物聯網（IOT）在過去十年一直是學術界的熱門研究課題。同樣地，產業界也抱有很大的期待。許多新興物聯網服務被廣泛討論，例如自動駕駛車和無人機、虛擬個人助理、智能工作場所、智能製造、智能物流、智能零售服務、智能醫療、智能穿戴、智能城市等。然而，很少有人意識到有限的全球能源供應和經濟規模會限制物聯網裝置和服務的成長。在未來幾年國內生產總值和能源消耗不會大幅增長的前提下，除非我們能大幅度降低物聯網系統和服務的總成本以及能源消耗，否則物聯網裝置將無法在幾年內達到預期的數量級增長。
由於產品壽命和總體成本與產品可靠性有關，我們以交通號誌控制器為例，使用共生系統（SS）模型設計可靠的物聯網系統。在共生系統模型中，修復機制可以是自我修復機制、同儕修復機制，或者兩者兼有。同儕修復機制允許裝置由相鄰裝置修復，而我們稱這些裝置為「同儕」。在不失一般性的情況下，我們的研究專注於將共生系統模型應用於交通號誌控制系統。藉由提出的共生系統模型和修復機制，我們可以評估單一個裝置的可靠性。實驗結果顯示，在故障率為10-6時，與基於三模組化冗餘所設計的控制器相比，基於共生系統所設計具有自我修復機制的控制器不僅延長了43％的使用壽命，而且降低了18％的硬體面積。功率消耗減少約19％。對於基於共生系統所設計具有同儕修復（包括自我修復）的控制器，與基於三模組化冗餘所設計控制器相比，延長了48％的使用壽命，並且稍微減少了2％的硬體面積。功率消耗稍微改善，約減少5％。總括來說，在邏輯修復中，共生設計方法比三模組化冗餘設計方法更具成本效益。此外，在共生設計中，因為同儕修復機制需要額外的通訊負擔，所以自我修復機制比同儕復機制更具成本效益。

Internet of Things (IOT) has been a hot research topic in academia in the past decade. Industry has had great expectation on it, likewise. There are many anticipated new IOT services widely discussed, e.g., autonomous vehicles and drones, virtual assistants, intelligent worksites, intelligent manufacturing, intelligent logistics, intelligent retail services, intelligent healthcare, intelligent wearables, smart city, etc. However, few people have yet to realize that the growth of IOT devices and services is restricted by the limited global energy supply and economy scale. Under the premise that GDP and energy consumption do not increase dramatically in the future, the IOT devices will not have the opportunity to reach the expected orders-of-magnitude growth in a few years, unless we can greatly reduce the total cost and energy consumption of the IOT systems and services.
Because product lifetime and overall cost is related to its reliability, we have worked on the design of reliable IOT systems by using the symbiotic system (SS) model, with the traffic light controller as an example. In the SS model, the repair mechanism can be the self-repair mechanism, the peer-repair mechanism, or both. The peer-repair mechanism allows the devices to be repaired by their neighboring devices, called peers. Again, without loss of generality, in this work we focus on applying the SS model to a traffic light control system. With the proposed SS model and repair mechanism, we can evaluate a single device reliability. Experimental results show that, given a failure rate of 10-6, the SS-based controller with the self-repair mechanism not only extends 43% of its lifetime, but also reduces 18% of the hardware area, as compared with a TMR-based controller. The power consumption reduces about 19%. For the SS-based controller with the peer-repair (self-repair included), compared with TMR-based controller, it extends the lifetime by about 48%, and slightly reduces the hardware area (2%). The power consumption is slightly improved, i.e., reduced by about 5%. To sum up, in logic repair, the symbiotic design methodology is more cost efficient than the TMR methodology. In addition, in symbiotic design, the self-repair mechanism is more cost efficient than the peer-repair mechanism, because the peer-repair mechanism needs additional communication overhead.

