Abstract Plant diseases are considered as the major bottleneck for the farmers to monitor and diagnosis with the super intelligent methods. With the onset of Artificial Intelligence, Internet of Things(IoT), predicting the plant diseases in early stages has given the bright light of hope to farmers for boosting the productivity of agriculture in which increases the country’s economy. But the current advances in the IoT-AI-driven data gathers data from the agricultural fields and integrates the strong communication system for the early prediction and diagnosis process. Though these intelligent drive systems have several advantages, these procedure have been suffering from the varied security challenges which accelerates an outcry for a cognitive systems in the form of data breaches and privacy problems. The protection of patient data remains a significant concern due to the sensitivity and value of healthcare information, especially when transmitted over the Internet. This has heightened the demand for secure systems to safeguard against data breaches and privacy issues. Similarly, in agriculture, security challenges have been addressed using Web 3.0 and Blockchain technologies, which are favored for their immutable and decentralized features. This research proposes an advanced Web 3.0 Ensemble hybrid blockchain framework to enhance authentication security within the agricultural sector. To improve the authentication process further, chaotic maps are used to generate highly dynamic hashes during the creation of genesis blocks, ensuring that all data is securely stored in the recommended approach. The framework was tested on the Ethereum blockchain using Web 3.0, with Python 3.19 as the primary programming language for developing various interfaces. The security strength of the framework was thoroughly assessed using NIST standard tests, and its robustness was compared with other blockchain models. The results demonstrate that the proposed framework provides stronger defenses against various attacks and surpasses varied approaches in terms of complexity and robustness.
"A distributed database that maintains an ever-expanding list of ordered records, called blocks," is how a blockchain is defined.These parts are connected by the use of cryptography. A timestamp contain by each, the preceding block of a cryptographic hash , and with a transaction information. Also we can say that distributed, public, decentralized digital ledger that keeps track of transactions across several computers is called a blockchain. Its goal is to stop record tampering without interfering with network consensus or all subsequent blocks. Because blockchain and smart contracts are developed using non-standard software life cycles, there may be security flaws and difficulties in getting users to adopt the technology. For instance, distributed applications may not receive regular updates or may have bugs that can only be fixed by releasing a new version. A detailed review of smart contracts was covered in this publication. In terms of security, privacy, communication channel, etc., it further differentiated and contrasted the security of smart contracts with that of traditional security. This study also discusses other smart contract systems, including Stellar, Monax, Ethereum, Bitcoin, and Lisk. For smart contracts certain suggested methods are applied in various contexts to address security risks. Furthermore, also smart contract classification of the security application was put out in an effort to address some of the shortcomings. Additionally, the paper offers a thorough security scenario for smart contracts using several methods. Finally, the dangers and weaknesses of the smart contracts that might lead to an attack are listed. Here we can find and focuses on security risks and weaknesses specific to smart contracts.
The rise of the Internet of Things (IoT) has driven significant advancements across sectors such as urbanization, manufacturing, and healthcare, all of which are focused on enhancing quality of life and stimulating the global economy. This survey offers an in-depth analysis of the integration of blockchain technology with IoT, addressing aspects such as architectural alignment, applications, security, limitations, scalability, and latency. Moreover, this survey focuses on security, integration techniques, and future research directions. The primary contributions of this review include a taxonomy of security concerns specific to IoT, an analysis of integration methods, and insights into consensus mechanisms suitable for resource-constrained environments. These findings highlight the unique challenges and opportunities in IoT–blockchain integration, providing a foundation for advancing secure and scalable IoT applications. By exploring consensus mechanisms and resource-constrained deployments, this paper provides a framework for developing secure and efficient IoT applications utilizing blockchain technology and providing a basis for future research and practical applications. In addition, this survey investigates innovative trends, including AI-driven blockchain for IoT.
