Haijun Liao, Zhao Wang, Zhenyu Zhou, Yang Wang · 7 authors
Power systems impose stringent security and delay requirements on computation offloading, which cannot be satisfied by existing power Internet of Things (PIoT) networks. In this paper, we tackle this challenge by combining blockchain, space-air-ground integrated PIoT (SAG-PIoT) and machine learning. Low earth orbit (LEO) satellites assist in broadcasting a consensus message to reduce the block creation delay, and unmanned aerial vehicles (UAVs) provide flexible coverage enhancement. Specifically, we propose a Blockchain and semi-distributed leaRning-based secure and low-latency electromAgnetic interferenCe-awarE computation offloading algorithm (BRACE) to minimize the total queuing delay under the long-term security constraint. First, the task offloading is decoupled from the computational resource allocation by Lyapunov optimization. Second, the task offloading problem is solved by the proposed federated deep actor-critic-based electromagnetic interference-aware task offloading algorithm (FDAC-EMI). Finally, the resource allocation problem is solved by smooth approximation and Lagrange optimization. Simulation results verify that BRACE achieves superior delay and security performance.
The integration of blockchain technology and RAFT consensus algorithm could address many of the IoT application security and privacy issues. However, each node in RAFT consensus has equivalent probability of being elected as a leader, which takes no consideration on the communication and computing status of the node. Therefore, in this paper, we propose a weighted RAFT consensus algorithm for IoT applications, which allows IoT gateway with better capacity to have a higher possibility of being elected as the leader compared to the normal RAFT mechanism. Thus, the latency of uploading IoT data on blockchain can be reduced a lot. Simulation results show that the weighted RAFT can reduce the system forwarding latency by 24% at most.
Chengjie Li, Lidong Zhu, M. Luglio, Zhongqiang Luo · 5 authors
Due to satellite physical constraints in terms of available power and area, data processing capacity is low, storage and security are limited. It is a challenge to protect satellite network from illegal information access and use storage space effectively. In this paper, a blockchain technology-based on authentication and privacy protection scheme is proposed for a satellite communication network. to this aim, an architecture consisting of both conventional and restricted devices connected to the blockchain via a wireless heterogeneous network is deployed. The communication is carried out through registration, authentication and revocation. In this scheme, the satellite will forward the collected information to the ground base station, which will record all key parameters on the distributed blockchain and all malicious node certificates will be cleared from the blockchain by the ground base station. The simulation results show that the scheme has been greatly improved in terms of communication security and communication overhead.
This paper proposes a method of using a blockchain to solve the privacy problem and forgery of black box image data, which plays an essential role in determining the responsibility for traffic accidents and preventing accidents. Blockchain that can operate inside vehicle black box IoT or connected car is used, and for this purpose, the size is reduced for operation in low-power, low-capacity devices. By enabling consensus, security problems can be solved through a lightweight blockchain that can operate inside a black box device. As a result of the experiment, it was confirmed that the IPFS upload and download delay time increased linearly, and the proposed consensus algorithm decreased 63% compared to PBFT.
Wider coverage and a better solution to a latency reduction in 5G necessitate its combination with multi-access edge computing (MEC) technology. Decentralized deep learning (DDL) such as federated learning and swarm learning as a promising solution to privacy-preserving data processing for millions of smart edge devices, leverages distributed computing of multi-layer neural networks within the networking of local clients, whereas, without disclosing the original local training data. Notably, in industries such as finance and healthcare where sensitive data of transactions and personal medical records is cautiously maintained, DDL can facilitate the collaboration among these institutes to improve the performance of trained models while protecting the data privacy of participating clients. In this survey paper, we demonstrate the technical fundamentals of DDL that benefit many walks of society through decentralized learning. Furthermore, we offer a comprehensive overview of the current state-of-the-art in the field by outlining the challenges of DDL and the most relevant solutions from novel perspectives of communication efficiency and trustworthiness.
This article deals with recent developments in distributed ledger technologies using IOTA Tangle. We focus on the context of massive Internet of Things sensors with the specific need to secure near real time events. We will explain some general basics of IOTA Tangle and why it can perform well to address some transaction processing limitations related to data validation through consensus mechanisms.
The use of blockchain technology is becoming more widespread. Governments have expanded their use of the technology from online polls to business management of smaller local governments while private institutions have increased their services from financial to medical services management. This paper presents the modified pBFT blockchain consensus algorithm for a more efficient data management method in cases of applying blockchains in authorized nodes such as governmental agencies. The network communication cost was minimized while the consensus accuracy was maximized by applying a method of simplifying the request management process and electing the reliability-based consensus node during the pBFT consensus algorithm process. By applying the modified pBFT consensus algorithm, stability and speed of the consensus and verification process among various organizations can be guaranteed as well as application in efficient management and value creation of data.
