Researches On Performance Optimization In Wireless Sensor Networks

As a research focus in the information field, wireless sensor networks have been popularly used in a wide range of applications such as military defense, industry and agriculture, biomedical health monitoring, environment monitoring, military target tracking and surveillance, real-time monitoring and vehicle tracking, and so on. Of all the factors that affect the performance of wireless sensor networks, sensor deployment, topology control and routing are the fundamental and key technologies. In this paper, in order to improve the performance of wireless sensor networks, the above three technologies are studied. The main works can be summarized as follows:1. In view of the defects of the LMN and LMA algorithms based on node degree, a transmission power control algorithm in wireless sensor networks is presented under the minimum connected average node degree constraint. Firstly, we find a convenient and exact mathematical description of the minimum transmission range to achieve a specified connectivity probability under the typical Poisson distribution, and then the minimum connected average node degree is obtained. Based on the above results, a new algorithm is designed, which allows the network to satisfy a specified connectivity probability and maintain a certain coverage degree. Simulation results show that our proposed algorithm has a more optimized objective function value with high connectivity rate and coverage rate compared with other related algorithms. Having the advantages of distributed structure and low requirement for nodes, the new algorithm can quickly adjust the node transmission range according to the requirement of network, so it is more convenient for application in the real network.2. Topology control is a critical issue for energy efficient wireless sensor networks. Distance between sensors plays an important role in the problem of topology control in that it directly determines the accuracy of the node position. However, distance is generally affected by uncertain external factors, such as measurement error, actual interference. The actual performance of a topology control strategy can be severely influenced by distance uncertainty. Based on the robust discrete optimization theory and methodology, a power control algorithm is proposed to deal with distance uncertainty. Firstly, related works to robust optimization are introduced. Then the problem of power control is formulated as a robust minimum spanning tree model under distance uncertainty, which is solved by Prim’s algorithm. In the computational experiments, the influence of the adjusting parameter on network performance is researched. Simulation results show that a robust solution can provide an improvement when the distance is uncertain at the expense of less optimal value compared with a deterministic solution.3. We consider the sensor deployment problem in the context of distance uncertainty. It is characterized by differentiated arrangement of specific detection probability thresholds at different locations. The problem is formulated as an integer linear programming(ILP) model firstly, aiming at optimizing the number of sensors and their locations. Based on the robust discrete optimization methodology, the uncertain model is transformed into an equivalent ILP problem considering distance uncertainty. The proposed approach can control the tradeoff between optimality and robustness by varying the parameters named protection levels. Uniform and non-uniform event detection probability distributions are considered in the experiment. The results show that, as the distance uncertainty increases, the constraint violation can be avoided in the robust model and the robust solution can provide a significant improvement at the expense of a small loss in optimality when compared to the optimal solution of a deterministic scenario.4. As an effective routing mechanism, the clustering hierarchy algorithm has become a hot research. Based on LEACH with unequal cluster, a density-based clustering protocol is proposed. While selecting cluster heads, the protocol can set the scale of each cluster based on the density of nodes, with the network load and energy distribution balanced. Simulation results show that our proposed protocol is more effective in prolonging the network lifetime and reducing the energy dissipation compared with LEACH. To overcome the defects of LEACH, another clustering hierarchy algorithm based on graphic tessellation is proposed. By tessellating regular hexagon among the monitoring area, clustering is completed, following which the cluster head is selected according to relative residual energy connected with the communication distance. Simulation results show that our algorithm is more effective than LEACH in the aspects of network lifetime, load-balancing and the total amount of data communications.