Crowdsensing, driven by the proliferation of sensor-rich mobile devices, has emerged as a promising data sensing and aggregation paradigm. Despite useful, traditional crowdsensing systems typically rely on a centralized third-party platform for data collection and processing, which leads to concerns like single point of failure and lack of operation transparency. Such centralization hinders the wide adoption of crowdsensing by wary participants. We therefore explore an alternative design space of building crowdsensing systems atop the emerging decentralized blockchain technology. While enjoying the benefits brought by the public blockchain, we endeavor to achieve a consolidated set of desirable security properties with a proper choreography of latest techniques and our customized designs. We allow data providers to safely contribute data to the transparent blockchain with the confidentiality guarantee on individual data and differential privacy on the aggregation result. Meanwhile, we ensure the service correctness of data aggregation and sanitization by delicately employing hardware-assisted transparent enclave. Furthermore, we maintain the robustness of our system against faulty data providers that submit invalid data, with a customized zero-knowledge range proof scheme. The experiment results demonstrate the high efficiency of our designs on both mobile client and SGX-enabled server, as well as reasonable on-chain monetary cost of running our task contract on Ethereum.
Jinliang Xu, Shangguang Wang, Bharat Bhargava, Fangchun Yang
Crowd-intelligence tries to gather, process, infer and ascertain massive useful information by utilizing the intelligence of crowds or distributed computers, which has great potential in Industrial Internet of Things (IIoT). A crowd-intelligence ecosystem involves three stakeholders, namely the platform, workers (e.g., individuals, sensors or processors), and task publisher. The stakeholders have no mutual trust but interest conflict, which means bad cooperation of them. Due to lack of trust, transferring raw data (e.g., pictures or video clips) between publisher and workers requires the remote platform center to serve as a relay node, which implies network congestion. First we use a reward-penalty model to align the incentives of stakeholders. Then the predefined rules are implemented using blockchain smart contract on many edge servers of the mobile edge computing network, which together function as a trustless hybrid human-machine crowd-intelligence platform. As edge servers are near to workers and publisher, network congestion can be effectively improved. Further, we proved the existence of the only one strong Nash equilibrium, which can maximize the interests of involved edge servers and make the ecosystem bigger. Theoretical analysis and experiments validate the proposed method respectively.
Ananda Maiti, Ali Raza, Byeong Ho Kang, Lachlan Hardy
Internet of Things (IoT) plays a big role in automating information generation and consumption in industrial monitoring applications. Blockchain can allow this information to be stored in a manner that is both accessible and reliable for the IoT devices to work with. Blockchain has the capability to collect data from IoT devices and store it in a distributed manner that prevents tampering with the data. This paper discusses the use of blockchain to calculate the Service Quality (SQ) in an Industrial IoT for monitoring application. The proposed framework looks at the blockchain as a finite number of fragmented pieces of data corresponding to a specific industrial process. The SQ is expressed as penalties which is the difference between the expected IoT sensor values and the actual sensor data in reported events from the IoT devices. It also moderates the penalty between similar industrial processes based on each other. The moderation allows better understanding of the system functions and identification of specific problems rather than simply recording the sensor data for a single process. Furthermore, this paper analyzes private blockchains for suitability in IIoT and summarizes some key challenges for IoT to be used with blockchain in context of the proposed framework. The paper uses supply chain as a use case scenario for describing the proposed framework and presents results on its technical feasibility.