Feilong Wang, Yipeng Ji, Mingsheng Liu, Yangyang Li · 7 authors
At present, the transaction delay of the consortium block chain applying the Practical Byzantine Fault Tolerance (PBFT) consensus protocol can only reach 2 to 5 seconds, and the throughput cannot reach tens of thousands. In addition as the number of nodes increases, the performance of the consortium block chain declines very quickly. The main challenge of previous research are to realize communication network topology of PBFT algorithm and high information exchange in the case of Byzantine failure, thus, this paper proposes an optimized Byzantine fault-tolerant algorithm to solve the performance bottleneck of the consortium chain. First of all, for the communication network structure of the whole network broadcast, we have reached an agreement on the transaction according to the pre-prepare and prepare phases of PBFT, and generally enter the commit phase, there is a high probability that the leader is honest, so we will communicate with the commit phase The network is optimized as a star communication structure. Second, combined with Tendermint, merge the view-change process of Byzantine failures of the normal consensus process, and switch the leader according to the longest chain principle. The algorithm is based on a partially synchronized network model to ensure the security and liveness of the protocol, and improve the performance and effective robustness.
Long Shi, Taotao Wang, Jun Li, Shengli Zhang · 5 authors
As the underlying consensus protocol of Bitcoin and Ethereum blockchains, Proof-of-Work (PoW) features a cryptographic mathematical puzzle whose solution is easy to verify but extremely hard to solve. Under PoW, miners maintain the security of blockchain by devoting computing powers to solve the puzzle; the miner who has solved the puzzle successfully generates a block, along with a reward (e.g., a set of cryptocurrency). The average waiting time to generate a block is inversely proportional to the computing power of the miner. To reduce the average block generation time, a group of individual miners can form a centralized mining pool to aggregate their computing power to solve the puzzle together and share the reward contained in the block. However, if the aggregated computing power of the pool forms a substantial portion of the total computing power in the network, the pooled mining undermines the core spirit of blockchain, i.e., the decentralization, and harms its security. To discourage the pooled mining, we develop a new consensus protocol called Proof-of-Age (PoA) that builds upon the native PoW protocol. The core idea of PoA lies in using Age-of-Work (AoW) to measure the effective mining periods that the miners have devoted to maintaining the security of blockchain. Unlike in the native PoW protocol, in our PoA protocol, miners benefit from its effective mining periods even if they have not successfully mined a block. We first employ a continuous time Markov chain (CTMC) to model the block generation process of the PoA based blockchain. Based on this CTMC model, we then analyze the block generation rates of the mining pool and solo miners respectively. Our analytical results verify that under PoA, the block generation rates of miners in the mining pool are reduced compared to that of solo miners, thereby disincentivizing the pooled mining. Finally, we simulate the mining process in the PoA blockchain to demonstrate the consistency of the analytical results.
Currently, The Internet of Things (IoT), as an essential infrastructure and new technology, has been applied in a wide range of fields. The fact that many devices in IoT are battery-operated makes the energy-saving network protocols necessary. Therefore, the Long Range Wide Area Network (LoRaWAN) is getting popular for its long range and low power consumption. To facilitate LoRaWAN key management, this study proposes a new secure architecture that can enhance the availability and security based on the permissioned blockchain and a new Join Request that shortens the network access time and shares the pressure of JS(Join Server). In addition, this study also puts up with a new approach to update the root key, solving the problem that the root key remains unchanged through the device's life. To demonstrate the feasibility of the proposed architecture, we put forward a workable prototype using open-source tools. Moreover, Automated Validation of Internet Security Protocols and Applications (AVISPA) is used to verify the security, and the performance analysis shows the superiority of our scheme in delay and execution time. All results demonstrate that our scheme is lightweight, efficient and secure.
Abstract The increasing number of drones brings various issues like security risks, increasing air traffic, misuse or crash of drones, and so on. Blockchain can provide the solution to mitigate these problems of drones. Hence, in this article, we have presented the Blockchain of Drones (BCoD), a conceptual approach of collaboration between blockchain and IoD to make drones advantageous through blockchain properties. But the main challenge for adopting blockchain is scalability as consensus in blockchain relies on the message communications by replicated nodes to attain flexibility against faulty nodes. Considering this, we present an advanced byzantine fault tolerance (ABFT) consensus for drone‐based applications. This protocol provides scalability with minimum cost and resources for achieving general agreement. It uses only f + 1 resources in normal case and 2f + 1 resources in faulty case with O(n) communication complexity. The systematic analysis and evaluation show that the ABFT improves the performance of the system for a wide range of networks compared with existing BFT systems.
