List of Titles

A hybrid computing solution and resource scheduling strategy for edge computing in smart manufacturing

- Li, Xiaomin, Wan, Jiafu, Dai, Hong-Ning, Imran, Muhammad, Xia, Min, Celesti, Antonio

Authors: Li, Xiaomin , Wan, Jiafu , Dai, Hong-Ning , Imran, Muhammad , Xia, Min , Celesti, Antonio
Date: 2019
Type: Text , Journal article
Relation: IEEE Transactions on Industrial Informatics Vol. 15, no. 7 (2019), p. 4225-4234
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Description: At present, smart manufacturing computing framework has faced many challenges such as the lack of an effective framework of fusing computing historical heritages and resource scheduling strategy to guarantee the low-latency requirement. In this paper, we propose a hybrid computing framework and design an intelligent resource scheduling strategy to fulfill the real-time requirement in smart manufacturing with edge computing support. First, a four-layer computing system in a smart manufacturing environment is provided to support the artificial intelligence task operation with the network perspective. Then, a two-phase algorithm for scheduling the computing resources in the edge layer is designed based on greedy and threshold strategies with latency constraints. Finally, a prototype platform was developed. We conducted experiments on the prototype to evaluate the performance of the proposed framework with a comparison of the traditionally-used methods. The proposed strategies have demonstrated the excellent real-time, satisfaction degree (SD), and energy consumption performance of computing services in smart manufacturing with edge computing. © 2005-2012 IEEE.

A lightweight federated learning based privacy preserving B5G pandemic response network using unmanned aerial vehicles: A proof-of-concept

- Nasser, Nasser, Fadlullah, Zubair, Fouda, Mostafa, Ali, Asmaa, Imran, Muhammad

Authors: Nasser, Nasser , Fadlullah, Zubair , Fouda, Mostafa , Ali, Asmaa , Imran, Muhammad
Date: 2022
Type: Text , Journal article
Relation: Computer Networks Vol. 205, no. (2022), p.
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Description: The concept of an intelligent pandemic response network is gaining momentum during the current novel coronavirus disease (COVID-19) era. A heterogeneous communication architecture is essential to facilitate collaborative and intelligent medical analytics in the fifth generation and beyond (B5G) networks to intelligently learn and disseminate pandemic-related information and diagnostic results. However, such a technique raises privacy issues pertaining to the health data of the patients. In this paper, we envision a privacy-preserving pandemic response network using a proof-of-concept, aerial–terrestrial network system serving mobile user entities/equipment (UEs). By leveraging the unmanned aerial vehicles (UAVs), a lightweight federated learning model is proposed to collaboratively yet privately learn medical (e.g., COVID-19) symptoms with high accuracy using the data collected by individual UEs using ambient sensors and wearable devices. An asynchronous weight updating technique is introduced in federated learning to avoid redundant learning and save precious networking as well as computing resources of the UAVs/UEs. A use-case where an Artificial Intelligence (AI)-based model is employed for COVID-19 detection from radiograph images is presented to demonstrate the effectiveness of our proposed approach. © 2021 Elsevier B.V.

A reconfigurable method for intelligent manufacturing based on industrial cloud and edge intelligence

- Tang, Hao, Li, Di, Wan, Jiafu, Imran, Muhammad, Shoaib, Muhammad

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
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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.

Complementing IoT services through software defined networking and edge computing : a comprehensive survey

- Rafique, Wajid, Qi, Lianyong, Yaqoob, Ibrar, Imran, Muhammad, Rasool, Raojan, Dou, Wanchun

Authors: Rafique, Wajid , Qi, Lianyong , Yaqoob, Ibrar , Imran, Muhammad , Rasool, Raojan , Dou, Wanchun
Date: 2020
Type: Text , Journal article
Relation: IEEE Communications Surveys and Tutorials Vol. 22, no. 3 (2020), p. 1761-1804
Full Text: false
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Description: Millions of sensors continuously produce and transmit data to control real-world infrastructures using complex networks in the Internet of Things (IoT). However, IoT devices are limited in computational power, including storage, processing, and communication resources, to effectively perform compute-intensive tasks locally. Edge computing resolves the resource limitation problems by bringing computation closer to the edge of IoT devices. Providing distributed edge nodes across the network reduces the stress of centralized computation and overcomes latency challenges in the IoT. Therefore, edge computing presents low-cost solutions for compute-intensive tasks. Software-Defined Networking (SDN) enables effective network management by presenting a global perspective of the network. While SDN was not explicitly developed for IoT challenges, it can, however, provide impetus to solve the complexity issues and help in efficient IoT service orchestration. The current IoT paradigm of massive data generation, complex infrastructures, security vulnerabilities, and requirements from the newly developed technologies make IoT realization a challenging issue. In this research, we provide an extensive survey on SDN and the edge computing ecosystem to solve the challenge of complex IoT management. We present the latest research on Software-Defined Internet of Things orchestration using Edge (SDIoT-Edge) and highlight key requirements and standardization efforts in integrating these diverse architectures. An extensive discussion on different case studies using SDIoT-Edge computing is presented to envision the underlying concept. Furthermore, we classify state-of-the-art research in the SDIoT-Edge ecosystem based on multiple performance parameters. We comprehensively present security and privacy vulnerabilities in the SDIoT-Edge computing and provide detailed taxonomies of multiple attack possibilities in this paradigm. We highlight the lessons learned based on our findings at the end of each section. Finally, we discuss critical insights toward current research issues, challenges, and further research directions to efficiently provide IoT services in the SDIoT-Edge paradigm. © 1998-2012 IEEE.

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Cloudlet computing : recent advances, taxonomy, and challenges

- Babar, Mohammad, Khan, Muhammad, Ali, Farman, Imran, Muhammad, Shoaib, Muhammad

Authors: Babar, Mohammad , Khan, Muhammad , Ali, Farman , Imran, Muhammad , Shoaib, Muhammad
Date: 2021
Type: Text , Journal article
Relation: IEEE Access Vol. 9, no. (2021), p. 29609-29622
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Description: A cloudlet is an emerging computing paradigm that is designed to meet the requirements and expectations of the Internet of things (IoT) and tackle the conventional limitations of a cloud (e.g., high latency). The idea is to bring computing resources (i.e., storage and processing) to the edge of a network. This article presents a taxonomy of cloudlet applications, outlines cloudlet utilities, and describes recent advances, challenges, and future research directions. Based on the literature, a unique taxonomy of cloudlet applications is designed. Moreover, a cloudlet computation offloading application for augmenting resource-constrained IoT devices, handling compute-intensive tasks, and minimizing the energy consumption of related devices is explored. This study also highlights the viability of cloudlets to support smart systems and applications, such as augmented reality, virtual reality, and applications that require high-quality service. Finally, the role of cloudlets in emergency situations, hostile conditions, and in the technological integration of future applications and services is elaborated in detail. © 2013 IEEE.

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