Towards Effective Communication Management in Cooperative Robotic-enabled Healthcare Systems: Open Challenges and Future Research Directions
Links to Files
Author/Creator ORCID
Date
Type of Work
Department
Program
Citation of Original Publication
Adil, Muhammad, Muhammad Khurram Khan, Aitizaz Ali, et al. “Towards Effective Communication Management in Cooperative Robotic-Enabled Healthcare Systems: Open Challenges and Future Research Directions.” IEEE Internet of Things Journal, 2025, 1–1. https://doi.org/10.1109/JIOT.2025.3631333.
Rights
© 2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Subjects
Cooperative Robotic-enabled Healthcare
Robots
Quality of service
Cloud computing
Deep Learning
Quality of Service
Deep learning
Reinforcement learning
Resource management
Reliability
Medical services
Robot sensing systems
Communication Challenges
Reviews
UMBC Security and Optimization for Networked Globe Laboratory (SONG Lab)
Reinforcement Learning
Measurement
Robots
Quality of service
Cloud computing
Deep Learning
Quality of Service
Deep learning
Reinforcement learning
Resource management
Reliability
Medical services
Robot sensing systems
Communication Challenges
Reviews
UMBC Security and Optimization for Networked Globe Laboratory (SONG Lab)
Reinforcement Learning
Measurement
Abstract
Cooperative robotic healthcare systems (CRHS) are advanced technologies that enhance medical services by allowing robots to collaborate with healthcare professionals, making clinical practices safer and more efficient. However, for these systems to work efficiently, they need fast and reliable communication and computation, all while managing the limited resources and energy available in robot-embedded sensors. Therefore, this survey focuses on clarifying how various networking and computing decisions impact different aspects of this technology, such as latency, reliability, Quality of Service (QoS), and scalability, etc. We evaluated the recent research on resource allocation, as well as orchestration in edge, fog, and cloud computing, to have a holistic overview of what has been done so far in this field. Moreover, we analyzed communication technologies such as 5G, Ultra-Reliable Low-Latency Communication (URLLC), Time-Sensitive Networking (TSN), Software-Defined Networking (SDN), Network Function Virtualization (NFV), and network slicing to understand their role in RHCS QoS metrics. Our synthesis finds that (i) placing perception/control close to the edge consistently decreases end-to-end delay, (ii) SDN/NFV and time-sensitive networking improve predictable and real-time operation in multi-robot hospital environments; and (iii) learning-based scheduling and offloading often outperform static heuristics in variable workloads. Despite these advancements, we have identified several challenges in the literature, such as limited interoperability between different vendors and a lack of standardized benchmarks for Quality of Service (QoS), etc. Therefore, we conducted a comparative analysis to understand how specific design choices influence the QoS metrics of this technology. In addition, we have proposed potential research directions that address the open challenges to ensure the real deployment of this technology.
