This perspective proposes that, by virtue of its sophisticated trust and consensus finding mechanisms, blockchain has the clear potential to substantially upgrade the processes and organization traditionally underpinning academic science and commercial technology development comprising funding, project delivery, generation of intellectual property, documentation and publication. For supporting this hypothesis, striking analogies between the concepts underlying blockchain technology with research are identified, and applied to the generation of verified knowledge in science and technology development. It is then elaborated how a blockchain-enabled token economy can efficiently and transparently incentivize and coordinate an integrative and community-inclusive participatory approach to fuel crowdsourcing of collective intelligence for contributing ideas, work, infrastructure, funding, data, validation, management, assessment, governance, arbitration and exploitation of projects. Quality, credibility and direction of projects are optimized by demanding collateral “skin-in-the-game” from contributors based on blockchain-enabled staking, reputation systems and prediction markets. This way research progress emerges as a chain of community generated and independently vetted blocks of scientific knowledge; these new blocks are concatenated with the state-of-the-art according to transparent consensus mechanisms.
The major challenges of operating data-intensive of Distributed Ledger Technology (DLT) are (1) to reach consensus on the main chain as a set of validators cast public votes to decide on which blocks to finalize and (2) scalability on how to increase the number of chains which will be running in parallel. In this paper, we introduce a new proximal algorithm that scales DLT in a large-scale Internet of Things (IoT) devices network. We discuss how the algorithm benefits the integrating DLT in IoT by using edge computing technology, taking the scalability and heterogeneous capability of IoT devices into consideration. IoT devices are clustered dynamically into groups based on proximity context information. A cluster head is used to bridge the IoT devices with the DLT network where a smart contract is deployed. In this way, the security of the IoT is improved and the scalability and latency are solved. We elaborate on our mechanism and discuss issues that should be considered and implemented when using the proposed algorithm, we even show how it behaves with varying parameters like latency or when clustering.
The development of Blockchain-based mobile applications are impeded due to the resource limitations of mobile devices. Computation offloading can be a viable solution. In this paper, we consider a two-layer computation offloading paradigm including 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. We study two practical scenarios: 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 isconnected(to the CSP) orstandalone, 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.
Development of large-scale and complex software systems requires multiple teams, including software development teams, domain experts, user representatives, and other project stakeholders, to work collaboratively to achieve software development goals. These teams rely on the use of agreed software development processes, knowledge management tools, and communication channels collaboratively in the software development project. Software testing is an important and complicated process due to reasons such as difficulties in achieving testing goals with the given time constraint, absence of efficient data sharing policies, vague testing acceptance criteria at various levels of testing, and lack of trusted coordination among the teams involved in software testing. The efficiency of the software testing relies on efficient, reliable, and trusted information sharing among these teams. Existing approaches to software testing for collaborative software development use centralized or decentralize tools for software testing, knowledge management, and communication channels. Existing approaches have the limitations of centralized authority, a single point of failure/compromise, lack of automatic requirement compliance checking and transparency in information sharing, and lack of unified data sharing policy, and reliable knowledge management repositories for sharing and storing past software testing artifacts and data. In this paper, a software testing approach for collaborative software development using private blockchain is presented, and the desirable properties of private blockchain, such as distributed data management, tamper-resistance, auditability and automatic requirement compliance checking, are incorporated to greatly improve the quality of software testing for collaborative software development.
Collaborative learning techniques allow numerous clients conjointly to improve artificial intelligence models using their private datasets. The clients carry out the training locally and periodically exchanging gradient values through devices. Unlike conventional training approaches, the training data in the collaborative techniques are not revealed publicly. Regardless of privacy merits, clients are often less motivated to improve the model due to inadequate incentives procedural. In short, the resources owned are not maximally utilized. To tackle the issue, we design a collaborative learning model with a secure, fair, and immutable incentive mechanism by leveraging blockchain technology. Incentives are distributed proportionately to clients according to their respective contributions. We implement our incentive schemes on Ethereum. We also evaluate the performance of collaborative learning in a different setting. The results indicate that the design objectives are met.
