As a result of IoT-based Wireless Sensor Networks (IoT-WSNs), resource-constrained environments are becoming more efficient and dynamic. Even though IoT-WSNs have many advantages, they also face significant challenges, including their high energy consumption, limited lifespan, and security vulnerabilities. IoT-WSNs for smart cities could be made more energy-efficient and secure by using a blockchain-based approach. Blockchain technology improves energy efficiency and reduces communication costs while ensuring decentralized, secure spectrum management. A smart contract and distributed ledger mechanism reduce redundant data transmissions and facilitate network trust. A blockchain-enabled clustering mechanism allows energy-aware sensing and resource allocation, as well as cognitive radio technology used for efficient spectrum utilization. Compared to existing techniques, the proposed method is more energy efficient, more accurate in sensing spectrums, and more secure. IoT-WSNs provide a solution to energy and security challenges in smart city infrastructure, contributing to sustainable development.
MintMart is a decentralized Web3 platform designed for simplifying the process of buying, selling and creating NFTs (Non Fungible Tokens). Currently there are various existing platforms in the market which have features like transparent transactions, auctions in marketplace etc. However they lack some focus in areas of secure transactions, irregular platform fee and royalty compensation for owners which plays an important role in such platforms. The proposed system is designed with an objective to encounter such problems and make the use of NFT marketplace more seamless for the users. The proposed system uses blockchain technology to confront the existing issues. Royalty compensation distribution becomes easier using smart contracts and libraries like OpenZeppelin which helps in better integration of royalty features using its ERC-721 standard. Also MintMart offers fixed minim MintMart is a decentralized Web3 platform designed for simplifying the process of buying, selling and creating NFTs (Non Fungible Tokens). Currently there are various existing platforms in the market which have features like transparent transactions, auctions in marketplace etc. However they lack some focus in areas of secure transactions, irregular platform fee and royalty compensation for owners which plays an important role in such platforms. The proposed system is designed with an objective to encounter such problems and make the use of NFT marketplace more seamless for the users. The proposed system uses blockchain technology to confront the existing issues. Royalty compensation distribution becomes easier using smart contracts and libraries like OpenZeppelin which helps in better integration of royalty features using its ERC-721 standard. Also MintMart offers fixed minimal platform fee making it more accessible for users The proposed system can successfully support multiple digital formats like images, videos etc. and has also achieved cross chain compatibility allowing users to interact with various blockchain networks. MintMart plans to expand its features in the near future. These include personalized recommendations, live bidding for NFTs on the marketplace, etc., thus aiming to increase user engagement. The proposed system can successfully support multiple digital formats like images, videos, etc., and has also achieved cross-chain compatibility, allowing users to interact with various blockchain networks. MintMart plans to expand its features in the near future. These include personalized recommendations, live bidding for NFTs on the marketplace, etc., thus aiming to increase user engagement.
In recent years, the term Metaverse emerged as one of the most compelling concepts, captivating the interest of international companies such as Tencent, ByteDance, Microsoft, and Facebook. These company recognized the Metaverse as a pivotal element for future success and have since made significant investments in this area. The Metaverse is still in its developmental stages, requiring the integration and advancement of various technologies to bring its vision to life. One of the key technologies associated with the Metaverse is blockchain, known for its decentralization, security, trustworthiness, and ability to manage time-series data. These characteristics align perfectly with the ecosystem of the Metaverse, making blockchain foundational for its security and infrastructure. This paper introduces both blockchain and the Metaverse ecosystem while exploring the application of the blockchain within the Metaverse, including decentralization, consensus mechanisms, hash algorithms, timestamping, smart contracts, distributed storage, distributed ledgers, and non-fungible tokens (NFTs) to provide insights for researchers investigating these topics.
