8 Conclusions and future work.docVIP

8 Conclusions and future work In this paper, we present an innovative framework for efficiently monitoring WSNs, to achieve both efficiency and query result quality. Our framework, coined KSpot, Distrib Parallel Databases (2011) 29: 113–150 147 utilizes a novel top-k query processing algorithm we developed, in conjunction with the concept of in-network views, in order to minimize the cost of query execution. In particular, we formulate the problem of constructing a hierarchy of recursively defined top-k views. We then describe the MINT Views algorithm that identifies the K highest-ranked answers efficiently. We also present a stateless, non-materialized version of MINT, coined INT (In-Network Top-k) Views that is appropriate for sensing device with limited memory. To illustrate the efficiency of our framework, we have implemented a real system in nesC, which combines the traditional advantages of declarative acquisition frameworks, like TinyDB, with the ideas presented in this work. Extensive real-world testing and experimentation with traces from UC-Berkeley, University of Washington and Intel Research Berkeley, show that KSpot presents an up to 66% of energy savings compared to TinyDB, minimizes both the size and number of packets transmitted onto the network (up to 77%), prolonging in that way the longevity and health of a WSN deployment. In the future we plan to incorporate an automated transceiver operation module that will automatically tune the waking window of each sensor device using application layer semantics [2, 64]. Additionally, we plan to investigate the applicability of these ideas over Mobile Sensor Networks and Networks of Smartphone Devices. References 1. Agrawal, D., Ganesan, D., Sitaraman, R.K., Diao, Y., Singh, S.: Lazy-adaptive tree: an optimized index structure for flash devices. Proc. VLDB Endow. 2(1), 361–372 (2009) 2. Andreou, P., Zeinalipour-Yazti, D., Chrysanthis, P.K., Samaras, G.: Workload-aware query routing t