Mobile Data Gathering And Energy Replenishing Programming In Wireless Sensor Networks

Wireless sensor networks(WSNs) are supported by microelectronic technology, embedded technology, sensor technology and communication technology, etc. WSNs widely used in military, environmental, health and disaster relief, industrial, household, commercial and other fields. But the network lifetime problem has always been a bottleneck in the field. How to prolong the lifetime of wireless sensor network is an important problem.With the introduction of mobile node and the rise of wireless charging technology, replenishing sensor nodes energy by mobile nodes and prolonging lifetime of WSNs becomes a new research direction. Design of the path planning of mobile node is a hot research topic in the research direction. The path planning of mobile node as the main research content, the main work and contributions are summarized as follows:(1) This paper briefly introduces the concepts, characteristics, background and the current some of the hot areas in view of prolonging the network lifetime on wireless sensor networks(WSNs).(2) This paper briefly introduces the wireless charging technology, wireless sensor network in the mobile charging, the path of mobile data collection and mobile security related work at home and abroad.(3) This paper established a problem model, which mobile node replenishes the energy of sensor nodes and collects the data of sensor nodes. Meanwhile, this paper proposes a mobile data collection and charge of path planning algorithm and a mobile node routing protocol. This paper implements the proposed algorithms and two related algorithms as comparing algorithm. These algorithms are tested by omnet++ network simulation software. In experiments, we set the rate of mobile node and data collection time delay as variable, which proves that these algorithms are indeed prolong the network lifetime.(4) This paper establishs a mobile node in the process of moving to ensure the efficiency and safety of the path model of the problem. The unpredictable path planning algorithm is proposed based on the problem model. Firstly, we input monitoring point coordinates into TSP algorithm and generate a minimum Hamiltonian circuit. Secondly, we proposes an access probability function Pij. Finally, we use the probability matrix of function generation to determine the path. The generated path of the algorithm be tested by SPSS statistical analysis software and the proper β of access probability function Pij will be found. Through a lot of experiments by omnet++ network simulation software, which proves the path length had significantly lower than of a random path and the path is random.