Carmen-Ionela ROTUNĂ, Alexandru Gheorghiță, Alin Zamfiroiu, Dragos-Marian Smada
With cities' continued growth and development, the implementation of incorporated, compatible, secure online services may improve the level of life for urban residents. Governmental organizations are aware of how digital currencies might be used to solve social challenges.. The concept of block-chain, initially associated with the digital currency bitcoin, offers a novel approach to the organization of smart-cities and a more transparent economic framework for the management of resources. The present research endeavors to examine the prospective advantages of utilizing block-chain-based services to facilitate the expansion of smart cities. Additionally, a Smart-city ecosystem model is suggested, which is founded on smart contracts established between firms, inhabitants, and government agencies. Additionally, it provides a synopsis of the potential application domains of the technology. The results of this study could potentially facilitate the implementation of blockchain technology as a means of facilitating interactions and transactions within the public sector at a regional level.
In the IoT (Internet of Things) environment, smart homes, smart grids, and telematics constantly generate data with complex attributes. These data have low heterogeneity and poor interoperability, which brings difficulties to data management and value mining. The promising combination of blockchain and the Internet of things as BCoT (blockchain of things) can solve these problems. This paper introduces an innovative method DCOMB (dual combination Bloom filter) to firstly convert the computational power of bitcoin mining into the computational power of query. Furthermore, this article uses the DCOMB method to build blockchain-based IoT data query model. DCOMB can implement queries only through mining hash calculation. This model combines the data stream of the IoT with the timestamp of the blockchain, improving the interoperability of data and the versatility of the IoT database system. The experiment results show that the random reading performance of DCOMB query is higher than that of COMB (combination Bloom filter), and the error rate of DCOMB is lower. Meanwhile, both DCOMB and COMB query performance are better than MySQL (My Structured Query Language).
Cloud computing is an information technology that enables different users to access a shared pool of configurable system resources and different services without physically acquiring them. Most industries nowadays such as banking, healthcare and education are migrating to the cloud due to its efficiency of services especially when it comes to data security and integrity. Cloud platforms encounter numerous challenges such as Data deduplication, Data Transmission, Data Integrity, VM Security, Data Availability, Bandwidth usage… etc. In this paper we have adopted the Blockchain technology - which is a relatively new technology - that emerged for the first time as the cryptocurrency Bitcoin and proved its efficiency in securing data and assuring data integrity. It is mostly a distributed public ledger that holds transactions data in case of Bitcoin. In our work blockchains are adopted in a different way than its regular use in bitcoin. Three of the major challenges in Cloud Computing and Cloud services are Data Deduplication, Storage and Bandwidth usage are discussed in this paper.
With Industry 4.0, IT infrastructure has started to be used more effectively in the manufacturing sector. Cyber physical systems, IoT, cloud manufacturing, big data are some of the technologies that make up the concept of Industry 4.0. These technologies have solved many problems in the manufacturing sector. One of these technologies, cloud manufacturing technology, has emerged with the idea of pay as you go. This technology has enabled manufacturing resources to be leased and shared on a global scale. However, it has problems arising from its central structure and the need for a reliable 3rd party. Reliability, security, continuity, scalability, data lock-in, single point failure, data manipulation are some of the main problems. Blockchain (BC) is a decentralized and distributed technology. The data stored on the BC network cannot be altered in any way. With these features, we believe that BC-supported cloud manufacturing systems can overcome the aforementioned problems and eliminates the need for a reliable 3rd party. Based on this belief, in this study the agreements and communication between the resource provider and the customer, which is one of the basic functions of cloud manufacturing platforms, are realized with a decentralized application using BC-based smart contracts (SCs). The designed application is called the decentralized cloud manufacturing application (DCMApp). DCMApp does not operate on a fully public BC network, it has a hybrid structure and uses the Ethereum network as a public BC network. These features make DCMApp different from other BC-based cloud manufacturing applications. DCMApp's hybrid structure has enabled more transparent, economic and safe manufacturing agreements. It is also possible to store agreements on the BC network at a low cost without installing any server infrastructure. The use of Ethereum network makes it almost impossible to manipulate agreements.