Public blockchains have emerged as a promising direction in revolutionizing existing data-driven systems relying on centralized service providers. Among others, one kind of such systems is the popular crowdsensing systems which promise convenient data collection and aggregation. Although promising, leveraging public blockchains to build crowdsensing systems is non-trivial and has to overcome several barriers. First, public blockchains are transparent and lack support for data privacy. Second, participants from the open blockchain environment may misbehave in serving crowdsensing applications, like providing invalid data or doing aggregation incorrectly. Further, on-chain processing incurs monetary cost, so simply putting all workload on-chain is highly uneconomical and a delicate joint on-chain and off-chain design is required. In this paper, we take the first research attempt and explore a new design point to bridge public blockchains with crowdsensing systems. We propose a framework for building private, robust, and verifiable blockchain-empowered crowdsensing systems. It features an open service paradigm where blockchain nodes can rent out their computing resources to serve crowdsensing applications, with custom and full-fledged mechanisms to foster a healthy and economical ecosystem and to simultaneously tackle the challenges of data privacy, robustness against misbehaving participants, and service correctness assurance. Extensive experiments demonstrate our designs practicality.
Task matching in crowdsourcing is designed to provide convenient task information retrieval and has been extensively explored. In general, the task matching process is required to be reliable and to meet privacy requirements. However, most existing privacy-preserving task matching solutions for crowdsourcing focus on privacy issues but ignore the reliability of the process. In this paper, we propose a blockchain-based task matching scheme for crowdsourcing with a secure and reliable matching. Instead of utilizing a centralized cloud server, we employ smart contracts, an emerging blockchain technology, to provide reliable and transparent matching. In this way, data confidentiality and identity anonymity are achieved effectively and efficiently. The extensive privacy analysis and performance evaluation show that our solution is secure and feasible.
Mobile crowdsensing (MCS) is an emerging data collection paradigm that exploits the potential of individual mobile devices to acquire mass data in a cost-effective manner. One of the important challenges in MCS application is to resist malicious users who provide false data to disturb the system. In the existing work, the reputation management scheme is an effective way to overcome the challenge. However, most reputation management schemes rely on a semi-honest server and process data in the plaintext domain without considering server security and user privacy. In this paper, we integrate the blockchain and edge computing in the MCS scenario to construct a credible and efficient blockchain-based MCS system, called BC-MCS. To resist malicious users, we present a privacy-preserving reputation management scheme based on the proposed system. Furthermore, we design a delegation protocol to solve the inherent problem of user dynamics in the MCS. The prototype system implemented on the Hyperledger Sawtooth and Android client demonstrates that our scheme can achieve higher utility and security levels in handling malicious users compared with the previous centralized reputation management schemes.
The mining process in public blockchains with the Nakamoto consensus protocol requires solving a computational puzzle, i.e., proof-of-work, which is resource expensive to implement in lightweight devices with limited computing resources and energy. Thus, renting mining service from cloud providers becomes a reasonable solution, which is called cloud mining. This enables users who want to mine, i.e., miners, to purchase and lease an amount of hashing power from the cloud/edge providers without any hassle of managing the infrastructure. In this paper, we study the interactions among the cloud/edge providers and miners in blockchain using a multi-leader multi-follower game-theoretic approach, in order to support proof-of-work based blockchains application. Due to the inherent complexity of the formulated game, we employ the Alternating Direction Method of Multipliers (ADMM) algorithm to investigate the optimum solution. Utilizing the decomposition characteristics and fast convergence of ADMM, we obtain the optimum results in a distributed manner. Simulation results demonstrate that with the proposed solutions, the optimization of the utilities of miners and the profits of providers can be jointly achieved.
Alexander Raschendorfer, Benjamin Mörzinger, Eric Steinberger, Patrick Pelzmann · 7 authors
Recently, the manufacturing industry became aware of distributed ledger technologies, a protocol that, amongst other things, allows trustless transactions between machines. In this paper, we investigate whether an M2M economy would be feasible within the IOTA network, a popular cryptocurrency for IoT. We build and present a simple industrial lot-size one production system involving three agents that cooperate to create an artistic painting. Payments between agents and users are autonomously executed via IOTA. Amongst other points of criticism, we found that an M2M economy would benefit from the support of smart-contracts and conclude that IOTA is not a fitting solution.
