Distributed task processing platform for infrastructure-less IoT networks: A multi-dimensional optimisation approach

Author Identifier

Iftekhar Ahmad: https://orcid.org/0000-0003-4441-9631

Document Type

Journal Article

Publication Title

IEEE Transactions on Parallel and Distributed Systems

Publisher

IEEE

School

School of Engineering

RAS ID

75871

Funders

Australian Research Council

Grant Number

ARC Number : LP190100594

Comments

Zheng, Q., Jin, J., Shen, Z., Wu, L., Ahmad, I., & Xiang, Y. (2024). Distributed task processing platform for infrastructure-less IoT Networks: A multi-dimensional optimisation approach. IEEE Transactions on Parallel and Distributed Systems, 35(12), 2392-2404. https://doi.org/10.1109/TPDS.2024.3469545

Abstract

With the rapid development of artificial intelligence (AI) and the Internet of Things (IoT), intelligent information services have showcased unprecedented capabilities in acquiring and analysing information. The conventional task processing platforms rely on centralised Cloud processing, which encounters challenges in infrastructure-less environments with unstable or disrupted electrical grids and cellular networks. These challenges hinder the deployment of intelligent information services in such environments. To address these challenges, we propose a distributed task processing platform (DTPP) designed to provide satisfactory performance for executing computationally intensive applications in infrastructure-less environments. This platform leverages numerous distributed homogeneous nodes to process the arriving task locally or collaboratively. Based on this platform, a distributed task allocation algorithm is developed to achieve high task processing performance with limited energy and bandwidth resources. To validate our approach, DTPP has been tested in an experimental environment utilising real-world experimental data to simulate IoT network services in infrastructure-less environments. Extensive experiments demonstrate that our proposed solution surpasses comparative algorithms in key performance metrics, including task processing ratio, task processing accuracy, algorithm processing time, and energy consumption.

DOI

10.1109/TPDS.2024.3469545

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