HealthCare 4.0 stands as a cutting-edge technology that integrates human-sourced data with communication systems to enable highly accurate clinical diagnostics and treatments. While sensor-based devices have simplified many aspects of daily life, these advancements also face significant security challenges, particularly in safeguarding patient information. In response, this research introduces a novel blockchain-based hybrid chaotic encryption and authentication for maintaining EHR transmissions aimed at robust and secure authentication within HealthCare 4.0. The complete framework incorporates the Infura Web API, using Python 3.19. To strengthen authentication security, modified Hippopotamus-evoked scroll maps are used to generate dynamic hashes in the genesis block formation, securing all EHR data in the proposed architecture. This entire framework was evaluated on the Ethereum Blockchain, leveraging Web 3.0, with Python 3.19 serving as the core programming environment for interface development. Security analysis was performed utilizing Burrows-Abadi-Needham (BAN) logic, while the framework's resilience was rigorously tested against NIST standards. Comparative testing highlights the model's superior defense capabilities against various attacks, surpassing other frameworks in complexity and robustness. The findings ultimately provide a comprehensive view of the optimized encryption scheme on blockchain technology and the future pathways for secure authentication frameworks in HealthCare 4.0.
After the emergence of the Internet of Things (IoT), the way devices interact with each other changed, as it allowed automation and seamless communication in various fields. However, various challenges related to security and trust have emerged, hindering the widespread adoption of the IoT. Blockchain technology is considered the ideal solution to face these challenges because of its immutable and decentralized nature. This paper explores the potential of blockchain technology to address critical security and trust challenges within the rapidly growing IoT ecosystem. Through a systematic literature review, this study examines how blockchain’s decentralized, immutable, and transparent features contribute to enhancing security and trust in IoT networks. Key findings indicate that blockchain integration can prevent data manipulation, ensure robust identity management, and facilitate transparent, verifiable transactions, supporting both security and trust in IoT systems. These attributes not only improve IoT security but also promote sustainable practices by optimizing resource efficiency, reducing environmental impact, and enhancing resilience in systems like supply chain management and smart grids. Additionally, this study identifies open research challenges and suggests future directions for optimizing blockchain in IoT environments, focusing on scalability, energy-efficient consensus mechanisms, and efficient data processing.
As unmanned aerial vehicle (UAV) technology expands into diverse applications, the demand for enhanced performance intensifies. Blockchain sharding technology offers promising avenues for improving data processing capabilities and security in drone networks. However, the inherent mobility of UAVs and their dynamic operational environment pose significant challenges to conventional sharding techniques, often resulting in communication latencies and data synchronization delays that compromise efficiency. This study presents a novel blockchain-based adaptive sharding framework specifically designed for UAV ecosystems. Our research extends beyond improving data transmission rates to encompass an enhanced Asynchronous Advantage Actor-Critic algorithm, tailored to address long-term optimization objectives in aerial networks. The proposed optimizations focus on dual objectives: enhancing data security while concurrently accelerating processing speeds. By addressing the limitations of traditional approaches, this work aims to facilitate seamless communication and foster innovation in UAV networks. The adaptive sharding framework, coupled with the refined A3C algorithm, presents a comprehensive solution to the unique challenges faced by mobile aerial systems in blockchain implementation.
The increasing ability of internet-connected daily life electronic gadgets has propelled smart homes into a global trend. The Internet of Things (IoT) enables ambient devices to communicate and interact seamlessly through various sensors. Emerging technical concepts like Web3 and Industry 5.0 require decentralised and intelligent systems near the network's edge. Petabytes of IoT sensor-generated data cause a shortage of storage on the Cloud servers, adding a delay factor to the IoT system. Standard cloud-based IoT systems can't fully function in areas with unstable internet. This paper addresses these challenges and proposes a solution to integrate edge computing concepts. The proposed system is developed using a Raspberry Pi 3 Home Server (RHS) driven by the Support Vector Machine (SVM) algorithm. The designed prototype includes a fire and smoke detection system with MQ2 gas, dust, temperature, and flame sensors. The SVM and these sensors form a data fusion module integrating with Network Mapper (NMAP), Message Queuing Telemetry Transport (MQTT) broker, MariaDB SQL server, and InfluxDB time series database. The experiments demonstrate a fundamental edge operation with a latency of 2.45 ms (milliseconds), while NMAP integration ensures data security and device verification for sensor data storage. The synthetic simulations show positive outcomes for the data fusion-based monitoring system, where alerts are promptly triggered as sensor values change, with an overall system latency of approximately 24 ms. The developed system manages home automation, real-time monitoring for fire, smoke, gas leaks, network scans, anomaly detection, appliance usage tracking, and cloud data backup. A multi-level alert system ensures early threat mitigation, with alarms, SMS, notifications, and email alerts to maximize awareness.
