Tangle is a distributed ledger technology that stores data as a directed acyclic graph (DAG). Unlike blockchain, Tangle does not require dedicated miners for its operation; this makes Tangle suitable for Internet of Things (IoT) applications. Distributed ledgers have a built-in transaction rate control mechanism to prevent congestion and spamming; this is typically achieved by increasing or decreasing the proof of work (PoW) difficulty level based on the number of users. Unfortunately, this simplistic mechanism gives an unfair advantage to users with high computing power. This paper proposes a principal-agent problem (PAP) framework from microeconomics to control the transaction rate in Tangle. With users as agents and the transaction rate controller as the principal, we design a truth-telling mechanism to assign PoW difficulty levels to agents as a function of their computing power. The solution of the PAP is achieved by compensating a higher PoW difficulty level with a larger weight/reputation for the transaction. The mechanism has two benefits, (1) the security of Tangle is increased as agents are incentivized to perform difficult PoW, and (2) the rate of new transactions is moderated in Tangle. The solution of PAP is obtained by solving a mixed-integer optimization problem. We show that the optimal solution of the PAP increases with the computing power of agents. The structural results reduce the search space of the mixed-integer program and enable efficient computation of the optimal mechanism. Finally, via numerical examples, we illustrate the transaction rate control mechanism and study its impact on the dynamics of Tangle.
Marthe Fogstad Dynge, Ugur Halden, Gro Klæboe, Ümit Cali
Support schemes like the Feed-in-Tariff (FiT) have for many years been an important driver for the deployment of distributed energy resources, and the transition from consumerism to prosumerism. This democratization and decarbonization of the energy system has led to both challenges and opportunities for the system operators, paving the way for emerging concepts like local energy markets. The FiT approach has often been assumed as the lower economic bound for a prosumer's willingness to participate in such markets but is now being phased out in several countries. In this paper, a new pricing mechanism based on the Levelized Cost of Electricity is proposed, with the intention of securing profitability for the prosumers, as well as creating a transparent and fair price for all market participants. The mechanism is designed to function on a Distributed Ledger Technology-based platform and is further set up from a holistic perspective, defining the market framework as interactions in a Cyber-Physical-Social-System. Schemes based on both fixed and variable contracts with the wholesale supplier are analyzed and compared with both the conventional FiT and to its proposed replacement options. The results show a cost reduction for the consumers and a slight loss in revenue for the prosumers compared to the FiT scheme. Comparing it to the actual suggested replacements to the FiT, however, it is clear that the pricing mechanism proposed in this study provides a substantial increase of benefits for both prosumers and consumers.
What happens to mining when the Bitcoin price changes, when there are mining supply shocks, the price of energy changes, or hardware technology evolves? We give precise answers based on the technical forces and incentives in the system. We then build on these dynamics to consider value: what is the cost and purpose of mining, and is it worth it? Does it use too much energy, is it bad for the environment? Finally we extend our analysis to the long term: is mining economically feasible forever? What will the global hash rate be in 40 years? How is mining impacted by the limits of computation and energy? Is it physically sustainable in the long run? From first principles, we derive a fundamental scale-invariant feasibility constraint, which enables us to analyze the interlocking dynamics, find key invariants, and answer these questions mathematically.
May Alhajri, Carsten Rudolph, Ahmad Salehi Shahraki
Wearable fitness devices are widely used to track an individual’s health and physical activities to improve the quality of health services. These devices sense a considerable amount of sensitive data processed by a centralized third party. While many researchers have thoroughly evaluated privacy issues surrounding wearable fitness trackers, no study has addressed privacy issues in trackers by giving control of the data to the user. Blockchain is an emerging technology with outstanding advantages in resolving consent management privacy concerns. As there are no fully transparent, legally compliant solutions for sharing personal fitness data, this study introduces an architecture for a human-centric, legally compliant, decentralized and dynamic consent system based on blockchain and smart contracts. Algorithms and sequence diagrams of the proposed system’s activities show consent-related data flow among various agents, which are used later to prove the system’s trustworthiness by formalizing the security requirements. The security properties of the proposed system were evaluated using the formal security modeling framework SeMF, which demonstrates the feasibility of the solution at an abstract level based on formal language theory. As a result, we have shown that blockchain technology is suitable for mitigating the privacy issues of fitness providers by recording individuals’ consent using blockchain and smart contracts.
