Routing tree maintenance based on trajectory prediction in mobile sensor networks

With the wireless sensor networks (WSNs) becoming extremely widely used, mobile sensor networks (MSNs) have recently attracted more and more researchers' attention. Existing routing tree maintenance methods used for query processing are based on static WSNs, most of which are not directly applicable to MSNs due to the unique characteristic of mobility. In particular, sensor nodes are always moving in real world, which seriously affects the stability of the routing tree. Therefore, in this paper, we propose a novel method, named routing tree maintenance based on trajectory prediction in mobile sensor networks (RTTP), to guarantee a long term stability of routing tree. At first, we establish a trajectory prediction model based on extreme learning machine (ELM), by which we can predict sensor node's trajectory to choose an appropriate parent node for each non-effective node. Then, an Improved version of RTTP method (I-RTTP) that using probabilistic method to minimize the error and improve the accuracy is proposed, to improve the performance of RTTP. Therefore, the state of the routing tree in MSNs can be made more stable. Finally, extensive experimental results show that RTTP and I-RTTP can effectively improve the stability of routing tree and greatly reduce energy consumption of mobile sensor nodes.