Underwater Wireless Sensor Networks Real-Time Localization Algorithm

With the development of global economy,science and living standard gradually,people have pay more attention to marine resources,economy,education and protection increasingly.Underwater wireless sensor networks utilized to monitor ocean have become the focus of marine information technology.With great concerns,localization is the basis and key of majority application in underwater wireless sensor networks.In underwater wireless sensor networks,it is great challenge for localization that nodes whose energy is limited move with ocean currents.To solve the problems,a realtime localization based on current model(RTLC)is proposed.The current model representing the mobility model of nodes eliminates the influence of fluidity and the speed of nodes underwater is simulated precisely.The mobility model is optimized by kalman prediction combined observed values with theoretical values to be more in line with movement rules.The recording and updating mechanism are adopted to ensure information efficiency.The main content of this paper is as follows:1)In order to eliminate the influence of fluidity,the ocean current model has been constructed with the historical data analysis.It is composed of time basis function and space basis function and the speed of nodes underwater is simulated precisely.This model has been initialized by the parameters with lower temporal and spatial resolution.The historical current data is interpolated into the model by least square.The center of the radial basis function which can be updated in time is selected by K-means clustering algorithm.2)The localization is divided into two parts: anchors positioning and ordinary nodes positioning.The former is used to help finish the latter.Anchor location errors are compared with the error threshold to determine whether the mobility model needs to be updated by Kalman prediction to be more in line with movement rules.The recording and updating mechanism are adopted to realize ordinary nodes positioning and ensure information efficiency.3)According to the influences of location coverage,average positioning error and average communication cost,there are some simulation analysis.The result shows that RTLC algorithm is better than SLMP on node density,error threshold,confidence threshold and prediction window size.It has low communication costs,high average localization coverage and positioning accuracy.