Bruno Veloso, Fátima Leal, Benedita Malheiro, Fernando Moreira
Crowdsourced repositories have become an increasingly important source of information for users and businesses in multiple domains. Everyday examples of tourism crowdsourcing platforms focusing on accommodation, food or travelling in general, influence consumer behaviour in modern societies. These repositories, due to their intrinsic openness, can strongly benefit from independent data quality modelling mechanisms. In this context, building trust & reputation models of contributors and storing crowdsourced data using distributed ledger technology allows not only to ascertain the quality of crowdsourced contributions, but also ensures the integrity of the built models. This paper presents a survey on distributed trust & reputation modelling using blockchain technology and, for the specific case of tourism crowdsourcing platforms, discusses the open research problems and identifies future lines of research.
ETH Zurich, Switzerland, Mathieu Chanson, Andreas Bogner, ETH Zurich, Switzerland · 8 authors
An ever growing variety of smart, connected Internet of Things (IoT) devices poses completely new challenges for businesses regarding security and privacy. In fact, the adoption of smart products may depend on the ability of organizations to offer systems that ensure adequate sensor data integrity while guaranteeing sufficient user privacy. In light of these challenges, previous research indicates that blockchain technology could be a promising means to mitigate issues of data security arising in the IoT. Building upon the existing body of knowledge, we propose a design theory, including requirements, design principles, and features, for a blockchain-based sensor data protection system (SDPS) that leverages data certification. To support this, we designed and developed an instantiation of an SDPS (CertifiCar) in three iterative cycles intented to prevent the fraudulent manipulation of car mileage data. Following the explication of our SDPS, we provide an ex post evaluation of our design theory considering CertifiCar and two additional use cases in the areas of pharmaceutical supply chains and energy microgrids. Our results suggest that the proposed design ensures the tamper-resistant gathering, processing, and exchange of IoT sensor data in a privacy-preserving, scalable, and efficient manner.
Jiannan Li, Mohamad Kassem, Angelo Luigi Camillo Ciribini, Marzia Bolpagni
The complementarity among Building Information Modelling (BIM), distributed ledger technology (DLT), smart contracts, and the Internet of Things (IoT) is increasingly acknowledged in industry reports and major national digital transformation initiatives. However, theoretical foundation and empirical evidence to ascertain such a prerogative are still very limited. This paper analyses the interactions between these technologies and proposes an approach that capitalises on their complementarity by linking the physical environment; the digital environment; agreements representing the contract; and the DLT environment. A simulated installation activity is used to verify the conceptual interrelations included in the proposed framework as a proof-of-concept. The simulation reveals how a mini smart contract -for a limited scope such as an installation activity work -can be executed within the proposed approach and how payments can be automated when project delivery is coupled with machine-readable BIM requirements and contract clauses. The paper also discusses the key limitations and challenges facing the adoption of the proposed approach and in particular the diffusion of smart contracts.
With the rapid development of smart mobile terminals (MTs), various industrial Internet of things (IIoT) applications can fully leverage them to collect and share data for providing certain services. However, two key challenges still remain. One is how to achieve high-quality data collection with limited MT energy resource and sensing range. Another is how to ensure security when sharing and exchanging data among MTs, to prevent possible device failure, network communication failure, malicious users or attackers, etc. To this end, we propose a blockchain-enabled efficient data collection and secure sharing scheme combining Ethereum blockchain and deep reinforcement learning (DRL) to create a reliable and safe environment. In this scheme, DRL is used to achieve the maximum amount of collected data, and the blockchain technology is used to ensure security and reliability of data sharing. Extensive simulation results demonstrate that the proposed scheme can provide higher security level and stronger resistance to attack than a traditional database based data sharing scheme for different levels/types of attacks.
