This article introduces${\sf FedRLChain}$, a novel framework for blockchain-based secure federated deep reinforcement learning, which allows users to securely and collaboratively train a Deep Reinforcement Learning (DRL) model by plugging appropriate aggregation and verification algorithms for specific problems. Unlike existing systems,${\sf FedRLChain}$adopts 1) a novel verification algorithm to prevent malicious clients, 2) an aggregation weight scheme from preventing the global model from getting biased toward any client, and 3) a variant of traditional FedAverage algorithm to accelerate the convergence process. We perform a rigorous experimental evaluation of${\sf FedRLChain}$considering the classic cart-pole problem, and we show a significant improvement in the number of epochs and time required for model convergence w.r.t. the state-of-the-art frameworks – DDQL, BAFFLE, and BASE-PIoT.
Sharded blockchain offers scalability, decentralization, immutability, and linear improvement, making it a promising solution for addressing the trust problem in large-scale collaborative IoT. However, a high proportion of cross-shard transactions can severely limit the performance of decentralized blockchain. Furthermore, the dynamic assemblage characteristic of collaborative sensing in sharded blockchain is often ignored. To overcome these limitations, we propose HMMDShard, a dynamic blockchain sharding scheme based on the Hidden Markov Model. HMMDShard leverages fine-grained blockchain sharding and fully embraces the dynamic assemblage characteristic of IoT collaborative sensing. By integrating the Hidden Markov Model, we achieve adaptive dynamic incremental updating of blockchain shards, effectively reducing cross-shard transactions across all shards. We conduct a comprehensive analysis of the security issues and properties of HMMDShard, and evaluate its performance through the implementation of a system prototype. The results demonstrate that HMMDShard significantly reduces the proportion of cross-shard transactions and outperforms other baselines in terms of system throughput and transaction confirmation latency.
Reporting on the Non-Fungible Token (NFT) ecosystem overwhelmingly focuses on the community that drove its growth and price volatility, gaining widespread media attention in 2021. This overlooks the communities developing novel creative practices on NFT platforms. Interviews with 16 creatives utilizing NFTs reveal a vast Art World: networks of distinct communities maturing into cooperative ecosystems with unique artistic subcultures, philosophies, and interactions. We observe unique qualities of these decentralized distribution platforms and identify patterns of activity comparable to those of traditional art worlds. We identify how aspects of these systems might subvert, or replicate, existing systems of power, value, and access. The impacts of policy and platform design on online creative communities in the NFT Art World carry valuable lessons for developers of digital interventions into the creative industry, exemplifying pertinent considerations for the future of creative labor and cooperation online.
The proliferation of smart devices, sensors, autonomous robots, drones, and other similar instruments have profoundly changed the way of implementing and deploying systems in industrial and home environments, for diverse scenarios such as smart agriculture, healthcare, or manufacturing. Devices in these settings are not limited to simply observe and acquire data for monitoring, but they are also equipped with actuation capabilities, as well as the possibility of autonomously processing the incoming data through various techniques. However, given their intrinsic limitations regarding the capacity to store and process computations, it is often necessary to delegate some of these processing tasks to intermediary edge nodes in the network. These nodes, given their unique position can act as orchestrators guiding the decentralized work of the interconnected autonomous devices. Beyond static and pre-defined organization structures, in this work we propose the usage of agent and multi-agent-based models for designing and implementing swarms of edge nodes, conceived to dynamically orchestrate other devices, while meeting quality of service conditions. Allowing the control of intelligent edge nodes as conveyors and orchestrators on swarms of devices, we aim at providing intelligence to the self-organization of edge nodes, which may interchange streaming data, and represent their own capabilities through semantic models. Swarm-inspired behavioral patterns would guide the collaborative distribution of their computational tasks. Finally, we will implement and demonstrate the proposed technologies in an elderly home environment powered with a host of edge computing, sensing, and actuating devices.
