Follow blockchain research across journals, conferences, and preprint repositories.
163 results · page 4 of 7
A. Sasikumar, N. Senthilkumar, V. Subramaniyaswamy, Ketan Kotecha · 6 authors
No abstract is available for this record.
Ziaur Rahman, Xun Yi, Sk. Tanzir Mehedi, Rafiqul Islam · 5 authors
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.
Chonghe Zhao, Shengli Zhang, Taotao Wang, Soung Chang Liew
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.
Ioannis Skalidis, Olivier Müller, Stéphane Fournier
No abstract is available for this record.
Yunkai Wei, Zixian An, Supeng Leng, Kun Yang
Machine learning is an essential technology providing ubiquitous intelligence in Internet of Things (IoT). However, the model training in machine learning demands tremendous computing resource, bringing heavy burden to the IoT devices. Meanwhile, in the Proof-of-Work (PoW)-based blockchains, miners have to devote large amount of computing resource to compete for generating valid blocks, which is frequently disputed for tremendous computing resource waste. To address this dilemma, we propose an Evolved-PoW (E-PoW) consensus that can integrate the matrix computations in machine learning into the process of blockchain mining. The integrated architecture, the elaborated schemes of transferring matrix computations from machine learning to blockchain mining, and the reward adjustment scheme to affect the activity of the miners are, respectively, designed for E-PoW in detail. E-PoW can keep the advantages of PoW in blockchain and simultaneously salvage the computing power of the miners for the model training in machine learning. We conduct experiments to verify the availability and effect of E-PoW. The experimental results show that E-PoW can salvage by up to 80% computing power from pure blockchain mining for parallel model training in machine learning.
Ping Wang, Weiqian Chen, Songlian Lin, Liyan Liu · 6 authors
Blockchain systems based on the proof-of-work (PoW) consensus introduce entropy to the system in a natural way due to the randomness of mining. However, for non-PoW consensus (e.g., proof-of-stake and delegated proof-of-stake consensus) blockchain systems, a different approach to introducing entropy, such as the distributed random number generation (dRNG) algorithm, must be established. The dRNG algorithm is one of the key challenges in developing the consensus mechanism, as well as one of the relevant parameters for determining the merit of the consensus mechanism. In this paper, we first derive a publicly verifiable quantum random numbers generation protocol based on the certifiable randomness scheme from any untrusted quantum device, which offers features, such as fairness, no trusted third party, and publicly verifiable. Then, based on verifiable quantum random numbers, we propose a new consensus algorithm. The algorithm selects block proposer and block verification committees for each round using verifiable quantum random numbers, resulting in better randomness, fairness, and efficiency of the entire consensus process. In addition, the new consensus algorithm is not only resistant to adaptive adversary models as well as to collusion attacks, but also requires negligible computation for each user to avoid unnecessary consumption of power resources. Finally, we analyze the verifiable randomness, fairness, liveness, and communication complexity of the consensus algorithm.
Tanweer Alam, Arif Ullah, Mohamed Benaida
No abstract is available for this record.
T. Abhiroop, Sarath Babu, B. S. Manoj
Blockchain is considered as an important technique for maintaining the integrity of data in enterprises. However, the computationally intensive proof-of-work forms a major bottleneck in adopting blockchains to energy-constrained environments such as Internet of Things (IoT). In this paper, we propose a Machine learning Consensus based Light-weight Blockchain (MCLB) for resource-constrained edge devices in IoT to detect malicious data besides providing the consensus for maintaining the integrity of data. Our light-weight approach reduces the overhead by eliminating the nonce of traditional blockchains, thereby bringing down the delay in arriving at a consensus. Each edge device is equipped with a machine learning algorithm to classify data and the predicted classifications are used to arrive at the consensus. The use of different machine learning algorithms at the edge nodes improves the robustness of our system. Our framework is realized on a sensor network consisting of four edge devices and the results show that MCLB outperforms existing blockchain models in terms of computational delay, communication overhead, and block addition delay.
Shivani Wadhwa, Shalli Rani, Gagandeep Kaur, Deepika Koundal · 6 authors
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.
Wendy Charles
No abstract is available for this record.
Long Liu, Zhichao Li
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.
Narendra Kumar Dewangan, Preeti Chandrakar
No abstract is available for this record.
Armando Ruggeri, Antonio Celesti, Maria Fazio, Massimo Villari
No abstract is available for this record.
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.
