Cloud-based query evaluation for energy-efficient mobile sensing

In this paper, we reduce the energy overheads of continuous mobile sensing, specifically for the case of context-aware applications that are interested in collective context or events, i.e., events expressed as a set of complex predicates over sensor data from multiple smartphones. We propose a cloud-based query management and optimization framework, called CloQue, that can support thousands of such concurrent queries, executing over a large number of individual smartphones. Our central insight is that the context of different individuals & groups often have significant correlation, and that this correlation can be learned through standard association rule mining on historical data. CloQue's exploits such correlation to reduce energy overheads via two key innovations: (i) dynamically reordering the order of predicate processing to preferentially select predicates with not just lower sensing cost and higher selectivity, but that maximally reduce the uncertainty about other context predicates; and (ii) intelligently propagating the query evaluation results to dynamically update the confidence values of other correlated context predicates. We present techniques for probabilistic processing of context queries (to save significant energy at the cost of a query fidelity loss) and for query partitioning (to scale CloQue to a large number of users while meeting latency bounds). An evaluation, using real cellphone traces from two different datasets, shows significant energy savings (between 30% and 50% compared with traditional short-circuit systems) with little loss in accuracy (5% at most). In addition, we utilize parallel evaluation to reduce overall latency. The experiments show our approaches save up to 70% latency.