En Wang, Jiatong Cai, Yongjian Yang, Wenbin Liu · 7 authors
With the development of communications, networking, and information technology, Crowdsensed Data Trading (CDT) becomes a novel data trading paradigm. In CDT, the data requesters publish crowdsensing tasks with specific data requirements, and then workers complete these tasks, upload the data and obtain corresponding rewards. To efficiently deal with data trading, most of the existing CDT systems assume a trusted centralized platform. However, we argue that the platform may collude with workers or requesters to trick others for achieving more benefits. For example, according to the workers’ uploaded data, the platform can modify the reward functions by colluding with the requester. Similarly, the platform might collude with workers to let them know the reward function, then workers could forge data. Meanwhile, requesters and workers may also be malicious. For example, requesters may post tasks but fail to pay and workers can upload wrong data to mislead the system. To solve the above problems, we combine the Crowdsensed Data Trading system with intelligent Blockchain (CDT-B), which contains a smart contract called CDToken. As a credible third-party, the CDToken is used to record the requesters’ reward function and workers’ data uploading function to avoid targeted trick. At the same time, we not only design a Data Uploading and Preprocessing (DUP) mechanism in CDToken to collect and process the workers’ sensed data, but also propose a Grouping Truth Discovery (GTD) to evaluate their data quality for determining the payments. Moreover, to hold a large number of requesters and workers in CDT-B, we propose a Layered Sharding blockchain based on Membership Degree (LSMD) to solve the blockchain inefficiency problem. Finally, we deploy CDToken to an experimental environment based on Ethereum and demonstrate its efficient performance and practicability.
Weizheng Wang, Yaoqi Yang, Zhimeng Yin, Kapal Dev · 8 authors
Given the explosive growth of portable devices, mobile crowdsensing (MCS) is becoming an essential approach that fully utilizes pervasive idle resources to accomplish sensing tasks. The traditional MCS relies on the centralized server for task handle is susceptible to a single point of failure. Targeting this security issue, researchers have proposed a series of blockchain-based MCS. However, nodes in the blockchain suffer from high computation cost for data processing. Simultaneously, most blockchain-based MCS systems lack an efficient incentive mechanism for service requesters and workers. In this work, we integrate the smart contract and mobile devices to establish a secure, interactive, and fair blockchain-based MCS system called BSIF. To prevent illegitimate participants, BSIF requests all users to verify their identities using private keys from the registration phase. In the case of worker location privacy leakage, the location-based symmetric key generator is adopted to coordinate a session key for target range worker selection. Besides, we transfer the data evaluation process to the requester side (e.g., a personal computer), reducing computation cost in the blockchain nodes. Due to the homomorphic feature of the Paillier Cryptosystem and common interest, the requester cannot violate the directives from the blockchain. Subsequently, the Stackelberg game is adopted to investigate the participation level of the workers and the fair reward mechanism for the requesters to achieve a dynamic balance. Finally, the security analysis and performance evaluation demonstrate that our BSIF can defend against possible adversaries while significantly cutting overhead and giving participants the utmost incentive.
Igor Y. Mazurok, Yevhen Leonchyk, Sergii Grybniak, Oleksandr S. Nashyvan · 5 authors
Decentralized public platforms are becoming increasingly popular due to a growing number of applications for various areas of business, finance, and social life. Authorless nodes can easily join such networks without any confirmation, making a transparent system of rewards and punishments crucial for the self-sustainability of public platforms. To achieve this, a system for incentivizing and punishing Workers' behavior should be tightly harmonized with the corresponding consensus protocol, taking into account all of its features, and facilitating a favorable and supportive environment with equal rights for all participants. The main purpose of rewards is to incentivize Workers to follow the protocol properly, and to penalize them for any type of misbehavior. The issues of block rewarding and punishing in decentralized networks have been well studied, but the DAG referential structure of the distributed ledger forces us to design methods that are more relevant. Since referential structures cannot be reliably validated due to the fact that they are built on the basis of the instantaneous visibility of blocks by a certain node, we propose to set rewards for blocks in the DAG network based on the degree of confidence of topological structures. In doing so, all honest nodes make common decisions based only on information recorded into the ledger, without overloading the network with additional interactions, since such data are always identical and available. The main goal of this work is to design a fair distribution of rewards among honest Workers and establish values for penalties for faulty ones, to ensure the general economic equilibrium of the Waterfall platform. The proposed approach has a flexible and transparent architecture that allows for its use for a wide range of PoS-based consensus protocols. The core principles are that Workers' rewards depend on the importance of the conducted work for block producing and achieving consensus and their penalties must not be less than the potential profit from possible attacks. The incentivizing system can facilitate protection from various kinds of attacks, namely, so-called Nothing-at-stake, Rich-get-richer, Sybil, and Splitting attacks, and from some specific threats related to a DAG structure.
