Blockchain Papers

Bhupendra sahu

No abstract is available for this record.

Usman Khalil, Mueen Uddin, Hashem Alaidaros, Adnan Akhunzada

Abstract In the rapidly evolving landscape of IoT-enabled smart devices, significant challenges persist in integration to web3, security, and data reliability. This research presents the design and integration of IoT assets, particularly devices, through the Novel Decentralized Smart City of Things (DSCoT) framework. ESP32 microcontrollers serve as Ethereum clients, generating Externally Owned Accounts (EOA) for device identification and authentication. Despite resource constraints, including limited computational capabilities, essential libraries that manage tasks such as Wi-Fi module control, interaction with Ethereum-based blockchains, TCP connection management, and EEPROM operations for persistent data storage. The code is structured with functions for Wi-Fi setup, TCP API requests, and secure communication challenges. Integration involves compiling and flashing the code onto ESP32 devices, verifying EOA generation, and mapping devices, fog nodes, and users through smart contract interactions. The deployment process culminates in the generation of Non-Fungible Tokens (NFTs) for user authentication, with transaction verification on the Goerli testnet confirming successful DSCoT edge system implementation. This research underscores the importance of secure and decentralized integration of IoT-enabled smart devices to the blockchain, enhancing performance while ensuring security and transparency.

Chong Shao, Xumin Liu, Ding Li, Xiaoting Chen

This study presents a distributed electricity trading system using smart contracts to improve transaction efficiency and reduce costs in power markets. Three trading models are analyzed: centralized trading, blockchain-based decentralized trading, and smart contract-driven automated trading. The advantages and challenges of each model are examined, focusing on factors like node inclusion time, transaction costs, and price stability. The results show that the smart contract-driven model outperforms the others by increasing market efficiency, lowering transaction costs, and reducing price fluctuations. Through simulations and real-world analysis, this study provides support for using blockchain technology in power markets and offers practical advice for improving electricity trading systems. The findings suggest that the proposed system could greatly enhance transparency, efficiency, and cost-effectiveness in distributed energy markets, even in uncertain market conditions.

Amit Kumar Vishwakarma, Pratyush Kumar Patro, Adolf Acquaye, Raja Jayaraman · 5 authors

In attempts to progress the transition towards decarbonizing energy systems and achieving sustainable development goals linked to energy, renewable energy sources (RES), which are decentrally deployed, are being widely promoted. However, RES are constrained by many barriers, some of which are technical in nature, such as energy losses. For instance, traditional peer-to-peer (P2P) trading systems have been used as a mechanism to advance RES because they offer a promising solution for decentralized energy trading without third-party intermediaries. However, it faces significant challenges, notably in accounting for energy losses during transmission and distribution, thus reducing overall system efficiency. We propose an end-to-end blockchain-based solution to provide traceability of energy loss in a distributed network and enhance transaction security, trust, and operational efficiency by linking prosumers and consumers. Our solution uses smart contracts to automate business transactions among the stakeholders. We develop six algorithms and deploy the smart contracts to demonstrate the successful deployment of the P2P energy trading process. Our solution effectively provides traceability of energy loss and reliably secured P2P energy transaction verification. We also present the cost and security analysis to demonstrate the affordability and reliability of our solution. We make our smart contract codes publicly available on GitHub.

Alaa Awad Abdellatif, Khaled Shaban, Ahmed Massoud

This study introduces a secure, adaptable, and decentralized learning framework empowered by blockchain technology to enhance smart grid security and efficiency. Security is achieved through blockchain’s ledger, ensuring data integrity, privacy, and resilience. Adaptability refers to the framework’s ability to adjust to changing conditions, supporting multiple learning paradigms . Decentralization enhances fault tolerance by distributing control across nodes. Our framework excels in scalability, data-exchange security, and rapid response times , aiming to establish an intelligent blockchain-based smart grid supporting centralized learning (CL), federated learning (FL), and active federated learning (AFL). We present an innovative blockchain-based architecture customized to optimize information sharing and security within the blockchain. Our solution addresses various learning paradigm requirements by: (i) Selecting reliable entities for participation based on high-quality training data models; (ii) Acquiring a reliable subset of data for CL and AFL, balancing learning performance , latency, and cost; (iii) Adjusting blockchain configuration to align with specific learning paradigm requirements. Results from real-world datasets demonstrate superior performance compared to existing solutions. Our framework achieves high learning performance while minimizing latency and blockchain costs.