This research contributes to the security design of an advanced smart drone swarm network based on a variant of the Blockchain Governance Game (BGG), which is the theoretical game model to predict the moments of security actions before attacks, and the Strategic Alliance for Blockchain Governance Game (SABGG), which is one of the BGG variants which has been adapted to construct the best strategies to take preliminary actions based on strategic alliance for protecting smart drones in a blockchain-based swarm network. Smart drones are artificial intelligence (AI)-enabled drones which are capable of being operated autonomously without having any command center. Analytically tractable solutions from the SABGG allow us to estimate the moments of taking preliminary actions by delivering the optimal accountability of drones for preventing attacks. This advanced secured swarm network within AI-enabled drones is designed by adapting the SABGG model. This research helps users to develop a new network-architecture-level security of a smart drone swarm which is based on a decentralized network.
Yang Xiao, Shanghao Shi, Wenjing Lou, Chonggang Wang · 8 authors
Spectrum access system (SAS) is widely considered the de facto solution to coordinating dynamic spectrum sharing (DSS) and protecting incumbent users. The current SAS paradigm prescribed by the FCC for the CBRS band and standardized by the WInnForum follows a centralized service model in that a spectrum user subscribes to a SAS server for spectrum allocation service. This model, however, neither tolerates SAS server failures (crash or Byzantine) nor resists dishonest SAS administrators, leading to serious concerns about SAS system reliability and trustworthiness. This is especially concerning for the evolving DSS land-scape where an increasing number of SAS service providers and heterogeneous user requirements are coming up. To address these challenges, we propose a novel blockchain-based decentralized SAS architecture called BD-SAS that provides SAS services securely and efficiently, without relying on the trust of each individual SAS server for the overall system trustworthiness. In BD-SAS, a global blockchain (G-Chain) is used for spectrum regulatory compliance while smart contract-enabled local blockchains (L-Chains) are instantiated in individual spectrum zones for automating spectrum access assignment per user request. We hope our vision of a decentralized SAS, the BD-SAS architecture, and discussion on future challenges can open up a new direction toward reliable spectrum management in a decentralized manner.
Critical Infrastructures (CIs) such as power grid, water and gas distribution are controlled by Industrial Control Systems (ICS). Sensors and actuators of a physical plant are managed by the ICS. Data and commands transmitted over the network from the Programmable Logic Controllers (PLCs) are saved and parsed within the Historian. Generally, this architecture guarantees to check for any process anomalies that may occur due to component failures and cyber attacks. The other use of this data allows activities such as forensic analysis. To secure the network is also crucial to protect the communication between devices. A cyber attack on the log devices could jeopardize any forensic analysis be it for maintenance, or discovering an attack trail. In this paper is proposed a strategy to secure plant operational data recorded in the Historian and data exchange in the network. An integrity checking mechanism, in combination with blockchain, is used to ensure data integrity. Data redundancy is achieved by applying an efficient replication mechanism and enables data recovery after an attack.
Roman Overko, Rodrigo Ordóñez-Hurtado, Sergiy Zhuk, Robert Shorten
In this paper, we describe an approach to guide drivers searching for a parking space (PS). The proposed system suggests a sequence of routes that drivers should traverse in order to maximise the expected likelihood of finding a PS and minimise the travel distance. This system is built on our recent architecture SPToken, which combines both Distributed Ledger Technology (DLT) and Reinforcement Learning (RL) to realise a system for the estimation of an unknown distribution without disturbing the environment. For this, we use a number of virtual tokens that are passed from vehicle to vehicle to enable a massively parallelised RL system that estimates the best route for a given origin-destination (OD) pair, using crowdsourced information from participant vehicles. Additionally, a moving window with reward memory mechanism is included to better cope with non-stationary environments. Simulation results are given to illustrate the efficacy of our system.
