Satellite communication (SATCOM) networks are essential in delivering long-range and high-capacity data transmission on a global basis. With emerging satellite-ground-air integrated networks (SAGIN) responding to growing demands for communication and low-latency connections, their unique, dynamic infrastructure raises novel issues in security. Traditional centralized defenses are increasingly ineffective against advanced attacks such as distributed denial-of-service (DDoS). This research advocates an integrated solution that combines blockchain infrastructure with deep learning approaches to address these security challenges. The model was simulated in an NS-3 environment, and normal and attack traffic were generated to train a hybrid CNN-LSTM-based anomaly detection model. Distinct types of threats were recorded on a private Ethereum-based blockchain using smart contracts, enabling decentralized blacklist control and automated response behaviors. With decentralized control of threats, detection efficiency is enhanced by application of AI-driven analysis, and trust is ensured by virtue of immutable logging. The test results hold promise for this solution in delivering scalable, robust, and autonomous security for modern SATCOM networks.
The smart grid is the next evolution of electrical power systems, a continuation of the old grids that involves a mix of digital and traditional power grid technologies to allow the potential to communicate in both directions, decentralized energy production and real-time monitoring. However, such a connection exposes it to cyber attacks, data fraud, and unauthorized access as well. Blockchain technology is one of these technologies because it is transparent, immutable, and decentralized to overcome these security obstacles. In this paper, an overview of blockchain technology smart grid security, architecture, consensus algorithm, and application are presented. Some of the most notable blockchain works in the smart grid include secure energy trading, decentralised identity management, detecting attacks and preserving privacy. When applied in smart grids, reviewed blockchain protocols also comprise Proof of Work (PoW) and Proof of Stake (PoS) along with Practical Byzantine Fault Tolerance (PBFT). Top of that, there are hybrid types of blockchain such as artificial intelligence (AI) and the Internet of things (IoT) that are also covered as the next picture to enable the system to become more scalable and interoperable. Power consumption, time wastage, and regulation hurdle is greatly considered. This paper has concluded that blockchain is a bottom-up technology, which can cause smart grid infrastructures to be much more resilient, transparent, and efficient.
Manuel Uche-Soria, Antonio Martínez Raya, Alberto Muñoz Cabanes, Jorge Moya Velasco
Rapid depletion of fossil fuel reserves forces the global energy sector to transition to sustainable energy sources. Specifically, distributed energy markets have emerged in the renewable energy sector in recent years, partly because blockchain technology is becoming a successful way to promote secure and transparent transactions. Using its decentralized structure, transparency, and even pseudonymity, blockchain is increasingly adopted worldwide for large-scale energy trading, peer-to-peer exchanges, project financing, supply chain management, and asset tracking. The research comprehensively analyzes blockchain applications across multiple fields related to energy, bibliographically evaluating their transformative potential. In addition, the study explores the architecture of various blockchain systems, assesses critical security and privacy challenges, and discusses how blockchain can enhance operational efficiency, transparency, and reliability in the energy sector. The paper’s findings provide a roadmap for future developments and the strategic adoption of blockchain technologies in the evolving energy landscape for an effective energy transition.
Rhana Elsayed, Mohamed I. Ismail, Ahmed F. Ashour, Hesham A. Sakr · 6 authors
Cyber-physical Systems (CPS) are increasingly utilized by Smart Building Management Systems (SBMS) to achieve intelligent, energy-efficient operations. The significant energy footprint of buildings (approximately 40% of worldwide energy use) and the desire to improve sustainability without compromising occupant comfort are the primary drivers of this movement. However, complicated cybersecurity issues are also brought about by the close integration of Internet of Things (IoT) sensors, automated controls, and networked management. In this study, we provide a comprehensive examination of CPS-based smart building monitoring and control technologies, exploring how state-of-the-art sensors, data analytics, and Artificial Intelligence (AI)-powered decision engines are utilized to enhance responsiveness and energy efficiency. We present a comparison between contemporary CPS-enabled SBMS and conventional building management systems, emphasizing the enhancements in security, flexibility, and energy efficiency. A thorough analysis of energy optimization processes demonstrates how machine learning algorithms and feedback loops may dynamically strike a balance between efficiency and comfort. We also examine new security models being developed to defend these cyber-physical infrastructures against attacks. This survey is unique because it comprehensively covers recent CPS developments and identifies emerging trends that will influence next-generation smart building management, including blockchain, Decentralized Autonomous Organization (DAO)based decentralized management architectures, and federated learning for collaborative optimization. In addition to outlining substantial obstacles and future research opportunities, our findings highlight the crucial role that CPS plays in enabling safe, self-sufficient, and sustainable buildings.
