Research On Distributed Scheduling Approach For Wireless Sensor Network

Wireless sensor network (WSN) is virtually a resource constrained network system. In most cases, WSN nodes are battery-powered, which restricts the lifetime of WSN. As an embedded system, WSN nodes are also characterized by restrained computation capacity and storage capacity. Moreover, communication bandwidth is much narrower among WSN nodes. Therefore, given these restrictions, it is significantly important to adopt scheduling approaches into the real applications of WSN in order to improve the network performance.This dissertation conducts an in-depth study of the distributed scheduling approach for wireless sensor network, and proposes four effective distributed scheduling approaches to address the issues of restrained energy. The main contents and contribution of this dissertation are given below:(1) A family of collaborative distributed scheduling approaches (CDSAs) is proposed to reduce the energy consumption of WSN. The family of CDSAs comprises of two scheduling approaches, i.e. one-step collaborative distributed scheduling approach (O-CDSA) and two-step collaborative distributed scheduling approach (T-CDSA). The family of CDSAs, based on the Markov chain, enables nodes to learn the behavior information of other nodes collaboratively and integrate sleep scheduling with transmission scheduling to reduce the energy consumption. In this dissertation, the adaptability and practicality features of the CDSAs are analyzed and the convergence feature is proved. The test results show that the two proposed approaches can effectively reduce nodes'energy consumption.(2) A distributed self-learning scheduling approach (SSA) is proposed in order to reduce energy consumption and latency for wireless sensor network. This approach employs reinforcement learning algorithm in scheduling approach of WSN and extends the Q-learning method. Based on a proposed approximate computational method of scheduling parameters, SSA enables nodes to learn continuous transmission parameters and sleep parameter through interacting with the WSN. Then, the WSN nodes can schedule its sleep and transmission through this approach. We implement the SSA in a MAC protocol. The simulation results show that the SSA can effectively reduce energy consumption, and can reduce the average latency of data packs in tree topology network.(3) A distributed evolutionary self-learning scheduling approach (ESSA) is proposed in order to reduce energy consumption and latency for wireless sensor network. Considering the similarities of working features among the nodes, which have the same parent node and are located close to each other, this approach optimizes their scheduling policy. Given the feature of network bandwidth restriction, ESSA adopts an approach to integrate PSO algorithm and SSA, which enable nodes to profit the experience from other nodes. This approach speeds up the learning rate in great deal. The simulation results further demonstrate that ESSA can largely reduce much more energy than S-MAC with duty cycle 10% while the latency for data packs approximates to that of S-MAC with duty cycle 60%. Comparing with SSA, the ESSA can further reduce the energy consumption and latency of data packs at the completion of the simulation test.