Recently, crowd sensing has been intensively researched, due to the rapid growth of sensor-integrated mobile devices. Crowd sensing is a participatory sensing service where a server gathers and analyzes sensing data submitted from mobile devices of lots of users. In crowd sensing, the user's anonymity is desired, since the server gathers sensitive data from the participants including their GPS locations and moving path. However, the anonymous data submission may compromise the trust of the sensing data, because anonymous users may submit inappropriate sensing data, but they cannot be traced. Therefore, as the system to achieve both anonymity and trust in crowd sensing, ARTSense has been proposed. In the system, the trust of the sensing data is assessed on the sensed environment, other users' sensing, and the reputation of the user, and furthermore the reputation of the user is anonymously managed on the feedback from the trust assessment for the data. However, the anonymous reputation system of ARTSense has the efficiency problem, i.e., the user needs to wait a random time after the data submission phase before requesting the reputation update, which causes the communication delay. In this paper, we propose an efficient anonymous reputation system for crowd sensing, which can be integrated to the trust assessment in ARTSense. In the proposed system, during the data submission, the reputation update is anonymously completed. This is because the server does not manage the reputation of each user, but each user manages his/her reputation in the user side, where the the validity of the reputation is ensured by a certificate and anonymously checked by zero-knowledge proofs. Therefore, the proposed system achieves the better efficiency with no delay.
With the rapid development of Internet of Things (IoT), mobile crowdsensing (MCS), i.e., outsourcing sensing tasks to mobile devices or vehicles, has been proposed to address the problem of data collection in the scenarios such as smart city. Despite its benefits for a wide range of applications, MCS lacks an efficient incentive mechanism, restricting the development of IoT applications, especially for Internet of Vehicles (IoV)-a typical example of IoT applications; this is because vehicles are usually reluctant to participate these sensing tasks. Moreover, in practice, some sensing tasks may arrive suddenly (called an emergent task) in the IoV environment, but the resources of a single vehicle may be insufficient to handle, and thus multivehicles collaboration is required. In this case, the incentive mechanisms for the participation of multiple vehicles and the task scheduling for their collaborations are collectively needed. To address this important problem, we first propose a new model for the scenario of two vehicles collaboration, considering the situation of the emergent appearance of a task. In this model, for a general sensing task, we propose a bidding mechanism to better encourage vehicles to contribute their resources, and the tasks for those vehicles are scheduled accordingly. Second, for an emergent task, a novel time-window-based method is devised to manage the tasks among vehicles and to incent the vehicles to participate. Finally, we develop a blockchain framework to achieve the secured information exchange through smart contract for the proposed models in IoV.
Currently, data sharing for the Internet of Vehicles (IoV) applications has drawn much attention in the framework of developing smart cities and smart transportation. A critical challenge for data sharing is to incentivize users to participate in collecting and sharing data. The traditional incentive mechanism of crowdsourcing is not practical for IoV because of its trust issues. Although blockchain technology has been introduced to address trust issues and security challenges, ensuring trust in off-chain data for the blockchain-based approaches is still an open issue. In this article, we propose a quality-driven auction-based incentive mechanism based on a consortium blockchain that guarantees trust in both on-chain data and off-chain data. We first introduce a consortium blockchain that is used as an open and distributed hyperledger to address the security issue of on-chain data. Then, we formulate the problem as a reverse auction in which the platform acts as an auctioneer that purchases data from users. By utilizing a data quality-driven auction model, the evaluated data quality via expectation maximization is used to ensure the trust in off-chain data. The quality-driven, auction-based incentive mechanism can obtain the high-quality data and optimal social welfare with low social cost. Otherwise, we design a smart contract to perform the data sharing automatically. Finally, the extensive simulations show that our proposed algorithm achieves maximum social welfare, outperforms other solutions, and scales well when the number of users or tasks increase. Moreover, the performance of the smart contract shows its low computing cost.
Given the devastating outcomes of the recent hurricanes, earthquakes, and floods across the globe, governments all over the world started to pay serious attention to their disaster management protocols. The current reliance on centralized, infrastructure-dependent solutions is a serious threat to the whole management process. The entire process is mainly about processing help requests based on real-time information about the disaster, and efficiently responding to such requests by dispensing a set of limited resources available at the management's disposal. Relying on independent anonymous help givers can expand the resource availability and facilitate help provisioning. Further, Crowdsourcing can be a valuable source of information that can better guide help provisioning process. However, establishing dynamic trust relationship between help requesters, information providers, and help givers is the key enabler to such enhancement. Help requests should be probably authenticated not to waste the valuable resources, help provisioning should be rewarded to motivate help givers, and Data sharing should be verified not to act on false information. In this paper, we present a decentralized Blockchain based trust management framework to facilitate cooperative anonymous help provisioning at disaster time considering infrastructure-supported scenario.
