Rescarch On Ronting Protocols With Low Energy Consumption For Wireless Sensor Networks

Recent advances in processor, memory, and radio technology will enable small and cheap sensing nodes capable of wireless communication and significant computation. Sensor networks can contain hundreds or thousands of sensing nodes. It is desirable to make these nodes as cheap and energy-efficient as possible and rely on their large num-bers to obtain high quality results. Sensor networks are usually unattended with low energy-consumption so that the need for maintenance is minimized. This is especially desirable in those applications where the sensors may be embedded in the structure or are in inhospitable terrain and are inaccessible for any service.Routing protocol is one of the significant techniques of sensor networks, and also is the field with most attention of network researchers. Routing protocols must be designed to achieve robust in the presence of individual node failure earlier. In addition, since the limited wireless channel bandwidth must be shared among all the sensors in the network, routing protocols for these networks should be able to perform local collaboration to increase transmission efficiency. In the practical view, the research on energy consumption of wireless sensor network routing protocol is more meaningful. The research on energy consumption is from the following two directions:decreasing the number of forwarding and decreasing the amount of data.According to different applications, this thesis brings opportunistic routing into studying the design and evaluation of routing protocols with Low Energy Consumption for wireless sensor networks. Chapter three, chapter five and chapter six are based on the direction of decreasing the number of forwarding, chapter four is based on the direction of decreasing the amount of data. First, in the applications of one source with a lot of data to be sent, chapter 3 studies a protocol based on data and query. Second, in the applications of mass sources with redundancy data, chapter 4 studies a multi-level clustering protocol. Finally, in the applications of using mobile sinks, chapter 5 and chapter 6 study two protocols to decrease and balance energy consumptions. The contributions of this thesis are as following:1. An advanced Directed Diffusion protocol, named DDOR, is presented in this paper based on Opportunistic Routing to improve the disadvantage on mono-forwarder of traditional routing protocol. Packet head has been modified in order to implement a forwarder list in DDOR. Instead of fixed next-hop forwarding in transmission, DDOR corresponding mechanism selects the optimal node to forward. DDOR mechanism can effectively save energy of WSNs by decreasing the hops from sources to sinks, thereby extends the life of WSNs.2. A multi-level LEACH protocol based on opportunistic routing (LEACH-MLOR) is presented in this paper. In the proposed protocol, the threshold of choosing cluster heads is improved based on the levels of nodes, and the route paths between cluster heads are established also based on the levels of nodes. Furthermore, in order to im-prove the energy efficiency of packet retransmission, assistant nodes are introduced to reduce the cost of retransmission. This architecture achieves better energy-efficiency by reducing retransmissions and cluster hierarchy establishment.3. A Beacon-Buoy based routing protocol for wireless sensor networks with mobile sinks (BBR-MS) is presented in this paper. BBR-MS introduces beacon nodes to reduce refreshment of topology, while broadcasting the interest messages. Furthermore, to avoid the extra overhead of last hop, the protocol introduces buoy nodes direct reporting data to sinks if possible. An optimization mechanism based on biconnected graph is also presented in BBR-MS to balance the energy consumption of networks.4. A hotspot-tracking-based routing protocol with mobile sinks for wireless sensor networks (HTR-MS) is presented in this paper. HTR-MS is a combination of hotspot-tracking and hotspot time learning. HTR-MS has the following advantages. First, the overhead of HTR-MS is very low. Second, HTR-MS establishes the network gradient much more rapidly than Bread Crumbs and DDRP. Third, a learning mechanism is employed in HTR-MS, which achieves efficiently establishment and updating of network gradient.