Blockchain Papers

Kaile Xiao, Zhipeng Gao, Weisong Shi, Xuesong Qiu · 6 authors

No abstract is available for this record.

Hao Xu, Lei Zhang, Yinuo Liu, Bin Cao

Blockchain shows great potential to be applied in wireless IoT ecosystems for establishing the trust and consensus mechanisms without central authority's involvement. Based on RAFT consensus mechanism, this letter investigates the security performance of wireless blockchain networks in the presence of malicious jamming. We first map and model the blockchain transaction as a wireless network composed of uplink and downlink transmissions by assuming the follower nodes' position as a Poisson Point Process (PPP) with selected leader location. The probability of achieving successful blockchain transactions is derived and verified by extensive simulations. The results provide analytical guidance for the practical deployment of wireless blockchain networks.

Jun Feng, Laurence T. Yang, Ronghao Zhang, Benard Safari Gavuna

Tucker decomposition has been widely used to extract meaningful and underlying data from heterogeneous data generated by different kinds of devices in a wide range of industrial Internet of Things (IIoT) applications. IIoT data uploaded to the cloud contain personal and sensitive information; thus, there is a growing concern about data privacy. Current existing data analysis solutions, however, assume that the data are reliably and securely collected from different IIoT data providers, an assumption that is not always true in the real world. To address the issues, in this article we propose a privacy-preserving tucker train decomposition based on gradient descent over blockchain-based encrypted IIoT data. Specifically, we use blockchain techniques to enable IIoT data providers to reliably and securely share their data by encrypting them locally before recording them in the blockchain. We use tensor train (TT) theory to build an efficient TT-based tucker decomposition based on gradient descent that tremendously reduces the number of elements to be updated during the tucker decomposition. We utilize the massive resources of fogs and clouds to implement an efficient privacy-preserving tucker train decomposition scheme. We use homomorphic encryption to build our scheme that does complete tucker train decomposition without the involvement of users. Results from a series of extensive experiments on synthetic datasets and real-world datasets demonstrate that our proposed scheme is efficient.

Soohyeong Kim, Sejong ­Lee, C. Jeong, Sunghyun Cho

Blockchain is a distributed, reliable, and secure ledger that maintains data by consensus among network participants. The consensus algorithms provide data reliability but increase the data processing time. In this paper, we propose the multi-block consensus algorithm based on Byzantine Fault Tolerance to enhance throughput. The key point of the proposed algorithm is that the primary propagates the disjoint-transaction sets to other replicas. After receiving the propagated blocks, the replicas verify the propagation part and the content part of the blocks. As sharing the verifying result, the replicas could add the valid blocks to the blockchain at a time. We evaluate the performance of the proposed algorithm comparing to the Practical Byzantine Fault Tolerance algorithm which is the most ordinary Byzantine Fault Tolerance based algorithm. By the simulation results, throughput increases as the number of users increases.

Zhenyu Zhou, Xinyi Chen, Yan Zhang, Shahid Mumtaz

In the future 5G paradigm, billions of machinetype devices will be deployed to enable wide-area and ubiquitous data sensing, collection, and transmission. Considering the traffic characteristics of machine-to-machine (M2M) communications and the spectrum shortage dilemma, a cost-efficient solution is to share the underutilized spectrum allocated to human-to-human (H2H) users with M2M devices in an opportunistic manner. However, the implementation of large-scale spectrum sharing in 5G heterogeneous networks confronts many challenges, including lack of incentive mechanism, privacy leakage, security threats, and so on. This motivates us to develop a privacy-preserved, incentive-compatible, and spectrum-efficient framework based on blockchain, which is implemented in two stages. First, H2H users sign a contract with the base station for spectrum sharing, and receive dedicated payments based on their contributions. Next, the shared spectrum is allocated to M2M devices to maximize the total throughput. We elaborate the operation details of secure spectrum sharing, incentive mechanism design, and efficient spectrum allocation. A case study is presented to demonstrate the security and efficiency of the proposed framework. Finally, we outline several open issues and conclude this article.