In this article, we study the pricing and resource management in the Internet of Things (IoT) system with blockchain-as-a-service (BaaS) and mobile-edge computing (MEC). The BaaS model includes the cloud-based server to perform blockchain tasks and the set of peers to collect data from local IoT devices. The MEC model consists of the set of terrestrial and aerial base stations (BSs), i.e., unmanned aerial vehicles (UAVs), to forward the tasks of peers to the BaaS server. Each BS is also equipped with an MEC server to run some blockchain tasks. As the BSs can be privately owned or controlled by different operators, there is no information exchange among them. We show that the resource management and pricing in the BaaS-MEC system are modeled as a stochastic Stackelberg game with multiple leaders and incomplete information about actions of leaders/BSs and followers/peers. We formulate a novel hierarchical reinforcement learning (RL) algorithm for the decision makings of BSs and peers. We also develop an unsupervised hierarchical deep learning (HDL) algorithm that combines deep $Q$ -learning (DQL) for BSs with the Bayesian deep learning (BDL) for peers. We prove that the proposed algorithms converge to stable states in which the peers' actions are the best responses to optimal actions of BSs.
Jie Feng, F. Richard Yu, Qingqi Pei, Xiaoli Chu · 6 authors
Mobile-edge computing (MEC) is a promising paradigm to improve the quality of computation experience of mobile devices because it allows mobile devices to offload computing tasks to MEC servers, benefiting from the powerful computing resources of MEC servers. However, the existing computation-offloading works have also some open issues: 1) security and privacy issues; 2) cooperative computation offloading; and 3) dynamic optimization. To address the security and privacy issues, we employ the blockchain technology that ensures the reliability and irreversibility of data in MEC systems. Meanwhile, we jointly design and optimize the performance of blockchain and MEC. In this article, we develop a cooperative computation offloading and resource allocation framework for blockchain-enabled MEC systems. In the framework, we design a multiobjective function to maximize the computation rate of MEC systems and the transaction throughput of blockchain systems by jointly optimizing offloading decision, power allocation, block size, and block interval. Due to the dynamic characteristics of the wireless fading channel and the processing queues at MEC servers, the joint optimization is formulated as a Markov decision process (MDP). To tackle the dynamics and complexity of the blockchain-enabled MEC system, we develop an asynchronous advantage actor–critic-based cooperation computation offloading and resource allocation algorithm to solve the MDP problem. In the algorithm, deep neural networks are optimized by utilizing asynchronous gradient descent and eliminating the correlation of data. The simulation results show that the proposed algorithm converges fast and achieves significant performance improvements over existing schemes in terms of total reward.
Turki Ali Alghamdi, Ishtiaq Ali, Nadeem Javaid, Muhammad Shafiq
The Internet of Things (IoT) industry is growing very fast to transform factories, homes, farms and practically everything else to make them efficient and intelligent. IoT is applied in different resilient scenarios and applications. IoT faces lots of challenges due to lack of computational power, battery and storage resources. Fortunately, the rise of blockchain technology facilitates IoT in many security solutions. Using blockchain, communication between IoT and emerging computing technologies is made efficient. In this work, we propose a secure service provisioning scheme with a fair payment system for Lightweight Clients (LCs) based on blockchain. Furthermore, an incentive mechanism based on reputation is proposed. We use consortium blockchain with the Proof of Authority (PoA) consensus mechanism. Furthermore, we use Smart Contracts (SCs) to validate the services provided by the Service Providers (SPs) to the LCs, transfer cryptocurrency to the SPs and maintain the reputation of the SPs. Moreover, the Keccak256 hashing algorithm is used for converting the data of arbitrary size to the hash of fixed size. AES128 encryption technique is used to encrypt service codes before sending to the LCs. The simulation results show that the LCs receive validated services from the SPs at an affordable cost. The results also depict that the participation rate of SPs is increased because of the incentive mechanism.
