Smart Real-Time IoT mHealth-based Conceptual Framework for Healthcare Services Provision during Network Failures


  • O.S. Albahri Victorian Institute of Technology, Melbourne, Australia
  • Amneh Alamleh Department of Artificial Intelligence, Faculty of Information Technology, Zarqa University, Zarqa, Jordan
  • Tahsien Al-Quraishi Victorian Institute of Technology, Melbourne, Australia
  • Rahul Thakkar Victorian Institute of Technology, Melbourne, Australia



mHealth, IoT Network Failures, multi-criteria decision-making, telemedicine


A series of healthcare problems related to frequent failures in telemedicine architecture, particularly in multi-sensors (Tier 1), medical center servers (Tier 3), and potential failures in network integration between these system components, needed to be addressed. The objective of this research was to present a novel smart real-time IoT mHealth framework within the context of IoT that could select an appropriate hospital during the aforementioned failures. The research methodology involved a new local multi-sensor fusion triage algorithm called the three-level localization triage (3LLT). This aimed to exclude the control process of patient triage and sensor fusion from the medical center, while also alarming failures related to medical body sensors. Additionally, the proposed framework was implemented using the multi-criteria decision-making (MCDM) method, connecting mHealth directly with distributed hospital servers. The distribution of hospitals was determined using the AHP (Analytic Hierarchy Process) based on the crossover between ‘healthcare services/time of arrival of the patient at the hospital’ and ‘hospitals list’ to estimate small power consumption. Validation processes were conducted for the proposed framework. The expected output from this research is to enhance the provision of healthcare services during various network failures.


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How to Cite

Albahri, O., Alamleh , A., Al-Quraishi , T., & Thakkar, R. (2023). Smart Real-Time IoT mHealth-based Conceptual Framework for Healthcare Services Provision during Network Failures . Applied Data Science and Analysis, 2023, 110–117.
DOI: 10.58496/ADSA/2023/010
Published: 2023-11-02