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研究生： 王志航
Wang, Chih-Hang
論文名稱： 一些針對節能社群物聯網之最佳化演算法
Some Optimization Algorithms for Energy-Efficiency Social Internet of Things
指導教授： 陳文村
Chen, Wen-Tsuen
楊得年
Yang, De-Nian
許健平
Sheu, Jang-Ping
口試委員: 曾煜棋
Tseng, Yu-Chee
陳志成
Chen, Jyh-Cheng
逄愛君
Pang, Ai-Chun
王志宇
Wang, Chih-Yu
學位類別： 博士
Doctor
系所名稱：
論文出版年： 2018
畢業學年度： 107
語文別： 英文
論文頁數： 120
中文關鍵詞： 社群物聯網 、最佳化 、近似演算法 、資源分配 、獵能
外文關鍵詞： social IoT, optimization, approximation algorithm, resource allocation, energy harvesting
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    • 隨著人工智慧晶片技術的發展，社群物聯網近年來已經受到廣泛的關注。在社群物聯網中，不同的智慧裝置之間具有社群關係且能夠組成團體在不經由遠端伺服器的協助下，直接共同辨識並處理偵測到的事件。為了處理大量的社群物聯網資料及提升其網路強健性，行動邊端運算和無線能量傳輸的無線能量傳輸技術是其中兩個重要的技術。行動邊端運算能夠有效地在網路邊端處理社群物聯網的大數據使得整體網路的傳輸量和計算量能卸載至邊端伺服器，而獵能技術能夠延長網路生命以增進社群物聯網的強健性和覆蓋率使得社群物聯服務能夠廣泛地佈建。同時，由於資訊通訊科技的爆炸性成長，節能問題近年來已受到高度重視。因此，本博士論文廣泛地探討節能社群物聯網，並提出了不同的最佳化和近似演算法使得整體網路的資源配置和能量消耗最小化。首先，本論文研究多攝影機監視系統在蜂巢式網路的資源配置問題，在最小化資源區塊配置數量的情形下，同時確保監視系統的覆蓋需求和無線通訊系統的限制能被滿足。本論文針對一般性情況設計一近似演算法最小化配置給攝影機的資源區塊數量，接著，在曼哈頓街道圖上探討此問題以了解監視器選擇的問題本質，並提出了最佳化攝影機選擇演算法。根據真實的攝影機地圖和大量模擬資料所做的實驗結果顯示，所提出之方法和現存的其他方法相比，能有效地減少配置的資源區塊數量。接著，本論文探討在無線邊端運算網路下的物聯社群建立和物聯裝置選擇問題，其中每一個物聯裝置皆具有人工智慧晶片技術，使得這些物聯裝置能自動地建立社群，並針對使用者的服務請求進行共同決策。針對此問題，本論文提出一近似演算法有效解決1)通訊和計算能量的權衡、2)社群物聯網的跨層設計及封包傳送和3)資料聚合的能量效益權衡，使得整體能量消耗最小化。根據真實網路拓樸的模擬，結果顯示所消耗之總能量能有效地減少超過50%。最後，為了提升社群物聯網的強健性，本論文在獵能和人體健康的限制下，研究基於健康需求的波束成型方法對選擇的物聯裝置進行充能。本論文設計一近似演算法以最小化輻射暴曬和最大化物聯網覆蓋，模擬結果顯示所提出的方法其效能顯著地勝過現有的獵能方法200%以上。值得一提的是以上的演算法可以提供社群物聯網總解決方案，首先，資源配置演算法可以分配資源區塊給物聯裝置和物聯社群群體，此外，基於健康需求的波束成型技術可以安全地幫物聯裝置充能。

    With the development of artificial intelligence (AI) on chips, social internet of things (SIoT) has drawn increasing attention because a group of devices with social relationships can collaborate to directly identify and handle local events without the help of servers. In order to cope with local SIoT data and enhance the robustness of SIoT, mobile edge computing and wireless power transfer are two of the most important paradigms. With mobile edge computing, SIoT data can be locally managed to alleviate data transmission and computation in the backhaul networks, whereas wireless power transfer can prolong the network
    lifetime to enhance the robustness and coverage of SIoT such that SIoT services can be widely deployed. Meanwhile, optimizing energy efficiency is important due to the explosive growth of information and communication technology. Therefore, this dissertation investigates the energy-efficiency SIoT and proposes some optimization and approximation algorithms to minimize allocated resources and energy consumption in the networks. Firstly, this dissertation explores the uplink resource allocation problem for multi-camera surveillance systems in cellular networks. The objective is to minimize the number of allocated resource blocks (RBs), while simultaneously ensuring the coverage requirement for
    the surveillance system and coping with the wireless communication limitation in cellular networks. An approximation algorithm is designed for the general case and optimal solutions for the camera deployments in the Manhattan street map are proposed to find the
    intrinsic properties of camera selections. Simulation results, based on two real surveillance maps and synthetic datasets, show that the number of allocated RBs can be effectively reduced compared to the existing approaches for cellular networks. Secondly, this dissertation investigates the SIoT group construction and device selection problem, where the IoT devices encompass AI-on-chips technologies such that the devices are able to automatically build social groups to make decisions. An approximation algorithm is proposed to minimize the overall energy consumption by exploring communication and computation trade-off, cross-layer design in an SIoT, and forwarding and aggregation trade-off in mobile edge computing networks. Simulations on two real networks show that the overall energy consumption can be effectively reduced by more than 50%. To enhance the robustness of
    SIoT, this dissertation finally explores the health-aware beamforming to charge the selected IoT devices under the energy harvesting and human safety constraints. An approximation algorithm is proposed to minimize radiation exposure and maximize IoT coverage and simulation results manifest that it significantly outperforms the previous energy harvesting approaches by more than 200%. It is worth noting that the above algorithms can jointly provide the total solution to SIoT. The resource allocation algorithm is used to allocate RBs to the selected devices and collaborative groups of SIoT. On the other hand, the health-aware
    beamforming safely charges the selected IoT devices.

