Data aggregation in sensor networks using learning automata
Wireless Networks
Market-based resource allocation for distributed data processing in wireless sensor networks
ACM Transactions on Embedded Computing Systems (TECS)
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Recent research has led to the advent of software systems capable of performing query processing in sensor networks. They perform query processing in a sensor network by constructing a routing tree rooted at an access point (i.e., a base station), where the queries are submitted, parsed, and optimized. This approach to query processing is centralized in nature. Performing query optimization at a single node (base station) does not generate efficient query plans, and requires each node to report metadata to the access point. In addition, the routing tree infrastructure ineffi ciently aggregates data packets. To address these issues, this paper proposes several decentralized query processing systems that utilize sensor node驴s innate spatial and semantic characteristics. Experimental results conclude that decentralizing query processing significantly reduces energy costs. In addition, experimental results show that the spatial and semantic properties of nodes are in?uential in designing a decentralized query processing system.