Network Partition-Based Localization Algorithms For Wireless Sensor Networks

Abstract:Wireless Sensor Networks (WSNs) have been a popular field in nowadays. Localization is a practically important issue in WSNs. Location information of sensor nodes, is critical in many wireless sensor network (WSN) applications and protocols. Although many localization algorithms have been proposed in recent years, they usually target at dense networks and perform poorly in sparse networks in which the average node degree is smaller than10. In fact, those algorithms that perform well in dense networks might not work well in sparse networks.In this paper, we focus on the localization problem in sparse WSNs. We propose two component-based localization algorithms that can localize many more nodes in sparse networks than state-of-the-art localization algorithms do. Furthermore, the new algorithm can reduce the impact of noisy on localization accuracy. The main works are as follows:We put up new theories of merging conditions in sparse network and prove them in paper. Still, we design the simulation and run our algorithms. The simulation result proves that our algorithms outperform the state-of-the-art localization algorithms. Simulations results show that CLAD (Component-Based Localization Algorithm for Sparse Sensor Networks Combining Angle and Distance Information) can locate12%more nodes than CALL (Component-Based Localization Algorithm) do in a sparse network with average node degree7.5. And the accuracy of CLAD also improves about8%compared to CALLAt last, we propose a novel3D localization Algorithm based on the theories discussed above. To solve the localized problems in three dimentional space, we come up with theories and build up3D localization model:3D Component-Based conception. Additionally, we design Three Dimentional Component-Based Algorithm with Anchor (TDCA) and simulate it and USP in terms of localization ratio and localization accuracy. Simulations show that TDCA can locate30%more nodes than USP do in3D WSNs with average node degree12. And the accuracy of CLAD also improves about5%compared to CALL.