Energy-aware data sharing protocol for vision disability management
The Internet of Medical Things (IoMT) is an emerging and impactful research area in healthcare, focused on enabling seamless data exchange among medical devices and sensors to enhance patient care and lifestyle. One promising application of IoMT is integrative medical imaging, where data from various imaging devices are shared with a central server to facilitate accurate diagnostics and prevent potential disabilities. IoMT also plays a critical role in managing vision-related impairments, such as monitoring patients' walking patterns to prevent falls and injuries. Timely intervention is essential for patients at risk of falling, necessitating continuous online monitoring of their vital signs. To support this, various wireless sensors are deployed on patients to track their health conditions and mobility. These sensors typically operate with limited energy reserves and are often designed to harvest energy from their environment to extend their operational lifetime. However, during emergencies, the demand for data transmission surges, leading to rapid energy depletion. To ensure energy efficiency and reliable communication, it is crucial to avoid data collisions. Therefore, contention-free Medium Access Control (MAC) protocols like Time Division Multiple Access (TDMA) are preferred over contention-based alternatives. This work proposes a TDMA-based MAC protocol, referred to as TMPH (TDMA-based MAC Protocol for Health monitoring), aimed at facilitating efficient transmission of health and imaging-related data. TMPH integrates the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS), a multi-criteria decision-making approach, to optimize transmission rates, maximize link utilization, and prolong the lifecycle of wireless sensor nodes in response to varying data traffic demands. The proposed protocol is evaluated against traditional First Come First Serve (FCFS) and Shortest Job First (SJF)-based BS-MAC protocols. Simulation results demonstrate that TMPH significantly enhances data transmission performance, improves link utilization, and extends the network's operational life.
Authors
- Mohammad Zubair Khan (ORCID: https://orcid.org/0000-0002-2409-7172)
- Mohammad Mahyoob (ORCID: https://orcid.org/0000-0002-6664-1017)
- Muhammad Awais Javed (ORCID: https://orcid.org/0000-0001-5816-097X)
- Nadim Rana (ORCID: https://orcid.org/0000-0002-6215-4414)
- Jeehaan Algaraady (ORCID: https://orcid.org/0000-0003-3901-4648)
- Ahmad Naseem Alvi (ORCID: https://orcid.org/0000-0002-7771-9115)
- Ali Haider (ORCID: https://orcid.org/0000-0002-2876-3996)
Institutions
- COMSATS University Islamabad (PK)
- Taibah University (SA)
- Taiz University (YE)
- King Salman Center for Disability Research (SA)
- Jazan University (SA)
Publication Details
- Journal
- Ain Shams Engineering Journal
- Published
- 2025-06-17
- DOI
- https://doi.org/10.1016/j.asej.2025.103547
- Primary Topic
- IoT and Edge/Fog Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- King Salman Center for Disability Research