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1. Powering next-generation industry 4.0 by a self-learning and low-power neuromorphic system

   https://doi.org/doi.org/10.1145/3411295.3411302  

   Hongyu An, Dong Sam ( September 2020 , ACM International Conference on Nanoscale Computing and Communication)

   null (Ed.)

   With the continuous development of technologies, our society is approaching the next stage of industrialization. The Fourth Industrial Revolution also referred to as Industry 4.0, redefines the manufacturing system as a smart and connected machinery system with fully autonomous operation capability. Several advanced cutting-edge technologies, such as cyber-physical systems (CPS), the internet of things (IoT), and artificial intelligence, are believed to the essential components to realize Industry 4.0. In this paper, we focus on a comprehensive review of how artificial intelligence benefits Industry 4.0, including potential challenges and possible solutions. A panoramic introduction of neuromorphic computing is provided, which is one of the most promising and attractive research directions in artificial intelligence. Subsequently, we introduce the vista of the neuromorphic-powered Industry 4.0 system and survey a few research activities on applications of artificial neural networks for IoT. 

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2. Dynamic Electricity Pricing – Modeling Manufacturer Response and an Application to Cement Processing

   https://doi.org/10.5539/eer.v9n2p1  

   Eryilmaz, Derya ; Apland, Jeffrey ; Smith, Timothy M. ( November 2019 , Energy and Environment Research)

   Dynamic pricing, also known as real-time pricing, provides electricity users with an economic incentive to adjust electricity use based on changing market conditions. This paper studies the economic implications of real-time pricing mechanisms in a cement manufacturing plant. Production for a representative cement manufacturing plant is modeled using stochastic mathematical programming. The results show that a cement plant can a) reduce electricity costs by shifting electricity load of certain processes to times when electricity prices are lower, and b) profitably reduce electricity use during peak prices through more efficient scheduling of production under real-time pricing compared to fixed pricing. The results suggest that building scheduling flexibility into certain industrial manufacturing processes to reschedule electricity consumption when the electricity prices at their peak may be economical. The results also suggest that shifts in the production schedule of a cement manufacturer that result from real-time pricing may also influence environmental impacts. The modelling framework modeled real-time pricing as a source of risk in this study, which is also applicable to other energy intensive industries. As such, dynamic pricing strategies that include the non-market impacts of electricity generation should be further explored. 

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3. Analysis of Joint Scheduling and Power Control for Predictable URLLC in Industrial Wireless Networks

   Wang, Ling ; Zhang, Hongwei ( January 2019 , IEEE International Conference on Industrial Internet (ICII))

   Wireless networks are being applied in various industrial sectors, and they are posed to support mission-critical industrial IoT applications which require ultra-reliable, low-latency communications (URLLC). Ensuring predictable per-packet communication reliability is a basis of predictable URLLC, and scheduling and power control are two basic enablers. Scheduling and power control, however, are subject to challenges such as harsh environments, dynamic channels, and distributed network settings in industrial IoT. Existing solutions are mostly based on heuristic algorithms or asymptotic analysis of network performance, and there lack field-deployable algorithms for ensuring predictable per-packet reliability. Towards addressing the gap, we examine the cross-layer design of joint scheduling and power control and analyze the associated challenges. We introduce the Perron–Frobenius theorem to demonstrate that scheduling is a must for ensuring predictable communication reliability, and by investigating characteristics of interference matrices, we show that scheduling with close-by links silent effectively constructs a set of links whose required reliability is feasible with proper transmission power control. Given that scheduling alone is unable to ensure predictable communication reliability while ensuring high throughput and addressing fast-varying channel dynamics, we demonstrate how power control can help improve both the reliability at each time instant and throughput in the long-term. Based on the analysis, we propose a candidate framework of joint scheduling and power control, and we demonstrate how this framework behaves in guaranteeing per-packet communication reliability in the presence of wireless channel dynamics of different time scales. Collectively, these findings provide insight into the cross-layer design of joint scheduling and power control for ensuring predictable per-packet reliability in the presence of wireless network dynamics and uncertainties. 

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4. Smart Transportation Delay and Resiliency Testbed based on Information Flow of Things Middleware

   https://doi.org/10.1109/SMARTCOMP.2019.00022  

   Talusan, Jose Paolo ; Tiausas, Francis ; Yasumoto, Keiichi ; Wilbur, Michael ; Pettet, Geoffrey ; Dubey, Abhishek ; Bhattacharjee, Shameek. ( June 2019 , 2019 IEEE International Conference on Smart Computing (SMARTCOMP))

   Edge and Fog computing paradigms are used to process big data generated by the increasing number of IoT devices. These paradigms have enabled cities to become smarter in various aspects via real-time data-driven applications. While these have addressed some flaws of cloud computing some challenges remain particularly in terms of privacy and security. We create a testbed based on a distributed processing platform called the Information flow of Things (IFoT) middleware. We briefly describe a decentralized traffic speed query and routing service implemented on this framework testbed. We configure the testbed to test counter measure systems that aim to address the security challenges faced by prior paradigms. Using this testbed, we investigate a novel decentralized anomaly detection approach for time-sensitive distributed smart transportation systems 

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5. Strategic Behavior of Distributed Energy Resources in Energy and Reserves Co-Optimizing Markets

   https://doi.org/10.1109/CDC.2018.8619550  

   Yanikara, Fatma Selin ; Andrianesis, Panagiotis ; Caramanis, Michael ( January 2019 , 2018 IEEE Conference on Decision and Control (CDC), Miami Beach, FL, 2018, pp. 4875-4881. doi: 10.1109/CDC.2018.8619550)

   null (Ed.)

   We consider decentralized scheduling of Distributed Energy Resources (DERs) in a day-ahead market that clears energy and reserves offered by both centralized generators and DERs. Recognizing the difficulty of scheduling transmission network connected generators together with distribution feeder connected DERs that have complex intertemporal preferences and dynamics, we propose a tractable distributed algorithm where DERs self-schedule based on granular Distribution Locational Marginal Prices (DLMPs) derived from LMPs augmented by distribution network costs. For the resulting iterative DER self-scheduling process, we examine the opportunity of DERs to engage in strategic behavior depending on whether DERs do or do not have access to detailed distribution feeder information. Although the proposed distributed algorithm is tractable on detailed real-life network models, we utilize a simplified T&D network model to derive instructive analytical and numerical results on the impact of strategic DER behavior on social welfare loss, and the distribution of costs and benefits to various market participants. 

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