Shahriar Rahman Fahim, SM Katibur Rahman, Sharfuddin Mahmood
Since the inception of Blockchain, the computer database has been evolving into innovative technologies. Recent technologies emerge, the use of Blockchain is also flourishing. All the technologies from Blockchain use a mutual algorithm to operate. The consensus algorithm is the process that assures mutual agreements and stores information in the decentralized database of the network. Blockchain's biggest drawback is the exposure to scalability. However, using the correct consensus for the relevant work can ensure efficiency in data storage, transaction finality, and data integrity. In this paper, a comparison study has been made among the following consensus algorithms: Proof of Work (PoW), Proof of Stake (PoS), Proof of Authority (PoA), and Proof of Vote (PoV). This study aims to provide readers with elementary knowledge about blockchain, more specifically its consensus protocols. It covers their origins, how they operate, and their strengths and weaknesses. We have made a significant study of these consensus protocols and uncovered some of their advantages and disadvantages in relation to characteristics details such as security, energy efficiency, scalability, and IoT (Internet of Things) compatibility. This information will assist future researchers to understand the characteristics of our selected consensus algorithms.
Abdullah Lakhan, Mazin Abed Mohammed, Jan Nedoma, Radek Martínek · 6 authors
Industrial Internet of Things (IIoT) is the new paradigm to perform different healthcare applications with different services in daily life. Healthcare applications based on IIoT paradigm are widely used to track patients health status using remote healthcare technologies. Complex biomedical sensors exploit wireless technologies, and remote services in terms of industrial workflow applications to perform different healthcare tasks, such as like heartbeat, blood pressure and others. However, existing industrial healthcare technoloiges still has to deal with many problems, such as security, task scheduling, and the cost of processing tasks in IIoT based healthcare paradigms. This paper proposes a new solution to the above-mentioned issues and presents the deep reinforcement learning-aware blockchain-based task scheduling (DRLBTS) algorithm framework with different goals. DRLBTS provides security and makespan efficient scheduling for the healthcare applications. Then, it shares secure and valid data between connected network nodes after the initial assignment and data validation. Statistical results show that DRLBTS is adaptive and meets the security, privacy, and makespan requirements of healthcare applications in the distributed network.
Blockchain technology provides a data structure with inherent security properties that include cryptography, decentralization, and consensus, which ensure trust in transactions. It covers widely applicable usages, such as in intelligent manufacturing, finance, the Internet of things (IoT), medicine and health, and many different areas, especially in medical health data security and privacy protection areas. Its natural attributes, such as contracts and consensus mechanisms, have leading-edge advantages in protecting data confidentiality, integrity, and availability. The security issues are gradually revealed with in-depth research and vigorous development. Unlike traditional paper storage methods, modern medical records are stored electronically. Blockchain technology provided a decentralized solution to the trust-less issues between distrusting parties without third-party guarantees, but the “trust-less” security through technology was easily misunderstood and hindered the security differences between public and private blockchains appropriately. The mentioned advantages and disadvantages motivated us to provide an advancement and comprehensive study regarding the applicability of blockchain technology. This paper focuses on the healthcare security issues in blockchain and sorts out the security risks in six layers of blockchain technology by comparing and analyzing existing security measures. It also explores and defines the different security attacks and challenges when applying blockchain technology, which promotes theoretical research and robust security protocol development in the current and future distributed work environment.
The research topics of this paper are the data security of the edge devices and terminals of the Internet of Things (IoT) and the consensus design of a lightweight blockchain for the Internet of Things. These devices have self-organization capabilities to overcome the bandwidth delay and service-congestion problems caused by excessive concentration in existing scenarios, but they face the challenges of limited computing, storage, and communication resources. As a result, a non- financial lightweight blockchain consensus design with low energy consumption, low latency, and greater stability should be investigated. We propose a hierarchical proof-of-capability (HPoC) consensus mechanism combined with the asynchronous proof-of-work (PoW) mechanism for improving the computing capacity, storage capacity, and communication capacity of IoT edge devices that can generate blocks with low latency, low power consumption, and strong stability in resource-constrained edge device nodes, while ensuring that the security of the edge devices is enhanced asynchronously. We simulated a smart-home scenario, with the number of device nodes ranging from 15 to 75, and conducted comparative experiments between HPoC and PoW based on different difficulty bits. The experimental results showed that HPoC is a consensus mechanism with scalability and stability that can flexibly adjust time consumption and accurately select nodes with strong capabilities to generate blocks in heterogeneous devices.