    1 Introduction 1 2 The Survey of SIoT and Its Applications 7 2.1 Uplink Resource Allocation . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2 IoT and MEC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.3 SIoT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.4 Energy Harvesting with Wireless Power Transfer . . . . . . . . . . . . . . 13 3 Resource Allocation for SIoT 15 3.1 System Model and Problem Description . . . . . . . . . . . . . . . . . . . 15 3.2 Hardness Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 3.3 Algorithm Design for the General Camera Deployment . . . . . . . . . . . 19 3.3.1 Baseline Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.3.2 Approximation Algorithm . . . . . . . . . . . . . . . . . . . . . . 21 3.3.3 Complexity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 24 3.3.4 Approximation Ratio of MRAMC . . . . . . . . . . . . . . . . . . 25 3.3.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.4 Algorithm Design for the Camera Deployments on the Manhattan Street Map 28 3.4.1 CSRAP with Arbitrary Camera Directions and Channel Qualities . 28 3.4.2 CSRAP with the Outward Camera Direction and Arbitrary Channel Qualities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.4.3 CSRAP with the Outward Camera Direction and Equal Channel Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 3.5 Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 3.5.1 Simulation Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 3.5.2 Camera Deployments on the Manhattan Street Map . . . . . . . . . 42 3.5.3 General Camera Deployments . . . . . . . . . . . . . . . . . . . . 46 3.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 4 SIoT Device Selection and Group Construction 54 4.1 System Model and Problem Description . . . . . . . . . . . . . . . . . . . 54 4.2 Hardness Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 4.3 Collaborative Groups in Different SIoT Scenarios . . . . . . . . . . . . . . 59 4.3.1 Grid Deployment . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 4.3.2 All-Pair Argumentation . . . . . . . . . . . . . . . . . . . . . . . . 63 4.4 Algorithm Design for EICGS . . . . . . . . . . . . . . . . . . . . . . . . . 66 4.4.1 Approximation Algorithm . . . . . . . . . . . . . . . . . . . . . . 67 4.4.2 Complexity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 73 4.4.3 Approximation Ratio of EICGS . . . . . . . . . . . . . . . . . . . 74 4.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 4.5.1 Distributed Implementation of EICGS . . . . . . . . . . . . . . . . 77 4.5.2 Dynamic User Request . . . . . . . . . . . . . . . . . . . . . . . . 79 4.6 Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 4.6.1 Simulation Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 4.6.2 Simulation Result . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 4.6.3 Performance of D-EICGS . . . . . . . . . . . . . . . . . . . . . . 85 4.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 5 Wireless Power Transfer for SIoT 88 5.1 System Model and Problem Description . . . . . . . . . . . . . . . . . . . 88 5.1.1 Two Safety Models: PD and SAR . . . . . . . . . . . . . . . . . . 88 5.1.2 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . 89 5.2 Hardness Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 5.3 Algorithm Design for HABIS . . . . . . . . . . . . . . . . . . . . . . . . . 92 5.3.1 Approximation Algorithm . . . . . . . . . . . . . . . . . . . . . . 93 5.3.2 Health-Aware Wireless Power Transfer System . . . . . . . . . . . 97 5.3.3 Optimal Health-Aware Beamforming (OHAB) . . . . . . . . . . . 98 5.3.4 Approximation Ratio of MREMIC . . . . . . . . . . . . . . . . . . 99 5.3.5 Complexity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 103 5.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 5.4.1 MREMIC for Multi-BS . . . . . . . . . . . . . . . . . . . . . . . 103 5.4.2 User Mobility and Simultaneous Downlink Transmission . . . . . . 104 5.5 Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 5.5.1 Simulation Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 5.5.2 Simulation Result . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 5.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 6 Concluding Remarks and Future Directions 110

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