The novel sensing paradigm known as crowdsensing leverages ubiquitous smart devices to collect data in Internet of Things (IoT) applications. Traditional crowdsensing schemes assume a central framework to execute truth discovery algorithm to assure data quality, which may introduce reliability and privacy issues. Blockchain is a promising technology that provides a decentralized, transparent, and immutable platform. However, designing a blockchain-based quality assurance scheme in crowdsensing is not a trivial problem. First, truth discovery is a time-consuming iterative algorithm, which is not practical to execute on blockchain. Second, privacy-preserving schemes always require that the participants join in multiround communications, which is not acceptable in open blockchain because of users’ highly unpredictable behaviors. Finally, on-chain data are publicly accessible, and achieving a good balance between data utility and privacy is an important issue. In this article, we propose a lightweight quality assurance framework atop blockchain to build a reliable, privacy preserving, and fair crowdsensing system. Specifically, we carefully design two kinds of smart contracts to cooperatively maintain a long-term reliable platform to execute crowdsensing tasks. In the contracts, we devise a reputation-based aggregator selection algorithm to reach the consensus on truthful results while avoiding expensive on-chain iterative processes. The participant selection scheme and reward policy are further utilized to filter appropriate participants to complete the task. Our scheme also protects data privacy and does not require communications between participants. Finally, we implement and deploy the contracts on Ethereum and conduct extensive experiments to demonstrate that the contracts can practically execute crowdsensing tasks.
Long Zhang, Gang Feng, Shuang Qin, Xiaoqian Li · 6 authors
Blockchain is envisioned as one of the promising technologies to address trust concern brought by mobile crowdsensing (MCS), due to its auditability, immutability and decentralization. Nevertheless, blockchain cannot fundamentally guarantee that the valuable sensed data outside the chain can enter the chain, although data integrity and consistency can be ensured once it is confirmed inside the chain. In addition, simply applying blockchain in MCS while ignoring possible abnormal saboteurs hidden in numerous devices may mislead the normal operation of blockchain, resulting in untrustworthy interactions. Consequently, it is highly desirable to build a trust-preserving mechanism (TPM) to fully enjoy the benefits of using blockchain in MCS. To this end, we first resort to a probabilistic trust assessment inferred from the interaction outcomes in blockchain, to incentivize participants to maintain the trustworthiness of interactions. By inferring trust to aid decision-making, trust decision is further made, including leader election and transaction data generation, to filter untrusted nodes from participating in blockchain process. Finally, extensive simulations are conducted to validate the effectiveness and efficiency of TPM, and improve the performance in terms of contribution rate, consensus accuracy and system stability.
The spread of infectious diseases in crowded spaces such as shopping malls, markets, and hospitals is a growing concern. In order to mitigate this risk, it is crucial to develop a method that leverages the power of distributed crowd to learn, de- tect, and alert individuals about potential health hazards. Hence, the integration of federated learning (FL), and blockchain (BC) to provide intelligent platforms that facilitate pervasive AI and trust amongst IoT devices and smart phones can play a significant role in achieving this goal. In this study, we propose a new technique named BC-FL Location-Based, which utilizes smart applications installed on IoT devices and smart phones to detect and predict imminent health risks. The technique works by using algorithms such as maximal clique to detect individuals in close proximity and sharing their health data through a blockchain network. A smart contract then triggers a node with sufficient resources to gather users' learning experiences from the blockchain, aggregate it, and run a model to determine if any of the individuals present in the area are infected. To demonstrate the effectiveness of the proposed technique, we conducted simulation experiments using Ethereum-based private blockchain network, where nodes represent individuals in different locations. We used the maximal clique algorithm to simulate the movement of individuals and compared the results of the model run on individual data versus aggregated data. Experiments showed promising results, with accuracy of detection increasing to 99% when using iid data and 90% when using non-iid data.
The development of the Metaverse is completely changing how business is done in the physical world. The Metaverse considerably improves intelligent manufacturing by mapping out operations and spreading them into virtual space. The Metaverse can access data from numerous production and operation lines thanks to the Internet of Things (IoT), enabling efficient data analysis and decision-making. However, the problem of sharing sensitive and private data remains a challenge when integrating the Metaverse with IoT. Federated learning (FL) has emerged as a distributed machine learning (ML) setting that can overcome the security problems related to data sharding With FL, several devices can work together to create an ML model under the direction of a central server while maintaining the privacy and security of their local training data. FL in the Metaverse continues to face significant challenges due to a lack of transparency, learning forgetting caused by streaming industrial data, and problems with non-independent and identically dispersed (non-iid) data. In this paper, we develop a FL framework for transparent and secure model learning in the Metaverse using blockchain technology. The blockchain ledger stores and verifies the model updates which ensures that all updates are tamper-proof and transparent to all parties involved. Furthermore, we propose a scheduling approach to distribute the bandwidth between reliable devices, hence minimizing communication across FL devices and giving devices with reliable behavior priority. The numerical result demonstrates that our framework performed better on the chosen indicators.