The wearable healthcare equipment is primarily designed to alert patients of any specific health conditions or to act as a useful tool for treatment or follow-up. With the growth of technologies and connectivity, the security of these devices has become a growing concern. The lack of security awareness amongst novice users and the risk of several intermediary attacks for accessing health information severely endangers the use of IoT-enabled healthcare systems. In this paper, a blockchain-based secure data storage system is proposed along with a user authentication and health status prediction system. Firstly, this work utilizes reversed public-private keys combined Rivest–Shamir–Adleman (RP2-RSA) algorithm for providing security. Secondly, feature selection is completed by employing the correlation factor-induced salp swarm optimization algorithm (CF-SSOA). Finally, health status classification is performed using advanced weight initialization adapted SignReLU activation function-based artificial neural network (ASR-ANN) which classifies the status as normal and abnormal. Meanwhile, the abnormal measures are stored in the corresponding patient blockchain. Here, blockchain technology is used to store medical data securely for further analysis. The proposed model has achieved an accuracy of 95.893% and is validated by comparing it with other baseline techniques. On the security front, the proposed RP2-RSA attains a 96.123% security level.
Proof-of-Work (PoW) was the first blockchain consensus protocol discovered, followed by Proof-of-Stake and others. The disadvantage of the PoW is that it requires high energy consumption compared to other consensus protocols. Based on this weakness, some researchers proposed a lightweight blockchain technology, a modified blockchain that has a simplified algorithm but does not reduce the security factor. This lightweight blockchain is suitable for applications requiring data reliability but with limited computing resources, such as Internet of Things devices. This paper discussed and modified the SimBlock simulator as one of the existing blockchain simulators. It has a visualization tool to look further into the propagation transition of the block. Unfortunately, the existing PoW consensus on the SimBlock simulator is unable to pinpoint the actual hash computation method. Therefore, the hashing process in the SimBlock simulator was modified by including the difficulty level for finding the hash target. The purpose of including the difficulty level was to determine how long it takes to create a block. By knowing the time needed, a recommendation could be obtained for the most suitable difficulty level for a lightweight blockchain and its implementation with IoT devices. There are two options of approaches to the difficulty level referred to in this paper; finding the number of zeros that appear sequentially and are in front of a hash value (leading zero) and finding the number of zeros that appear arbitrarily (count zero). For example, the first difficulty level on a leading-zero quest has the same meaning as searching for a leading zero, the second level of difficulty is the search for the two leading zeros, etc. The block generation time on a blockchain network using the PoW consensus highly depends on the difficulty level. Block generation time and resource utility have been analyzed and compared with other blockchain simulators and existing networks, such as Ethereum and Bitcoin. The modified SimBlock simulator was tested in this experiment using 100-600 nodes, with the expected result of creating 100-1000 blocks. Based on the experiments, creating a block using leading zeros as the hash target for the first to fourth difficulty levels took less than 1 s, whereas when using count zeros (zero-count) as the target hash, it took less than 1 s for the first to fifteenth difficulty levels. Using leading zeros took approximately 237.4 s at difficulty level 7, while count-zero took approximately 633.8 s at difficulty level 19. The experiment was not continued at the next difficulty level because it required a longer compilation time. With the modifications made, the creation of a block on a blockchain network using the PoW consensus can be clearly seen. The difficulty level added to the simulator can also provide information for determining the difficulty level to be implemented on the lightweight blockchain.