Abstract: This research investigates the transformative potential of incorporating blockchain technology into computer science education. In light of the rapid evolution of the digital landscape, traditional educational frameworks often fail to meet the demands of the industry, resulting in a significant skills gap among graduates. This paper analyzes how the integration of blockchain can revolutionize computer science curricula by enhancing learning experiences and equipping students to navigate future technological challenges. The study highlights the advantages of blockchain in educational contexts, including increased security and transparency of academic records, streamlined credentialing processes, and the establishment of decentralized learning platforms that promote collaboration and innovation. It presents case studies of institutions that have successfully implemented blockchain, along with strategies for educators to effectively integrate this technology into their pedagogical approaches. Additionally, the research addresses the challenges and limitations associated with blockchain integration, such as the requisite learning curve and infrastructure demands. The findings indicate that the incorporation of blockchain in computer science education can significantly boost student engagement, provide verifiable skill sets, and align academic outcomes more closely with industry requirements. This study contributes to the ongoing discourse on educational innovation and offers a strategic framework for institutions aiming to utilize blockchain technology in preparing students for the future job market.
Paras Shah, Chetna Patel, Jaykumar Patel, Akash Shah · 6 authors
Blockchain is a decentralized, secure, and immutable public ledger that offers significant benefits over conventional centralized systems by preventing data breaches and cyber-attacks. It has a great potential to improve data security, privacy, and interoperability in healthcare and biomedical research. This review discusses the basic principles and the historical evolution of blockchain and evaluates the implications of blockchain for the existing healthcare infrastructure. It also highlights blockchain technology's advantages in electronic health records, supply chain management, clinical trials, and telemedicine. However, this technology faces several hurdles, including regulatory issues, technical complexity, and economic costs, which suggest a gradual adoption over time. In addition, the review emphasizes its ability to ensure data integrity, enhance collaboration, and protect intellectual property in biomedical research. This review shows that blockchain can enhance healthcare data management by providing secure, efficient, and patient-centric solutions. Furthermore, it also discusses the implications of blockchain for the future of healthcare and biomedical research and suggests that ongoing research and interdisciplinary approaches are essential for overcoming current barriers and realizing the full potential of this technology. Future research should focus on developing privacy-preserving hybrid data storage solutions that comply with international laws and regulations, thus enhancing the sustainability and scalability of this technology in healthcare.
This paper surveys the landscape of security and data attacks on machine unlearning, with a focus on financial and e-commerce applications. We discuss key privacy threats such as Membership Inference Attacks and Data Reconstruction Attacks, where adversaries attempt to infer or reconstruct data that should have been removed. In addition, we explore security attacks including Machine Unlearning Data Poisoning, Unlearning Request Attacks, and Machine Unlearning Jailbreak Attacks, which target the underlying mechanisms of unlearning to manipulate or corrupt the model. To mitigate these risks, various defense strategies are examined, including differential privacy, robust cryptographic guarantees, and Zero-Knowledge Proofs (ZKPs), offering verifiable and tamper-proof unlearning mechanisms. These approaches are essential for safeguarding data integrity and privacy in high-stakes financial and e-commerce contexts, where compromised models can lead to fraud, data leaks, and reputational damage. This survey highlights the need for continued research and innovation in secure machine unlearning, as well as the importance of developing strong defenses against evolving attack vectors.