There is growing attention in the metaverse from a variety of fields, and many Internet of Things (IoT) companies are exploring the possibility of integrating their existing business into the metaverse space. However, it still remains challenging to put the metaverse into practice. Among those challenges, trust management is critical to guarantee secure interactions and effective sharing among metaverse assets. In this article, we construct a TMETA system as a pioneer work for further establishing the metaverse and utilizing digital assets, which helps IoT startup (IS) companies expand their business during the cold start phase with limited business knowledge and seed budget. In the TMETA system, IoT companies treat their interaction experience with resource providers (RPs) as digital assets. When an IS initiates IoT services with the assistance of expertise-diverse RPs, task assignments are determined by leveraging the knowledge and trustworthy advice of expert IoT companies. With the aid of digital twin (DT) and blockchain technology, IoT tasks will be assigned to their suitable RPs as the secured instruction from trust DTs of expert companies, which are evaluated and updated by trust evolution and advice aggregation. We demonstrate our proposed TMETA system’s practicability with synthetic and real-world data. The experimental results indicate that our proposed TMETA system can help ISs attain more favorable outcomes and reduce the task failure rate in various trust environments, thereby accelerating the cold start process.
Blockchain (BC) is a promising Distributed Ledger Technology (DLT) that has attracted attention in recent years due to its characteristics of maintaining privacy and security. This technology, which removes the need for a trusted third party, can be applied in various trust-related domains, such as energy trading, crowdsourcing, and the Internet of Things. This paper presents GuRuChain, a platform framework for online service trading. It combines on-chain and off-chain trust management to provide a blockchain system suitable for trust-related applications. The platform does not only ensure the recording of online transactions; but also the deliverance of offline services. In GuRuChain, we propose a mathematical model to assess the trustworthiness of each participant. We also introduce an incentive mechanism based on reputation and guarantee to monitor the participants' behavior. Finally, we maintain the consistency of the BC through the proposed consensus scheme called Proof Guarantee and Reputation (PoGR). PoGR selects nodes based on a scoring formula that uses three parameters, reputation score, guarantee balance, and risk taken to ensure fairness. The results of the performance evaluation demonstrate the feasibility, efficiency, and scalability of GuRuChain.
Sijie Huang, Guoju Gao, He Huang, Yu-E Sun · 8 authors
Mobile crowdsensing (MC), an excellent solution to large-scale spatiotemporal data sensing problems, has recently received lots of attention from both industry and academia. In the MC system, any requester can acquire the sensing data for his points of interest (PoIs) by offering some payments to attract a group of mobile users capable of completing these PoI-related sensing tasks. However, the current MC work neglected three vital factors, more or less. First, they assume that these distributed users are mutually independent in MC, ignoring the social effects. Actually, the sensing data collected by one user may be corroborated by others’ sensing data, so-called information corroboration. Second, all rational and selfish users are inclined to gather to perform these tasks due to information corroboration. Meanwhile, they may be strategic about their participation levels to maximize profits. However, more similar sensing data will undoubtedly lower the information value, so any user has a tradeoff between gather and scatter. Third, although mobile users can obtain some payments, privacy issues may still prevent them from participating in MC. In this article, we propose a secure blockchain-assisted socially-aware MC framework by adopting the smart contract technique of Ethereum. For this framework, we further devise a two-stage Stackelberg game model to assist the requester (i.e., the leader in the game) in properly pricing each PoI-related sensing task, so that mobile users (i.e., the followers in the game) can exactly select their tasks and determine their participation levels. To analyze the game equilibrium, we extend the traditional Hessian matrix method to a multidimension case involving the multiuser multitask hyperspace setting. We conduct extensive experiments to prove the equilibrium and effectiveness of the proposed solution. We also implement a prototype and deploy the smart contract to an official Ethereum test network to demonstrate the practicability of the proposed framework.
The Metaverse, envisioned as the next-generation Internet, will be constructed via twining a practical world in a virtual form, wherein Meterverse service providers (MSPs) are required to collect massive data from Meterverse users (MUs). In this regard, a critical demand exists for MSPs to motivate MUs to contribute computing resources and data while preserving user privacy. Federated learning (FL), as a privacy-preserving collaborative machine learning paradigm, can support distributed intensive computation in the Metaverse. In this work, we first investigate minting the machine learning models into NFT with FL assistance (referred to as FL-NFT), such that MUs as stakeholders can control the ownership and share the economic value of user-generated content (UGC). Specifically, MUs are encouraged to establish a decentralized autonomous organization (i.e., MU-DAO) to aggregate local models and mint FL-NFT. MUs and MSPs optimize the strategies by formulating an imperfect information Stackelberg game to trade off the cost and benefit. We apply the backward induction to derive the equilibrium solution. Then, we construct a privacy-preserving multi-winner sealed-bid auction mechanism (PMS-AM), in which the Hidden Markov Model assists MSPs in choosing rational bidding strategies according to historical bids, and the double auction mechanism determines the winners and price of FL-NFT. Finally, the numerical results based on theoretical analysis and simulations demonstrate that the proposed PMS-AM can increase the quality of FL-NFT and achieve the economic properties of incentive mechanisms such as individual rationality and incentive compatibility.
