(3.238.88.35) 您好!臺灣時間:2021/04/10 20:05
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果

詳目顯示:::

我願授權國圖
: 
twitterline
研究生:楊承翰
研究生(外文):Yang, Cheng-Han
論文名稱:於行動邊緣網路中考量準確度及精密度的物聯網裝置選擇
論文名稱(外文):Accuracy and Precision-Aware IoT Device Selection in Mobile Edge Networks
指導教授:陳文村陳文村引用關係
指導教授(外文):Chen, Wen-Tsuen
口試委員:許健平楊得年
口試委員(外文):Sheu, Jang-PingYang, De-Nian
口試日期:2018-12-20
學位類別:碩士
校院名稱:國立清華大學
系所名稱:資訊工程學系所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2018
畢業學年度:107
語文別:英文
論文頁數:34
中文關鍵詞:物聯網行動邊緣運算準確度精確度能量
外文關鍵詞:IoTMECaccuracyprecisionenergy
相關次數:
  • 被引用被引用:0
  • 點閱點閱:84
  • 評分評分:系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
物聯網 (Internet of Things, IoT) 已被認為是未來最重要的網路示例之一。對於物聯網,確保其偵測的正確性是極為重要的。正確性包含準確度 (Accuracy) 及精確度 (Precision) 兩個要素。另一方面,為了減少網路中運算及傳輸的能量消耗,行動邊緣運算 (Mobile Edge Computing, MEC),即於行動邊緣處理物聯網所產生的大數據已成為了一個很有前途的方法。然而,確保物聯網之準確度及精確度需求,且同時在行動邊緣網路中最小化能量消耗的問題,在過去並未被探討。因此,在本篇論文中,我們探討了在行動邊緣網路中之能量消耗最小化問題,並且同時考慮了:(1) 物聯網之準確度、精確度及覆蓋 (coverage) (2) 行動邊緣伺服器 (MEC server) 上的大數據處理 (3) 物聯網網路和行動邊緣網路之能量消耗。具體來說,給定:(1) 物聯網裝置集合 (2) 目標集合 (3) 行動邊緣網路 (4) 能量消耗模型 (5) 準確度和精確度需求,我們制定了一個新的最佳化問題,稱為Accuracy and Precision-Aware IoT Device Selection (APAIDS),以最小化於行動邊緣網路中之整體能量消耗。我們證明APAIDS是NP-hard,並且提出了一個新的演算法,稱為Energy Efficient Device and MEC Server Selection (EDMS),藉由共同選擇物聯網裝置、配置行動邊緣運算連接及選擇伺服器處理每個目標的資料,以最小化能量消耗。最後,我們在兩個真實網路拓樸中評估EDMS。相較於基線的方法,模擬結果顯示整體能量消耗能夠減少超過60%。
Internet of Things (IoT) has been regarded as one of the most significant network paradigms in the future. For IoT, it is crucial to ensure the correctness of detection which includes the factors of accuracy and precision. On the other hand, Mobile Edge Computing (MEC) has emerged as a promising way to process big IoT data at the network edge so as to reduce the computation and transmission energy in the networks. Nevertheless, minimizing the energy consumption in MEC networks while ensuring both the accuracy and precision requirements of IoT has not been explored before. In this thesis, therefore, we explore the energy minimization problem in MEC networks by considering the accuracy, precision, and coverage of IoT, big data processing on MEC servers as well as the dissipated energy in both IoT and MEC networks. Specifically, given 1) a set of IoT devices, 2) a set of observed targets, 3) an MEC network, 4) the energy consumption model, and 5) the accuracy and precision requirements, we formulate a new optimization problem, named Accuracy and Precision-Aware IoT Device Selection (APAIDS), to minimize the overall energy consumption in MEC networks. We prove the NP-hardness of APAIDS and then propose a new algorithm, named Energy Efficient Device and MEC Server Selection (EDMS), to minimize energy consumption by jointly selecting IoT devices, configuring MEC association, and selecting processing servers for dealing with the data of each target. Finally, we evaluate EDMS on two real networks. In comparison with the baseline schemes, the results manifest that the overall energy consumption can be reduced by more than 60%.
1 Introduction 1
2 Related Work 4
2.1 IoT in Mobile Edge Networks . . . . . . . . . . . . . . . . . . . . . . . . 4
2.2 The Correctness of IoT Services . . . . . . . . . . . . . . . . . . . . . . . 5
3 Accuracy and Precision-Aware IoT Device Selection 7
3.1 System Model and Problem Formulation . . . . . . . . . . . . . . . . . . . 7
3.2 The Hardness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
4 Energy Efficient Device and MEC Server Selection 14
4.1 Algorithm Concept . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
4.2 Algorithm Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
4.2.1 Group Construction (GC) . . . . . . . . . . . . . . . . . . . . . . 15
4.2.2 Device Selection (DS) . . . . . . . . . . . . . . . . . . . . . . . . 18
4.2.3 MEC Selection and Energy Reduction (MSER) . . . . . . . . . . . 21
4.3 Time Complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
5 Simulation 25
5.1 Simulation Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
5.2 Simulation Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
6 Conclusion 31
[1] P. Mach and Z. Becvar, “Mobile edge computing: A survey on architecture and computation offloading,” IEEE Communications Surveys Tutorials, vol. 19, no. 3, pp. 1628–1656, 2017.