Blockchain, a promising decentralized para-digm, can be exploited not only to overcome the shortcomings of the traditional crowdsourcing systems, but also to bring technical innovations, such as decentralization and accountability. Nevertheless, some critical inherent limitations of blockchain have been rarely addressed in the literature when it is incorporated into crowdsourcing, which may yield the performance bottleneck in the crowdsourcing systems. To further leverage the superiority of combining blockchain and crowdsourcing, in this article, we propose an innovative hybrid blockchain crowdsourcing platform, named zkCrowd. Our zkCrowd integrates with a hybrid blockchain structure, smart contract, dual ledgers, and dual consensus protocols to secure communications, verify transactions, and preserve privacy. Both the theoretical analysis and experiments are performed to evaluate the advantages of zkCrowd over the state of the art.
Sep 9, 2019·Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers
Regulatory compliance is an essential exercise in the modern societies confirming safety and prevention of harm to consumers. Despite many efforts from international and national quality control authorities, transparency and accountability in regulatory compliance remain a challenging technical-legal problem sitting atop a heavy reliance on trust. This paper presents a theoretical model of regulatory compliance aiming at improving accountability for systems and data audit and introduces a higher degree of transparency in management and quality control. It explores the technical aspects of two emerging technologies the Internet of Things (IoT) and Blockchain, and using a common use-case in practice shows how to better align these technologies with legal concerns and trust in regulatory compliance.
Dimitrios G. Kogias, Helen C. Leligou, Michael G. Xevgenis, Maria Polychronaki · 8 authors
Crowdsourcing has been pursued as a way to leverage the power of the crowd for many different purposes in diverse sectors from collecting information, aggregating funds, and gathering employees to perform tasks of different sizes among other targets. Data integrity and nonrepudiation are of utmost importance in these systems and are currently not guaranteed. Blockchain technology has been proven to improve on these aspects. In this article, we investigate the benefits that the adoption of Blockchain technology can bring in crowdsourcing systems. To this end, we provide examples of real-life crowdsourcing use cases and explore the benefits of using Blockchain, mainly as a database.
Naman Goel, Cyril van Schreven, Aris Filos-Ratsikas, Boi Faltings
Blockchain based systems allow various kinds of financial transactions to be executed in a decentralized manner. However, these systems often rely on a trusted third party (oracle) to get correct information about the real-world events, which trigger the financial transactions. In this paper, we identify two biggest challenges in building decentralized, trustless and transparent oracles. The first challenge is acquiring correct information about the real-world events without relying on a trusted information provider. We show how a peer-consistency incentive mechanism can be used to acquire truthful information from an untrusted and self-interested crowd, even when the crowd has outside incentives to provide wrong informations. The second is a system design and implementation challenge. For the first time, we show how to implement a trustless and transparent oracle in Ethereum. We discuss various non-trivial issues that arise in implementing peer-consistency mechanisms in Ethereum, suggest several optimizations to reduce gas cost and provide empirical analysis.
