Towards Green IoT: Energy Conservation in Core IoT Network Using Network Coding and Soft Actor Critic

Summary

This study integrates network coding with soft actor-critic reinforcement learning to achieve energy-efficient routing in core IoT networks, demonstrating a 40% reduction in energy consumption with a 97% packet delivery ratio.

Abstract

The rapid expansion of internet of things (IoT) networks has intensified energy efficiency challenges in core IoT networks, where data routing places high demands on node energy. This paper proposes a hybrid model integrating network coding (NC) with soft actor-critic (SAC) reinforcement learning to address these challenges. NC minimizes redundant transmissions by combining packets, while SAC agents dynamically select energy-aware routing paths based on node state, link quality, and residual energy.

The system was implemented using the NS-3 simulator with a 5 × 5 grid topology of IoT nodes (512-byte packets, 100s runtime) under varying traffic conditions. Results demonstrate that NC + SAC achieves a 40% reduction in energy consumption, a 97% packet delivery ratio, 50% improvement in throughput, and an extended network lifetime compared to traditional routing and standalone approaches.

Key contributions include novel NC-SAC integration, comprehensive NS-3 validation across multiple metrics, and demonstration of learning-driven coded communication for sustainable IoT infrastructures. The hybrid approach balances reliability, energy preservation, and performance, offering a scalable framework for next-generation green IoT networks.

Introduction

The internet of things (IoT) has emerged as one of the most transformative technologies of the 21st century, seamlessly connecting billions of devices to enable smarter homes, cities, industries, and healthcare systems¹. With this unprecedented growth, however, comes a pressing challenge: the issue of energy consumption. IoT devices, especially those deployed in the core network, are often battery-powered or resource-constrained. As the density of devices and data traffic rises, energy efficiency becomes a crucial factor for ensuring the long-term sustainability of IoT infrastructures. In this context, designing intelligent communication and ro....

Protocol

Data transmission and packet processing workflow
Node initialization: Each node starts with complete network topology information, its maximum energy capacity, and a pre-trained SAC agent ready for decision-making. The network coding module activates and prepares for packet combination operations.

Packet encoding process: When transmission begins, source nodes gather multiple data packets from sensors. The network coding module combines these packets into fewer coded packets using efficient mathematical operations, significantly reducing the total number of transmissions needed.

SAC routing d....

Results

We conducted simulations comparing four different approaches: (i) A traditional model without optimization, (ii) A system employing only Network Coding (NC), (iii) A setup using only the soft actor-critic (SAC) algorithm for routing, and (iv) The proposed hybrid model combining both NC and SAC.

Energy consumption
The most significant improvement was observed in energy efficiency. Traditional IoT networks consume energy rapidly due to reliance on fixed routes and frequent r.......

Discussion

This research introduces a novel energy-aware communication strategy for core IoT networks that integrates network coding (NC) with the soft actor-critic (SAC) reinforcement learning algorithm. The objective of this hybrid approach is to minimize energy consumption while maintaining reliable and efficient data transmission. NC reduces redundant transmissions by encoding data packets, while SAC dynamically learns and selects energy-efficient routing paths based on observed network states. The simulation results presented .......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
6 GB NVIDIA GeForce RTX 3050 (Computer system)	nVIDIA	https://www.nvidia.com/en-in/geforce/graphics-cards/30-series/rtx-3050/	For simulation and training: CPU RAM: 16 GB, GPU RAM
AMD Ryzen (Computer system)	AMD	Ryzen 7	For simulation and training: Processor
Hardware Testbed (optional future work)	Raspberry Pi	https://www.raspberrypi.com/products/raspberry-pi-4-model-b/	Raspberry Pi 4 nodes with wireless modules for real-world IoT deployment and validation
Intel Core i7 (Computer system)	Intel Core	i7-7700 Processor	For simulation and training: Processor
Jupyter Notebook	Jupyter	https://jupyter.org/	Environment for iterative development, debugging, and visualization
Matplotlib	Python	https://matplotlib.org/	Python visualization libraries for plotting performance metrics (Energy, Throughput, PDR, Network Lifetime)
Network Coding Module			Custom Python/NS-3 integration for implementing linear network coding and packet encoding
NS-3 Simulator	ns-3	nsnam.org	Open-source discrete-event network simulator used for IoT network simulation and routing evaluation
NumPy	NumPy	numpy.org	Python packages for handling simulation logs, statistical analysis, and structured datasets
Pandas		pandas.pydata.org	Python packages for handling simulation logs, statistical analysis, and structured datasets
Python 3.10	Python	Version 3.10.0	Programming language used for SAC agent training, coding coefficient optimization, and data processing
PyTorch	PyTorch	https://pytorch.org/	Deep learning library for implementing the Soft Actor-Critic algorithm and reinforcement learning models
Seaborn	Python	https://seaborn.pydata.org/	Python visualization libraries for plotting performance metrics (Energy, Throughput, PDR, Network Lifetime)
Ubuntu 20.04 (Computer system)	Ubuntu	Version 20.04	For simulation and training: operating system