Abstract In this work, the issue of predicting the edge weight in Bitcoin network has been addressed by leveraging community structure that involves members who trust with each other in their transactions. The proposed model consists of two main stages; the first one is the detection of trusted Bitcoin communities by implementing Newman- Girvan algorithm. In the context, the attributes of node have been modeling in different ways to get different structure of communities each time. Secondly, prediction the missing edge weight based on the neighbors of edge-source in community. In other words, the trust values that pointed to edge-target by neighbors are averaged to represent the prediction of missing edge weight. Practically, the model has been evaluated using two real-world datasets; Bitcoin-OTC and Bitcoin-Alpha datasets. The experimental results explicate the effectiveness of the proposed model comparable with other methods, where the minimization percentage for Bitcoin-OTC dataset is 4% and 18% for all and partial edges respectively. As for Bitcoin-Alpha dataset are 0% and 30% for all and partial edges respectively.
Renato Vargas-Gomez, Juan Carlos Pérez-Arriaga, Jorge Octavio Ocharán-Hernández, Ángel J. Sánchez-García
Distributed Ledger Technologies (DLT) open new opportunities for data protection since they bring decentralization and sovereignty over the ownership of data. On the practical side, implementations of this technology are limited. Due to its novelty, the fundamentals for their design and development are still emerging. An essential feature of DLT is the consensus mechanism, which is used so that the members of the DLT network validate and append data to the ledger. The design decision of which mechanism to use will significantly impact the functionality of the system. Therefore, it is a decision that cannot be taken lightly. Software engineers, developers, and other practitioners in the field can use the knowledge and understanding of the implementations that already exist to help with these decisions. This paper presents the results of a multivocal literature review carried out to identify the consensus mechanisms used in DLT for the protection of confidential data. Twenty-seven studies were selected; in those studies, twenty-one different consensus mechanisms were identified. The review showcases the mechanisms that have been identified and their contexts, alongside a discussion of their relevance and characteristics.
Md Shohel Khan, Ajoy Kanti Das, Md. Shohrab Hossain, Husnu S. Narman
The impact of global transformation due to mosquito-borne diseases like dengue is noticeable and according to the World Health Organization, approximately 96 million people are infected by dengue per year. Moreover, the climate of tropical countries, e.g., Bangladesh is highly in favor of dengue. The initiatives taken by different organizations every year are not enough to face the challenges of dengue. To mitigate the effect of dengue, we propose a distributed crowdsourcing framework, the Dengue Tracker System in which the infected patients and the conscious citizen can submit the possible infectious locations. With the submitted data, two separate heatmaps can be generated so that the people and the concerned authority can get ready to face the challenges of dengue. Moreover, the system is deployed on the Ethereum-blockchain to enhance the security of the system. To prevent fake location data, different token generation methods are implemented.
Edoardo Puggioni, Arash Shaghaghi, Robin Doss, Salil S. Kanhere
Internet of Things (IoT) devices are being deployed in huge numbers around the world, and often present serious vulnerabilities. Accordingly, delivering regular software updates is critical to secure IoT devices. Manufactures face two predominant challenges in providing software updates to IoT devices: 1) scalability of the current client-server model and 2) integrity of the distributed updates - exacerbated due to the devices' computing power and lightweight cryptographic primitives. Motivated by these limitations, we propose CrowdPatching, a blockchain-based decentralized protocol, allowing manufacturers to delegate the delivery of software updates to self-interested distributors in exchange for cryptocurrency. Manufacturers announce updates by deploying a smart contract (SC), which in turn will issue cryptocurrency payments to any distributor who provides an unforgeable proof-of-delivery. The latter is provided by IoT devices authorizing the SC to issue payment to a distributor when the required conditions are met. These conditions include the requirement for a distributor to generate a zero-knowledge proof, generated with a novel proving system called zk-SNARKs. Compared with related work, CrowdPatching protocol offers three main advantages. First, the number of distributors can scale indefinitely by enabling the addition of new distributors at any time after the initial distribution by manufacturers (i.e., redistribution among the distributor network). The latter is not possible in existing protocols and is not account for. Secondly, we leverage the recent common integration of gateway or Hub in IoT deployments in our protocol to make CrowdPatching feasible even for the more constraint IoT devices. Thirdly, the trustworthiness of distributors is considered in our protocol, rewarding the honest distributors' engagements. We provide both informal and formal security analysis of CrowdPatching using Tamarin Prover.
