Research Article
A Unified Low-Cost Embedded IoT Framework for Concurrent Physiological and Ambient Air-Quality Monitoring in Patient-Centred Care
Issue:
Volume 14, Issue 2, June 2026
Pages:
26-37
Received:
8 July 2026
Accepted:
23 July 2026
Published:
10 August 2026
Abstract: Continuous tracking of a patient's vital signs is now routine, but the air the patient actually breathes is seldom measured by the same inexpensive device, even though air quality is a modifiable factor that affects cardiorespiratory health. Because embedded health monitors and air-quality monitors are usually built as separate products, a caregiver must install, power, and reconcile two devices, and the vital-sign readings arrive without any picture of the environment that produced them. To close this gap, we describe a single low-cost embedded platform that combines a physiological subsystem (a pulse sensor and an LM35 temperature sensor) with an environmental subsystem based on an MQ-135 gas sensor. Both share one Arduino Uno (ATmega328P) edge node, and an ESP32 Wi-Fi gateway relays the readings to the ThingSpeak cloud. A 10-bit ADC digitises every channel; the firmware converts the counts into clinical and air-quality indices, checks them against calibrated thresholds, and reports the outcome through a 16×2 I2C LCD, a graduated LED and PWM-buzzer alert stage, and a USART log. Each subsystem was built in hardware and checked against a Proteus simulation. In testing, the physiological subsystem separated normal, tachycardic, and febrile states cleanly (pulse 72-120 bpm; temperature 98.2-101.2°F), and the environmental subsystem classified all four severity levels correctly across five controlled gas trials, with an 8-12 s response time and full simulation-hardware agreement. The assembled prototype costs roughly BDT 1,347 (≈ USD 12.3). Three contributions follow: a single-node architecture serving both domains, a shared threshold-classification and multi-modal alerting scheme that works across them, and a cost model grounded in the actual build. Placing patient vitals and ambient air quality on one affordable node lets the device raise context-aware alerts—such as calling for ventilation when pollutant levels climb near a vulnerable patient—which makes it a practical fit for homes, clinics, and resource-limited settings.
Abstract: Continuous tracking of a patient's vital signs is now routine, but the air the patient actually breathes is seldom measured by the same inexpensive device, even though air quality is a modifiable factor that affects cardiorespiratory health. Because embedded health monitors and air-quality monitors are usually built as separate products, a caregive...
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Research Article
AI-Driven Energy-Efficient Routing and UAV Trajectory Optimization for UAV-Assisted Internet of Things Sensor Networks in 6G Environments
Mojtaba Nasehi*
Issue:
Volume 14, Issue 2, June 2026
Pages:
38-55
Received:
6 July 2026
Accepted:
16 July 2026
Published:
18 August 2026
Abstract: The rapid expansion of the Internet of Things (IoT) and the emergence of sixth-generation (6G) wireless networks have created unprecedented opportunities for large-scale intelligent sensing, real-time data collection, and ubiquitous connectivity. However, the deployment of massive IoT sensor networks faces significant challenges, including limited energy resources, dynamic network topologies, communication reliability issues, routing inefficiencies, and coverage constraints, particularly in remote, disaster-stricken, and infrastructure-deficient environments where conventional terrestrial communication systems often fail to provide reliable services. Unmanned Aerial Vehicles (UAVs) have emerged as a promising solution for enhancing network coverage, improving data collection efficiency, and supporting communication services in IoT ecosystems; nevertheless, their integration introduces additional challenges related to energy consumption, trajectory planning, routing optimization, and resource allocation. To address these issues, this paper proposes an AI-Driven Energy-Efficient Routing and UAV Trajectory Optimization Framework for UAV-assisted IoT sensor networks operating in 6G environments. The proposed framework integrates intelligent routing, adaptive energy management, and dynamic UAV trajectory optimization within a unified cross-layer architecture and develops a comprehensive mathematical model to characterize the relationships among energy consumption, communication delay, packet delivery performance, routing decisions, and UAV mobility. Furthermore, a Deep Reinforcement Learning (DRL)-based optimization algorithm is introduced to enable autonomous decision-making and adaptive network control under dynamic environmental conditions. The proposed approach continuously monitors key network parameters, including residual sensor energy, link quality, transmission distance, traffic load, UAV battery status, and data collection requirements, and dynamically determines optimal routing paths and UAV flight trajectories to minimize overall energy consumption while maximizing network lifetime, packet delivery ratio, and data collection efficiency. In addition, the framework leverages the ultra-reliable low-latency communication capabilities envisioned for future 6G infrastructures to facilitate intelligent coordination between UAV platforms and IoT sensor nodes. Performance evaluation under various network densities, mobility scenarios, and communication conditions demonstrates that the proposed framework significantly reduces energy consumption, improves routing efficiency, extends network lifetime and enhances packet delivery performance, and decreases communication overhead and data collection latency compared with conventional approaches. The results confirm that the integration of artificial intelligence, energy-aware routing, and UAV trajectory optimization provides an effective and scalable solution for next-generation UAV-assisted IoT systems and establishes a robust foundation for intelligent 6G-enabled wireless sensor networks.
Abstract: The rapid expansion of the Internet of Things (IoT) and the emergence of sixth-generation (6G) wireless networks have created unprecedented opportunities for large-scale intelligent sensing, real-time data collection, and ubiquitous connectivity. However, the deployment of massive IoT sensor networks faces significant challenges, including limited ...
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