Md. Mainul Islam, Rachad Atat, Muhammad Ismail, Katherine Davis · 5 authors

Abstract Enhancing the resilience and reliability of power grids is crucial amid rising cyber threats and system complexities. To address these challenges, this paper proposes an energy‐efficient, consortium blockchain‐based global alarm system for power grid management. Using smart contracts and the proof of‐authority consensus algorithm, the alarm system triggers global alarms upon detecting local anomalies, ensuring a prompt response to partition the power grid and mitigate failures. The effectiveness is validated by simulating the Iberian power system with 15 providers from various regions. Key metrics, such as load shedding, damage reduction, energy consumption, latency, and transaction costs, are used to assess the performance. Through simulations, we show that the blockchain‐based system effectively limits the damage propagation and the load shedding during cascading failures by delaying the onset of instability and maintaining lower damage levels compared to non‐blockchain scenarios. Our investigations reveal that the proposed global alarm mechanism reduces the damage and load shedding by up to 29% and 87%, respectively, showcasing its potential for preventing widespread outages.

Aswani Devi Aguru, Amrit Pandey, Suresh Babu Erukala, Ali Kashif Bashir · 7 authors

Routing protocol for low-power and lossy network (RPL) is a routing protocol for resource-constrained Internet of Things (IoT) network devices. RPL has become a widely adopted protocol for routing in low-powered device networks. However, it lacks essential security features, including end-to-end security, robust authentication, and intrusion detection capabilities. Blockchain is a decentralized and immutable digital ledger that records transactions across multiple computers. It provides privacy, transparency, security, and trust. In this work, we proposed a blockchain-based reliable RPL protocol called reliable-RPL, which uses node reliability, link reliability, and relative trust scores of RPL-enabled IoT devices. The parent selection and network topology formulation are based on the proposed reliability-aware objective function. A lightweight ECC-based scheme performs registration, identification, and authentication of RPL-enabled IoT devices. The consistent topological updates from these authenticated IoT devices are used to secure routing paths in RPL-enabled networks. Using a modified trickle algorithm, we employed a reputation-based trust system that monitors and labels malicious nodes based on their reliable activities. The novelty of the proposed framework relies on integrating Contiki-NG (as fronted for IoT network simulation) and Hyperledger Fabric (as a backend for blockchain-based device authentication and trust-based attack resilience regarding rank, replay, sinkhole, and route poisoning attacks). The experimental evaluation of reliable-RPL has demonstrated its effectiveness compared to state-of-the-art methods regarding significant performance metrics, including packet loss, routing overhead, and throughput on Hyperledger Caliper.

Yi Yu, Shuai Liu, Yi Huang, C. Y. Chung · 5 authors

With the ongoing development of renewable energy sources, information technologies and physical energy systems are further integrated, which leads to challenges in ensuring the secure and stable operation of renewable energy power systems in the face of potential cyber threats. The strengths of blockchain in cybersecurity make it a promising solution to these challenges. However, existing blockchains are not well-suited for control tasks due to their low real-time performance. Here, we present a consensus mechanism that enables real-time security control of systems, called Proof of Task. Instead of solving meaningless hash puzzles in Proof of Work, Proof of Task addresses problems closely related to the stable operation and control performance of these systems. With the proposed verification mechanism, Proof of Task significantly enhances the real-time performance of blockchain while mines its computational resources for tasks of interest. To demonstrate the effectiveness and necessity of Proof of Task, it is deployed across three renewable energy power systems. The results show that Proof of Task markedly fortifies the security and computing capability of these systems, ensuring their reliable and stable operation. This work highlights the promise of blockchain to facilitate security control and trusted computing of large-scale, complex-dynamic systems. For control systems, blockchain usually exhibits a trade-off between security and real-time performance. Here, authors propose a new mechanism that utilizes multi-party computation and verification with prediction, specifically tailored for real-time security control of renewable energy power systems.

