Adaptive Communication Protocols for Manager-Worker Small LLM Multi-Agent Systems in Resource-Constrained Environments
adaptive protocols; edge computing; IoT coordination; Multi-agent systems; resource-constrained environments; semantic communication; small language models; topology control
The proliferation of Internet of Things (IoT) and edge computing paradigms has necessitated the deployment of intelligent multi-agent systems in resource-constrained environments, where traditional communication protocols fail to optimize bandwidth utilization and energy consumption. This paper presents a novel adaptive communication framework specifically designed for manager-worker small Large Language Models (LLMs) multi-agent systems operating under stringent computational, memory, and energy constraints. Our approach integrates three synergistic innovations: (1) a lightweight semantic filtering module employing knowledge-distilled small LLMs (DistilBERT 66M parameters, TinyBERT 4.4M parameters) for real-time extraction of task-relevant information with minimal computational overhead, (2) a dynamic hierarchical coordination scheme enabling runtime role reassignment based on evolving task complexity and resource availability, and (3) an adaptive topology control mechanism leveraging algebraic connectivity measures to optimize network robustness while minimizing communication overhead. Comprehensive simulation-based evaluation across five distinct IoT deployment scenarios demonstrates substantial improvements in communication efficiency, achieving an average token reduction of 5 7. 4 % (range: 5 4. 1 - 5 9. 4 %) while maintaining task completion rates within 8.3 percentage points of baseline performance. The framework exhibits superior coordination quality improvements of 6.0 %, coupled with significant resource optimization including 5 1. 2 % CPU usage reduction and 50.4 % energy savings, validating its practical suitability for edge computing deployments in resource-critical applications. 2025 IEEE.
Yashu, Kumar; Gogi, Vyshali J; Azarudheen, S.
Proceedings - 2025 IEEE DELCON: International Conference on Recent Smart Technologies in Engineering for Sustainable Development;
Institute of Electrical and Electronics Engineers Inc.
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1109/DELCON68055.2025.11400330 https://www.scopus.com/pages/publications/105036380674?origin=resultslist
Yashu K., Christ University, Dept. Of Statistics And Data Science, Karnataka, Bengaluru, India; Gogi V.J., Christ University, Dept. Of Statistics And Data Science, Karnataka, Bengaluru, India; Azarudheen S., Christ University, Dept. Of Statistics And Data Science, Karnataka, Bengaluru, India