Title :
Join of Multiple Data Streams in Sensor Networks
Author :
Zhu, Xianjin ; Gupta, Himanshu ; Tang, Bin
Author_Institution :
Microsoft, Inc., Seattle, WA, USA
Abstract :
Sensor networks are multihop wireless networks of resource-constrained sensor nodes used to realize high-level collaborative sensing tasks. To query or access data generated by the sensor nodes, the sensor network can be viewed as a distributed database. In this paper, we develop algorithms for communication-efficient implementation of join of multiple (two or more) data streams in a sensor network. The distributed implementation of join in sensor networks is particularly challenging due to unique characteristics of the sensor networks such as limited memory and battery energy on individual nodes, arbitrary and dynamic network topology, multihop communication, and unreliable infrastructure. One of our proposed approaches, viz., the perpendicular approach (PA), is load balanced, and in fact, incurs near-optimal communication cost for the special case of binary joins in grid networks under the assumption of uniform generation of tuples across the network. We compare the performance of our designed approaches through extensive simulations on the ns2 simulator, and show that PA results in substantially prolonging the network lifetime compared to other approaches, especially for joins involving spatial constraints.
Keywords :
distributed algorithms; distributed databases; query processing; resource allocation; telecommunication network topology; wireless sensor networks; battery energy; binary join; communication-efficient implementation; distributed algorithm; distributed database; dynamic network topology; grid network; high-level collaborative sensing task; limited memory; load balancing; multihop communication; multihop wireless network; multiple data stream; near-optimal communication cost; network lifetime; ns2 simulator; perpendicular approach; query processing; resource-constrained sensor node; sensor network; spatial constraint; uniform tuple generation; unreliable infrastructure; Distributed query processing; Sensor networks.;
Journal_Title :
Knowledge and Data Engineering, IEEE Transactions on
DOI :
10.1109/TKDE.2009.38