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Multi-Objective Task-Aware Offloading and Scheduling Framework for Internet of Things Logistics.

Asif Umer1, Mushtaq Ali1, Ali Imran Jehangiri1

  • 1Department of Computer Science & Information Technology, Hazara University, Mansehra 21130, Pakistan.

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|April 27, 2024
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Summary
This summary is machine-generated.

This study introduces a new framework for Internet of Things (IoT) logistics, optimizing task offloading and scheduling to reduce response times and energy use. The proposed system enhances efficiency and reliability in smart transportation by prioritizing tasks and selecting optimal nodes.

Keywords:
IoT task offloading & schedulinganalytical hierarchy process (AHP)computation-intensive tasksdelay-sensitive tasksenergy consumptionfault-tolerant managersmart transportation of logisticstask-aware scheduler

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Area of Science:

  • * Internet of Things (IoT)
  • * Smart Transportation Systems
  • * Cloud and Fog Computing

Background:

  • * IoT devices in smart transportation have limited computational power, necessitating task offloading to remote servers.
  • * Existing task offloading mechanisms often overlook fault tolerance, data awareness, and multi-parameter node selection for logistics.
  • * This gap hinders optimal performance in energy consumption and response time for IoT logistics trucks.

Purpose of the Study:

  • * To propose a novel Multi-Objective Task-Aware Offloading and Scheduling Framework for IoT Logistics (MT-OSF).
  • * To address limitations in fault-tolerance, task/data awareness, priority-based offloading, and multi-parameter fog node selection.
  • * To enhance the efficiency and reliability of IoT-based smart transportation.

Main Methods:

  • * Developed a priority-based offloader to categorize tasks into delay-sensitive and computation-intensive.
  • * Implemented a Task-Aware Scheduler (TAS) using the Analytical Hierarchy Process (AHP) for multi-criterion fog node selection (energy, bandwidth, RAM, MIPS).
  • * Integrated a fault-tolerant manager for task re-execution and node failure handling, utilizing the First-Come, First-Served (FCFS) algorithm for scheduling.

Main Results:

  • * Achieved a 7% reduction in response time compared to existing methods.
  • * Demonstrated a 16% decrease in energy consumption.
  • * Showcased a significant 22% reduction in the task failure ratio.

Conclusions:

  • * The MT-OSF framework effectively optimizes task offloading and scheduling in IoT logistics.
  • * The proposed system significantly improves performance metrics, including response time, energy efficiency, and fault tolerance.
  • * MT-OSF offers a robust solution for enhancing the reliability and efficiency of smart transportation systems.