Preethi Preethi, Mohammed Mujeer Ulla, R. Sapna, Raghavendra M Devadas
Over the last few years, the conceptualization of Smart Home has received acceptance. The extensive issues regarding a smart home include offloading computational tasks, data security aspects, privacy issues, authentication of Internet of Things (IoT) devices, and so on. Presently, existing smart home automation addresses either of these issues, nevertheless, Smart Home automation that also necessitates decision-making for offloading computational tasks with improved QoS (i.e., latency and throughput) and systematic features apart from being reliable and safe is a definite necessity. To address these gaps in this, work a QoS-improved method called, Blockchain-modeled Swarm Optimized Lyapunov Smart Contract Deep Reinforced Tasks Offloading (BSOLSC-DRTO) in smart home is proposed. The BSOLSC-DRTO method is split into two sections, namely, Offloading Computational Tasks based on the Particle Swarm Optimized Lyapunov model and Temporal Difference Deep Reinforced Secured Offloading. First to solve the offloading issue and therefore improve the QoS, we developed a Particle Swarm Optimized Lyapunov model using a Lyapunov optimization function. This optimization problem aims to minimize latency and improve throughput considerably. Second, to boost the offloading security, we propose a trustworthy access control using the Temporal Difference Deep Reinforced Secured Offloading model that can safeguard devices against illegal offloading. Then to handle the computation management for addressing the offloading decisions in the queue temporal difference function is applied, therefore improving the smart contract accuracy and precision involved in offloading computational tasks. Evaluation results from experiments and numerical simulations exhibit the notable advantages of the proposed BSOLSC-DRTO method over existing methods.•Develop a Particle Swarm Optimized Lyapunov model to minimize latency and significantly improve throughput.•Proposed a Temporal Difference Deep Reinforced Secured Offloading model for trustworthy access control, protecting devices against illegal offloading
The integration of Artificial Intelligence (AI) and Blockchain technology has opened new possibilities in secure distributed systems, addressing several inherent challenges in cybersecurity, trust management, and data privacy. This article explores the synergies between AI and Blockchain, focusing on how they can complement each other in creating secure, transparent, and efficient distributed systems. AI's capabilities in predictive analytics, machine learning, and decision-making combined with Blockchain's decentralized, immutable ledger offer enhanced security and operational efficiency for applications ranging from financial transactions to healthcare and supply chain management. Through a comprehensive analysis of recent advancements and case studies, we highlight the practical implications of these technologies in fostering secure distributed systems and provide a roadmap for their future integration.
ABSTRACT IoT is a rapidly developing technology with a wealth of creative application possibilities. However, IoT wireless sensor networks are vulnerable to Denial of Service (DoS) attacks due to their insecure nature. Although network integrity and security have been ensured through the use of distributed ledger and blockchain technologies, privacy preservation concerns frequently arise with traditional approaches. So, deep learning‐based Physics‐informed neural networks (PINN) and Honesty‐based Distributed Proof‐of‐Authority (HDPoA) are developed to enhance transaction security and detect intrusions. Initially, the mobile nodes were deployed in different regions to gather transactions and an intrusion detection system to analyze attacks. First, the Intrusion Detection System (IDS) uses a deep learning approach for detecting the intrusion in the network. For that, the collected data from the deployed nodes are pre‐processed using Variational auto‐encoder and min‐max normalization to standardize input dataset values. Then the features are selected using wild horse optimization and classified using PINN to predict data attack or non‐attack. After that, Homomorphic variable tag generation is used for normal transactions with multiple copies in the same document, which are then converted into hash values using the Keccak hashing function. The miner validates transactions based on rank‐based priority. Honesty‐based Distributed Proof‐of‐Authority (HDPoA) was used for network security, making it suitable for deployment in blockchain‐based IoT applications. The proposed deep learning‐based PINN classifier reached 97.2% accuracy and 96.52% specificity. Homomorphic variable (HV) tag generation takes 0.4 s, while the Keccak algorithm takes 0.3 s for hash generation, and the HDPoA protocol has 420 s for block generation time.
