The rapid advancement of smart grids, propelled by the integration of distributed energy resources (DERs) and renewable energy technologies, has exposed key challenges such as interoperability, standardization, and data security. These issues impede the efficient operation and scalability of smart grid applications, particularly in enabling decentralized, real-time energy trading and demand response management. Effective management of DERs and demand response systems relies heavily on seamless information exchange, data-driven decision-making, and robust digital communication frameworks to maintain system stability and operational efficiency. To address these challenges, this study presents the Blockchain Consortium-Based Demand Energy Trading System (BC-DETS), a blockchain-powered framework designed to enhance interoperability, security, and standardized energy trading mechanisms within smart grid ecosystems. Comprehensive simulations have validated the effectiveness of BC-DETS using Key Performance Indicators (KPIs) such as transaction latency, demand response participation rate, operational cost reductions, and overall grid efficiency under diverse scenarios. Findings reveal a 35% boost in grid efficiency through optimized energy distribution and minimized energy losses, alongside a 15% decrease in operational costs due to reduced transaction overhead and improved energy allocation. Moreover, demand response participation rates increased by 40%, facilitated by secure and transparent blockchain-enabled real-time energy transactions. These numerical findings underscore blockchain’s transformative potential in enhancing the scalability, security, and inclusivity of smart grids, establishing a foundational platform for future advancements in blockchain standardization and sustainable energy management solutions.
Data integrity in Smart Grids (SG) systems can be vulnerable with the implementation of the novel Community Blockchain-Driven Traceability Framework (CBDTF). It enhances Detection Rates (DR), maintains low End-to-End Delay (EED), and uses less energy by using distributed ledger technology and community-based validation. This model deployed a Delegated Proof of Stake (DPoS) consensus mechanism and community-driven testing, resulting in an average Detection Rate (DR) of 98.7% for Data Tampering attacks and a False Positive Rate (FPR) of 1.78%. It outperforms conventional Blockchain (BC) solutions with an EED of 120.8 ms and an average CPU utilization of 1,113 tx/kWh. When compared with conventional Proof-of-Work (PoW), CBDTF requires 60% less energy while proving 96.2% consensus resilience against distinct attacks. Applying real-world SG data collected by a distributed network of 100 nodes, the accuracy of this model was tested. The present study makes a valuable contribution to the field by signifying how BC platforms driven by the public can address SG's data security issues while maintaining the accuracy of real-time operations.
Mohammad Nasrinasrabadi, Maryam A. Hejazi, Ehsan Chaharmahali, Mousa Hussein
The integration of blockchain and the Internet of Things (IoT) within smart grids offers transformative potential for enhancing energy management, security, and operational efficiency. Smart grids rely on advanced digital technologies to enable bidirectional communication between energy producers and consumers, optimizing the integration of renewable energy sources and promoting demand-side management. Blockchain technology, with its decentralized and immutable nature, ensures secure and transparent energy transactions while fostering trust without the need for centralized authorities. Meanwhile, IoT facilitates real-time data collection and monitoring, enabling dynamic energy management and transactive energy systems. This review explores the synergies between blockchain and IoT in addressing critical challenges such as cybersecurity, data security, and network security. By leveraging mechanisms like smart contracts and consensus algorithms, these technologies enhance grid resilience and privacy, providing robust solutions to manage distributed energy resources and decentralized energy markets. The integration also supports peer-to-peer energy trading, improves scalability, and reduces reliance on intermediaries, aligning with sustainability goals by promoting renewable energy adoption. Despite significant advancements, challenges such as regulatory barriers, high computational costs, and scalability limitations persist. This paper emphasizes the need for innovative approaches to overcome these issues and highlights emerging trends such as hybrid blockchain models and AI-enabled solutions. By addressing these gaps, blockchain and IoT can redefine smart grid infrastructures, ensuring a secure, efficient, and sustainable energy future.