Afnan Alsadhan, Areej Alhogail, Hessah A. Alsalamah
The Internet of Medical Things (IoMT) is a rapidly expanding network comprising medical devices, sensors, and software that collect and exchange patient health data. Today, the IoMT has the potential to revolutionize healthcare by offering more personalized care to patients and improving the efficiency of healthcare delivery. However, the IoMT also introduces significant privacy concerns, particularly regarding data privacy. IoMT devices often collect and store large amounts of data about patients’ health. These data could be used to track patients’ movements, monitor their health habits, and even predict their future health risks. This extensive data collection and surveillance could be a major invasion of patient privacy. Thus, privacy-preserving research in an IoMT context is an important area of research that aims to mitigate these privacy issues. This review paper comprehensively applies the PRISMA methodology to analyze, review, classify, and compare current approaches of preserving patient data privacy within IoMT blockchain-based healthcare environments.
P. Chinnasamy, G. Charles Babu, Ramesh Kumar Ayyasamy, S. Amutha · 6 authors
6G mobile network technology will set new standards to meet performance goals that are too ambitious for 5G networks to satisfy. The limitations of 5G networks have been apparent with the deployment of more and more 5G networks, which certainly encourages the investigation of 6G networks as the answer for the future. This research includes fundamental privacy and security issues related to 6G technology. Keeping an eye on real-time systems requires secure wireless sensor networks (WSNs). Denial of service (DoS) attacks mark a significant security vulnerability that WSNs face, and they can compromise the system as a whole. This research proposes a novel method in blockchain 6G-based wireless network security management and optimization using a machine learning model. In this research, the deployed 6G wireless sensor network security management is carried out using a blockchain user datagram transport protocol with reinforcement projection regression. Then, the network optimization is completed using artificial democratic cuckoo glowworm remora optimization. The simulation results have been based on various network parameters regarding throughput, energy efficiency, packet delivery ratio, end-end delay, and accuracy. In order to minimise network traffic, it also offers the capacity to determine the optimal node and path selection for data transmission. The proposed technique obtained 97% throughput, 95% energy efficiency, 96% accuracy, 50% end-end delay, and 94% packet delivery ratio.
Mithul Raaj A T, B. Saravana Balaji, Sai Arun Pravin R R, Rani Chinnappa Naidu · 10 authors
In response to the growing need for enhanced energy management in smart grids in sustainable smart cities, this study addresses the critical need for grid stability and efficient integration of renewable energy sources, utilizing advanced technologies like 6G IoT, AI, and blockchain. By deploying a suite of machine learning models like decision trees, XGBoost, support vector machines, and optimally tuned artificial neural networks, grid load fluctuations are predicted, especially during peak demand periods, to prevent overloads and ensure consistent power delivery. Additionally, long short-term memory recurrent neural networks analyze weather data to forecast solar energy production accurately, enabling better energy consumption planning. For microgrid management within individual buildings or clusters, deep Q reinforcement learning dynamically manages and optimizes photovoltaic energy usage, enhancing overall efficiency. The integration of a sophisticated visualization dashboard provides real-time updates and facilitates strategic planning by making complex data accessible. Lastly, the use of blockchain technology in verifying energy consumption readings and transactions promotes transparency and trust, which is crucial for the broader adoption of renewable resources. The combined approach not only stabilizes grid operations but also fosters the reliability and sustainability of energy systems, supporting a more robust adoption of renewable energies.
The healthcare industry has witnessed a transformative impact due to recent advancements in sensing technology, coupled with the Internet of Medical Things (IoMTs)-based healthcare systems. Remote monitoring and informed decision-making have become possible by leveraging an integrated platform for efficient data analysis and processing, thereby optimizing data management in healthcare. However, this data is collected, processed, and transmitted across an interconnected network of devices, which introduces notable security risks and escalates the potential for vulnerabilities throughout the entire data processing pipeline. Traditional security approaches rely on computational complexity and face challenges in adequately securing sensitive healthcare data against evolving threats, thus necessitating robust solutions that ensure trust, enhance security, and maintain data confidentiality and integrity. To address these challenges, this paper introduces a two-phase framework that integrates blockchain technology with IoMT to enhance trust computation, resulting in a secure cluster that supports the quality-of-service (QoS) for sensitive data. The first phase utilizes the decentralized interplanetary file system and hashing functions to create a smart contract for device registration, establishing a resilient storage platform that encrypts data, improves fault tolerance, and facilitates data access. In the second phase, communication overhead is optimized by considering power levels, communication ranges, and computing capabilities alongside the smart contract. The smart contract evaluates the trust index and QoS of each node to facilitate device clustering based on processing capabilities. We implemented the proposed framework using OMNeT++ simulator and C++ programming language and evaluated against the cutting-edge IoMT security approaches in terms of attack detection, energy consumption, packet delivery ratio, throughput, and latency. The qualitative results demonstrated that the proposed framework enhanced attack detection by 6.00%, 18.00%, 20.00%, and 27.00%, reduced energy consumption by 6.91%, 8.19%, 12.07%, and 17.94%, improved packet delivery ratio by 3.00%, 6.00%, 9.00%, and 10.00%, increased throughput by 7.00%, 8.00%, 11.00%, and 13.00%, and decreased latency by 4.90%, 8.81%, 11.54%, and 20.63%, against state-of-the-art methods and was supported by statistical analysis.
