Njabulo S. Mtetwa, Nombuso Sibeko, Paul Tarwireyi, Adnan M. Abu‐Mahfouz
LoRa WAn is one of the LPWAN technologies that has become popular in both industries and research. LoRaWAN aims to provide long-range communication and empowers low-powered devices to last for years in the field. LoRaWAN relies on the symmetric cryptography to provide end-to-end encryption. Even though LoRaWAN relies on symmetric cryptography, there are recent works that try to enhance security of LoRaWAN by incorporating technologies like Blockchain. Blockchain is a decentralized peer-to-peer network that provides tamperproof and immutability of data. This paper proposes a Blockchain-based firmware update mechanism to enhance firmware update in LoRaWAN as well as managing the update process. This mechanism aims to provide updates by ensuring authenticity, and integrity of the firmware. The mechanism focuses more on devices that are too constrained in resources, hence for that purpose we evaluated the cost involved in some cryptographic operations taken to ensure security during firmware updates. We conclude that the approach is feasible for constrained devices in LoRaWAN network by evaluating the memory usage of the cryptographic operation used by the end device.
Minsu Kim, Sungho Lee, Chanwon Park, Jemin Lee · 5 authors
The Age of Information (AoI) is a recently proposed metric for quantifying data freshness in real-time status monitoring systems, where timeliness is of importance. In this article, the problem of characterizing and controlling the AoI is studied in the context of blockchain-enabled monitoring networks (BeMNs). In BeMN, status updates from sources are transmitted and recorded in a blockchain. To investigate the statistical characteristics of the AoI in BeMN, the transmission latency and the consensus latency are first rigorously modeled. Then, the average AoI, the AoI violation probability, and the peak AoI violation probability are derived in a closed form so as to quantify the performance of BeMN. Furthermore, a simplified form is derived for the AoI violation probability, and it is shown that this quantity can capture the upper or lower bounds of the actual AoI violation probability. Simulation results show that each BeMN parameters (i.e., target successful transmission probability, block size, and timeout) can have conflicting effects on the AoI-related performance. Subsequently, design insights are provided to maintain the freshness of the status data in BeMN. Then, experimental results with a real Hyperledger Fabric platform further validate the accuracy of our modeling and analysis.
Abstract Fog robotics is an entirely new direction in the robotic field, inspired by the fogcomputing concept. Some fog architectures have been developed for robots groups and robot swarms, yet, to the best of our knowledge, there are no developed mechanisms of data sharing and replication in such structures. So, they are in the focus of this paper. The distributed ledger-based architecture for the fog robot servers is considered and described, as well as some models have been developed to estimate the time needed for data sharing. Simulation results show the expediency of consensus methods usage for distributed ledger-based.
The most recent development of the Internet of Things brings massive timely sensitive and bursty data flows. Also, joint optimization on storage, computation, and communication is in need for multiaccess edge computing frameworks. The adaptive network control has been explored using deep reinforcement learning (RL), but it is not sufficient for bursty network traffic flows, especially when the network traffic pattern may change over time. We formulate the routing control in an environment with time-variant link delays as a Lyapunov optimization problem. We identify that there is a tradeoff between optimization performance and modeling accuracy when the propagation delays are included. We propose a novel deep RL (DRL)-based adaptive network routing method to tackle the issues mentioned above. A Lyapunov optimization technique is used to reduce the upper bound of the Lyapunov drift, improving queuing stability in networked systems. By modeling the network traffic pattern using the Markovian arrival process, we show that network routing problems can be modeled as Markov decision processes and value-iteration-based RL methods can be used to solve them. We design a blockchain-based protocol using proof of elapsed time consensus mechanism to ensure a trustworthy network statistics information exchange for the routing framework. Experiment results show that the proposed method can learn a routing policy and adapt to the changing environment. The proposed method outperforms the baseline backpressure method in multiple settings and converges faster than existing methods. Moreover, the DRL module can effectively learn a better estimation of the long-term Lyapunov drift and penalty functions, providing superior results in terms of the backlog size, end-to-end latency, age of information, and throughput. Furthermore, the blockchain-based network statistics exchange can provide the routing framework against malicious nodes. In addition, the proposed model performs well under various topologies, and thus can be used in general cases.