Classifying brain signals collected by wearable Internet of Things (IoT) sensors, especially brain-computer interfaces (BCIs), is one of the fastest-growing areas of research. However, research has mostly ignored the secure storage and privacy protection issues of collected personal neurophysiological data. Therefore, in this article, we try to bridge this gap and propose a secure privacy-preserving protocol for implementing BCI applications. We first transformed brain signals into images and used generative adversarial network to generate synthetic signals to protect data privacy. Subsequently, we applied the paradigm of transfer learning for signal classification. The proposed method was evaluated by a case study and results indicate that real electroencephalogram data augmented with artificially generated samples provide superior classification performance. In addition, we proposed a blockchain-based scheme and developed a prototype on Ethereum, which aims to make storing, querying and sharing personal neurophysiological data and analysis reports secure and privacy-aware. The rights of three main transaction bodies - construction workers, BCI service providers and project managers - are described and the advantages of the proposed system are discussed. We believe this paper provides a well-rounded solution to safeguard private data against cyber-attacks, level the playing field for BCI application developers, and to the end improve professional well-being in the industry.
Blockchain technology is becoming a promising technological solution for enterprise applications with the rise of interoperable solutions. A cross-chain architecture facilitates interoperability, thus improves its chain efficiency, reduces fragmentation, and allows users and features to flow more freely across multiple blockchains. However, enabling interoperability in silo networks will make a significant functional trade-off on the security and performance of the system. This paper review trade-offs in blockchain technologies related to interoperability.
Chenyuan Wu, B.R. Mehta, Mohammad Javad Amiri, Ryan Marcus · 5 authors
This paper presents AdaChain, a learning-based blockchain framework that adaptively chooses the best permissioned blockchain architecture in order to optimize effective throughput for dynamic transaction workloads. AdaChain addresses the challenge in the Blockchain-as-a-Service (BaaS) environments, where a large variety of possible smart contracts are deployed with different workload characteristics. AdaChain supports automatically adapting to an underlying, dynamically changing workload through the use of reinforcement learning. When a promising architecture is identified, AdaChain switches from the current architecture to the promising one at runtime in a way that respects correctness and security concerns. Experimentally, we show that AdaChain can converge quickly to optimal architectures under changing workloads, significantly outperform fixed architectures in terms of the number of successfully committed transactions, all while incurring low additional overhead.
Anupama Ray, Sai Sakunthala Guddanti, Vishnu Ajith, Dhinakaran Vinayagamurthy
Ethereum is one of the most valuable blockchain networks in terms of the total monetary value locked in it, and arguably been the most active network where new blockchain innovations in research and applications are demonstrated. But, this also leads to Ethereum network being susceptible to a wide variety of threats and attacks in an attempt to gain unreasonable advantage or to undermine the value of the users. Even with the state-of-art classical ML algorithms, detecting such attacks is still hard. This motivated us to build a hybrid system of quantum-classical algorithms that improves phishing detection in financial transaction networks. This paper presents a classical ensemble pipeline of classical and quantum algorithms and a detailed study benchmarking existing Quantum Machine Learning algorithms such as Quantum Support Vector Machine and Variational Quantum Classifier. With the current generation of quantum hardware available, smaller datasets are more suited to the QML models and most research restricts to hundreds of samples. However, we experimented on different data sizes and report results with a test data of 12K transaction nodes, which is to the best of the authors knowledge the largest QML experiment run so far on any real quantum hardware. The classical ensembles of quantum-classical models improved the macro F-score and phishing F-score. One key observation is QSVM constantly gives lower false positives, thereby higher precision compared with any other classical or quantum network, which is always preferred for any anomaly detection problem. This is true for QSVMs when used individually or via bagging of same models or in combination with other classical/quantum models making it the most advantageous quantum algorithm so far. The proposed ensemble framework is generic and can be applied for any classification task
Speech is one form of biometric that combines both physiological and behavioral features. It is beneficial for remote-access transactions over telecommunication networks. Presently, this task is the most challenging one for researchers. People's mental status in the form of emotions is quite complex, and its complexity depends upon internal behavior. Emotion and facial behavior are essential characteristics through which human internal thought can be predicted. Speech is one of the mechanisms through which human's various internal reflections can be expected and extracted by focusing on the vocal track, the flow of voice, voice frequency, etc. Human voice specimens of different ages can be emotions that can be predicted through a deep learning approach using feature removal behavior prediction that will help build a step intelligent healthcare system strong and provide data to various doctors of medical institutes and hospitals to understand the physiological behavior of humans. Healthcare is a clinical area with data concentrated where many details are accessed, generated, and circulated periodically. Healthcare systems with many existing approaches like tracing and tracking continuously disclose the system's constraints in controlling patient data privacy and security. In the healthcare system, majority of the work involves swapping or using decisively confidential and personal data. A key issue is the modeling of approaches that guarantee the value of health-related data while protecting privacy and observing high behavioral standards. This will encourage large-scale perception, especially as healthcare information collection is expected to continue far off this current ongoing pandemic. So, the research section is looking for a privacy-preserving, secure, and sustainable system by using a technology called Blockchain. Data related to healthcare and distribution among institutions is a very challenging task. Storage of facts in the centralized form is a targeted choice for cyber hackers and initiates an accordant sight of patients' facts which will cause a problem in sharing information over a network. So, this research paper's approach based on Blockchain for sharing sufferer data in a secured manner is presented. Finally, the proposed model for extracting optimum value in error rate and accuracy was analyzed using different feature removal approaches to determine which feature removal performs better with different voice specimen variations. The proposed method increases the rate of correct evidence collection and minimizes the loss and authentication issues and using feature extraction based on text validation increases the sustainability of the healthcare system.