Peng Lin, Qingyang Song, F. Richard Yu, Dan Wang · 5 authors
Wireless virtual reality (VR)-enabled medical treatment (WVMT) system, integrating the VR technology and the platform of the Internet of Medical Things (IoMT), is a promising application in future medical industries. Multiaccess edge computing (MEC) is an effective approach to support the ubiquitous applications of WVMT systems. Due to the high requirements of medical services, the computation efficiency and security are two issues in WVMT systems. In this article, we propose a blockchain-enabled task offloading scheme, where the viewport rendering tasks of VR devices (VDs) can be offloaded to edge access points (EAPs). The blockchain is integrated into the system to reach the consensus of the global information of task offloading and data processing to resist malicious attacks. To reduce VDs’ computation load under the promise of high VR QoE, we formulate the computation offloading and resource allocation to be a Markov decision problem, considering block consensus, content correlation, and fluctuating channel conditions. Then, a novel collective reinforcement learning (CRL) algorithm is proposed to adaptively allocate resources based on the requirements of viewport rendering, block consensus, and content transmission. In the simulations, the convergence rate and the performance in terms of energy consumption and stalling rate are evaluated. simulation results demonstrate the effectiveness of the proposed scheme.
Abstract With the continuous in-depth application of new technologies such as big data and Internet of Things in the oil and gas field, the scale and value of oil and gas data continue to rise, laying the foundation for oil and gas big data sharing. Oil and gas big data sharing can effectively integrate different data sources and break the dilemma of “data islands” where data in the oil and gas field are separated from each other. However, there are problems in actual practice such as (1) difficulties in trust among different government departments, scientific research institutions and enterprises, (2) insufficient data privacy protection, and (3) weak data traceability. In response to the above problems, this article proposes a model of oil and gas big data sharing based on blockchain technology. We build a blockchain for oil and gas big data sharing alliances to secure oil and gas big data by utilizing the decentralization and non-tampering characteristics. We also provide supports for oil and gas big data sharing by controlling data access strictly based on the smart contracts. The evaluation and analysis results show that this model makes oil and gas big data more secure and authoritative compared with the traditional centralized sharing method.
Abstract The distributed ledger or blockchain technologies originated from the Bitcoin have been rapidly widespread in recent years. However, it also gives incentive to malicious users who would like to break the system or take advantage of it (steal money, hide some information stored in the ledger, isolate a particular node from the rest of the network, and so forth). Thus, research focusing on overcoming potential attacks to distributed ledgers is required. In this article, we focus on attacks that damage underlying networks of distributed ledgers. Underlying networks offer useful communication primitives such as an atomic broadcast, however, such attacks may degrade the property of the primitives and make distributed ledgers relying on the primitives no longer work. Hence we should design algorithms to make the distributed ledgers still work even when some attacks degrade the primitives. As the first study for such situations, we consider a problem to implement distributed ledgers tolerating the degradation of an underlying atomic broadcast service that distributed ledgers are relying on. We consider the case where the uniform agreement property of the atomic broadcast is degraded, and propose new algorithms that could ensure to reach eventual consistency despite degraded atomic broadcast.
Federated learning (FL) is seen as a road toward privacy-preserving distributed artificial intelligence while keeping raw training data on local devices. By leveraging blockchain, this article puts forward a blockchain and FL fusioned framework to manage the security and trust issues when applying FL over mobile edge networks. First, a two-layered architecture is proposed that consists of two types of blockchains: local model update chain (LMUC) assisted by device-to-device (D2D) communication and global model update chain (GMUC) supporting task sharding. The D2D-assisted LMUC is designed to chronologically and efficiently record all of the local model training results, which can help to form long-term reputations of local devices. The GMUC is proposed to provide both security and efficiency by preventing mobile edge computing nodes from malfunctioning and dividing them into logically isolated FL task-specific chains. Then a reputation-learning-based incentive mechanism is introduced to make participating local devices more trustful with a reward implemented by a smart contract. Finally, a case study is given to show that the proposed framework performs well in terms of FL learning accuracy and blockchain time delay.