The arrival of Machine Learning (ML) completely changed how we can unlock valuable information from data. Traditional methods, where everything was stored in one place, had big problems with keeping information private, handling large amounts of data, and avoiding unfair advantages. Machine Learning has become a powerful tool that uses Artificial Intelligence (AI) to overcome these challenges. We started by learning the basics of Machine Learning, including the different types like supervised, unsupervised, and reinforcement learning. We also explored the important steps involved, such as preparing the data, choosing the right model, training it, and then checking its performance. Next, we examined some key challenges in Machine Learning, such as models learning too much from specific examples (overfitting), not learning enough (underfitting), and reflecting biases in the data used. Moving beyond centralized systems, we looked at decentralized Machine Learning and its benefits, like keeping data private, getting answers faster, and using a wider variety of data sources. We then focused on a specific type called federated learning, where models are trained without directly sharing sensitive information. Real-world examples from healthcare and finance were used to show how collaborative Machine Learning can solve important problems while still protecting information security. Finally, we discussed challenges like communication efficiency, dealing with different types of data, and security. We also explored using a Zero Trust framework, which provides an extra layer of protection for collaborative Machine Learning systems. This approach is paving the way for a bright future for this groundbreaking technology.
In the era of big data, information security and privacy protection have become important issues facing today's society. This study proposes a distributed network security architecture based on blockchain to enhance the security of information privacy protection. The proposed architecture consists of three primary levels: equipment layer, network service layer, and application layer. It also integrates smart contracts. In addition, this study also proposes a vulnerability detection method based on improved tree convolutional neural networks. The incorporation of a "continuous binary tree" approach effectively addresses the limitation inherent to conventional tree convolution, wherein the number of nodes is fixed. This refinement enables a more effective capture of the hierarchical structure and semantic nuances inherent to smart contract code. The experiment used multiple datasets, each containing multiple IoT attack types and smart contract vulnerability code snippets. These datasets were evaluated based on a set of criteria, including but not limited to accuracy, recall, F1 scores, gas costs, and execution delays. Experiments have shown that the proposed method performs well in accuracy, precision, recall, and F1 scores compared to existing state-of-the-art methods, with an accuracy range of 89.62% to 98.36%, significantly better than Oyente (about 75%) and Securify (about 85%). Specifically, the proposed method achieved 96.14% accuracy in detecting reentrant attacks, compared to 78% for Oyente and 82% for Securify. The findings indicate that the architectural design exerts a substantial influence on enhancing network security performance, thereby ensuring the stability of the system by effectively mitigating the variability in response time.
Over the past few years, the financial industry has undergone significant transformation as a result of a flurry of technological advancements.This examination of the most recent innovations that have altered the financial landscape focuses on fintech, blockchain technology, artificial intelligence (AI), and digital currencies.Startups in the fintech sector have created brand-new financial services with the goal of improving customer service, efficiency, and accessibility.Block chain technology has revolutionized data management and secure transactions, paving the way for decentralized finance (DeFi) and smart contracts.AI has improved risk assessment, fraud detection, and personalized financial services through advanced data analytics.Digital currencies, particularly crypto currencies and central bank digital currencies (CBDCs), have also challenged conventional monetary systems and introduced new paradigms for global financial transactions.Consumers, regulators, and financial institutions face both challenges and opportunities as a result of these innovations, according to this research.To fully utilize these technologies' potential and address associated risks, the findings emphasize the need for industry-wide adaptation and ongoing collaboration.
Phishing is a serious threat to cryptocurrency networks; Bitcoin and Ethereum are prime targets for these attacks. This paper discusses some aspects of phishing attacks on these platforms. While the simpler architecture of Bitcoin leads to more direct phishing attempts, the more complex ecosystem in Ethereum introduces a wide range of attack vectors through dApps and smart contracts. A comparative analysis of phishing attacks in both blockchains shows that while both have their fair share of attacks, Bitcoin seems to bear the brunt of phishing attacks. Current defense strategies, like 2FA and anti-phishing tools, as well as recommendations for increasing network security against phishing are discussed in this paper. Understanding these phishing mechanisms is crucial in strengthening the security of blockchain platforms and mitigating future attacks.
