The Energy-efficiency Node Scheduling Schemes In Wireless Sensor Networks

With the development and maturity of computer technology, wireless communication technology, and microelectronic technology, wireless sensor network has become an important way to collect and handle massive amounts of data. In wireless sensor networks, plenty of sensor nodes, which integrate sensing, computing and communication capabilities, are deployed in a particular area. They build the data collection network in a self-organized manner and transmit the data to remote users in the effective time. Due to the good performance in adaptability and scalability, wireless sensor networks can be deployed in some complex environment which is not easy to be managed manually. It has broad application prospects in the military, commercial and civilian fields.Energy efficiency is an important index to evaluate the performance of wireless sensor networks. Due to the micro sensor nodes with limited battery capacity are usually deployed in the wide area which is difficult to recharge. How to improve network energy efficiency has become a key issue in the research of wireless sensor network. Node scheduling is an effective solution to optimize the network energy consumption. The high-density deployment of sensor nodes results in a higher degree of data redundancy. A large number of similar data transmission will bring an additional burden for the wireless communication of network. Node scheduling methods manage the nodes working conditions in time and space to optimize network data transmission and energy efficiency. It can reduce the nodes energy consumption while ensuring the quality of service, thereby greatly prolong the lifetime of the network.In this paper, we first study the node scheduling problem in the uneven task distribution network and design a node state transition model according to the task load condition. Moreover, the task prediction mechanism based on Markov chain is proposed, through which the workload prediction results can be obtained by calculating the transition probabilities of different node states. Then we further propose an energy-efficiency node scheduling game which considers both of the two key parameters of average workload and energy condition. Nodes are able to obtain the optimal scheduling strategy based on local information in this method. In addition, we also study the node scheduling problem of intrusion detection applications in wireless sensor networks and construct a novel globoid model to ensure the all-directional detection in 3D environment. A full coverage scheduling scheme is adopted in the outermost shell to guarantee the recognition quality of intruding events. We also present a node scheduling algorithm based on trajectory prediction in the interior region, through which nodes are scheduled to track the moving target by the statistical learning of the intruder’s moving tendency. Therefore, the energy consumption of the irrelevant nodes is saved to a large extent. Moreover, a trajectory correction strategy is proposed to relocate the missing intruders quickly by extending the coverage range. This scheme greatly improves the energy efficiency of the network while ensuring the high quality of monitoring.