Blockchain technology has been recognized as a promising solution to enhance the security and privacy of Internet of Things (IoT) and Edge Computing scenarios. Taking advantage of the Proof-of-Work (PoW) consensus protocol, which solves a computation intensive hashing puzzle, Blockchain assures the security of the system by establishing a digital ledger. However, the computation intensive PoW favors members possessing more computing power. In the IoT paradigm, fairness in the highly heterogeneous network edge environments must consider devices with various constraints on computation power. Inspired by the advanced features of Digital Twins (DT), an emerging concept that mirrors the lifespan and operational characteristics of physical objects, we propose a novel Miner-Twins (MinT) architecture to enable a fair PoW consensus mechanism for blockchains in IoT environments. MinT adopts an edge-fog-cloud hierarchy. All physical miners of the blockchain are deployed as microservices on distributed edge devices, while fog/cloud servers maintain digital twins that periodically update miners’ running status. By timely monitoring miner’s footage that is mirrored by twins, a lightweight Singular Spectrum Analysis (SSA) based detection achieves to identify individual misbehaved miners that violate fair mining. Moreover, we also design a novel Proof-of-Behavior (PoB) consensus algorithm to detect byzantine miners that collude to compromise a fair mining network. A preliminary study is conducted on a proof-of-concept prototype implementation, and experimental evaluation shows the feasibility and effectiveness of proposed MinT scheme under a distributed byzantine network environment.
The Internet of Things (IoT) and the integration of medical devices perform hand-to-hand solutions and comfort to their users. With the inclusion of IoT under medical devices a hybrid (IoMT) is formulated. This features integrated computation and processing of data via dedicated servers. The IoMT is supported with an edge server to assure the mobility of data and information. The backdrop of IoT is a networking framework and hence, the security of such devices under IoT and IoMT is at risk. In this article, a framework and prototype for secure healthcare application processing via blockchain are proposed. The proposed technique uses an optimized Crow search algorithm for intrusion detection and tampering of data extraction in IoT environment. The technique is processed under deep convolution neural networks for comparative analysis and coordination of data security elements. The technique has successfully extracted the instruction detection from un-peer source with a source validation of 100 IoT nodes under initial intervals of 25 nodes based on block access time, block creation, and IPFS storage layer extraction. The proposed technique has a recorded performance efficiency of 92.3%, comparable to trivial intrusion detection techniques under Deep Neural Networks (DNN) supported algorithms.
Considering the computation resources available with sensor devices and the value and validity of Cognitive Wireless Communication Network (CWCN), traditional blockchain is not feasible for CWCN. Further, considering the security and privacy for CWCN that can directly impact human life (as in the case of ambient assisted living applications), blockchain provides a good solution for such applications, however, with some simplicity in the computation of Proof of Work (PoW). Therefore, the fourth objective solution comes up with a simplified energy-efficient blockchain implementation for CWCN that consumes less energy in computation time. The energy-hungry blockchain has been implemented on resource-constrained CWCN for ambient assisted living applications specialized for elderly care. The process includes a collection of physical environmental parameters on a single board computer-based CWCN. The implementation includes possible simplification in the most energy-consuming process, i.e., the mining process, which makes it energy efficient in computation time as energy consumption is a computation time factor.
To bridge the current gap between the Blockchain expectancies and their intensive computation constraints, the present paper advances a lightweight processing solution, based on a load-balancing architecture, compatible with the lightweight/embedding processing paradigms. In this way, the execution of complex operations is securely delegated to an off-chain general-purpose computing machine while the intimate Blockchain operations are kept on-chain. The illustrations correspond to an on-chain Tezos configuration and to a multiprocessor ARM embedded platform (integrated into a Raspberry Pi). The performances are assessed in terms of security, execution time, and CPU consumption when achieving a visual document fingerprint task. It is thus demonstrated that the advanced solution makes it possible for a computing intensive application to be deployed under severely constrained computation and memory resources, as set by a Raspberry Pi 3. The experimental results show that up to nine Tezos nodes can be deployed on a single Raspberry Pi 3 and that the limitation is not derived from the memory but from the computation resources. The execution time with a limited number of fingerprints is 40% higher than using a classical PC solution (value computed with 95% relative error lower than 5%).