Thanks to its decentralized structure and immutability, blockchain technology has the potential to address relevant security and privacy challenges in the Internet of Things (IoT). In particular, by hosting and executing smart contracts, blockchain allows secure, flexible, and traceable message communication between IoT devices. The unique characteristics of IoT systems, such as heterogeneity and pervasiveness, however, pose challenges in designing smart contracts for such systems. In this paper, we study these challenges and propose a design approach for smart contracts used in IoT systems. The main goal of our design model is to enhance the development of IoT smart contracts based on the inherent pervasive attributes of IoT systems. In particular, the design model allows the smart contracts to encapsulate functionalities such as contractlevel communication between IoT devices, access to data-sources within contracts, and interoperability of heterogeneous IoT smart contracts. The essence of our approach is structuring the design of IoT smart contracts as self-contained software services, inspired by the microservice architecture model. The flexibility, scalability and modularity of this model make it an efficient approach for developing pervasive IoT smart contracts.
With the gradual promotion, crowdsourcing has become an efficient way to solve problems that are very complicated for computers and simple for human crowd intelligence in recent years. Traditional crowdsourcing is based on a central system where requesters post tasks on a crowdsourcing central server or platform, however, this centralized model currently faces various challenges such as prohibitive cost, single point of failure, and vulnerability to malicious attacks. To this end, this paper proposes a smart contract-based decentralized online crowdsourcing mechanism, which includes task assignment rules and reward payment rules, etc. The mechanism has the characteristics like decentralization, unalterable, truthfulness and so on. In addition, the corresponding smart contract is designed, so that the mechanism can really run and process the actual data, and the effectiveness is shown by experiments. In this way, the entire crowdsourcing process no longer requires the participation of trusted third-party agencies, information and privacy security is guaranteed, and the cost is lower.
Jian An, Danwei Liang, Xiaolin Gui, Yang He · 6 authors
With the popularization of intelligent terminals, crowdsensing has become increasingly prominent because of its advantages, such as low cost, high convenience, and fast speed in conducting tasks. However, the quality of the data collected through crowdsensing is varied and is difficult to evaluate. Furthermore, the existing crowdsensing quality control methods are mostly based on a central platform, which is not completely trusted in reality and results in the existence of fraud and other problems. To solve these two questions, a crowdsensing quality control model based on a two-consensus blockchain is proposed in this paper. First, the idea of a blockchain is introduced into this model. The credit-based verifier selection mechanism and the two-consensus approach are proposed to realize the nonrepudiation and nontampering of information in crowdsensing. Then, to help task publishers obtain higher-quality sensing data, the methods of node matching and QGE are proposed. The former method uses the idea of the calculation of matching degree to select workers, and the latter uses the idea of clustering and fuzzy theories to evaluate the quality of the sensing data. Finally, the experiments show that the running time of the block generation in our model is acceptable, and comparing with the other methods, our model can acquire data of higher ioj.
Ognjen Šćekić, Stefan Nastić, Schahram Dustdar
Recent technological advances are creating possibilities for novel forms of interaction, collaboration, and organization of labor in Smart Cities. In this paper, we present a reward-driven, Blockchain-backed platform acting as the technological enabler for enactment of ad-hoc, decentralized neighborhood-scale co-creation, collaboration, and citizen engagement activities.
Bing Jia, Tao Zhou, Wuyungerile Li, Zhenchang Liu · 5 authors
Crowd sensing is a perception mode that recruits mobile device users to complete tasks such as data collection and cloud computing. For the cloud computing platform, crowd sensing can not only enable users to collaborate to complete large-scale awareness tasks but also provide users for types, social attributes, and other information for the cloud platform. In order to improve the effectiveness of crowd sensing, many incentive mechanisms have been proposed. Common incentives are monetary reward, entertainment & gamification, social relation, and virtual credit. However, there are rare incentives based on privacy protection basically. In this paper, we proposed a mixed incentive mechanism which combined privacy protection and virtual credit called a blockchain-based location privacy protection incentive mechanism in crowd sensing networks. Its network structure can be divided into three parts which are intelligence crowd sensing networks, confusion mechanism, and blockchain. We conducted the experiments in the campus environment and the results shows that the incentive mechanism proposed in this paper has the efficacious effect in stimulating user participation.