Edoardo Puggioni, Arash Shaghaghi, Robin Doss, Salil S. Kanhere
We propose CrowdPatching, a blockchain-based decentralized protocol, allowing\nInternet of Things (IoT) manufacturers to delegate the delivery of software\nupdates to self-interested distributors in exchange for cryptocurrency.\nManufacturers announce updates by deploying a smart contract (SC), which in\nturn will issue cryptocurrency payments to any distributor who provides an\nunforgeable proof-of-delivery. The latter is provided by IoT devices\nauthorizing the SC to issue payment to a distributor when the required\nconditions are met. These conditions include the requirement for a distributor\nto generate a zero-knowledge proof, generated with a novel proving system\ncalled zk-SNARKs. Compared with related work, CrowdPatching protocol offers\nthree main advantages. First, the number of distributors can scale indefinitely\nby enabling the addition of new distributors at any time after the initial\ndistribution by manufacturers (i.e., redistribution among the distributor\nnetwork). The latter is not possible in existing protocols and is not account\nfor. Secondly, we leverage the recent common integration of gateway or Hub in\nIoT deployments in our protocol to make CrowdPatching feasible even for the\nmore constraint IoT devices. Thirdly, the trustworthiness of distributors is\nconsidered in our protocol, rewarding the honest distributors' engagements. We\nprovide both informal and formal security analysis of CrowdPatching using\nTamarin Prover.\n
Large commercial buildings are complex cyber-physical systems containing expensive and critical equipment that ensure the safety and comfort of their numerous occupants. Yet occupant and visitor access to spaces and equipment within these buildings are still managed through unsystematic, inefficient, and human-intensive processes. As a standard practice, long-term building occupants are given access privileges to rooms and equipment based on their organisational roles, while visitors have to be escorted by their hosts. This approach is conservative and inflexible. In this paper, we describe a methodology that can flexibly and securely manage building access privileges for long-term occupants and short-term visitors alike, taking into account the risk associated with accessing each space within the building. Our methodology relies on blockchain smart contracts to describe, grant, audit, and revoke fine-grained permissions for building occupants and visitors, in a decentralised fashion. The smart contracts are specified through a process that leverages the information compiled from Brick and BOT models of the building. We illustrate the proposed method through a typical application scenario in the context of a real office building and argue that it can greatly reduce the administration overhead, while, at the same time, providing fine-grained, auditable access control.CCS Concepts: Security and privacy; Security services; Computer systems organisation; Embedded and cyber-physical systems; Sensors and actuators
Owing to its benefits such as flexibility, scalability, and interoperability, Software-Defined Networking (SDN) has been incorporated into Internet of Vehicles (IoV) to cope with the increasing demands of vehicular applications. The integration of SDN and IoV, namely SDN-IoV, can enrich many new applications for intelligent transportation such as traffic monitoring, smart navigation, and self-driving. The spatial crowdsourcing technology has been adopted as an effective data collection and processing method that is the premise of various SDN-IoV applications. However, as huge amounts of data are generated in spatial crowdsourcing services, the data privacy and security has become a key challenge for SDN-IoV. To overcome abovementioned challenge, a Deep Reinforcement Learning (DRL) and Blockchain empowered Spatial Crowdsourcing System (DB-SCS) is proposed. In DB-SCS, we design an improved multi-blockchain structure and a blockchain-based hierarchical task management method, which divide the spatial tasks into different categories according to the privacy requirements and the areas of the task and then decompose different categories of tasks and task receivers into sub-blockchains. While guaranteeing the data privacy, DB-SCS can also enhance the spatial crowdsourcing performance by using the proposed DRL-based management strategy to dynamically select the consensus algorithm, block size, and block generation rule. Extensive simulation experiments demonstrate that the DB-SCS can obtain high throughput, low overhead, and data privacy under various SDN-IoV scenarios.