This study delves into the vulnerability of the smart grid to infiltration by hackers and proposes methods to safeguard it by leveraging blockchain and artificial intelligence (AI). A categorization and analysis of cyberattacks against smart grids will be conducted, focusing on those targeting their communication layers. The main goal of the work is to address the challenges in this area by implementing novel detection and defense strategies. The authors categorize attacks on smart grid networks based on the communication classes they want to compromise. They propose novel taxonomies specifically designed to detect and implement defense strategies. The study investigates artificial intelligence and blockchain techniques to identify cyber-attacks that employ deceptive data injection. The study indicates that cyberattacks against smart grids are increasing in frequency and complexity. The paper proposes innovative strategies for defense, such as enhancing cybersecurity with artificial intelligence and blockchain technology. The research further enumerates several challenges, such as counterfeit topological data, imprecise data identification, and combining big data with blockchain technology. Given the increasing risks, the study emphasizes the crucial need for robust cybersecurity safeguards in smart grids. This work contributes to the protection of smart grid infrastructures by categorizing attacks, suggesting novel defenses, and exploring solutions integrating artificial intelligence and blockchain technology. Research should prioritize enhancing technology to maximize security and counter emerging attack methods. The intended audience of our paper comprises graduate-level academics and independent researchers.
Matt Tsao, Karthik Gopalakrishnan, Kaidi Yang, Marco Pavone
Networked Unmanned Aerial Vehicles (UAVs) can be used for complex tasks such as surveillance and reconnaissance, inspection of dangerous environments, and target pursuit. Typically, coordination between multiple UAVs has been shown to improve the ability and performance of accomplishing such tasks. However, networked UAVs introduce new vulnerabilities enabling cyber-attacks which can target critical elements and prevent the UAVs from achieving their goal. This chapter presents a distributed system architecture for coordination of UAVs that provides resilience against denial-of-service and integrity cyber-attacks. The developed architecture consists of a distributed ledger implementing an asynchronous Byzantine fault tolerant protocol exchanging data between distributed agents and a distributed learning algorithm based on vector consensus implemented on top of the distributed ledger. Performance and resilience of the architecture are evaluated using a target pursuit case study based on a hardware-in-the-loop testbed. The experimental results demonstrate that that the UAVs that are not under attack are still able to successfully cooperate and accomplish the desired task. [160 words] privacy risks to users. If left unaddressed, potential privacy risks may deter users from contributing their data to cyber-physical systems, thereby limiting the effectiveness of data-driven tools employed by the systems. To ensure that users are protected from privacy risks and feel comfortable sharing their data with cyber-physical systems, this chapter discusses how differential privacy and cryptography, techniques originally developed for privacy in health care and computer systems respectively, can be used to conduct data analysis and optimization with principled privacy guarantees in cyber-physical systems. Along the way, we will discuss and compare the properties, strengths and weaknesses of these approaches. We will also show why these techniques address many of the privacy-related issues that arise when using data anonymization and data aggregation, two of the most common approaches to privacy-aware data sharing.
The cybersecurity threats targeting industrial control systems (ICS) are evolving with increasing sophistication. Addressing the detection blind spots in existing source code analysis techniques, this study reveals a dual security paradox arising from code sensitivity: privacy leakage risks caused by decompilation techniques and integrity verification deficiencies in reverse engineering. This paper investigates three critical challenges: (1) What are the component flow process and detection elements of ICS component source code? (2) How can high-performance and reliable tracing and traceability be provided for ICS component source code exceptions and routine detection? (3) How can privacy enhancement and trusted detection of ICS component source code with high sensitivity be achieved? This paper proposes a blockchain-integrated trusted detection framework for ICS (BCTD-ICS), delivering groundbreaking solutions: (1) Establishing a lifecycle circulation model that systematically maps component types, stakeholders, and detection parameters; (2) Developing a tripartite collaborative architecture (Blockchain-Identification Resolution Zero-knowledge proofs (ZKPs)), featuring a traceability mechanism with trusted identification codes (resolution efficiency: 40ms/105 queries) to eliminate decompilation-induced privacy risks; (3) Creating an industrial-oriented privacy enhancement system utilizing DBSCAN clustering for intelligent sampling (26% compression rate on BCN3D Moveo) and optimizing ZK-SNARK protocols through Shamir’s Secret Sharing, establishing a backdoor-resistant distributed parameter generation system (time delay increment < 100ms). Experimentally verified, our solution enables ICS component code detection supply-chain-wise without sensitive data leakage in real-world industries. This work establishes a novel trusted detection paradigm for ICS, advancing detection efficiency and credibility under strict privacy preservation requirements, meeting Industry 4.0 security demands.