The Internet of Things (IoT) is gradually becoming mature and has already entered our daily life, which interconnects more machines and makes communication more convenient and more intelligent. Massive IoT devices produce innumerable data which need to be analyzed in joint cloud computation (JointCloud) with diversified services. However, due to the weak security of IoT devices, the existing JointCloud architecture hardly provides a secure trusted trade environment for users, which affects severely the application in the IoT network. In this article, we propose a hierarchical trust networking architecture based on permissioned blockchain to implement JointCloud (HTJC). The proposed Hyperledger fabric-based architecture has a better performance than those based on Ethereum in latency. By introducing the credit bonus-penalty strategy (CBPS), HTJC can solve the trust problem and provide users with a secure trusted trade environment. The availability of the proposed architecture is evaluated and compared to the existing models. The numerical results show that the HTJC can defend distributed denial-of-service (DDoS) attacks and provide users with a trusted and effective trade platform.
Blockchain, Software Defined Networking (SDN) and the Internet of Things (IoT) are the leading emerging technology all over the world. Blockchain is the main contributor to Bitcoin which aims to ignore the third party among the proceedings. On the contrary, the aims of the IoT network is connected with a massive number of objects over the internet. For smart cities, a distributed secure Blockchain-based SDN-IoT architecture has been proposed in this research paper with Network Function Virtualization (NFV) implementation. We have also revolved around the blockchain network which is a highly secured technology that provides good results in network performances, adaptability, privacy, security, accessibility, versatility, confidentiality, and efficiency. Moreover, we have presented a cluster head selection algorithm which aim is to select the desired head of the clusters. In addition, an approach has been also developed that is more efficient for generating a cluster head utilizing SDN-IoT. SDN-IoT with the NFV conception bring in benefits to the associated areas in terms of energy preservation and load equilibrating. In the projected architecture, the Blockchain network allows more prominent security of each network layer relative to the schematic network.
In recent years, the emergence of blockchain technology (BT) has become a unique, most disruptive, and trending technology. The decentralized database in BT emphasizes data security and privacy. Also, the consensus mechanism in it makes sure that data is secured and legitimate. Still, it raises new security issues such as majority attack and double-spending. To handle the aforementioned issues, data analytics is required on blockchain based secure data. Analytics on these data raises the importance of arisen technology Machine Learning (ML). ML involves the rational amount of data to make precise decisions. Data reliability and its sharing are very crucial in ML to improve the accuracy of results. The combination of these two technologies (ML and BT) can provide highly precise results. In this paper, we present a detailed study on ML adoption for making BT-based smart applications more resilient against attacks. There are various traditional ML techniques, for instance, Support Vector Machines (SVM), clustering, bagging, and Deep Learning (DL) algorithms such as Convolutional Neural Network (CNN) and Long short-term memory (LSTM) can be used to analyse the attacks on a blockchain-based network. Further, we include how both the technologies can be applied in several smart applications such as Unmanned Aerial Vehicle (UAV), Smart Grid (SG), healthcare, and smart cities. Then, future research issues and challenges are explored. At last, a case study is presented with a conclusion.
While the large-scale Internet of Things (IoT) makes many new applications feasible, like Smart Cities, IoT also brings new concerns on data reliability, security, and privacy. The rapid evolution in blockchain technologies, which relied on a decentralized, immutable and distributed ledger system for transaction data auditing, provides a prospective solution to address the issues in IoT. The blockchain and smart contract enabled security mechanism for IoT applications have attracted increasing interests from both academia and industry. However, integrating cryptocurrency-oriented blockchain technologies into IoT systems meets tremendous challenges on scalability, storage capacity, security, and privacy. Particularly, the performance of blockchain networks significantly relies on the performance of consensus mechanisms, e.g., in terms of data confidentiality, transaction throughput, and network scalability. In this chapter, given an in-depth review of state-of-the-art blockchain networks, the key matrix of designing consensus mechanism for IoT networks are identified in terms of throughput, scalability, and security. To demonstrate a case study on designing scalable, lightweight blockchain protocols for IoT systems, a Microchain framework is introduced and a proof-of-concept prototype is implemented in a physical network environment. The experimental results verify the feasibility of integrating the Microchain into IoT systems.
