Challenges and recommended technologies for the industrial internet of things: A comprehensive review
چالش ها و فن آوری های پیشنهادی برای اینترنت اشیا صنعتی: مرور جامع-2020
Physical world integration with cyber world opens the opportunity of creating smart environments; this new paradigm is called the Internet of Things (IoT). Communication between humans and objects has been extended into those between objects and objects. Industrial IoT (IIoT) takes benefits of IoT communications in business applications focusing in interoperability between machines (i.e., IIoT is a subset from the IoT). Number of daily life things and objects connected to the Internet has been in increasing fashion, which makes the IoT be the dynamic network of networks. Challenges such as heterogeneity, dynamicity, velocity, and volume of data, make IoT services produce inconsistent, inaccurate, incomplete, and incorrect results, which are critical for many applications especially in IIoT (e.g., health-care, smart transportation, wearable, finance, industry, etc.). Discovering, searching, and sharing data and resources reveal 40% of IoT benefits to cover almost industrial applications. Enabling real-time data analysis, knowledge extraction, and search techniques based on Information Communication Technologies (ICT), such as data fusion, machine learning, big data, cloud computing, blockchain, etc., can reduce and control IoT and leverage its value. This research presents a comprehensive review to study state-of-the-art challenges and recommended technologies for enabling data analysis and search in the future IoT presenting a framework for ICT integration in IoT layers. This paper surveys current IoT search engines (IoTSEs) and presents two case studies to reflect promising enhancements on intelligence and smartness of IoT applications due to ICT integration.
Keywords: Industrial IoT (IIoT) | Searching and indexing | Blockchain | Big data | Data fusion Machine learning | Cloud and fog computing
A 2020 perspective on “Digital blockchain networks appear to be following Metcalfe’s Law”
چشم انداز سال 2020 در مورد -2020
The value of several blockchain assets was shown to possibly be modeled based on Metcalfe’s law as well as a proposed network effects law in the original paper published in 2017. The models showed the growth in the value of the assets to be correlated to the growth in the number of daily unique addresses used on the network; data that is easily extracted from the blockchain. An update to that study is presented, extending the data from May 2017 to current, and showing the sustained correlation between current data and the models. A discussion of potential improvements to that model and its approach from a 2020 perspective is presented.
Keywords: Blockchains | Data analytics | Machine learning | Metcalfe’s law | Network effects
Blockchain, AI and Smart Grids: The Three Musketeers to a Decentralized EV Charging Infrastructure
بلاکچین ،هوش مصنوعی و شبکه های هوشمند: سه تفنگدار به زیرساخت شارژ EV غیرمتمرکز-2020
The proliferation of Internet of Things (IoT) has brought an array of different services, from smart health-care, to smart transportation, all the way to smart cities. For a truly connected environment, different sectors need to collaborate. One use case of such overlap is between smart grids and Intelligent Transportation System (ITS) giving rise to Electric Vehicles and their charging infrastructure. Being such a lucrative opportunity for investors and the research community, many efforts have been made toward providing the end-user with an extraordinary Quality of Service (QoS). However, given the current protocols and deployment of the Electric Vehicle (EV) charging infrastructure, some key challenges still need to be addressed. In particular, we identify two main EV challenges: (1) vulnerable charging stations and EVs, and (2) non-optimal charging schedules. With these issues in mind, we evaluate the integration of Blockchain and AI with the EV charging infrastructure. Specifically, we discuss the current AI and Blockchain charging solutions available in the market. In addition, we propose a couple of use cases where both technologies complement each other for a secure, efficient and decentralized charging ecosystem. This article serves as starting point for stakeholders and policymakers to help identify potential directions and implementations of better charging systems for EVs.
AI, Blockchain, and Vehicular Edge Computing for Smart and Secure IoV: Challenges and Directions
هوش مصنوعی ، بلاکچین و محاسبه لبه ای برای هوشمند و ایمن IoV: چالش ها و دستورالعمل ها-2020
Internet of Things (IoT) is turning into an undeniably developing point of discussion in both research and industrial fields. A key area that is witnessing a quick development in the utilization of IoT devices is the Internet of Vehicles (IoV), which allows information exchange among vehicles and infrastructures. Notably, Artificial Intelligence (AI) has been widely adopted for solving challenging vehicular problems and managing the IoV infrastructure. Despite the advantages AI carries for IoV, its deployment can be negatively affected by lacking computation resources and processing unreliable data. On the other hand, Blockchain is a decentralized and distributed peer-to-peer network architecture that can be employed to empower security and resist against undesirable data modification. However, integrating both technologies (i.e., AI and Blockchain) exhausts, even more, the IoV infrastructure. Therefore, we present in this paper an overview discussing the AI and Blockchain approaches and models for IoV and propose a new Vehicular Edge Computing based architecture embedding both technologies and overcoming the aforementioned limitations. We then discuss the main challenges and give notice to the concerned parties and stakeholders about promising directions that arise from enabling the three technologies for providing smart, secure, and efficient IoV.
