A novel cooperative link selection mechanism for enhancing the robustness in scale-free IoT networks
- Authors: Khan, Muhammad , Javaid, Nadeem , Javaid, Sakeena , Khalid, Adia , Nasser, Nidal , Imran, Muhammad
- Date: 2020
- Type: Text , Conference paper
- Relation: 16th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2020, Limassol, Cyrprus, 15 to 19 June 2020, 2020 International Wireless Communications and Mobile Computing, IWCMC 2020 p. 2222-2227
- Full Text: false
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- Description: In today's world, Internet of Things (IoT) helps people in many fields by enabling smart city projects in health monitoring, smart parking, industrial optimization, home energy management, etc. Daily life objects are connected with the Internet to allow access to their owners to keep an eye on their surroundings. The IoT network is comprised of nodes that are smart enough to perform any function and provide benefits to the people. However, any fault in the network opens up the risk of leaking personal information. The aim is to develop a scale-free network, which controls the effects of malicious attacks and consequently improves the network robustness. In this paper, our prime focus is to mitigate the effect of malicious nodes by providing a robust strategy to maintain the network stability. In this regard, we propose a topology named as a Cooperation based Edge Swap (CES) for improving the network robustness in the scale-free network. The CES uses the edge/link selection mechanism by involving the cooperation using a Rayleigh fading to swap the network topology for improving the network robustness. The simulations' outcome depicts the performance of the CES in terms of improving the network robustness. © 2020 IEEE.
A reconfigurable method for intelligent manufacturing based on industrial cloud and edge intelligence
- Authors: Tang, Hao , Li, Di , Wan, Jiafu , Imran, Muhammad , Shoaib, Muhammad
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Internet of Things Journal Vol. 7, no. 5 (2020), p. 4248-4259
- Full Text: false
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- Description: The development of Industry 4.0 has provided the possibility to meet frequent changes in product type and batches, a sharp decline in the delivery cycle, constraints of quality cost, and other relevant parameters of customized production mode. Intelligent manufacturing, as a core of Industry 4.0, represents a deep integration of new IT technologies, such as the industrial Internet of Things and service-oriented architecture, and manufacturing process. To realize intelligent manufacturing, this article introduces a cloud-assisted and edge-decision-making manufacturing architecture that contains a cloud and production edges. An intelligent production edge is designed to provide the traditional devices the abilities of data access and self-decision making. Besides, the proposed architecture is modeled as a multiagent system with the edge intelligence support, describing the agent-based reconfiguration mechanism from the three aspects, namely, agent interaction, agent behavior, and negotiation mechanism. The experimental results show that the reconfigurable method based on the proposed architecture can be used in the mixed-flow production scenario based on random orders, to improve the adaptability and robustness. © 2014 IEEE.
A robust consistency model of crowd workers in text labeling tasks
- Authors: Alqershi, Fattoh , Al-Qurishi, Muhammad , Aksoy, Mehmet , Alrubaian, Majed , Imran, Muhammad
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 168381-168393
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- Description: Crowdsourcing is a popular human-based model to acquire labeled data. Despite its ability to generate huge amounts of labelled data at moderate costs, it is susceptible to low quality labels. This can happen through unintentional or intentional errors by the crowd workers. Consistency is an important attribute of reliability. It is a practical metric that evaluates a crowd workers' reliability based on their ability to conform to themselves by yielding the same output when repeatedly given a particular input. Consistency has not yet been sufficiently explored in the literature. In this work, we propose a novel consistency model based on the pairwise comparisons method. We apply this model on unpaid workers. We measure the workers' consistency on tasks of labeling political text-based claims and study the effects of different duplicate task characteristics on their consistency. Our results show that the proposed model outperforms the current state-of-the-art models in terms of accuracy. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion
- Authors: Ali, Farman , El-Sappagh, Shaker , Islam, S. , Kwak, Daehan , Ali, Amjad , Imran, Muhammad , Kwak, Kyung-Sup
- Date: 2020
- Type: Text , Journal article
- Relation: Information Fusion Vol. 63, no. (2020), p. 208-222
- Full Text: false