Shijing Yuan, Jie Li, Jinghao Liang, Yuxuan Zhu · 7 authors
With the growth of data scale in the mobile edge computing (MEC) network, data security of the MEC network has become a burning concern. The application of blockchain technology in MEC enhances data security and privacy protection. However, throughput becomes the bottleneck of the blockchain-enabled MEC system. Hence, this paper proposes a novel hierarchical and partitioned blockchain framework to improve scalability while guaranteeing the security of partitions. Next, we model the joint optimization of throughput and security as a Markov decision process (MDP). After that, we adopt deep reinforcement learning (DRL) based algorithms to obtain the number of partitions, the size of micro blocks and the large block generation interval. Finally, we analyze the security and throughput performance of proposed schemes. Simulation results demonstrate that proposed schemes can improve throughput while ensuring the security of partitions.
Mohammad S. Obaidat, Sanjeev Kumar Dwivedi, Ruhul Amin, Kuei-Fang Hsiao
The upcoming industry is continuously changing, and the current systems and technologies are not suitable in their present form. They need to upgrade to solve the complex problems that are faced by the different applications. The healthcare industry is one of them. In the current healthcare industries, the patient’s information is stored in the central cloud servers, and the third party (TP) is required to share these details among the service providers. As a result, sharing information without adopting TP in a distributed manner is a challenging task. The distributed ledger technology blockchain has the potential to resolve the problems mentioned above. The blockchain provides the immutability, transparency, and traceability of records where the records are distributed among the many nodes. This article first presents the various problems of the current electronic medical record (EMR) system, discusses the importance of blockchain in EMR systems, and finally summarizes the existing approaches with their future scopes. Based on it, this paper proposed a novel architecture for a blockchain-enabled secure sharing of health records. In addition to it, possible research directions with the relevant security attacks for blockchain systems are also highlighted in the paper.
Yuan Liu, Yixiao Lan, Boyang Li, Chunyan Miao · 5 authors
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.
Thomas Lavigne, Bacem Mbarek, Tomáš Pitner
Smart health systems have the potential to improve the life and the provided health quality. Since the collaborative working is important in healthcare, the integration of Blockchain for the distribution of real-time patient health becomes more and more important because non-authorized access can result in health risks or private information disclosure. Many Blockchain healthcare applications have been proposed to manage the patient information, however, there is still a lack of providing a smart real-time healthcare monitoring and tracking system. This paper therefore proposes a Real-Time Healthcare system (RTH-Care) based on Blockchain technology that supports the life cycle of a patient in the healthcare system. In particular, we improve the quality of the healthcare system by ensuring a consensus around tests and information about the patients and by centralizing the prescription system. Furthermore, we collect patient health data by integrating medical IoT devices. Subsequently, we provide a data access control to restrict access to the granted persons. The performance analysis of the implementation of RTH-Care with the Hyperledger Fabric tool is showing promising results. An analysis of the communication failures is discussed by using Hyperledger Caliper. Indeed, the obtained results show that our solution is reliable and scalable in terms of the number of transactions per second processed by the network, and the average latency measurements.
Qian Qu, Ronghua Xu, Yu Chen, Erik Blasch · 5 authors
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.
Hemlata Kohad, Sunil Kumar, Asha Ambhaikar
Many industries implement the blockchain for their application as it is a distributed ledger with a trust. The first application of blockchain is Bitcoin. Depending on the applicability of blockchain we have to consider different parameters and accordingly, we have to select the consensus algorithm. Consensus algorithm is the crucial part while designing any blockchain for the desired application. To confirm any transaction different consensus algorithms are used. In this paper, we will discuss different consensus algorithms. In addition to this, we will compare different algorithms concerning different parameters like time required to confirm the transaction, throughput, network used for the implementation. This article summarizes the factors to be considered while implementing the blockchain.
Filippos Pelekoudas Oikonomou, José Ribeiro, Γεώργιος Μαντάς, Joaquim Bastos · 5 authors
Although blockchain is a promising technology that can bring significant benefits into current centralized IoT-based health monitoring systems in order to address security challenges, the resource-constrained IoT devices of these systems cannot afford complex and heavyweight operations due to their limited processing power, storage capacity, and battery life. Therefore, in this paper, we propose a Hyperledger Fabric-based blockchain architecture to: i) enhance security in IoT-based health monitoring systems, ii) achieve better storage handling due to the limited storage capacity of sensors and gateways, iii) facilitate decentralized accountability, and iv) eliminate single point of failure.
N. K. Al-Shammari, Tehreem Syed, M. B. Syed
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.