Rimvydas LauĆŸikas, Tadas ĆœiĆŸiĆ«nas, Vladislav V. Fomin
The conflict between heritage protection and urban infrastructure development rationales creates a context for inclusion, participation and dialogue of different heritage-related communities. However, developed in the pre-computer age of administrative practice, are often incapable, partially or completely, to accommodate the ânew-eraâ community oriented participatory practices. In this article, authors discuss the mutual effects of IT in the process of democratization of urban heritage preservation. The authors create and argue the conceptual model of distributed ledger technologies (DLT) in participatory UHP. The model demonstrates how technologies can become catalysts for democratization in situations when the regulatory and administrative change (on its own) is too inert. The article hypothesizes that novel technological developments which aim at or have the potential for increasing community involvement and democratization of administrative practice, exert their effects directly through technology-based participatory practices.
Retail energy markets are increasingly consumer-oriented, thanks to a growing number of energy plans offered by a plethora of energy suppliers, retailers and intermediaries. To maximize the benefits of competitive retail energy markets, group purchasing is an emerging paradigm that aggregates consumers' purchasing power by coordinating switch decisions to specific energy providers for discounted energy plans. Traditionally, group purchasing is mediated by a trusted third-party, which suffers from the lack of privacy and transparency. In this paper, we introduce a novel paradigm of decentralized privacy-preserving group purchasing, empowered by privacy-preserving blockchain and secure multi-party computation, to enable users to form a coalition for coordinated switch decisions in a decentralized manner, without a trusted third-party. The coordinated switch decisions are determined by a competitive online algorithm, based on users' private consumption data and current energy plan tariffs. Remarkably, no private user consumption data will be revealed to others in the online decision-making process, which is carried out in a transparently verifiable manner to eliminate frauds from dishonest users and supports fair mutual compensations by sharing the switching costs to incentivize group purchasing. We implemented our decentralized group purchasing solution as a smart contract on Solidity-supported blockchain platform (e.g., Ethereum), and provide extensive empirical evaluation.
Abstract With the boom in 5G technology, mobile spatial crowdsourcing has shown great dynamism in industrial mobile communications and edge computing node management. But the traditional crowdsourcing system is not advanced enough to adapt to the new environment. Typically, traditional crowdsourcing workflow is hosted by a centralized crowdsourcing platform. However, the centralized crowdsourcing platform faces the following problems: (1) single point of failure, (2) user privacy leakage, (3) subjective arbitration, (4) additional service fee, and (5) non-transparent task assignment process. To improve those problems, we replaced the centralized crowdsourcing platform with a decentralized blockchain infrastructure. And we analyzed the challenge problems of multi-skilled spatial crowdsourcing tasks in the blockchain crowdsourcing system. In addition, a crowdsourcing task allocation algorithm has been proposed, which implements a transparent task distribution process and can adapt to the computing-constrained environment on the blockchain. Compared with the TSWCrowd blockchain-based crowdsourcing model, our system has a higher task allocation rate under the same conditions. And the experimental result shows our work has good economic feasibility, which decentralizes the crowdsourcing process and significantly reduces the additional consumption of the crowdsourcing process.
Crowdsourcing as a computing paradigm, has been widely used in industries and services. Accountability in crowdsourcing services enables participants to work honestly and improves the quality of services. The realization of accountability requires trusted evidence, multiparty verification, and fair reward or punishment. Blockchain technology, which is inherently tamper-resistant, traceable, and decentralized, puts forward a direction for realizing the requirements. However, in the process of data management and decentralized verification, it is hard to achieve a better trade-off between efficiency and security. This paper proposes a blockchain-based verification scheme integrated by trust management, âvalidatorRepâ, that is suitable to enhance accountability in the crowdsourcing system. In detail, a decoupled blockchain model is proposed for the differentiated storage of business transactions and log transactions during data interaction. Additionally, a fine-grained trust model is proposed, including both the rep-utation of participants and the trust relationship between participants. Based on fine-grained trust, the decentralized verification scheme is designed to guarantee secure data access, trusted verification, and fair reward or punishment. Finally, the proposed framework is deployed on the Ethereum platform to observe its effectiveness and overall performance. Simulation results also reveal that the proposed fine-grained trust model can provide efficient accountability for crowdsourcing.
