Scheduling and sampling technologies for sensor data

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)


1 Introduction
1.1 Background
1.2 Objectives and Contributions
1.3 Outlineof the Thesis
2 Related Works
2.1 Scheduling Algorithms for Maintaining Data Freshness
2.1.1 TemporalValidityforDataFreshnes
2.1.2 Half-Half and More-Less
2.2 Querying Sensor Data in Wireless Sensor Network
2.3 Location Update Schemes for Supporting LDCQ
3 Deferrable Scheduling Algorithm for Maintaining Data Freshness
3.1 Deferrable Scheduling
3.1.1 Intuition and principle of DS-FP
3.1.2 Deferrable Scheduling Algorithm
3.1.3 Theoretical Analysis of DS-FP Utilization
3.2 Performance Evaluation
3.2.1 Simulation Model and Parameters
3.2.2 Expt.1:Comparison of CPU Workloads
3.2.3 Expt.2:Co-scheduling of Mixed Workloads
3.3 Chapter Summary
4 Deferrable Scheduling: Schedulability Analysis and Overhead Re-duction
4.1 Feasibility Analysis for DS-FP
4.2 Optimality of DS-FP for Minimizing Processor Utilization
4.3 Deferrable Scheduling with Hyperperiod
4.3.1 DEferrable Scheduling with Hyperperiod: Schedule Construc-tion
4.3.2 DEferrable Scheduling with Hyperperiod: Schedule Adjustment
4.4 Performance Evaluation
4.4.1 Simulation Model and Parameters
4.4.2 Experimental Results
4.5 Chapter Summary
5 Statistics-based Sensor Selection Scheme for Continuous Queries in Wireless Sensor Network
5.1 System Model and Continuous Probabilistic Query Model
5.1.1 Continuous Probabilistic Query Model
5.1.2 Sensor Data and the Role of the Coordinator Node
5.2 Statistics-Based Sensor Selection Scheme for CPQ
5.2.1 Computing the Region’s Initial Statistic Properties
5.2.2 Adaptive Sampling Period
5.2.3 Deriving Maximum Allowed Variance
5.2.4 Time Complexity Analysis
5.2.5 Determining Sample Size and the Set of Sensors
5.3 PerformanceEvaluation
5.4 Chapter Summary
6 A Probabilistic Continuous Update Scheme for Location Depen-dent Continuous Queries
6.1 System Model and Definitions
6.1.1 Uncertainty Model
6.1.2 Fidelity of the Query
6.2 The Probabilistic Continuous Update Scheme
6.2.1 Overview
6.2.2 Evaluation of the Potential Set
6.2.3 Generation of OLU and QAU
6.3 Simulation Results and Performance Analysis
6.3.1 Simulation ……


Author: Han, Song

Source: City University of Hong Kong

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