.The problem is solved,which effectively improves the charging efficiency and prolongs the network life cycle.Details as follows:(1)For the static deployment problem of charging nodes in wireless rechargeable sensor networks,the network model and wireless charging model are described,and the multi-objective optimization deployment method is proposed.This method is a complex nonlinear optimization problem,and the traditional algorithm is difficult to solve.In this paper,the firefly algorithm in the group intelligent optimization algorithm is used to solve the problem,and the solution coding of the problem is defined as a firefly in the form of a one-dimensional array.By simulating the process of the firefly moving according to the light intensity,the evolutionary calculation is continued until the optimal solution of the problem is found.This paper further improves the attractiveness formula for the weak attraction of the firefly algorithm in large-scale scenarios,enabling it to adaptively adjust the attractiveness according to the scale of the scene.This paper also proposes a dynamic location update mechanism.By increasing the dynamic global optimal value factor,it affects the firefly movement together with the attraction,so as to improve the shortcomings of the firefly algorithm's slow convergence speed and easy to fall into local optimum.Experiments show that the improved firefly algorithm can not only solve the static deployment problem of charging nodes,but also has faster solution speed and higher solution accuracy than other group intelligent optimization algorithms.(2)Since the dynamic deployment of charging vehicles in wireless rechargeable sensor networks is more complicated and involves specific network energy consumption problems,this paper adopts a first-order radio charging model,and on this basis,the definition of charging urgency is proposed.The charging car is scheduled to be charged according to priority and an optimization problem model is established.This problem is a multi-objective optimization problem and has proven to be an NPcomplete problem.The complexity of the dynamic deployment problem requires the algorithm with higher precision.Therefore,the firefly algorithm is further improved.The firefly algorithm is easy to fall into the local optimal value,combined with the differential evolution idea and introduces the local search operator.When fireflies tend to local optimum values resulting in reduced population diversity,other evolutionary information can be extracted to jump out of local optimums to increase individual activity.Experimental verification shows that the improved firefly algorithm has higher accuracy than other group optimization algorithms,which can effectively extend the life cycle of the network.