An Adaptive Timeout Aggregation for Periodic Data Gathering in Wireless Sensor Networks

This paper proposes a novel timeout scheduling scheme for data aggregation in wireless sensor networks, which adaptively configures both the length of timeout and the collecting period according to the potential level of event occurrence. At normal times, the proposed scheme decreases the length of timeout that is a waiting time for messages from child nodes, and it makes longer the collecting period. That reduces the dissipated energy of the sensor node. Conversely, the proposed scheme increases the length of timeout and makes shorter the collecting period under event occurrences in order to fulfill more accurate data aggregation and early event detection. Extensive simulation work under various workloads has revealed that the proposed scheme enhances data accuracy and energy consumption by 15% and 25% respectively, as compared with the previous TAG data aggregation. Also, the proposed scheme detects the event occurrence six times earlier than the previous TAG and Cascading timeout aggregation.