As Industry 5.0 emerges, the convergence of advanced technologies like the Internet of Things (IoT) and blockchain is vital in shaping the future of industrial automation. Industry 5.0 emphasizes the collaborative relationship between humans and machines, requiring robust, decentralized systems to ensure security, accountability, and trust in interconnected ecosystems. Currently, IoT data processing is cloud-centric, which introduces challenges like fragmented data silos, limiting the potential for seamless and secure real-time analytics. Blockchain technology offers a solution by providing a decentralized and transparent ledger that can enhance data integrity and security across IoT applications. This study investigates the integration of blockchain with the IoT in the context of Industry 5.0, highlighting the potential for improved data management, security, and human-machine collaboration. By conducting a comprehensive analysis of IoT application designs and blockchain platforms, we evaluate existing literature to uncover the challenges, benefits, and limitations of this integration. Our research contributes by proposing a framework for selecting optimal blockchain platforms for IoT applications in Industry 5.0, providing actionable recommendations for enhanced data trust and resilience. Future research directions are also outlined to address the evolving demands of this technological convergence, ensuring that IoT ecosystems are secure, scalable, and human-centered in the era of Industry 5.0.
The integration of Internet of Things (IoT) devices in healthcare has revolutionized patient care by enabling real-time monitoring, personalized treatments, and efficient data management. However, this technological advancement introduces significant security risks, particularly concerning the confidentiality, integrity, and availability of sensitive medical data. Traditional security measures are often insufficient to address the unique challenges posed by IoT environments, such as heterogeneity, resource constraints, and the need for real-time processing. To tackle these challenges, we propose a comprehensive three-phase security framework designed to enhance the security and reliability of IoT-enabled healthcare systems. In the first phase, the framework assesses the reliability of IoT devices using a reputation-based trust estimation mechanism, which combines device behavior analytics with off-chain data storage to ensure scalability. The second phase integrates blockchain technology with a lightweight proof-of-work mechanism, ensuring data immutability, secure communication, and resistance to unauthorized access. The third phase employs a lightweight Long Short-Term Memory (LSTM) model for anomaly detection and classification, enabling real-time identification of cyber threats. Simulation results demonstrate that the proposed framework outperforms existing methods, achieving a 2% increase in precision, accuracy, and recall, a 5% higher attack detection rate, and a 3% reduction in false alarm rate. These improvements highlight the framework's ability to address critical security concerns while maintaining scalability and real-time performance.
Increasing adoption of electric vehicles (EVs) and the expansion of EV charging infrastructure present opportunities for enhancing sustainable transportation within smart cities. However, the interconnected nature of EV charging stations (EVCSs) exposes this infrastructure to various cyber threats, including false data injection, man-in-the-middle attacks, malware intrusions, and denial of service attacks. Financial attacks, such as false billing and theft of credit card information, also pose significant risks to EV users. In this work, we propose a Hyperledger Fabric-based blockchain network for EVCSs to mitigate these risks. The proposed blockchain network utilizes smart contracts to manage key processes such as authentication, charging session management, and payment verification in a secure and decentralized manner. By detecting and mitigating malicious data tampering or unauthorized access, the blockchain system enhances the resilience of EVCS networks. A comparative analysis of pre- and post-implementation of the proposed blockchain network demonstrates how it thwarts current cyberattacks in the EVCS infrastructure. Our analyses include performance metrics using the benchmark Hyperledger Caliper test, which shows the proposed solution’s low latency for real-time operations and scalability to accommodate the growth of EV infrastructure. Deployment of this blockchain-enhanced security mechanism will increase user trust and reliability in EVCS systems.
