A Research Of Air Data Collection For Large-scale WSN

With the development of information technology, wireless communication technology and sensor technology, Wireless Sensor Networks(WSN) shows a highlighted advantage in the capability to access information. Recent years, WSN has been applied in various industries. With the rapid development of wireless sensor network, there are many technical problems at the same time, including how to solve the large-scale deployment of data collection in wireless sensor network, which is one of the most important and urgent problem.Using the minimum coverage of wireless sensor network, this paper adopts the advanced unmanned aerial vehicle(UAV) as mobile data collector, and proposed a fast path plan algorithm for UAV, which is the base for air data collection.For air data collection based on controllable unmanned aerial vehicle in this paper, we do the following work:Analysis and research the current domestic and foreign existing research on data collection of wireless sensor network. The data collection way, depending on the use of the means of transportation, can be divided into: static data collection, surface data collection, air data collection and at the same time we point out their features and application scenarios.Based on the minimum cover in large-scale wireless sensor networks, and considering the uniformity of its deployment, this paper proposed a fast path plan algorithm, and this algorithm can obtain the node access path with a short time. Through the experiments, we have verified the feasibility of the algorithm. At the same time, by the comparison with other algorithms, we can conclude that the quick fast path plan algorithm can effectively realize the path planning with the nodes deployed uniformly.For the main process and key steps of air data collection, we proposed a simulation platform in this paper. The platform can be implemented according to the different input parameters and system configuration of large-scale wireless sensor network, and according to the simulation results, this platform can provide real network a feedback.Finally, in order to verify the feasibility of air data collection platform and pick up the simulation results, the simulation test platform is developed in this paper. Choose c# as development language, NS2 as simulation environment, set up the relevant configuration and simulation parameters, and the different UAV speed, network scale, then record and analysis the simulation results. Through this experiment, we proved the feasibility of the air data collection simulation platform. At the same time, through the analysis of NS2 simulation results, the parameters of the simulation platform to air data collection, such as time-consuming, flight distance, memory capacity can be evaluated and feedback.