Anudit Nagar

For the modern world where data is becoming one of the most valuable assets,\nrobust data privacy policies rooted in the fundamental infrastructure of\nnetworks and applications are becoming an even bigger necessity to secure\nsensitive user data. In due course with the ever-evolving nature of newer\nstatistical techniques infringing user privacy, machine learning models with\nalgorithms built with respect for user privacy can offer a dynamically adaptive\nsolution to preserve user privacy against the exponentially increasing\nmultidimensional relationships that datasets create. Using these privacy aware\nML Models at the core of a Federated Learning Ecosystem can enable the entire\nnetwork to learn from data in a decentralized manner. By harnessing the\never-increasing computational power of mobile devices, increasing network\nreliability and IoT devices revolutionizing the smart devices industry, and\ncombining it with a secure and scalable, global learning session backed by a\nblockchain network with the ability to ensure on-device privacy, we allow any\nInternet enabled device to participate and contribute data to a global privacy\npreserving, data sharing network with blockchain technology even allowing the\nnetwork to reward quality work. This network architecture can also be built on\ntop of existing blockchain networks like Ethereum and Hyperledger, this lets\neven small startups build enterprise ready decentralized solutions allowing\nanyone to learn from data across different departments of a company, all the\nway to thousands of devices participating in a global synchronized learning\nnetwork.\n

Yiyang Pei, Shisheng Hu, Feng Zhong, Dusit Niyato · 5 authors

Traditionally, dynamic spectrum access (DSA) based on cooperative spectrum sensing relies on a centralized fusion centre to fuse and store the sensing results, which is vulnerable to single point of failure. In this paper, we propose a sensing-based DSA framework which is enabled by blockchain. The proposed DSA framework includes a protocol that specifies a time-slotted-based five-phase operations. In the proposed framework, each secondary user (SU) acts as both a sensing node for cooperatively sensing the spectrum and a node, i.e., a miner and a verifier, in the blockchain network for mining and updating the sensing and access results in a distributed and secure manner without the need for a fusion centre. In order to incentivize SUs for participating in such energy-consuming operations of the blockchain network, we reward them with tokens for sensing and mining, which can be used to bid for the access to the spectrum opportunities. The sensing and mining policies which they use to determine when to sense and mine affect the number of tokens they can obtain and subsequently how they bid for the spectrum. Hence, the performance of the system depends on their sensing-access-mining policy. Therefore, we consider a heuristic sensing-access-mining policy that determines whether to participate in sensing and mining in a probabilistic manner and that determines how much to bid based on its buffer occupancy and the number of available tokens. Simulation results show that although increasing sensing and mining probabilities can increase average transmission rate, it also leads to higher energy consumption. Moreover, there exists an optimal set of sensing and mining probabilities that maximize the system energy efficiency.

Zhenyu Zhou, Haijun Liao, Bo Gu, Shahid Mumtaz · 5 authors

With the rapid development of smart devices and compute-intensive applications, fog computing has emerged as a promising solution to accommodate the ever-increasing computational demands. Particularly, in the peak time, the computational tasks can be offloaded from the overloaded base stations to fog servers by leveraging the under-utilized computational resources at the demand side. However, there are two major obstacles hindering the wide deployment of fog computing in Internet of things, which are the lack of an effective incentive mechanism and a task offloading algorithm. In this paper, we develop a two-stage resource sharing and task offloading approach by integrating contract theory with computational intelligence. In the first stage, we propose an efficient incentive mechanism to encourage servers to share their residual computational resources by employing the contract theory. In the second stage, a distributed task offloading algorithm is proposed by leveraging the online learning capability of multi-armed bandit. Specifically, we propose a distance-aware, occurrence-aware, and task-property-aware volatile upper confidence bound algorithm to minimize the long-term delay of task offloading. Finally, extensive simulations are carried out to validate the performance of the proposed algorithm.

Wanyang Dai

We model the hardware and software architecture for generalized Internet of Things (IoT) by quantum cloud-computing and blockchain. To reduce the measurement error and increase the efficiency of quantum entanglement (i.e. the capability of fault tolerance) in the current quantum computers and communications, we design a quantum-computing chip by modelling it as a multi-input multi-output (MIMO) quantum channel and obtain its channel capacity via our recently derived mutual information formula. To capture the internal qubit data flow dynamics of the channel, we model it via a deep convolutional neural network (DCNN) with generalized stochastic pooling in terms of resource-competition among different quantum eigenmodes or users. The pooling is corresponding to a resource allocation policy with two levels of competitions as in cognitive radio: the first one is on users’ selection in a ‘win–lose’ manner; the second one is on resourcesharing among selected users in a ‘win–win’ manner. To wit, our scheduling policy is the one by mixing a saddle point to a zero-sum game problem and a Pareto optimal Nash equilibrium point to a nonzero- sum game problem. The effectiveness of our policy is proved by diffusion modelling with theory and numerical examples.