Kamil Erdayandı, Lucas C. Cordeiro, Mustafa Mustafa

This paper proposes a novel Privacy-preserving and Accountable Billing (PA-Bill) protocol for peer-to-peer energy trading markets. It addresses the challenges of discrepancies between committed and delivered energy volumes, ensuring accurate billing, privacy, and accountability. PA-Bill employs a universal cost-splitting mechanism to enhance fairness and prevent indirect privacy leakage. The protocol leverages homomorphic encryption to protect user data and uses blockchain technology to maintain accountability through an immutable and transparent distributed ledger. Additionally, it includes a dispute resolution mechanism to rectify erroneous bill calculations and identify responsible parties, thus ensuring non-repudiation. Our experimental and theoretical evaluations demonstrates that PA-Bill effectively supports large communities of up to 2000 households, offering a computationally efficient, privacy-preserving, and accountable billing solution in a semi-decentralised manner.

Kai Su, Yun Yu, Jianzhong Zhang

Abstract To improve the security and efficiency of smart grid power trading, this study designs a smart grid trading system based on blockchain technology. The transaction module of the system composed of smart contracts and order-matching algorithms is proposed. The credit consensus mechanism of the system adopts a designed credit-based hybrid consensus mechanism. The experimental results showed that under this system, the successful transaction prices of consumers bidding were between the grid price and the recycling price, making bidding more reasonable. The highest power utilization rate was 0.84, the lowest was 0.67, and the transaction throughput exceeded 350TPS. The highest CPU usage was 0.74, the lowest was 0.5, and the highest memory usage was only 0.44. Compared with other systems, the order matching of this system was more reasonable and its performance was better. Furthermore, the system has been subjected to rigorous security testing and is reliable, thereby providing a secure and efficient trading platform for smart grid electricity trading.

Jalalud Din, Hongsheng Su

This research investigates the integration of blockchain technology into smart grids, focusing on optimizing both electrical billing and peer-to-peer energy trading between producers and consumers. Using blockchain smart contracts, the system automates and secures energy consumption recording, bill calculation, payment processing, and energy transactions. In the electrical billing framework, a blockchain-based approach was developed to model these functionalities, utilizing an EnergyBilling smart contract to calculate bills and an EnergyPayment smart contract to ensure payment accuracy. Validation using actual consumption data from Sinoma Handan’s project site confirmed the system’s accuracy and reliability when cross-verified with mathematical models. Simultaneously, the study explores peer-to-peer energy trading, where producers (represented by Askari Cement Plant.Nizampur, Pakistan) and consumers (Sinoma Handan Ltd, Handan, China.) conduct automated, transparent transactions. Blockchain’s decentralized nature ensures transparency, data immutability, and a secure, tamper-proof record of transactions. The system eliminates intermediaries, enhancing operational efficiency and reducing costs. Key outcomes demonstrate successful transaction execution with detailed settlements, ensuring financial accountability. Our research highlights blockchain’s transformative potential in revolutionizing electrical billing and energy trading. It offers a secure, transparent, and efficient solution while acknowledging scalability, transaction costs, and regulatory hurdles. Future work could focus on real-world implementation, integration with IoT devices for real-time data collection, and scaling these technologies for broader industrial applications in global energy markets.

Yinfeng Chen, B.K.H. Sun, Yaofei Wang, Zhixin Qiu · 5 authors

IOTA is an emerging decentralized computing paradigm for developing blockchain-based Internet of Things (IoT) applications. It has the advantages of zero transaction fees, incremental scalability, and high-performance transaction rates. Despite its well-understood benefits, IOTA nodes need to withstand considerable resource costs to generate the distributed ledger. The main reason for this is that IOTA abandons the original blockchain reward mechanism and does not charge transaction fees. Therefore, in this paper we address the cost optimization issue for IOTA based on Lyapunov optimization theory. We take the first step in investigating the cost optimization problem of IOTA and exploring a new optimization scheme using Lyapunov optimization theory. Our proposed scheme enables IOTA to minimize the total cost of IOTA nodes through a computational optimization algorithm. Then, an optimized transaction rate control algorithm can be designed based on the large deviation theory to reduce orphan tangles that waste computational costs. In addition, we define and deduce the effective width of the tangle to monitor the total throughput and reduce the time spent on cost optimization to avoid unnecessary waste of resources. Lastly, a comprehensive theoretical analysis and simulation experiments demonstrate that the proposed strategy is both efficient and practical.

