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A Special Issue of Sensors (ISSN 1424-8220) belonging to the section "Sensor Networks".

Deadline for manuscript submissions: 20 May 2027 | Viewed by 5994

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Editors

Dr. Zaib Ullah

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Guest Editor

Artificial Intelligence & Robotics Laboratory, Giustino Fortunato University, 82100 Benevento, Italy
Interests: wireless sensor networks; IoT; MIMO wireless communications; signal processing; next-generation mobile cellular systems; UAV utilization for 5G and B5G communications
Special Issues, Collections and Topics in MDPI journals

Dr. Muddasar Naeem

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Guest Editor

Research Center on ICT Technologies for Healthcare and Wellbeing, Università Telematica Giustino Fortunato, 82100 Benevento, Italy
Interests: reinforcement learning; deep learning; eHealth systems; communication; MIMO systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid advancement of the Internet of Things (IoT) and sensor technologies is transforming healthcare, enabling innovative solutions for patient monitoring, diagnostics, and personalized treatment. These technologies facilitate real-time data collection, seamless connectivity, and intelligent analytics, enhancing clinical decision-making and improving patient outcomes. From wearable devices tracking vital signs to smart implants and remote health monitoring systems, the IoT and sensors are revolutionizing how healthcare is delivered, making it more proactive, efficient, and accessible. This Special Issue explores cutting-edge developments in IoT and sensor technologies tailored for healthcare applications. It highlights advancements in sensor design, data integration, security, and interoperability, addressing challenges such as privacy, scalability, and regulatory compliance. Contributions in this issue showcase novel frameworks, case studies, and interdisciplinary approaches that drive the future of connected healthcare, fostering innovations that bridge technology and patient care.

Dr. Zaib Ullah
Dr. Muddasar Naeem
Guest Editors

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Keywords

Internet of Things (IoT)
sensor technologies
healthcare monitoring
wearable devices
remote patient care
data analytics
blockchain application in healthcare
personalized medicine

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Published Papers (5 papers)

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Research

27 pages, 11819 KB

Open AccessArticle

Dual-Layer PSO-Enhanced Federated Heterogeneous Data Fusion for Hemodialysis Complication Prediction

by Chihhsiong Shih, Cheng-Hsu Chen and Xiuyuan Yeah

Sensors 2026, 26(16), 5209; https://doi.org/10.3390/s26165209 - 17 Aug 2026

Viewed by 342

Abstract

Taiwan has one of the highest dialysis prevalences worldwide, making safe and reliable hemodialysis monitoring a critical sensor-based healthcare challenge. Modern hemodialysis machines integrate heterogeneous multimodal sensors (pressure, flow, conductivity, temperature, and cardiovascular signals), but differences in machine brands, data formats, and privacy constraints hinder centralized learning and robust complication prediction. This work proposes a Medical IoT-oriented federated learning framework, PSOFed-HD, that performs dual-layer Particle Swarm Optimization (PSO) to enhance heterogeneous sensor fusion for predicting dialysis-related hypotension and discomfort events. The events are defined as abnormal blood-pressure states, defined as systolic blood pressure <90 mmHg. Each hemodialysis machine is paired with an edge gateway acting as an FL client, where local PSO optimizes CNN feature weights over non-IID sensor subsets, while the central server applies PSO-driven aggregation to adaptively weight client models according to validation performance. Experiments on real-world hemodialysis datasets with 17 most commonly seen HD physiological features demonstrate that standard FedAvg yields an accuracy of 65.24% and F1-score of 0.5318, server-side PSO improves accuracy to 75.11%, and client-side PSO further raises accuracy to 81.97%. The proposed dual-layer PSO framework achieves the best performance, with 90.56% accuracy and an F1-score of 0.8533, along with superior ROC characteristics (AUC = 0.908) and stable cross-validation across 11 folds. State-of-the-art federated learning techniques for non-IID data such as SCAFFOLD and FedProx are also examined using the same heterogeneous HD dataset. The performance is close to our client-only PSO techniques, proving the merits of our dual-layer PSO architecture. These results confirm that jointly optimizing local feature representations and global aggregation weights enables effective fusion of heterogeneous hemodialysis sensor data under privacy-preserving Medical IoT constraints, providing a practical decision-support approach for real-time complication prediction in dialysis units. Future work will incorporate temporal models such as LSTM or Transformer architectures to achieve early event prediction. Full article

