Load-balanced join processing in shared-nothing systems
Journal of Parallel and Distributed Computing
Proof verification and the hardness of approximation problems
Journal of the ACM (JACM)
Introduction to Algorithms
Dynamic Load Distribution in the Borealis Stream Processor
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
TinyDB: an acquisitional query processing system for sensor networks
ACM Transactions on Database Systems (TODS) - Special Issue: SIGMOD/PODS 2003
In-network execution of monitoring queries in sensor networks
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Two-Tier Multiple Query Optimization for Sensor Networks
ICDCS '07 Proceedings of the 27th International Conference on Distributed Computing Systems
Network-aware query processing for stream-based applications
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Model-driven data acquisition in sensor networks
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Multi-query optimization for sensor networks
DCOSS'05 Proceedings of the First IEEE international conference on Distributed Computing in Sensor Systems
Multiobjective hypergraph-partitioning algorithms for cut and maximum subdomain-degree minimization
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Query Allocation in Wireless Sensor Networks with Multiple Base Stations
DASFAA '09 Proceedings of the 14th International Conference on Database Systems for Advanced Applications
Journal of Network and Computer Applications
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In this paper, we consider a large scale sensor network comprising multiple, say K, base stations and a large number of wireless sensors. Such an infrastructure is expected to be more energy efficient and scale well with the size of the sensor nodes. To support a large number of queries, we examine the problem of allocating queries across the base stations to minimize the total data communication cost among the sensors. In particular, we examine similarity-aware techniques that exploit the similarities among queries when allocating queries, so that queries that require data from a common set of sensor nodes are allocated to the same base stations. We first approximate the problem of allocating queries to K base stations as a max-K-cut problem, and adapts an existing solution to our context. However, the scheme only works in a static context, where all queries are known in advance. In order to operate in a dynamic environment with frequent query arrivals and termination, we further propose a novel similarity-aware strategy that allocates queries to base stations one at a time. We also propose several heuristics to order a batch of queries for incremental allocation. We conducted experiments to evaluate our proposed schemes, and our results show that our similarity-aware query allocation schemes can effectively exploit the sharing among queries to greatly reduce the communication cost.