IoT and Edge/Fog Computing

Latest papers

• Momentum-Guided Federated Split Distillation for Personalized Temporal Edge Intelligence

  Ahmed-Rafik Baahmed (LINEACT), Jean-Fran\c{c}ois Dollinger (LINEACT), Amine Brahmia (LINEACT), Mourad Zghal (LINEACT) · 28 September 2026

  We propose a momentum-guided federated split distillation framework for personalized, efficient, and autonomous temporal edge intelligence. We introduce TeRR-SAtt, our novel temporal reservoir student attention design that combines fixed reservoir representations, a lightweight temporal student, and…

• A Rapid Pipeline for Training and Deploying ML Models on WeBe Band

  Ehsan Kourkchi, Asmita Asmita, Houman Homayoun, Mahdi Eslamimehr · 25 September 2026

  Developing optimized machine-learning algorithms for edge devices with limited computational and memory resources is challenging, time-consuming, and highly dependent on device-specific constraints. In this work, we streamline an edge ML workflow to enable rapid development, optimization, and deploy…

• TopoCompress: Topology Aware Token Compression Algorithm for Distributed Edge MoE Inference

  Ning Li, Xinyu Wang, Xin Yuan, Wenchao Xu, Athanasios V. Vasilakos, Song Guo, Haijun Zhang · 23 September 2026

  Mixture-of-experts (MoE) models improve capacity with moderate overhead by sparsely activating experts per token. However, deploying MoE across resource-constrained edge servers incurs substantial cross-server communication as experts are distributed across heterogeneous servers. Existing placement …

• MECAIL: Communication-Aware Incremental Learning for Object Detection with 14.6 KB Spatiotemporal Experts

  Matthias Neuwirth-Trapp, Maarten Bieshaar, Danda Paudel, Konrad Schindler, Luc Van Gool, Christos Sakaridis · 22 September 2026

  Intelligent transportation systems require Incremental Learning (IL) to continually improve their overall performance in dynamic environments. However, most edge devices lack the computational resources to support on-device IL, requiring updates to be transmitted from centralized servers. We propose…

• Trust in Edge-Enabled IoT Security: Features, Challenges and Research Directions

  Esin Ece Ayd{\i}n, \c{S}erif Bahtiyar, G\"urkan G\"ur · 22 September 2026

  Providing autonomous intelligence, pervasive connectivity and usability to human life and industry has led to the emergence of the Internet of Things (IoT). To support time-sensitive and resource-constrained applications, IoT systems nowadays increasingly rely on edge computing. This brings computat…

• The Other Half of the Memory Wall: Serving 35B MoEs from SSD with Trained Routing Prediction

  Yu Lin, Yiming Wang, Runyuan Cai, Hanze Liu, Xiaodong Zeng · 17 September 2026

  Mixture-of-experts (MoE) inference on consumer hardware is bounded by weight memory: a 35B-class model is 19.5GB at 4-bit, and sparsity shrinks the compute per token, not the bytes that must be held. Naive offloading to SSD does not help on its own, because layer N+1's experts must be chosen before …

• CIDERS: Cloud-Edge LLM Collaborative Learning via Accelerating Personalized Bilevel Optimization

  Victor H. Chen, Hairui Yu, Stella K. Chung, Hong Yan · 16 September 2026

  Amid the rapid advancement of physical-world intelligence, cloud-edge collaborative large language models (LLMs) have emerged as a promising roadmap for practical LLM deployment. However, existing cloud-edge paradigms struggle to balance global consensus with local personalization, which fails to sa…

• MANE: A Multi-Path Adaptive Network for Edge Onloading of Deep Neural Networks

  Sokratis Nikolaidis, Stylianos I. Venieris, Leonidas Malachias, Iakovos S. Venieris · 15 September 2026

  Split computing constitutes a widely used distributed inference approach, where a lightweight head model is onloaded onto the device and a heavier tail model resides on an edge server, leveraging the growing computational capabilities of modern System-on-Chips while alleviating server load. As intel…