The conventional architecture of vehicular ad hoc networks (VANETs) with a centralized approach has difficulty overcoming the increasing complexity of intelligent transportation system (ITS) applications as well as challenges in providing large amounts of data storage, trust management, and information security. Therefore, vehicular edge computing networks (VECNets) have emerged to provide massive storage resources with powerful computing on network edges. However, a centralized server in VECNets is insufficient due to potential data leakage and security risks as it can still allow a single point of failure (SPoF). We propose consortium blockchain and smart contracts to ensure a trustworthy environment for secure data storage and sharing in the system to address these challenges. Practical byzantine fault tolerance (PBFT) is utilized because it is suitable for consortium blockchain to audit publicly, store data sharing, and records the whole consensus process. It can defend against system failures with or without symptoms to reach an agreement among consensus participants. Furthermore, we use an incentive mechanism to motivate the vehicle to contribute and honestly share their data. The simulation results satisfy the proposed model’s design goals by increasing vehicular networks’ performance in general.
Florian Jacob, Carolin Beer, Norbert Henze, Hannes Hartenstein
Matrix is a new kind of decentralized, topic-based publish-subscribe middleware for communication and data storage that is getting particularly popular as a basis for secure instant messaging. By comparison with traditional decentralized communication systems, Matrix replaces pure message passing with a replicated data structure. This data structure, which we extract and call the Matrix Event Graph (MEG), depicts the causal history of messages. We show that this MEG represents an interesting and important replicated data type for decentralized applications that are based on causal histories of publish-subscribe events: First, we prove that the MEG is a Conflict-Free Replicated Data Type for causal histories and, thus, provides Strong Eventual Consistency (SEC). With SEC being among the best known achievable trade-offs in the scope of the well-known CAP theorem, the MEG provides a powerful consistency guarantee while being available during network partition. Second, we discuss the implications of byzantine attackers on the data type's properties. We note that the MEG, as it does not strive for consensus or strong consistency, can cope with n > f environments with n participants, of which f are byzantine. Furthermore, we analyze scalability: Using Markov chains, we study the number of forward extremities of the MEG over time and observe an almost optimal evolution. We conjecture that this property is inherent to the underlying spatially inhomogeneous random walk. With the properties shown, a MEG represents a promising element in the set of data structures for decentralized applications, but with distinct trade-offs compared to traditional blockchains and distributed ledger technologies.
Xianjia Yu, Jorge Peña Queralta, Jukka Heikkonen, Tomi Westerlund
Autonomous systems are becoming inherently ubiquitous with the advancements of computing and communication solutions enabling low-latency offloading and real-time collaboration of distributed devices. Decentralized technologies with blockchain and distributed ledger technologies (DLTs) are playing a key role. At the same time, advances in deep learning (DL) have significantly raised the degree of autonomy and level of intelligence of robotic and autonomous systems. While these technological revolutions were taking place, raising concerns in terms of data security and end-user privacy has become an inescapable research consideration. Federated learning (FL) is a promising solution to privacy-preserving DL at the edge, with an inherently distributed nature by learning on isolated data islands and communicating only model updates. However, FL by itself does not provide the levels of security and robustness required by today’s standards in distributed autonomous systems. This survey covers applications of FL to autonomous robots, analyzes the role of DLT and FL for these systems, and introduces the key background concepts and considerations in current research.
Distributed ledgers (DLs) provide many advantages over centralized solutions in Internet of Things projects, including but not limited to improved security, transparency, and fault tolerance. To leverage DLs at scale, their well-known limitation (i.e., performance) should be adequately analyzed and addressed. Directed acyclic graph-based DLs have been proposed to tackle the performance and scalability issues by design. The first among them, IOTA, has shown promising signs in addressing the preceding issues. IOTA is an open source DL designed for the Internet of Things. It uses a directed acyclic graph to store transactions on its ledger, to achieve a potentially higher scalability over blockchain-based DLs. However, due to the uncertainty and centralization of the deployed consensus, the current IOTA implementation exposes some performance issues, making it less performant than the initial design. In this article, we first extend an existing simulator to support realistic IOTA simulations and investigate the impact of different design parameters on IOTA’s performance. Then, we propose a layered model to help the users of IOTA determine the optimal waiting time to resend the previously submitted but not yet confirmed transaction. Our findings reveal the impact of the transaction arrival rate, tip selection algorithms, weighted tip selection algorithm randomness, and network delay on the throughput. Using the proposed layered model, we shed some light on the distribution of the confirmed transactions. The distribution is leveraged to calculate the optimal time for resending an unconfirmed transaction to the DL. The performance analysis results can be used by both system designers and users to support their decision making.