By training a machine learning algorithm across multiple decentralized edge nodes, federated learning (FL) ensures the privacy of the data generated by the massive Internet-of-Things (IoT) devices. To economically encourage the participation of heterogeneous edge nodes, a transparent and decentralized trading platform is needed to establish a fair market among distinct edge companies. In this article, we propose a hybrid blockchain-based resource trading system that combines the advantages of both public and consortium blockchains. We design and implement a smart contract to facilitate an automatic, autonomous, and auditable rational reverse auction mechanism among edge nodes. Moreover, we leverage the payment channel technique to enable credible, fast, low-cost, and high-frequency payment transactions between requesters and edge nodes. Simulation results show that the proposed reverse auction mechanism can achieve the properties, including budget feasibility, truthfulness, and computational efficiency.
Since its emergence, blockchain technology has received great attention because of its advantages in terms of decentralization, transparency, traceability, and the ability to be tamper proof. These advantages help blockchain become a better option for fields, such as digital currency and information storage. Specifically, consortium blockchain is preferred by researchers because it provides a certain degree of access control and a supervisory mechanism. However, in real-world applications, blockchain platforms pervasively show bottlenecks, such as ultrahigh energy consumption, time inefficiency, low transaction throughput, vulnerability to targeted attacks, and poor fairness of user profits, which seriously influence the performance of this technology and thus hinder its development and adoption. In this article, we try to enhance the performance of blockchain platforms by optimizing the quality of its core module, known as the consensus algorithm. To do so, we introduce proof of assets and proof of reputation to design a voting-based decentralized consensus (VDC) algorithm for consortium blockchain. Combined with the verifiable random function (VRF), VDC realizes better fairness of user profits and time efficiency with acceptable energy consumption and without sacrificing security. The simulation results show that the proposed algorithm achieves a faster consensus process and better user fairness than existing algorithms while still maintaining a negligible energy cost and adequate security.
To achieve software crowdsourcing, it is significant to develop a suitable framework for software development and quality control to motivate workers to participate and well-perform in crowdsourcing. Although the existing works have achieved some positive results, they do not achieve secure and efficient software crowdsourcing due to the “trust” and “efficiency” issues. For example, the existing centralized software crowdsourcing suffers from lack of trust, low reliability, and high costs. We first exploit blockchain technologies for software crowdsourcing and propose a blockchain-empowered decentralized framework for software development and quality control to achieve secure and efficient software crowdsourcing. Then we describe and analyze the blockchain-empowered decentralized framework for software crowdsourcing in detail. Ultimately, we analyze the security and efficiency performance of the proposed blockchain-empowered decentralized crowdsourcing framework.
Sadaf MD Halim, Latifur Khan, Bhavani Thuraisingham
Mobile devices are a rich source of sensitive location data. In this paper, we propose a method for harnessing this data to provide better location predictions without sacrificing the privacy of the users generating this data. To this end, we propose utilizing Federated Learning to train locally on a user's mobile device, while simultaneously identifying and combatting the possibility of bad actors or adversaries that may deliberately report problematic data to hurt the training process. Furthermore, we propose using a blockchain instead of a centralized server for the training process, to ensure that the process is secure.
Motivated by the increasingly powerful computing capabilities of end-user equipment, and by the growing privacy concerns over sharing sensitive raw data, a distributed machine learning paradigm known as federated learning (FL) has emerged. By training models locally at each client and aggregating learning models at a central server, FL has the capability to avoid sharing data directly, thereby reducing privacy leakage. However, the conventional FL framework relies heavily on a single central server, and it may fail if such a server behaves maliciously. To address this single point of failure, in this work, a blockchain-assisted decentralized FL framework is investigated, which can prevent malicious clients from poisoning the learning process, and thus provides a self-motivated and reliable learning environment for clients. In this framework, the model aggregation process is fully decentralized and the tasks of training for FL and mining for blockchain are integrated into each participant. Privacy and resource-allocation issues are further investigated in the proposed framework, and a critical and unique issue inherent in the proposed framework is disclosed. In particular, a lazy client can simply duplicate models shared by other clients to reap benefits without contributing its resources to FL. To address these issues, analytical and experimental results are provided to shed light on possible solutions, i.e., adding noise to achieve local differential privacy and using pseudo-noise (PN) sequences as watermarks to detect lazy clients.