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- Description: The accurate prediction of heart disease is essential to efficiently treating cardiac patients before a heart attack occurs. This goal can be achieved using an optimal machine learning model with rich healthcare data on heart diseases. Various systems based on machine learning have been presented recently to predict and diagnose heart disease. However, these systems cannot handle high-dimensional datasets due to the lack of a smart framework that can use different sources of data for heart disease prediction. In addition, the existing systems utilize conventional techniques to select features from a dataset and compute a general weight for them based on their significance. These methods have also failed to enhance the performance of heart disease diagnosis. In this paper, a smart healthcare system is proposed for heart disease prediction using ensemble deep learning and feature fusion approaches. First, the feature fusion method combines the extracted features from both sensor data and electronic medical records to generate valuable healthcare data. Second, the information gain technique eliminates irrelevant and redundant features, and selects the important ones, which decreases the computational burden and enhances the system performance. In addition, the conditional probability approach computes a specific feature weight for each class, which further improves system performance. Finally, the ensemble deep learning model is trained for heart disease prediction. The proposed system is evaluated with heart disease data and compared with traditional classifiers based on feature fusion, feature selection, and weighting techniques. The proposed system obtains accuracy of 98.5%, which is higher than existing systems. This result shows that our system is more effective for the prediction of heart disease, in comparison to other state-of-the-art methods. © 2020
Adversarial learning-based bias mitigation for fatigue driving detection in fair-intelligent IoV
- Authors: Han, Mingzhe , Wu, Jun , Bashir, Ali , Yang, Wu , Imran, Muhammad , Nasser, Nidal
- Date: 2020
- Type: Text , Conference paper
- Relation: 2020 IEEE Global Communications Conference, GLOBECOM 2020, Virtual, Taipei, China, 7 to 11 December 2020, 2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings
- Full Text: false
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- Description: Fatigue driving is one of main causes of traffic accidents. To avoid such traffic accidents, divers' fatigue detection has been used in Intelligent Internet of Vehicles (IIoV). IIoV usually dynamically allocate computing resources according to drivers' fatigue degree to improve the real-time of fatigue detection model. However, the traditional fatigue detection model may have bias on certain groups, which would further cause unfair resource allocation. To solve the problem, this paper proposes an improved IIoV framework, named Fair-Intelligent Internet of Vehicles (FIIoV). Compared with IIoV, we improve two layers in FIIoV, i.e., the detection layer and the normalization layer. The detection layer uses Convolutional Neural Network (CNN) to detect drivers' fatigue degree, and then uses adversarial network to achieve fairness of detection models. The normalization layer achieves the distribution of different sensitive feature values from historical detection results generated in the detection layer, and then uses the distribution to normalize the output of the detection layer to improve the fairness and accuracy of fatigue detection models. Simulation results show that both accuracy and fairness of FIIoV is improved compared with the original IIoV. © 2020 IEEE.
An application development framework for internet-of-things service orchestration
- Authors: Rafique, Wajid , Zhao, Xuan , Yu, Shui , Yaqoob, Ibrar , Imran, Muhammad , Dou, Wanchun
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Internet of Things Journal Vol. 7, no. 5 (2020), p. 4543-4556
- Full Text: false
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- Description: Application development for the Internet of Things (IoT) poses immense challenges due to the lack of standard development frameworks, tools, and techniques to assist end users in dealing with the complexity of IoT systems during application development. These challenges invoke the use of model-driven development (MDD) along with the representational state transfer (REST) architecture to develop IoT applications, supporting model generation at different abstraction levels while generating software implementation artifacts for heterogeneous platforms and ensuring loose coupling in complex IoT systems. This article proposes an IoT application development framework, named IADev, which uses attribute-driven design and MDD to address the above-mentioned challenges. This framework is composed of two major steps, including iterative architecture development using attribute-driven design and generating models to guide the transformation using MDD. IADev uses attribute-driven design to transform the requirements into a solution architecture by considering the concerns of all involved stakeholders, and then, MDD metamodels are generated to hierarchically transform the design components into the software artifacts. We evaluate IADev for a smart vehicle scenario in an intelligent transportation system to generate an executable implementation code for a real-world system. The case study experiments proclaim that IADev achieves higher satisfaction of the participants for the IoT application development and service orchestration, as compared to conventional approaches. Finally, we propose an architecture that uses IADev with the Siemens IoT cloud platform for service orchestration in industrial IoT. © 2014 IEEE.