Blockchain-powered edge computing (BEC) is a promising extension to strengthen the security and the trustworthiness among collaborative edge clouds for delivering computation-intensive and delay-sensitive services in the environments of IoT and 5G. A fundamental challenge is how to respond to the maximum number of IoT requests at the network edge instead of the remote cloud. Although some work has been done to consider service provisioning and request scheduling in collaborative edge clouds, they assume that a single service is used to respond to each request. This assumption, however, is not practical to meet the demand of emerging IoT applications. In reality, the request needs to call a set of services with a chain-based structure. To tackle this challenge, in this article, we first propose a chain-based service request model for emerging IoT applications and further study the joint service provisioning and request scheduling problem for chain-based service requests at the network edge. We characterize this problem as an integer linear programming (ILP) model and prove the NP-hardness of this joint optimization problem. Furthermore, we prove that the related problem is of approximate submodularity with an approximation ratio guarantee. Finally, a novel two-stage optimization (TSO) scheme is proposed, and the results of extensive experiments show the efficiency and the effectiveness of the TSO scheme.
Nowadays, blockchain is very common and widely used in various fields. The properties of blockchain-based algorithms such as being decentralized and uncontrolled by institutions and governments, are the main reasons that has attracted many applications. The security and the scalability limitations are the main challenges for the development of these systems. Using second layer network is one of the various methods proposed to improve the scalability of these systems. This network can increase the total number of transactions per second by creating extra channels between the nodes that operate in a different layer not obligated to be on consensus ledger. In this paper, the optimal structure for the second layer network has been presented. In the proposed structure we try to distribute the parameters of the second layer network as symmetrically as possible. To prove the optimality of this structure we first introduce the maximum scalability bound, and then calculate it for the proposed structure. This paper will show how the second layer method can improve the scalability without any information about the rate of transactions between nodes.
Yixin Li, Bin Cao, Liang Liang, Lei Zhang · 6 authors
Blockchain, a distributed ledger technology, has attracted many attentions to enable a decentralized and safe wireless networks for various applications. Considering the high density of nodes and the massive service requests in next-generation wireless network will result in a surge of blockchain forking, this paper proposes a Block Access Control (BAC) approach to address forking problem and transmit block effectively while improving transaction throughput and saving computational power. Then, using a Markov chain model, we analyse the performance of a wireless blockchain network by involving the effect of BAC approach. The results show that the BAC approach can help the network to achieve a high transaction throughput while addressing forking problem.
Atomic broadcast protocols are increasingly used to build distributed ledgers. The most robust protocols achieve byzantine fault tolerance (BFT) and operate in asynchronous networks. Recent proposals such as HoneyBadgerBFT (ACM CCS `16) and BEAT (ACM CCS `18) achieve optimal communication complexity, growing linearly as a function of the number of nodes present. Although asymptotically optimal, their practical performance precludes their use in demanding applications. Further performance improvements to HoneyBadgerBFT and BEAT are not obvious as they run two separate sub-protocols for broadcast and voting, each of which has already been optimized. We describe how hashgraph - an asynchronous BFT atomic broadcast protocol (ABFT) - departs in structure from prior work by not using communication to vote, only to broadcast transactions. We perform an extensive empirical study to understand how hashgraph's structure affects performance. We observe that hashgraph can improve latency by an order of magnitude over HoneyBadgerBFT and BEAT, while keeping throughput constant with the same number of nodes; similarly, throughput can increase by up to an order of magnitude while maintaining latency. Furthermore, we test hashgraph's capability for high performance, and conclude that it can achieve sufficiently high throughput and low latency to support demanding practical applications.