Arnau Rovira-Sugranes, Abolfazl Razi

Blockchain is an emerging technology that uses distributed ledgers for transparent, reliable, and traceable information exchange among network nodes. Blockchain and its 3rd generation Tangle-based implementations quickly extend their territory beyond crypto-currency to a broad range of applications using fee-less transactions over the Internet of things (IoT). However, this technology suffers from sluggishness in consensus-based validation of information that restricts its applicability to time-sensitive applications such as smart health. In this letter, we propose an optimized policy for sampling rate by IoT sensors that utilize blockchain and Tangle technologies for their transmission with the goal of minimizing the age of information (AoI) experienced by the end-users, considering both processing and networking resource constraints. Simulation results confirm the efficacy of the proposed algorithm compared to the current fixed-rate update policy. Further, a closed-form solution is obtained for the optimal sampling rate in a network of homogeneous IoT nodes as a benchmark system.

Adam Gągol, Damian Leśniak, Damian Straszak, Michał Świętek

The spectacular success of Bitcoin and Blockchain Technology in recent years has provided enough evidence that a widespread adoption of a common cryptocurrency system is not merely a distant vision, but a scenario that might come true in the near future. However, the presence of Bitcoin's obvious shortcomings such as excessive electricity consumption, unsatisfying transaction throughput, and large validation time (latency) makes it clear that a new, more efficient system is needed.

Eryk Schiller, Sina Rafati Niya, Timo Surbeck, Burkhard Stiller

This paper studies various methods that improve the performance of Blockchain systems integrated with the Internet of Things (BIoT) using the LoRaWAN access method. Duty Cycle Enforcement (DCE) and Listen Before Talk (LBT) mechanisms as the channel access methods, Automatic Repeat reQuest (ARQ) on the Transport Layer, and transaction aggregation on the Application Layer are evaluated. The main focus is put on the system performance studying the maximal number of transactions submitted, reliability of transport schemes, and the energy efficiency of the BIoT system. The combination of LBT-based MAC, the ARQ-enabled Transport Layer, and transaction aggregation at the Application Layer provides a good trade-off between submitted transaction count, packet loss, and energy efficiency. The proposed scheme complies to the data integrity demands of BIoT applications by specifying a reliable data transmission scheme from IoT devices to the BC.

Seongjoon Park, Hwangnam Kim

In this paper, we propose a scheme that implements a Distributed Ledger Technology (DLT) based on Directed Acyclic Graph (DAG) to generate, validate, and confirm the electricity transaction in Smart Grid. The convergence of the Smart Grid and distributed ledger concept has recently been introduced. Since Smart Grids require a distributed network architecture for power distribution and trading, the Distributed Ledger-based Smart Grid design is a spotlighted research domain. However, only the Blockchain-based methods, which are a type of the distributed ledger scheme, are currently either being considered or adopted in the Smart Grid. Due to computation-intensive consensus schemes such as Proof-of-Work and discrete block generation, Blockchain-based distributed ledger systems suffer from efficiency and latency issues. We propose a DAG-based distributed ledger for Smart Grids, called PowerGraph, to resolve this problem. Since a DAG-based distributed ledger does not need to generate blocks for confirmation, each transaction of the PowerGraph undergoes the validation and confirmation process individually. In addition, transactions in PowerGraph are used to keep track of the energy trade and include various types of transactions so that they can fully encompass the events in the Smart Grid network. Finally, to ensure that PowerGraph maintains a high performance, we modeled the PowerGraph performance and proposed a novel consensus algorithm that would result in the rapid confirmation of transactions. We use numerical evaluations to show that PowerGraph can accelerate the transaction processing speed by over 5 times compared to existing DAG-based DLT system.