An efficient caching policy for content retrieval in autonomous connected vehicles
- Authors: Rahim, Muddasir , Javed, Muhammad , Alvi, Alvi , Imran, Muhammad
- Date: 2020
- Type: Text , Journal article
- Relation: Transportation Research Part A: Policy and Practice Vol. 140, no. (2020), p. 142-152
- Full Text: false
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- Description: Connected vehicles will enable the smart and autonomous transportation systems in the future. Cellular Vehicle-to-Everything (C-V2X) communication will provide wireless connectivity to enable large number of connected vehicle applications. Vehicles will receive traffic and infotainment contents from the city traffic command center using C-V2X communications. In this context, infrastructure Road Side Units (RSUs) will cache urgent and popular data in their memory storage, hence providing vehicles to retrieve information from a closer vicinity at a RSU. In this paper, we present a content caching policy for the connected vehicles operator to improve the efficiency of the content retrieval in terms of download rate and delay. We propose the utility functions for the RSUs and vehicles to cache a particular content at a given RSU. Moreover, Gale-Shapley stable matching algorithm is used to efficiently allocate RSU cache to the contents. We also provide rules to update the cache slots. The proposed caching scheme is compared with random caching policy and market matching based caching policies. Results show that the proposed content caching policy improves the efficiency of the content retrieval with 60% more data transmission with reduced downloading time and better link utilization as compared to other two scheme. © 2020 Elsevier Ltd
An incentive scheme for VANETs based on traffic event validation using blockchain
- Authors: Iftikhar, Muhammad , Javaid, Nadeem , Samuel, Omaji , Shoaib, Muhammad , Imran, Muhammad
- Date: 2020
- Type: Text , Conference paper
- Relation: 16th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2020, Limassol, Cyprus, 15 to 19 June 2020, 2020 International Wireless Communications and Mobile Computing, IWCMC 2020 p. 2133-2137
- Full Text: false
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- Description: A large amount of data is involved in an effective and timely exchange of traffic information between vehicles in Vehicular Ad-hoc Networks (VANETs), which ensures efficiency and reliability. VANETs assist in sharing traffic information effectively and timely to improve traffic efficiency and reliability. However, less storage capability and selfish behavior of the vehicles are important issues that need to be tackled. Moreover, traditional storage mechanisms require the involvement of third parties, which are insecure, untrustworthy, non-transparent, and unreliable. To overcome these issues, we proposed a blockchain-based data storage scheme for VANETs by exploiting the benefits of the Interplanetary File System (IPFS), which is deployed on Road Side Units (RSUs). Furthermore, RSUs are able to receive the aggregation packet comprising of the event information acquired from the vehicles. After receiving and verifying the aggregation packet, the RSU stores the event's information in IPFS and the reputation values of vehicles in blockchain. Moreover, we proposed an incentive mechanism in this work, in which monetary incentives are given to the repliers who agree with the vehicle regarding the event information. The incentives are given by the initiator after verifying the signatures of the repliers. All the transactions involved in the incentive process are stored in blockchain. The simulation results prove the efficiency of the proposed scheme in terms of transaction cost and storage savings in VANETs. © 2020 IEEE.
An intelligent healthcare monitoring framework using wearable sensors and social networking data
- Authors: Ali, Farman , El-Sappagh, Shaker , Islam, S. , Ali, Amjad , Imran, Muhammad
- Date: 2020
- Type: Text , Journal article
- Relation: Future Generation Computer Systems Vol. 114, no. (2020), p. 23-43
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- Description: Wearable sensors and social networking platforms play a key role in providing a new method to collect patient data for efficient healthcare monitoring. However, continuous patient monitoring using wearable sensors generates a large amount of healthcare data. In addition, the user-generated healthcare data on social networking sites come in large volumes and are unstructured. The existing healthcare monitoring systems are not efficient at extracting valuable information from sensors and social networking data, and they have difficulty analyzing it effectively. On top of that, the traditional machine learning approaches are not enough to process healthcare big data for abnormality prediction. Therefore, a novel healthcare monitoring framework based on the cloud environment and a big data analytics engine is proposed to precisely store and analyze healthcare data, and to improve the classification accuracy. The proposed big data analytics engine is based on data mining techniques, ontologies, and bidirectional long short-term memory (Bi-LSTM). Data mining techniques efficiently preprocess the healthcare data and reduce the dimensionality of the data. The proposed ontologies provide semantic knowledge about entities and aspects, and their relations in the domains of diabetes and blood pressure (BP). Bi-LSTM correctly classifies the healthcare data to predict drug side effects and abnormal conditions in patients. Also, the proposed system classifies the patients’ health condition using their healthcare data related to diabetes, BP, mental health, and drug reviews. This framework is developed employing the Protégé Web Ontology Language tool with Java. The results show that the proposed model precisely handles heterogeneous data and improves the accuracy of health condition classification and drug side effect predictions. © 2020 Elsevier B.V.
