A probabilistic filter protocol for Continuous Nearest-Neighbor Query

Emerging location-based application and sensor monitoring management system collect user´s locations with limited power, which cannot report very accurate position values. An important query is the Continuous Nearest-Neighbor Query (CNNQ), which returns the closest mobile object given a query point over inaccurate location data collected from positioning devices. This paper proposes the Probabilistic Threshold filter and its pruning algorithm for CNNQ over imperfect data to utilize energy efficiently, which returns sets of objects that satisfy the query with probabilities higher than some threshold P. Probabilistic filter, Scenario analyzing and pruning algorithm employed here can handle CNNQ wisely to avoid computational and I/O expensive eEmerging location-based application and sensor monitoring management system collect user´s locations with limited power, which cannot report very accurate position values. An important query is the Continuous Nearest-Neighbor Query (CNNQ), which returns the closest mobile object given a query point over inaccurate location data collected from positioning devices. This paper proposes the Probabilistic Threshold filter and its pruning algorithm for CNNQ over imperfect data to utilize energy efficiently, which returns sets of objects that satisfy the query with probabilities higher than some threshold P. Probabilistic filter, Scenario analyzing and pruning algorithm employed here can handle CNNQ wisely to avoid computational and I/O expensive evaluation. The algorithm can be applied in global positioning system, military reconnaissance, communication technique, traffic and transportation management etc.valuation.