Spatial crowdsourcing emerges as a new computing paradigm that enables mobile users to accomplish spatio- temporal tasks in order to solve human-intrinsic problems. Existing crowdsourcing systems critically use centralized servers for interacting with workers and making task assignment decisions. These systems are hence susceptible to issues such as the single point of failure and the lack of operational transparency. Prior work, therefore, turns to blockchain-based decentralized crowdsourcing systems, yet still suffers from problems of lacking efficient task assignment scheme, requiring a deposit to an untrusted system, low block generation speed, and high transaction fees. To address these issues, we design a blockchain-based decentralized framework for spatial crowdsourcing, which we call SC-EOS. Our system does not rely on any trusted servers, while providing efficient and user-customizable task assignment, low monetary cost, and fast block generation. More importantly, it frees users from making a deposit into an untrusted system. Our framework can also be extended and applied to generic crowdsourcing systems. We implemented the proposed system on the EOS blockchain. Trace-driven evaluations involving real users show that our system attains the comparable task assignment performance against a clairvoyant scheme. It also achieves 10× cost savings than an Ethereum-based implementation.
Web archives store born-digital documents, which are usually collected from the Internet by crawlers and stored in the Web Archive (WARC) format. The trustworthiness and integrity of web archives is still an open challenge, especially in the news portal domain, which face additional challenges of censorship even in democratic societies. The aim of this paper is to present a light-weight, blockchain-based solution for web archive validation, which would ensure that documents retrieved by crawlers are authentic for many years to come. We developed our archive validation solution as an extension and continuation of our work in web crawler development mainly targeting news portals. The system is designed as an overlay over a blockchain with a proof-of-stake (PoS) distributed consensus algorithm. PoS was chosen due to its lower ecological footprint compared to proof-of-work solutions (e.g. Bitcoin) and lower expected investment in computing infrastructure. We based our prototype on the open-source Nxt blockchain and implemented it in Python. The prototype was tested on web archive content crawled from Hungarian news portals at two different timestamps with more than 1 million articles in total. We concluded that the proposed solution is accessible, usable by different stakeholders to validate crawled content, deployable on cheap commodity hardware, tackles the archive integrity challenge and is capable to efficiently manage duplicate documents.
Christopher Gorog, Pam Russell, Terrance E. Boult, Philip N. Brown
This work introduces a carbon-neutral approach for Distributed Ledger. With computationally intensive consensus models creating exceptional levels of wasted energy worldwide, it is crucial that distributed ledger technology progresses in a direction that alleviates this trend. This work offers both a novel consensus model and a user incentive that departs from the usage of computational processes for consensus. This work considers the need for longevity of distributed ledger and reflects the desires of the different user archetypes engaged in the recent upswing of cryptocurrency. We anticipate that some users engage for investment or growth speculation, some engage for transactional purposes, and others engage for analytics and transaction verification. We introduce a sustainable distributed ledger that provides engagement considerations for each archetype while eliminating wasteful side effects of computational proof-of-work algorithms. The incentive for contribution included for this novel distributed ledger is based on participation over time. This work shows the ability to incentivize each of the archetypes aligned for the purpose of maintaining ledger data and transactions over time. In the model introduced, the key user engagement focus is redirected from computational waste found in current cryptocurrencies to a model that incentivizes constant uptime to receive tokens released for use over time. In addition, we demonstrate that current incentive models which result in global energy waste are converted to a user incentive where all rewards are aligned to maintain continually available data storage.