The resurgence of the Taliban in Afghanistan has significantly exacerbated educational challenges for marginalized women and girls, deepening gender disparities and impeding socio-economic development. Addressing these issues, this article introduces the Blockchain and Artificial Intelligence Non-Formal Education System (BANFES), an innovative educational solution specifically designed for Afghan girls deprived of formal schooling. BANFES leverages advanced artificial intelligence technologies, including personalized data analysis, to provide customized learning experiences. Additionally, blockchain technology ensures secure record management and data integrity, facilitating a decentralized educational ecosystem where various nodes offer hybrid learning methodologies without intermediaries. This system not only adapts to individual learning speeds and styles to enhance engagement and outcomes but also employs an independent assessment mechanism to evaluate learners. Such evaluations promote transparency and maintain the quality and reputation of educational contributions within the network. The BANFES initiative also addresses implementation challenges, including local distrust and integration with existing educational structures, providing a robust model to overcome barriers to education. Furthermore, the paper explores the scalability of BANFES, proposing its application as a global strategy for non-formal education systems facing similar geopolitical and infrastructural challenges. By creating a secure, flexible, and learner-focused environment, BANFES aims to empower Afghan women and girls with essential skills for personal and professional growth, thus fostering socioeconomic advancement within their communities and setting a new standard for informal education worldwide.
Blockchain technology relies on consensus mechanisms, which are essential systems for validating transactions and ensuring their authenticity. These mechanisms maintain a permanent record of all valid transactions within a blockchain, establishing trust among users of cryptocurrencies like Bitcoin and Ethereum. By verifying and confirming transactions, consensus mechanisms Safeguard the integrity and protection of the blockchain network, safeguarding it from fraud or malicious activities. Once validated, a transaction is permanently added to the blockchain, becoming an immutable part of the network’s ledger. The process of building trust in a decentralized blockchain environment hinges on the consensus mechanism's ability to facilitate agreement among network participants (nodes). A variety of consensus methodologies have been developed to fulfill this need, each with its unique approach to ensuring security, transparency, and efficiency. For instance, Proof of Work (PoW) involves participants solving intricate mathematical puzzles to validate transactions, whereas Proof of Stake (PoS) grants validation rights based on the amount of cryptocurrency a participant owns. Moreover, more energy-efficient and scalable models like Delegated Proof of Stake (DPoS) and Practical Byzantine Fault Tolerance (PBFT) have been developed to tackle the challenges of blockchain scalability and sustainability. In this paper, we explore the various consensus mechanisms used in blockchain systems, comparing their strengths, weaknesses, and real-world applications. We aim to provide a comprehensive overview of how these mechanisms function, their role in maintaining security, and their impact on the broader blockchain ecosystem
The adoption of blockchain platforms to bolster the security of Internet of Things (IoT) systems has attracted significant attention in recent years. Currently, there is a lack of comprehensive and systematic survey papers in the literature addressing these platforms. This paper discusses six of the most popular emerging blockchain platforms adopted by IoT systems and analyses their usage in state-of-the-art works to solve security problems. The platform was compared in terms of security features and other requirements. Findings from the study reveal that most blockchain components contribute directly or indirectly to IoT security. Blockchain platform components such as cryptography, consensus mechanism, and hashing are common ways that security is achieved in all blockchain platform for IoT. Technologies like Interplanetary File System (IPFS) and Transport Layer Security (TLS) can further enhance data and communication security when used alongside blockchain. To enhance the applicability of blockchain in resource-constrained IoT environments, future research should focus on refining cryptographic algorithms and consensus mechanisms to optimise performance and security.
