Towards Effective Communication Management in Cooperative Robotic-enabled Healthcare Systems: Open Challenges and Future Research Directions
| dc.contributor.author | Adil, Muhammad | |
| dc.contributor.author | Khan, Muhammad Khurram | |
| dc.contributor.author | Ali, Aitizaz | |
| dc.contributor.author | Abulkasim, Hussein | |
| dc.contributor.author | Farouk, Ahmed | |
| dc.contributor.author | Song, Houbing | |
| dc.contributor.author | Jin, Zhanpeng | |
| dc.date.accessioned | 2026-01-22T16:19:00Z | |
| dc.date.issued | 2025-11-10 | |
| dc.description.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. | |
| dc.description.sponsorship | The authors extend their appreciation to the Deputyship for Research and Innovation, Ministry of Education in Saudi Arabia, for funding this research (IFKSU-HCRA-3-3). This work was also supported in part by the Guangdong Provincial Department of Science and Technology under Grant 2023CX10X070; in part by the Guangdong Provincial Key Laboratory of Human Digital Twin under Grant 2022B1212010004; in part by the Guangzhou Basic Research Program under Grant SL2023A04J00930; and in part by the Shenzhen Holdfound Foundation Endowed Professorship. | |
| dc.description.uri | https://ieeexplore.ieee.org/document/11237130 | |
| dc.format.extent | 35 pages | |
| dc.genre | journal articles | |
| dc.genre | postprints | |
| dc.identifier | doi:10.13016/m2ny8o-i8ic | |
| dc.identifier.citation | 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. | |
| dc.identifier.uri | https://doi.org/10.1109/JIOT.2025.3631333 | |
| dc.identifier.uri | http://hdl.handle.net/11603/41529 | |
| dc.language.iso | en | |
| dc.publisher | IEEE | |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Information Systems Department | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.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. | |
| dc.subject | Cooperative Robotic-enabled Healthcare | |
| dc.subject | Robots | |
| dc.subject | Quality of service | |
| dc.subject | Cloud computing | |
| dc.subject | Deep Learning | |
| dc.subject | Quality of Service | |
| dc.subject | Deep learning | |
| dc.subject | Reinforcement learning | |
| dc.subject | Resource management | |
| dc.subject | Reliability | |
| dc.subject | Medical services | |
| dc.subject | Robot sensing systems | |
| dc.subject | Communication Challenges | |
| dc.subject | Reviews | |
| dc.subject | UMBC Security and Optimization for Networked Globe Laboratory (SONG Lab) | |
| dc.subject | Reinforcement Learning | |
| dc.subject | Measurement | |
| dc.title | Towards Effective Communication Management in Cooperative Robotic-enabled Healthcare Systems: Open Challenges and Future Research Directions | |
| dc.type | Text | |
| dcterms.creator | https://orcid.org/0000-0003-2631-9223 |
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