This thesis addresses three important problems related to sensor data processing with the purpose to improve the correctness of results in execution of sensor queries. The first problem focuses on how to schedule updates to maintain the temporal validity of sensor data with minimal workload. The second problem is how to select the right set of sensors for sensor data aggregation to obtain data values that are precise enough to meet the probabilistic requirements of sensor queries. The third problem is how to guarantee the accuracy of the query results without incurring significant update cost in the context of Location Dependent Continuous Query (LDCQ). In the first part, we study the real-time scheduling algorithms for update trans-actions associated with sensor data to minimize CPU utilization. Different from the traditional real-time scheduling approaches which adopt periodic update transac-tion model, we propose a novel algorithm, namely deferrable scheduling algorithm for fixed priority transactions (DS-FP), in which update transactions are scheduled following a sporadic task model. DS-FP exploits the semantics of temporal valid-ity constraint of sensor data by judiciously deferring the sampling times of update transaction jobs as late as possible. The schedulability of the algorithm is examined and a sufficient condition is presented in this thesis. We also present a theoretical analysis of its CPU utilization and prove that DS-FP is optimal for fixed priority schedules in terms of minimizing processor workload. To reduce the on-line scheduling overhead of DS-FP, we further propose two hyperperiod-based approaches with lower scheduling overhead and also satisfying the validity constraint. The first algorithm, namely DEferrable Scheduling with Hyperperiod by Schedule Construction (DESH-SC), searches for a hyperperiod in the DS-FP schedule, and repeats the hyperperiod infinitely. The second algorithm, namely DEferrable Scheduling with Hyperperiod by Schedule Adjustment (DESH-SA), adjusts the DS-FP schedule in an interval so that the adjusted schedule in the interval can be repeated infinitely. In this manner, we reduce the on-line schedulin
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