Mobile edge computing (MEC), as a promising technology, provides proximate and prompt computing service for mobile users on various applications. With appropriate incentives, profit-driven users can offload multi-task requests across heterogeneous edge servers. However, such incentive trade lacks a trustworthy platform. Due to the decentralized nature of MEC, trading information from players is easily tampered with by edge servers, which poses a threat to cross-server resource allocation. In this paper, we jointly consider incentives and cross-server resource allocation in blockchain-driven MEC, where the blockchain prevents malicious edge servers from tampering with player information by maintaining a continuous tamper-proof ledger database. Particularly, we propose two double auction mechanisms, namely a double auction mechanism based on breakeven (DAMB) and a more efficient breakeven-free double auction mechanism (BFDA), in which users request multi-task service with claimed bids and edge servers cooperate with each other to serve users. A delegated proof of stake (DPoS) based blockchain technology is leveraged to realize decentralized, untampered, safe and fair resource allocation consensus mechanism. The simulation results show that the proposed DAMB and BFDA can significantly improve the system efficiency of MEC.
Recent advances in blockchain technology have led to a significant interest in developing blockchain-based applications. While data can be retained in a blockchain, the stored values can be deleted or updated. From a user viewpoint that searches for data, it is unclear whether the discovered data from the blockchain storage is relevant for real-time decision-making processes for block-chain-based applications. The data freshness issue serves as a critical factor, especially in dynamic networks handling real-time information. In general, transactions to renew data require additional processing time inside the blockchain network, which is called ledger-commitment latency. Due to this problem, some users may receive outdated data. As a result, it is important to investigate if the blockchain is suitable for providing real-time data services. In this article, we first describe block-chain-enabled (BCE) networks with Hyperledger Fabric (HLF). Then, we define age-of-information (AoI) of BCE networks and investigate influential factors on this AoI. Experiments are conducted to explore the impacts of the influential factors on data freshness in BCE networks. Lastly, we conclude by discussing future challenges.
Recently, software-defined Industrial Internet of Things (SDIIoT), the integration of software-defined networking (SDN) and Industrial Internet of Things (IIoT), has emerged. It is perceived as an effective way to manage IIoT dynamically. Aiming to improve scalability and flexibility of SDIIoT, multi-SDN has been applied to form a physically distributed control plane to handle the large amount of data generated by industrial devices. However, as the core of multi-SDN, reaching consensus among multiple SDN controllers is a thorny issue. To meet the required design principle, this paper proposes a blockchain-enabled distributed architecture with SDIIoT to synchronize local views between distinct SDN controllers and finally reach the consensus of global view. On the other hand, both the cryptographic operations of blockchain and the noncryptographic computational tasks have access to the same computational resource pool of mobile edge cloud (MEC). In order to simultaneously optimize the throughput of blockchain and the energy consumption caused by computing, we adaptively allocate computational resources and the block size by jointly considering the trust features of SDN controllers and the resource requirements of non-cryptographic operations. To implement the truly distributed manner of blockchain, we describe our problem as a partially observable Markov decision process (POMDP) and propose a novel deep recurrent Q-network (DRQN) approach to solve it. In the simulation results, we compare two different protocols of blockchain and show the effectiveness of our scheme in either of them.
Yang Gao, Wenjun Wu, Haixiang Nan, Yang Sun · 5 authors
Nowadays, the Internet of Things (IoT) has developed rapidly. To deal with the security problems in some of the IoT applications, blockchain has aroused lots of attention in both academia and industry. In this paper, we consider the mobile blockchain supporting IoT applications, and the mobile edge computing (MEC) is deployed at the Small-cell Base Station (SBS) as a supplement to enhance the computation ability of IoT devices. To encourage the participation of the SBS in the mobile blockchain networks, the long-term revenue of the SBS is considered. The task scheduling problem maximizing the long-term mining reward and minimizing the resource cost of the SBS is formulated as a Markov Decision Process (MDP). To achieve an efficient intelligent strategy, the deep reinforcement learning (DRL) based solution named policy gradient based computing tasks scheduling (PG-CTS) algorithm is proposed. The policy mapping from the system state to the task scheduling decision is represented by a deep neural network. The episodic simulations are built and the REINFORCE algorithm with baseline is used to train the policy network. According to the training results, the PG-CTS method is about 10% better than the second-best method greedy. The generalization ability of PG-CTS is proved theoretically, and the testing results also show that the PG-CTS method has better performance over the other three strategies, greedy, first-in-first-out (FIFO) and random in different environments.
