2026· ITEGAM- Journal of Engineering and Technology for Industrial Applications (ITEGAM-JETIA)· Vol 12, pp. 1368-1381· 0 citations
TL;DR
This paper examines the way phones and IoT devices run deep neural network (DNN) inference with help from a nearby edge server to minimize total user energy and connects theory, algorithms and experiments into one unified framework for energy minimization tasks.
Abstract
This paper examines the way phones and IoT devices run deep neural network (DNN) inference with help from a nearby edge server. It uses semantic communication (SC) to shrink the data sent over the air. The work notes that local computing power is limited. It connects two aspects: the level of data compression and the amount of local computation needed to achieve that compression. This link is modeled as a piecewise linear function across DNN layers. Early layers compute less and compress less. Deeper layers compute more and compress more. The objective is to minimize total user energy. The threshold depends on the radio channel quality and the local energy cost per compute cycle. A user above the threshold should upload earlier with less compression. A user below the threshold should compute more locally to compress the data. Next, a low-complexity iterative algorithm is designed for the general case. It iteratively updates the split layer and resources. The proposed scheme saves much more energy than full offloading and then conventional partial offloading. The gains are largest when wireless bandwidth is scarce. The approach scales to many users and suits real mobile edge scenarios. The model is transparent. Assumptions suit edge networks well. It connects theory, algorithms and experiments into one unified framework for energy minimization tasks.
With the rapid development of the Internet of Things (IoT) and artificial intelligence (AI) technologies, edge computing emerges as a crucial computing paradigm. By processing data near the source, edge computing enables faster and more efficient intelligent services. However, edge devices usually limit computational resources, and existing DNN inference latency profiling methods often rely on internal model details or large-scale latency measurements, making them costly and unsuitable for black-box deployment scenarios. This paper proposes ProfEdge, a fast construction framework for deep neural network (DNN) latency profiling models based on Gaussian process regression and Bayesian optimization. ProfEdge adaptively samples real latency measurements under different batch-size states and tunes profiling-model hyperparameters to reduce construction cost while improving profiling accuracy. Specifically, ProfEdge builds an adaptive sampling module based on Gaussian process regression to locate high-error regions through coarse-grained sampling and dynamically refine the sampling process. It further designs a dynamic Bayesian optimization mechanism to improve the accuracy of the latency profiling model. Finally, ProfEdge constructs a cross-device performance mapping model to migrate an existing profiling model to a target device with lightweight stratified calibration, thereby avoiding full reconstruction of the target-device profiling model. Experiments on various edge devices and DNN models, including CNN-based and transformer-based workloads, show that ProfEdge reduces profiling errors by up to 80% and saves over 70% of profiling construction cost compared with existing methods. The cross-device migration results further demonstrate that ProfEdge can achieve competitive profiling accuracy on new devices with only a small number of target-device calibration samples.
Wei-Long Wang, Song-Tao Lu, Jia-Wei Liu et al.· ACM Transactions on Internet...· 0 citations
The impact of memory frequency and computing frequency on DNN inference time is considered, and a low-complexity heuristic algorithm is proposed to effectively solve the overall problem with polynomial time complexity.
Deep joint source-channel coding-enabled (DeepJSCC) semantic communication (SemCom) has excelled at delivering high perceptual quality at low channel-bandwidth ratios, which positions it as a pillar for next-generation wireless networks. However, the existing works have difficulty accommodating user heterogeneity in terms of communication channel quality, expected quality-of-service (QoS) targets, and the available local energy. Therefore, in this paper, we explicitly reflect the heterogeneity of user devices in terms of the differences in expected QoS, channel condition, and local energy, and then mathematically formulate the problem. Next, we propose an energy-aware compression-computation co-adaptation (CoCo) framework, in which the base station can meet the expected user QoS by transmitting a longer signal or offloading the task to a local device. The user has to dedicate energy to denoising the signal to recover higher-fidelity latent features before feeding it to the semantic decoder. To solve the formulated problem, we first decompose it into two sub-problems: parameter optimization and resource allocation problems. Specifically, we propose a robust codec that effectively works under a diversity of compression rates and channel noise without re-training, while the greedy sub-carrier allocation lowers the communication time. Finally, we present simulation results on standard image datasets over additive white Gaussian noise to demonstrate the effectiveness of CoCo, which reduces total latency relative to rate-only adaptive DeepJSCC or denoising-only, thereby ensuring the demands of each individual user are met.