Mobile Crowdsensing (MCS) utilizes sensing data collected from users' mobile devices (MDs) to provide high-quality and personalized services, such as traffic monitoring, weather prediction, and service recommendation. In return, users who participate in crowdsensing (i.e., MCS participants) get payment from cloud service providers (CSPs) according to the quality of their shared data. Therefore, it is vital to guarantee the security of payment transactions between MCS participants and CSPs. As a distributed ledger, the blockchain technology is effective in providing secure transactions among users without a trusted third party, which has found many promising applications such as virtual currency and smart contract. In a blockchain, the proof-of-work (PoW) executed by users plays an essential role in solving consensus issues. However, the complexity of PoW severely obstructs the application of blockchain in MCS due to the limited computational capacity of MDs. To solve this issue, we propose a new framework based on Deep Reinforcement Learning (DRL) for offloading computation-intensive tasks of PoW to edge servers in a blockchain-based MCS system. The proposed framework can be used to obtain the optimal offloading policy for PoW tasks under the complex and dynamic MCS environment. Simulation results demonstrate that our method can achieve a lower weighted cost of latency and power consumption compared to benchmark methods.
When a blockchain application runs on data from the real world, it relies on an oracle mechanism that transports data from external sources to the blockchain. The blockchain oracle problem arises around the need to procure trustworthy data from external sources. Previous works have addressed data authenticity/integrity by building a secure channel between blockchain and external sources while employing a decentralized oracle network to avoid a single point of failure. However, the truthful data challenge, which emerges when legitimate external sources submit fraudulent or deceitful data, remains unsolved. In this paper, we introduce a new decentralized truth-discovering oracle architecture called DecenTruth to address the truthful data challenge using a data-centric approach. DecenTruth aims to elevate the "truthfulness" of external data input by enabling decentralized oracle nodes to discover and reach consensus on truthful values of common data objects from multi-sourced inputs in an off-chain manner. It harmonizes techniques in both the data plane and consensus plane—truth discovery (TD) and asynchronous BFT consensus—and enables nodes to finalize the same estimated truths on data objects with high accuracy, amid the harsh asynchronous network condition and presence of Byzantine sources and nodes. We implemented DecenTruth and evaluated its performance in a simulated oracle service scenario. The results demonstrate significantly higher Byzantine resilience and long-term data feed accuracy of DecenTruth, compared to existing median-based aggregation methods.
In mobile crowdsensing (MCS), truth discovery (TD) plays an important role in sensing task completion. Most of the existing studies focus on the privacy preservation of mobile users, and the reliability of mobile users is evaluated by their weights which are calculated based on the submitted sensing data. However, if mobile users are unreliable, the submitted sensing data and their weights are also unreliable, which may influence the accuracy of the ground truths of sensing tasks. Therefore, this article proposes a privacy-preserving and reputation-based truth discovery framework named PRTD which can generate the ground truths of sensing tasks with high accuracy while preserving privacy. Specifically, we first preserve sensing data privacy, weight privacy, and reputation value privacy by utilizing the Paillier algorithm and Pedersen commitment. Then, to verify whether the reputation values of mobile users are tampered with and select mobile users that satisfy the corresponding reputation requirements, we design a privacy-preserving reputation verification algorithm based on reputation commitment and zero-knowledge proof and propose a concept of reliability level to select mobile users. Finally, a general TD algorithm with reliability level is presented to improve the accuracy of the ground truths of sensing tasks. Moreover, theoretical analysis and performance evaluation are conducted, and the evaluation results demonstrate that the PRTD framework outperforms the existing TD frameworks in several evaluation metrics in the synthetic dataset and real-world dataset.
Raising ambition inside the Paris Agreement calls for collaboration on climate action. Efficient a nd trustworthy monitoring, reporting and verification (MRV) of greenhouse gas (GHG) emissions is important in decarbonization efforts. This article proposes a collaborative approach, and exemplifies two scenarios in MRV. Therefore, we propose a consortium blockchain as platform, and aim on the reduction of costs and efforts by collaboration and automation of different aspects in GHG management. We present a demonstrator with Hyperledger Fabric, which employs a industry grade RFID security approach to distribute and manage certificate material, a low-code interface to interact with the network, and the operation of Fabric nodes on industrial edge devices. We argue that a toolkit approach like CarbonEdge may be a step towards ease of use for blockchain based GHG emissions management.