As the acceptance of Internet of Things (IoT) systems quickens, guaranteeing their sustainability and reliability poses an important challenge. Faults in IoT systems can result in resource inefficiency, high energy consumption, reduced security, and operational downtime, obstructing sustainability goals. Thus, blockchain (BC) technology, known for its decentralized and distributed characteristics, can offer significant solutions in IoT networks. BC technology provides several benefits, such as traceability, immutability, confidentiality, tamper proofing, data integrity, and privacy, without utilizing a third party. Recently, several consensus algorithms, including ripple, proof of stake (PoS), proof of work (PoW), and practical Byzantine fault tolerance (PBFT), have been developed to enhance BC efficiency. Combining fault detection algorithms and BC technology can result in a more reliable and secure IoT environment. Thus, this study presents a sustainable BC-Driven Edge Verification with a Consensus Approach-enabled Optimal Deep Learning (BCEVCA-ODL) approach for fault recognition in sustainable IoT environments. The proposed BCEVCA-ODL technique incorporates the merits of the BC, IoT, and DL techniques to enhance IoT networks’ security, trustworthiness, and efficacy. IoT devices have a substantial level of decentralized decision-making capacity in BC technology to achieve a consensus on the accomplishment of intrablock transactions. A stacked sparse autoencoder (SSAE) model is employed to detect faults in IoT networks. Lastly, the Piranha Foraging Optimization Algorithm (PFOA) approach is used for optimum hyperparameter tuning of the SSAE approach, which assists in enhancing the fault recognition rate. A wide range of simulations was accomplished to highlight the efficacy of the BCEVCA-ODL technique. The BCEVCA-ODL technique achieved a superior FDA value of 100% at a fault probability of 0.00, outperforming the other evaluated methods. The proposed work highlights the significance of embedding sustainability into IoT systems, underlining how advanced fault detection can provide environmental and operational benefits. The experimental outcomes pave the way for greener IoT technologies that support global sustainability initiatives.
In recent times, the number of fake drugs has increased dramatically, which has resulted in millions of victims severely affected by poisoning and treatment failures, resulting in a need for Drug Supply Chain (DSC) traceability. The DSC is generally reluctant to share traceability data and includes several parties having heterogeneous interests. Moreover, existing provenance and traceability systems for DSCs need more trust, data sharing transparency, and separated data storage. By realizing decentralized, trustless systems, a decentralized Blockchain (BC)-based solution is proposed to tackle these constraints. BC is an immutable, decentralized, shared network that allows management directly through a peer-to-peer (P2P) network without the necessity of a central authority to check transactions. This study proposes a new Blockchain Non-Fungible Token-based Drug Traceability with Enhanced Pharmaceutical Supply Chain Management (BNFTDT-EPSCM) model. The proposed BNFTDT-EPSCM model presents transparent and more secure reporting of changes in the operating condition of transported pharmaceutical products to prevent drug recalls. The Ethereum BC enables transactions and computational services using the cryptocurrency Ether (ETH). Simultaneously, an enhanced Byzantine fault-tolerant consensus (RB-BFT) leverages a reputation system to address reliability issues of primary nodes and reduce communication complexity inherent in the Practical Byzantine algorithm (PBFT). The BNFTDT-EPSCM model presents a decentralized solution using Non-Fungible Tokens (NFTs) to improve the traceability and tracking capabilities of the standard serialization process. In addition, the BNFTDT-EPSCM model employs a Deep Belief Network (DBN) approach to perform the inbound logistics task prediction process. Finally, the Tasmanian Devil Optimization (TDO) method is utilized to enhance the hyperparameter tuning of the DBN approach. A detailed set of simulations was executed to examine the effectiveness of the BNFTDT-EPSCM approach, demonstrating a higher throughput at the highest user count of 6000 and achieving 551.22 TPS, significantly outperforming existing models.
The goal of this current study is to address important concerns about data security, privacy, and integrity by amalgamating blockchain technology with the Internet of Medical Things. The IoMT ecosystem consists of wearables, implanted sensors, and remote monitoring tools that generate sensitive medical data continuously, revealing several security vulnerabilities. Blockchain, with its principles of decentralization, transparency, immutability, and cryptographic security, opens up new avenues for securing health data without the use of third-party authorities. This paper outlines the methodology used in this review, including a systematic analysis of relevant literature, utilizing the PRISMA framework to evaluate sources. The analysis identifies key protocols and components of blockchain relevant to IoMT, highlights challenges, and provides solutions. Key findings emphasize blockchain’s ability to reduce attacks using distributed ledgers, permissioned access, and encrypted transactions. Furthermore, blockchain may improve patient care by providing real-time data exchange and enabling interoperability across health systems.
