Sustainable AI: Energy-Efficient Algorithms for Edge Computing in IoT Networks

International Journal of Advanced Scientific and Technical Research ISSN 2249-9954 Available online on http://www.rspublication.com/ijst/index.html volume 15, No. 5, 2025 DOI: 10.5281/zenodo.17484664 Original Article ©2025 RS Publicaon, rspublica[email protected] 535 Sustainable AI: Energy-Efficient Algorithms for Edge Computing in IoT Networks Adhith S Menon * Abel Jopaul V P** *(Postgraduate Student (MCA), PG Department of Computer Applications, LEAD College (Autonomous), Palakkad. Email: [email protected]) *(Assistant Professor, PG Department of Computer Applications, LEAD College (Autonomous), Palakkad. Email: [email protected]) 1. Introduction Artificial intelligence has become integral to Internet of Things ecosystems, enabling real-time analytics for applications ranging from smart agriculture to industrial automation. However, the computational demands of deep learning models impose substantial energy costs: training a single large-scale neural network can emit approximately 284 tons of CO₂ equivalent (Strubell et al., 2019). With IoT networks generating 79.4 zettabytes of data annually by 2025, centralized cloud processing exacerbates this environmental burden through transmission energy overhead and data center operations consuming 1-2% of global electricity. Edge computing—processing data proximate to IoT sensors—offers a decentralization paradigm that reduces latency and bandwidth requirements. Yet edge devices (e.g., Raspberry Pi, NVIDIA Jetson Nano) operate under stringent power budgets (1-10W) and memory constraints (512MB-4GB RAM), necessitating algorithmic innovations. Sustainable AI Internaonal Journal of Advanced Scienfic and Technical Research Available online on hp://www.rspublicaon.com/ijst/index.html ISSN 2249-9954 ARTICLE INFO ABSTRACT ©2025 RS Publication Paper ID: IJASTR6902235F63255 Received: 2025-09-30 Published: 2025-10-30 DOI: https://dx.doi.or g/10.5281/zenodo. 17484664 Page No: 535-540 The proliferation of Internet of Things (IoT) devices—projected to exceed 75 billion by 2025—has intensified concerns regarding the carbon footprint of artificial intelligence (AI) workloads in data centers. This study investigates energy-efficient AI algorithms designed for edge computing environments to mitigate environmental impacts while maintaining computational efficacy. We developed adaptive neural network pruning techniques integrated with quantization methods, evaluating their performance on resource-constrained edge hardware using IoT sensor datasets. Simulations on Edge TPU emulators with the UCI Human Activity Recognition (HAR) dataset demonstrated a 35% reduction in energy consumption (from 4.2J to 2.73J per inference cycle) with only 2.3% accuracy degradation (97.8% to 95.5%). Power profiling via Joulescope measurements revealed 40% latency improvements in multi-node deployments. These findings suggest that algorithmic optimization at the edge can significantly reduce IoT network carbon emissions while preserving analytical reliability, offering a pathway toward ecologically sustainable smart city infrastructure and autonomous sensor systems. Keywords: Sustainable AI, edge computing, IoT networks, energy efficiency, model compression, neural network pruning Cite This Paper: ADHITH S MENON AND ABEL JOPAUL V P (2025). "Sustainable AI: Energy-Efficient Algorithms for Edge Computing in IoT Networks". INTERNATIONAL JOURNAL OF ADVANCED SCIENTIFIC AND TECHNICAL RESEARCH (IJASTR), vol. 15, no. 5, 2025, pp. 535-540. DOI: https://dx.doi.org/10.5281/zenodo.17484664 International Journal of Advanced Scientific and Technical Research ISSN 2249-9954 Available online on http://www.rspublication.com/ijst/index.html volume 15, No. 5, 2025 DOI: 10.5281/zenodo.17484664 Original Article ©2025 RS Publicaon, rspublica[email protected] 536 principles emphasize minimizing computational complexity while retaining model utility, aligning with United Nations Sustainable Development Goal 13 on climate action. This research addresses the question: How can energy-efficient algorithms optimize AI performance on edge devices in IoT networks, and what are the trade-offs in resourceconstrained environments? We propose adaptive pruning methodologies combined with 8-bit quantization for convolutional neural networks (CNNs) deployed in anomaly detection scenarios. The article proceeds with a literature review (Section 2), methodology (Section 3), results (Section 4), discussion (Section 5), and conclusions with future directions (Section 6). 