In blockchain applications, transaction latency is crucial for determining the quality of service (QoS). Transaction latency is measured as the time between its issuance and its inclusion in a block in the chain. When different applications use the same blockchain network, a block proposer often prioritizes its own application transactions over other applications transactions to minimize its own latency. To maintain fairness, a block proposer is typically supposed to select the included transactions randomly providing each transaction similar chances to be included. The random selection might cause some transactions to experience high latency since this selection implies a high variance in the time a transaction waits until it is selected. We suggest an alternative, age-aware approach towards fairness so that transaction priority is increased upon observing a large waiting time. The challenge with this approach is that the age of a transaction is not absolute due to transaction propagation. Moreover, a node might present its transactions as older to obtain priority. We consider three network restrictions on transaction propagation and explain how to enhance fairness in each one of them. We describe three declaration schemes in which a node declares its pending transactions providing the ability to validate transaction age. We demonstrate the advantages of the solutions on Ethereum and synthetic data in reducing tail latency.
In public distributed ledger technologies (DLTs), such as Blockchains, nodes can join and leave the network at any time. A major challenge occurs when a new node joining the network wants to retrieve the current state of the ledger. Indeed, that node may receive conflicting information from honest and Byzantine nodes, making it difficult to identify the current state. In this paper, we are interested in protocols that are stateless, i.e., a new joining node should be able to retrieve the current state of the ledger just using a fixed amount of data that characterizes the ledger (such as the genesis block in Bitcoin). We define three variants of stateless DLTs: weak, strong, and probabilistic. Then, we analyze this property for DLTs using different types of consensus.
This paper proposes a real-time chain and a novel embedded Markovian queueing model with variable bulk arrival (VBA) and variable bulk service (VBS) in order to establish and assure a theoretical foundation to design a blockchain-based real-time system with particular interest in Ethereum. Based on the proposed model, various performances are simulated in a numerical manner in order to validate the efficacy of the model by checking good agreements with the results against intuitive and typical expectations as a baseline. A demo of the proposed real-time chain is developed in this work by modifying the open source of Ethereum Geth 1.9.11. The work in this paper will provide both a theoretical foundation to design and optimize the performances of the proposed real-time chain, and ultimately address and resolve the performance bottleneck due to the conventional block-synchrony by employing an asynchrony by the real-time deadline to some extent.
There is evidence that blockchain plays a crucial role in the Internet of Things (IoT)-based implementation due to its transparency, traceability, and immutability, in which the participants are incentivized to behave authentically and precisely for rewards. Despite the domination of subsidy in reward, the decrease of the mining rate and the imperativeness of fees make the fee market become a pivotal role to motivate miners in the blockchain. However, the current mechanism for selecting transactions into a block poses a risk to the stability of the system, which stems from the vicious competition of users and the insufficient incentives of miners. In this article, we propose a novel transaction selection mechanism by leveraging the Lyapunov optimization and large deviation theory. This article is: 1) fair because the proposed mechanism is not single-factor dominated, both personal utility of the miner and overall utility of the system are taken into account; 2) sustainable since miners are incentivized greatly to guarantee the mining behavior; and 3) robust. The analysis based on the large deviation theory enhances the robustness of the blockchain. To the best of our knowledge, we are the first to consider both miner's benefit as well as system benefit to establish a better fee market in the blockchain for IoT enhancement. Our theoretical analyses and simulation results demonstrate the effectiveness of the proposed mechanism.
