Title :
Routing and data compression in sensor networks: stochastic models for sensor data that guarantee scalability
Author_Institution :
Sch. of Electr. & Comput. Eng., Cornell Univ., Ithaca, NY, USA
fDate :
29 June-4 July 2003
Abstract :
This paper discusses the compression of correlated data samples in multihop sensor network by means of distributed source coding techniques under plausible stochastic models. This paper shows that there are cases where high density sensor networks are not only possible but increased density can potentially be used to increase the precision of the measurements or decrease the transmission error. The routing algorithms and reencoding of the sensor data is proposed in this paper to provide a perfect feasible sensor networks.
Keywords :
correlation theory; source coding; stochastic processes; telecommunication network routing; wireless sensor networks; by means of distributed source coding technique; data compression; data sample correlation; multihop sensor network; reencoding; routing; sensor data; stochastic model; transmission error; Broadcasting; Data compression; Distortion measurement; Intelligent networks; Routing; Sampling methods; Scalability; Source coding; Stochastic processes; Telecommunication traffic;
Conference_Titel :
Information Theory, 2003. Proceedings. IEEE International Symposium on
Print_ISBN :
0-7803-7728-1
DOI :
10.1109/ISIT.2003.1228188
