Optimizing Resource Allocation and Task Offloading for Real-Time IoT Applications

Dhanush M

Optimizing Resource Allocation and Task Offloading for Real-Time IoT Applications

July 2025

Vol-11, Issue-4

Paper ID: 27089

ISSN: 2395-4396

Downloads: 0

Abstract & Details

Research Area

Computer Applications

Keywords

Edge computing task offloading resource allocation real-time IoT applications mobile edge computing (MEC) latency reduction energy efficiency scheduling algorithms fairness-aware allocation ultra-dense networks bandwidth optimization low-latency communication quality of service (QoS) distributed computing edge intelligence context-aware computing dynamic resource management vehicular edge computing cloud-edge collaboration multi-access edge computing (MAEC).

Abstract

This research investigates strategies to optimize resource allocation and task offloading in Edge Computing environments for real-time Internet of Things (IoT) applications. As IoT devices continue to proliferate, edge computing has emerged as a vital approach to meet demands for low latency and real-time responsiveness. However, limited computational and energy resources at edge nodes pose significant challenges. This study reviews existing algorithms and proposes a hybrid offloading framework that combines heuristic-based task scheduling with AI-driven resource prediction. Simulations were conducted using a testbed of emulated IoT devices and edge nodes under variable workloads and network conditions. The results demonstrate a noticeable improvement in processing delay, energy consumption, and resource utilization compared to static or cloud-only approaches. By enhancing decision-making at the edge, this paper contributes to the growing body of work aiming to make distributed computing more intelligent, sustainable, and responsive to real-world constraints in IoT environments.

License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author Information

#	Name	Institute / Affiliation
1	Dhanush M	CMR University

How to Cite

Use the following formats to cite this article in your research.

APA Style

M, Dhanush (2025). Optimizing Resource Allocation and Task Offloading for Real-Time IoT Applications. International Journal of Advance Research and Innovative Ideas In Education, 11(4), 555-559.

MLA Style

M, Dhanush. "Optimizing Resource Allocation and Task Offloading for Real-Time IoT Applications." International Journal of Advance Research and Innovative Ideas In Education, vol. 11, no. 4, 2025, pp. 555-559.

IEEE Style

Dhanush M, "Optimizing Resource Allocation and Task Offloading for Real-Time IoT Applications," International Journal of Advance Research and Innovative Ideas In Education, vol. 11, no. 4, pp. 555-559, 2025.

Vancouver Style

M Dhanush. Optimizing Resource Allocation and Task Offloading for Real-Time IoT Applications. International Journal of Advance Research and Innovative Ideas In Education. 2025;11(4):555-559.

Harvard Style

M, Dhanush (2025) 'Optimizing Resource Allocation and Task Offloading for Real-Time IoT Applications', International Journal of Advance Research and Innovative Ideas In Education, 11(4), pp. 555-559.

Chicago Style

M, Dhanush. "Optimizing Resource Allocation and Task Offloading for Real-Time IoT Applications." International Journal of Advance Research and Innovative Ideas In Education 11, no. 4 (2025): 555-559.

Turabian Style

M, Dhanush. "Optimizing Resource Allocation and Task Offloading for Real-Time IoT Applications." International Journal of Advance Research and Innovative Ideas In Education 11, no. 4 (2025): 555-559.