Adaptive Load Balancing Model on Edge Gateways to Support IoT Scalability in 5G Networks
Abstract
The rapid proliferation of Internet of Things (IoT) devices, particularly within the context of smart cities, industrial automation, and connected vehicles, poses significant challenges to network scalability and real-time data processing. With the advent of 5G networks, promising ultra-low latency and massive connectivity, the role of edge computing and specifically edge gateways becomes critical. However, the dynamic and heterogeneous nature of IoT traffic, coupled with varying computational demands, can lead to uneven resource utilization and performance bottlenecks on edge gateways. This paper proposes an adaptive load balancing model designed to optimize resource distribution and enhance IoT scalability in 5G networks. We explore how artificial intelligence (AI) and machine learning (ML) techniques can be leveraged for real-time traffic prediction, dynamic task offloading, and intelligent resource allocation across multiple edge gateways. The proposed model aims to minimize latency, maximize throughput, and ensure high availability for diverse IoT applications. We discuss the architectural components, key adaptive mechanisms, and the integration with 5G network capabilities, alongside outlining persistent challenges and promising future research directions to build more resilient and efficient IoT ecosystems.
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References
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