Dinh C. Nguyen, Pubudu N. Pathirana, Ming Ding, Aruna Seneviratne
\n\t\t\t\t\tBlockchain technology with its secure, transparent and decentralized nature has been recently employed in many mobile applications. However, the process of executing extensive tasks such as computation-intensive data applications and blockchain mining requires high computational and storage capability of mobile devices, which would hinder blockchain applications in mobile systems. To meet this challenge, we propose a mobile edge computing (MEC) based blockchain network where multi-mobile users (MUs) act as miners to offload their data processing tasks and mining tasks to a nearby MEC server via wireless channels. Specially, we formulate task offloading, user privacy preservation and mining profit as a joint optimization problem which is modelled as a Markov decision process, where our objective is to minimize the long-term system offloading utility and maximize the privacy levels for all blockchain users. We first propose a reinforcement learning (RL)-based offloading scheme which enables MUs to make optimal offloading decisions based on blockchain transaction states, wireless channel qualities between MUs and MEC server and user’s power hash states. To further improve the offloading performances for larger-scale blockchain scenarios, we then develop a deep RL algorithm by using deep Q-network which can efficiently solve large state space without any prior knowledge of the system dynamics. Experiment and simulation results show that the proposed RL-based offloading schemes significantly enhance user privacy, and reduce the energy consumption as well as computation latency with minimum offloading costs in comparison with the benchmark offloading schemes.\n\t\t\t\t
The blockchain-empowered mobile-edge computing (MEC) is a promising solution for enhancing the computation capabilities of mobile equipments (MEs) to process computation-intensive tasks such as the real-time data processing tasks and mining tasks. However, because of the “cold start” and “long return” problems, efficient computation offloading cannot be achieved in blockchain-empowered MEC because the MEs do not always have enough coins to afford the offloading service cost. In this article, we study the joint computation-offloading and coin-loaning problem for blockchain-empowered MEC to minimize the total cost of all MEs. We introduce the banks that can provide loan services to the MEs to address the above two issues. We formulate the problem as a noncooperative game to model the competitions between the myopic MEs. By using a potential game method, we prove the existence of a pure-strategy Nash equilibrium (NE) and design a distributed algorithm to achieve the NE point with low computational complexity. We also provide an upper bound on the price of anarchy of the game by theoretical proof. Besides, two smart contracts are designed to automatically perform the computing resource trading and coin loaning processes. Lastly, our simulation results show that our proposed algorithm can significantly reduce the total cost of all MEs, has better performance compared with other solutions, and scales well as the number of MEs increases. Moreover, the financial cost for executing the two smart contracts on the Ethereum network is low.
Blockchain is a decentralized digital ledger to capture and record economic transactions of mobile applications. In blockchain operation process, mining tasks play a key role and pose intensive computation demands on resource constrained mobile devices. Mobile edge computing (MEC) is a promising solution to alleviate the heavy burden on the devices through task offloading. Since only the miner device that first obtains a block can get rewards, there are risks of the devices for consuming edge computing resources without any profit. However, traditional task offloading schemes that purely rely on expected utilities of the devices always ignore the difference of various devices in balancing risks and rewards, thus make the offloading inefficient. To address this problem, we adopt a prospect theoretic approach to design an optimal offloading scheme for MEC-empowered blockchain, where diverse preferences of devices for profits and risks are explicitly taken into account. Specifically, we formulate the offloading process as a Stackelberg game that incorporates notions from prospect theory. We design an efficient algorithm to obtain the optimal offloading strategies, which maximize the utilities of both the miner devices and the MEC service providers. Numerical results are presented to illustrate the performance of the proposed offloading schemes.
Aug 1, 2019·2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS)
Blockchain technique has been widely applied in various fields, such as finance, Internet of Things, law, etc. However, it is a challenge to apply blockchain technique for mobile applications, as the mobile devices cannot afford computing resources required by the mining processes. This paper proposes a mechanism based on a combinatorial double auction to offload the mining process of miners to the edge servers. The mechanism is formulated as a resource allocation problem. The corresponding allocation algorithms and payment scheme are proposed to allocate resources and calculate trade prices, respectively. Moreover, this paper proves that the proposed mechanism is efficient in terms of computation, and it satisfies three properties of economic auction which are budget balance, individual rationality and truthfulness. Experimental results show that the proposed mechanism is able to yield higher total utility, together with good scalability. For the case of 900 miners in the auction, the total utility of the proposed mechanism is higher than two existing works by 39.2% and 569.9% on average, respectively.
The Internet of Things is stepping out of its infancy into full maturity, requiring massive data processing and storage. Unfortunately, because of the unique characteristics of resource constraints, short-range communication, and self-organization in IoT, it always resorts to the cloud or fog nodes for outsourced computation and storage, which has brought about a series of novel challenging security and privacy threats. For this reason, one of the critical challenges of having numerous IoT devices is the capacity to manage them and their data. A specific concern is from which devices or Edge clouds to accept join requests or interaction requests. This paper discusses a design concept for developing the IoT data management platform, along with a data management and lineage traceability implementation of the platform based on blockchain and smart contracts, which approaches the two major challenges: how to implement effective data management and enrich rational interoperability for trusted groups of linked Things; And how to settle conflicts between untrusted IoT devices and its requests taking into account security and privacy preserving. Experimental results show that the system scales well with the loss of computing and communication performance maintaining within the acceptable range, works well to effectively defend against unauthorized access and empower data provenance and transparency, which verifies the feasibility and efficiency of the design concept to provide privacy, fine-grained, and integrity data management over the IoT devices by introducing the blockchain-based data management platform.
