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


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Guest Editor
Department of Computer Science, Harbin Institute of Technology, Harbin 150028, China
Interests: edge computing; edge intelligence; network measurement

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Guest Editor
School of Cyber Science and Engineering, Southeast University, Nanjing 211189, China
Interests: privacy protection and optimization techniques for edge intelligence

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

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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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Published Papers (2 papers)

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Research

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29 pages, 2267 KB  
Article
EdgeElderCare: A Resource-Aware, Scene-Adaptive Edge-Cloud Collaborative System for Long-Term Elderly Safety and Health Monitoring
by Lihao Luo, Yuting Li, Lin Wei, Di Han, Ruifeng Cao, Bo Chen, Yuechen Pan and Yunfan Chen
Electronics 2026, 15(12), 2601; https://doi.org/10.3390/electronics15122601 (registering DOI) - 12 Jun 2026
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Abstract
Driven by global population aging, long-term in-home and institutional elderly care faces challenges in delivering continuous, privacy-aware, and resource-efficient safety and health monitoring. Existing edge-based solutions struggle to jointly balance detection accuracy, privacy, and resource overhead during continuous operation, and often have limited [...] Read more.
Driven by global population aging, long-term in-home and institutional elderly care faces challenges in delivering continuous, privacy-aware, and resource-efficient safety and health monitoring. Existing edge-based solutions struggle to jointly balance detection accuracy, privacy, and resource overhead during continuous operation, and often have limited situational awareness and inflexible management. We propose EdgeElderCare, a resource-aware, scene-adaptive edge-cloud collaborative system for continuous elderly safety and health monitoring. Its contributions are threefold: (1) a scene-adaptive multi-sensor task-sharing architecture that deploys vision-based fall detection in public areas and privacy-aware millimeter-wave radar in private spaces. Combined with edge-side task scheduling, it provides spatially complementary coverage of public and private areas, mitigates the accuracy–privacy conflict, and reduces computing and bandwidth consumption relative to data-level fusion; (2) a lightweight myocardial infarction detection module deployed on an edge platform, enabling local ECG analysis with low resource overhead; (3) a 3D digital-twin edge-cloud management platform that maps multi-source sensing data to a virtual scene in real time and supports hierarchical visual alerting. Experiments in a real nursing home environment show that the system operated stably on resource-constrained edge hardware: UWB positioning achieved centimeter-level RMSE, visual fall detection reached a recall of 0.90, millimeter-wave radar fall detection achieved accuracy, and F1 above 0.90, and myocardial infarction detection exceeded 0.99 accuracy on the public PTB/PTB-XL benchmark. These results indicate an engineering-feasible approach to intelligent elderly care. Larger-scale and longer-term validation remains the focus of future work. Full article

Review

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38 pages, 7564 KB  
Review
The Evolution of the Robot Operating System Communication Ecosystem: An Overview of the DDS Architecture and Emerging Communication Protocols
by Zhe Wei, Huitong You, Haibo Xu and Zhipan Deng
Electronics 2026, 15(12), 2632; https://doi.org/10.3390/electronics15122632 (registering DOI) - 14 Jun 2026
Abstract
As robotic systems evolve toward large-scale distributed architectures and cloud-edge collaboration, communication middleware has become a critical infrastructure that impacts system real-time performance and scalability. The traditional Robot Operating System 1 (ROS 1) communication architecture, which relies on a centralized master node, has [...] Read more.
As robotic systems evolve toward large-scale distributed architectures and cloud-edge collaboration, communication middleware has become a critical infrastructure that impacts system real-time performance and scalability. The traditional Robot Operating System 1 (ROS 1) communication architecture, which relies on a centralized master node, has limitations in dynamic network environments. Robot Operating System 2 (ROS 2) achieves decentralized communication through the introduction of DDS. However, the single Data Distribution Service (DDS) mechanism remains inadequate for cross-network communication and high-performance local data exchange. Addressing the current issue in ROS communication research: the coexistence of multiple mechanisms without a unified analytical framework or guidance for selection. This paper systematically traces the evolution of the ROS communication architecture from centralized to distributed systems. It constructs a unified analytical framework covering two dimensions: communication models and data transmission paths. Crucially, to overcome the unreliability of cross-protocol comparisons based on heterogeneous literature, this paper designs and executes a set of unified benchmark experiments on a controlled testbed. These experiments systematically evaluate the performance of two mainstream DDS implementations (CycloneDDS and FastDDS) across five key metrics: latency, throughput, jitter, scalability, and packet loss rate under load. Additionally, a comprehensive comparative analysis of the performance of three transmission modes is conducted. Based on this comprehensive evaluation, this paper summarizes the performance characteristics of different mechanisms and further proposes an optimization-based middleware selection method for quantitative communication mechanism selection under different workload and application requirements. This paper provides a systematic reference for the design and optimization of ROS communication systems and offers guidance for promoting the application of multi-middleware collaborative architectures in robotic systems. Full article
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