社交物聯網（SIoT）已成為新興的網絡範例，具有人工智慧（AI）和社群關係的物聯網設備可以自動建立合作式群組以在本地端偵測事件。另一方面，移動用戶可以充當無處不在的通用感測器，以提高物聯網事件偵測的準確性。在本篇論文中，我們探討了SIoT與眾包 (Crowdsourcing) 的合作式問題，以共同選擇社群物聯網裝置並僱用用戶來監視具有準確性要求的事件和位置，同時最大程度地減少射群物聯網的通信和計算的總成本以及用戶的僱用成本。我們證明SCC是NP-hard，除非P = NP，否則不能用任何因子近似。接著，我們提出了一個新的演算法，具有準確性和社群意識的物聯網裝置和用戶選擇（ASSUS），使用了合作式樹（CT）和準確性利潤（AP）的想法，其中CT利用用戶的社群關係來正確選擇中間的物聯網裝置。模擬結果表明，與最新的演算法相比，ASSUS可以有效地減少總成本的50％以上。

Social Internet of Things (SIoT) has become an emerging network paradigm, where IoT devices with Artificial Intelligence (AI) and social relations can automatically establish a collaborative group to identify events locally. On the other hand, mobile users can act as ubiquitous and versatile sensors to improve the accuracy of SIoT event detection. In this thesis, we explore the SIoT Collaboration with Crowdsourcing (SCC) problem to jointly select SIoT devices and hire users to monitor events and locations with accuracy requirements, while minimizing the total SIoT communication and computation costs and the user hiring cost. We prove that SCC is NP-hard and cannot be approximated by any factor unless P = NP. Then, we propose a new algorithm, Accuracy- and Social-aware SIoT and User Selection (ASSUS), with the idea of Collaborative Tree (CT) and Accuracy Profit (AP), where CT exploits users' social relations to properly choose intermediate SIoTs. Simulation results manifest that ASSUS can effectively reduce more than $50\%$ of the total cost compared with state-of-the-art algorithms.

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