The sixth generation (6G) systems are generally recognized to be established on ubiquitous Artificial Intelligence (AI) and distributed ledger such as blockchain. However, the AI training demands tremendous computing resource, which is limited in most 6G devices. Meanwhile, miners in Proof-of-Work (PoW) based blockchains devote massive computing power to block mining, and are widely criticized for the waste of computation. To address this dilemma, we propose an Evolved-Proof-of-Work (E-PoW) consensus that can integrate the matrix computations, which are widely existed in AI training, into the process of brute-force searches in the block mining. Consequently, E-PoW can connect AI learning and block mining via the multiply used common computing resource. Experimental results show that E-PoW can salvage by up to 80 percent computing power from pure block mining for parallel AI training in 6G systems.
The adoption of Internet of Things (IoT) technology across many applications, such as autonomous systems, communication, and healthcare, is driving the market’s growth at a positive rate. The emergence of advanced data analytics techniques such as blockchain for connected IoT devices has the potential to reduce the cost and increase in cloud platform adoption. Blockchain is a key technology for real-time IoT applications providing trust in distributed robotic systems running on embedded hardware without the need for certification authorities. There are many challenges in blockchain IoT applications such as the power consumption and the execution time. These specific constraints have to be carefully considered besides other constraints such as number of nodes and data security. In this paper, a novel approach is discussed based on hybrid HW/SW architecture and designed for Proof of Work (PoW) consensus which is the most used consensus mechanism in blockchain. The proposed architecture is validated using the Ethereum blockchain with the Keccak 256 and the field-programmable gate array (FPGA) ZedBoard development kit. This implementation shows improvement in execution time of 338% and minimizing power consumption of 255% compared to the use of Nvidia Maxwell GPUs.
Shakila Zaman, Muhammad R. A. Khandaker, Risala Tasin Khan, Faisal Tariq · 5 authors
The Internet-of-Things (IoT) is an emerging technology that connects and integrates a massive number of smart physical devices with virtual objects operating in diverse platforms through the internet. Due to massive size and physical spread of many applications such as smart healthcare, IoT is increasingly implemented in distributed setting. This distributed nature of implementation of the entities connected to the IoT networks are exposed to an unprecedented level of privacy and security threats. This is particularly severe for IoT healthcare system as it involves huge volume of sensitive and personal data. Although blockchain has posed to be the solution in this scenario thanks to its inherent distributed ledger technology (DLT), it suffers from a major drawback of rapidly increasing storage and computation requirements with the increase in network size which makes its implementation impractical. This paper proposes a holochain-based security and privacy-preserving framework for IoT healthcare systems that overcomes the scalability challenge and is particularly suited for resource constrained IoT scenarios. Through thorough analysis and performance results, we have demonstrated that the holochain based IoT healthcare solution outperforms blockchain based solution in terms of resource requirements while ensuring appropriate level of privacy and security.
As Internet of Things (IoT) revolutionized over the years, it has become a crucial part in many application such as smart city and smart transportation, ensuring smooth functioning of the human life in a more reliable manner. As the amount of data that is to be collected, stored and analyzed increases, there is need for a system that perform these tasks in a more efficient manner. Artificial intelligence plays a crucial part in delivering these requirements. But when AI is involved, it will also result in the use of big tools that are necessary for tackling the issue at hand. Hence in order to ensure that AI can function in a smooth manner, blockchain support is introduced such that it develops a decentralized architecture. Block chain will provide a safe and secure environment to share the information and resources and can also address the drawbacks of Artificial intelligence. The purpose of this proposed work is to develop a hybrid architecture, combining the advantages of blockchain and artificial intelligence to enable big data analysis. We have performed an analysis and results have been observed to conclude the impact of blockchain in Artificial Intelligence in the aspect of latency and accuracy. The results indicate that the proposed architecture excels over the other architectures and can also overcome some of the challenges prevalent.
