Research On DV-Hop Localization Algorithm Based On Polynomial Average Optimization Improvement

Wireless Sensor Network(WSN)consists of a large number of sensor nodes that can communicate with each other,it plays a great role in applications such as military defense,environmental monitoring,and smart cities.Node localization is the top priority for WSN applications,as the data collected by the nodes need to be processed in conjunction with their locations.The Distance Vector-Hop(DV-Hop)algorithm has been one of the most popular node localization algorithms due to its easy implementation and low cost.However,the DV-Hop algorithm performs poorly in anisotropic networks.To address the problem,this thesis presents a detailed analysis of the error sources of the DV-Hop algorithm and proposes a corresponding improvement scheme.The main contents include:(1)In this thesis,an improved DV-Hop localization algorithm based on polynomial averaging and anchor node filtering(PAFDV-Hop)is proposed.Firstly,according to the relationship between received signal strength indication(RSSI)and distance,RSSI technique is used to reduce the minimum hop error by graded refinement of the first hop between nodes.Subsequently,polynomial is used to approximate the relationship between the number of hops and the distance between nodes for the uneven distribution of nodes,and the estimated distance is averaged and optimized to reduce the estimated distance error.Finally,in the process of calculating the coordinates of the unknown node,the validation error is used to filter the trusted anchor nodes and the coordinates of the position of the unknown node are calculated based on the information of the trusted anchor nodes.Simulation results show that the localization accuracy of the proposed algorithm is improved in both isotropic and anisotropic networks.(2)For the problem that the DV-Hop localization algorithm has large localization error due to matrix irreversibility in the process of location calculation,this thesis proposes a DV-Hop localization algorithm based on improved bald eagle search algorithm with polynomial average optimization.In order to improve the accuracy of the search results of the bald eagle search algorithm,this thesis adds a t-distribution perturbation term on the originally selected search space to balance the global search ability and local search ability of the bald eagle search algorithm,and also introduces a reverse learning strategy after the hunting phase to enhance the ability of the algorithm to jump out of the local optimum.Based on the polynomial average optimized DV-Hop localization algorithm,the bald search algorithm is used to replace the original least squares method.Simulation experiments are performed under square,C-shaped and O-shaped network areas,and the performance of the proposed algorithm is analyzed by varying the ratio of anchor nodes in the network and the communication radius of the sensor nodes.The experimental results prove that the improved algorithm proposed in this thesis has higher positioning accuracy and better stability.