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The role of big data analytics in industrial internet of things

- Rehman, Muhammad, Yaqoob, Ibrar, Salah, Khaled, Imran, Muhammad, Jayaraman, Prem, Perera, Charith

  • Authors: Rehman, Muhammad , Yaqoob, Ibrar , Salah, Khaled , Imran, Muhammad , Jayaraman, Prem , Perera, Charith
  • Date: 2019
  • Type: Text , Journal article
  • Relation: Future Generation Computer Systems Vol. 99, no. (2019), p. 247-259
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  • Description: Big data production in industrial Internet of Things (IIoT) is evident due to the massive deployment of sensors and Internet of Things (IoT) devices. However, big data processing is challenging due to limited computational, networking and storage resources at IoT device-end. Big data analytics (BDA) is expected to provide operational- and customer-level intelligence in IIoT systems. Although numerous studies on IIoT and BDA exist, only a few studies have explored the convergence of the two paradigms. In this study, we investigate the recent BDA technologies, algorithms and techniques that can lead to the development of intelligent IIoT systems. We devise a taxonomy by classifying and categorising the literature on the basis of important parameters (e.g. data sources, analytics tools, analytics techniques, requirements, industrial analytics applications and analytics types). We present the frameworks and case studies of the various enterprises that have benefited from BDA. We also enumerate the considerable opportunities introduced by BDA in IIoT. We identify and discuss the indispensable challenges that remain to be addressed, serving as future research directions. © 2019 Elsevier B.V.

Blockchain for digital twins : recent advances and future research challenges

- Yaqoob, Ibrar, Salah, Khaled, Uddin, Mueen, Jayaraman, Raja, Omar, Mohammed, Imran, Muhammad

  • 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
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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.

A privacy-preserving framework for smart context-aware healthcare applications

- Azad, Muhammad, Arshad, Junaid, Mahmoud, Shazia, Salah, Khaled, Imran, Muhammad

  • Authors: Azad, Muhammad , Arshad, Junaid , Mahmoud, Shazia , Salah, Khaled , Imran, Muhammad
  • Date: 2022
  • Type: Text , Journal article
  • Relation: Transactions on Emerging Telecommunications Technologies Vol. 33, no. 8 (2022), p.
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  • Description: Smart connected devices are widely used in healthcare to achieve improved well-being, quality of life, and security of citizens. While improving quality of healthcare, such devices generate data containing sensitive patient information where unauthorized access constitutes breach of privacy leading to catastrophic outcomes for an individual as well as financial loss to the governing body via regulations such as the General Data Protection Regulation. Furthermore, while mobility afforded by smart devices enables ease of monitoring, portability, and pervasive processing, it introduces challenges with respect to scalability, reliability, and context awareness. This paper is focused on privacy preservation within smart context-aware healthcare emphasizing privacy assurance challenges within Electronic Transfer of Prescription. We present a case for a comprehensive, coherent, and dynamic privacy-preserving system for smart healthcare to protect sensitive user data. Based on a thorough analysis of existing privacy preservation models, we propose an enhancement to the widely used Salford model to achieve privacy preservation against masquerading and impersonation threats. The proposed model therefore improves privacy assurance for smart healthcare while addressing unique challenges with respect to context-aware mobility of such applications. © 2019 John Wiley & Sons, Ltd.

Device-centric adaptive data stream management and offloading for analytics applications in future internet architectures

- Rehman, Muhammad, Liew, Chee, Wah, Teh, Imran, Muhammad, Salah, Khaled, Nasser, Nidal, Svetinovic, Davor

  • Authors: Rehman, Muhammad , Liew, Chee , Wah, Teh , Imran, Muhammad , Salah, Khaled , Nasser, Nidal , Svetinovic, Davor
  • Date: 2021
  • Type: Text , Journal article
  • Relation: Future Generation Computer Systems Vol. 114, no. (2021), p. 155-168
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  • Description: Information-Centric Networking (ICN) enables in-network data management and communication between multiple parties by replicating data and activating interactions between decoupled senders and receivers. Existing data management and offloading schemes in ICNs primarily use the transport layer hence it becomes inefficient to actively develop and update the ICN standards because of continuously evolving heterogeneous future internet architectures such as mobile edge cloud computing (MECC) architectures. In this paper, we present an adaptive execution model for mobile data stream mining (MDSM) applications in MECC environments to enable device-centric adaptive data management and offloading. We designed the proposed execution model considering multiple factors of complexity such as volume and velocity of continuously streaming data, the selection of data fusion and data preprocessing methods, the choice of learning models, learning rates, learning modes, mobility, limited computational and memory resources in mobile devices, the high coupling between application components, and dependency over Internet connections. We integrated the proposed execution model with multiple MDSM applications mapping to a real-word use-case for activity detection using MECC as a future network architecture. We thoroughly evaluated the proposed execution model in terms of battery power consumption, memory utilization, makespan, accuracy, and the amount of data reduced during in-network communication. The comparison showed that our proposed adaptive execution model outperformed the static and dynamic execution models which were deployed in the same ICN architecture. © 2020 Elsevier B.V.
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