Smart Grid Security and Resilience
Physical Unclonable Functions (PUFs) and Hardware Security
S. M. Eswara Moorthi, G. Ravishankar, D. David Neels Ponkumar
Decentralized public ledger blockchain secures transactions across untrusted network nodes. Bitcoin systems rely on it to safeguard and decentralize transaction records, attracting attention. Over the last decade, blockchain has garnered interest from numerous businesses because of its potential to transform multiple areas, including cybersecurity. This research area is new, and blockchain's cybersecurity efficacy has to be addressed. This qualitative study examines blockchain-based security applications and their suitability in the current cybersecurity environment. A single point of failure may expose a centralized administration and validation system to malware, Distributed Denial of Services (DDoS), and Denial of Services (DoS) assaults. Blockchain technology creates secure, private decentralized networks without third-party control. Blockchain allows irreversible and verifiable storage of current and historical data in a sealed ledger distributed throughout the network. Blockchain technology distributes encrypted data throughout the network, improving data security and privacy. A decentralized e-government peer-to-peer system employing blockchain technology to secure and anonymize data and boost public sector credibility is proposed in this research. The suggested system's security and privacy consequences are theoretically and qualitatively analyzed, along with a prototype.
With the rapid adoption of Electric Vehicles (EVs) and the increasing need for Vehicle-to-Vehicle (V2V) electricity trading, ensuring privacy, security, and efficiency in decentralized transactions remains a critical challenge. Existing solutions face vulnerabilities such as identity exposure, high computational costs, and inefficiencies in consensus mechanisms. To address these issues, this paper proposes a Novel Hybrid IntelligenceDriven Blockchain Framework for Secure and Efficient V2V Electricity Trading in the Internet of Vehicles (IoV). The framework integrates a hybrid privacy-preserving mechanism utilizing a dual-layer dynamic pseudo-identity system and certificateless aggregate signcryption to enhance transactional privacy while enabling fraudulent activity traceability. Additionally, a multilayered blockchain architecture leveraging digital twins, edge computing, and decentralized storage optimizes transaction verification and reduces latency. A novel Hybrid Reinforcement Learning-based Proof-of-Reputation and Adaptive Stake (HRLPoRAS) consensus mechanism is designed, dynamically adjusting validation participation based on EV reputation scores, stake levels, and environmental factors, ensuring scalability and energy efficiency. Furthermore, a Deep Q-Network and MultiAgent Deep RL (DQN-MADRL) hybrid optimization strategy is introduced to enhance smart contract execution and relay selection, minimizing computational overhead while maintaining network resilience. Performance evaluations demonstrate that the proposed framework significantly improves privacy protection, reduces communication overhead, and enhances transaction throughput compared to existing blockchain-based V2V electricity trading schemes. This work contributes to the development of a highly scalable, intelligent, and secure electricity trading system for the future IoV ecosystem.
With the shifting from traditional grids to smart grids, there is an immense shift towards decentralized energy trading wherein “prosumers” can enter peer-to-peer transactions. This model decreases dependence on centralized utilities and maximizes efficient, flexible, and resilient energy distribution. It enhances transparency and trust by automating and securing trades via smart contracts. No intermediaries are required; hence transaction costs are low. However, an attack that would breach the security guarantees of blockchain systems-by tremendous quantum computers-might break a few of the older cryptographic methods or even reveal very significant portions of their keys. This paper introduces a blockchain-based decentralized framework for energy trading in smart grids, with a strong emphasis on post-quantum cryptography to safeguard transactions against quantum threats. We explore post-quantum cryptographic techniques, particularly lattice-based algorithms due to its compact signature sizes and strong security capability for the future-proof blockchain enabled smart grids. The proposed system model ensures secure and decentralized energy trading while incorporating off-chain signature validation to enhance computational efficiency. Unlike previous studies that primarily focus on market structure or consensus protocols, this work introduces a quantum-resilient architecture with an off-chain transaction validation mechanism, enabling high-throughput trading secured against future cryptographic vulnerabilities. The novel feature of the proposed model is the integration of off-chain post-quantum cryptographic verification into a blockchain energy trading architecture that is practically deployable on embedded hardware. Compared to existing solutions, the proposed method ensures quantum-resilient authentication while reducing gas costs by 40% and improving computational efficiency achieving a signing time of 0.327 ms and verification time of 0.127 ms. The proposed framework represents a significant step toward future-proofing blockchain-enabled smart grids while maintaining performance, transparency, and resilience. The outcome of this research work presents a quantum-safe solution that strengthens the resilience of smart grid operations and ensures the security of decentralized energy trading.