(This article belongs to the Special Issue IoT and Sensor Technologies for Healthcare)

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22 pages, 430 KB

Open AccessArticle

A Symmetric ICA-Based CDMA Receiver for Dense IoT-Enabled Healthcare Monitoring Systems

by Muhammad Irfan Anjum, Abdullah Waqas and Asad Saleem

Sensors 2026, 26(14), 4501; https://doi.org/10.3390/s26144501 - 15 Jul 2026

Viewed by 423

Abstract

The rapid development of Internet of Things (IoT)-enabled healthcare systems, including wearable medical sensors and remote patient monitoring devices, has led to dense multiuser communication scenarios in which numerous low-power devices simultaneously transmit physiological data. In such environments, multiuser interference significantly degrades reliability and increases error rates, which may compromise clinical decision-making. This paper proposes a real-time symmetric Independent Component Analysis (ICA)-based Code Division Multiple Access (CDMA) receiver, termed symmetric independent CDMA (i-CDMA) receiver, to enhance multiuser detection in dense healthcare IoT networks. Unlike conventional ICA-CDMA receivers that utilize the orthogonality of users’ spreading codes and higher-order statistics (HoS) of users’ message symbols in signal separation, the proposed approach utilizes a symmetric independence process that jointly utilizes the independence of separating codes and the statistical independence of users’ message symbols. The proposed receiver avoids the runtime optimization of scaling parameter for different user densities and SNR conditions. Furthermore, a modified whitening transform is introduced to exploit the complete signal-plus-noise subspace while avoiding overlearning. Simulation results demonstrate significant performance improvement over symmetric Gram–Schmidt orthogonalization-based standard ICA-CDMA receiver in terms of bit error rate (BER) under varying SNR and user-density scenarios for both uplink and downlink systems. The proposed receiver provides a scalable and interference-resilient communication framework suitable for real-time IoT-enabled healthcare monitoring applications. Full article

(This article belongs to the Special Issue IoT and Sensor Technologies for Healthcare)

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31 pages, 7717 KB

Open AccessArticle

Design and Validation of a Cyber–Physical Medication Dispensing Platform Integrating Edge AI Verification, Distributed Control, and Cloud Synchronization

by Buddharaksa Phatcharasaksakol, Supaphan Sittithanon, Veerinrada Pianapitham, Vipas Chantrapanichkul, Jing Tang and Ratchatin Chancharoen

Sensors 2026, 26(12), 3823; https://doi.org/10.3390/s26123823 - 16 Jun 2026

Viewed by 1739

Abstract

Medication dispensing errors remain a significant concern in healthcare systems, particularly in elderly care and long-term medication management, where incorrect medication delivery may compromise patient safety and treatment outcomes. This study presents the design and experimental validation of a cyber–physical medication dispensing platform integrating robotic manipulation, edge AI-based visual verification, distributed motion control, and cloud synchronization. The platform combines a rotary medication storage mechanism, vacuum-based pill handling, a Klipper-based control framework, and a YOLOv8 perception subsystem deployed on a Hailo AI accelerator for real-time edge inference. Experimental evaluation was conducted under controlled laboratory conditions. Using an environment-specific validation dataset, the perception subsystem achieved a precision of 0.627, recall of 0.739, and mAP@0.5 of 0.786. An adaptive verification strategy was subsequently evaluated to improve dispensing verification under varying pill occupancy conditions. End-to-end system testing comprising 80 dispensing trials achieved an overall dispensing success rate of 86.25%, with no incorrect dispensing events observed. The results demonstrate the feasibility of integrating edge AI verification, distributed control, and cloud connectivity within a cyber–physical medication dispensing platform. The presented system provides a foundation for future research on perception-assisted medication dispensing, long-term deployment, and clinical validation in smart healthcare environments. Full article