The sharing of high-quality traffic information plays a crucial role in enhancing the driving experience and safety performance for vehicular networks, especially in the development of electric vehicles (EVs). The crowdsourcing-based real-time navigation of charging piles is characterized by low delay and high accuracy. However, due to the lack of an effective incentive mechanism and the resource-consuming bottleneck of sharing real-time road conditions, methods to recruit or motivate more EVs to provide high-quality information gathering has attracted considerable interest. In this paper, we first introduce a blockchain platform, where EVs act as the blockchain nodes, and a reputation-based incentive mechanism for vehicular networks. The reputations of blockchain nodes are calculated according to their historical behavior and interactions. Further, we design and implement algorithms for updating honest-behavior-based reputation as well as for screening low-reputation miners, to optimize the profits of miners and address spatial crowdsourcing tasks for sharing information on road conditions. The experimental results show that the proposed reputation-based incentive method can improve the reputation and profits of vehicle users and ensure data timeliness and reliability.
Daniel Montero Hernández, Jorge Peña Queralta, Tomi Westerlund
The increased use of Internet of Things (IoT) devices -- from basic sensors to robust embedded computers -- has boosted the demand for information processing and storing solutions closer to these devices. Edge computing has been established as a standard architecture for developing IoT solutions, since it can optimize the workload and capacity of systems that depend on cloud services by deploying necessary computing power close to where the information is being produced and consumed. However, as the network scale in size, reaching consensus becomes an increasingly challenging task. Distributed ledger technologies (DLTs), which can be described as a network of distributed databases that incorporate cryptography, can be leveraged to achieve consensus among participants. In recent years DLTs have gained traction due to the popularity of blockchains, the most-well known type of implementation. The reliability and trust that can be achieved through transparent and traceable transactions are other key concepts that bring IoT and DLT together. We present the design, development and conducted experiments of a proof-of-concept system that uses DLT smart contracts for efficiently selecting edge nodes for offloading computational tasks. In particular, we integrate network performance indicators in smart contracts with a Hyperledger Blockchain to optimize the offloading on computation under dynamic connectivity solutions. The proposed method can be applied to networks with varied topologies and different means of connectivity. Our results show the applicability of blockchain smart contracts to a variety of industrial use cases.
Advances in communications and networking technologies are driving the computing paradigm toward the end-edge-cloud collaborative architecture to leverage ubiquitous data and resources. Opposite to centralized intelligence, Hierarchical Federated Learning (HFL) relieves overwhelmed communication overhead and enjoys the advantages of high bandwidth as well as abundant computing resources while retaining privacy-preserving benefits of Federated Learning (FL). It is difficult to balance system overhead and model performance in the HFL framework, while it could be solved by introducing an incentive mechanism. Although the incentive mechanism can alleviate the above anxiety by compensating relevant participants, some limitations (multi-dimensional properties, incomplete information and unreliable participants) will significantly degrade the performance and efficiency of the designed mechanism. To address the challenges caused by the above limitations, we propose InFEDge, a blockchain-based incentive mechanism in the HFL. The InFEDge considers 1) multi-dimensional individual properties to model system participants and proves the uniqueness of Nash equilibrium with the closed-form solution. Meanwhile, 2) we transform the problem under incomplete information into a contract game where we obtain the optimal solution. Moreover, 3) we also leverage the blockchain to provide economic incentives, prevent unreliable participants’ disturbance and further ensure data privacy by implementing the mechanism in the smart contract to offer a credible, faster, and transparent resource trading system. Experimental evaluations on a proof-of-concept testbed along with real traces demonstrate the superiority of our mechanism. Further, our method solves a real-world user allocation problem for future communications and networking.