This paper presents the design and implementation of a smart home system in the context of Internet of Things (IoT) with Ethereum private Blockchain, Raspberry Pi, Blynk platform, DHT11 temperature and humidity sensors. By Raspberry Pi, it collects real-time room humidity and temperature information by DHT11. The data is then uploaded to the Blynk App, which is stored on the smart contract deployed with the Ethereum private Blockchain. When the real-time humidity or temperature value exceeds a predefined threshold value, warnings are given by turning on LEDs. The system functions as a proof-of-concept prototype, showing the feasibility of applying blockchain in smart homes with IoT functionalities.
Roben Castagna Lunardi, Henry Cabral Nunes, Vinicius da Silva Branco, Bruno Hugentobler Lipper · 6 authors
Blockchain emerged as a solution for data integrity, non-repudiation, and availability in different applications. Data sensitive scenarios, such as Health Care, can also benefit from these blockchain properties. Consequently, different research proposed the adoption of blockchain in Health Care applications. However, few are discussed about incentive methods to attract new users, as well as to motivate the system or application usage by existing end-users. Also, little is discussed about performance during code execution in blockchains. In order to tackle these issues, this work presents the preliminary evaluation of TokenHealth, an application for collaborative health practice monitoring with gamification and token-based incentives. The proposed solution is implemented through smart contracts using Solidity in the Ethereum blockchain. We evaluated the performance of both in Ropsten test network and in a Private instance. The preliminary results show that the execution of smart contracts takes less than a minute for a full cycle of different smart contracts. Also, we present a discussion about costs for using a Private instance and the public Ethereum main network.
Justice Odoom, Richlove Samuel Soglo, Samuel Akwasi Danso, Huang Xiao-fang
It is undisputable fact that Coronavirus pandemic will go into the annals of history as one of the devastating plagues. From the healthcare perspective, a lot of efforts are underway geared towards testing, management and vaccination whereas industry and research communities explore innovative solutions. Quite a number of solutions have emerged zooming in on contact tracing, combating misinformation, data aggregation and analysis as well as test result certification with blockchain technology been the core. Aside the reliance on centralized architectures and use of permissioned/consortium blockchain, conspicuously missing in existing solutions based on blockchain is the work around the immutability feature of the technology given the fact that a person's test result is not static but dynamic. In this paper, we propose a solution using blockchain and smart contract that allows for state changes to be made by authorized entities. We leverage distributed storage technology using InterPlanetary File System (IPFS) for storage of user encrypted records and subsequent retrieval for verification purposes. We extend our solution by incorporating vaccination status to provide comprehensive source of information and show proof of concept. The full code of our proposed solution is made publicly available on GitHub.
Blockchain software development is becoming more and more important for any modern software developer and IT startup. Nonetheless, blockchain software production still lacks a disciplined, organized and mature development process, as demonstrated by the many and (in)famous failures and frauds occurred in recent years. In this paper we present ABCDE, a complete method addressing blockchain software development. The method considers the software integration among the blockchain components—smart contracts, libraries, data structures—and the out-of-chain components, such as web or mobile applications, which all together constitute a complete DApp system. We advocate for ABCDE the use of agile practices, because these are suited to develop systems whose requirements are not completely understood since the beginning, or tend to change, as it is the case of most blockchain-based applications. ABCDE is based on Scrum, and is therefore iterative and incremental. From Scrum, we kept the requirement gathering with user stories, the iterative-incremental approach, the key roles, and the meetings. The main difference with Scrum is the separation of development activities in two flows—one for smart contracts and the other for out-of-chain software interacting with the blockchain—each performed iteratively, with integration activities every 2–3 iterations. ABCDE makes explicit the activities that must be performed to design, develop, test and integrate smart contracts and out-of-chain software, and documents the smart contracts using formal diagrams to help development, security assessment, and maintenance. A diagram derived from UML class diagram helps to effectively model the data structure of smart contracts, whereas the exchange of messages between the entities of the system is modeled using a modified UML sequence diagram. The proposed method has also specific activities for security assessment and gas optimization, through systematic use of patterns and checklists. ABCDE focuses on Ethereum blockchain and its Solidity language, but preserves generality and with proper modifications might be applied to any blockchain software project. ABCDE method is described in detail, and an example is given to show how to concretely implement the various development steps.