Lam Duc Nguyen, Anders E. Kalør, Israel Leyva‐Mayorga, Petar Popovski
The data collected from Internet of Things (IoT) devices on various emissions or pollution, can have a significant economic value for the stakeholders. This makes it prone to abuse or tampering and brings forward the need to integrate IoT with a Distributed Ledger Technology (DLT) to collect, store, and protect the IoT data. However, DLT brings an additional overhead to the frugal IoT connectivity and symmetrizes the IoT traffic, thus changing the usual assumption that IoT is uplink-oriented. We have implemented a platform that integrates DLTs with a monitoring system based on narrowband IoT (NB-IoT). We evaluate the performance and discuss the tradeoffs in two use cases: data authorization and real-time monitoring.
Blockchain-enabled Federated Learning (BFL) enables mobile devices to\ncollaboratively train neural network models required by a Machine Learning\nModel Owner (MLMO) while keeping data on the mobile devices. Then, the model\nupdates are stored in the blockchain in a decentralized and reliable manner.\nHowever, the issue of BFL is that the mobile devices have energy and CPU\nconstraints that may reduce the system lifetime and training efficiency. The\nother issue is that the training latency may increase due to the blockchain\nmining process. To address these issues, the MLMO needs to (i) decide how much\ndata and energy that the mobile devices use for the training and (ii) determine\nthe block generation rate to minimize the system latency, energy consumption,\nand incentive cost while achieving the target accuracy for the model. Under the\nuncertainty of the BFL environment, it is challenging for the MLMO to determine\nthe optimal decisions. We propose to use the Deep Reinforcement Learning (DRL)\nto derive the optimal decisions for the MLMO.\n
Yuris Mulya Saputra, Diep N. Nguyen, Dinh Thai Hoang, Thang X. Vu · 6 authors
In this paper, we propose a novel energy-efficient framework for an electric vehicle (EV) network using a contract theoretic-based economic model to maximize the profits of charging stations (CSs) and improve the social welfare of the network. Specifically, we first introduce CS-based and CS clustering-based decentralized federated energy learning (DFEL) approaches which enable the CSs to train their own energy transactions locally to predict energy demands. In this way, each CS can exchange its learned model with other CSs to improve prediction accuracy without revealing actual datasets and reduce communication overhead among the CSs. Based on the energy demand prediction, we then design a multi-principal one-agent (MPOA) contract-based method. In particular, we formulate the CSs' utility maximization as a non-collaborative energy contract problem in which each CS maximizes its utility under common constraints from the smart grid provider (SGP) and other CSs' contracts. Then, we prove the existence of an equilibrium contract solution for all the CSs and develop an iterative algorithm at the SGP to find the equilibrium. Through simulation results using the dataset of CSs' transactions in Dundee city, the United Kingdom between 2017 and 2018, we demonstrate that our proposed method can achieve the energy demand prediction accuracy improvement up to 24.63% and lessen communication overhead by 96.3% compared with other machine learning algorithms. Furthermore, our proposed method can outperform non-contract-based economic models by 35% and 36% in terms of the CSs' utilities and social welfare of the network, respectively.
Providing an acceptable level of security for Internet of Things (IoT)-based critical infrastructures, such as the connected vehicles, considers as an open research issue. Nowadays, blockchain overcomes a wide range of network limitations. In the context of IoT and blockchain, Byzantine Fault Tolerance (BFT)-based consensus protocol, that elects a set of authenticated devices/nodes within the network, considers as a solution for achieving the desired energy efficiency over the other consensus protocols. In BFT, the elected devices are responsible for ensuring the data blocks’ integrity and preventing the concurrently appended blocks that might contain some malicious data. In this paper, we evaluate the fault-tolerance with different network settings, i.e., the number of connected vehicles. We verify and validate the proposed model with MATLAB/Simulink package simulations. The results show that our proposed hybrid scenario performed over the non-hybrid scenario taking throughput and latency in the consideration as the evaluated metrics.
