A new arXiv preprint introduces an event-native symbolic-temporal spike encoding framework designed to handle heterogeneous cyber data streams. The approach builds on spiking neural networks, which compute sparsely and keep internal state, making them a fit for low-power edge devices. The authors argue these traits suit cyber monitoring, where data arrives asynchronously and continuously.
A new arXiv paper introduces FREDI, a security-focused wireless edge-intelligence framework for event-triggered inference across user devices, edge servers, and the cloud. It combines proportional-fair resource allocation with a dual-threshold early-exit scheme so that each user device can partially process inference locally before offloading. The work aims to balance fairness, latency, and efficiency in cooperative multi-layer edge deployments.
A new arXiv preprint proposes a hybrid feedback-guided optimal learning method for delivering interactive panoramic content over wireless networks. The work addresses the tight latency, frame rate, and synchronization demands of immersive VR and AR applications, where edge servers must render scenes and stream them to users. It frames the problem as jointly optimizing rendering and transmission decisions under feedback from the wireless channel and viewer interaction.
Researchers present EdgeHAR, a compact foundation model for sensor-based human activity recognition that is built to run on edge devices rather than in the cloud. The work targets common real-world problems such as shifts in sensing conditions, including unfamiliar users and devices. It is described as an edge-native approach to wearable and ubiquitous computing.
An arXiv preprint surveys how falling launch and hardware costs have led to more satellites and a fast-growing volume of data generated in orbit. Because downlinking that data to Earth is expensive and slow, the paper looks at processing it in space, including onboard edge AI and orbital data-center concepts. It is a cross-listed replacement submission and has not been peer reviewed.
A new arXiv preprint introduces SH-WRNN, a neural architecture that replaces conventional static fully connected weight matrices with a routing scheme based on implicit spherical harmonics weight fields. The authors frame the work as a challenge to the standard synapse-layer design that most deep learning models still rely on. The paper targets asymmetric edge intelligence settings, where compute and bandwidth are unevenly distributed across devices.
Researchers describe a fog-computing setup that runs deep learning inference near the source of sensor data to forecast temperatures in perishable food cold chains. The work targets the latency and connectivity limits that make cloud-only inference impractical for LoRaWAN-linked monitoring of fresh produce. The paper reports on a real-world deployment and characterises its performance.
A new arXiv preprint proposes a routing approach that decides whether function-calling LLM requests should be handled at the edge or sent to cloud models, with the aim of reducing energy consumption and carbon emissions. The work targets agentic systems where inference is currently concentrated in large cloud-hosted models. It argues that latency, compute placement, and grid carbon intensity can be balanced when choosing where an inference runs.
Researchers propose MANE, a multi-path adaptive network designed to improve split computing, where a small head model runs on a device and a larger tail model runs on an edge server. The approach targets efficient distributed inference by adapting how computation is divided between the device and the edge. It is described in a new arXiv preprint (2609.14660v1) listed under cross-submissions.
Researchers propose BiHDTrans, a transformer variant that uses binary hyperdimensional computing to classify multivariate time series from IoT sensors. The design targets resource-constrained edge devices, where large data volumes and limited compute make standard models impractical. It is presented as an arXiv preprint focused on balancing efficiency with classification accuracy.
A new arXiv paper surveys how large language models can support deep reinforcement learning in managing resources across IoT, edge, and cloud layers. The work focuses on continuous, context-aware decision-making in environments where constraints shift constantly. It positions LLMs as a complement to established DRL techniques for adaptive computing across the computing continuum.
A new arXiv paper proposes a perception pipeline for Formula Student Driverless vehicles that relies only on LiDAR and a random forest classifier, avoiding deep learning and multi-sensor fusion. The authors aim to cut latency and hardware demands so the system can run without GPU acceleration.