An overview on smart contracts : challenges, advances and platforms
- Authors: Zheng, Zibin , Xie, Shaoan , Dai, Hong-Ning , Chen, Weili , Imran, Muhammad
- Date: 2020
- Type: Text , Journal article
- Relation: Future Generation Computer Systems Vol. 105, no. (2020), p. 475-491
- Full Text: false
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- Description: Smart contract technology is reshaping conventional industry and business processes. Being embedded in blockchains, smart contracts enable the contractual terms of an agreement to be enforced automatically without the intervention of a trusted third party. As a result, smart contracts can cut down administration and save services costs, improve the efficiency of business processes and reduce the risks. Although smart contracts are promising to drive the new wave of innovation in business processes, there are a number of challenges to be tackled. This paper presents a survey on smart contracts. We first introduce blockchains and smart contracts. We then present the challenges in smart contracts as well as recent technical advances. We also compare typical smart contract platforms and give a categorization of smart contract applications along with some representative examples. © 2019 Elsevier B.V.
Artificial noise aided scheme to secure UAV-assisted internet of things with wireless power transfer
- Authors: Wang, Qubeijian , Dai, Hong-Ning , Li, Xuran , Shukla, Mahendra , Imran, Muhammad
- Date: 2020
- Type: Text , Journal article
- Relation: Computer Communications Vol. 164, no. (2020), p. 1-12
- Full Text: false
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- Description: The proliferation of massive Internet of Things (IoT) devices poses research challenges especially in unmanned aerial vehicles(UAV)-assisted IoT. In particular, the limited battery capacity not only restricts the life time of UAV-assisted IoT but also brings security vulnerabilities since computation-complex cryptographic algorithms cannot be adopted in UAV-assisted IoT systems. In this paper, artificial noise and wireless power transfer technologies are integrated to secure communications in UAV-assisted IoT (particularly in secret key distribution). We present the artificial noise aided scheme to secure UAV-assisted IoT communications by letting UAV gateway transfer energy to a number of helpers who will generate artificial noise to interfere with the eavesdroppers while the legitimate nodes can decode the information by canceling additive artificial noise. We introduce the eavesdropping probability and the security rate to validate the effectiveness of our proposed scheme. We further formulate an eavesdropping probability constrained security rate maximization problem to investigate the optimal power allocation. Moreover, analytical and numerical results are provided to obtain some useful insights, and to demonstrate the effect of crucial parameters (e.g., the transmit power, the main channel gain) on the eavesdropping probability, the security rate, and the optimal power allocation. © 2020 Elsevier B.V.
Autonomous driving cars in smart cities : recent advances, requirements, and challenges
- Authors: Yaqoob, Ibrar , Khan, Latif , Kazmi, S. , Imran, Muhammad , Guizani, Nadra , Hong, Choong
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Network Vol. 34, no. 1 (2020), p. 174-181
- Full Text: false
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- Description: An unprecedented proliferation of autonomous driving technologies has been observed in recent years, resulting in the emergence of reliable and safe transportation services. In the foreseeable future, millions of autonomous cars will communicate with each other and become prevalent in smart cities. Thus, scalable, robust, secure, fault-tolerant, and interoperable technologies are required to support such a plethora of autonomous cars. In this article, we investigate, highlight, and report premier research advances made in autonomous driving by devising a taxonomy. A few indispensable requirements for successful deployment of autonomous cars are enumerated and discussed. Furthermore, we discover and present recent synergies and prominent case studies on autonomous driving. Finally, several imperative open research challenges are identified and discussed as future research directions. © 2019 IEEE.
