Predictive software update management for the internet of things

DOI: 10.31673/2412-9070.2025.051172

Abstract

The rapid proliferation of the Internet of Things (IoT) has created unprecedented challenges in maintaining and updating software across millions of heterogeneous devices operating in dynamic environments. This research paper addresses the critical problem of inefficient software update management in large-scale IoT networks, where traditional deployment methodlogies often prove insufficiently flexible, secure, and reliable. The study introduces a groundbreaking intelligent framework that synergistically combines Artificial Intelligence (AI) algorithms with the canary release strategy to revolutionize the update process for distributed IoT ecosystems. At the core of this innovative approach lies a sophisticated mathematical model that enables real-time optimization of deployment parameters through continuous monitoring of system performance metrics and failure patterns.
The proposed framework employs Reinforcement Learning (RL) techniques to create an autonomous decision-making system capable of dynamically adjusting rollout strategies based on actual network conditions and device performance. The AI agent operates within a formally defined state space encompassing critical parameters such as the number of successfully updated devices, current error rates, and system load indicators. Through iterative learning, the system develops an optimal policy for managing update deployments by evaluating actions against a comprehensive cost function that balances stability requirements with operational efficiency. This function incorporates weighted factors including failure rates, performance degradation, and total deployment duration, enabling the system to make intelligent choices between continuing, pausing, or rolling back updates.
Experimental results demonstrate that the AI-enhanced canary release model achieves remarkable improvements in deployment reliability and resource utilization compared to conventional approaches. The system reduces rollout-related failures while decreasing overall deploy ment time, significantly enhancing operational continuity in critical IoT applications. Further more, the framework optimizes network bandwidth consumption through intelligent scheduling and prioritization mechanisms, addressing one of the most pressing constraints in large-scale IoT environments. The mathematical formalization of the deployment process provides a solid theoretical foundation for reproducible results and further academic investigation. The proposed solution not only addresses immediate operational challenges but also paves the way for developing self-healing IoT infrastructures capable of adaptive behavior in increasingly complex networked environments. The paper concludes by outlining promising directions for future work, including the integration of federated learning for privacy-preserving analytics and the development of predictive maintenance capabilities for proactive system management.

Keywords: Internet of Things; software updates; artificial intelligence; reinforcement learning; canary release; deployment optimization; information systems; mathematical model.