• TASTE: Throughput-Aware Batch Size Tuning for On-Device Edge Learning

  Avik Bhatnagar, Federico Nicolas Peccia, Oliver Bringmann · 9 September 2026

  The rise of privacy-preserving artificial intelligence (AI) has shifted the focus of model adaptation and personalization towards on-device learning, where deep learning models are finetuned directly on edge hardware using local user data. However, this shift requires optimization of deep learning t…

• PLATOS: A Power and Latency-Aware Task-Oriented Scheduling Strategy for Healthcare IoT in Fog Computing

  Mohammed Alaa Ala'anzy, Zulfiqar Ahmad, Zhanar Mukash · 9 September 2026

  Healthcare Internet of Things (HIoT) technology is revolutionising the healthcare industry by enabling real-time data collection and analysis for personalised patient care. However, the rapid expansion of HIoT technology introduces challenges such as increased latency and higher energy consumption i…

• Diffusion Language Models for Mobile Edge Agentic AI: Foundations, Applications, and Challenges

  Chenqi Li, Minghui Min, Dusit Niyato, Wei Ni · 7 September 2026

  Diffusion language models (DLMs) offer a non-autoregressive alternative for mobile edge agentic artificial intelligence (AI) by refining tokens through iterative denoising rather than left-to-right decoding. Compared with autoregressive Transformer-based large language models (LLMs), DLMs can update…

• PPO-STGNN: A Proximal Policy Optimization Approach with Spatio-Temporal Graph Neural Networks for DAG Task Scheduling in Cloud-Edge-End Computing

  Yangshuo Qi, Chenwei Wang, Zihan Shen, Songlin Sun · 4 September 2026

  With the rapid development of the Internet of Things, computation intensive directed acyclic graph (DAG) tasks have become increasingly common in cloud-edge-end collaborative environments. However, cloud, edge, and end nodes are highly heterogeneous in computing capacity, network bandwidth, and ener…

• Intelligent Edge Computing

  Kalgi Gandhi, Minal Bhise · 2 September 2026

  The number of edge devices in large-scale edge systems is rapidly increasing. Edge devices have limited processing power, memory, and network bandwidth, making resource utilization and data management during edge query processing challenging. Joins are among the costliest database operations in term…

• DART-FL: Burst-Aware Multitask Federated Learning under Dynamic Inference Demand at the Edge

  Yiming Xie, Pinrui Yu, Geng Yuan, Xue Lin, Ningfang Mi · 31 August 2026

  Edge intelligence systems increasingly require model training and online inference to coexist on resource-constrained devices, while inference demand can vary substantially across tasks over time. This creates two coupled challenges: sufficient computation must be reserved for inference to maintain …

• SAREF-based Ontology for Distributed AI Workflows across the Edge-Fog-Cloud Continuum

  Viorica Rozina Chifu, Tudor Cioara, Vasile Ofrim, Liana Toderean, Ionut Anghel, Laura Daniele, Cornelis Bouter · 28 August 2026

  Nowadays semantic models provide limited support for representing distributed AI workflows and their execution across heterogeneous edge, fog, and cloud environments. Therefore, AI processes and resources are often described using incompatible semantic representations, affecting the interoperability…

• Trustworthy mobile edge caching: a blockchain approach to mitigate malicious nodes and incentivize cache sharing

  Motahare Ebrahimi, Nastooh Taheri Javan, Seyedakbar Mostafavi, Fatemeh Pakzaban · 21 August 2026

  As mobile network traffic continues to grow, content caching on edge servers is critical for reducing latency. However, challenges such as malicious edge servers that may delete or manipulate cached content, along with the limited capacity of these servers, need to be addressed. To overcome the capa…

• EcoVLA: Energy-Efficient Device-Edge Co-Inference for Vision-Language-Action Models under Real-Time Constraints

  Ao Zhou, Bo Dai, Le Yu, Xingyu Liu, Zeyu Hao, Lingkun Long, Chunming Hu, Jianlei Yang · 18 August 2026

  Vision-Language-Action (VLA) models have emerged as a promising foundation for Embodied AI, but their high inference cost poses significant challenges for deployment in robotic systems. In practice, on-device inference is constrained by limited compute capacity and energy budgets, struggling to simu…