Blockchain energy: Blockchain in future energy systems
انرژی بلاکچین : بلاکچین در سیستم های انرژی آتی-2020
The ongoing, in-depth transformation of the electricity sector towards increased use of alternative, renewable energy sources extends beyond a simple decentralisation drive in the electricity market. The transformation process is characterised by the interplay of old and new technologies from the energy sector as well as structural coupling with other sectors, such as the information and communications technology (ICT), enabling the technology transfer as well as market entry by information technology (IT) actors. Blockchain-based technologies have the potential to play a key role in this transition by offering decentralised interfaces and systems as well as an alternative approach to the current organisation form of the energy market. This paper discusses the applicability and prospects for blockchain-based technologies in the energy sector, which are described using the term “blockchain energy”. For the purposes of this study, blockchain energy encompasses all socio-technical and organisational configurations in the energy sector based on the utilisation of the blockchain principle for energy trading, information storage, and/or increased transparency of energy flows and energy services. In the following chapters, the authors present and discuss the current transformation in the electricity market, followed by a review of the different utilisation possibilities for blockchain technologies in the energy sector and a discussion of the barriers and potential for blockchain energy using a transition studies perspective. Finally, the opportunities and risks of blockchain energy are discussed.
Keywords: Blockchain energy management | Crowd energy | Transition research
Explainable AI and Mass Surveillance System-Based Healthcare Framework to Combat COVID-19 Like Pandemics
چارچوب بهداشتی مبتنی بر سیستم نظارت گسترده و هوش مصنوعی برای مبارزه با COVID-19 مانند موارد همه گیر-2020
Tactile edge technology that focuses on 5G or beyond 5G reveals an exciting approach to control infectious diseases such as COVID-19 internationally. The control of epidemics such as COVID-19 can be managed effectively by exploiting edge computation through the 5G wireless connectivity network. The implementation of a hierarchical edge computing system provides many advantages, such as low latency, scalability, and the protection of application and training model data, enabling COVID-19 to be evaluated by a dependable local edge server. In addition, many deep learning (DL) algorithms suffer from two crucial disadvantages: first, training requires a large COVID-19 dataset consisting of various aspects, which will pose challenges for local councils; second, to acknowledge the outcome, the findings of deep learning require ethical acceptance and clarification by the health care sector, as well as other contributors. In this article, we propose a B5G framework that utilizes the 5G network’s low-latency, high-bandwidth functionality to detect COVID-19 using chest X-ray or CT scan images, and to develop a mass surveillance system to monitor social distancing, mask wearing, and body temperature. Three DL models, ResNet50, Deep tree, and Inception v3, are investigated in the proposed framework. Furthermore, blockchain technology is also used to ensure the security of healthcare data.
Blockchain for Internet of Energy management: Review, solutions, and challenges
بلاکچین برای مدیریت انرژی اینترنت: بررسی ، راه حل ها و چالش ها-2020
After smart grid, Internet of Energy (IoE) has emerged as a popular technology in the energy sector by integrating different forms of energy. IoE uses Internet to collect, organize, optimize and manage the networks energy information from different edge devices in order to develop a distributed smart energy infrastructure. Sensors and communication technologies are used to collect data and to predict demand and supply by consumers and suppliers respectively. However, with the development of renewable energy resources, Electric Vehicles (EVs), smart grid and Vehicle-to-grid (V2G) technology, the existing energy sector started shifting towards distributed and decentralized solutions. Moreover, the security and privacy issues because of centralization is another major concern for IoE technology. In this context, Blockchain technology with the features of automation, immutability, public ledger facility, irreversibility, decentralization, consensus and security has been adopted in the literature for solving the prevailing problems of centralized IoE architecture. By leveraging smart contracts, blockchain technology enables automated data exchange, complex energy transactions, demand response management and Peer-to-Peer (P2P) energy trading etc. Blockchain will play vital role in the evolution of the IoE market as distributed renewable resources and smart grid network are being deployed and used. We discuss the potential and applications of blockchain in the IoE field. This article is build on the literature research and it provides insight to the end-user regarding the future IoE scenario in the context of blockchain technology. Lastly this article discusses the different consensus algorithm for IoE technology.
