An Advanced Intelligence Protocol for Context-Aware Edge Computing Devices in Distributed Systems

Vishnu Suryawanshi; Nakul Sharma; Nandkishor P. Karlekar; Abhijeet Cholke; Vishal Bogam; Raju Prakash Gurav; Bharat Devhare

doi:10.48084/etasr.18467

Authors

Vishnu Suryawanshi School of Computing, MIT Art, Design and Technology University, Pune, India
Nakul Sharma Department of Computer Science Engineering (IOT CS inc. Blockchain Technology), Vishwakarma Institute of Technology, Pune, India
Nandkishor P. Karlekar School of Computing, MIT Art, Design and Technology University, Pune, India
Abhijeet Cholke School of Computing, MIT Art, Design and Technology University, Pune, India
Vishal Bogam School of Computing, MIT Art, Design and Technology University, Pune, India
Raju Prakash Gurav School of Computing, MIT Art, Design and Technology University, Pune, India
Bharat Devhare Department of Computer Engineering, Bharti Vidyapeeth College of Engineering, Pune, India

Volume: 16 | Issue: 3 | Pages: 36856-36863 | June 2026 | https://doi.org/10.48084/etasr.18467

Received: 1 March 2026 | Revised: 25 March 2026, 19 April 2026, and 22 April 2026 | Accepted: 23 April 2026 | Online: 6 June 2026

Corresponding author: Vishnu Suryawanshi

Abstract

The increasing complexity of integrated computing environments requires sustainable architectures that can adapt, evolve, and remain reliable throughout their lifetime. Current edge computing environments are frequently deficient in dynamic context management, adaptive scheduling, and continuous compliance. This work aims to mitigate these limitations and presents an open-source framework that unifies multidimensional contextual analysis, adaptive task scheduling, lifecycle-conscious security, and continuous compliance within a single closed-loop system. The proposed solution utilizes a hybrid methodology consisting of Bayesian inference for probabilistic context reasoning and Deep Reinforcement Learning (DRL) for adaptive decision-making. Experimental analysis shows that the proposed protocol framework is superior to currently available models, resulting in a 24 % reduction in latency, a 14 % decrease in power consumption, and a 96 % compliance rate. These findings confirm that the proposed approach is scalable and enables intelligent, secure, and compliance-aware edge computing systems.

Keywords:

Model Context Protocol (MCP), distributed computing, edge intelligence, Reinforcement Learning (RL), Bayesian inference, IoT security, context modeling

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