摘要 i
Abstract ii
Contents iv
List of Figures vi
List of Tables viii
Chapter 1 Introduction 1
1.1 Motivation and Objective 1
1.2 Method and Result 3
1.3 Organization 5
Chapter 2 Symbiotic System 6
2.1 Symbiotic System Model 6
2.2 Symbiotic Traffic Light Controller 7
Chapter 3 Traffic Light Controller 10
3.1 Traffic Light Control 10
3.2 Specifications of the Traffic Light Controller 13
3.3 Proposed Symbiotic Traffic Light Controller 14
3.4 Triple Module Redundancy Based Traffic Light Controller 18
Chapter 4 Experimental Results 20
4.1 Hazard Generation 20
4.2 Behavior-Level Simulation 21
4.3 RTL-Level Simulation 22
4.4 Parameter 25
4.5 Reliability Comparison 27
4.5.1 Mathematic Model 27
4.5.2 Behavior-Level Simulation Result 28
4.5.3 RTL-Level Simulation Result 31
4.6 Synthesis Report 33
4.6.1 Performance Comparison 33
4.6.2 Hardware Complexity Comparison 33
4.6.3 Power Consumption Comparison 34
Chapter 5 Conclusions and Future Work 36
5.1 Conclusions 36
5.2 Future Work 37
Bibliography 38

[1] D. Evans, “The Internet of Things: How the Next Evolution of the Internet Is Changing Everything,” Cisco White Paper, Apr. 2011.
[2] Cisco, “The Zettabyte Era: Trends and Analysis,” Cisco White Paper, Jun. 2017.
[3] S. Dale, “Group chief economist’s analysis,” BP Statistical Review of World Energy, 66th Ed., Jun. 2017, pp. 3-7.
[4] G. Fagas, J. P. Gallagher, L. Gammaitoni, and D. J. Paul, “Energy Challenges for ICT,” in ICT-Energy Concepts for Energy Efficiency and Sustainability, Ed. G. Fagas, L. Gammaitoni, J. P. Gallagher, and D. J. Paul, InTech, Mar. 2017, pp. 1-35.
[5] J. Manyika, M. Chui, P. Bisson, J. Woetzel, R. Dobbs, J. Bughin, D. Aharon, “The internet of things: Mapping the value beyond the hype,” Mckinsey & Company, Jun. 2015.
[6] D. Schrank, B. Eisele, T. Lomax, and J. Bak, “2015 Urban Mobility Scorecard,” Published jointly by The Texas A&M Transportation Institute and INRIX, Aug. 2015.
[7] A. G. Sims and K. W. Dobinson, “The Sydney coordinated adaptive traffic (SCAT) system philosophy and benefits,” IEEE Transactions on Vehicular Technology, vol. 29, issue 2, May. 1980, pp. 130-137.
[8] D. I. Robertson and R. D. Bretherton, “Optimizing networks of traffic signals in real time-the SCOOT method,” in IEEE Transactions on Vehicular Technology, vol. 40, issue 1, Feb. 1991, pp. 11-15.
[9] S. F. Smith, G. J. Barlow, X.-F. Xie, and Z. B. Rubinstein, “SURTRAC: Scalable Urban Traffic Control,” in Proc. Transp. Res. Board 92nd Annu. Meet. Compendium Papers, pp. 1-15, Jan. 2013.
[10] Y.-C. Kao, “A Machine Learning Model for Adaptive Traffic Light Control System with Self-Organizing Map and Reinforcement Learning,” MS Thesis, Dept. CS, National Tsing Hua University, Hsinchu, Taiwan, Oct. 2017.
[11] C.-W. Wu, B.-Y. Lin, H.-W. Hung, S.-M. Tseng, and C. Chen, “Symbiotic System Models for Efficient IOT System Design and Test,” in Proc. 1st Int. Test Conference in Asia (ITC-Asia), 2017.
[12] B.-Y. Lin, H.-W. Hung, S.-M. Tseng, C. Chen, and C.-W. Wu, “Highly Reliable and Low-Cost Symbiotic IOT Devices and Systems,” in Proc. Int. Test Conference (ITC), 2017.
[13] P. H. Bardell, W. H. McAnney, J. Savir, Built-in test for VLSI: pseudorandom techniques, New York, NY, USA: John Wiley & Sons (Wiley-Interscience), 1987.
[14] L.-T. Wang, C.-W. Wu, and X. Wen, VLSI Test Principles and Architectures: Design for Testability, San Francisco, CA, USA: Elsevier (Morgan Kaufmann), 2006.
[15] E. Dubrova, Fault-Tolerant Design: An Introduction, Kluwer Academic Publishers, Mar. 2008.
[16] U. S. DoD, Military Handbook Reliability Prediction of Electronic Equipment (MIL-HDBK-217E), Oct. 1986.
[17] J. Grad and J. E. Stine. “A Standard Cell Library for Student Projects,” in Proc. International Conference on Microelectronic Systems Education, Jun. 2003, pp. 98-99.