[2] Y. Mao, C. You, J. Zhang, K. Huang, and K. B. Letaief, “A survey on mobile edge computing: The communication perspective,” IEEE Communications Surveys Tutorials, vol. 19, no. 4, pp. 2322–2358, 2017.
[3] N. Abbas, Y. Zhang, A. Taherkordi, and T. Skeie, “Mobile edge computing: A survey,” IEEE Internet of Things Journal, vol. 5, no. 1, pp. 450–465, 2018.
[4] J. N. Al-Karaki and A. Gawanmeh, “The optimal deployment, coverage, and connectivity problems in wireless sensor networks: Revisited,” IEEE Access, vol. 5, pp. 18 051–18 065, 2017.
[5] S. Kekki, W. Featherstone, Y. Fang, P. Kuure, A. Li, A. Ranjan, D. Purkayastha, F. Jiangping, D. Frydman, G. Verin, et al., MEC in 5G networks, White Paper, ETSI, 2018.
[6] IoT edge, deliver Google AI capabilities at the edge, Google. [Online]. Available: https://cloud.google.com/iot-edge/.
[7] Microsoft Azure IoT edge, Microsoft. [Online]. Available: https://azure.microsoft. com/en-us/services/iot-edge/.
[8] Develop IoT business applications at the edge, Cisco. [Online]. Available: https : //www.cisco.com/c/en/us/products/cloud-systems-management/iox/index.html.
[9] M. A. Mahmood,W. K. Seah, and I.Welch, “Reliability in wireless sensor networks: A survey and challenges ahead,” Computer Networks, vol. 79, pp. 166–187, 2015.
[10] H. Wen, Z. Xiao, A. Markham, and N. Trigoni, “Accuracy estimation for sensor systems,” IEEE Transactions on Mobile Computing, vol. 14, no. 7, pp. 1330–1343, 2015.
[11] A. De Paola, P. Ferraro, S. Gaglio, G. L. Re, and S. K. Das, “An adaptive bayesian system for context-aware data fusion in smart environments,” IEEE Transactions on Mobile Computing, vol. 16, no. 6, pp. 1502–1515, 2017.
[12] B. Ao, Y. Wang, L. Yu, R. R. Brooks, and S. Iyengar, “On precision bound of distributed
fault-tolerant sensor fusion algorithms,” ACM Computing Surveys, vol. 49, no. 1, p. 5, 2016.
[13] R. R. Brooks and S. S. Iyengar, “Robust distributed computing and sensing algorithm,” Computer, vol. 29, no. 6, pp. 53–60, 1996.
[14] Sensor terminology, White Paper, National Instruments, 2013. [Online]. Available: http://www.ni.com/white-paper/14860/en/.
[15] ISO 5725-1: 1994: Accuracy (Trueness and Precision) of Measurement Methods and Results-Part 1: General Principles and Definitions. International Organization for Standardization, 1994.
[16] C. Perera, A. Zaslavsky, C. H. Liu, M. Compton, P. Christen, and D. Georgakopoulos,“Sensor search techniques for sensing as a service architecture for the internet of things,” IEEE Sensors Journal, vol. 14, no. 2, pp. 406–420, 2014.
[17] Five questions about sensor accuracy, answered, AML Oceanographic. [Online]. Available: https://amloceanographic.com/blog/sensor-accuracy.
[18] K. Marzullo, “Tolerating failures of continuous-valued sensors,” ACM Transactions on Computer Systems, vol. 8, no. 4, pp. 284–304, 1990.
[19] M. Cardei, M. T. Thai, Y. Li, and W. Wu, “Energy-efficient target coverage in wireless sensor networks,” in Proceedings of IEEE INFOCOM, 2005.
[20] C.-H. Wang, J.-J. Kuo, D.-N. Yang, and W.-T. Chen, “Green software-defined internet of things for big data processing in mobile edge networks,” in Proceedings of IEEE ICC, 2018.
[21] W. Vereecken,W. Van Heddeghem, D. Colle, M. Pickavet, and P. Demeester, “Overall ICT footprint and green communication technologies,” in Proceedings of IEEE ISCCSP, 2010.
[22] J. Sorber, A. Balasubramanian, M. D. Corner, J. R. Ennen, and C. Qualls, “Tula: Balancing energy for sensing and communication in a perpetual mobile system,” IEEE Transactions on Mobile Computing, vol. 12, no. 4, pp. 804–816, 2013.
