Research On Improvement Of DV-Hop Algorithm In Wireless Sensor Networks

Wireless sensor network is a new distributed network which is composed of sensor nodes with functions of gathering data, process information, and wireless communication of sensor nodes. It is widely applied in earthquake monitoring, agricultural production, smart home, battlefield reconnaissance, and other fields. On the one hand, in the actual application, Data collected by sensor nodes need to determine the corresponding position, otherwise the data obtained will be meaningless. On the other hand, location is an indispensable supporting technology in wireless sensor network topology control, routing optimization, target tracking and other functions. Therefore, node localization became an important topic in current research of wireless sensor network.According to whether need to directly measure distance or not, localization algorithm can be divided into range free localization algorithm and range based localization algorithm. Thereinto the former is characterized by simple implementation, low-cost hardware, small power consumption and so on, relatively more suitable for wireless sensor networks. DV-Hop(Distance Vector-Hop) localization algorithm is a hotspot in the research of range free localization algorithm, has attracted many scholars at home and abroad. Paper deeply studied the principle and error sources of DV-Hop algorithm, and for different application scenarios, three different improved algorithms are proposed:(1)The improved DV-Hop optimization algorithm based on RSSI and vector correction(RVDV-Hop) is designed for the problem of too low localization accuracy of DV-Hop algorithm in sparse region of node. The algorithm is commonly suitable for open environment which requires high positioning speed and positioning accuracy. The algorithm is improved from minimum hop, average hopping distance and correction vector aspect. Firstly, set reference value of received signal strength indicator(RSSI) in one standard hop. The ratio between the signal strength value which the node receives and the reference value is used to correct hops. Secondly, the average hop distance of unknown nodes is weighted sum of average hopping distance of all anchor nodes or average hopping distance of the corresponding anchor node. Then, the estimating coordinates of the unknown node is corrected by correction vector calculated by the deviation between distance from estimated position to anchor node and corresponding estimated distance. Simulation results show that positioning precision of this algorithm is significantly higher than the traditional DV-Hop algorithm and the algorithm of literature [44]. The algorithm has also certain improvement in stability.(2)The improved DV-Hop algorithm based on improved particle swarm optimization algorithm(MPSO-DV-Hop) is designed for the problem of unsatisfactory location accuracy of the DV-Hop algorithm in the open environment. The algorithm is commonly suitable for open environment which harshly requires high positioning accuracy and stability. The algorithm combines with optimization method of hops calculation improved by the RSSI ratio in RVDV-Hop algorithm and use improved particle swarm algorithm to optimize the node positioning results. Thereinto particle swarm optimization algorithm is improved mainly from the particle velocity, inertia weight, learning strategy and mutation aspect. First of all, the best optimal position of part of the particles in history is adopted to lead particles to move, instead of global optimal location. Secondly, the inertia weight use a linear gradient strategy of random fluctuations, and four kinds of adaptive learning strategies is presented. Finally, step length of Levy distribution is adopted to vary the current optimal position. The simulation results show that compared with the traditional DV-Hop algorithm, the improved DV-Hop based on chaoic particle swarm algorithm in literature [57] and the DV-Hop algorithm based on improved particle swarm optimization in literature [58], the algorithm has higher positioning accuracy and better stability.(3)The improved DV-Hop algorithm based on hybrid algorithm of bat algorithm and quasi-newton algorithm(BNDV-Hop) is designed for the problem of the unsatisfactory performance of the DV-Hop algorithm in the environment with obstacles or interference. The algorithm is commonly suitable for environment with obstacles or interference which requires high positioning accuracy and stability. The algorithm firstly adopts the improved bat algorithm instead of the least squares to compute estimated location of nodes, and then quasi-newton algorithm was used to continue to search for the node location from the estimated location as the initial searching point. The bat algorithm was improved from three aspects. Firstly, the random vector β is adjusted adaptively according to fitness of bats. Secondly, bats are guided to move by the average position of all the best individuals before the current iteration. Thirdly, combination of learning and random walk method is adopted to search for new solutions. The simulation results show that, compared with the traditional DV-Hop algorithm and the improved algorithm of DV-Hop based on bat algorithm in literature [59], the algorithm has obvious advantages in positioning accuracy and stability.