Federated Deep Learning (FDL) is helping to realize distributed machine learning in the Internet of Vehicles (IoV). However, FDL's global model needs multiple clients to upload learning model parameters, thus still existing unavoidable communication overhead and data privacy risks. The recently proposed Swarm Learning (SL) provides a decentralized machine-learning approach uniting edge computing and blockchain-based coordination without the need for a central coordinator. This paper proposes a Swarm-Federated Deep Learning framework in the IoV system (IoV-SFDL) that integrates SL into the FDL framework. The IoV-SFDL organizes vehicles to generate local SL models with adjacent vehicles based on the blockchain empowered SL, then aggregates the global FDL model among different SL groups with a proposed credibility weights prediction algorithm. Extensive experimental results demonstrate that compared with the baseline frameworks, the proposed IoV-SFDL framework achieves a 16.72% reduction in edge-to-global communication overhead while improving about 5.02% in model performance with the same training iterations.
Most self-service payment terminals require network connectivity for processing electronic payments. The necessity to maintain network connectivity increases costs, introduces cybersecurity risks, and significantly limits the number of places where the terminals can be installed. Leading payment service providers have proposed offline payment solutions that rely on algorithmically generated payment tokens. Existing payment token solutions, however, require complex mechanisms for authentication, transaction management, and most importantly, security risk management. In this paper, we present VolgaPay, a blockchain-based system that allows merchants to deploy secure offline payment terminal infrastructure that does not require collection and storage of any sensitive data. We design a novel payment protocol which mitigates security threats for all the participants of VolgaPay, such that the maximum loss from gaining full access to any component by an adversary incurs only a limited scope of harm. We achieve significant enhancements in security, operation efficiency, and cost reduction via a combination of polynomial multi-hash chain micropayment channels and blockchain grafting for off-chain channel state transition. We implement the VolgaPay payment system, and with thorough evaluation and security analysis, we demonstrate that VolgaPay is capable of delivering a fast, secure, and cost-efficient solution for offline payment terminals.
Blockchain is a promising technology for future dynamic spectrum access (DSA) management due to its decentralization, immutability, and traceability. However, many challenges need to be addressed to integrate the blockchain to DSA, such as the trustworthiness of participating nodes’ spectrum sensing results, privacy protection of sensing nodes’ identities, and affordable lightweight consensus algorithms for IoT devices. In this article, we propose a trust-centric privacy-preserving blockchain for DSA in IoT networks. To be specific, we propose a trust evaluation mechanism to evaluate the trustworthiness of sensing nodes and design a Proof-of-Trust (PoT) consensus mechanism to build a scalable blockchain with high transaction-per-second (TPS). Moreover, a privacy protection scheme is proposed to protect sensors’ real-time geolocation information when they upload sensing data to the blockchain. Two smart contracts are designed to make the whole procedure (spectrum sensing, spectrum auction, and spectrum allocation) run automatically. Simulation results demonstrate the expected computation cost of the PoT consensus algorithm for reliable nodes is low, and the cooperative sensing performance is improved with the help of the trust evaluation mechanism. In addition, incentivization and security are also analyzed, which show that our system can not only encourage nodes’ participation, but also resist many kinds of attacks which are frequently arise in the trust management mechanism and blockchain-based IoT systems.
Xu Wang, Wei Ni, Xuan F. Zha, Guangsheng Yu · 7 authors
As distributed ledgers, blockchains run consensus protocols which trade capacity for consistency, especially in non-ideal networks with incomplete connectivity and erroneous links. Existing studies on the tradeoff between capacity and consistency are only qualitative or rely on specific assumptions. This paper presents discrete-time Markov chain models to quantify the capacity of Proof-of-Work based public blockchains in non-ideal networks. The comprehensive model is collapsed to be ergodic under the eventual consistency of blockchains, achieving tractability and efficient evaluations of blockchain capacity. A closed-form expression for the capacity is derived in the case of two miners. Another important aspect is that we extend the ergodic model to analyze the capacity under strong consistency, evaluating the robustness of blockchains against double-spending attacks. Validated by simulations, the proposed models are accurate and reveal the effect of link quality and the distribution of mining rates on blockchain capacity and the ratio of stale blocks.