Participants can complete tasks on crowdsensing platform with smart mobile devices. They go to specific locations to collect data and upload them. Then, the platform pays them some rewards. However, most of the existing crowdsensing platforms are deployed on centralized servers, which are vulnerable to attack, intrusion, and manipulation. In addition, the location information in task records increases the potential risk of privacy leakage. To reach a balance between privacy protection and functional availability of the system. We design and implement Crowdchain, which is a location preserve anonymous payment system based on a permissioned blockchain. In order to disassociate participants identity information from task records, Crowdchain contains a novel protocol that makes participants transfer wallet address to payment system bypass the crowdsensing platform and receive task rewards correctly. We prove the security of our anonymous payment system by zero-knowledge proof and give the analysis result of system stability.
Aug 1, 2019·2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)
Nitin Sukhija, Elizabeth Bautista, Moon Moore, John-George Sample
In recent years, the crowdsourced data analytics have gained an unprecedented interest and adoption from the research communities. In data crowdsourcing, large scientific problems can be solved by utilizing collective human intelligence, where various researchers or groups work on solving various sub-problems. However, the main challenges that encompasses a crowdsourcing system are data access, management, and analysis along with user privacy and trust preservation. In this paper, we present a decentralized data access and control framework to handle data crowdsourcing using blockchain technology. Leveraging on blockchain, our permissioned system enables confidentiality, accountability and traceability of the Crowdsourced data while maintaining control of it via Proof of Stake consensus algorithm. The proposed framework will be employed to crowdsource operational data to the National Energy Research Scientific Computing Center (NERSC) users at Lawrence Berkeley National Laboratory.
With the rapid development of the Internet of Things (IoT) in the era of big data, the amount of collected data has increased dramatically. Data are one of the most important commodities in IoT. To maximize the utility of the collected data, it is crucial to design an open IoT data market that enables data owners and consumers to carry out data trading securely and efficiently. To address the challenge of security presented by an untrusted and nontransparent data market, we propose an edge/cloud-computing-assisted, blockchain-enhanced data market framework to support secure and efficient IoT data trading, with a particular focus on an optimal pricing mechanism. In this mechanism, an authorized market-agency works as a scheduler, determining the win-owner and its pricing strategy to the consumer. We formulate a two-stage Stackelberg game to solve the pricing and purchasing problem of the data consumer and the market-agency. In the first stage of the game, the market-agency gives the win-owner and its pricing strategy. In the second stage, the data consumer decides on its purchasing quantity of data. We consider competition between data owners and propose a competition-enhanced pricing scheme (CPS). We apply backward induction to analyze the subgame perfect equilibrium at each stage for both independent and CPSs. Lastly, we validate the existence and uniqueness of Stackelberg equilibrium, and the numerical results show the efficiency of the CPS.
Jernej Mihelj, Yuan Zhang, Andrej Kos, Urban Sedlar
Real-time data about various traffic events and conditions-offences, accidents, dangerous driving, or dangerous road conditions-is crucial for safe and efficient transportation. Unlike roadside infrastructure data which are often limited in scope and quantity, crowdsensing approaches promise much broader and comprehensive coverage of traffic events. However, to ensure safe and efficient traffic operation, assessing trustworthiness of crowdsourced data is of crucial importance; this also includes detection of intentional or unintentional manipulation, deception, and spamming. In this paper, we design and demonstrate a road traffic event detection and source reputation assessment system for unreliable data sources. Special care is taken to adapt the system for operation in decentralized mode, using smart contracts on a Turing-complete blockchain platform, eliminating single authority over such systems and increasing resilience to institutional data manipulation. The proposed solution was evaluated using both a synthetic traffic event dataset and a dataset gathered from real users, using a traffic event reporting mobile application in a professional driving simulator used for driver training. The results show the proposed system can accurately detect a range of manipulative and misreporting behaviors, and quickly converges to the final trust score even in a resource-constrained environment of a blockchain platform virtual machine.
Yury Zhauniarovich, Yazan Boshmaf, Husam Al Jawaheri, Mashael Al Sabah
Web-based hosting services for version control, such as GitHub, have made it easier for people to develop, share, and donate money to software repositories. In this paper, we study the use of Bitcoin to make donations to open source repositories on GitHub. In particular, we analyze the amount and volume of donations over time, in addition to its relationship to the age and popularity of a repository. We scanned over three million repositories looking for donation addresses. We then extracted and analyzed their transactions from Bitcoin's public blockchain. Overall, we found a limited adoption of Bitcoin as a payment method for receiving donations, with nearly 44 thousand deposits adding up to only 8.3 million dollars in the last 10 years. We also found weak positive correlation between the amount of donations in dollars and the popularity of a repository, with highest correlation (r=0.013) associated with number of forks.
