An Adaptive State-Aware Routing Algorithm for Data Aggregation in Wireless Sensor Networks

Due to different data correlation among events in driven Wireless Sensor Networks (WSNs), over-overlapping paths from events in order to maximize the data aggregation will weaken the monitoring ability of WSNs instead of improving the network performance. There should be a tradeoff between data aggregation maximization and energy balance. Excessive pursuit of high data aggregation regardless of the actual state of network could bring about premature death of some backbone nodes, leading to unstable network structure. Based on the problems, in this paper, a novel adaptive stateaware routing algorithm for data aggregation is proposed. The algorithm maximizes the possible data aggregation by building and updating a Hop-Tree, takes the local state of nodes to build and maintain Hop-Tree to gain better adaptation to heterogeneous wireless sensor networks, depends on Time-ToLive (TTL) mechanism to limit the Hop-Tree update range to avoid over-overlapping of paths according to the correlation of events, and designs a forced path building strategy to balance the data load on the backbone nodes of Hop-Tree to further balance the energy consumption. Theoretical analysis and simulation results show that our algorithm can maximize the possible data aggregation while balance the energy consumption among nodes and enhance the monitoring ability of WSNs significantly. 1