Optimizing Mobile Learning Networks in Resource-Constrained Educational Environments Using Autonomic Computing
Abstract
This work discusses how autonomic computing may assist in optimizing mobile learning networks in educational environments where resources are often limited. The emergence of mobile learning has heightened the demand for scalable and cost-effective network management systems capable of coping with hardware, bandwidth, and infrastructure resource limitations often seen in most educational environments, especially in developing countries. Autonomic systems leverage autonomic computing, which centers on self-management and self-adaptation as mechanisms to respond to changing conditions and continuously optimize performance. This paper proposes a framework for mobile learning networks driven by autonomic computing, where mobile learning networks can change based on adapting conditions such as network load, device performance, and network resource availability. The provision for autonomic computing in mobile environments would include intelligent algorithms that will optimize resource allocation, improve the user experience, and allow learning access without interruption to the learner because of limitations to the underlying infrastructure. This paper discusses autonomous integration of machine learning, artificial intelligence-based decision-making, and cloud computing developments and applications in an autonomic context, and their contribution to efficiencies and optimal scalability in mobile learning environments. The review of case studies and examples where the aforementioned technologies have been successfully implemented also recounts the challenges and benefits encountered in resource-constrained education contexts. In conclusion, this research will contribute to postulating a framework for optimizing mobile learning networks to provide broader access to quality education, especially in resource-poor environments. PDF Date December 2025 Page Number 197-210