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 I 2 C 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.
Rwanda’s pursuit of a high-income, knowledge-based economy under Vision 2050 depends on scalable, resilient, and cost-efficient telecommunications infrastructure. Despite achieving near-universal 4G LTE population coverage (97–99%), a persistent usage gap — approximately 62% of the population remains unconnected — reveals that supply-side infrastructure alone is insufficient to bridge the digital divide. This paper investigates how Software-Defined Networking (SDN) and Network Function Virtualization (NFV) can serve as strategic enablers of telecommunications modernization in Rwanda, addressing the twin challenges of cost and operational complexity in a landlocked, resource-constrained environment. Employing a Design Science Research (DSR) methodology, this study evaluates Rwanda’s infrastructure readiness, identifies technical, economic, and regulatory barriers to SDN/NFV adoption, and proposes a context-adapted, phased deployment artifact the Frugal SDN/NFV Framework aligned with Rwanda’s ICT Sector Strategic Plan 2024–2029. The framework is supported by five formally specified mathematical optimization models: the Controller Placement Problem for Rwanda’s 30-district fiber topology, the VNF Resource Allocation mixed integer program, a Network Slice SLA Allocation model, a CAPEX/OPEX Net Present Value cost model, and a joint SDN-MEC task offloading optimization. Numerical projections derived from these models informed by comparable African SDN/NFV deployments including Safaricom Ethiopia’s 2022 greenfield virtualized network and MTN South Africa’s cloud-native 5G core suggest potential CAPEX reductions of 20–68% and OPEX reductions of 20–67%. The proposed three-phase roadmap (Pilot 2025–2026; Scale 2026–2028; Optimize 2028–2029) positions SDN/NFV as a leapfrogging catalyst for equitable digital growth, contributing the first academically grounded SDN/NFV deployment framework for an African national telecommunications network.
The rapid convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) is reshaping the hospitality industry by enabling intelligent, automated, and hyper-personalized service ecosystems. This study examines how the integration of AI and IoT—referred to as AIoT—enhances smart service delivery and strengthens customer engagement within hotels and related hospitality environments. The research explores key AI and IoT applications such as smart guest rooms, predictive maintenance, automated check-in systems, and personalized service recommendations, highlighting their impact on operational efficiency and guest satisfaction. Findings indicate that AIoT-driven solutions not only streamline service processes but also create immersive, interactive experiences that increase convenience, engagement, and loyalty. Despite challenges related to security, cost, and technological integration, AIoT offers significant potential for transforming hospitality service models. The study concludes that embracing AI–IoT convergence is essential for hospitality organizations seeking competitive advantage in an increasingly digital and experience-driven marketplace.
In order to solve the problems of effective resource allocation in low-power wide-area networks, this thesis investigates the scheduling of end devices in Internet of Things applications using LoRaWAN technology. The main goal of this research is to use RL to improve QoS measures including energy efficiency, throughput, latency, and dependability. This was accomplished by using a simulation-based approach that evaluated the effectiveness of the RL-based scheduling algorithm using NS3 simulations. The main findings show that, in comparison to current scheduling practices, the RL agent greatly improves data transmission reliability and improves network throughput. Furthermore, the suggested approach efficiently lowers average system latency and overall energy usage, improving network resource utilization. These findings imply that using reinforcement learning (RL) for job scheduling in LoRaWAN networks can offer a reliable and expandable solution to present problems, resulting in more intelligent and environmentally friendly IoT systems. In the end, this study finds that using RL-based techniques can help improve resource management in contexts that are dynamic and resource-constrained.
The temperature-sensitive industries including healthcare, agriculture and cold chain logistics the Internet of Things (IoT) has greatly increased monitoring and management of environmental conditions. Minor temperature fluctuations can lead to the deterioration of products, diminished effectiveness of pharmaceuticals, or suboptimal agricultural results. This work presents the design and development of a thermologger system for real-time temperature monitoring and data recording system based on the Internet of Things (IoT). The Internet of Things (IoT) has emerged as a revolutionary solution, facilitating real-time monitoring, sophisticated analysis and automated decision-making across several sectors. The system incorporates low-power digital temperature sensors, a microcontroller unit (MCU) and a wireless communication module based on the ESP-8266. These components work together to collect temperature data and transmit it to a cloud platform for storage and analysis. A key feature of the system is its user-friendly interface, available through a mobile app and a web dashboard. These platforms enable users to view temperature trends, receive alerts when temperatures fall outside of safe ranges and generate reports for further analysis. The alert mechanism is especially useful in high-risk areas such as vaccine storage, greenhouse operations, large industrial cold storage, datacenter and food transportation where timely intervention can prevent significant loss. Real-world testing demonstrates the system’s accuracy, responsiveness, and dependability. These findings validate the system’s efficacy and affordability as a continuous temperature monitoring solution for critical applications. All things considered the proposed thermologger system may enhance operational decision-making, boost safety and optimize resource use across a range of industries.