Amirhossein Adavoudi Jolfaei, Seyed Farhad Aghili, Dave Singelée
Recently, blockchain-based Internet of Medical Things (IoMT) has started to receive more attention in the healthcare domain as it not only improves the care quality using real-time and continuous monitoring but also minimizes the cost of care. However, there is a clear trend to include many entities in IoMT systems, such as IoMT sensor nodes, IoT wearable medical devices, patients, healthcare centers, and insurance companies. This makes it challenging to design a blockchain framework for these systems where scalability is a most critical factor in blockchain technology. Motivated by this observation, in this survey we review the state-of-the-art in blockchain-IoMT systems. Comparison and analysis of such systems prove that there is a substantial gap, which is the negligence of scalability. In this survey, we discuss several approaches proposed in the literature to improve the scalability of blockchain technology, and thus overcoming the above mentioned research gap. These approaches include on-chain and off-chain techniques, based on which we give recommendations and directions to facilitate designing a scalable blockchain-based IoMT system. We also recommended that a designer considers the well-known trilemma along with the various dimensions of a scalable blockchain system to prevent sacrificing security and decentralization as well. Moreover, we raise several research questions regarding benchmarking; addressing these questions could help designers determining the existing bottlenecks, leading to a scalable blockchain.
The blockchain technology is quickly becoming the most disruptive technology of recent times. This technology has generated a lot of buzz and optimism, and it has gained a lot of attention from both the public and private sectors. Information can be transferred using blockchain technology in a manner that is confidential, safe, reliable, and open to scrutiny. This analysis focuses on the ways in which blockchain technology can be used to solve problems related to scalability and give solutions in the context of the healthcare industry. In light of this, the proposed solutions can be categorized into two primary categories: storage optimization and blockchain redesign. However, there are still several restrictions, such as the block size, the enormous volume of data and transactions, the number of nodes, and the obstacles posed by the protocol. It has seen widespread application in decentralized crypto currencies like Bitcoin and Ethereum, for example. An example of a public blockchain application that was successful, Bitcoin, was the impetus for a significant increase in research and development into blockchain technology. Nevertheless, scalability continues to be a significant obstacle. According to the facts that we've compiled, the primary causes of scalability issues appear to be bottlenecks in the transaction throughput, the network latency, and the consensus methods. On the other side, the potential for scalability includes the application of techniques like as sharding, hybrid systems, and off-chain computations. We also examine the consequences of these findings as well as prospective paths for future study to help solve the scalability problems that blockchain-based systems provide.
The combination of blockchain technology and Internet of Things (IoT) technology has brought many significant advantages and new development directions. With the development of embedded technology and 5G communication technology, the performance limitations and network limitations that are traditionally believed to restrict the application of blockchain technology to IoT devices have been broken. The development of "blockchain + 5G + IoT" provides reliable data from the source for the blockchain, linking the credible mapping of physical assets and digital assets. However, at the beginning of the blockchain design, the application of the IoT was not fully considered, so there have been some obvious defects in applying the blockchain technology in the IoT. In the Byzantine fault tolerance (BFT) consensus algorithm of traditional blockchain, the entire blockchain network will become paralyzed when more than 1/3 of the nodes in the network are offline. However, in IoT applications, this situation is likely to occur and greatly limits the security and stability of the application of blockchain technology in the IoT. In order to solve this problem, we proposed an IoT adaptive dynamic blockchain networking method based on discrete heartbeat signals. The feature of the method is to set a different monitoring time for each group of nodes, that is, discrete heartbeat signals monitoring. When the number of nodes gradually decreases, the IoT adaptive dynamic blockchain network can dynamically adapt to this process. Even when more than 1/3 of the IoT are offline, the adaptive dynamic IoT blockchain network can maintain stable running. This method also has the advantages of a short network expectation recovery time and avoids instantaneous system paralysis caused by the thundering herd effect. This research improves the security and stability of the application of blockchain technology in the IoT, and provides the necessary technical foundation for the better combination of blockchain technology and IoT technology.
