Abstract:

Continuous queries in wireless sensor networks are established on the premise of a routing tree that provides each sensor with a path over which answers can be transmitted to the query processor. The number of tuples received by S in a given epoch e denotes the workload of S. Since the exact workload of a node is not known ahead of time, a node has to enable its transceiver for a sub-optimal amount of time in order to collect the results from its children. We found that this leads to an enormous waste of energy in predominant data acquisition frameworks such as TAG and Cougar. We found that these structures are sub-optimality constructed in predominant data acquisition systems leading to an enormous waste of energy. In this paper we present MicroPulse, a workload-aware optimization algorithm for query routing trees in wireless sensor networks. Our algorithm is established on profiling recent data acquisition activity and on identifying the bottlenecks using an in-network execution of the critical path method. A node S utilizes this information in order to locally derive the time instance during which it should wake up, the interval during which it should deliver its workload and the workload increase tolerance of its parent node. We additionally provide an elaborate description of energy-conscious algorithms for disseminating and maintaining the critical path cost in a distributed manner. Our trace-driven experimentation with real sensor traces from Intel Research Berkeley shows that MicroPulse can reduce the data acquisition costs by many orders.