Monitoring Cost Reduction in Sensor Networks using Proximity Queries
Event detection and notification is a common task in a Wireless Sensor Networks (WSN). Efficient data aggregation and the minimization of energy consumption are the great research challenges in WSN. In WSN, normally the events are detected by more than one node and it is more reliable if the consistent event information is received from more than one node. Therefore, aggregated event information is more important than individual event information for energy saving and reliability. Proximity queries can be used to reduce the complexity of data aggregation and energy consumption. This paper presents an efficient and scalable hybrid framework for processing spatial and temporal proximity queries in WSN which we call Spatial and Temporal Processing (STP). STP builds tree structure with less overhead and reduces the event propagation cost through proximity queries. STP reduces energy consumption by reducing the number of aggregator nodes, which ultimately increases the network life time. STP eliminates the unnecessary aggregation of events using a tunable temporal proximity threshold. We compare STP’s performance with another spatial query processing method and we show that STP performs better.