Big data analytics for manufacturing internet of things: opportunities, challenges and enabling technologies
- Authors: Dai, Hong-Ning , Wang, Hao , Xu, Guangquan , Wan, Jiafu , Imran, Muhammad
- Date: 2020
- Type: Text , Journal article
- Relation: Enterprise Information Systems Vol. 14, no. 9-10 (2020), p. 1279-1303
- Full Text: false
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- Description: Data analytics in massive manufacturing data can extract huge business values while can also result in research challenges due to the heterogeneous data types, enormous volume and real-time velocity of manufacturing data. This paper provides an overview on big data analytics in manufacturing Internet of Things (MIoT). This paper first starts with a discussion on necessities and challenges of big data analytics in manufacturing data of MIoT. Then, the enabling technologies of big data analytics of manufacturing data are surveyed and discussed. Moreover, this paper also outlines the future directions in this promising area. © 2019 Informa UK Limited, trading as Taylor & Francis Group.
Big data analytics for preventive medicine
- Authors: Razzak, Muhammad , Imran, Muhammad , Xu, Guandong
- Date: 2020
- Type: Text , Journal article
- Relation: Neural Computing and Applications Vol. 32, no. 9 (2020), p. 4417-4451
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- Description: Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention. This review first introduces disease prevention and its challenges followed by traditional prevention methodologies. We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and association as well as their respective advantages, drawbacks and guidelines for selection of specific model followed by discussion on recent development and successful application of disease prevention methods. The article concludes with open research challenges and recommendations. © 2019, Springer-Verlag London Ltd., part of Springer Nature.
Big data management in participatory sensing : issues, trends and future directions
- Authors: Karim, Ahmad , Siddiqa, Aisha , Safdar, Zanab , Razzaq, Maham , Imran, Muhammad
- Date: 2020
- Type: Text , Journal article
- Relation: Future Generation Computer Systems Vol. 107, no. (2020), p. 942-955
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- Description: Participatory sensing has become an emerging technology of this era owing to its low cost in big sensor data collection. Prior to participatory sensing, large-scale deployment complexities were found in wireless sensor networks when collecting data from widespread resources. Participatory sensing systems employ handheld devices as sensors to collect data from communities and transmit to the cloud, where data are further analyzed by expert systems. The processes involved in participatory sensing, such as data collection, transmission, analysis, and visualization, exhibit certain management issues. This study aims to identify big data management issues that must be addressed at the cloud side during data processing and storing and at the participant side during data collection and visualization. It then proposes a framework for big data management in participatory sensing to resolve the contemporary big data management issues on the basis of suggested principles. Moreover, this work presents case studies to elaborate the existence of the highlighted issues. Finally, the limitations, recommendations, and future research directions for academia and industry in the domain of participatory sensing are discussed. © 2017. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Muhammad Imran” is provided in this record**
Bio-inspired network security for 5G-enabled IoT applications
- Authors: Saleem, Kashif , Alabduljabbar, Ghadah , Alrowais, Nouf , Al-Muhtadi, Jalal , Imran, Muhammad , Rodrigues, Joel
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE access Vol. 8, no. (2020), p. 1-1
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- Description: Every IPv6-enabled device connected and communicating over the Internet forms the Internet of things (IoT) that is prevalent in society and is used in daily life. This IoT platform will quickly grow to be populated with billions or more objects by making every electrical appliance, car, and even items of furniture smart and connected. The 5th generation (5G) and beyond networks will further boost these IoT systems. The massive utilization of these systems over gigabits per second generates numerous issues. Owing to the huge complexity in large-scale deployment of IoT, data privacy and security are the most prominent challenges, especially for critical applications such as Industry 4.0, e-healthcare, and military. Threat agents persistently strive to find new vulnerabilities and exploit them. Therefore, including promising security measures to support the running systems, not to harm or collapse them, is essential. Nature-inspired algorithms have the capability to provide autonomous and sustainable defense and healing mechanisms. This paper first surveys the 5G network layer security for IoT applications and lists the network layer security vulnerabilities and requirements in wireless sensor networks, IoT, and 5G-enabled IoT. Second, a detailed literature review is conducted with the current network layer security methods and the bio-inspired techniques for IoT applications exchanging data packets over 5G. Finally, the bio-inspired algorithms are analyzed in the context of providing a secure network layer for IoT applications connected over 5G and beyond networks.