• Joint Optimization of Memory and Computing Frequency for Energy-Efficient DNN Inference

  Yunchu Han, Zhaojun Nan, Sheng Zhou, Zhisheng Niu · 17 August 2026

  Deep neural network (DNN) inference on mobile devices often incurs high latency and energy consumption due to limited computing and memory resources. To enable energy-efficient DNN inference, most existing studies focus on dynamic voltage and frequency scaling (DVFS) for adjusting the computing freq…

• Removing Infrastructure Barriers in Human-Robot Collaboration Through Wireless Reconfigurable Cells

  Emma Takács, Mátyás Hajós, Ádám Juniki, Ádám Fischer, Zoltán Komáromi, Kristóf Abai, Dániel Horváth, Sándor Máthé, Konstantinos Kousias, Bence Tipary · 11 August 2026

  Human-Robot Collaboration (HRC) plays a vital role in dynamic, high mix, low volume industrial scenarios such as remanufacturing, which frequently face workcell rearrangements. Traditional setups are constrained by power and data cabling, restricting modularity and reconfigurations, while the select…

• BALANCE: Hybrid Autoregressive-Speculative LLM Inference in Wireless Edge Networks

  Guanqiao Qu, Shuo Chen, Qian Chen, Kin K. Leung, Xianhao Chen · 7 August 2026

  Edge inference is a promising paradigm to provide large language model (LLM) inference services in next-generation mobile networks. LLM inference mainly relies on two approaches: Autoregressive decoding (AD) generates output tokens sequentially, resulting in long latency; Speculative decoding (SD) a…

• Multi-Agent Reinforcement Learning for Online Traffic Scheduling in Time-Sensitive Application

  Marcos Carvalho, Fatih Temiz, Shavbo Salehi, Melike Erol-Kantarci, Daniel F. Macedo · 7 August 2026

  Time-sensitive networking (TSN) is increasingly integrated into mobile edge computing (MEC) to support applications with stringent latency requirements, such as extended reality (XR). However, existing TSN scheduling solutions predominantly rely on static optimization techniques or centralized learn…

• Design-Time Optimization of Deep Neural Networks for Intermittent Learning on Microcontrollers

  Jakob Schubert, Maximilian Kasper, Maximilian Linke, Benedict Herzog, Mark Deutel, Axel Plinge, Dominik Seuss, Christopher Mutschler · 5 August 2026

  We present a method for designing deep neural networks (DNNs) for intermittent, energy-autonomous, on-device learning on microcontroller units (MCUs). In mobile applications where the energy can run out, e.g., when solar-powered, executing artificial intelligence (AI) faces a technical issue as lear…

• HetRoute Heterogeneous and Cost-aware Collaborative Routing Framework for Distributed Edge MoE Inference

  Xin Yuan, Ning Li, Wenchao Xu, Song Guo, Haijun Zhang · 4 August 2026

  Mixture-of-Experts (MoE) models have become a dominant architecture for large-scale AI services, yet deploying them over geo-distributed heterogeneous edge servers remains challenging. When the Top-k activated experts of a token are spread across multiple servers, the optimal routing depends jointly…

• TrimMoE A communication aware and adaptive depth framework for distributed edge inference

  Ning Li, Shuting Bai, Xin Yuan, Wenchao Xu, Song Guo, Haijun Zhang · 4 August 2026

  Serving Mixture-of-Experts (MoE) large language models across distributed edge servers is bottlenecked by the cross-server expert transmission. The existing approaches mainly focus on how to reach a remote expert faster. However, in this paper, we instead consider whether a given layer, and the laye…

• Learning-Based Collaborative MEC for LLM Inference with Soft-Deadline Awareness via Transformer-Enhanced PPO

  Ngoc Hung Nguyen, Bjorn Landfeldt · 4 August 2026

  This paper investigates collaborative mobile edge computing (MEC) servers for large language model (LLM) inference under soft deadline constraints. In this system, to improve the quality of service, computations are expected to be completed within their deadlines. However, due to dependencies among …