Crowdsourcing for Human Intelligence Tasks (HIT) has been widely used to crowdsource human knowledge, such as image annotation for machine learning. We use a public blockchain to play the role of traditional centralized HIT systems, such that the blockchain deals with cryptocurrency payments and acts as a trustworthy judge to resolve disputes between a worker and a requester in a decentralized setting, preventing false-reporting and free-riding. Our approach neither uses expensive cryptographic tools, such as zero-knowledge proofs, nor sends the worker's answers to the blockchain. Compared with prior works, our approach significantly reduces on-chain cost: it only requires O(1) on-chain storage and O(log N ) smart contract computation, where N is the question number of a HIT. Additionally, our approach uses known answers or gold standards to determine the worker's answer quality. To motivate the requester to use honest known answers, the requester cannot learn the worker's answers if the answer quality does not meet the requirement. We further provide formal security definitions for our decentralized HIT and prove security of our construction.
Liangde Tao, Lin Chen, Lei Xu, Weidong Shi · 6 authors
Many research works in computational social choice assume a fixed set of voters in an election and study the resistance of different voting rules against electoral manipulation. In recent years, however, a new technique known as random sample voting has been adopted in many multi-agent systems. One of the most prominent examples is blockchain. Many proof-of-stake based blockchain systems like Algorand will randomly select a subset of participants of the system to form a committee, and only the committee members will be involved in the decision of some important system parameters. This can be viewed as running an election where the voter committee (i.e., the voters whose votes will be counted) is randomly selected. It is generally expected that the introduction of such randomness should make the election more resistant to electoral manipulation, despite the lack of theoretical analysis. In this paper, we present a systematic study on the resistance of an election with a randomly selected voter committee against bribery. Since the committee is randomly generated, by bribing any fixed subset of voters, the designated candidate may or may not win. Consequently, we consider the problem of finding a feasible solution that maximizes the winning probability of the designated candidate. We show that for most voting rules, this problem becomes extremely difficult for the briber as even finding any non-trivial solution with non-zero objective value becomes NP-hard. However, for plurality and veto, there exists a polynomial time approximation scheme that computes a near-optimal solution efficiently. The algorithm builds upon a novel integer programming formulation together with techniques from n-fold integer programming, which may be of a separate interest.
In the Internet of Things (loT) era, edge computing is a promising paradigm to improve the quality of service for latency sensitive applications by filling gaps between the loT devices and the cloud infrastructure. Highly geo-distributed edge computing resources that are managed by independent and competing service providers pose new challenges in terms of resource allocation and effective resource sharing to achieve a globally efficient resource allocation. In this paper, we propose a novel blockchain-based model for allocating computing resources in an edge computing platform that allows service providers to establish resource sharing contracts with edge infrastructure providers apriori using smart contracts in Ethereum. The smart contract in the proposed model acts as the auctioneer and replaces the trusted third-party to handle the auction. The blockchain-based auctioning protocol increases the transparency of the auction-based resource allocation for the participating edge service and infrastructure providers. The design of sealed bids and bid revealing methods in the proposed protocol make it possible for the participating bidders to place their bids without revealing their true valuation of the goods. The truthful auction design and the utility-aware bidding strategies incorporated in the proposed model enables the edge service providers and edge infrastructure providers to maximize their utilities. We implement a prototype of the model on a real blockchain test bed and our extensive experiments demonstrate the effectiveness, scalability and performance efficiency of the proposed approach.
Crowd funding and crowd innovation can boost creativity of creators at a low cost. However, how to protect rights and benefits of relative stakeholders during the process in a credible way remains a problem. By introducing fungible tokens, non-fungible tokens and on-chain governance based on blockchain, we propose a set of smart contracts supporting crowd funding and crowd innovation to better reward participants and govern the process in a trusted way. Furthermore, based on these smart contracts, we abstract and encapsulate a series of common operations and implement a service platform for trustworthy crowd funding and crowd innovation.
