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A Time Slot Assignment Scheme for Sensor Data Compression  

Yeo, Myung-Ho (충북대학교 정보통신공학과)
Kim, Hak-Sin (충북대학교 정보통신공학과)
Park, Hyoung-Soon (충북대학교 정보통신공학과)
Yoo, Jae-Soo (충북대학교 정보통신공학과)
Abstract
Recently, wireless sensor networks have found their way into a wide variety of applications and systems with vastly varying requirements and characteristics such as environmental monitoring, smart spaces, medical applications, and precision agriculture. The sensor nodes are battery powered. Therefore, the energy is the most precious resource of a wireless sensor network since periodically replacing the battery of the nodes in large scale deployments is infeasible. Energy efficient mechanisms for gathering sensor readings are indispensable to prolong the lifetime of a sensor network as long as possible. There are two energy-efficient approaches to prolong the network lifetime in sensor networks. One is the compression scheme to reduce the size of sensor readings. When the communication conflict is occurred between two sensor nodes, the sender must try to retransmit its reading. The other is the MAC protocol to prevent the communication conflict. In this paper, we propose a novel approaches to reduce the size of the sensor readings in the MAC layer. The proposed scheme compresses sensor readings by allocating the time slots of the TDMA schedule to them dynamically. We also present a mathematical model to predict latency from collecting the sensor readings as the compression ratio is changed. In the simulation result, our proposed scheme reduces the communication cost by about 52% over the existing scheme.
Keywords
Sensor network; Compression; TDMA; MAC Protocol;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 S. Ramaswamy, R. Rastogi, and K. Shim. “Effi-cient Algorithms for Mining Outliers from Large Data Sets,” Proceedings of ACM SIGMOD Inter-national Conference on Management of Data, pp.427-438, 2000   DOI   ScienceOn
2 A. Lazarevic and V. Kumar. "Feature Bagging for Outlier Detection," Proceedings of the eleventh ACM SIGKDD International Conference on Know-ledge Discovery in Data Mining, pp.157-166, 2005   DOI
3 W. Ye, J. Heidemann, and D. Estrin, “Medium Access Control With Coordinated Adaptive sleep-ing for Wireless Sensor Networks,” IEEE/ACM Transactions on Networking (TON), vol.12, no.3, pp.493-506, 2004   DOI   ScienceOn
4 J. Polastre, J. Hill, and D. Culler, “Versatile Low Power Media Access for Wireless Sensor Net-works,” Proceedings of the 2nd International Con-ference on Embedded Networked Sensor Systems, pp.95-107, 2004   DOI
5 A. El-Hoiydi and J. Decotignie, “WiseMAC: An Ultra Low Power MAC Protocol for the Downlink of Infrastructure Wireless Sensor Networks,” Pro-ceedings of the 9th International Symposium on Computer and Communications, pp.244-251, 2004   DOI
6 S. Pattern, B. Krishnamachari and R. Govindan, "The Impact of Spatial Corrleation on Routing with Compression in Wireless Sensor Networks," ACM Transactions on Sensor Networks (TOSN), vol.4, no.4, 2004   DOI
7 여명호, 이미숙, 박종국, 이석재, 유재수 "무선 센서 네트워크에서 네트워크 트래픽 감소를 위한 데이타 중심 클러스터링 알고리즘", 정보과학회논문지 정보통신, 제35권, 제2호, pp.139-148, 2008년 4월   과학기술학회마을
8 S. Subramaniam, T. Palpanas, D. Papadopoulos, V. Kalogeraki, and D. Gunopulos. “Online Outlier Detection in Sensor Data Using Non-Parametric Models,” Proceedings of the 32nd International Conference on Very Large Data Bases, pp.187-198, 2006
9 X. Meng, L. Li, T. Nandagopal and S. Lu, "Event contour: An Efficient and Robust Mechanism for Tasks in Sensor Networks," Technical Report, UCLA, 2004
10 M. Sharaf, J. Beaver, A. Labrinidis and P. Chry-anthis, “Tina: A Scheme for Temporal Coherency-Aware in-Network Aggregation,” Proceedings of the 3rd ACM International Workshop on Data Engineering for Wireless and Mobile Access, pp.69-76, 2003   DOI
11 Intel Lab Data, http://berkeley.intel-research.net/labdata/, 2004