Preeti Prasada, S.J. Suji Prasad

Cyber-Physical Systems (CPS) have become a research hotspot due to their vulnerability to stealthy network attacks like ZDA and PDA, which can lead to unsafe states and system damage. Recent defense mechanisms for ZDA and PDA often rely on model-based observation techniques prone to false alarms. In this paper, we present an innovative approach to securing CPS against Advanced Persistent Threat (APT) injection attacks by integrating machine learning with blockchain technology. Our system leverages a robust ML model trained to detect APT injection attacks with high accuracy, achieving a detection rate of 99.89%. To address the limitations of current defense mechanisms and enhance the security and integrity of the detection process, we utilize blockchain technology to store and verify the predictions made by the ML model. We implemented a smart contract on the Ethereum blockchain using Solidity, which logs the input features and corresponding predictions. This immutable ledger ensures the integrity and traceability of the detection process, mitigating risks of data tampering and reducing false alarms, thereby enhancing trust in the system's outputs. The implementation includes a user-friendly interface for inputting features, a backend for data processing and model prediction, and a blockchain interaction module to store and verify predictions. The integration of blockchain with Machine learning enhances both the precision and resilience of APT detection while providing an additional layer of security by ensuring the transparency and immutability of the recorded data. This dual approach represents a substantial advancement in protecting CPS from sophisticated cyber threats.

Sabah Suhail, Mubashar Iqbal, Rasheed Hussain, Saif Ur Rehman Malik · 5 authors

Cyber–physical systems (CPSs) are being increasingly adopted for industrial applications, yet they involve a dynamic threat landscape that requires CPSs to adapt to emerging threats during their operation. Recently, digital twin (DT) technology (which refers to a virtual representation of a product, process, or environment) has emerged as a suitable candidate to address the security challenges faced by dynamic CPSs. DT has the capability of strengthening the security of CPSs by continuously mapping the physical to twin counterparts to detect inconsistencies. The existing DT-based security solutions are constrained by untrustworthy data dissemination as well as limited data sharing among the involved stakeholders, which, in turn, limit the ability of DTs to run accurate simulations or make valid decisions. To address these challenges, this paper proposes a modular framework called TR usted and I ntelligent cyber- P hysica L syst E m (TRIPLE), that leverages blockchain , DTs, and threat intelligence (TI) to secure CPSs. The blockchain-based DT components in the framework provide data integrity, traceability, and availability for trusted DTs. Furthermore, to accurately and comprehensively model system states, the framework envisions fusing process knowledge for modeling DTs from system specification-based and learning-based information and other sources, including infrastructure-as-code (IaC) and knowledge base (KB). The framework also integrates TI for future-proofing against emerging threats, such that threats can be detected either reactively by mapping the behavior of physical and virtual spaces or proactively by TI and threat hunting. We demonstrate the viability of the framework through a proof of concept . Finally, we formally verify the TRIPLE framework to demonstrate its correctness and effectiveness in enhancing CPS security.

Jiajing Wu, Kaixin Lin, Dan Lin, Bozhao Zhang · 6 authors

Cross-chain bridges are essential decentralized applications (DApps) to facilitate interoperability between different blockchain networks. Unlike regular DApps, the functionality of cross-chain bridges relies on the collaboration of information both on and off the chain, which exposes them to a wider risk of attacks. According to our statistics, attacks on cross-chain bridges have resulted in losses of nearly 4.3 billion since 2021. Therefore, it is particularly necessary to understand and detect attacks on cross-chain bridges. In this paper, we collect the largest number of cross-chain bridge attack incidents to date, including 49 attacks that occurred between June 2021 and September 2024, of which 22 were attacks on cross-chain bridge business logic. Our analysis reveal that attacks against cross-chain business logic cause significantly more damage than those that do not. These cross-chain attacks exhibit different patterns compared to normal transactions in terms of call structure, which effectively indicates potential attack behaviors. Given the significant losses in these cases and the scarcity of related research, this paper aims to detect attacks against cross-chain business logic, and propose the BridgeGuard tool. Specifically, BridgeGuard models cross-chain transactions from a graph perspective, and employs a two-stage detection framework comprising global and local graph mining to identify attack patterns in cross-chain transactions. We conduct multiple experiments on the datasets with 203 attack transactions and 40,000 normal cross-chain transactions. The results show that BridgeGuard's reported recall score is 36.32% higher than that of state-of-the-art tools and can detect unknown attack transactions.