Zhaofeng Ma, Lingyun Wang, Wang Xiaochang, Zhen Wang · 5 authors
With the fast development of Internet-of-Things (IoT) technologies, IoT big data and its applications are getting more and more useful. However, traditional IoT data management is fragile and vulnerable. Once the gathered data are untrusted or the stored data are tampered with deliberately from the internal users or attacked by an external hacker, then the tampered data have a serious problem to be utilized. To solve the problems of trust and security of IoT big data management, in this article, we propose a permissioned blockchain-based decentralized trust management and secure usage control scheme of IoT big data (called BlockBDM), upon which all the data operations and management, such as data gathering, invoking, transfer, storage, and usage, are processed over the blockchain smart contract. To encourage the IoT client to supply high-quality content, in our scheme, we design public-blockchain-based tokens reward mechanism for the high-quality data supply contribution. All the data processing and usage procedure can be recorded in a cryptography-signed and Merkle tree-based transaction(s) and block(s) with high-level security in a global and distributed ledger with tamper resistance. For data utilization and consumption, we propose secure usage control for digital rights management and token-based data consumption approach of high-value data from being violated or spread without any limitation. We implemented the BlockBDM scheme based on public and permissioned blockchain for IoT big data management. Finally, a large amount of evaluation manifests that the proposed BlockBDM scheme is feasible, secure, and scalable for decentralized trust management of IoT big data.
We consider a public blockchain realized in the mobile edge computing (MEC) network, where the blockchain miners compete against each other to solve the proof-of-work puzzle and win a mining reward. Due to limited computing capabilities of their mobile terminals, miners offload computations to the MEC servers. The MEC servers are maintained by the service provider (SP) that sells its computing resources to the miners. The SP aims at maximizing its long-term profit subject to miners' budget constraints. The miners decide on their hash rates, i.e., computing powers, simultaneously and independently, to maximize their payoffs without revealing their decisions to other miners. As such, the interactions between the SP and miners are modeled as a stochastic Stackelberg game under private information, where the SP assigns the price per unit hash rate, and miners select their actions, i.e., hash rate decisions, without observing actions of other miners. We develop a hierarchical learning framework for this game based on fully- and partially-observable Markov decision models of the decision processes of the SP and miners. We show that the proposed learning algorithms converge to stable states in which miners' actions are the best responses to the optimal price assigned by the SP.
Md Ashraf Uddin, Andrew Stranieri, Iqbal Gondal, Venki Balasurbramanian
The Internet of Things (IoT) has facilitated services without human intervention for a wide range of applications, including underwater monitoring, where sensors are located at various depths, and data must be transmitted to surface base stations for storage and processing. Ensuring that data transmitted across hierarchical sensor networks are kept secure and private without high computational cost remains a challenge. In this paper, we propose a multilevel sensor monitoring architecture. Our proposal includes a layer-based architecture consisting of Fog and Cloud elements to process and store and process the Internet of Underwater Things (IoUT) data securely with customized Blockchain technology. The secure routing of IoUT data through the hierarchical topology ensures the legitimacy of data sources. A security and performance analysis was performed to show that the architecture can collect data from IoUT devices in the monitoring region efficiently and securely.
Fog computing makes up for the shortcomings of cloud computing. It brings many advantages, but various peculiarities must be perceived, such as security, resource management, storage, and other features at the same time. This paper investigates the resource contribution model between the fog node and cloud or users when fog computing introduces blockchain. The proposed model practices the reward and punishment mechanism of the blockchain to boost the fog nodes to contribute resources actively. The behavior of the fog node in contributing resources and the completion degree of the task also for contributing resources are packaged into blocks and stored in the blockchain system to form a transparent, open, and tamper-free service evaluation index. The differential game method is employed to model and solve the above process and address the interaction between the optimal resource contribution strategy of the fog node and the optimal benefit under the optimal resource contribution strategy. Indirectly, this service evaluation index also brings long-term economic benefits to fog service providers. Besides, taking advantage of the performance characteristics of the collective maintenance of blockchain and the ability to establish a credible consensus mechanism in an untrusted environment, fog computing nodes, under the proposed architecture, can have specific security protection capabilities.