This paper opts to mitigate the energy-inefficiency of the Blockchain Proof-of-Work (PoW) consensus algorithm by rationally repurposing the power spent during the mining process. The original PoW mining scheme is designed to consider one block at a time and assign a reward to the first place winner of a computation race. To reduce the mining-related energy consumption, we propose to compensate the computation effort of the runner(s)-up of a mining round, by granting them exclusivity of solving the upcoming block in the next round. This will considerably reduce the number of competing nodes in the next round and consequently, the consumed energy. Our proposed scheme divides time into epochs, where each comprises two mining rounds; in the first one, all network nodes can participate in the mining process, whereas in the second round only runners-up can take part. Thus, the overall mining energy consumption can be reduced to nearly $50\%$. To the best of our knowledge, our proposed scheme is the first to considerably improve the energy consumption of the original PoW algorithm. Our analysis demonstrates the effectiveness of our scheme in reducing energy consumption, the probability of fork occurrences, the level of mining centralization presented in the original PoW algorithm, and the effect of transaction censorship attack.
Conventional certificate verification, whether paper-based or backed by a centralized digital registry, remains exposed to forgery, single points of failure, and slow manual cross-checking between issuers, holders, and verifiers. This paper presents a decentralized, blockchain-based framework for issuing, storing, and verifying academic and professional certificates that addresses these weaknesses without placing the full document on-chain. Each certificate is reduced to a SHA-256 hash, signed with the issuing institution's RSA private key, and recorded through an Ethereum smart contract, while the original file is retained off-chain on IPFS and referenced by its Content Identifier. Only institutions that pass a unanimous, vote-based onboarding process administered by a validator consortium are permitted to issue certificates, which constrains the system to a trusted-issuer model while preserving decentralization across the validator set. A hash-mapped Bloom Filter sits in front of the blockchain query path and performs a fast probabilistic existence check, allowing forged or non-existent certificates to be rejected before an on-chain lookup is triggered. A prototype was implemented with Solidity smart contracts on the Ethereum Sepolia testnet, a Node.js/Web3.js application layer, and a React.js frontend with MetaMask-based authentication. Evaluation on the testnet shows that the Bloom Filter pre-check lowers the average lookup time for invalid certificates by roughly 87%, keeps verification of valid certificates under two seconds end-to-end, holds the false-positive rate below 0.5% for up to 50,000 stored certificate hashes, and reduces the gas cost of issuing a certificate to approximately 4.57 USD. These results indicate that combining consortium governance, cryptographic hashing, and probabilistic filtering yields a certificate validation pipeline that is simultaneously tamper-resistant, low-cost, and fast enough for real-time institutional use.
Furqan Jameel, Uzair Javaid, Wali Ullah Khan, Muhammad Naveed Aman · 6 authors
Blockchain is emerging as a promising candidate for the uberization of Internet services. It is a decentralized, secure, and auditable solution for exchanging, and authenticating information via transactions, without the need of a trusted third party. Therefore, blockchain technology has recently been integrated with industrial Internet-of-things (IIoT) networks to help realize the fourth industrial revolution, Industry 4.0. Though blockchain-enabled IIoT networks may have the potential to support the services and demands of next-generation networks, the gap analysis presented in this work highlights some of the areas that need improvement. Based on these observations, the article then promotes the utility of reinforcement learning (RL) techniques to address some of the major issues of blockchain-enabled IIoT networks such as block time minimization and transaction throughput enhancement. This is followed by a comprehensive case study where a Q-learning technique is used for minimizing the occurrence of forking events by reducing the transmission delays for a miner. Extensive simulations have been performed and the results have been obtained for the average transmission delay which relates to the forking events. The obtained results demonstrate that the Q-learning approach outperforms the greedy policy while having a reasonable level of complexity. To further develop the blockchain-enabled IIoT networks, some future research directions are also documented. While this article highlights the applications of RL techniques in blockchain-enabled IIoT networks, the provided insights and results could pave the way for rapid adoption of blockchain technology.