(This article belongs to the Special Issue IoT and Sensor Technologies for Healthcare)

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21 pages, 323 KB

Open AccessArticle

IoT-Based Intervention and Home Support to Address Frailty-Related Vulnerability and Well-Being in Older Adults Living in Rural Areas

by Jessica Fernández-Solana, Rodrigo Vélez-Santamaría, Ana I. Sánchez-Iglesias, Maria Isabel Villanueva-Alameda, Jerónimo J. González-Bernal and Josefa González-Santos

Sensors 2026, 26(3), 975; https://doi.org/10.3390/s26030975 - 2 Feb 2026

Viewed by 957

Abstract

Background: Spain has an increasingly aging population in rural areas. These individuals often face the burden of illness and the limitations it causes in solitude, leading to greater impacts on their health and quality of life. Therefore, the aim of this study was to evaluate the effectiveness of a combined IoT-based home monitoring and Silver Caregiver support intervention on health-related quality of life and functional, cognitive, emotional, and social outcomes in older adults living alone in rural settings. Material and methods: A longitudinal study was conducted with a sample of 144 older adults from rural areas who received home support through a Silver Caregiver and IoT technology. Results: Statistically significant differences were observed in cognitive status, anxiety, depression, family functionality, social support, life satisfaction, and quality of life. Conclusions: The findings indicate that the combined intervention primarily enhances psychological well-being, social connectedness, and perceived quality of life while contributing to the maintenance of basic physical function in older adults living in rural areas. Full article

(This article belongs to the Special Issue IoT and Sensor Technologies for Healthcare)

32 pages, 6543 KB

Open AccessArticle

Synergy of Information in Multimodal Internet of Things Systems—Discovering the Impact of Daily Behaviour Routines on Physical Activity Level

by Mohsen Shirali, Zahra Ahmadi, Jose Luis Bayo-Monton, Zoe Valero-Ramon and Carlos Fernandez-Llatas

Sensors 2025, 25(18), 5619; https://doi.org/10.3390/s25185619 - 9 Sep 2025

Cited by 3 | Viewed by 1578

Abstract

Background and Objective: The intricate connection between daily behaviours and health necessitates robust monitoring, particularly with the advent of Internet of Things (IoT) systems. This study introduces an innovative approach that exploits the synergy of information from various IoT sources to assess the alignment of behavioural routines with health guidelines. The goal is to improve the readability of behaviour models and provide actionable insights for healthcare professionals. Method: We integrate data from ambient sensors, smartphones, and wearable devices to acquire daily behavioural routines by employing process mining (PM) techniques to generate interpretable behaviour models. These routines are grouped according to compliance with health guidelines, and a clustering method is used to identify similarities in behaviours and key characteristics within each cluster. Results: Applied to an elderly care case study, our approach categorised days into three physical activity levels (Insufficient, Sufficient, Desirable) based on daily step thresholds. The integration of multi-source data revealed behavioural variations not detectable through single-source monitoring. We demonstrated that the proposed visualisations in calendar and timeline views aid health experts in understanding patient behaviours, enabling longitudinal monitoring and clearer interpretation of behavioural trends and precise interventions. Notably, the approach facilitates early detection of behaviour changes during contextual events (e.g., COVID-19 lockdown and Ramadan), which are available in our dataset. Conclusions: By enhancing interpretability and linking behaviour to health guidelines, this work signifies a promising path for behavioural analysis and discovering variations to empower smart healthcare, offering insights into patient health, personalised interventions, and healthier routines through continuous monitoring with IoT-driven data analysis. Full article

(This article belongs to the Special Issue IoT and Sensor Technologies for Healthcare)

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