@Article{cmc.2023.042403,
AUTHOR = {Muhammad Tahir, Mingchu Li, Irfan Khan, Salman A. Al Qahtani, Rubia Fatima, Javed Ali Khan, Muhammad Shahid Anwar},
TITLE = {Towards Cache-Assisted Hierarchical Detection for Real-Time Health Data Monitoring in IoHT},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {77},
YEAR = {2023},
NUMBER = {2},
PAGES = {2529--2544},
URL = {http://www.techscience.com/cmc/v77n2/54789},
ISSN = {1546-2226},
ABSTRACT = {Real-time health data monitoring is pivotal for bolstering road services’ safety, intelligence, and efficiency within
the Internet of Health Things (IoHT) framework. Yet, delays in data retrieval can markedly hinder the efficacy
of big data awareness detection systems. We advocate for a collaborative caching approach involving edge devices
and cloud networks to combat this. This strategy is devised to streamline the data retrieval path, subsequently
diminishing network strain. Crafting an adept cache processing scheme poses its own set of challenges, especially
given the transient nature of monitoring data and the imperative for swift data transmission, intertwined with
resource allocation tactics. This paper unveils a novel mobile healthcare solution that harnesses the power of our
collaborative caching approach, facilitating nuanced health monitoring via edge devices. The system capitalizes
on cloud computing for intricate health data analytics, especially in pinpointing health anomalies. Given the
dynamic locational shifts and possible connection disruptions, we have architected a hierarchical detection system,
particularly during crises. This system caches data efficiently and incorporates a detection utility to assess data
freshness and potential lag in response times. Furthermore, we introduce the Cache-Assisted Real-Time Detection
(CARD) model, crafted to optimize utility. Addressing the inherent complexity of the NP-hard CARD model, we
have championed a greedy algorithm as a solution. Simulations reveal that our collaborative caching technique
markedly elevates the Cache Hit Ratio (CHR) and data freshness, outshining its contemporaneous benchmark
algorithms. The empirical results underscore the strength and efficiency of our innovative IoHT-based health
monitoring solution. To encapsulate, this paper tackles the nuances of real-time health data monitoring in the
IoHT landscape, presenting a joint edge-cloud caching strategy paired with a hierarchical detection system. Our
methodology yields enhanced cache efficiency and data freshness. The corroborative numerical data accentuates
the feasibility and relevance of our model, casting a beacon for the future trajectory of real-time health data
monitoring systems.},
DOI = {10.32604/cmc.2023.042403}
}