Yang Yang, Min Lin, Yangfei Lin, Chen Zhang · 5 authors
In the area of agriculture and livestock management, the integration of the Internet of Things (IoT) has emerged as a groundbreaking strategy to enhance operational efficiency and advance intelligent process management. However, this sector faces significant challenges, including ambiguity in product origins and limited regulatory oversight of IoT devices. This paper explores the innovative integration of blockchain technology within the agricultural and livestock IoT, highlighting how this convergence significantly enhances operational security and transparency. We provide an in-depth review of the latest applications and advancements of blockchain in these domains, offering a comprehensive analysis of the current state of technology and its implications. Furthermore, this paper discusses the potential future development trajectories in agricultural and livestock IoT, emphasizing blockchain’s role in addressing current challenges and shaping future innovations. The findings suggest that blockchain technology not only improves data security and trustworthiness but also opens new avenues for efficient and transparent management in agriculture and animal husbandry.
The food and agriculture sector is a cornerstone of critical infrastructure (CI), underpinning global food security, public health, and economic stability. However, the increasing digitalization and connectivity of operational technologies (OTs) in this sector expose it to significant cybersecurity risks. Blockchain technology (BT) has emerged as a transformative solution for addressing these challenges by enhancing network security, traceability, and system resilience. This study presents a comprehensive review of BT applications in OT security for food and agriculture CI, employing bibliometric and content analysis methods. A total of 124 relevant articles were identified from six databases, including the Web of Science Core Collection and MEDLINE®. Bibliometric analysis was conducted across five dimensions: publication year, literature type, journal distribution, country contributions, and keyword trends. The findings are meticulously organized through tables, charts, and graphs. The year 2018 marked a surge in research within this domain, with the IEEE Internet of Things Journal and IEEE ACESS emerging as the most prolific journals, each boasting nine publications. The United States, China, and India are at the forefront in terms of journal citation counts. Our analysis determined that a reference count of 37 serves as an appropriate threshold. Otoum Safa stands out as the author with the highest number of published articles, totaling four. Keywords such as “blockchain”, “internet of things”, “smart contract”, “security”, and “critical infrastructure” appear with significant frequency. The statistics, trends, and insights gleaned from this bibliometric analysis can guide researchers in the OTCI field to forge a coherent and logical research trajectory. Content analysis further identified six key research areas within this domain: identity authentication and data verification, secure access control, attack detection and perception, data security and protection, data backup and recovery, and attack assessment and attribution. Based on these insights, a general framework is proposed to guide future research and practical applications of BT in securing OT within food and agriculture CI. This study systematically analyzes the current research landscape, challenges, and opportunities for BT in securing the OT critical to food and agriculture CI. By bridging the gap between blockchain innovations and the operational needs of the food and agriculture sector, this work contributes to advancing strategic implementation and improving the security of CI systems.
In smart applications, streaming IoT data is essential to building trust in sustainable IoT solutions. However, most existing systems for storing and disseminating IoT data streams lack reliability, security, and transparency, primarily due to centralized architectures that create single points of failure. To address these limitations, this research introduces TraVel, a blockchain and transfer learning-based framework for secure IoT data management. TraVel leverages decentralized IPFS storage to handle large data volumes effectively, integrating with a private Ethereum blockchain to enhance data integrity and accessibility. In the proposed scheme, the smart home ([Formula: see text]) data is collected securely and accessed over the BC with a unique hash key generated on the IPFS for all the files. Self-executing Ethereum smart contracts enforce access control and verify data integrity, allowing only validated, non-malicious data to be stored. An adversarial domain adaptation (DA) learning model is employed to detect and filter malicious data before it enters the blockchain. TraVel's performance is evaluated on blockchain parameters, with simulations conducted on REMIX IDE and InterPlanetary File System (IPFS), demonstrating its reliability and scalability for secure IoT data dissemination.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Blockchain technology has significant applications in medicine, particularly in ensuring the confidentiality of medical records and enhancing the transparency of healthcare data management. Rapid digitalization in healthcare presents challenges related to interoperability and privacy, which traditional centralized systems struggle to address. Blockchain, as immutable and distributed ledger technology initially designed for cryptocurrencies, offers a solution by securing and decentralizing healthcare data management. The NAIBHSC framework exemplifies this integration, utilizing blockchain alongside IoT, cloud computing, and AI to enhance the supply chain management of healthcare products and electronic health records. Additionally, its implementation in car insurance systems showcases automated damage identification and secure premium transactions. The study concludes that by combining blockchain with modern technologies, solutions can be developed that enhance security, transparency, and efficiency across various domains, ultimately fostering trust and innovation within critical industries.