Incentive plays an important role in knowledge discovery, as it impels users to provide high-quality knowledge. To promise incentive schemes with transparency, blockchain technology has been widely used in incentive schemes. Currently, privacy, reliability, streamlined processing, and quality awareness are major challenges in designing blockchain-based incentive schemes. In this paper, we design a blockchain-based eFficient and pRivacy-preserving qUality-aware IncenTive scheme called FRUIT. With well-designed smart contracts, FRUIT achieves privacy, reliability, streamlined processing, and quality awareness during the whole procedure. Specifically, we design a novel lightweight encryption method by combining matrix decomposition with proxy re-encryption and a privacy-preserving task allocation based on the polynomial fitting function and hash function. Then, we leverage our proposed lightweight encryption and task allocation to build an efficient and privacy-preserving knowledge discovery protocol in order to securely calculate the data quality and truthful knowledge. To promise user reliability in the incentive scheme, we utilize the Dirichlet distribution to realize the automatic reputation prediction based on the data quality by deploying the reputation management on the blockchain. Moreover, we also deploy the payment management on the blockchain, endowing the incentive scheme to reward participants based on the data quality automatically. Through a detailed security analysis, we demonstrate that data privacy and task privacy are well preserved during the whole process. Theoretical analysis and extensive experiments on real-world datasets demonstrate that FRUIT has acceptable efficiency and affordable performance in terms of computation cost, communication overhead, and gas consumption.
Michael Froehlich, Benjamin Moser, Florian Alt, Albrecht Schmidt
There is an increasingly diverse range of smart-contract blockchains on which decentralized applications (dApps) are built. However, HCI research has so far failed to address them, focusing primarily on Bitcoin and Ethereum. This is problematic as these new blockchains come with an increasingly diverse set of properties that influence the usability of dApps for end-users. For blockchain interface design guidelines to be valuable for practitioners, they need to acknowledge the heterogeneity of blockchains. However, evaluating novel interface concepts across different blockchains is resource-intensive as each blockchain has to be integrated manually, slowing down research. To address this challenge, we propose a system to support interface experimentation for blockchain applications. The system allows researchers and developers to connect interfaces to a unified API simulating different blockchains and facilitates the configuration, distribution, and evaluation of online experiments. A preliminary evaluation showed promising results.
Abdullah Alghamdi, Jiang Zhu, Guocai Yin, Mohammad Shorfuzzaman · 7 authors
Resource constraint Consumer Internet of Things (CIoT) is controlled through gateway devices (e.g., smartphones, computers, etc.) that are connected to Mobile Edge Computing (MEC) servers or cloud regulated by a third party. Recently Machine Learning (ML) has been widely used in automation, consumer behavior analysis, device quality upgradation, etc. Typical ML predicts by analyzing customers' raw data in a centralized system which raises the security and privacy issues such as data leakage, privacy violation, single point of failure, etc. To overcome the problems, Federated Learning (FL) developed an initial solution to ensure services without sharing personal data. In FL, a centralized aggregator collaborates and makes an average for a global model used for the next round of training. However, the centralized aggregator raised the same issues, such as a single point of control leaking the updated model and interrupting the entire process. Additionally, research claims data can be retrieved from model parameters. Beyond that, since the Gateway (GW) device has full access to the raw data, it can also threaten the entire ecosystem. This research contributes a blockchain-controlled, edge intelligence federated learning framework for a distributed learning platform for CIoT. The federated learning platform allows collaborative learning with users' shared data, and the blockchain network replaces the centralized aggregator and ensures secure participation of gateway devices in the ecosystem. Furthermore, blockchain is trustless, immutable, and anonymous, encouraging CIoT end users to participate. We evaluated the framework and federated learning outcomes using the well-known Stanford Cars dataset. Experimental results prove the effectiveness of the proposed framework.
Matthias Babel, Vincent Gramlich, Marc-Fabian Körner, Johannes Sedlmeir · 6 authors
Abstract In the energy transition, there is an urgent need for decreasing overall carbon emissions. Against this background, the purposeful and verifiable tracing of emissions in the energy system is a crucial key element for promoting the deep decarbonization towards a net zero emission economy with a market-based approach. Such an effective tracing system requires end-to-end information flows that link carbon sources and sinks while keeping end consumers’ and businesses’ sensitive data confidential. In this paper, we illustrate how non-fungible tokens with fractional ownership can help to enable such a system, and how zero-knowledge proofs can address the related privacy issues associated with the fine-granular recording of stakeholders’ emission data. Thus, we contribute to designing a carbon emission tracing system that satisfies verifiability, distinguishability, fractional ownership, and privacy requirements. We implement a proof-of-concept for our approach and discuss its advantages compared to alternative centralized or decentralized architectures that have been proposed in the past. Based on a technical, data privacy, and economic analysis, we conclude that our approach is a more suitable technical backbone for end-to-end digital carbon emission tracing than previously suggested solutions.
