TY - JOUR
T1 - Data sweeper
T2 - A proactive filtering framework for error-bounded sensor data collection
AU - Wang, Dan
AU - Liu, Jiangchuan
AU - Xu, Jianliang
AU - Jiang, Hongbo
AU - Wang, Chonggang
N1 - Funding Information:
The work of D. Wang was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 61272464 and in part by the Research Grants Council/General Research Fund under Grant PolyU 5264/13E. The work of J. Liu was supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant, in part by the NSERC Strategic Project Grant, and in part by the Major Program of International Cooperation through the NSFC under Grant 61120106008. The work of J. Xu was supported by the Hong Kong Research Grants Council under Grant HKBU12202414 and Grant HKBU12200114. The work of H. Jiang was supported by the NSFC under Grant 61271226.
PY - 2016/10
Y1 - 2016/10
N2 - This paper presents data sweeper-a novel framework that attempts to reduce network traffic for error-bounded data collection in wireless sensor networks. Unlike existing passive filters, a data sweeper migrates in the network and proactively suppresses data updates while maintaining the user-defined error bound. Intuitively, the migration of a data sweeper learns the data change of each sensor node on the fly, which helps to maximize the filtering capacity. We design the data sweeper framework in such a way that it can accommodate diverse query specifications and be easily incorporated into the existing sensor network protocols. Moreover, we develop efficient strategies for query precision maintenance, sweeper migration, and data suppression within the framework. In particular, in order to maximize traffic reduction and adapt to online data updates, a Lagrangian relaxation-based algorithm is proposed for data suppression. Extensive simulations based on real-world traces show that the data sweeper significantly reduces the network traffic and extends the system lifetime under various network configurations.
AB - This paper presents data sweeper-a novel framework that attempts to reduce network traffic for error-bounded data collection in wireless sensor networks. Unlike existing passive filters, a data sweeper migrates in the network and proactively suppresses data updates while maintaining the user-defined error bound. Intuitively, the migration of a data sweeper learns the data change of each sensor node on the fly, which helps to maximize the filtering capacity. We design the data sweeper framework in such a way that it can accommodate diverse query specifications and be easily incorporated into the existing sensor network protocols. Moreover, we develop efficient strategies for query precision maintenance, sweeper migration, and data suppression within the framework. In particular, in order to maximize traffic reduction and adapt to online data updates, a Lagrangian relaxation-based algorithm is proposed for data suppression. Extensive simulations based on real-world traces show that the data sweeper significantly reduces the network traffic and extends the system lifetime under various network configurations.
KW - Data sweeper
KW - Sensor data collection
UR - http://www.scopus.com/inward/record.url?scp=85027714594&partnerID=8YFLogxK
U2 - 10.1109/TETC.2015.2411215
DO - 10.1109/TETC.2015.2411215
M3 - Journal article
AN - SCOPUS:85027714594
SN - 2168-6750
VL - 4
SP - 487
EP - 501
JO - IEEE Transactions on Emerging Topics in Computing
JF - IEEE Transactions on Emerging Topics in Computing
IS - 4
ER -