This paper presents iBatch, a middleware system running on top of an operational Ethereum network to enable secure batching of smart-contract invocations against an untrusted relay server off-chain. iBatch does so at a low overhead by validating the server's batched invocations in smart contracts without additional states. The iBatch mechanism supports a variety of policies, ranging from conservative to aggressive batching, and can be configured adaptively to the current workloads. iBatch automatically rewrites smart contracts to integrate with legacy applications and support large-scale deployment. For cost evaluation, we develop a platform with fast and cost-accurate transaction replaying, build real transaction benchmarks on popular Ethereum applications, and build a functional prototype of iBatch on Ethereum. The evaluation results show that iBatch saves 14.6%-59.1% Gas cost per invocation with a moderate 2-minute delay and 19.06%-31.52% Ether cost per invocation with a delay of 0.26-1.66 blocks.
This work presents the design and implementation of a blockchain system that enables the trustable transactive energy management for distributed energy resources (DERs). We model the interactions among DERs, including energy trading and flexible appliance scheduling, as a cost minimization problem. Considering the dispersed nature and diverse ownership of DERs, we develop a distributed algorithm to solve the optimization problem using the alternating direction method of multipliers (ADMM) method. Furthermore, we develop a blockchain system, on which we implement the proposed algorithm with the smart contract, to guarantee the transparency and correctness of the energy management. We prototype the blockchain in a small-scale test network and evaluate it through experiments using real-world data. The experimental results validate the feasibility and effectiveness of our design.
Shayan Eskandari, Mehdi Salehi, Wanyun Catherine Gu, Jeremy Clark
One fundamental limitation of blockchain-based smart contracts is that they execute in a closed environment. Thus, they only have access to data and functionality that is already on the blockchain, or is fed into the blockchain. Any interactions with the real world need to be mediated by a bridge service, which is called an oracle. As decentralized applications mature, oracles are playing an increasingly prominent role. With their evolution comes more attacks, necessitating greater attention to their trust model. In this systemization of knowledge paper (SoK), we dissect the design alternatives for oracles, showcase attacks, and discuss attack mitigation strategies.
The advent of distributed energy resources (DERs), such as distributed renewables, energy storage, electric vehicles, and controllable loads, \rv{brings} a significantly disruptive and transformational impact on the centralized power system. It is widely accepted that a paradigm shift to a decentralized power system with bidirectional power flow is necessary to the integration of DERs. The virtual power plant (VPP) emerges as a promising paradigm for managing DERs to participate in the power system. In this paper, we develop a blockchain-based VPP energy management platform to facilitate a rich set of transactive energy activities among residential users with renewables, energy storage, and flexible loads in a VPP. Specifically, users can interact with each other to trade energy for mutual benefits and provide network services, such as feed-in energy, reserve, and demand response, through the VPP. To respect the users' independence and preserve their privacy, we design a decentralized optimization algorithm to optimize the users' energy scheduling, energy trading, and network services. Then we develop a prototype blockchain network for VPP energy management and implement the proposed algorithm on the blockchain network. By experiments using real-world data-trace, we validated the feasibility and effectiveness of our algorithm and the blockchain system. The simulation results demonstrate that our blockchain-based VPP energy management platform reduces the users' cost by up to 38.6% and reduces the overall system cost by 11.2%.
Sajjad Asefi, Yash Madhwal, Yury Yanovich, Elena Gryazina
The application of renewable energy sources in the power grid increases the necessity of tracking the system's state, especially in smart grids, where there is a bidirectional transfer of data and power. The complexity of coupling between communication and the electrical infrastructure in a smart grid will create a higher chance for security breach. Increasing the state estimation accuracy will help the smart grid operator efficiently manage the system. The paper proposes an integration of distributed state estimation with a blockchain designed communication platform. Additionally, the asynchronous manner for data transmission, which is more likely to happen in the real world, has been considered as the second task of this research. Finally, a detailed analysis of the blockchain-based application in distributed state estimation is provided. The numerical analysis shows that the proposed method meets real-world performance requirements and brings high security and reliability to the distributed state estimation process.
