群眾外包可以有效地利用社交物聯網（Social Internet of Things, SIoT）設備和移動用戶來收集數據並偵測事件。近年來隨著數位孿生（Digital twins, DTs）出現，能透過預測和模擬對應的SIoTs和用戶的狀態和信息以提高收集數據的準確性。一些研究還提出了社交數位孿生（Social Digital twins, SDTs）的概念，以描述DTs的社交能力。我們可以建立一個協作小組來分享由SIoT、SDTs和移動用戶收集的數據，以共同偵測事件。然而據我們所知，目前沒有相關研究探討SIoT、SDTs和移動用戶之間合作，以實現對準確性偵測事件的檢測。在本論文中，我們制定了一個新的優化問題，名為具準確性提升感知的社交物聯網、社交數位孿生與用戶選擇 (Accuracy Improvement-aware SIoT, SDT, and User Selection, AISSUS)，用於選擇一組SIoTs、SDTs與用戶在有準確性要求情況下去偵測事件並同時最小化計算成本、通信成本、維護成本和用戶僱傭成本之總和。我們證明了AISSUS是NP-hard的問題和它的不可近似性。為了解決這個問題，我們提出了一種啟發式演算法名為準確性和社交感知的社交物聯網、用戶和社交數位孿生選擇(Accuracy- and Social-aware SIoT, User and SDT Selection, ASSUDS)，其中想法包含協作節點選擇(Collaborative Node Selection, CNS)、協作樹建構(Collaborative Tree Construction, CTC)、協作樹節點替換與修剪(CT Nodes Replacement and Pruning, CTNRP)以及用戶交換與用戶調整(User Swapping and User Adjustment with SDT of user, USUAS)。為了找到選擇SIoTs和SDTs問題的固有特性，我們還設計了問題的特殊情況(Specialized SIoT and SDT Selection Problem, SSDSP)，只考慮準確度高於閾值的公共SIoTs和SDTs。為了解決SSDSP，我們提出了協作式SIoT和SDT選擇(Collaborative SIoT and SDT Selection, CSDS)，並證明了CSDS的近似比率。模擬結果顯示，相比基線演算法，ASSUDS在AISSUS減少了35％的總成本，而CSDS在SSDSP下減少了50％的總成本。

Crowdsourcing can effectively utilize the Social Internet of Things (SIoT) devices and mobile users to collect data and monitor events. Recently, Digital Twins (DTs) have emerged to improve the accuracy of collected data by utilizing prediction and simulation of the states and information of the corresponding SIoTs and users. Some research also proposes the concept of Social Digital Twins (SDTs) to describe the social capabilities of DTs. We can construct a collaborative group to share data collected by SIoT, SDTs, and mobile users to detect events collaboratively. However, to the best of our knowledge, there is no work investigating the collaboration among SIoT, SDTs, and mobile users for accuracy-ware event detection. In this thesis, we formulate a new optimization problem, named Accuracy Improvement-aware SIoT, SDT, and User Selection (AISSUS) to select a set of SIoTs, SDTs, and mobile users to monitor the events with accuracy requirements while minimizing the total computation cost, communication cost, maintenance cost, and user hiring cost. We then prove the NP-hardness and inapproximability of AISSUS. To solve the problem, we propose a heuristic algorithm Accuracy- and Social-aware SIoT, User and SDT Selection (ASSUSS) with the ideas of Collaborative Node Selection (CNS), Collaborative Tree Construction (CTC), CT Nodes Replacement and Pruning (CTNRP), and User Swapping and User Adjustment with SDT of user (USUAS). To find the intrinsic properties of the selection of SIoTs and SDTs, we also design the problem’s special case, called Specialized SIoT and SDT Selection Problem (SSDSP), by considering only public SIoTs and SDTs with the accuracy of monitoring locations above the accuracy threshold. To solve SSDSP, we propose Collaborative SIoT and SDT Selection (CSDS) and show the approximation ratios of CSDS. Simulation results show that ASSUSS achieves a 35% reduction in total cost for AISSUS and CSDS achieves a 50% reduction in total cost for SSDSP compared to the baseline algorithms.

1 Introduction 1
2 Related Work 11
2.1 Social Internet of Things (SIoT) 11
2.2 Digital Twins (DTs) 11
2.3 Social Digital Twins (SDTs) 13
2.4 Crowdsourcing 13
3 System Model and Problem Formulation 18
3.1 System Model 18
3.2 Problem Formulation 21
3.3 Theoretical Analysis 25
3.4 Summary 27
4 Algorithm Design 29
Algorithm Design 29
4.1 Overview of Algorithm 29
4.2 Accuracy- and Social-aware SIoT, User and SDT Selection (ASSUSS) 30
4.3 Specialized SIoT and SDT Selection Problem (SSDSP) 45
4.4 Collaborative SIoT and SDT Selection (CSDS) 46
4.5 Discussion 50
4.6 Summary 53
5 Simulation 54
5.1 Simulation Setup 54
5.2 Baseline Algorithm 56
5.3 Simulation Results and Analyses 57
5.4 Summary 68
6 Conclusions 69

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