As the Internet of Things (IoT) concept materialized worldwide in complex ecosystems, the related data security and privacy issues became apparent. While the system elements and their communication paths could be protected individually, generic, ecosystem-wide approaches were sought after as well. On a parallel timeline to IoT, the concept of distributed ledgers and blockchains came into the technological limelight. Blockchains offer many advantageous features in relation to enhanced security, anonymity, increased capacity, and peer-to-peer capabilities. Although blockchain technology can provide IoT with effective and efficient solutions, there are many challenges related to various aspects of integrating these technologies. While security, anonymity/data privacy, and smart contract-related features are apparently advantageous for blockchain technologies (BCT), there are challenges in relation to storage capacity/scalability, resource utilization, transaction rate scalability, predictability, and legal issues. This paper provides a systematic review on state-of-the-art approaches of BCT and IoT integration, specifically in order to solve certain security- and privacy-related issues. The paper first provides a brief overview of BCT and IoT’s basic principles, including their architecture, protocols and consensus algorithms, characteristics, and the challenges of integrating them. Afterwards, it describes the survey methodology, including the search strategy, eligibility criteria, selection results, and characteristics of the included articles. Later, we highlight the findings of this study which illustrates different works that addressed the integration of blockchain technology and IoT to tackle various aspects of privacy and security, which are followed by a categorization of applications that have been investigated with different characteristics, such as their primary information, objective, development level, target application, type of blockchain and platform, consensus algorithm, evaluation environment and metrics, future works or open issues (if any), and further notes for consideration. Furthermore, a detailed discussion of all articles is included from an architectural and operational perspective. Finally, we cover major gaps and future considerations that can be taken into account when integrating blockchain technology with IoT.

/pdf/leveraging-blockchain-technology-for-ensuring-security-and-2e77b3q3.pdf

Leveraging Blockchain Technology for Ensuring Security and Privacy Aspects in Internet of Things: A Systematic Literature Review

Central bank digital currency (CBDC) has been the global hot issue in financial and money filed, and is expected to serve as the core digital infrastructure in the future. The environmental impact of CBDC is essential to sustainable development, and it remains debates to scholars, entrepreneurs, and policymakers. This article aims in exploring how CBDC in China affects green finance and sustainable development practically through an empirical investigation on CBDC pilot program. The results suggest that CBDC significantly promotes the issuing of green bonds. Specifically, CBDC increases green bonds issuing more in manufacturing industries, and in state-owned enterprises. Moreover, CBDC is found to decrease SO2 emission, NOx emission, Smoke emission, and improve green land ratio, which is beneficial to sustainable development. Our findings offer a preliminary exposition about the environmental impact of CBDC. We argue that, CBDC is useful to accelerate green finance and hence advances sustainable development, which needs future robust empirical examination. We present some implications of issuing CBDC for policymakers around the world. 

The Role of CBDC in Green Finance and Sustainable Development

This chapter explores the benefits and issues surrounding the digital Rupee, also known as the eRupee or the central bank digital currency in India. The study found that Indian people who were interested in ‘cryptocurrency' information were also interested in ‘central bank digital currency' information. The study also showed that the introduction of CBDC has potential benefits such as reduced dependency on cash, higher seigniorage due to lower transaction costs, and reduced settlement risk. However, the India CBDC has associated risks that need to be carefully evaluated against the potential benefits. The introduction of a digital Rupee or CBDC in India will require legal and regulatory changes to make the phased CBDC implementation possible. 

Central Bank Digital Currency in India

As global challenges of population growth, climate change, and resource scarcity intensify, the agricultural landscape is at a critical juncture. Sustainable vertical farming emerges as a transformative solution to address these challenges by maximizing crop yields in controlled environments. This paradigm shift necessitates the integration of cutting-edge technologies, with Artificial Intelligence (AI) at the forefront. The paper provides a comprehensive exploration of the role of AI in sustainable vertical farming, investigating its potential, challenges, and opportunities. The review synthesizes the current state of AI applications, encompassing machine learning, computer vision, the Internet of Things (IoT), and robotics, in optimizing resource usage, automating tasks, and enhancing decision-making. It identifies gaps in research, emphasizing the need for optimized AI models, interdisciplinary collaboration, and the development of explainable AI in agriculture. The implications extend beyond efficiency gains, considering economic viability, reduced environmental impact, and increased food security. The paper concludes by offering insights for stakeholders and suggesting avenues for future research, aiming to guide the integration of AI technologies in sustainable vertical farming for a resilient and sustainable future in agriculture.