Zhixin Liu, Lu Gao, Yang Liu, Xinping Guan · 6 authors

Blockchain-based femtocell networks aim to build decentralized frameworks which enable easy deployment and low power consumption, thus they have been seen promising technologies to make up the coverage of cellular networks in the next generation communication system. This article aims to employ power control to support quality-of-service provisioning, especially the guarantee for the transmission rate of a macrocell user (MUE) and the time delay of femtocell users (FUEs) in two-tier femtocell networks, where the MUE and FUEs share the same communication channel. We formulate the interactions among the macrocell base station and FUEs as a Stackelberg game to maximize the utilities of MUE and FUEs by obtaining the optimal power allocation and pricing strategy. Considering the uncertainty of channel gain which is expressed as a function of transmission distance, we propose a worst-case method to transform the uncertain optimization problem into a deterministic one. We then design two algorithms by considering the dynamics of FUEs, i.e., FUEs may join and leave femtocells. Numerical results verify the convergence and superior performance of our proposed algorithms.

Mohammad M. Jalalzai, Costas Busch, Golden G. Richard

There have been numerous solutions to improve the message complexity of Byzantine Fault Tolerant (BFT) protocols. Unfortunately, these solutions do not guarantee consistent performance and fall back to quadratic message complexity if a certain threshold of node failures is encountered in the network. Furthermore, reliance on a single primary to forward a proposed blockchain block to all replicas in the network can provide a potential attack vector, in which the primary can create discrepancies among histories of honest replicas. This results in increased latency during the view change (denial of service). Therefore, we propose a BFT-based protocol that guarantees consistent performance and shifts the reliance from a single primary to broadcast a candidate block to a sub-committee of replicas.

Nafissatou Diarra

A key issue for Distributed Ledger Technologies is how to agree on any changes to the Ledger; the way to reach such an agreement is known as consensus protocol. There are currently many platforms and implementations of DLTs, each offering a more or less effective way to reach consensus. As a result, it is sometimes difficult to know which consensus mechanism is most appropriate for a given use case. We propose in this short paper a draft methodology to choose between a lottery-based consensus and a voting-based consensus. We take in account several indicators, both related to the requirements of the use case in question, but also to its position relatively to the tradeoff ”Security - Scalability - Decentralization”.

Arnaud Durand, Pascal Gremaud, Jacques Pasquier

Summary This paper introduces a fully decentralized low‐power wide‐area network (LPWAN) infrastructure for the Internet of Things (IoT) using the LoRa protocol. While global LPWANs typically require roaming agreements between network providers and a trusted third party for server resolution, we propose a trustless model where the network servers are resolved using a blockchain application. Since LoRaWAN relies on symmetric cryptography, we also propose a new security model that adds non‐repudiation using digital signatures. This paves the way for linking devices to decentralized applications. We finally analyze the impact of this new model on message size and energy requirements.

Lachlan J. Gunn, Jian Liu, Bruno Vavala, N. Asokan

Consensus mechanisms used by popular distributed ledgers are highly scalable but notoriously inefficient. Byzantine fault tolerance (BFT) protocols are efficient but far less scalable. Speculative BFT protocols such as Zyzzyva and Zyzzyva5 are efficient and scalable but require a trade-off: Zyzzyva requires only $3f + 1$ replicas to tolerate $f$ faults, but even a single slow replica will make Zyzzyva fall back to more expensive non-speculative operation. Zyzzyva5 does not require a non-speculative fallback, but requires $5f + 1$ replicas in order to tolerate $f$ faults. BFT variants using hardware-assisted trusted components can tolerate a greater proportion of faults, but require that every replica have this hardware. We present SACZyzzyva, addressing these concerns: resilience to slow replicas and requiring only $3f + 1$ replicas, with only one replica needing an active monotonic counter at any given time. We experimentally evaluate our protocols, demonstrating low latency and high scalability. We prove that SACZyzzyva is optimally robust and that trusted components cannot increase fault tolerance unless they are present in greater than two-thirds of replicas.