Recently, we have seen increasing popularity of using the blockchain technology to secure sensing data generated by traditional wireless sensor networks and Internet of Things (IoT). One of the biggest obstacles for integrating the IoT and blockchain technologies is the limited throughput of the current distributed ledgers. In this paper, we propose a novel method for hierarchical processing and logging of potentially large amount of sensing data with the blockchain technology, which could drastically address the issue of limited throughput in blockchains. Depending on the scale of the sensing need, two or more levels of processing and logging could be involved, where only the highest level of summative sensor data are placed on the blockchain or a secure distributed ledger. By establishing a strong linkage between different level of data, all lower-level and raw data are also made immutable once the highest level of summative data are placed on the blockchain.
PBFT is a consensus algorithm based on Byzantine fault tolerance that is widely used in current systems like blockchains. However, this algorithm has some problems that slow down its use on a large scale. In the interest of solving its problems, we have combined it with the Distributed Proof of Stake (DPoS) algorithm and smart contract technology to improve it and make it better. For this, Smart contracts were deployed in the network to improve the selection process of accounting nodes and participated in the operating process of the PBFT algorithm in order to make the selection process more transparent, incorruptible, and secure. Concerning the problem of the scalability of the nodes of the system, it will be possible to make a readjustment of the consensus algorithm to make it more flexible. The modification can be done by implementing readjustment counters, which will count the number of nodes in the network each time a consensus is reached or a node is ejected from the network, then automatically distributes the list of new nodes in the network. This new list of nodes will constitute the new network on which the new consensus will be based. To make it more secure and more sensitive to Byzantine faults, the sensitivity margin is improved.
Fengxian Guo, F. Richard Yu, Heli Zhang, Hong Ji · 6 authors
In this paper, we present a blockchain-based mobile edge computing (B-MEC) framework for adaptive resource allocation and computation offloading in future wireless networks, where the blockchain works as an overlaid system to provide management and control functions. In this framework, how to reach a consensus between the nodes while simultaneously guaranteeing the performance of both MEC and blockchain systems is a major challenge. Meanwhile, resource allocation, block size, and the number of consecutive blocks produced by each producer are critical to the performance of B-MEC. Therefore, an adaptive resource allocation and block generation scheme is proposed. To improve the throughput of the overlaid blockchain system and the quality of services (QoS) of the users in the underlaid MEC system, spectrum allocation, size of the blocks, and number of producing blocks for each producer are formulated as a joint optimization problem, where the time-varying wireless links and computation capacity of the MEC servers are considered. Since this problem is intractable using traditional methods, we resort to the deep reinforcement learning approach. Simulation results show the effectiveness of the proposed approach by comparing with other baseline methods.
Vicent Cholvi, Antonio Fernández Anta, Chryssis Georgiou, Nicolas Nicolaou · 5 authors
A Distributed Ledger Object (DLO) is a concurrent object that maintains a totally ordered sequence of records, and supports two basic operations: append, which appends a record at the end of the sequence, and get, which returns the sequence of records. In this work we provide a proper formalization of a Byzantine-tolerant Distributed Ledger Object (BDLO), which is a DLO in a distributed system in which processes may deviate arbitrarily from their indented behavior, i.e. they may be Byzantine. Our formal definition is accompanied by algorithms to implement BDLOs by utilizing an underlying Byzantine Atomic Broadcast service. We then utilize the BDLO implementations to solve the Atomic Appends problem against Byzantine processes. The Atomic Appends problem emerges when several clients have records to append, the record of each client has to be appended to a different BDLO, and it must be guaranteed that either all records are appended or none. We present distributed algorithms implementing solutions for the Atomic Appends problem when the clients (which are involved in the appends) and the servers (which maintain the BDLOs) may be Byzantine.