Advancements in power system intelligence and the integration of emergent technologies have significantly transformed grid communication needs. This paper conducts a thorough analysis of developments within this pivotal area. Examined is the function of communicative solutions in facilitating real-time observation, management, and harmonization within smart energy systems. Discussed are how techniques including the Internet of Things, fifth generation cellular technology, artificial intelligence, and distributed ledger systems are integrating to reshape the domain of intelligent grid interconnectivity. Moreover, difficulties such as cyber risks and interworking capabilities are explored. Via a comprehensive evaluation of contemporary works and practical applications, this paper illuminates the present condition and forthcoming path of intelligent system communications within the energy sector.
Road-Side Units (RSUs) are deployed along the road to facilitate Vehicle-to-Infrastructure (V2I) communication, a critical component of Vehicle-to-Everything (V2X) services. However, the presence of rogue RSUs, which are unauthorized access points, poses significant threats to V2X communications and safety applications. These rogue RSUs, installed by adversaries, can mimic legitimate RSUs and establish connections with vehicles, enabling various attacks such as data interception, spoofing, and denial of service. Therefore, Software-defined Networking (SDN) has been leveraged to employ various traffic engineering, network management, and secure verification of RSUs and vehicle functionalities. The SDN controller (SDNC), which manages RSUs, can periodically verify their identity. This mechanism ensures the association of vehicles with legitimate RSUs and the detection of rogue RSUs. To periodically verify the RSUs' identity, a novel Fiat-Shamir Transformation-enabled Non-Interactive Zero-knowledge Proof ($\text{Z K P}$) -based identity verification mechanism has been proposed. The RSUs are initially registered with an SDNC in this protocol. Subsequently, SDNC verifies their identity periodically using a unique ZKP-based challenge-response mechanism. As per the performance and security analysis, the proposed protocol surpasses state-of-the-art authentication protocols and achieves notable improvements.
Joan Ferré-Queralt, Jordi Castellà‐Roca, Alexandre Viejo
Smart grid technology has transformed electricity generation , distribution, and consumption by incorporating advanced communication systems and distributed energy resources , including solar panels and energy storage solutions . This integration enables prosumers to actively participate in energy markets, benefiting from real-time monitoring, dynamic pricing , and load balancing. However, the detailed data collected during these processes raise significant privacy concerns, as it may expose sensitive information about users’ lifestyles. This work presents an innovative energy trading system operating within decentralized energy distribution networks . The system leverages blockchain-based hierarchical smart contracts to enhance privacy protection for users. It automates energy trades, ensures accurate transaction verification, and obscures user identities and energy consumption patterns through its hierarchical structure, preventing unauthorized profiling or data breaches. Additionally, mechanisms to detect and penalize dishonest behavior are incorporated, ensuring the integrity and fairness of the energy market. The feasibility of the proposed system is experimentally evaluated in an IoT environment through a small-scale implementation using actual IoT devices, yielding positive results in terms of scalability and privacy features. Lastly, a comparative analysis is presented to demonstrate the advantages of the proposed system over existing state-of-the-art solutions.