Waqas Amin, Qi Huang, Jianping Li, Abdullah Aman Khan · 6 authors
An increase in the popularity of peer-to-peer energy trading in smart grids due to the massive integration of renewable energy sources demands effective and competitive pricing and energy allocation policies to ensure fairness within the market framework. Considering the scalability issues, technical complexity, and operational costs of distributed ledger technology such as blockchain, the reputation of the participants becomes a prominent factor to ensure trustworthiness, reduce risk, and increase market efficiency. This paper proposes a novel method to determine the reputation of participants within the energy market. Based on the evaluated reputation of the participants, an effective pricing method along with an energy distribution technique is devised by considering several market dynamics that significantly affect the pricing and energy allocation method. Extensive experiments have been conducted to validate the effectiveness of the proposed model. The results demonstrate that through the proposed model, the energy bills of the buyers can be reduced by 44%. This highlights the tangible benefits and practical applicability of the proposed approach in optimizing energy costs for consumers in the P2P energy trading ecosystem.
T. Ratha Jeyalakshmi, Alamma Bh, H S Harshitha, K. Agarwal R.
Privacy of users and security of data are important issues that will be exposed to use in the Metaverse by use of Digital Twins (DTs). The current paper suggests a privacy-preserving system, which combines Zero-Knowledge Proofs (ZKPs) of secure identity verification and Federated Learning (FL) of decentralized model training. The framework allows for alleviating the risk of storing data in central facilities and preventing unauthorized access by locally processing data and using cryptographic solutions. The results produced by the evaluation prove that the proposed system is capable of attaining the necessary level of privacy of its users and ensuring reliable and scalable communications within the Metaverse applications.
Timed signatures are cryptographic primitives that enable senders to predefine the validity period of a signature. Currently, two primary types of timed signatures have been developed. The first type, known as Verifiable Timed Signatures (CCS'2020), implements a delay before a signature becomes effective. The second type is Short-Lived Signatures (ASIACRYPT'2022), which allows for the setting of an expiration time for signatures upon creation. However, certain applications requiring time-sensitive authorization demand both activation and expiration times to be set, a requirement not fulfilled by the existing timed signature schemes. To overcome this limitation, we propose a novel flexible timed signature scheme called Time Interval Signatures (TIS). TIS combines Verifiable Delay Functions and Short-Lived Signatures with our Zero-Knowledge Proof of Product, facilitating the flexible setting of both activation and expiration times for the signature. Building on TIS, we present TimeGuardian, a time-bound NFT rights protocol that enables presetting authorization and revocation periods for NFT usage rights. Experimental results show that TIS achieves signature size reductions of 98.67% and 57.14% compared to existing verifiable timed signature solutions.
Farid Hamzeh Aghdam, Aleksandr Zavodovski, Mehdi Rasti, Éva Pongrácz
The energy domain worldwide is experiencing high transformative pressure due to the imperative of climate change and new opportunities brought by various rapidly evolving digital technologies. Particularly, this change is affecting smart grids (SGs), which are increasingly shifting toward utilizing renewable and distributed energy sources. The natural intermittency of these energy sources increases the complexity of SG operations, such as ensuring continuity of energy supply and demand response balancing. In mitigation of these challenges, the tools and complex approaches that digitalization can provide have shown themselves particularly advantageous. There is a solid body of work showing how technologies like the Internet of Things (IoT), distributed ledgers, edge and cloud computing, machine learning (ML), etc., can be applied to address a variety of technical and economic problems in the energy sector, emphasizing SGs. This paper presents the most comprehensive literature review to date on digitalization in renewable energy source-based SGs, synthesizing over 200 studies across data analytics, artificial intelligence and ML, digital twins, edge–fog–cloud computing, the IoT, advanced metering infrastructure, and distributed ledger technologies. Unlike previous reviews, which are often limited to a single technology or narrow application, this work provides a cross-technology synthesis linking technical and financial aspects, identifies consolidated research gaps, and proposes a unified research agenda. The review further highlights future trends, including large language models, 6G communications, and distributed autonomous organizations, and discusses their implications for both industry practice and academic research.