[23] C. Long, Y. Cao, T. Jiang, and Q. Zhang, “Edge computing framework for cooperative video processing in multimedia IoT systems,” IEEE Transactions on Multimedia, vol. 20, no. 5, pp. 1126–1139, 2018.
[24] K. Zhang, S. Leng, Y. He, S. Maharjan, and Y. Zhang, “Mobile edge computing and networking for green and low-latency internet of things,” IEEE Communications Magazine, vol. 56, no. 5, pp. 39–45, 2018.
[25] B. Chen, J.Wan, A. Celesti, D. Li, H. Abbas, and Q. Zhang, “Edge computing in IoT-based manufacturing,” IEEE Communications Magazine, vol. 56, no. 9, pp. 103–109, 2018.
[26] M. Qin, L. Chen, N. Zhao, Y. Chen, F. R. Yu, and G. Wei, “Power-constrained edge computing with maximum processing capacity for IoT networks,” IEEE Internet of Things Journal, 2018.
[27] X. Sun and N. Ansari, “EdgeIoT: Mobile edge computing for the internet of things,” IEEE Communications Magazine, vol. 54, no. 12, pp. 22–29, 2016.
[28] X. Li, S. Liu, F. Wu, S. Kumari, and J. J. Rodrigues, “Privacy preserving data aggregation scheme for mobile edge computing assisted IoT applications,” IEEE Internet of Things Journal, 2018.
[29] S. Liu, S. P. Chepuri, M. Fardad, E. Ma¸sazade, G. Leus, and P. K. Varshney, “Sensor selection for estimation with correlated measurement noise,” IEEE Transactions on Signal Processing, vol. 64, no. 13, pp. 3509–3522, 2016.
[30] V. Isler and R. Bajcsy, “The sensor selection problem for bounded uncertainty sensing models,” IEEE Transactions on Automation Science and Engineering, vol. 3, no. 4, pp. 372–381, 2006.
[31] S. P. Chepuri and G. Leus, “Sparsity-promoting sensor selection for non-linear measurement models.,” IEEE Transactions on Signal Processing, vol. 63, no. 3, pp. 684– 698, 2015.
[32] D. Wang, T. Abdelzaher, L. Kaplan, and C. C. Aggarwal, “Recursive fact-finding: A streaming approach to truth estimation in crowdsourcing applications,” in Proceedings of IEEE ICDCS, 2013.
[33] M. Ding, D. Chen, A. Thaeler, and X. Cheng, “Fault-tolerant target detection in sensor networks,” in Proceedings of IEEE WCNC, 2005.
[34] T. Clouqueur, K. K. Saluja, and P. Ramanathan, “Fault tolerance in collaborative sensor networks for target detection,” IEEE Transactions on Computers, vol. 53, no. 3, pp. 320–333, 2004.
[35] J. Moy, OSPF version 2, RFC 2178, 1997.
[36] J.-H. Chang and L. Tassiulas, “Maximum lifetime routing in wireless sensor networks,” IEEE/ACM Transactions on Networking, vol. 12, no. 4, pp. 609–619, 2004.
[37] W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, “Energy-efficient communication protocol for wireless microsensor networks,” in Proceedings of IEEE HICSS, 2000.
[38] C. Mobius, W. Dargie, and A. Schill, “Power consumption estimation models for processors, virtual machines, and servers,” IEEE Transactions on Parallel and Distributed Systems, vol. 25, no. 6, pp. 1600–1614, 2014.
[39] P. Mahadevan, P. Sharma, S. Banerjee, and P. Ranganathan, “A power benchmarking framework for network devices,” in Proceedings of International Conference on Research in Networking, Springer, 2009.
[40] U. Feige, “A threshold of ln n for approximating set cover,” Journal of the ACM, vol. 45, no. 4, pp. 634–652, 1998.
[41] Internet topology zoo. [Online]. Available: http://www.topology-zoo.org.
[42] T. M. Nam, N. H. Thanh, N. Q. Thu, H. T. Hieu, and S. Covaci, “Energy-aware routing based on power profile of devices in data center networks using SDN,” in Proceedings of IEEE ECTI-CON, 2015.
[43] A. Krioukov, P. Mohan, S. Alspaugh, L. Keys, D. Culler, and R. H. Katz, “Napsac: Design and implementation of a power-proportional web cluster,” in Proceedings of the First ACM SIGCOMM Workshop on Green Networking, ACM, 2010.
[44] R. A. da Silva and N. L. da Fonseca, “Resource allocation mechanism for a fog-cloud infrastructure,” in Proceedings of IEEE ICC, 2018.
連結至畢業學校之論文網頁點我開啟連結
註: 此連結為研究生畢業學校所提供,不一定有電子全文可供下載,若連結有誤,請點選上方之〝勘誤回報〞功能,我們會盡快修正,謝謝!
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
系統版面圖檔 系統版面圖檔