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MSCET : a multi-scenario offloading schedule for biomedical data processing and analysis in cloud-edge-terminal collaborative vehicular networks

- Ni, Zhichen, Chen, Honglong, Li, Zhe, Wang, Xiaomeng, Yan, Na, Liu, Weifeng, Xia, Feng

  • Authors: Ni, Zhichen , Chen, Honglong , Li, Zhe , Wang, Xiaomeng , Yan, Na , Liu, Weifeng , Xia, Feng
  • Date: 2023
  • Type: Text , Journal article
  • Relation: IEEE/ACM Transactions on Computational Biology and Bioinformatics Vol. 20, no. 4 (2023), p. 2376-2386
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  • Description: With the rapid development of Artificial Intelligence (AI) and Internet of Things (IoTs), an increasing number of computation intensive or delay sensitive biomedical data processing and analysis tasks are produced in vehicles, bringing more and more challenges to the biometric monitoring of drivers. Edge computing is a new paradigm to solve these challenges by offloading tasks from the resource-limited vehicles to Edge Servers (ESs) in Road Side Units (RSUs). However, most of the traditional offloading schedules for vehicular networks concentrate on the edge, while some tasks may be too complex for ESs to process. To this end, we consider a collaborative vehicular network in which the cloud, edge and terminal can cooperate with each other to accomplish the tasks. The vehicles can offload the computation intensive tasks to the cloud to save the resource of edge. We further construct the virtual resource pool which can integrate the resource of multiple ESs since some regions may be covered by multiple RSUs. In this paper, we propose a Multi-Scenario offloading schedule for biomedical data processing and analysis in Cloud-Edge-Terminal collaborative vehicular networks called MSCET. The parameters of the proposed MSCET are optimized to maximize the system utility. We also conduct extensive simulations to evaluate the proposed MSCET and the results illustrate that MSCET outperforms other existing schedules. © 2004-2012 IEEE.

Blockchain leveraged decentralized IoT eHealth framework

- Uddin, Ashraf, Stranieri, Andrew, Gondal, Iqbal, Balasubramanian, Venki

  • Authors: Uddin, Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
  • Date: 2020
  • Type: Text , Journal article
  • Relation: Internet of Things Vol. 9, no. March 2020 p. 100159
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  • Description: Blockchain technologies recently emerging for eHealth, can facilitate a secure, decentral- ized and patient-driven, record management system. However, Blockchain technologies cannot accommodate the storage of data generated from IoT devices in remote patient management (RPM) settings as this application requires a fast consensus mechanism, care- ful management of keys and enhanced protocols for privacy. In this paper, we propose a Blockchain leveraged decentralized eHealth architecture which comprises three layers: (1) The Sensing layer –Body Area Sensor Networks include medical sensors typically on or in a patient body transmitting data to a smartphone. (2) The NEAR processing layer –Edge Networks consist of devices at one hop from data sensing IoT devices. (3) The FAR pro- cessing layer –Core Networks comprise Cloud or other high computing servers). A Patient Agent (PA) software replicated on the three layers processes medical data to ensure reli- able, secure and private communication. The PA executes a lightweight Blockchain consen- sus mechanism and utilizes a Blockchain leveraged task-offloading algorithm to ensure pa- tient’s privacy while outsourcing tasks. Performance analysis of the decentralized eHealth architecture has been conducted to demonstrate the feasibility of the system in the pro- cessing and storage of RPM data.

Quantum particle swarm optimization for task offloading in mobile edge computing

- Dong, Shi, Xia, Yuanjun, Kamruzzaman, Joarder

  • Authors: Dong, Shi , Xia, Yuanjun , Kamruzzaman, Joarder
  • Date: 2023
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Industrial Informatics Vol. 19, no. 8 (2023), p. 9113-9122
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  • Description: Mobile edge computing (MEC) deploys servers on the edge of the mobile network to reduce the data transmission delay between servers and mobile devices, and can meet the computing demand of mobile computing tasks. It alleviates the problem of computing power and delay requirements of mobile computing tasks and reduces the energy consumption of mobile devices. However, the MEC server has limited computing and storage resources and mobile network bandwidth, making it impossible to offload all mobile computing tasks to MEC servers for processing. Therefore, MEC needs to reasonably offload and schedule mobile computing tasks, to achieve efficient utilization of server resources. To solve the above-mentioned problems, in this article, the task offloading problem is formulated as an optimization problem, and particle swarm optimization (PSO) and quantum PSO based task offloading strategies are proposed. Extensive simulation results show that the proposed algorithm can significantly reduce the system energy consumption, task completion time, and running time compared with recent advanced strategies, namely ant colony optimization, multiagent deep deterministic policy gradients, deep meta reinforcement learning-based offloading, iterative proximal algorithm, and parallel random forest. © 2005-2012 IEEE.
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