Vehicular CrowdSensing (VCS) has become a promising paradigm to employ mobile vehicles for performing sensing tasks in supporting location-based services and applications. In traditional VCS, a central agency is highly depended on, from collecting task requests of requesters to employment and reward assignment to workers. However, the centralized manner causes critical problems, such as potential privacy leakage and unexpected free-riding and false-reporting behaviors due to the lack of recorded proofs. The challenging problems hinder the wide-spread deployment of VCS. In this paper, we utilize consomum blockchain to elaborately design a dedicated blockchain, called by VeSenChain, to provision VCS services in a decentralized, authentic and transparent manner. For the implementation of VeSenChain, we develop an interactive protocol to support smart contract based operations among different entities. Stackelberg game approach is further used to formulate and solve the sensing task scheduling problem between a requester and multiple workers, and thus enable optimal smart contract design. Numerical results demonstrate that VeSenChain is effective and efficient for promoting VCS in network security and efficiency.
Computation offloading has been considered as a viable solution to blockchain mining in mobile environments. In this paper, we present a two-layer computation offloading paradigm that includes an edge computing service provider (ESP) and a cloud computing service provider (CSP). We formulate a multi-leader multi-follower Stackelberg game to address the computing resource management problem in such a network, by jointly maximizing the profits of each service provider (SP) and the payoffs of individual miners. Two practical scenarios are investigated: a fixed-miner-number scenario for permissioned blockchains and a dynamic-miner-number scenario for permissionless blockchains. For the fixed-miner-number scenario, we discuss two different edge operation modes, i.e., the ESP is connected (to the CSP) or standalone, which form different miner subgames based on whether each miner's strategy set is mutually dependent. The existence and uniqueness of Stackelberg equilibrium (SE) in both modes are analyzed, according to which algorithms are proposed to achieve the corresponding SE(s). For the dynamic-miner-number scenario, we focus on the impact of population uncertainty and find that the uncertainty inflates the aggressiveness in the ESP resource purchasing. Numerical evaluations are presented to verify the proposed models.
The crowdsourcing schemes which utilize the social network to solve complex tasks are an important part of open cooperation over the Internet. Although blockchain-based crowdsourcing schemes have considerable advantages in decentralization and data sharing, there is still a challenge to gurantee the security of crowdsourced-sensitive information and the fairness of crowdsourcing on the blockchain. To this end, this article investigates a crowdsourcing scheme based on blockchain. First, we define the basic requirements of blockchain-based crowdsourcing schemes including fairness, confidentiality, and integrity. And then, using secure hash, commitment, and homomorphic encryption, we propose a blockchain-based secure and fair crowdsourcing scheme, that is, BFC. The analysis results show that our scheme can satisfy the above requirements. Finally, the experimental results show that the computational overhead of the BFC scheme is acceptable to both the requester and the workers. In a word, our proposed crowdsourcing scheme has good expansibility in reality.
Blockchain has been treated as one of the most promising technologies to promote crowdsourcing by providing new nice features, such as decentralization and accountability. Unfortunately, some inherent limitations of blockchain have been rarely addressed by the most existing works when applying blockchain into crowdsourcing, which becomes the performance bottleneck of crowdsourcing systems. In this paper, we propose a novel hybrid blockchain crowdsourcing platform to achieve decentralization and privacy preservation. Our platform integrates with a hybrid blockchain structure, dual-ledgers, and dual consensus algorithms to ensure secure communication between the requesters and the workers. Moreover, the smart contract and zero-knowledge proof are employed to ensure automatic operation of the tasks and the protection users' privacy, respectively. Finally, we conduct experiments to confirm the efficiency of the adopted consensus algorithm on our platform by comparing it with the state-of-the-art.
The sharing economy has made great inroads with services like Uber or Airbnb enabling people to share their unused resources with those needing them. The computing world, however, despite its abundance of excess computational resources has remained largely unaffected by this trend, save for few examples like SETI@home. We present DeCloud, a decentralized market framework bringing the sharing economy to on-demand computing where the offering of pay-as-you-go services will not be limited to large companies, but ad hoc clouds can be spontaneously formed on the edge of the network. We design incentive compatible double auction mechanism targeted specifically for distributed ledger trust model instead of relying on third-party auctioneer. DeCloud incorporates innovative matching heuristic capable of coping with the level of heterogeneity inherent for large-scale open systems. Evaluating DeCloud on Google cluster-usage data, we demonstrate that the system has a near-optimal performance from an economic point of view, additionally enhanced by the flexibility of matching.