Impact of Data Compression on Energy Consumption of Wireless-Networked Handheld Devices
ICDCS '03 Proceedings of the 23rd International Conference on Distributed Computing Systems
The design of an acquisitional query processor for sensor networks
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
A Survey on Data Compression in Wireless Sensor Networks
ITCC '05 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'05) - Volume II - Volume 02
TinyDB: an acquisitional query processing system for sensor networks
ACM Transactions on Database Systems (TODS) - Special Issue: SIGMOD/PODS 2003
Energy aware lossless data compression
Proceedings of the 1st international conference on Mobile systems, applications and services
An Analysis of Spatio-Temporal Query Processing in Sensor Networks
ICDEW '05 Proceedings of the 21st International Conference on Data Engineering Workshops
Adaptive Localized QoS-Constrained Data Aggregation and Processing in Distributed Sensor Networks
IEEE Transactions on Parallel and Distributed Systems
Query Processing in Sensor Networks
IEEE Pervasive Computing
Multi-query optimization for sensor networks
DCOSS'05 Proceedings of the First IEEE international conference on Distributed Computing in Sensor Systems
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Environment monitoring is one key application of wireless sensor networks. In a monitoring application, continuous query is adopted to periodically retrieve data from the network. Sensor nodes have limited energy resources and their functionality continues until their energy drains. Data collection algorithm has to be designed to extend the lifetime of sensors to the best extent and at the same time keep the data accuracy to a certain level. This paper presents a novel data gathering algorithm for continuous query in wireless sensor networks. In particular, the problem of adaptive determination of data granularity for QoS-constraint query execution is address. Application specifies its QoS requirements with the query. Then, each node can choose the optimum data granularity for local data collection and transmission. The proposed algorithm is in a distributed fashion, and executed at each local sensor node. The proposed algorithm is verified and the impact of and tradeoff between various design parameters are analyzed.