Abstract Machine‐as‐a‐Service (MaaS) is an emerging service model for industrial appliances. With MaaS, machines are rented instead of being acquired, and their lifecycle is handled by an ecosystem of specialized actors, such as different independent maintenance companies certified for interventions on specific hardware. As the number of actors, clients, and providers involved in a MaaS ecosystem grows, maintaining mutual trust relationships between all involved parties and orchestrating MaaS operations in centralized fashion quickly becomes intractable. We present a blockchain‐based approach to providing MaaS in industrial settings where rented machines are equipped with IoT sensors, and where MaaS operations are orchestrated in a transparent, decentralized, and scalable way using a collection of smart contracts deployed over an infrastructure combining the Ethereum and InterPlanetary File System decentralized services. We detail the operations of MaaS, such as the lifecycle of management operations, and report on the performance of a prototype implementation deployed in the cloud.
Autonomous mobile robots (AMRs) use decentralized, AI-driven decision-making processes to providing material handling capabilities in industrial settings. Essay 1 examines how firms organize and engage to mitigate uncertainty during external technology integration (ETI), using an abductive approach with dyadic customer-supplier data to extend prior ETI models by exploring firm engagement, organizational adaptation, and distinct uncertainty types in AMR ETI projects. Essay 2 applies a grounded theory approach to examine AMR integration, using constant comparison and theoretical sampling to develop core categories explaining how suppliers, customers, and users exchange knowledge impacting AMR integration and project performance. Finally, Essay 3 is a conceptual paper examining the importance of end-user adoption by integrating ETI and technology acceptance model (TAM) frameworks, exploring important relationships between managerial interventions, cognitive constructs, user acceptance, and project success in AMR ETIs. As a whole, these essays contribute to the body of knowledge by extending the breadth and depth of current ETI models, emerging a substantive theory of AMR AIU, and extending TAM by grounding managerial interventions and individual cognitive constructs in an AMR context. Managers can use these frameworks to differentiate AMRs and other autonomous collaborative technology from traditional automation, and develop strategies enabling timely and effective AMR implementation.
From tracking pandemics to applications, such as Google Maps, Uber, environmental monitoring, journalism, healthcare, crisis/disaster response, air quality control, noise and traffic monitoring, urban planning, etc., mobile crowdsourcing (MCS) systems are interweaved with the society and daily lives. This survey outlines major security and privacy challenges in MCS systems along with solutions and approaches. Comprehensive countermeasures, leveraging the capabilities of blockchains, smart contracts, machine learning, games, incentives, spatiotemporal cloaking, etc., are presented to preserve privacy and security of mobile workers, task requestors, and other aspects of crowdsourcing systems. Security recommendations for use cases, such as Industrial IoT, Internet of Vehicles, wireless crowdsensed systems, social crowdsourcing, edge computing, personalized and privacy-preserving recommendation, and mobile worker recruitment are further elaborated.
With the rapid development of cloud computing and the sharing economy, crowdsourcing aroused widespread interest and adoption in providing intelligent and efficient services for humans. The majority of existing works focus on effective crowdsourcing task assignment and privacy protection, mostly relying on central servers and assuming that participants are$honest$-$and$-$curious$and proactive. However, in reality, workers may be unwilling to participate, and there may be malicious behavior among participants, thus harming the enthusiasm and interests of other participants. The central server has weaknesses such as single point of failure. To address above problems, we propose a blockchain-based framework for crowdsourcing with reputation and incentive. We first design a worker selection scheme to select credible and capable workers. We leverage reputation as a metric of workers’ credibility, which is calculated through the improved subjective logic model. Then we utilize contract theory to design incentive mechanisms to attract more workers, especially high-quality workers to participate. Experimental results show that our proposed method can detect and prevent malicious participants and resist malicious collusion when the proportion of malicious participants is no more than 1/3. And encourage more workers to actively, honestly and continuously participate in crowdsourcing.
Actual challenges with data in physical infrastructure include: 1) the adversity of its velocity based on access and retrieval, thus integration; 2) its value as its intrinsic quality; 3) its extensive volume with a limited variety in terms of systems; and finally, 4) its veracity, as data can be modified to obtain an economical advantage. Physical infrastructure design based on Agile project management and minimum viable products provides benefits against the traditional waterfall method. Agile supports an early return on investment that promotes circular reinvesting while making the product more adaptable to variable social-economical environments. However, Agile also presents inherent issues due to its iterative approach. Furthermore, project information requires an efficient record of the aims, requirements, and governance not only for the investors, owners, or users but also to keep evidence in future health & safety and other statutory compliance. In order to address these issues, this article presents a Validation and Verification (V&V) model for data marketplaces with a hierarchical process; each data V&V stage provides a layer of data abstraction, value-added services, and authenticity based on Artificial Intelligence (AI). In addition, this proposed solution applies Distributed Ledger Technology (DLT) for a decentralised approach where each user keeps and maintains the data within a ledger. The presented model is validated in real data marketplace applications: 1) live data for the Newcastle Urban Observatory Smart City Project, where data are collected from sensors embedded within the smart city via APIs; 2) static data for University College London (UCL)—Real Estate—PEARL Project, where different project users and stakeholders introduce data into a Project Information Model (PIM).