Federated Learning (FL) is a promising solution for training using data collected from heterogeneous sources (e.g., mobile devices) while avoiding the transmission of large amounts of raw data and preserving privacy. Current FL approaches operate in an iterative manner by selecting a subset of participants each round, asking them to training using their latest local data over the most recent version of the global model, before collecting these local model updates and aggregating them to form the next iteration of the global model, and so forth until convergence is reached. Unfortunately, existing FL approaches typically select randomly the set of clients to use each round, which can negatively impact the quality of the model trained, as well the training round time due to the straggler problem. Moreover, clients, especially mobile devices with limited resources, should be incentivized to participate as federated learning is essentially a form of crowdsourcing for AI which requires monetization. We argue that the integration of blockchain and smart contract technologies to FL can solve the two aforementioned issues. In this paper, we present FL-MAB (FL- Multi-Auction using Blockchain), a client selection mechanism for FL operating in a smart contract which rewards clients for their participation using cryptocurrencies. FL-MAB employs a multidimensional auction mechanism for selecting users based on the compute and network resources offered by each client, as well as the quality of their local data. This auction is realized in a reliable and auditable manner through a smart contract. This allows FL-MAB to measure the relative contribution of each client by calculating a Shapley value, and allocating rewards accordingly. We have implemented FL-MAB using Solidity and tested on the Ethereum blockchain with various popular datasets. Our results show that FL-MAB outperforms existing baseline schemes by improving accuracy and reducing the no. of FL rounds.
Federated learning (FL) has become a new form of data sharing by aggregating multi-party local models. Although the existing FL incentive system has reduced insufficient data supply under comprehensive information, it still confronts issues including free-riding, unfairness, and unreliability. Therefore, this paper proposes an incomplete information FL incentive mechanism based on blockchain and Bayesian games. The data transaction process is modeled by quantifying the cost-utility of the data providers and the payment reward of the data requesters, in which Shapley value is used to realize the fairness of reward distribution of data providers. We consider the heterogeneity and privacy protection of participating individuals. The data providers' resource allocation strategies are built as a Bayesian game model, which optimizes the local training strategy to realize the incentive effect on the data providers. Furthermore, we consider the effectiveness of the incentive mechanism, a privacy-preserving Bayesian game action strategy consensus algorithm (PPBG-AC) is proposed, which enables the data providers to realize Bayesian Nash equilibrium under a data trading platform based on blockchain. The comparison and analysis of the schemes reveal that the incentive mechanism presented in our paper assures benefit distribution fairness and resource allocation credibility. Simulation experiments and performance evaluations based on real datasets demonstrate the effectiveness of our incentive mechanism.
The rapid growth of connected and autonomous vehicles (CAVs) shows an urgent demand for driving and transportation-related data, which gives rise to vehicular crowdsensing systems (VCSs). Nevertheless, the existing centralized VCS framework mainly faces the system reliability problem while the decentralized one cannot satisfy the management flexibility. In addition, when the privacy preservation scheme that prevents information leakage encounters the user selection scheme that desires detailed information of participants, how to balance this seemingly irreconcilable contradiction is inevitable for VCS. To remedy that, we take the first research attempt and explore the balance point between the system management, privacy preservation, and quality of experience (QoE) of participants. By fully exploiting the characters of participating entities, a blockchain-enabled conditional decentralized VCS is proposed in this paper. Firstly, we propose a privacy-preserving scheme where the zk-SNARK proof combines with the mixed-task smart contract to guarantee the interaction process will not reveal any private information of participants. Secondly, we propose an efficient reputation management mechanism that renders certain the participants can get a satisfactory QoE even under the condition that the private information of users is secured. And also, the malicious operations in the system will be effectively supervised. Theoretical analysis and extensive simulations demonstrate the security and efficiency properties of privacy preservation and indicate the effectiveness of reputation management.
The application of distributed ledger technologies, including blockchain, is rapidly growing in governance, transport, supply chain, and logistics. Today, blockchain technology is promoted as the heart of Smart Cities. This study reviews the potential of blockchain application in water management systems. We surveyed the literature and organized the previous studies based on three main application topics: Smart Water Systems, Water Quality Monitoring, and Storm Water Management. Also, we addressed technical, organizational, social, and institutional challenges that may hinder the adoption of Blockchain technology. Water management systems need to have a long-term commitment plan, update their organizational policies, and acquire relevant knowledge and expertise before successfully adopting any distributed ledger technology.
