基于全局人工鱼群算法优化的DV-Hop定位算法

无线传感器网络具有大规模、自组织、可靠性、以数据为中心、集成化等特点，被广泛应用于军事、医疗、矿山监测、安全生产等领域。然而现有的无线传感器网络非测距定位算法还存在定位偏差较大问题。针对上述问题，本文提出一种基于全局人工鱼群算法优化的DV-Hop（Distance Vector Hop）定位算法（DEWF-D）。该算法对非测距定位算法中的DV-Hop算法出现误差的步骤进行优化处理，通过减小算法过程中出现的误差，最终得到较为精准的定位坐标。首先使信标节点以两种不同的通信半径传递消息，将跳数进行精确化处理，以减少跳数带来的误差，然后用最小均方误差准则和误差加权方式计算平均每跳距离，最后利用人工鱼群算法替换三边测量法进行坐标计算，同时又在人工鱼选择下一个位置时引入全局最优信息，并引入人工鱼的吞食行为，提高人工鱼群算法的精度以及收敛速度。通过仿真验证，在不同信标节点密度下，本算法与DV-Hop算法以及其他算法相比定位精度分别提升28.3%、6.9%、12.5%，而在不同通信半径下，定位精度提升了24.4%、7.6%、14.8%。证明DEWF-D算法能有效提升定位精度，解决了定位算法中出现的定位偏差较大问题。

DV-Hop Localization Algorithm Optimized Based on Global Artificial Fish Swarm Algorithm

Wireless sensor networks have the characteristics of large scale, self-organization, reliability, data-centricity, integration, etc., and are widely used in military, medical, mine monitoring, safety production and other fields. However, the existing non-ranging positioning algorithms for wireless sensor networks suffer from large positioning errors. Aiming at this problem, a DV-Hop (distance vector-hop) localization algorithm based on the global artificial fish swarm algorithm optimization was proposed, namely DEWF-D localization algorithm. In the algorithm, the error-prone steps of the DV-Hop algorithm were optimized in the non-ranging positioning algorithm, and finally more accurate positioning coordinates were obtained by reducing the errors in the algorithm process. First, messages were transmitted by the beacon nodes with two different communication radii, and the number of hops was precisely processed to reduce the error caused by the number of hops; then, the average distance per hop was calculated using the minimum mean square error criterion and the error weighting method; Finally, the global artificial fish swarm algorithm was used to replace the trilateration method for coordinate calculation. Simulation results showed that, compared with the DV-Hop algorithm and other algorithms, the positioning accuracy of the proposed algorithm is respectively improved by 28.3%, 6.9%, and 12.5% under different beacon node densities; and under different communication radii, the positioning accuracy is improved by 24.4%, 7.6%, 14.8%, respectively. It was demonstrated that the DEWF-D algorithm can effectively improve the positioning accuracy and solve the problem of large positioning errors in previous positioning algorithms.