With the evolution of information technology, the use of internet of things has increased. It affects several areas such as medical field, smart cities, and information systems. In this work, we will use this technological development in the context of health, particularly e-health. We present a platform based on IoMT to allow the monitoring of patient’s health. To meet the constraint of medical secrecy and confidentiality of information, we will use the Blockchain as a secure system. Our system will use the data collected by several smart sensors such as blood pressure, SPO2 concentration, and EEG signals. These encrypted data will be collected by an embedded Raspberry PI 4 platform (working as a smart data relay) before being processed (on a backend server) and then saved in an embedded Blockchain node. The preliminary results show the effectiveness of the proposed platform as a candidate of a low-cost example of secured Electronic Health Record (EHR).
Giulia Rafaiani, Paolo Santini, Marco Baldi, Franco Chiaraluce
The growing interest in Internet of Things (IoT) and Industrial IoT (IIoT) poses the challenge of finding robust solutions for the certification and notarization of data produced and collected by embedded devices. The blockchain and distributed ledger technologies represent a promising solution to address these issues, but rise other questions, for example regarding their practical feasibility. In fact, IoT devices have limited resources and, consequently, may not be able to easily perform all the operations required to participate in a blockchain. In this paper we propose a minimal architecture to allow IoT devices performing data certification and notarization on the Ethereum blockchain. We develop a hardware-software platform through which a lightweight device (e.g., an IoT sensor), holding a secret key and the associated public address, produces signed transactions, which are then submitted to the blockchain network. This guarantees data integrity and authenticity and, on the other hand, minimizes the computational burden on the lightweight device. To show the practicality of the proposed approach, we report and discuss the results of benchmarks performed on ARM Cortex-M4 hardware architectures, sending transactions over the Ropsten testnet. Our results show that all the necessary operations can be performed with small latency, thus proving that an IoT device can directly interact with the blockchain, without apparent bottlenecks.
Our brain is made of thousands of neurons that create a chain of different episodes stored in the human brain in a decentralized manner that shapes human conscious experience just like blockchain technology. Although the human brain can voluntarily delete some episodes stored by the mechanism of distributed neurons, this ability is limited. Thus, each episode is open to access and saved securely so that we do not confuse our childhood memories with movie characters same as blocks in a decentralized ledger (Allegri, 2021). In this paper, it is investigated that some properties of the human brain can be to some extent analogical to blockchain and that certain blockchain concepts can be applied to the development of neuroscience as knowledge of the workings of the human brain and memories. At the same time, the latest advancements in blockchain technology can be fruitful in the development of neuroscience. To closely examine the correlation between neuroscience and blockchain, decentralized ledger technology concepts and neuroscience basics are presented [1].