USDOE, Emilio C. Piesciorovsky, Gary Hahn, Raymond Borges Hink · 5 authors
In this study is presented two power system applications with distributed ledger technology (DLT) and smart contracts (SC) that were assessed in a Cyber-Grid-Guard System (CGGS) advanced testbed, with protective relays, power meters, communication devices, DLT devices, synchronized time source, clock displays and real time simulator. This CGGS testbed was set in the Advanced Protection Lab, 252 lab space of the Grid Research Integration and Deployment Center (GRID- C), at Oak Ridge National Laboratory. In power grids, customer-owned distributed energy resources (DERs) are more frequent than in the past, and the numbers of points of interconnection (POI) with customer-owned DERs have increased. Disruptive operation from DERs presents a risk to grid operations, and protective relays located at the POI are used to isolate out-of-tolerance or poorly behaving of DERs. Ensuring the integrity of data from the relays at the POI, and DLT could enhance the security of the power grids. The first application is a SC to define and control the allowable total power factor (TPF) of the DER (wind farm) output, and the terms of the SC are implemented using DLT with a CGGS for a customer-owned DER. The TPF SC was implemented by the CGGS using DLT. The experimental model was performed with a real-time simulator using a CGGS and relay in-the-loop. The data collected from the CGGS were used to execute the TPF SC. The TPF limits were between +0.9 and +1.0, and the breakers’ operation in the POI was controlled by the relay using the SC. The events were collected from the real-time simulator, CGGS, and SEL 700GT relay to validate a successful application of the TPF SC using DLT. The second application is a SC to measure and control the allowable voltage service limits (VSL) by the CGGS using DLT. The tests were performed by using a real-time simulator, CGGS and relay in-the-loop. The data was collected from the CGGS that executed the SC. The main constraints were defined based on ANSI C84.1 service voltage limits, and the operation of the breakers in the POI. The events were collected from the CGGS, and SEL 700GT relay to assess a successful operation of the VSL SC using DLT.
In an era where the intersection of artificial intelligence (AI) and energy finance drives critical infrastructure decision-making, designing resilient AI architectures has become imperative.Predictive energy finance systems-spanning investment forecasting, carbon pricing, and grid demand-supply modeling-face mounting complexity due to shifting policy landscapes, data sovereignty regulations, and the escalating risk of adversarial threats.This paper presents a multidisciplinary framework for constructing AI architectures that maintain operational integrity, adaptability, and security in volatile environments.At a macro level, the study outlines the integration of federated learning, edge analytics, and privacy-preserving AI techniques to ensure compliance with crossborder data governance regimes while enabling decentralized energy financial modeling.It further examines adversarial machine learning risks-such as data poisoning and model inversion-that compromise predictive validity in high-stakes financial applications.Through threat modeling and robust training paradigms, the architecture includes defense-in-depth strategies like adversarial regularization, ensemble resilience, and real-time anomaly detection.The paper also analyzes the effects of dynamic policy shiftssuch as carbon credit revaluation and renewable energy subsidies-on model reliability and system adaptation.A scenario-based approach illustrates how the proposed architecture adjusts to policy-induced discontinuities through modular retraining, real-time policy rule parsing, and simulation-informed decision loops.Case studies from green energy bonds, smart grid investment portfolios, and climate-linked derivatives are used to validate the architectural robustness under varying policy, regulatory, and cyber conditions.Ultimately, this work provides a systems-engineered blueprint for resilient AI in predictive energy finance, enabling trustworthy, secure, and sovereign-compliant deployment.
Aiming at the problems of data security and privacy protection in the traditional power metering data sharing mode, this paper puts forward an architecture design of power metering data sharing platform based on blockchain, and deeply analyzes its multi-party security. The platform adopts hierarchical architecture, including data layer, network layer, consensus layer, contract layer and application layer. The data can not be tampered with and can be traced through distributed ledger technology, and the improved DPoS consensus algorithm and intelligent contract technology are used to ensure data consistency and automatic processing. In terms of security, differential privacy, zero-knowledge proof and improved PBFT fault-tolerant model are adopted to effectively resist data tampering, unauthorized access and potential attacks. Through case analysis, the results show that the platform has obvious advantages in data integrity protection, access control, privacy protection and inter-agency collaboration efficiency improvement, and shows good adaptability in resource consumption. The research shows that blockchain technology provides a safe, efficient and reliable solution for power metering data sharing.
