Sleepwalk: Scalable and Energy-Efficient Processing of Continuous Range Queries for Location-Aware Mobile Computing

Recently, monitoring queries are getting attention for various real-life applications such as safety, security, and personalization services. This work proposes a distributed sensing and monitoring technique (called Sleepwalk ) for continuous range queries with energy- and computation-efficient optimizations. In our scheme, each mobile client (MC) is aware of its nearby monitoring queries by leveraging its processing power. The proposed Sleepwalk has three major contributions. First, with piecewise linear movement assumption and motion vector v ̅ , it can locally preevaluate every possible query result in advance in bulk and sends them to the server at once. We also provide a timestamp-based invalidation technique for efficiently removing failed preevaluated results by computing the smallest valid timestamp. Second, an energy-conserving technique that repeatedly sleeps off MCs whenever possible is proposed by calculating the safely sleepable time. Third, we provide a set of localized query optimization techniques for MCs' local query subset using plane-sweeping, which effectively minimize search space. Extensive experiments indicate that Sleepwalk technique remarkably outperforms existing state-of-the-art techniques in terms of server scalability, communication cost, and energy consumption of MCs.