Blockchain has recently been able to draw wider attention throughout the research community. Since its emergence, the world has seen the mind-blowing expansion of this new technology, which was initially developed as a pawn of digital currency more than a decade back. A self-administering ledger that ensures extensive data immutability over the peer-to-peer network has made it attractive for cybersecurity applications such as a sensor-enabled system called the Internet of things (IoT). Brand new challenges and questions now demand solutions as huge IoT devices are now online in a distributed fashion to ease our everyday lives. After being motivated by those challenges, the work here has figured out the issues and perspectives an IoT infrastructure can suffer because of the wrong choice of blockchain technology. Though it may look like a typical review, however, unlike that, this paper targets sorting out the specific security challenges of the blockchain-IoT eco-system through critical findings and applicable use-cases. Therefore, the contribution includes directing Blockchain architects, designers, and researchers in the broad domain to select the unblemished combinations of Blockchain-powered IoT applications. In addition, the paper promises to bring a deep insight into the state-of-the-art Blockchain platforms, namely Ethereum, Hyperledger, and IOTA, to exhibit the respective challenges, constraints, and prospects in terms of performance and scalability.
Despite numerous prior attempts to boost transaction per second (TPS) of blockchain systems, many sacrifice decentralization and security. This paper proposes a bodyless block propagation (BBP) scheme for which the blockbody is not validated and transmitted during block propagation, to increase TPS without compromising security. Nodes in the blockchain network anticipate the transactions and their ordering in the next upcoming block so that these transactions can be pre-executed and pre-validated before the block is born. For a network with $N$ nodes, our theoretical analysis reveals that BBP can improve TPS scalability from $O(1/log(N))$ to $O(1)$. Ensuring consensus on the next block's transaction content is crucial. We propose a transaction selection, ordering, and synchronization algorithm to drive this consensus. To address the undetermined Coinbase address issue, we further present an algorithm for such unresolvable transactions, ensuring a consistent and TPS-efficient scheme. With BBP, most transactions require neither validation nor transmission during block propagation, liberating system from transaction-block dependencies and rendering TPS scalable. Both theoretical analysis and experiments underscore BBP's potential for full TPS scalability. Experimental results reveal a 4x reduction in block propagation time compared to Ethereum blockchain, with TPS performance being limited by node hardware rather than block propagation.
Kai Chen, Cheng Xu, Hongzhe Liu, Pengfei Wang · 5 authors
The development of 5G network communication has brought technological innovation to smart city communication, making the realization of V2X (vehicle to everything) technology possible. Vehicles wirelessly communicate with other vehicles, sensors, pedestrians, and roadside units, raising data security issues while driving. In order to ensure driving safety, the risk map cognitive model is established with the help of blockchain technology. In this model, the key map data and personal privacy information are encrypted and uploaded to form a blockchain, and the smart contract technology is used for automatic script processing. Then, according to different risk scenarios, cognitive learning is carried out for different risk levels, the cognitive results and corresponding operations are fed back to the intelligent vehicle, and these operations ensure the safe operation of the vehicle according to the intelligent vehicle. Finally, the feasibility of the model was verified by comparing different dangerous scenarios. The experimental results show that this risk cognition model can cognize the data of the intelligent vehicle according to different danger scenarios, and the model can transmit acceleration, deceleration, braking, and other behaviors to the intelligent vehicle to ensure smart city driving safety.
Cognitive learning is progressively prospering in the field of Internet of Things (IoT). With the advancement in IoT, data generation rate has also increased, whereas issues like performance, attacks on the data, security of the data, and inadequate data resources are yet to be resolved. Recent studies are mostly focusing on the security of the data which can be handled by blockchain. Blockchain technology records the learned data into the block which is generated after completing proper consensus mechanism. In this paper, Hetero Federated Learning approach is used to apply cognitive learning on data produced by Internet of Thing devices. Security on cognitiveIoT data is provided by blockchain using Proof of Work consensus mechanism. By applying blockchain over heteroFL approach, we have conducted various simulations to check the performance of our proposed framework. Parameters taken into consideration during performance evaluation are effect of number of blocks on memory utilization and impact of data sample size on accuracy according to different learning rates.
