Research On Low Energy Routing Protocol Of Wireless Sensor Networks

With the development of MEMS technology, embedded computer technology and wireless communication technology, wireless sensor networks are becoming more and more cared. Usually, wireless sensor nodes are tiny devices with limited power, and change battery is almost impossible in some applications. This makes the lifetime of nodes mostly depend on the battery, so reduced power consumption to extend the lifetime of network is the chief objective in the research.Routing protocol, which is responsible for reliable transmission of data, is one of the crucial techniques in WSNs. Routing mechanism can affect the network's status from the network layer; simple, effective and low energy consumption route becomes an important target of a high performance network. Taking energy efficient and performance of QoS as the goal, this dissertation mainly researches on WSNs routing protocol. Two different application-oriented routing protocols are proposed, and the corresponding theoretical analysis and simulation on these protocols are implemented as well. The main contents include:(1) An energy-adaptive cluster head election algorithm based on fuzzy inference system of Mamdani(CEFM) is proposed. The residual energy level of candidates, the position of candidates, the number of the neighbors and the rate of retransmission are synthetically considered together to optimize the process of cluster head in CEFM algorithm, so that the partition of cluster is more equal and reasonable. In addition, an inter-cluster multiple-hop routing is formed for communication between cluster and the base station, which can reduce the energy consumption of cluster heads. The simulation results demonstrate that the novel algorithm improves network energy efficient and provides longer network lifetime.(2) An energy balancing multi-path source routing protocol(EBMSR) is proposed. According to the nodes location, EBMSR establishes node disjoint multipath between the source and destination nodes to reduce the energy cost of establishing the path. We introduce the parameter about mean energy of path, energy variance of path, link quality and hops of path to define the cost model of path. In addition, EBMSR selects a path dynamically in data transferring phase to balance energy consumption of different paths and extend the network life cycle. The simulations show that EBMSR not only enhance energy efficiency, but also improve the QoS performance.