Decentralizing crowdsourcing using blockchain removes the trusted mediator who may cause social biases in data aggregation and uncertainties in ensuring proper rewards to workers. Permissionless blockchain discloses all data on public ledgers, which compromises the privacy and anonymity of workers and induces free-riders. State-of-the-art anonymous crowdsourcing systems enable anonymity through identity registration of workers and a trusted setup for key generation. However, these systems fail to support anonymous payments to workers, which may compromise the identities of workers. In this paper, we incorporate anonymous payments in crowdsourcing and dispense with identity registration and trusted setup to support open anonymous participation from any worker. Our solution is based on the decentralized anonymous payment systems (e.g., Zerocoin), commitment schemes, and efficient non-interactive zero-knowledge proofs.
Smart cities are data driven and collect data from a variety of sources. Certain types of data such as building data is under-represented and remains harder to find despite its value. Our goal is to incentivise the stakeholders to make building data easier to avail by turning it into an asset. We use tokenized building data assets on a blockchain to improve data accessibility. This is achieved by connecting building data owners with the consumers of building information via tokens (fungible and non-fungible), which serves the purpose of coordinating the activities of the built ecosystem. Further, we present our system architecture designed to sustain the economic incentives for interested parties and individuals.
Xuelian Cai, Lingling Zhou, Fan Li, Yuchuan Fu · 7 authors
With the increase of on-board sensors, as a new paradigm of mobile crowdsensing (MCS), vehicular crowdsensing (VCS) shows great potential in realizing low-cost, large-scale sensing tasks. In order to improve the user engagement and task completion quality of VCS, an appropriate incentive mechanism can promote enough users to participate in the sensing activities and further provide high-quality sensing data. However, due to the contradiction between personal interests and user data security protection, the development of the incentive mechanism is seriously affected. To deal with these challenges, this article aims to propose a security protection incentive mechanism with data quality assurance (SPIM-DQA) for the VCS system. First, we adopt the blockchain-enabled VCS framework, and propose a series of smart contracts to ensure the automatic execution of the incentive mechanism, which solves the user data security issues existing in the traditional incentive mechanism. Then, based on this framework and these smart contracts, a data quality-aware incentive mechanism is proposed from the perspective of data quality. After selecting low-cost and high-quality users to perform the crowdsensing task, user reputation is updated by evaluating the quality of the provided data. In particular, there is a correlation between user reputation and reward distribution, which incentivizes users to consistently provide high-quality data to increase their rewards. Finally, extensive simulation results show that SPIM-DQA can effectively improve data quality while meeting security requirements.
Regardless of which community, incentivizing users is a necessity for well-sustainable operations. In the blockchain-backed Web3 communities, known for their transparency and security, airdrop serves as a widespread incentive mechanism for allocating capital and power. However, it remains a controversy on how to justify airdrop to incentive and empower the decentralized governance. In this paper, we use ParaSwap as an example to propose a role taxonomy methodology through a data-driven study to understand the characteristic of community members and the effectiveness of airdrop. We find that users receive more rewards tend to take positive actions towards the community. We summarize several arbitrage patterns and confirm the current detection is not sufficient in screening out airdrop hunters. In conjunction with the results, we discuss from the aspects of interaction, financialization, and system design to conclude the challenges and possible research directions for decentralized communities.
Founded in 2017, Algorand is one of the world's first carbon-negative, public blockchains inspired by proof of stake. Algorand uses a Byzantine agreement protocol to add new blocks to the blockchain. The protocol can tolerate malicious users as long as a supermajority of the stake is controlled by non-malicious users. The protocol achieves about 100x more throughput compared to Bitcoin and can be easily scaled to millions of nodes. Despite its impressive features, Algorand lacks a reward-distribution scheme that can effectively incentivize nodes to participate in the protocol. In this work, we study the incentive issue in Algorand through the lens of game theory. We model the Algorand protocol as a Bayesian game and propose a novel reward scheme to address the incentive issue in Algorand. We derive necessary conditions to ensure that participation in the protocol is a Bayesian Nash equilibrium under our proposed reward scheme even in the presence of a malicious adversary. We also present quantitative analysis of our proposed reward scheme by applying it to two real-world deployment scenarios. We estimate the costs of running an Algorand node and simulate the protocol to measure the overheads in terms of computation, storage, and networking.