Loc X. Nguyen, Y. Park, Avi Deb Raha et al.· 0 citations
The growing smart devices (SDs) in the Industrial Internet of Things (IIoT) generate complex computations that strain the performance and energy of local processing. Mobile Edge Computing (MEC) addresses this by providing nearby computing resources for low-latency offloading. However, achieving efficient computation offloading under massive device concurrency and densely distributed computation offloadings remains a key challenge. To address this, this paper constructs a multi-server MEC system model for IIoT and introduces Mean-Field Game (MFG) theory to model the offloading competition among SDs. This effectively reduces the dimensionality and complexity of multi-agent interactions. A novel Mean-Field Computation Offloading (MFCO) algorithm is proposed, which combines MFG with Rainbow Deep Q-Network under a Multi-Agent Deep Reinforcement Learning framework. By incorporating advanced components such as distributional value estimation, prioritized experience replay, multi-step learning, and dueling architecture, each SD acts as an autonomous agent, optimizing its policy based on local observations and mean-field approximations. Further enhancements include Boltzmann exploration, adaptive learning rates, and a mean Q-network structure, which improve convergence speed and training stability. Extensive simulations on a large-scale IIoT platform (100 SDs, 9 MEC servers) demonstrate that MFCO reduces computation latency and improves long-term rewards while maintaining robust server performance.
Xinmin Cheng, Chengquan Yu, Lu Gao et al.· IEEE Transactions on Green C...· 0 citations
Edge IoT devices are increasingly targeted for on-device intelligence using federated learning (FL). However, conventional FL imposes heavy communication and energy costs that make it impractical for battery-constrained, bandwidth-limited deployments with heterogeneous (non-IID) data. In this paper we present a practical, communication-efficient FL framework that combines update quantization with top-k sparsification and evaluates its performance under realistic edge conditions. We implement the framework in MATLAB and conduct an empirical study on EMNIST and a synthetic IoT sensor dataset across varied Dirichlet non-IID severities, client dropout rates, and multiple random seeds. Our experiments show that combining low-bit quantization (4 bits) with sparsification (top 2–5%) yields large reductions in transmitted bytes and estimated uplink energy while maintaining useful model fidelity: Compared with uncompressed FedAvg, the proposed 4-bit quantization and top-1% sparsification with error feedback reduces cumulative communication by approximately 98.87%, while achieving a final accuracy of 70.30% ± 0.40% across three random seeds. We provide detailed convergence analysis, confidence intervals across seeds, and an energy model translating bytes to Joules to quantify device-level savings. Finally, we analyze failure modes and robustness under extreme heterogeneity and client dropout, and provide reproducible MATLAB code and result artifacts.
The proliferation of optical sensors (e.g., surveillance cameras) in edge Internet of Things (IoT) environments generates massive video streams, posing critical challenges of high latency, network congestion, and data privacy for cloud-centric deep learning inference. To address this, we propose a novel edge-cloud collaborative inference framework. Our approach unifies model partitioning and task offloading decisions for lightweight vision models. We formulate a joint optimization problem aimed at maximizing the number of served tasks under stringent resource and latency constraints, and devise both offline and online heuristic scheduling algorithms. The online algorithm incorporates a novel dynamic threshold-based admission control mechanism for efficient real-time decision-making. Extensive simulations, based on real-world sensor data profiles and state-of-the-art lightweight vision models (e.g., MobileViT), validate the framework's efficacy. Compared to conventional baselines (FIFO, LBF), our strategy achieves: 1) The task acceptance rate has increased by 15-30%; 2) The uplink data volume for a single task has decreased by approximately 40%; 3) The system throughput has increased by more than 20%, and it strictly adheres to the task deadline. The proposed strategy provides an efficient and practical solution for resource-constrained optical sensing networks, significantly advancing the feasibility of deploying large-scale, real-time intelligent perception at the network edge.
Ying Gou· European Conference on Elect...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