The rapid advancement of quantum computing poses significant challenges to conventional cryptography, necessitating the adoption of post-quantum cryptography (PQC) solutions. This chapter proposes a Post-Quantum Lattice Security (PQLS) system for protecting power plant data in smart cities. It integrates Kyber for secure key exchange, Falcon for quantum-resistant digital signatures, ZKP for efficient authentication without revealing sensitive data, and JSON-LD for standardizing the format of data received from different smart meters. To evaluate the security of the proposed framework, we analyze its resistance to various threats, such as side-channel and message recovery attacks. We measured key performance indicators. The results showed an average CPU utilization of 2.4592 MS, memory consumption averaging 1843.899 KB, an execution time of 2.45 MS, and a level averaging 66.27677. This demonstrates that our proposed system offers high security and efficiency, making it a practical solution for protecting electrical infrastructure in smart cities in the quantum era.
Yagmur Yigit, Mehmet Ali Erturk, Kerem Gursu, Berk Canberk
Digital twin (DT) technology is rapidly becoming essential for smart city ecosystems, enabling real-time synchronisation and autonomous decision-making across physical and digital domains. However, as DTs take active roles in control loops, securely binding them to their physical counterparts in dynamic and adversarial environments remains a significant challenge. Existing authentication solutions either rely on static trust models, require centralised authorities, or fail to provide live and verifiable physical-digital binding, making them unsuitable for latency-sensitive and distributed deployments. To address this gap, we introduce PRZK-Bind, a lightweight and decentralised authentication protocol that combines Schnorr-based zero-knowledge proofs with elliptic curve cryptography to establish secure, real-time correspondence between physical entities and DTs without relying on pre-shared secrets. Simulation results show that PRZK-Bind significantly improves performance, offering up to 4.5 times lower latency and 4 times reduced energy consumption compared to cryptography-heavy baselines, while maintaining false acceptance rates more than 10 times lower. These findings highlight its suitability for future smart city deployments requiring efficient, resilient, and trustworthy DT authentication.
This paper proposes a blockchain framework integrating smart contracts and machine learning to enable secure, decentralized anomaly detection in energy grids. We deploy Ethereum-based smart contracts to trigger localized alerts by postcode, validated on the Ausgrid dataset. Experimental results demonstrate the framework’s ability to achieve 94.5% anomaly detection accuracy while reducing false alerts by 30% through a two-stage machine learning pipeline. First, the MeanShift clustering algorithm identifies irregular consumption patterns using adaptive interquartile range thresholds, followed by supervised classification. Various algorithms are investigated, with and without SMOTE, to tackle the problem of dataset imbalance. The Proof-of-Stake (PoS) consensus mechanism reduces energy overhead by 99% compared to traditional Proof-of-Work (PoW), ensuring scalability for real-time grid management. By automating postcode-specific alerts and leveraging blockchain’s tamper-proof data storage, the framework enhances operational responsiveness and transparency for decentralized energy systems. This work bridges the gap between decentralized ledger technology and AI-driven analytics, offering a practical solution for secure, low-latency anomaly management in modern smart grids.
Over the past few years, a strange industrial electricity customer has taken the industry by storm: Bitcoin mining. In 2021, the nascent Bitcoin mining industry, which had primarily been in China, shipped massive amounts of hardware and opportunity to capture global market share to the United States.
