Efficient Aggregation Processing in the Presence of Duplicately Detected Objects in WSNs

Abstract

Wireless sensor networks (WSNs) have received increasing attention in the past decades. Owing to an enhancement of MEMS technology, various types of sensors such as motion detectors, infrared radiation detectors, ultrasonic sensors (sonar), and magnetometers can detect the objects within a certain range. Under such an environment, an object without an identifier can be detected by several sensor nodes. However, existing studies for query processing in WSNs simply assume that the sensing regions of sensors are disjoint. Thus, for query aggregation processing, effective deduplication is vital. In this paper, we propose an approximate but effective aggregate query processing algorithm, called DE-Duplication on the Least Common Ancestor∗ (abbreviated as DELCA∗). In contrast to most existing studies, since we assume that each object does not have a unique identifier, we perform deduplication based on similarity. To recognize the duplicately detected events earlier, we utilize the locality-sensitive hashing (LSH) technique. In addition, since the similarity measures are not generally transitive, we adapt three duplicate semantics. In our experiments, by using a transmission cost model, we demonstrate that our proposed technique is energy-efficient. We also show the accuracy of our proposed technique.