Payment channel networks (PCNs) have emerged as a scalability solution for blockchains built on the concept of a payment channel: a setting that allows two nodes to safely transact between themselves in high frequencies based on pre-committed peer-to-peer balances. Transaction requests in these networks may be declined because of unavailability of funds due to temporary uneven distribution of the channel balances. In this paper, we investigate how to alleviate unnecessary payment blockage via proper prioritization of the transaction execution order. Specifically, we consider the scheduling problem in PCNs: as transactions continuously arrive on both sides of a channel, nodes need to decide which ones to process and when in order to maximize their objective, which in our case is the channel throughput. We introduce a stochastic model to capture the dynamics of a payment channel under random arrivals, and propose that channels can hold incoming transactions in buffers up to some deadline in order to enable more elaborate processing decisions. We describe a policy that maximizes the channel success rate/throughput for uniform transaction requests of fixed amounts, both in the presence and absence of buffering capabilities, and formally prove its optimality. We also develop a discrete event simulator of a payment channel, and evaluate different heuristic scheduling policies in the more general heterogeneous amounts case, with the results showing superiority of the heuristic extension of our policy in this case as well. Our work opens the way for more formal research on improving PCN performance via joint consideration of routing and scheduling decisions.
The emerging cryptocurrency market has lately received great attention for asset allocation due to its decentralization uniqueness. However, its volatility and brand new trading mode have made it challenging to devising an acceptable automatically-generating strategy. This study proposes a framework for automatic high-frequency bitcoin transactions based on a deep reinforcement learning algorithm-proximal policy optimization (PPO). The framework creatively regards the transaction process as actions, returns as awards and prices as states to align with the idea of reinforcement learning. It compares advanced machine learning-based models for static price predictions including support vector machine (SVM), multi-layer perceptron (MLP), long short-term memory (LSTM), temporal convolutional network (TCN), and Transformer by applying them to the real-time bitcoin price and the experimental results demonstrate that LSTM outperforms. Then an automatically-generating transaction strategy is constructed building on PPO with LSTM as the basis to construct the policy. Extensive empirical studies validate that the proposed method performs superiorly to various common trading strategy benchmarks for a single financial product. The approach is able to trade bitcoins in a simulated environment with synchronous data and obtains a 31.67% more return than that of the best benchmark, improving the benchmark by 12.75%. The proposed framework can earn excess returns through both the period of volatility and surge, which opens the door to research on building a single cryptocurrency trading strategy based on deep learning. Visualizations of trading the process show how the model handles high-frequency transactions to provide inspiration and demonstrate that it can be expanded to other financial products.
Railway operations require control systems to ensure safety and efficiency, and to coordinate infrastructure elements such as switches, signals and train protection. To compete with the traditional approaches to these systems, a blockchain-based approach has been proposed, with the intent to build a more resilient, integrated and cost-efficient system. Additionally, the developed blockchain-based architecture enables to run safety-relevant and security-focused business logic on off-the-shelf platforms such as cloud, rather than on specialized (and expensive) secure hardware. After implementing a prototype of the blockchain-based railway control system, scaling the approach to real-world mainline and branch operations required a thorough validation of the design choices. In this technical report, we show how performance calculations, long-term technology perspectives and law-mandated norms have impacted the architecture, the technology choices, and the make-buy-reuse decisions.
The fast growth of distributed energy resources (DERs), such as distributed renewables (e.g., rooftop PV panels), energy storage systems, electric vehicles, and controllable appliances, drives the power system toward a decentralized system with bidirectional power flow. The coordination of DERs through an aggregator, such as a utility, system operator, or a third-party coordinator, emerges as a promising paradigm. However, it is not well understood how to enable trust between the aggregator and DERs to integrate DERs efficiently. In this paper, we develop a trustable and distributed coordination system for DERs using blockchain technology. We model various DERs and formulate a cost minimization problem for DERs to optimize their energy trading, scheduling, and demand response. We use the alternating direction method of multipliers (ADMM) to solve the problem in a distributed fashion. To implement the distributed algorithm in a trustable way, we design a smart contract to update multipliers and communicate with DERs in a blockchain network. We validate our design by experiments using real-world data, and the simulation results demonstrate the effectiveness of our algorithm.