This paper presents an optimal peer-to-peer (P2P) energy transaction mechanism leveraging decentralized blockchain technology to enable a secure and scalable retail electricity market for the increasing penetration of distributed energy resources (DERs). A decentralized bidding strategy is proposed to maximize individual profits while collectively enhancing social welfare. The market design and transaction processes are simulated using the Ethereum testnet, demonstrating the blockchain network's capability to ensure secure, transparent, and sustainable P2P energy trading among DER participants.
The energy sector is experiencing a paradigm shift toward decentralized, renewable sources, necessitating efficient energy and data management solutions. In this paper, we propose methods to improve both scalability and storage efficiency within a Distributed Ledger Technology (DLT) based local energy trading framework. Our contributions include the improved use of directed acyclic graphs (DAGs) to manage Smart Contracts (SCs) separately, reducing the data retention burden on individual nodes, and implementing time based data pruning to enhance storage efficiency. We also demonstrate the scalability of our energy trading platform. Our results indicate significant improvements in storage usage and scalability, thereby supporting the long term viability and scalability of decentralized energy trading platforms in prosumer communities.
Sadly Syamsuddin, Salama Manjang, Muhammad Bachtiar Nappu, Ady Wahyudi Paundu
This study proposes the integration of a hybrid Proof of Work/Proof of Stake (PoW/PoS) consensus mechanism with a Long Short-Term Memory (LSTM) model for anomaly detection in blockchain-based microgrids. The hybrid PoW/PoS model is designed to address common issues in blockchain systems, such as 51% attacks, double-spending, and high energy consumption, by optimizing energy usage and enhancing security. Simulation results show that the system can process transactions with an average throughput of 37.25 transactions per second (TPS), an average latency of 26.84 milliseconds per transaction (ms/tx), and extremely efficient energy consumption per transaction (0.01 kWh/tx). The LSTM model applied for anomaly detection achieves an 89.10% detection rate, a 0.00% false positive rate, and a 0.12 s recovery time, indicating the system's reliability in facing attacks. The hybrid PoW/PoS system demonstrates advantages in both energy efficiency and resilience to attacks compared to individual PoW and PoS systems. This research contributes to the development of safer, more efficient, and scalable blockchain-based microgrids by integrating Artificial Intelligence (AI) to strengthen the system against anomalies and threats.
M. Zulfiqar, Muhammad Babar Rasheed, Daniel Rodríguez, María D. R‐Moreno
Contemporary power grid systems increasingly rely on sophisticated energy trading mechanisms to optimize resource allocation and operational performance. While prior studies have examined the coordination roles of energy intermediaries and utility operators, particularly through distributed ledger technologies that ensure data provenance and transaction verifiability in decentralized energy marketplaces, significant security vulnerabilities persist. Notably, fraudulent practices by energy suppliers characterized by payment collection without corresponding energy delivery pose substantial risks to market integrity and participant confidence. This research presents the Blockchain-based Energy Trading with Multi-Factor Trust Framework (BC-ET-MF), a novel architecture that addresses critical security deficiencies through advanced cryptographic protocols and consensus mechanisms. The framework utilizes anonymous credential systems to safeguard participant privacy while implementing time-locked commitment schemes that ensure transaction fairness and verifiability. The architecture incorporates granular access control mechanisms for secure service orchestration and establishes a consortium blockchain infrastructure among energy intermediaries to facilitate distributed transaction validation and immutable record-keeping. To mitigate computational overhead associated with conventional consensus algorithms, we introduce a Proof-of-Verifiability protocol that dynamically calibrates to real-time energy production and consumption patterns. This adaptive mechanism reduces system resource requirements while maintaining security guarantees. Experimental evaluation demonstrates that BC-ET-MF achieves substantial performance improvements: energy consumption reduction of 43.0 %, peak-to-average ratio optimization from 8.27 to 3.21 and 5.88 under 25 % and 50 % demand reduction scenarios respectively, and establishment of 92.5 % participant trust levels. The framework additionally yields 37.6 % transaction latency reduction while preserving user anonymity and enabling comprehensive audit capabilities, thus establishing a secure, efficient, and trustworthy energy trading ecosystem.