Artificial Intelligence in Sustainable Vertical Farming

The impact of projects is vital, from business operations to research to the national economy. Therefore, management science and operation research have extensively studied project scheduling and resource allocation for over six decades. Project databases were proposed to test algorithms, including simulated or real, single or multiprojects, and single-mode or multi-mode projects. However, the dozens of project databases are extremely heterogeneous regarding the file structure and the features of the modeled projects. Furthermore, the efficiency and performance of project scheduling and resource allocation algorithms are susceptible to the characteristics of projects. Therefore, the proposed Compound Matrix-Based Project Database (CMPD) collects and consolidates the most frequently used project databases. The proposed Unified Matrix-Based Project-Planning Model (UMP) sparse matrix-based model enables the addition of new features to existing project structures, such as completion priorities, structural flexibility, and quality parameters, to broaden the scope of considered projects and to take account of flexible approaches, such as agile, extreme, and hybrid projects. 

pdf/compound-matrix-based-project-database-cmpd-3746ptqdf3.pdf

Compound Matrix-Based Project Database (CMPD)

A matrix-based flexible project-planning library and indicators

The stakeholders of complex industrial-financial ecosystems and supply chains are interconnected in many ways, at many organisational and technical levels. Even in order to deliver a product or exchange various materials, various Information and Operational Technology solutions are required, etching the way towards the Industrial Internet of Things. Moreover, integrating the necessary legal and financial co-operations in the big picture is similarly important, where blockchains and Smart Contracts aim to provide the means. Nevertheless, new solutions should thrive not to disrupt current economic landscapes, but slowly transform and enhance instead. This article discusses solutions for supply chain trade use cases with over-the-counter derivatives, and argues the same in a world where Central Bank Digital Currency (CBDC) governs financial markets. It also provisions Arrowhead framework and Smart Contract based solutions for the issues there, such as matching bids, handling counterparty risks or physical settlements.

Towards CBDC-supported Smart Contracts for Industrial Stakeholders

It is a very popular era for machine learning (ML) applications, and Industry5.0 aims to have AI as one of its key technologies. Still, only a few ML initiatives make it to a production-grade implementation, mostly due to lacking proper Continuous Integration and Delivery framework and MLOps practices. This is especially true for industrial use cases, where the trust and reliability of ML applications are mission-critical. Most of these applications fail during the final stage of the development lifecycle, i.e. acceptance testing and validation of the ML application, while being integrated into Cyber-Physical System of Systems (CPSoS). This paper explores the key requirements for deploying ML applications in industrial scenarios, emphasizing the critical role of Digital Twins, edge AI, and responsible-explainable AI techniques in ensuring efficient and responsible operations. Building upon previous models, this paper suggests two process models: (i) the Olympics model for MLOps-coupled CPS engineering and (ii) the MLOps engineering toolchain for industrial applications. 

Tailoring MLOps Techniques for Industry 5.0 Needs

Current state of the art in vertical farming faces numerous challenges around optimisation and efficiency. This new domain of agriculture is targeting high level of automated and autonomous operations, which require advanced sensing and actuating capabilities with new types of process control. Although while several commercial solutions are already available, these only satisfy parts of the requirements, not enabling the desired level of autonomy and self-learning capabilities. To this end, this position paper examines state of the art and scopes the work on how to create and integrate Internet of Things and Machine Learning technologies to optimise the active ingredient output while growing medicinal plants in scalable vertical farming grow boxes.

Enabling Scalable Smart Vertical Farming with IoT and Machine Learning Technologies

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