The traditional centralized management mode has the risks of data tampering and privacy disclosure, and the sharing efficiency is limited. In this paper, an algorithm for safe sharing and intelligent contract optimization of power metering data based on blockchain is proposed to improve the security and efficiency of data sharing. The sharing model includes data generation layer, blockchain network layer, smart contract layer and data access layer, which ensures the data's tamper resistance and privacy protection through distributed account books and encryption technology. The smart contract optimization algorithm adopts a modular design, comprising data validation, access control, and transaction optimization modules. It integrates the Dynamic Proof of Stake (DPoS) consensus mechanism and Hash TimeLocked Contracts (HTLC) for cross-chain protocols, significantly enhancing the execution efficiency and security of smart contracts. Simulation experiments conducted on the Hyperledger Fabric platform show that the proposed algorithm increases transaction throughput (TPS) by 256 % under high load, reduces Gas consumption by 38.6 %, and markedly improves the security of cross-chain transactions. Specifically, the success rate of double-spending attacks is reduced 24-fold, the success rate of data tampering attacks drops to 0.01 %, and the risk of privacy leaks is decreased 75fold compared to traditional schemes.
The increasing digitization of the built environment, along with the growing demand for sustainable, resilient, and intelligent infrastructure, has led to the emergence of smart buildings as a critical domain of innovation. These buildings leverage Internet of Things (IoT) devices, building automation systems, data analytics, and artificial intelligence (AI) to optimize operations, reduce energy consumption, and enhance occupant comfort. However, the conventional design of smart building systems remains largely centralized. The reliance on centralized communication protocols and data architectures exposes building systems to single points of failure and cybersecurity threats, where a malfunction can disrupt its operational processes. Moreover, traditional facility management processes often predominantly rely on the centralized organizational structure which restricts participation from the building's stakeholders in the governance and operation of the facility, resulting in inefficiencies, misalignment with user needs, and missed opportunities for community participation. Furthermore, while traditional AI models and machine learning approaches have enabled a degree of building automation, their dependence on predefined rules or narrowly scoped training data limits their ability to reason adaptively in complex environments which is crucial in enabling autonomous building operations. This dissertation introduces a novel interdisciplinary framework that integrates generative artificial intelligence, digital twins, and distributed ledger technologies (DLTs) such as blockchain and decentralized autonomous organizations (DAOs) to develop a secure, decentralized, and autonomous building cyber-physical systems framework for building infrastructure. The overarching objective of this research is to transform how buildings are governed, operated, and maintained by shifting from centralized, administrator-led systems to inclusive, data-driven, and self-governing infrastructure. The research is structured around three primary goals: to enhance the security and resilience of IoT and digital twin data using decentralized communication protocols; to establish democratic and decentralized governance mechanisms for facility management; and to develop a prototype of an autonomous building cyber-physical system that integrates generative AI, digital twins, blockchain, and decentralized governance of building operation. To fulfill these objectives, the dissertation is composed of four interconnected studies. The first study focuses on enhancing the cybersecurity and resilience of smart building systems by developing blockchain-based protocols for transmitting IoT and digital twin data and building operation automation. The second study introduces a decentralized governance platform that allows building stakeholders to collectively participate in facility management decisions. With the addition of a real-time digital building twin and an AI assistant powered by a large language model, the platform promotes transparent, data-driven decision-making and greater inclusivity in building operations. Building on this foundation, the third study adds a blockchain-based incentivization model to encourage community involvement in the collective improvement and upkeeping of building infrastructure. The final study integrates the technological and governance components into a prototype of a Decentralized Autonomous Building Cyber-Physical System. This system brings together blockchain governance, digital twins, and AI agents/assistants capable of smart building appliances through natural human interaction and autonomous control of building operations. It also creates a collectively governed building infrastructure that functions autonomously with self-sustaining operation through blockchain-based protocols includes. The prototype was evaluated across several simulated and real-world scenarios, validating its effectiveness in enabling autonomous, secure, and community-governed building systems. This dissertation contributes to the growing body of knowledge at the intersection of smart buildings, blockchain governance, Generative AI-driven automation, and human-building interaction. By combining decentralized ledger technologies, AI, and digital twins, it lays the groundwork for a new paradigm of autonomous, secure, and community-driven building infrastructure. The research not only offers theoretical advancements in decentralized facility management and AI integration but also provides practical blueprints and open-source prototypes for broader adoption in the built environment.