Jelena Mišić, Vojislav B. Mišić, Xiaolin Chang, Haytham Qushtom
This work proposes Practical Byzantine Fault Tolerance (PBFT) ordering service needed for block formation in permissioned blockchain environments. Contrary to current PBFT implementations that only provide a single point of entry to the ordering service, we allow each ordering node to act as an entry point that proposes and conducts the consensus process of including new record in the distributed ledger. To ensure atomicity of record insertion in distributed ledger, we have developed a bandwidth reservation protocol that uses a modification of CSMA/CA protocol to regulate access to the broadcast medium formed by the P2P network of TCP connections between orderers. We have modeled record insertion service time in a cluster where ordering nodes have random position within Cartesian coordinate system. We have also modeled total request access time to the ledger which includes waiting time in the orderer's queue and record insertion time. These models are used to evaluate system performance under variable request rate ordering service, variable number of nodes and variable physical cluster dimensions. Our results show the interaction between decreased request waiting time in orderer's queue and increased contention among orderers when the number of orderers increases for the given total request arrival rate. This interaction is also investigated for two different physical cluster sizes which affect record insertion time. The interplay of request rate, number of orderers and physical cluster size determines system capacity expressed in total request rate. Our model can be used to make the trade-off between the required system capacity, number of orderers, and physical cluster dimensions under constraints on Byzantine fault rate.
The Intelligent Transportation System (ITS) has become essential for the economical and technological development of a country. The maturity of communication technologies (Vehicle to Infrastructure (V2I) and Vehicle to Vehicle (V2V)) and the amalgamation of smart grids, electric vehicles (EVs) and energy trading resulted in a storm of research opportunities for green ITS. In addition, the combination of vehicular communication technologies and ITS enable efficient selection of EV charging stations (CS) and scheduling EVs charging requirements in real-time. However, the untrusted centralized nature of energy markets and EV charging infrastructures result in several privacy and security threats to EV user's private information. These security and privacy threats include targeted advertisements, privacy leakage, selling data to third party, etc. In this work, we propose BlockEV, a blockchain-based efficient CS selection protocol for EVs to ensure the security and privacy of the EV users, availability of the reserved time slots at CSs, high Quality of Service (QoS) and enhanced EV user comfort. First, a blockchain-based framework is introduced to implement secure charging services and trusted reservation for EVs with the execution of smart contract. Second, we focus on the efficient CS selection and propose a mechanism for EVs to select the CS locally without sharing private information to CS, while fulfilling their service requirements. Evaluations show that the proposed BlockEV is scalable with significantly low blockchain transaction and storage overhead.
For several industrial applications, a sole data owner may lack sufficient training samples to train effective machine learning based models. As such, we propose a federated learning (FL) based approach to promote privacy-preserving collaborative machine learning for applications in smart industries. In our system model, a model owner initiates an FL task involving a group of workers, i.e., data owners, to perform model training on their locally stored data before transmitting the model updates for aggregation. There exists a tradeoff between service latency, i.e., the time taken for the training request to be completed, and age of information (AoI), i.e., the time elapsed between data aggregation from the deployed industrial Internet of Things devices to completion of the FL-based training. On one hand, if the data are collected only upon the model owner's request, the AoI is low. On the other hand, the service latency incurred is more significant. Furthermore, given that different training tasks may have varying AoI requirements, we propose a contract-theoretic task-aware incentive scheme that can be calibrated based on the weighted preferences of the model owner toward AoI and service latency. The performance evaluation validates the incentive compatibility of our contract amid information asymmetry, and shows the flexibility of our proposed scheme toward satisfying varying preferences of AoI and service latency.
Dynamic spectrum access (DSA) is crucial to improve the utilization efficiency of the limited and precious radio spectrum resources. Recently, the application of blockchain is proposed to improve the security, distribution and transparency of DSA. However, in opportunistic spectrum access (OSA), the implementation of blockchain consumes considerable amount time in each time slot so that the time left for spectrum sensing and access will be decreased. Therefore, in this letter, we aim to optimize the frame structure regarding the sensing time and mining time so that the average achievable throughput is maximized. We first decouple the original optimization problem into two sub-optimization problems with respect to sensing time and mining time, respectively, and then prove that there exists a unique maximum point for both the two sub-optimization problems. After that, an alternating algorithm is proposed for the optimization. Using the simulations, the sensing-mining-access tradeoff and effectiveness of our proposed algorithm to optimize such a tradeoff are illustrated.