Efficiency and privacy are two basic issues in moving object monitoring. This paper proposes a privacy-aware monitoring (PAM) framework that addresses each issues. The framework distinguishes itself from the present work by being the primary to holistically address the issues of location updating in terms of monitoring accuracy, potency, and privacy, particularly, when and the way mobile shoppers should send location updates to the server. Based on the notions of safe region and most probable result, PAM performs location updates solely when they would seemingly alter the question results. Furthermore, by planning various consumer update methods, the framework is flexible and in a position to optimize accuracy, privacy, or efficiency. We develop economical query evaluation/reevaluation and safe region computation algorithms within the framework. The experimental results show that PAM substantially outperforms traditional schemes in terms of monitoring accuracy, CPU price, and scalability whereas achieving close-to-optimal communication price.
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