Research On Data Collection Of Wireless Sensor Networks Based On Mobile Sink

The data collection is an important technology in wireless sensor networks. Mobile data collection has its unique advantages, for it can increase the survival time of the network and reduce the energy consumption of the nodes. Based on the previous studies, this paper studies the problem of wireless sensor network data collection based on mobile Sink by the analysis of related problems and the combination of new technology. For a number of important issues of mobile Sink wireless sensor network data collection, the study combines wireless signal transmission, node deployment, stochastic theory, queuing theory and optimization theory. The goal of this paper is to realize good data collection of the mobile Sink wireless sensor network, increase network lifetime, reduce the energy consumption of the network and shorten the delay.The main work and innovation of this paper are as follows:1. A method of the least node random deployment based on data packet receiving evaluation is proposed.Some large scale wireless sensor networks with low accuracy requirements need reducing the cost of deployment. This paper studies the random deployment of nodes with a certain packet loss rate, aiming at reducing the number of nodes in the network. By measuring different environment and different distance data packet reception, according to the probabilistic relationship of wireless signal receiving condition and distance, this paper builds packet receiving evaluation model and proposes the method of the least node random deployment based on data packet receiving evaluation. The method reduces the number of random deployment node.2. The paper presents the control method of single antenna mobile Sink based on queuing theory and the method of centralization.Mobile Sink can achieve the deployment of low cost and solve the load imbalance problem of nodes. In this paper, we control the data collection process of mobile Sink based on queuing theory, allowing the process to be interrupted. This queuing rule is suitable for the characteristics of mobile Sink data collection and based on the residual energy of nodes, so it increases the survival time of the network. This paper controls the trajectory of mobile Sink data collection based on the centralization method. Adding a number of virtual points forms a connected graph. According to the characteristics of connected graph, the method combines center method to optimize the trajectory of mobile Sink data collection, shortens the trajectory of the mobile Sink data collection and reduces the energy consumption of the network.3. A multi antennas mobile Sink data collection method based on speed control is proposed.The method of multi antennas mobile Sink data collection based on speed control divides the data collection process into a plurality of stages. Multi antennas mobile Sink completes data collection in the various stages by the regulation and control of Sink moving speed. The moving speed of multi antennas mobile Sink rate between stages can link. The area is divided into four categories, in order to play the advantage of multi antennas data collection, we choose convergence area firstly. At the same time, the cutting stage and maximum moving speed of Sink reduce the delay.4. The paper presents a method of node energy and load adaptation based on Markov flow prediction.Node energy and load need having a certain adaptation mechanism. In this paper, the adaptive method of energy and load based on Markov flow prediction is put forward, and the replacement of cluster head is optimized by using Markov flow prediction to solve the random dynamic decision problem. This paper selects the third order Markov flow prediction after analysis of multi hop forwarding mechanism of multi order Markov. The adaptive method of energy and load based on the Markov flow prediction prolongs the network lifetime.5. A data collection method based on improved simulated annealing algorithm for wireless charging of nodes is proposed.The data collecting device and the wireless charging device are arranged on the mobile device, and the wireless charging mode is adopted to carry out the energy supplement for the node. This paper analyzes the data collection and wireless charging problem, attributes the problem to the combinatorial optimization problem and proposes an improved simulated annealing algorithm. The algorithm optimizes the mobile device path and speed and maximizes the probability of the wireless charging and data collection at the same time. The method shortens the moving distance of mobile device and reduces the time required for data collection and wireless charging.