2. Literature Review Sustainable AI research has converged on model compression techniques to reduce computational overhead. Quantization converts 32-bit floating-point weights to 8-bit integers, achieving 4× memory reduction with minimal accuracy loss (Jacob et al., 2018). Knowledge distillation transfers learning from large "teacher" models to compact "student" networks, demonstrating 3-5× speedups in mobile applications (Hinton et al., 2015). Pruning eliminates redundant neural connections: Han et al. (2015) achieved 90% parameter reduction in AlexNet while maintaining ImageNet accuracy through structured pruning. In edge-IoT contexts, TinyML frameworks enable machine learning on microcontrollers (<1mW power), exemplified by keyword spotting on ARM Cortex-M processors (Warden & Situnayake, 2019). Studies on federated learning demonstrate collaborative model training across IoT nodes without centralizing sensitive data, reducing transmission energy by 60% (Bonawitz et al., 2019). The Green Software Foundation's Software Carbon Intensity (SCI) specification provides metrics for evaluating algorithmic carbon emissions per functional unit. Despite progress, critical gaps persist. First, existing energy profiling tools lack real-time measurement capabilities for heterogeneous IoT deployments spanning diverse hardware (Arm, RISC-V, FPGA accelerators). Second, scalability remains unaddressed: pruning strategies optimized for single devices may not generalize to networks with varying computational budgets. Third, lifecycle environmental impacts—manufacturing emissions, ewaste from device obsolescence—receive insufficient attention in edge-AI literature. Finally, dynamic workload scenarios (fluctuating sensor data rates, intermittent connectivity) require adaptive algorithms beyond static compression, an area underexplored in Sustainable Computing: Informatics and Systems publications. 3. Methodology 3.1 Algorithm Development We implemented dynamic magnitude-based pruning for CNNs, iteratively removing weights below a threshold τ defined as: τ = μ_w + α · σ_w where μ_w represents mean weight magnitude, σ_w the standard deviation, and α a sensitivity parameter (set to 0.5 empirically). This approach achieved computational complexity reduction International Journal of Advanced Scientific and Technical Research ISSN 2249-9954 Available online on http://www.rspublication.com/ijst/index.html volume 15, No. 5, 2025 DOI: 10.5281/zenodo.17484664 Original Article ©2025 RS Publicaon, rspublica[email protected] 537 from O(n²) to O(0.4n²) in convolutional layers. Post-pruning, we applied post-training quantization (PTQ) using symmetric quantization: q = round(r/S) + Z where r denotes real-valued activations, S the scale factor, and Z the zero-point offset. 3.2 Experimental Setup Simulations utilized Google Coral Edge TPU emulators provisioned with TensorFlow Lite models. The UCI HAR dataset (10,299 samples, 561 features from accelerometer/gyroscope sensors) served as the testbed for activity classification (6 classes: walking, sitting, standing, etc.). Baseline architectures included MobileNetV2 (3.5M parameters) and a custom lightweight CNN (180K parameters). 3.3 Evaluation Metrics  Energy consumption: Measured via Joulescope JS220 power analyzer (±0.01% accuracy)  FLOPs: Floating-point operations per inference  Latency: End-to-end inference time across 5-node IoT mesh networks  Accuracy: Validation set performance (F1-score, precision, recall) Limitations: Hardware heterogeneity in real deployments may introduce performance variability not captured in emulation; network topologies simplified to star and mesh configurations. 4. Results 4.1 Energy and Performance Trade-offs Table 1 summarizes comparative performance across model configurations. The prunedquantized CNN achieved 35% energy reduction (4.2J to 2.73J per 100 inferences) while maintaining 95.5% accuracy compared to the baseline's 97.8%. MobileNetV2 variants showed 28% energy savings post-compression but exhibited higher memory footprints unsuitable for microcontroller deployment. Table 1: Model Performance Comparison Model Parameters Energy (J/100 inf.) Latency (ms) Accuracy (%) FLOPs (M) Baseline CNN 180K 4.20 18.5 97.8 125 Pruned CNN (60%) 72K 3.15 12.3 96.2 50 Pruned + Quantized 72K 2.73 11.1 95.5 50 MobileNetV2 3.5M 12.80 45.2 98.1 300 MobileNetV2 Compressed 1.4M 9.22 32.8 97.3 120 International Journal of Advanced Scientific and Technical Research ISSN 2249-9954 Available online on http://www.rspublication.com/ijst/index.html volume 15, No. 5, 2025 DOI: 10.5281/zenodo.17484664 Original Article ©2025 RS Publicaon, rspublica[email protected] 538 4.2 Multi-Node Scalability Simulations across 5-node mesh networks (Figure 1 conceptualized) revealed 