Blending big data analytics : review on challenges and a recent study
- Authors: Amalina, Fairuz , Targio Hashem, Ibrahim , Azizul, Zati , Fong, Ang , Imran, Muhammad
- Date: 2020
- Type: Text , Journal article , Review
- Relation: IEEE Access Vol. 8, no. (2020), p. 3629-3645
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- Description: With the collection of massive amounts of data every day, big data analytics has emerged as an important trend for many organizations. These collected data can contain important information that may be key to solving wide-ranging problems, such as cyber security, marketing, healthcare, and fraud. To analyze their large volumes of data for business analyses and decisions, large companies, such as Facebook and Google, adopt analytics. Such analyses and decisions impact existing and future technology. In this paper, we explore how big data analytics is utilized as a technique for solving problems of complex and unstructured data using such technologies as Hadoop, Spark, and MapReduce. We also discuss the data challenges introduced by big data according to the literature, including its six V's. Moreover, we investigate case studies of big data analytics on various techniques of such analytics, namely, text, voice, video, and network analytics. We conclude that big data analytics can bring positive changes in many fields, such as education, military, healthcare, politics, business, agriculture, banking, and marketing, in the future. © 2013 IEEE.
Blockchain for cloud exchange : a survey
- Authors: Xie, Shaoan , Zheng, Zibin , Chen, Weili , Wu, Jiajing , Dai, Hong-Ning , Imran, Muhammad
- Date: 2020
- Type: Text , Journal article
- Relation: Computers and Electrical Engineering Vol. 81, no. (2020), p.
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- Description: Compared with single cloud service providers, cloud exchange provides users with lower price and flexible options. However, conventional cloud exchange markets are suffering from a number of challenges such as central architecture being vulnerable to malicious attacks and cheating behaviours of third-party auctioneers. The recent advances in blockchain technologies bring the opportunities to overcome the limitations of cloud exchange. However, the integration of blockchain with cloud exchange is still in infancy and extensive research efforts are needed to tackle a number of research challenges. To bridge this gap, this paper presents an overview on using blockchain for cloud exchange. In particular, we first give an overview on cloud exchange. We then briefly survey blockchain technology and discuss the issues on using blockchain for cloud exchange in aspects of security, privacy, reputation systems and transaction management. Finally, we present the open research issues in this promising area. © 2019
Blockchain for digital twins : recent advances and future research challenges
- Authors: Yaqoob, Ibrar , Salah, Khaled , Uddin, Mueen , Jayaraman, Raja , Omar, Mohammed , Imran, Muhammad
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Network Vol. 34, no. 5 (2020), p. 290-298
- Full Text: false
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- Description: The advent of blockchain technology can refine the concept of DTs by ensuring transparency, decentralized data storage, data immutability, and peer-to-peer communication in industrial sectors. A DT is an integrated multiphysics, multiscale, and probabilistic simulation, representation, and mirroring of a real-world physical component. The DTs help to visualize designs in 3D, perform tests and simulations virtually prior to creation of any physical component, and consequently play a vital role in sustaining and maintaining Industry 4.0. It is anticipated that DTs will become prevalent in the foreseeable future because they can be used for configuration, monitoring, diagnostics, and prognostics. This article envisages how blockchain can reshape and transform DTs to bring about secure manufacturing that guarantees traceability, compliance, authenticity, quality, and safety. We discuss several benefits of employing blockchain in DTs. We taxonomize the DTs literature based on key parameters (e.g., DTs levels, design phases, industrial use cases, key objectives, enabling technologies, and core applications). We provide insights into ongoing progress made towards DTs by presenting recent synergies and case studies. Finally, we discuss open challenges that serve as future research directions. © 1986-2012 IEEE.
Blockchain-based data privacy management with Nudge theory in open banking
- Authors: Wang, Hao , Ma, Shenglan , Dai, Hong-Ning , Imran, Muhammad , Wang, Tongsen
- Date: 2020
- Type: Text , Journal article
- Relation: Future Generation Computer Systems Vol. 110, no. (2020), p. 812-823
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- Description: Open banking brings both opportunities and challenges to banks all over the world especially in data management. A blockchain as a continuously growing list of records managed by a peer-to-peer network is widely used in various application scenarios; and it is commonly agreed that the blockchain technology can improve the protection of financial data privacy. However, current blockchain technology still poses some challenges in fully meeting the needs of financial data privacy protection. In order to address the existing problems, this paper proposes a new data privacy management framework based on the blockchain technology for the financial sector. The framework consists of three components: (1) a data privacy classification method according to the characteristics of financial data; (2) a new collaborative-filtering-based model; and (3) a data disclosure confirmation scheme for customer strategies based on the Nudge Theory. We implement a prototype and propose a set of algorithms for this framework. The framework is validated through field experiments and laboratory experiments. © 2019 Elsevier B.V.