Seth Larweh Kodjiku, Yili Fang, Tao Han, Kwame Omono Asamoah · 9 authors
Because of the rise of cryptocurrencies and decentralized apps, blockchain technology has generated a lot of interest. Among these is the emergent blockchain-based crowdsourcing paradigm, which eliminates the centralized conventional mechanism servers in favor of smart contracts for task and reward allocation. However, there are a few crucial challenges that must be resolved properly. For starters, most reputation-based systems favor high-performing employees. Secondly, the crowdsourcing platform’s expensive service charges may obstruct the growth of crowdsourcing. Finally, unequal evaluation and reward allocation might lead to job dissatisfaction. As a result, the aforementioned issues will substantially impede the development of blockchain-based crowdsourcing systems. In this study, we introduce ExCrowd, a blockchain-based crowdsourcing system that employs a smart contract as a trustworthy authority to properly select workers, assess inputs, and award incentives while maintaining user privacy. Exploration-based crowdsourcing employs the hyperbolic learning curve model based on the conduct of workers and analyzes worker performance patterns using a decision tree technique. We specifically present the architecture of our framework, on which we establish a concrete scheme. Using a real-world dataset, we implement our model on the Ethereum public test network leveraging its reliability, adaptability, scalability, and rich statefulness. The results of our experiments demonstrate the efficiency, usefulness, and adaptability of our proposed system.
With the fast development of the industrial Internet, its interconnectivity poses new challenges for the cooperation of industrial entities. Cooperation among these entities is built on trust, and trust is based on high-quality industrial products at reasonable prices. A traceability system can play an essential role in objectively reflecting the production process and promoting this trust. However, traditional traceability systems often have data privacy issues. Because traceability data are collected or generated during the production process (namely, production-related data), they could be considered privacy data. Several researchers have introduced privacy protection schemes into the traceability system, such as authentication or encryption. Nevertheless, when a privacy protection scheme is established, the original data are disclosed to the legal user of the system, but the data may still be leaked intentionally or unintentionally. Except for data privacy issues, a traditional traceability system can be vulnerable to network attacks, data unavailability, and reliability issues. The authors conducted a study to overcome these shortcomings, and this paper reports the results. We built a traceability prototype system using a blockchain protocol and a zero-knowledge proof method. First, we built a blockchain to record key production process data, aiming to maintain data reliability and availability. Second, through an analysis of traceability purpose using production knowledge, the traceability purpose could be divided into multiple provable statements. By introducing privacy protection through a zero-knowledge proof, the traceability process was converted to proving relative statements. Finally, the statements were validated by a smart contract that provided openness and reliability during the traceability process. Analysis has shown that our approach could meet the requirements for high security and privacy. In addition, the paper also discusses the calculation cost of the traceability process to show our work’s viability. The traceability system described in this paper creates new possibilities for constructing a healthy and reliable trust relationship between production entities to provide further support in the development of the industrial Internet.
With the rapid development of the Internet of Things (IoT) and the rapid popularization of 5 G networks, the data that needs to be processed in Mobile Crowdsourcing (MCS) system is increasing every day. Traditional cloud computing can no longer meet the needs of crowdsourcing for real-time data and processing efficiency, thus, edge computing was born. Edge computing can be calculated at the edge of network so that greatly improve the efficiency and real-time performance of data processing. In addition, most of the existing privacy protection technologies are based on the trusted third parties. Therefore, in view of the semi-trustworthiness of edge servers and the transparency of blockchain, this paper proposes a triple real-time trajectory privacy protection mechanism (T-LGEB) based on edge computing and blockchain. Through combining the localized differential privacy and multiple probability extension mechanism, the T-LGEB mechanism is proposed to send the requests and data to the edge server in this paper. Then, through the spatio-temporal dynamic pseudonym mechanism proposed in the paper, the entire trajectory of task participants is divided into multiple unrelated trajectory segments with different pseudonymous identities in order to protect the trajectory privacy of task participants while ensuring high data availability and real-time data. Through a large number of experiments and comparative analysis on multiple real data sets, the proposed T-LGEB has extremely high privacy protection capabilities and data availability, and the resource consumption caused is relatively low.