Energy efficient architecture of sensor network node based on compression accelerator
Proceedings of the 19th ACM Great Lakes symposium on VLSI
Minimizing energy consumptions in wireless sensor networks via two-modal transmission
ACM SIGCOMM Computer Communication Review
Energy-efficient node-level compression arbitration for wireless sensor networks
ICACT'09 Proceedings of the 11th international conference on Advanced Communication Technology - Volume 1
Information Sciences: an International Journal
Practical data compression in wireless sensor networks: A survey
Journal of Network and Computer Applications
International Journal of Computer Applications in Technology
Data-Aware, resource-aware, lossless compression for sensor networks
EWSN'13 Proceedings of the 10th European conference on Wireless Sensor Networks
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Since the inception of sensor networks, in-network processing has been touted as the enabling technology for long-lived deployments.Radio communication is the overriding consumer of energy in such networks.Therefore, data reduction before transmission, either by compression or feature extraction, will directly and significantly increase network lifetime. In many cases, it is premature to begin implementing feature extraction techniques.Users do not yet understand in what forms interesting data will appear and consequently can't risk automatically discarding what they presume to beuninteresting.Moreover, computer scientists are only beginning to develop algorithms to collect spatially distributed features in situ. Even for the many application where all of the data must be transported out of the network, data may be compressed before transport, so long as the chosen compression technique can operate under the stringent resource constraints of low-power nodes and induces only tolerable errors.This paper evaluates a simple temporal compression scheme designed specifically to be used by mica motes for the compaction of microclimate data.The algorithm makes use of the observation that over a small enough window of time, samples of microclimate data are linear. It finds such windows and generates a series of line segments that accurately represent the data.It compresses data up to 20-to-1 while introducing error on the order of the sensor hardware's specified margin of error.Furthermore it is simple, consumes little CPU and requires very little storage when compared to other compression techniques. This paper describes the technique and results using a dataset from a one-year microclimate deployment.