Yazeed Yasin Ghadi, Tehseen Mazhar, Tariq Shahzad, Muhammad Amir Khan · 7 authors
This study explores integrating blockchain technology into the Internet of Medical Things (IoMT) to address security and privacy challenges. Blockchain's transparency, confidentiality, and decentralization offer significant potential benefits in the healthcare domain. The research examines various blockchain components, layers, and protocols, highlighting their role in IoMT. It also explores IoMT applications, security challenges, and methods for integrating blockchain to enhance security. Blockchain integration can be vital in securing and managing this data while preserving patient privacy. It also opens up new possibilities in healthcare, medical research, and data management. The results provide a practical approach to handling a large amount of data from IoMT devices. This strategy makes effective use of data resource fragmentation and encryption techniques. It is essential to have well-defined standards and norms, especially in the healthcare sector, where upholding safety and protecting the confidentiality of information are critical. These results illustrate that it is essential to follow standards like HIPAA, and blockchain technology can help ensure these criteria are met. Furthermore, the study explores the potential benefits of blockchain technology for enhancing inter-system communication in the healthcare industry while maintaining patient privacy protection. The results highlight the effectiveness of blockchain's consistency and cryptographic techniques in combining identity management and healthcare data protection, protecting patient privacy and data integrity. Blockchain is an unchangeable distributed ledger system. In short, the paper provides important insights into how blockchain technology may transform the healthcare industry by effectively addressing significant challenges and generating legal, safe, and interoperable solutions. Researchers, doctors, and graduate students are the audience for our paper.
In recent years, the rapid integration of Internet of Things (IoT) devices into the healthcare sector has brought about revolutionary advancements in patient care and data management. While these technological innovations hold immense promise, they concurrently raise critical security concerns, particularly in safeguarding medical data against potential cyber threats. The sensitive nature of health-related information requires robust measures to ensure the confidentiality, integrity, and availability of patient data in IoT-enabled medical environments. Addressing the imperative need for enhanced security in IoT-based healthcare systems, we propose a comprehensive method encompassing three distinct phases. In the first phase, we implement Blockchain-Enabled Request and Transaction Encryption to strengthen data transaction security, providing an immutable and transparent framework. In the second phase, we introduce a Request Pattern Recognition Check that leverages diverse data sources to identify and block potential unauthorized access attempts. Finally, the third phase incorporates Feature Selection and a BiLSTM network to enhance the accuracy and efficiency of intrusion detection using advanced machine learning techniques. We compared the simulation results of the proposed method with three recent related methods: AIBPSF-IoMT, OMLIDS-PBIoT, and AIMMFIDS. The evaluation criteria include detection rate, false alarm rate, precision, recall, and accuracy - crucial benchmarks for assessing the overall performance of intrusion detection systems. Our findings show that the proposed method outperforms existing approaches across all evaluated criteria, demonstrating its effectiveness in improving the security of IoT-based healthcare systems.
The amount of power required to mine one Bitcoin (BTC) can vary significantly depending on several factors, including the type of mining hardware being used, its efficiency, the cost of electricity, and the overall network difficulty at any given time. Mining BTC involves solving complex mathematical problems to validate transactions on the blockchain network, which requires significant computational power. This research paper focuses on dedicated mining machines, combining essential data and information into a singular comparison evaluation of these machines.
Sri Sai Durga Mani Vasireddy, Supriya Yalagala, Jayasri Sikha, Rani Vullaganti · 7 authors
The primary objective of this project is to develop a robust and secure system that uses blockchain technology as well as Ethereum and IoT sensors to create a transparent and automated irrigation management solution for farmers. The key components of this innovative system include IoT sensors for real-time data collection, smart contracts on the Ethereum blockchain for transparent and immutable record keeping, and a user-friendly interface for farmers to monitor and control their irrigation systems remotely. The blockchain will provide data security to avoid the problem of data manipulation; because the IoT components will produce large volumes of data, the data must be securely maintained.