Nikos Kostopoulos, Yannis C. Stamatiou, Constantinos Halkiopoulos, Hera Antonopoulou
Background: Blockchain technology can transform military operations, increasing security and transparency and gaining efficiency. It addresses many problems related to data security, privacy, communication, and supply chain management. The most researched aspects are its integration with emerging technologies, such as artificial intelligence, the IoT, application in uncrewed aerial vehicles, and secure communications. Methods: A systematic review of 43 peer-reviewed articles was performed to discover the applications of blockchain in defense. Key areas analyzed include the role of blockchain in securing communications, fostering transparency, promoting real-time data sharing, and using smart contracts for maintenance management. Challenges were assessed, including scalability, interoperability, and integration with the legacy system, alongside possible solutions, such as sharding and optimized consensus mechanisms. Results: In the case of blockchain, great potential benefits were shown in enhancing military operations, including secure communication, immutable record keeping, and real-time integration of data with the IoT and AI. Smart contracts optimized resource allocation and reduced maintenance procedures. However, challenges remain, such as scalability, interoperability, and high energy requirements. Proposed solutions, like sharding and hybrid architecture, show promise to address these issues. Conclusions: Blockchain is set to revolutionize the efficiency and security of the military. Its potential is enormous, but it must overcome scalability, interoperability, and integration issues. Further research and strategic adoption will thus allow blockchain to become one of the cornerstones of future military operations.
Uncrewed aerial systems (UASs) were popularly used by hobbyists in the past, but they have now become critical enablers for managing disasters, handling emergencies, and so on. For example, one of their most critical applications is to provide seamless wireless communication services in remote rural areas. Thus, it is substantial to identify and consider the different security challenges in the research and development associated with advanced UAS-based B5G/6G architectures. Catering to this requirement, this article conducts a comprehensive review of the security aspects of UASs with respect to the 5G/6G system architecture, its enabling technologies, and privacy issues. It exhibits security integration at all the protocol stack layers and analyzes the existing mechanisms to secure UAS-based B5G/6G communications and its energy and power optimization factors. Last, this article also summarizes modern technological trends for establishing security and protecting UAS-based systems, along with the open challenges and strategies for future research work.
The pharmaceutical industry faces critical challenges like counterfeiting and supply chain inefficiencies, jeopardizing public health and the sector’s integrity. This paper introduces the efficient blockchain-enhanced transparent pharmaceutical supply chain management (EBETPSCM) model, which innovatively integrates blockchain and big data analytics to enhance traceability, security, and operational efficiency. At the heart of this model is the strategic use of Hyperledger fabric, renowned for its decentralized consensus mechanism and robust cryptographic methods. This ensures the security and reliability of the supply chain, with its decentralized nature bolstering data immutability, a key factor in maintaining the integrity of supply chain information. Concurrently, big data analytics provide real-time insights, enhancing stakeholder visibility across the chain. Our study critically appraises prevailing challenges, highlighting blockchain’s potential to achieve data immutability and transparency. Empirical evidence from existing studies affirms blockchain’s role in safeguarding pharmaceutical data and refining supply chain operations. The proposed EBETPSCM model integrates a comprehensive framework, addressing technical, methodological, and regulatory aspects. Theoretical outcomes include a well-defined conceptual model, technical insights into blockchain, and big data analytics methodologies. Practically, the study endeavors to implement a prototype system to demonstrate significant improvements in efficiency, transparency, and security. To overcome extant challenges, we advocate for resolving technological issues, enhancing collaborative efforts, and developing new legislative frameworks. The anticipated outcomes promise substantial advancements in safety, efficiency, and transparency within pharmaceutical supply chains. Conclusively, our study emphasizes the necessity of continuous research, collaborative engagement, and regulatory support for the successful adoption of these technologies in the pharmaceutical sector.