# Multi-Objective Load Balancing for Heterogeneous Edge-Based Object Detection Systems

Source: [Multi-Objective Load Balancing for Heterogeneous Edge-Based Object Detection Systems](<https://www.sota2.com/research/paper/69b8e0470636e5bf4afbee4c>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-03-18T05:00:18.507Z

Authors: Daghash K. Alqahtani, Maria A. Rodriguez, Muhammad Aamir Cheema, Adel N. Toosi

Published: 2026-03-16T15:15:56.000Z

## Abstract

The rapid proliferation of the Internet of Things (IoT) and smart applications has led to a surge in data generated by distributed sensing devices. Edge computing is a mainstream approach to managing this data by pushing computation closer to the data source, typically onto resource-constrained devices such as single-board computers (SBCs). In such environments, the unavoidable heterogeneity of hardware and software makes effective load balancing particularly challenging. In this paper, we propose a multi-objective load balancing method tailored to heterogeneous, edge-based object detection systems. We study a setting in which multiple device-model pairs expose distinct accuracy, latency, and energy profiles, while both request intensity and scene complexity fluctuate over time. To handle this dynamically varying environment, our approach uses a two-stage decision mechanism: it first performs accuracy-aware filtering to identify suitable device-model candidates that provide accuracy within the acceptable range, and then applies a weighted-sum scoring function over expected latency and energy consumption to select the final execution target. We evaluate the proposed load balancer through extensive experiments on real-world datasets, comparing against widely used baseline strategies. The results indicate that the proposed multi-objective load balancing method halves energy consumption and achieves an 80% reduction in end-to-end latency, while incurring only a modest, up to 10%, decrease in detection accuracy relative to an accuracy-centric baseline.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2603.15400>)

[PDF](<https://arxiv.org/pdf/2603.15400.pdf>)