M. Yu. Kataev, Larisa Bulysheva, Alexander Krupskiy
Abstract The Internet of Things (IoT) constitutes a network of physical objects, including devices and systems, capable of intercommunication both among themselves and with external systems via the Internet. Its applications span across various sectors such as manufacturing, medicine, commerce, security, forestry and agriculture. The application of IoT in agriculture involves gathering data from diverse sensors, offering timely and precise solutions. This technology facilitates the reduction of manual labour involved in tasks like animal monitoring, greenhouse management and field surveillance. Integrated sensors within the IoT framework enable the collection of data pertaining to soil, air, water, animal tracking, plant growth and other relevant metrics. Nevertheless, the swift evolution of IoT presents a susceptibility to unauthorized access and compromises in data security. To address this concern, the utilization of blockchain technology has been recommended. This paper proposes a novel IoT architecture integrated with blockchain technology to enhance data security in agricultural applications. A detailed schematic has been devised and implemented in software to illustrate this approach.
Inas Al Khatib, Abdulrahim Shamayleh, Malick Ndiaye
In recent years, the Internet of medical things (IoMT) has become a significant technological advancement in the healthcare sector. This systematic review aims to identify and summarize the various applications, key challenges, and proposed technical solutions within this domain, based on a comprehensive analysis of the existing literature. This review highlights diverse applications of the IoMT, including mobile health (mHealth) applications, remote biomarker detection, hybrid RFID-IoT solutions for scrub distribution in operating rooms, IoT-based disease prediction using machine learning, and the efficient sharing of personal health records through searchable symmetric encryption, blockchain, and IPFS. Other notable applications include remote healthcare management systems, non-invasive real-time blood glucose measurement devices, distributed ledger technology (DLT) platforms, ultra-wideband (UWB) radar systems, IoT-based pulse oximeters, accident and emergency informatics (A&EI), and integrated wearable smart patches. The key challenges identified include privacy protection, sustainable power sources, sensor intelligence, human adaptation to sensors, data speed, device reliability, and storage efficiency. The proposed mitigations encompass network control, cryptography, edge-fog computing, and blockchain, alongside rigorous risk planning. The review also identifies trends and advancements in the IoMT architecture, remote monitoring innovations, the integration of machine learning and AI, and enhanced security measures. This review makes several novel contributions compared to the existing literature, including (1) a comprehensive categorization of IoMT applications, extending beyond the traditional use cases to include emerging technologies such as UWB radar systems and DLT platforms; (2) an in-depth analysis of the integration of machine learning and AI in IoMT, highlighting innovative approaches in disease prediction and remote monitoring; (3) a detailed examination of privacy and security measures, proposing advanced cryptographic solutions and blockchain implementations to enhance data protection; and (4) the identification of future research directions, providing a roadmap for addressing current limitations and advancing the scientific understanding of IoMT in healthcare. By addressing current limitations and suggesting future research directions, this work aims to advance scientific understanding of the IoMT in healthcare.
Mui D. Nguyen, Tuan M. Nguyen, Thang C. Vu, Tien Minh Ta · 6 authors
The paper evaluates potential applications of blockchain technology in enhancing the security and reliability of Wireless Sensor Networks (WSNs). The existing vulnerabilities in WSNs, such as concerns regarding data integrity and security, demand innovative security solutions. Through systematic analysis, this paper provides valuable insights to expand understanding of WSNs security, explaining the feasibility and benefits of deploying blockchain technology. Possible attacks in the networks are classified to point out either risks or potential solusions to protect the networks. By exploring the integration of Blockchain within WSNs, the paper highlights its potential to minimize various security risks. In addition, this work discusses the challenges and considerations associated with implementing Blockchain in WSNs. Overall, this paper contributes on securing WSNs and underscores the role of blockchain technology as a promising way for enhancing security of WSNs.