Emilio C. Piesciorovsky, Gary Hahn, Raymond Borges Hink, Aaron Werth

In modern electrical grids, the numbers of customer-owned distributed energy resources (DERs) have increased, and consequently, so have the numbers of points of common coupling (PCC) between the electrical grid and customer-owned DERs. The disruptive operation of and out-of-tolerance outputs from DERs, especially owned DERs, present a risk to power system operations. A common protective measure is to use relays located at the PCC to isolate poorly behaving or out-of-tolerance DERs from the grid. Ensuring the integrity of the data from these relays at the PCC is vital, and blockchain technology could enhance the security of modern electrical grids by providing an accurate means to translate operational constraints into actions/commands for relays. This study demonstrates an advanced power system application solution using distributed ledger technology (DLT) with smart contracts to manage the relay operation at the PCC. The smart contract defines the allowable total power factor (TPF) of the DER output, and the terms of the smart contract are implemented using DLT with a Cyber Grid Guard (CGG) system for a customer-owned DER (wind farm). This article presents flowcharts for the TPF smart contract implemented by the CGG using DLT. The test scenarios were implemented using a real-time simulator containing a CGG system and relay in-the-loop. The data collected from the CGG system were used to execute the TPF smart contract. The desired TPF limits on the grid-side were between +0.9 and +1.0, and the operation of the breakers in the electrical grid and DER sides was controlled by the relay consistent with the provisions of the smart contract. The events from the real-time simulator, CGG, and relay showed a successful implementation of the TPF smart contract with CGG using DLT, proving the efficacy of this approach in general for implementing electrical grid applications for utilities with connections to customer-owned DERs.

Jingjie Xu, Ting Wang, Mingqi Lv, Tieming Chen · 6 authors

Abstract Smart contracts have significant losses due to various types of vulnerabilities. However, traditional vulnerability detection methods rely extensively on expert rules, resulting in low detection accuracy and poor adaptability to novel attacks. To address these problems, in this paper, deep learning methods are combined with smart contract vulnerability code detection approaches. Abstract syntax trees (ASTs), which are special isomorphic graph structures, are an important bridge between source code and graph neural networks. By learning the AST, the model can understand the semantics of the source code. Moreover, graph neural networks have an increasing ability to address complex heterogeneous graphs. Therefore, control flow graphs are fused with data flow graphs on the basis of the ASTs to build heterogeneous graphs with richer code semantics. Furthermore, multigranularity analysis of the vulnerability detection results is performed, including coarse-grained contract-level vulnerability detection and fine-grained line-level vulnerability detection. Through this multigranularity detection approach, vulnerabilities in contracts can be identified and analysed more comprehensively, providing a richer perspective and more solutions for vulnerability detection. The experimental results show that the proposed multigranularity vulnerability detection method based on heterogeneous graphs (MVD-HG) improves both the accuracy and range of the detected vulnerability types in contract-level vulnerability detection tasks; moreover, in the line-level vulnerability detection task, the MVD-HG model achieves significant results and addresses the shortcomings of existing methods. In addition, based on code generation methods used in related fields, a data enhancement method based on the source code is developed, which effectively expands the experimental dataset to address the reduced credibility of the results due to insufficient amounts of data.

Christian Rehtanz, Andreas Ulbig, Rajkumar Palaniappan, Timm Faulwasser · 7 authors

• Holonic automation, control, and operation architecture tackles complexity of power and energy systems. • Holonic architecture as an organization and coordination method makes grid operations more resilient. • Holonically designed power systems' ICT layer reduces the impacts of disturbances. • Distribution grid automation, digital twin approach, and flexibility assessment demonstrate benefits of holonic systems. In the ongoing transition towards distributed Renewable Energy Sources (RES) and the concurrent transformation of critical energy infrastructures, the efficient coordination of load, storage, and generation flexibilities while avoiding grid congestion is crucial. To orchestrate the growing myriad of distributed devices, digital solutions based on scalable information and communication technologies (ICT) that go far beyond the existing state-of-the-art, are the key enablers. To open a new avenue towards robust and resilient power and energy systems, this paper proposes the concepts of holarchies and holonic structures as underlying design principles for grid automation and coordination of flexibilities in power and energy systems. We argue that the holonic concept and its theoretic underpinning enables designing and building future resilient power systems that can cope with the otherwise overwhelming complexities of the energy transition. Our long-term vision is that the proposed holonic concept encompasses already existing trends in power and energy systems, i.e. decentralization, digitalization as well as observability and controllability improvements, into one holistic framework, whereby holistic integration is likewise pun and serious ambition. Beyond the existing holonic approach in general and partly for limited power system applications so far, our design proposal encompasses ICT infrastructures and the data domain into a consistent novel architectural approach. Holonic structures, or holarchies, extend and build upon the recursiveness and self-similarity of autonomous sub-structures, i.e. holons, of a system. It is a system-of-systems approach and, thus, conceptionally, very different from existing and well-known multi-agent system approaches. In essence, holonic concepts allow for the formalisation of hierarchical system relations regarding physics, information, and data using a part-whole architecture. Hence, they are well-suited for the conceptualisation of automation functionality across all dimensions of the cyber-physical domain of energy infrastructures and potentially also beyond. This paper investigates holonic structures from different novel perspectives, such as control and automation, system modeling and digital twins, as well as the corresponding ICT-infrastructure and data requirements. Three case studies are drawn upon as examples to illustrate how holonic concepts and approaches are already emerging in power and energy systems operation. © 2017 Elsevier Inc. All rights reserved.