To support the increasing computation-intensive applications in the Internet of Things (IoT), edge computing is introduced to provide mobile devices computing resources for performing low-latency tasks. Therefore, how to design an effective and secure computing resource allocation mechanism is attracting increasing attention. A lot of works have been done to design an effective computational resource market for IoT, but the problems of vulnerability and inefficiency still exist. In this article, we propose a two-level Stackelberg game-based computing resource trading mechanism for mobile IoT devices with a credit-based payment approach, which is implemented by smart contracts on blockchain. In our model, the Stackelberg game consists of two levels, i.e., leader-level and user-level. In the leader-level, the computing service provider (CSP) and its agent constitute a composite leader. The agent purchases computing resource from CSP on credit and acts as a broker among leader-level and user-level reselling these computing resources to users. In the user-level, every user experiences social externality, which means users are interdependent. The leader-level subgame makes credit payment easier by making loaning and trading become a joint credit payment. The user-level subgame makes the market more active and closer to reality by introducing social externality. Besides, smart contracts can prevent malicious behaviors such as delay payment. We also conduct equilibrium analysis and prove the existence and uniqueness of the Nash equilibrium in our Stackelberg game-based model. Finally, we conduct numerical experiments to evaluate the cost of smart contracts and the performance of each entity with the proposed pricing mechanism.
Device failure detection is one of most essential problems in Industrial Internet of Things (IIoT). However, in conventional IIoT device failure detection, client devices need to upload raw data to the central server for model training, which might lead to disclosure of sensitive business data. Therefore, in this article, to ensure client data privacy, we propose a blockchain-based federated learning approach for device failure detection in IIoT. First, we present a platform architecture of blockchain-based federated learning systems for failure detection in IIoT, which enables verifiable integrity of client data. In the architecture, each client periodically creates a Merkle tree in which each leaf node represents a client data record, and stores the tree root on a blockchain. Furthermore, to address the data heterogeneity issue in IIoT failure detection, we propose a novel centroid distance weighted federated averaging (CDW_FedAvg) algorithm taking into account the distance between positive class and negative class of each client data set. In addition, to motivate clients to participate in federated learning, a smart contact-based incentive mechanism is designed depending on the size and the centroid distance of client data used in local model training. A prototype of the proposed architecture is implemented with our industry partner, and evaluated in terms of feasibility, accuracy, and performance. The results show that the approach is feasible, and has satisfactory accuracy and performance.
Antonio Bordonaro, Alessandra De Paola, Giuseppe Lo Re, Marco Morana
The main goal of Ambient Intelligence (AmI) is to support users in their daily activities by satisfying and anticipating their needs. To achieve such goal, AmI systems rely on physical infrastructures made of heterogenous sensing devices which interact in order to exchange information and perform monitoring tasks. In such a scenario, a full achievement of AmI vision would also require the capability of the system to autonomously check the status of the infrastructure and supervise its maintenance. To this aim, in this paper, we extend some previous works in order to allow the self-management of AmI devices enabling them to directly interact with maintenance service providers. In particular, the combination of smart contracts and blockchains enables AmI systems to autonomously communicate with untrusted entities and complete secure transactions without the brokering of a trusted third party. The proposed approach has been adopted to design a sample AmI application capable of managing requests from faulty devices in a Smart home.
Paulo Valente Klaine, Lei Zhang, Bingpeng Zhou, Yao Sun · 6 authors
Due to the number of confirmed cases and casualties of the new COVID-19 virus diminishing day after day, several countries around the world are discussing how to return to the new normal way of life. In order to keep the spread of the disease under control and avoid a second wave of infection, one alternative being considered is the utilization of contact tracing. However, despite several alternatives being available, contact tracing still faces issues in terms of maintaining user privacy and security, making its mass adoption quite difficult. Based on that, a novel framework for contact tracing using blockchain as its infrastructure is presented. By integrating blockchain with contact tracing applications, user privacy can be guaranteed, while also providing people and government bodies with a complete public view of all confirmed cases. Moreover, we also investigate how public locations can aid in the contact tracing process by measuring the risk of exposure to COVID-19 to the general public and advertising it in a blockchain. By doing so, these locations can effectively report potential infection risks, while also guaranteeing privacy and trustworthiness in the information. Lastly, numerical results are shown in different scenarios and conclusions are drawn.