Optimizing Research Of Lifetime Density Deployment In Wireless Sensor Networks

WSN(wireless sensor networks) with its own advantages has become an important method to obtain information. Through the network deployment, sensor nodes send the data of target area to the destination node with multi-hops. However, as a tiny communication component, sensor nodes need to send and receive data frequently, which account for a large ratio in the energy consumption, therefore, we must take effective ways to improve the survival time of sensor nodes, so that the network can accomplish more service requests with a limited energy.Currently, extending the WSN lifetime has become an important research topic. Many key technologies have been proposed, mostly using a series of energy-saving protocols, such as topology control protocol, energy-saving routing protocol. In large-scale networks, the density deployment, as an energy dispersion method, is applied to extend the network lifetime, its application mode is that network deployment can be directed by computing density function and computer simulation, therefore, other energy consumption, such as node mobility, is not considered in the repeated deployment. Using the network energy consumption information, high energy consumption areas are set to be key service areas where have more excessive energy consumption because of the greater amount of data forwarding tasks, the death of the node in this region will cause the network service gap. Owing to the region division of the density deployment, nodes can be directed according to the actual needs, that is high energy consumption area can gain more nodes so as to extend the network lifetime.Although the density deployment can improve the network lifetime effectively, the network still has a considerable potential in energy-saving. Whether applying density deployment, sensor nodes always use the same transmission power which is an important parameter of the node lifetime for data sending and receiving. In order to satisfy the requirements of the network coverage, the transmission power is generally set larger. In the premise of the density deployment, reducing the transmission power of the node by topology control will further extend the network lifetime.Applying the topology control, WSN may generate a large number of unidirectional links, which would have a large impact on routing protocols and MAC protocols in the current network. The presence of the unidirectional link may result in the routing protocol failure, and effective route in the network difficult to be found. What's more, three handshakes of MAC protocol need to be done when forwarding the packet between nodes, while the existence of unidirectional link may cause the handshake failure, resulting in dropping packets. So, it is necessary to improve the existing routing protocol to adapt to the network with unidirectional links in topology control.Combining the density deployment, topology control, and routing protocol adapting to unidirectional links in topology control, an optimization framework for density deployment has been designed in this paper to further extend the network lifetime. Based on proximity graph theory, on the basis of RNG and the idea of MST construction, CTCRM topology control protocol is proposed, the edge compensation of the 1-connectivity nodes in RNG is done, so that the node has multiple routes to the destination node and edge compensation can be used several times to achieve the K-connectivity. At the same time, based on the AODV routing protocol, an improved protocol AODV-M is proposed to adapt to the unidirectional links. In the Linux environment, using the NS2 network simulator, the effectiveness of the optimization framework is evaluated, and the results show that with the same topology conditions and the same number of nodes, CTCRM protocol inherits the merits of RNG and it also increases the average node degree, expand the neighbor node set, avoiding the over-reliance on the unique neighbor node in 1-connectivity. At the same time, working under the AODV-M, compared with AODV and AODV-BL, CTCRM protocol has a higher packet delivery ratio. It means that AODV-M protocol can solve the unidirectional link problems caused by the topology control. Finally, in the network which has a 500m*500m topology range and 100 nodes, after eight rounds of the simulation, the result shows the impact that the density deployment optimization framework brings to the network lifetime, with the similar event sources, the network energy consumption level in different regions tends to be balanced, the total energy consumption is lower and the network lifetime is prolonged.In order to better support the density deployment, using Python language, a topology generator has been designed, and some functions such as adding and dragging the nodes, deploying the network with density, and generating the NS2 scene files are realized in this paper.