Resource-Aware Edge/on-Device Intelligence for Long-Term Autonomous Mobile Systems
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".
Deadline for manuscript submissions: 15 November 2026 | Viewed by 303
Special Issue Editors
Interests: edge computing; edge intelligence; network measurement
Special Issue Information
Dear Colleagues,
Autonomous mobile systems, such as robots, drones, intelligent vehicles, and mobile edge devices, are increasingly expected to operate continuously and independently over long periods of time. Unlike cloud-based intelligence, these systems rely on edge and on-device intelligence, where computation, energy, and memory are physically constrained, dynamically varying, and tightly coupled with sensing and actuation.
Recent progress in edge AI, adaptive inference, and foundation models enables more powerful on-device intelligence but also raises fundamental challenges: How should intelligent capability be provisioned, adapted, and degraded over time when compute and energy resources are limited? How can edge intelligence be co-designed with mobile systems to ensure long-term safety, efficiency, and robustness?
This Special Issue aims to bring together researchers and practitioners from mobile systems, edge computing, and autonomous intelligence to explore resource-aware edge/on-device intelligence. The focus is on models, systems, and algorithms that explicitly coordinate computation, energy, and intelligent capability to support long-term autonomous operation in dynamic environments.
Topics of Interest
We invite original research contributions, including early-stage and system-oriented work, on topics including but not limited to, the following:
Edge and On-Device Intelligence
- Edge/on-device AI for mobile and autonomous systems;
- Model compression, quantization, and adaptation for edge deployment;
- Mixture-of-Experts (MoE) and conditional computation on edge devices;
- Adaptive inference, early-exit, and anytime intelligence.
Resource-Aware Inference and Scheduling
- Resource-aware and energy-aware inference mechanisms;
- Joint scheduling of computation, energy, and intelligent capability;
- Capacity-region or budget-based intelligence control;
- Graceful degradation and proportional intelligence supply.
Systems and Model deployment on edge/device
- Systems and Middleware for adaptive edge intelligence;
- AI deployment on heterogeneous edge platforms (CPU/GPU/NPU/SSD);
- Expert caching, migration, and memory hierarchy management;
- Monitoring, profiling, and control of resource usage on devices.
Long-Term Autonomous Operation
- Long-term performance modeling and sustainability of edge intelligence;
- Closed-loop interaction between perception, decision-making, and actuation;
- Energy-aware planning, control, and risk-aware intelligence;
- Cross-layer co-design for long-term autonomous mobile systems.
Evaluation, Benchmarks, and Applications
- Long-duration evaluation and benchmarking of edge intelligence systems;
- Simulation platforms and real-world deployments;
- Applications in robotics, drones, vehicular systems, and mobile sensing;
- Case studies and lessons learned from deployed systems.
Dr. Ning Li
Dr. Kang Wei
Guest Editors
Manuscript Submission Information
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Keywords
- edge intelligence
- on-device intelligence
- mobile and autonomous systems
- model compression
- quantization
- edge deployment
- mixture-of-experts
- resource-aware inference
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