Lam Duc Nguyen, Amari N. Lewis, Israel Leyva-Mayorga, Amelia Regan · 5 authors
Urban areas are negatively impacted by Carbon Dioxide (CO2 ) and Nitrogen Oxide (NOx) emissions. In order to achieve a cost-effective reduction of greenhouse gas emissions and to combat climate change, the European Union (EU) introduced an Emissions Trading System (ETS) where organizations can buy or receive emission allowances as needed. The current ETS is a centralized one, consisting of a set of complex rules. It is currently administered at the organizational level and is used for fixed-point sources of pollution such as factories, power plants, and refineries. However, the current ETS cannot efficiently cope with vehicle mobility, even though vehicles are one of the primary sources of CO2 and NOx emissions. In this study, we propose a new distributed Blockchain-based emissions allowance trading system called B-ETS. This system enables transparent and trustworthy data exchange as well as trading of allowances among vehicles, relying on vehicle-to-vehicle communication. In addition, we introduce an economic incentive-based mechanism that appeals to individual drivers and leads them to modify their driving behavior in order to reduce emissions. The efficiency of the proposed system is studied through extensive simulations, showing how increased vehicle connectivity can lead to a reduction of the emissions generated from those vehicles. We demonstrate that our method can be used for full life-cycle monitoring and fuel economy reporting. This leads us to conjecture that the proposed system could lead to important behavioral changes among the drivers
Currently, drones represent a promising technology for combating Coronavirus disease 2019 (COVID-19) due to the transport of goods, medical supplies to a given target location in the quarantine areas experiencing an epidemic outbreak. Drone missions will increasingly rely on drone collaboration, which requires the drones to reduce communication complexity and be controlled in a decentralized fashion. Blockchain technology becomes a must in industrial applications because it provides decentralized data, accessibility, immutability, and irreversibility. Therefore, Blockchain makes data public for all drones and enables drones to log information concerning world states, time, location, resources, delivery data, and drone relation to all neighbors drones. This paper introduces decentralized independent multi-drones to accomplish the task collaboratively. Improving blockchain with a consensus algorithm can improve network partitioning and scalability in order to combat COVID-19. The multi-drones task is to combat COVID-19 via monitoring and detecting, social distancing, sanitization, data analysis, delivering goods and medical supplies, and announcement while avoiding collisions with one another. We discuss End to End (E2E) delivery application of combination blockchain and multi-drone in combating COVID-19 and beyond future pandemics. Furthermore, the challenges and opportunities of our proposed framework are highlighted.
Jan 14, 2021·International Workshop on Blockchain and Mobile Applications (BlockApp 2020) during the International Conference on Distributed Computing Systems (ICDCS 2020)
Peter Hillmann, Marcus Knüpfer, Erik Heiland, Andreas Karcher
The constantly growing size of blockchains becomes a challenge with the increasing usage. Especially the storage of unwanted data in a blockchain is an issue, because it cannot be removed naturally. In order to counteract this problem, we present the first concept for the selective deletion of single entries in a blockchain. For this purpose, the general consensus algorithm is extended by the functionality of regularly creating summary blocks. Previous data of the chain are summarized and stored again in a new block, leaving out unwanted information. With a shifting marker of the Genesis Block, data can be deleted from the beginning of a blockchain. In this way, the technology of the blockchain becomes fully transactional. The concept is independent of a specific block structure, network structure, or consensus algorithm. Moreover, this functionality can be adapted to current blockchains to solve multiple problems related to scalability. This approach enables the transfer of blockchain technology to further fields of application, among others in the area of Industry 4.0 and Product Life-cycle Management.