Muhammad Saad, Laurent Njilla, Charles Kamhoua, Joongheon Kim · 6 authors

In this paper, we present a new form of attack that can be carried out on the memory pools (mempools) of blockchain-based cryptocurrencies. Towards that end, we study such an attack on Bitcoin mempool and explore its effects on transactions fee paid by legitimate users. We also propose countermeasures to contain such an attack. Our countermeasures include fee-based and age-based designs, which optimize the mempool size and help in countering the effects of DDoS attacks. We further evaluate our designs by simulations and analyze their usefulness in varying attack conditions. Our analyses can be extended to other blockchain-based applications which use memory pools to cache network activities.

Mohammad M. Jalalzai, Costas Busch, Golden G. Richard

Byzantine Fault Tolerant (BFT) consensus exhibits higher throughput in comparison to Proof of Work (PoW) in blockchains. But BFT-based protocols suffer from scalability problems with respect to the number of replicas in the network. The main reason for this limitation is the quadratic message complexity of BFT protocols. Previously, proposed solutions improve BFT performance for normal operation, but will fall back to quadratic message complexity once the protocol observes a certain number of failures. This makes the protocol performance unpredictable as it is not guaranteed that the network will face a a certain number of failures. As a result, such protocols are only scalable when conditions are favorable (i.e., the number of failures are less than a given threshold). To address this issue we propose Proteus, a new BFT-based consensus protocol which elects a subset of nodes $c$ as a root committee. Proteus guarantees stable performance, regardless of the number of failures in the network and it improves on the quadratic message complexity of typical BFT-based protocols to $O(cn)$, where $c<<n$, for large $n$. Thus, message complexity remains small and less than quadratic when $c$ is asymptotically smaller than $n$, and this helps the protocol to provide stable performance even during the view change process (change of root committee). Our view change process is different than typical BFT protocols as it replaces the whole root committee compared to replacing a single primary in other protocols. We deployed and tested our protocol on $200$ Amazon $EC2$ instances, with two different baseline BFT protocols (PBFT and Bchain) for comparison. In these tests, our protocol outperformed the baselines by more than $2\times$ in terms of throughput as well as latency.

Gowri Ramachandran, Xiang Ji, Pavas Navaney, Licheng Zheng · 6 authors

Increasingly, connected cars are becoming a decentralized data platform. With greater autonomy, they have growing needs for computation and perceiving the world around them through sensors. While todays generation of vehicles carry all the necessary sensor data and computation on board, we envision a future where vehicles can cooperate to increase their perception of the world beyond their immediate view, resulting in greater safety, coordination and more comfortable experience for their human occupants. In order for vehicles to obtain data, compute and other services from other vehicles or road side infrastructure, it is important to be able to make micro payments for those services and for the services to run seamlessly despite the challenges posed by mobility and ephemeral interactions with a dynamic set of neighboring devices. We present MOTIVE, a trusted and decentralized framework that allows vehicles to make peer to peer micropayments for data, compute and other services obtained from other vehicles or road side infrastructure within radio range. The framework utilizes distributed ledger technologies including smart contracts to enable autonomous operation and trusted interactions between vehicles and nearby entities.

Evangelos Pournaras, Srivatsan Yadhunathan, Ada Diaconescu

Structure plays a key role in learning performance. In centralized computational systems, hyperparameter optimization and regularization techniques such as dropout are computational means to enhance learning performance by adjusting the deep hierarchical structure. However, in decentralized deep learning by the Internet of Things, the structure is an actual network of autonomous interconnected devices such as smart phones that interact via complex network protocols. Self-adaptation of the learning structure is a challenge. Uncertainties such as network latency, node and link failures or even bottlenecks by limited processing capacity and energy availability can significantly downgrade learning performance. Network self-organization and self-management is complex, while it requires additional computational and network resources that hinder the feasibility of decentralized deep learning. In contrast, this paper introduces a self-adaptive learning approach based on holarchic learning structures for exploring, mitigating and boosting learning performance in distributed environments with uncertainties. A large-scale performance analysis with 864,000 experiments fed with synthetic and real-world data from smart grid and smart city pilot projects confirm the cost-effectiveness of holarchic structures for decentralized deep learning.

Mohammad M. Jalalzai, Golden G. Richard, Costas Busch

No abstract is available for this record.

Shaochi Cheng, Yuan Gao, Xiang‐Yang Li, Yanchang Du · 6 authors

No abstract is available for this record.