Ethereum blockchain uses smart contracts (SCs) to implement decentralized applications (dApps). SCs are executed by the Ethereum virtual machine (EVM) running within an Ethereum client. Moreover, the EVM has been widely adopted by other blockchain platforms, including Solana, Cardano, Avalanche, Polkadot, and more. However, the EVM performance is limited by the constraints of the general-purpose computer it operates on. This work proposes offloading SC execution onto a dedicated hardware-based EVM. Specifically, EVMx is an FPGA-based SC execution engine that benefits from the inherent parallelism and high-speed processing capabilities of a hardware architecture. Synthesis results demonstrate a reduction in execution time of 61% to 99% for commonly used operation codes compared to CPU-based SC execution environments. Moreover, the execution time of Ethereum blocks on EVMx is up to 6x faster compared to analogous works in the literature. These results highlight the potential of the proposed architecture to accelerate SC execution and enhance the performance of EVM-compatible blockchains.
Ala’a Shamaseen, Mohammad Qatawneh, Basima Elshqeirat
Traditional systems in real life lack transparency and ease of use due to their reliance on centralization and large infrastructure. Furthermore, many sectors that rely on information technology face major challenges related to data integrity, trust, and counterfeiting, limiting scalability and acceptance in the community. With the decentralization and digitization of energy transactions in smart grids, security, integrity, and fraud prevention concerns have increased. The main problem addressed in this study is the lack of a secure, tamper-resistant, and decentralized mechanism to facilitate direct consumer-to-prosumer energy transactions. Thus, this is a major challenge in the smart grid. In the blockchain, current consensus algorithms may limit the scalability of smart grids, especially when depending on popular algorithms such as Proof of Work, due to their high energy consumption, which is incompatible with the characteristics of the smart grid. Meanwhile, Proof of Stake algorithms rely on energy or cryptocurrency stake ownership, which may make the smart grid environment in blockchain technology vulnerable to control by the many owning nodes, which is incompatible with the purpose and objective of this study. This study addresses these issues by proposing and implementing a hybrid framework that combines the features of private and public blockchains across three integrated layers: user interface, application, and blockchain. A key contribution of the system is the design of a novel consensus algorithm, Proof of Energy, which selects validators based on node roles and randomized assignment, rather than computational power or stake ownership. This makes it more suitable for smart grid environments. The entire framework was developed without relying on existing decentralized platforms such as Ethereum. The system was evaluated through comprehensive experiments on performance and security. Performance results show a throughput of up to 60.86 transactions per second and an average latency of 3.40 s under a load of 10,000 transactions. Security validation confirmed resistance against digital signature forgery, invalid smart contracts, race conditions, and double-spending attacks. Despite the promising performance, several limitations remain. The current system was developed and tested on a single machine as a simulation-based study using transaction logs without integration of real smart meters or actual energy tokenization in real-time scenarios. In future work, we will focus on integrating real-time smart meters and implementing full energy tokenization to achieve a complete and autonomous smart grid platform. Overall, the proposed system significantly enhances data integrity, trust, and resistance to counterfeiting in smart grids.
Alper Alimoğlu, Kamil Erdayandı, Mustafa Mustafa, Ümit Cali
This paper proposes a new decentralized framework, named EDGChain-E (Encrypted-Data-Git Chain for Energy), designed to manage version-controlled, encrypted energy data using blockchain and the InterPlanetary File System. The framework incorporates a Decentralized Autonomous Organization (DAO) to orchestrate collaborative data governance across the lifecycle of energy research and operations, such as smart grid monitoring, demand forecasting, and peer-to-peer energy trading. In EDGChain-E, initial commits capture the full encrypted datasets-such as smart meter readings or grid telemetry-while subsequent updates are tracked as encrypted Git patches, ensuring integrity, traceability, and privacy. This versioning mechanism supports secure collaboration across multiple stakeholders (e.g., utilities, researchers, regulators) without compromising sensitive or regulated information. We highlight the framework's capability to maintain FAIR-compliant (Findable, Accessible, Interoperable, Reusable) provenance of encrypted data. By embedding hash-based content identifiers in Merkle trees, the system enables transparent, auditable, and immutable tracking of data changes, thereby supporting reproducibility and trust in decentralized energy applications.