Vladislav Amelin, Ernest Gatiyatullin, Nikita S. Romanov, Ratmir Samarkhanov · 6 authors
This paper introduces a function for blockchain performance evaluation as a black-box. The function runs the Solana blockchain test network with the only differences between the main network in a configuration file and the physical network to operate in. The black-box takes setup parameters as input, launches blockchain in a cloud, emulates artificial users’ activity, and gives two outputs–transactions per second (tps) and drop rate. By default, the setup has six most important integer parameters and a network with three computers in the cloud, while one can vary eighty-nine parameters, the number of computers in the network and use local computers via black-box configuration files. The applied problem is to maximize the tps under a zero drop rate constraint. The black-box, like real blockchains, uses network communication, so reproducibility is an essential part of the design. We also provide an optimization baseline, showing the non-trivial results’ reachability.
Recently, the Healthcare Internet of Things (H-IoT) has been widely applied to alleviate the global challenge of the coronavirus disease 2019 (COVID-19) pandemic. However, security and limited energy capacity issues remain the two main factors that prevent the large-scale application of the H-IoT. Therefore, a permissioned blockchain and deep reinforcement learning (DRL)-empowered H-IoT system is presented in this research to address these two issues. The proposed H-IoT system can provide real-time security and energy-efficient healthcare services to control the propagation of the COVID-19 pandemic. To address the security issue, a permissioned blockchain method is adopted to guarantee the security of the proposed H-IoT system. As for handling the limited energy constraint, we employ the mobile edge computing (MEC) method to offload the computing tasks to alleviate the computational burden and energy consumption of the proposed H-IoT system. We also adopt an energy harvesting method to improve performance. In addition, a DRL method is employed to jointly optimize both the security and energy efficiency performance of the proposed system. The simulation results demonstrate that the proposed solution can balance the requirements of security and energy efficiency issues and hence can better respond to the COVID-19 pandemic.
Abdullah Ayub Khan, Asif Ali Laghari, Aftab Ahmed Shaikh, Mazhar Ali Dootio · 6 authors
A brain-computer interface (BCI) affords real-time communication, significantly improving the quality of lifecycle, brain-to-internet (B2I) connectivity, and communication between the brain and external digital devices. This assistive technology innovates information and communication development paradigms, such as directly connecting the brain and multimedia devices to the cyber world. The system converts brain information to understandable signals for multimedia devices without physical interference and replaces human-based languages with the external environment control protocols. This advancement challenges and limits security severely. For this reason, the rate of attacks, malware, ransomware, and other types of vulnerabilities is increasing drastically. Another reason is the need to improve traditional procedures to investigate cyberenvironment security aspects. Also, these malicious attackers' prime objective is to harm personal information, enable content security and privacy protocols and physical systems integrity, and create high risk between system and consumers. However, security's capital importance stems from the growing number of wearables (on-body) and in-body wireless devices. These limitations affect personal and healthcare wireless networks during the communication (such as on-chain and off-chain) between human and wearable sensors (sense and transmit) and actuators. This paper presents a novel, secure Blockchain Security Module (BSM) for BCI with Multimedia Life Cycle Framework (MLCF) (BSM-BCIMLCF) that safely connects wearables while investigating the present-day BCI life cycle (BCILC) protection. It homogenizes a Blockchain-based distributed permission network approach to overcome existing challenges. The Blockchain enables assistant cybersecurity for BCI distributed applications to identify brain operations in real-time.
The advent of neural network (NN) based deep learning, especially the recent development of the automatic design of networks, has brought unprecedented performance gains at heavy computational cost. On the other hand, in order to generate a new consensus block, Proof of Work (PoW) based blockchain systems routinely perform a huge amount of computation that does not achieve practical purposes but to solving a difficult cryptographic hash puzzle problem.In this study, we propose a new consensus mechanism, Proof of Learning (PoLe), which directs the computation spent for block consensus toward optimization of neural networks. In our design, the training and testing data are released to the entire blockchain network and the consensus nodes train NN models on the data, which serves as the proof of learning. As a core component of PoLe, we design a secure mapping layer (SML) to prevent consensus nodes from cheating, which can be straightforwardly implemented as a linear NN layer. When the consensus on the blockchain network is achieved, a new block is appended to the blockchain. We experimentally compare the PoLe protocol with Proof of Work (PoW) and show that PoLe can achieve a more stable block generation rate, which leads to more efficient transaction processing. Experimental evaluation also shows the PoLe can achieve a stable block generation rate without significantly sacrificing training performance.