Yu-Tse Lee, Jhan-Jia Lin, Jane Yung-jen Hsu, Ja‐Ling Wu
This paper presents a blockchain-based time bank system on the basis of the Hyperledger Fabric framework, which is one of the permissioned blockchain networks. Most of the services provided by existing Time Bank systems were recorded and conducted manually in the past; furthermore, jobs for matching services with receivers were managed by people. Running a time bank in this way will cost lots of time and human resources and, worse, it lacks security. This work designs and realizes a time bank system enabling all the service-related processes being executed and recorded on a blockchain. The matching between services’ supply-and-demand tasks can directly be done through autonomous smart contracts. Building a time bank system on blockchain benefits the transaction of time credit which plays the role of digital currency on the system. In addition, the proposed time bank also retains a grading system, allowing its members to give each other a grade for reflecting their degrees of satisfaction about the results provided by the system. This grading system will incentivize the members to provide a better quality of service and adopt a nicer attitude for receiving a service, which may positively endorse the development of a worldwide time bank system.
Designing an efficient difficulty control algorithm is an essential problem in Proof-of-Work (PoW) based blockchains because the network hash rate is randomly changing. This paper proposes a general difficulty control algorithm and provides insights for difficulty adjustment rules for PoW based blockchains. The proposed algorithm consists a two-layer neural network. It has low memory cost, meanwhile satisfying the fast-updating and low volatility requirements for difficulty adjustment. Real data from Ethereum are used in the simulations to prove that the proposed algorithm has better performance for the control of the block difficulty.
Muhammad Salek Ali, Massimo Vecchio, Guntur Dharma Putra, Salil S. Kanhere · 5 authors
Within the Internet of Things (IoT) and blockchain research, there is a growing interest in decentralizing health monitoring systems, to provide improved privacy to patients, without relying on trusted third parties for handling patients' sensitive health data. With public blockchain deployments being severely limited in their scalability, and inherently having latency in transaction processing, there is room for researching and developing new techniques to leverage the security features of blockchains within healthcare applications. This paper presents a solution for patients to share their biomedical data with their doctors without their data being handled by trusted third party entities. The solution is built on the Ethereum blockchain as a medium for negotiating and record-keeping, along with Tor for delivering data from patients to doctors. To highlight the applicability of the solution in various health monitoring scenarios, we have considered three use-cases, namely cardiac monitoring, sleep apnoea testing, and EEG following epileptic seizures. Following the discussion about the use cases, the paper outlines a security analysis performed on the proposed solution, based on multiple attack scenarios. Finally, the paper presents and discusses a performance evaluation in terms of data delivery time in comparison to existing centralized and decentralized solutions.
Xiuqing Chen, Hong Zhu, Deqin Geng, Wei Liu · 6 authors
The proliferation of physiological signals acquisition and monitoring system, has led to an explosion in physiological signals data. Additionally, RFID systems, blockchain technologies, and the fog computing mechanisms have significantly increased the availability of physiological signal information through big data research. The driver for the development of hybrid systems is the continuing effort in making health-care services more efficient and sustainable. Implantable medical devices (IMD) are therapeutic devices that are surgically implanted into patients' body to continuously monitor their physiological parameters. Patients treat cardiac arrhythmia due to IMD therapeutic and life-saving benefits. We focus on hybrid systems developed for patient physiological signals for collection, storage protection, and monitoring in critical care and clinical practice. In order to provide medical data privacy protection and medical decision support, the hybrid systems are presented, and RFID, blockchain, and big data technologies are used to analyse physiological signals.
