Optimizing Edge Computing for Real-Time Data Processing in IoT Networks: A Comparative Study of Lightweight Algorithms

: The explosive growth of the Internet of Things (IoT) has shifted real-time analytics toward the network edge to reduce latency, conserve bandwidth, and preserve privacy. However, stringent constraints on compute, memory, and energy at edge nodes demand algorithmic frugality without sacrificing responsiveness or accuracy. This paper surveys and comparatively analyzes lightweight approaches classical machine-learning models (e.g., Random Forests and gradient-boosted trees), compact deep networks (e.g., SqueezeNet, MobileNetV2, SqueezeDet), and model-compression strategies (pruning, quantization, and knowledge distillation). We situate these methods within an end-to-end edge reference stack that includes IoT messaging and application frameworks (MQTT, CoAP, ETSI MEC) and discuss deployment considerations such as hardware accelerators (e.g., Edge TPU), streaming dataflows, and privacy-aware training (federated learning). We present a practical comparison matrix linked to workload archetypes event detection, anomaly detection, time-series forecasting, and vision highlighting trade-offs across latency, memory footprint, energy, and maintainability. The study culminates in implementation patterns and a decision playbook to help practitioners select, compress, and operationalize models for real-time IoT pipelines at the edge.