Pierre Sedi Nzakuna, Vincenzo Paciello, A. Lay-Ekuakille, Angelo Kuti Lusala · 6 authors
The Internet of Things (IoT) demands scalable, secure, and feeless distributed ledger technologies (DLTs) to enable seamless machine-to-machine transactions. The IOTA DLT was developed to fulfill this vision through its feeless Directed Acyclic Graph (DAG) named the Tangle, whose announced upgrade to IOTA 2.0 promised feeless microtransactions and coordinator-free (Coordicide) decentralization via a Nakamoto Consensus mechanism and a Mana anti-spam system. However, its delayed decentralization and scalability limitations hindered ecosystem growth and practical IoT adoption, leading to a new ledger architecture named IOTA Rebased. This paper critically analyzes this architectural pivot and its implications for IoT applications, contrasting the abandoned IOTA 2.0 protocol-a leaderless, feeless DAG designed for the IoT-with the adoption of a Move Virtual Machine-based, object-oriented ledger secured by a Delegated Proof-of-Stake consensus via the Mysticeti protocol in IOTA Rebased. We evaluate IOTA Rebased trade-offs: enhanced programmability and speed versus compromised IoT suitability due to fees, and explore mitigation strategies such as sponsored transactions, lightweight clients, and hierarchical tiered transaction architecture to align IOTA Rebased with IoT environments where microtransactions are prevalent. A use case analysis is provided for the integration of IOTA Rebased in IoT scenarios. This study underscores the tension between technological innovation and decentralization, offering insights for balancing scalability with the unique demands of the IoT.
The exponential growth in power consumption demands a robust method to address and identify irregularities in distribution systems. This paper presents a novel approach integrating advanced machine learning with blockchain technology to enhance microgrid energy systems' anomaly detection and response times. The Isolation Forest algorithm is employed to identify outliers in power consumption. Custom statistical methods, such as Sudden Change Detection and Z-score, detect abrupt changes in power consumption patterns and statistical anomalies. To ensure prompt and automatic responses to identified irregularities, smart contracts are deployed on the Ethereum platform, enabling the instantaneous implementation of corrective measures. The system's real-time capabilities are enabled by the Web3 library, which establishes a direct connection between anomaly detection algorithms and smart contract execution, making the solution viable for practical deployment. The proposed model is demonstrated using a microgrid power consumption dataset, highlighting how smart contracts enable real-time detection and notification of anomalies. Upon identifying irregular power consumption, the smart contract recommends corrective actions, such as initiating load shedding and ensuring timely and transparent intervention. This integration of blockchain technology enhances the accuracy and efficiency of anomaly detection and provides a decentralized and autonomous solution for alerting system operators, reinforcing the security and reliability of microgrid energy systems.
Ahmad M. Almasabi, Ahmad B. Alkhodre, Maher Khemakhem, Fathy Eassa · 6 authors
IoT environments have introduced diverse logistic support services into our lives and communities, in areas such as education, medicine, transportation, and agriculture. However, with new technologies and services, the issue of privacy and data security has become more urgent. Moreover, the rapid changes in IoT and the capabilities of attacks have highlighted the need for an adaptive and reliable framework. In this study, we applied the proposed simulation to the proposed hybrid framework, making use of deep learning to continue monitoring IoT data; we also used the blockchain association in the framework to log, tackle, manage, and document all of the IoT sensor’s data points. Five sensors were run in a SimPy simulation environment to check and examine our framework’s capability in a real-time IoT environment; deep learning (ANN) and the blockchain technique were integrated to enhance the efficiency of detecting certain attacks (benign, part of a horizontal port scan, attack, C&C, Okiru, DDoS, and file download) and to continue logging all of the IoT sensor data, respectively. The comparison of different machine learning (ML) models showed that the DL outperformed all of them. Interestingly, the evaluation results showed a mature and moderate level of accuracy and precision and reached 97%. Moreover, the proposed framework confirmed superior performance under varied conditions like diverse attack types and network sizes comparing to other approaches. It can improve its performance over time and can detect anomalies in real-time IoT environments.