Spyros Voulgaris, Nikos Fotiou, Vasilios A. Siris, George C. Polyzos · 6 authors
In the last few years, we have been witnessing the convergence of the physical with the digital world. The Internet of Things (IoT) is progressing at a fast pace, and IoT devices are becoming pervasive in our physical environments, bringing the vision of Intelligent Environments closer to reality. At the same time, the newly-introduced blockchain technology is offering for the first time ever cryptographically proven trust based on a set of mutually untrusted nodes. Blockchain technology thus has the potential to become a key component of many IoT systems, offering them an unprecedented level of accountability, transparency, and reliability. This paper first lays out the principles on which blockchain systems are operating, along with descriptions of the most noteworthy blockchain implementations. It then presents a number of systems through which blockchains may interact with external systems and third-party data sources. Finally, it provides a survey of the state-of-the-art blockchain-based systems targeting IoT applications.
The emerging blockchain technology shows promising potential to enhance industrial systems and the Internet of things (IoT) by providing applications with redundancy, immutable storage, and encryption. In the past few years, many more applications in industrial IoT (IIoT) have emerged and the blockchain technologies have attracted huge amounts of attention from both industrial and academic researchers. In this paper, we address the integration of blockchain and IIoT from the industrial prospective. A blockchain-enabled IIoT framework is introduced and involved fundamental techniques are presented. Moreover, the main applications and key challenges are addressed. A comprehensive analysis for the most recent research trends and open issues is provided associated with the blockchain-enabled IIoT.
Industrial Internet of Things (IIoT) plays an indispensable role for Industry 4.0, where people are committed to implement a general, scalable, and secure IIoT system to be adopted across various industries. However, existing IIoT systems are vulnerable to single point of failure and malicious attacks, which cannot provide stable services. Due to the resilience and security promise of blockchain, the idea of combining blockchain and Internet of Things (IoT) gains considerable interest. However, blockchains are power-intensive and low-throughput, which are not suitable for power-constrained IoT devices. To tackle these challenges, we present a blockchain system with credit-based consensus mechanism for IIoT. We propose a credit-based proof-of-work (PoW) mechanism for IoT devices, which can guarantee system security and transaction efficiency simultaneously. In order to protect sensitive data confidentiality, we design a data authority management method to regulate the access to sensor data. In addition, our system is built based on directed acyclic graph -structured blockchains, which is more efficient than the Satoshi-style blockchain in performance. We implement the system on Raspberry Pi, and conduct a case study for the smart factory. Extensive evaluation and analysis results demonstrate that credit-based PoW mechanism and data access control are secure and efficient in IIoT.
Md. Abdur Rahman, M. Shamim Hossain, Md. Mamunur Rashid, Stuart J. Barnes · 6 authors
Although ElectroEncephaloGram (EEG) signals allow subjects suffering from neuromuscular disorders to interface their brains with the cyber-physical world, occupational therapy can be enhanced with the introduction of further modalities better assist the disabled person. In this paper, we propose an in-home occupational therapy environment, which leverages a rich set of occupational therapy-related activity recognition modalities, namely, EEG signals to understand brain activity, ElectroMyoGram (EMG) signals for muscle activity, gesture-tracking sensors for forward and inverse kinematics activities, and smart home appliance control sensors. To support a wide variety of disabled people's in-home occupational therapy, we have incorporated both selective attention and motor imagery processes for mapping a mental command with that of an occupational therapy-related command within a serious game environment. To attain higher accuracy and to avoid a higher number of false positives, a subject is first recommended to use a selective attention-based serious game in which a digital avatar of the subject acting as a model therapist will guide the therapy session. Once familiar with the generation of proper motor imagery, an advanced user can use self-paced motor imagery signals to perform occupational therapy activities within the serious game environment. The occupational therapy consists of a serious game environment in which smart home appliances are mapped with therapeutic activities through forward and inverse kinematics. The therapy data has been secured through blockchain and off-chain-based distributed repositories. The test results show the viability of using the framework in a clinical environment.