Dan Wang, Bo Li, Bin Song, Yingjie Liu · 6 authors
Nowadays, emerging sixth-generation (6G) mobile networks, the Internet of Things (IoT), and mobile-edge computing (MEC) technologies have played significant roles in developing a sustainable computing network. In sustainable computing networks, with the increasing scale of data-driven applications, massive privacy-sensitive data are generated. How to effectively process such data on resource-limited IoT devices is challenging. Although edge intelligence (EI) is designed to maintain an appropriate level of ultradelay reliability, low-latency communication (URLLC), real-time data processing, and security and privacy are concerning. In this article, we propose a novel blockchain-supported hierarchical digital twin IoT (HDTIoT) framework, which combines the digital twin to edge network and adopts blockchain technology to achieve secure and reliable real-time computation. We first propose a data and knowledge dual-driven learning solution to ensure real-time interaction and efficient optimization between the physical and the digital worlds. To improve communication and computation efficiency with data and knowledge dual-driven learning, the optimization goal is to minimize the system delay and energy consumption and ensure system reliability and the learning accuracy of IoT devices. Moreover, we propose a proximal policy optimization (PPO)-based multiagent reinforcement learning (MARL) algorithm to solve the resource allocation (RA) problem. Experimental results show that the proposed RA scheme can improve the efficiency of the HDTIoT system, guarantee learning accuracy, reliability, and security, and make a balance between system delay and energy consumption.
Leveraging various mobile devices to train the shared model collaboratively, federated learning (FL) can improve the privacy and security of 6G communication. To economically encourage the participation of heterogeneous mobile devices, an incentive mechanism and a fair trading platform are needed. In this paper, we implement a blockchain-based FL system and propose an incentive mechanism to establish a decentralized and transparent trading platform. Moreover, to better understand the mobile devicesâ behaviors, we provide economic analysis for this market. Specifically, we propose two strategy models for mobile devices, namely the discrete strategy model (DSM) and the continuous strategy model (CSM). Also, we formulate the interactions among the non-cooperative mobile devices as a dynamic game, where they adjust their strategies iteratively to maximize the individual payoff based on othersâ previous strategies. We further prove the existence in Nash equilibrium (NE) of two different models and propose algorithms to achieve them. Simulation results demonstrate the convergence of the proposed algorithms and show that the CSM can effectively increase the mobile devicesâ payoffs to 128.1 percent at most compared with DSM.
The devices at the edge of a network are not only responsible for sensing the surrounding environment but are also made intelligent enough to learn and react to the environment. Clustered Edge Intelligence (CEI) emphasizes intelligence-centric clustering instead of device-centric clustering. It allows the devices to share their knowledge and events with other devices and the remote fog or cloud servers. However, recent advancements facilitate the traceability of the eventsâ history by analyzing edge devicesâ event logs, which are compute intensive and easy to alter. This article focuses on a blockchain-based solution for CEI that makes the edge devicesâ events history immutable and easily traceable. This article further explains how the edge devicesâ activities and the environmental data can be secured from the source device to the cloud servers. Such a secured CEI mechanism can be applied in establishing a transparent and efficient smart city, supply chain, logistics, and transportation systems.
To understand how distributed ledger technology (DLT) enables people-centered IoT solutions we conducted a systematic literature review of tested implementations since 2017. We created a people-centered classification to analyze 39 implementations. We found that people-centered DLT-IoT architectures are in their infancy and detected no evidence of emerging patterns. We observed that Ethereum is the most used DLT. Fit-for-purpose technologies like IOTA and concepts like Self-Sovereign Identity (SSI) were underrepresented. We noted an increased interest in privacy-preserving and edge-computing mechanisms, and identified three areas for future research. We hope this survey will assist others learning more about people-centered IoT solutions.