Comparative Exploration of Multi-objective Optimization Approaches for Task Offloading in Healthcare IoT–Fog Networks

Document Type

Conference Proceeding

Source of Publication

Lecture Notes in Networks and Systems

Publication Date

6-1-2026

Abstract

Fog computing has emerged as a promising paradigm for supporting latency-sensitive healthcare services by bringing computation closer to Internet of Things (IoT) devices. Efficient task offloading in such systems is inherently a multi-objective optimization problem, where latency, energy consumption, throughput, task completion rate, and fairness must be jointly optimized. This paper presents a comparative exploration of three distinct optimization approaches for healthcare IoT–fog task offloading: Reinforcement Learning (RL), Genetic Algorithm (GA), and Mixed-Integer Linear Programming (MILP). Each method represents a different paradigm—learning-based, evolutionary, and deterministic mathematical optimization. A utility function is formulated to capture the trade-offs across multiple objectives, and the algorithms are evaluated under varying fog node configurations. Simulation results demonstrate that RL consistently achieves lower latency and energy consumption while balancing fairness and throughput, GA provides stronger performance in task completion and resource utilization, and MILP offers moderate but less adaptive results. The comparative analysis highlights the trade-offs between exploration capability, adaptability, and computational cost, establishing RL as the most effective and scalable solution for dynamic healthcare IoT–fog environments.

ISBN

[9783032248060]

ISSN

2367-3370

Publisher

Springer Nature Switzerland

Volume

1950 LNNS

First Page

128

Last Page

145

Disciplines

Computer Sciences

Keywords

Fog computing, Genetic algorithm, Healthcare IoT, MILP, Multi-objective optimization, Reinforcement learning, Task offloading

Scopus ID

105045702997

Indexed in Scopus

yes

Open Access

no

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