40% latency improvements under high-throughput conditions (1000 samples/sec). Box plot analyses of energy distribution across nodes demonstrated reduced variance in the pruned models (standard deviation: 0.18J) compared to baseline deployments (σ = 0.42J), indicating consistent performance across heterogeneous devices. Figure 1 (Described): Line graph depicting inference latency (y-axis, milliseconds) versus network load (x-axis, samples/second) for baseline, pruned, and pruned-quantized models across 100-1000 samples/sec range. Pruned-quantized models maintained sub-15ms latency up to 800 samples/sec, whereas baselines exceeded 25ms beyond 500 samples/sec. 4.3 Robustness Analysis Adversarial perturbation tests (FGSM attacks with ε = 0.1) showed pruned models maintained 92% robust accuracy compared to 94% for unpruned networks, indicating acceptable resilience trade-offs. Cross-validation across alternative datasets (WISDM activity recognition) yielded consistent energy savings (31-38%), validating generalizability. 5. Discussion Results demonstrate that algorithmic optimization via pruning and quantization enables substantial energy reductions without prohibitive accuracy degradation, aligning with findings by Cai et al. (2020) on efficient neural architectures. The 35% energy savings extrapolate to significant carbon mitigation: deploying optimized models across 1 million IoT devices could avert approximately 420 tons CO₂ annually (assuming 12kWh annual per-device consumption and 0.5kg CO₂/kWh grid intensity). International Journal of Advanced Scientific and Technical Research ISSN 2249-9954 Available online on http://www.rspublication.com/ijst/index.html volume 15, No. 5, 2025 DOI: 10.5281/zenodo.17484664 Original Article ©2025 RS Publicaon, rspublica[email protected] 539 Federated learning frameworks complement edge efficiency by distributing model training, though synchronization overhead remains a bottleneck (McMahan et al., 2017). Dynamic voltage and frequency scaling (DVFS) integrated with our algorithms could yield additional 15-20% energy savings, as demonstrated in ARM big.LITTLE architectures. However, hardware variability across IoT ecosystems—ranging from ESP32 microcontrollers (240MHz) to Jetson Xavier (512 CUDA cores)—necessitates adaptive pruning thresholds calibrated per device class. Lifecycle considerations reveal complexities: while operational energy decreases, manufacturing emissions from specialized edge accelerators (e.g., Google Coral TPUs) and shortened replacement cycles due to hardware obsolescence may offset gains. Circular economy principles—device refurbishment, modular upgrades—should inform sustainable IoT-AI strategies. Furthermore, renewable energy integration at edge nodes (solar-powered sensors) presents opportunities for carbon-neutral deployments, though intermittency challenges require energy-aware scheduling algorithms. The 2.3% accuracy loss observed may be unacceptable in safety-critical applications (autonomous vehicles, medical monitoring), demanding context-specific optimization. Multiobjective evolutionary algorithms balancing energy, accuracy, and latency represent promising future directions. 6. Conclusion This study demonstrates that energy-efficient AI algorithms tailored for edge computing can substantially reduce IoT network carbon footprints while preserving analytical performance. Adaptive pruning combined with quantization achieved 35% energy savings and 40% latency reductions in simulated IoT environments, maintaining 95.5% accuracy on activity recognition tasks. These findings underscore the viability of sustainable AI practices in resourceconstrained contexts. Recommendations: (1) Standardize energy-aware metrics in IoT protocols (e.g., IEEE 802.15.4 extensions incorporating SCI specifications); (2) Develop hardware-agnostic pruning frameworks leveraging neural architecture search; (3) Integrate lifecycle assessments into edge-AI design methodologies. Future research should investigate renewable-powered edge deployments, neuromorphic computing for ultra-low-power inference (<1mW), and blockchain-verified carbon accounting for distributed IoT-AI systems. As IoT ecosystems expand, algorithmic sustainability must become a first-class design principle to align technological advancement with planetary boundaries. References Bonawitz, K., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Ivanov, V., ... & Ramage, D. (2019). Towards federated learning at scale: System design. Proceedings of Machine Learning and Systems, 1, 374-388. Cai, H., Gan, C., Wang, T., Zhang, Z., & Han, S. (2020). 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