Keywords: Consensus algorithm | Blockchain | Internet of Energy | Smart grid | Vehicle-to-grid
System architecture for blockchain based transparency of supply chain social sustainability
معماری سیستم برای شفافیت مبتنی بر بلاکچین پایداری اجتماعی زنجیره تأمین-2020
Social sustainability is a major concern in global supply chains for protecting workers from exploitation and for providing a safe working environment. Although there are stipulated standards to govern supply chain social sustainability, it is not uncommon to hear of businesses being reported for noncompliance issues. Even reputable firms such as Unilever have been criticized for production labor exploitation. Consumers now increasingly expect sellers to disclose information on social sustainability, but sellers are confronted with the challenge of traceability in their multi-tier global supply chains. Blockchain offers a promising future to achieve instant traceability in supply chain social sustainability. This study develops a system architecture that integrates the use of blockchain, internet-of-things (IoT) and big data analytics to allow sellers to monitor their supply chain social sustainability efficiently and effectively. System implementation cost and potential challenges are analyzed before the research is concluded.
Keywords: Blockchain | Social sustainability | Multi-tier supply chain | Supply chain sustainability | Traceability
Financial portfolio optimization with online deep reinforcement learning and restricted stacked autoencoder-DeepBreath
بهینه سازی سبد مالی با یادگیری تقویتی عمیق آنلاین و محدود کردن خودکار رمزگذار-DeepBreath-2020
The process of continuously reallocating funds into financial assets, aiming to increase the expected re- turn of investment and minimizing the risk, is known as portfolio management. In this paper, a portfolio management framework is developed based on a deep reinforcement learning framework called Deep- Breath. The DeepBreath methodology combines a restricted stacked autoencoder and a convolutional neu- ral network (CNN) into an integrated framework. The restricted stacked autoencoder is employed in order to conduct dimensionality reduction and features selection, thus ensuring that only the most informative abstract features are retained. The CNN is used to learn and enforce the investment policy which consists of reallocating the various assets in order to increase the expected return on investment. The framework consists of both offline and online learning strategies: the former is required to train the CNN while the latter handles concept drifts i.e. a change in the data distribution resulting from unforeseen circum- stances. These are based on passive concept drift detection and online stochastic batching. Settlement risk may occur as a result of a delay in between the acquisition of an asset and its payment failing to deliver the terms of a contract. In order to tackle this challenging issue, a blockchain is employed. Finally, the performance of the DeepBreath framework is tested with four test sets over three distinct investment periods. The results show that the return of investment achieved by our approach outperforms current expert investment strategies while minimizing the market risk.
Keywords: Portfolio management | Deep reinforcement learning | Restricted stacked autoencoder | Online leaning | Settlement risk | Blockchain
AI-Powered Blockchain : A Decentralized Secure Multiparty Computation Protocol for IoV
بلاکچین با هوش مصنوعی: یک پروتکل محاسباتی محرمانه چند جانبه امن برای IoV-2020
The rapid advancements in autonomous technologies have paved way for vehicular networks. In particular, Vehicular Ad-hoc Network (VANET) forms the basis of the future of Intelligent Transportation System (ITS). ITS represents the communication among vehicles by acquiring and sharing the data. Though congestion control is enhanced by Internet of Vehicles (IoV), there are various security criteria where entire communication can lead to many security and privacy challenges. A blockchain can be deployed to provide the IoV devices with the necessary authentication and security feature for the transfer of data. Blockchain based IoV mechanism eliminates the single source of failure and remains secure at base despite having strong security, the higher level layers and applications are susceptible to attacks. Artificial Intelligence (AI) has the potential to overcome several vulnerabilities of current blockchain technology. In this paper, we propose an AI-Powered Blockchain which provides auto coding feature for the smart contracts making it an intelligent contract. Moreover, it speeds up the transaction verification and optimises energy consumption. The results show that intelligent contracts provide higher security compared to smart contracts considering range of different scenarios.
Index Terms: Blockchain | Artificial Intelligence | Smart contract | Internet of Vehicles | Vehicular Network