Anand Srivatsa, Ananthapadmanabha Thammaiah, M. V. Likith Kumar, D Rajeshwari · 5 authors

Recent advances in intelligent systems have significantly improved power management, load distribution, and resource management capabilities, far beyond past constraints. Despite these gains, the development of internet-connected technology has brought various vulnerabilities, leading to negative results. The integration of intelligent technology has unintentionally offered chances for hackers to enter networks and modify data sent to central systems for analysis. One of the most serious risks is the false data injection attack (FDIA), which may drastically impair analytical outcomes. Previous research has shown that standard approaches for recovering data affected by FDIA are unreliable and inefficient. This paper investigates the use of the proof of stake (PoS) consensus method in this framework improves data integrity and makes it easier to identify illegal changes. Participating nodes may reject or change block transactions, ensuring the ledger's correctness. Our results show that the PoS consensus method is exceptionally successful in creating and adding transactions to the blockchain. Furthermore, the PoS mechanism's simplicity in block formation enhances both time and energy efficiency, resulting in considerable benefits in operational performance.

André Augusto, Rafael Belchior, Jonas Pfannschmidt, André Vasconcelos · 5 authors

Cross-chain bridges are a type of middleware for blockchain interoperability that supports the transfer of assets and data across blockchains. However, several of these bridges have vulnerabilities that have caused 3.2 billion dollars in losses since May 2021. Some studies have revealed the existence of these vulnerabilities, but there is little quantitative research available, and there are no safeguard mechanisms to protect bridges from such attacks. Furthermore, no studies are available on the practices of cross-chain bridges that can cause financial losses. We propose \toolName~(Cross-Chain Watcher), a modular and extensible logic-driven anomaly detector for cross-chain bridges. It operates in three main phases: (1) decoding events and transactions from multiple blockchains, (2) building logic relations from the extracted data, and (3) evaluating these relations against a set of detection rules. Using \toolName, we analyze data from two previously attacked bridges: the Ronin and Nomad bridges. \toolName~was able to successfully identify the transactions that led to losses of \$611M and \$190M (USD) and surpassed the results obtained by a reputable security firm in the latter. We not only uncover successful attacks, but also reveal other anomalies, such as 37 cross-chain transactions (\CCTX) that these bridges should not have accepted, failed attempts to exploit Nomad, over \$7.8M worth of tokens locked on one chain but never released on Ethereum, and \$200K lost by users due to inadequate interaction with bridges. We provide the first open dataset of 81,000 \CCTXS~across three blockchains, capturing more than \$4.2B in token transfers.

