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

Abstract

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.

Introduction

1. Introduction

• The explosive growth of IoT devices has resulted in massive real-time data generation.

• Traditional cloud computing struggles with latency, bandwidth, and congestion issues.

• Edge computing brings processing closer to the data source, reducing latency, enhancing privacy, and improving responsiveness for critical applications like smart cities, healthcare, and industrial systems.

• However, edge devices are resource-constrained, requiring lightweight algorithms for efficient operation.

2. Research Focus

• This study compares and evaluates lightweight algorithms for edge computing, focusing on:

  • Latency

  • Throughput

  • Energy consumption

  • Memory footprint

  • Accuracy

• The goal is to help IoT practitioners choose suitable algorithms based on workload and resource constraints.

3. Background & Standards

• Edge computing complements cloud by enabling local, low-latency data processing.

• Key standards and tools:

  • MEC (Multi-access Edge Computing) for orchestration and APIs.

  • MQTT and CoAP protocols for low-power, efficient device-edge communication.

4. Research Gap

• Existing studies lack deployment-focused comparisons of lightweight algorithms.

• Questions remain about when to use compact CNNs, tree models, or apply model compression techniques.

5. Lightweight Algorithm Categories

A. Compact Deep Networks (Vision & Audio)

• SqueezeNet, MobileNetV2, SqueezeDet offer good accuracy with small model size.

• Best for tasks like image classification and object detection on devices with hardware accelerators (e.g., Edge TPU).

B. Classical Models (Tabular/Time-Series)

• Random Forests and XGBoost excel in anomaly detection, predictive maintenance, and interpretable results with low latency.

• Ideal for edge devices using CPU inference and structured sensor data.

C. Model Compression Tools

• Pruning, quantization, and knowledge distillation reduce model size and inference cost while preserving accuracy.

• Critical for adapting complex models to memory-limited edge hardware.

6. Methodology

• Four-phase approach:

  1. Literature Review: Identified top lightweight algorithms and compression techniques.

  2. Implementation: Used tools like EdgeSimPy and FogTorch, and frameworks such as TensorFlowLite, Scikit-learn, and PyTorch Mobile.

  3. Experimentation: Simulated real-world IoT conditions using datasets (e.g., Intel Berkeley, Smart City) on edge devices (Raspberry Pi 4, Jetson Nano).

  4. Evaluation: Measured performance metrics (latency, accuracy, energy, throughput) under variable network conditions. Statistical tools like ANOVA used for validation.

7. Key Findings & Discussion

Trade-offs

• Tree-based models: Lower latency and energy use, but less accurate than deep learning.

• Compact CNNs: Better accuracy but higher memory and power consumption.

• Pre-processing at the edge (e.g., noise filtering, feature selection) significantly boosts efficiency.

Scalability & Flexibility

• Frameworks like Apache Edgent and TensorFlowLite allow adaptable deployment across heterogeneous edge environments.

Security

• Lightweight models with anomaly detection, authentication, and encryption help secure edge systems without high overhead.

Hybrid Edge–Cloud Architectures

• Combining edge and cloud enables low-latency response with cloud-scale analytics.

• Hybrid models showed up to 40% latency improvement in complex IoT scenarios.

Future Directions

• Adoption of AI accelerators (e.g., Coral, Jetson Nano) and online learning algorithms for real-time adaptation.

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Conclusion

Edge computing enables real-time IoT analytics by keeping decision-making near data sources. Achieving this under tight device budgets requires choosing the right lightweight algorithm for the job and applying compression intelligently. For tabular and time-series telemetry, Random Forests and XGBoost offer reliable accuracy with low inference cost. For visual and acoustic tasks, MobileNetV2, SqueezeNet, and SqueezeDet provide compact backbones that compress and quantize effectively, especially when paired with Edge TPU or similar accelerators. A systematic pipeline—baseline ? INT8 quantization ? QAT ? pruning ? distillation—delivers the best blend of speed, size, and accuracy, while MEC, MQTT, and CoAP provide the surrounding fabric for low-latency, reliable operations. Finally, federated learning and lightweight cryptography align edge analytics with modern privacy and security expectations. Practitioners who adopt a workload-first mindset, design to the hardware, and iterate with compression will realize responsive, secure, and maintainable real-time IoT systems at scale.

References

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Copyright

Copyright © 2025 Ms. V. Manjula, Dheepthi M, Mrs. K. S. Hemalatha, Mrs. D. Gopika. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.