Mou Mahmood, Prangon Chowdhury, Rahbaar Yeassin, Mahmudul Hasan · 6 authors

• Different digitalization techniques in the energy sector is briefly outlined • Impacts of digitalization in smart grids is critically analyzed. • Integration of the renewable energy sources are given priority during the selection of digitalization methods. • Maximum energy efficiency attainment using demand response is ensured in the processes. Decarbonization, decentralization, and digitalization are essential for advanced energy systems (AES), which encompass smart grids, renewable energy integration, and demand response initiatives. Digitalization is a significant trend that transforms societal, economic, and environmental processes globally. This shift moves us from traditional power grids to decentralized, intelligent networks that enhance efficiency, reliability, and sustainability. By integrating data and connectivity, these technologies optimize energy production, distribution, and consumption. This article presents a comprehensive literature review of four closely related emerging technologies: Artificial Intelligence (AI), Internet of Things (IoT), Blockchain, and Digital Twin (DT) in AES. Our findings from the previous works indicate that AI significantly improves Demand Response strategies by enhancing the prediction, optimization, and management of energy consumption. Techniques like linear regression effectively predict power demand and aggregated loads, while more complex methods such as Support Vector Regression (SVR) and reinforcement learning (RL) optimize appliance scheduling and load forecasting. The integration of IoT technologies into Energy Management Systems (EMS) further enhances efficiency and sustainability through real-time monitoring and automated control. Additionally, DT technology aids in simulating energy scenarios and optimizing consumption in both residential and commercial smart grids. Our findings also emphasize blockchain’s role in creating decentralized energy trading platforms, facilitating peer-to-peer transactions, and enhancing trust through smart contracts. The insights gained from this review highlight the essential role of these emerging technologies in supporting decentralized, intelligent energy networks, offering valuable strategies for stakeholders to navigate the complexities of the evolving digital energy landscape.

Hamad Alrashede, Fathy Eassa, Abdullah Ali, Faisal Albalwy · 5 authors

Software-Defined Networking (SDN) has emerged as a revolutionary architecture in computer networks, offering comprehensive network control and monitoring capabilities. However, securing the east–west interface, which is crucial for communication between distributed SDN controllers, remains a significant challenge. This study proposes a novel blockchain-based security framework that integrates Ethereum technology with customized blockchain algorithms for authentication, encryption, and access control. The framework introduces decentralized mechanisms to protect against diverse attacks, including false data injection, man-in-the-middle (MitM), and unauthorized access. Experimental results demonstrate the effectiveness of this framework in securing distributed controllers while maintaining high network performance and low latency, paving the way for more resilient and trustworthy SDN infrastructures.

G.B. Bhavana, R. S. Anand, J. Ramprabhakar, Veerpratap Meena · 6 authors

Countries all over the world are shifting from conventional and fossil fuel-based energy systems to more sustainable energy systems (renewable energy-based systems). To effectively integrate renewable sources of energy, multi-directional power flow and control are required, and to facilitate this multi-directional power flow, peer-to-peer (P2P) trading is employed. For a safe, secure, and reliable P2P trading system, a secure communication gateway and a cryptographically secure data storage mechanism are required. This paper explores the uses of blockchain (BC) in renewable energy (RE) integration into the grid. We shed light on four primary areas: P2P energy trading, the green hydrogen supply chain, demand response (DR) programmes, and the tracking of RE certificates (RECs). In addition, we investigate how BC can address the existing challenges in these domains and overcome these hurdles to realise a decentralised energy ecosystem. The main purpose of this paper is to provide an understanding of how BC technology can act as a catalyst for a multi-directional energy flow, ultimately revolutionising the way energy is generated, managed, and consumed.

Juhani Latvakoski, Jouni Heikkinen, Jari Palosaari, Vesa Kyllönen · 5 authors

The aim of this research has been to enable the management of trustworthy relationships between stakeholders, service providers, and physical assets, which are required in critical energy and mobility cyber–physical systems (CPS) applications. The achieved novel contribution is the concept of trustworthy communities with respective experimental solutions, which are developed by relying on verifiable credentials, smart contracts, trust over IP, and an Ethereum-based distributed ledger. The provided trustworthy community solutions are validated by executing them in two practical use cases, which are called energy flexibility and hunting safety. The energy flexibility case validation considered the execution of the solutions with one simulated and two real buildings with the energy flexibility aggregation platform, which was able to trade the flexibilities in an energy flexibility marketplace. The provided solutions were executed with a hunting safety smartphone application for a hunter and the smartwatch of a person moving around in the forest. The evaluations indicate that conceptual solutions for trustworthy communities fulfill the purpose and contribute toward making energy flexibility trading and hunting safety possible and trustworthy enough for participants. A trustworthy community solution is required to make value sharing and usage of critical energy resources and their flexibilities feasible and secure enough for their owners as part of the energy flexibility community. Sharing the presence and location in mobile conditions requires a trustworthy community solution because of security and privacy reasons, but it can also save lives in real-life elk hunting cases. During the evaluations, the need for further studies related to performance, scalability, community applications, verifiable credentials with wallets, sharing of values and incentives, authorized trust networks, dynamic trust situations, time-sensitive behavior, autonomous operations with smart contracts through security assessment, and applicability have been detected.