Muhammad Imran Aslam

Diabetes mellitus affects over 589 million adults worldwide, with the majority in low and middle income countries where access to affordable, convenient monitoring tools is limited. Conventional finger-prick glucometry, while accurate, is invasive, painful, and prone to poor patient compliance. This paper presents an indigenously developed, low-cost, IoT-enabled non-invasive vitals monitoring system capable of estimating blood glucose level (BGL) alongside heart rate and oxygen saturation in real time. The system integrates a MAX30102 dual-wavelength photoplethysmography sensor mounted on a custom-designed printed circuit board, an ESP32-C3 microcontroller for edge-level signal processing, and a BMI-stratified 3rd-degree polynomial regression model for BGL estimation. Three ensemble machine learning models including Random Forest, Extra Trees, and LightGBM were evaluated on 266 clinically collected samples, with demographic anchoring via age and BMI as the highest-ranked predictive features. Results from our developed system achieved an overall RMSE of 14.92 mg/dL with 100% of predictions within Clarke Error Grid Zones A and B. A Flutter-based mobile application with Bluetooth connectivity and Firebase cloud integration enables real-time visualization and longitudinal data storage, offering a scalable, pain-free alternative to invasive glucose monitoring.

Rapid urbanization in developing countries poses air pollution challenges for urban areas caused by industry and vehicle emissions․ Challenges associated with traditional air pollution monitoring technologies include high costs and poor portability and geographic coverage‚ which make it difficult to monitor short-term and localized air pollution․ Other low-cost IoT solutions relying on Wi-Fi or LoRaWAN technology have their own limitations for outdoor use and dependence on local infrastructure‚ which can make monitoring and other applications difficult in other developing parts of South Asia․ This paper presents the design and testing of a low-cost‚ portable IoT system for monitoring and mapping short-term and localized air pollution․ The system monitors several air pollutants‚ including temperature‚ humidity‚ CO‚ and CH4‚ and includes GPS and a two-part communications system involving a GSM network for in-field data collection and Wi-Fi and the Firebase cloud platform for remote data reporting․ The system hardware is designed in two-layer format to separate the power supply circuitry from the sensing system. A real-time web and mobile dashboard provide interactive spatial visualization of pollution hotspots. To demonstrate the usability of the developed system, field tests were conducted over a 14․2-kilometer urban route in Karachi‚ Pakistan‚ in the morning and night․ Test results show positive GPS mapping and data collection at nine sample sites and differences in other measured parameters between morning and night samples․ Applications of this technology include a infrastructure-independent complement which is scalable to fixed monitoring networks in other areas lacking monitoring capabilities and‚ with calibration using data from other monitors‚ detailed air quality studies to support smart city and public health projects․

Millimeter wave (mmWave) technology offers vast bandwidth and ultra-high data rates but suffers from severe path loss and blockages. Reconfigurable intelligent surfaces (RISs) can mitigate the impact of blockages, but dense deployment of RISs adversely affect the overall energy requirement of the network. However, most of the existing work on RIS-assisted mmWave networks either assume passive RIS operation or rely on external power supply. The need to consider sustainable RIS operation in user association remains unaddressed, where sustainability refers to the RIS’s ability to operate continuously by harvesting energy from received RF signals without an external power source. This paper incorporates the concept of RIS energy sustainability into the user association probability of an RIS-assisted link. In this paper, a stochastic geometry based analytical framework for user association in a mmWave network assisted by self-sustainable RISs has been proposed. In addition, a baseline model for a conventional RIS-assisted link has also been developed that serves as a benchmark for comparison with the proposed model. Unlike conventional approach, each RIS in the proposed model operates in a self-sustainable manner. We have introduced a two-stage thinning process to identify RISs that are simultaneously in line-of-sight (LoS) with the user and capable of sustainable operation. Analytical expressions for RIS sustainability probability and user association probability have been derived, considering both direct, sustainable and conventional RIS-assisted communication links. We have performed a comparison of our proposed model with a learning based Deep Deterministic Policy Gradient (DDPG) model to validate the applicability of the proposed model for dense urban mmWave networks. The results obtained in this investigation reveal that increasing RIS deployment density significantly enhances the availability of sustainable LoS RISs, thereby improving the sustainability-aware user association probability and reducing outage in dense blockage environments. However, in highly dense environments, a tradeoff exists between increasing RIS deployment density to improve user association probability and ensuring self-sustainable RIS operation, thereby revealing a feasible RIS deployment region for sustainable operation. We demonstrate that, the proposed system remains fair and computationally inexpensive under varying network load, highlighting its applicability for large-scale and dense networks.

With the worsening environment quality credited to rapid urbanization and industrialization, ensuring optimal indoor air quality is a critical challenge with far-reaching implications for public health, environmental sustainability, and overall wellbeing. Despite its importance, real-time air quality monitoring solutions (AQMS) remain underdeveloped, leaving significant gaps in the ability to analyze and predict environment quality effectively. In this paper, we have presented the development of a Machine Learning Internet of Things (ML-IoT) enabled AQMS equipped with diverse sensors capable of sensing vital environmental parameters which include carbon monoxide, carbon dioxide, particulate matter PM2.5, PM10, temperature, and humidity. We deployed the developed AQMS in an indoor environment and collected environmental data from sensors. A comprehensive preprocessing pipeline, including outlier removal using the interquartile range method and feature scaling, was applied to improve data quality. Three machine learning models including Linear Regression (LR), Random Forest (RF), and Bidirectional Long Short-Term Memory (BiLSTM) were implemented for predictive analysis. Using state-of-the-art data analytics, we have tested these ML models to uncover trends, identify correlations, and predict air quality metrics with improved accuracy. To ensure methodological rigor, 5-fold cross-validation was applied to LR and RF models, while time-series cross-validation was used for BiLSTM to preserve temporal dependencies. The results show that LR achieves high accuracy for temperature prediction with R ² of 96.45% and cross validation R ² of 95.4%, while RF provides moderate performance for CO ₂ prediction with R ² of 52.7% and 64.05% in cross validation. BiLSTM improves CO ₂ prediction with R ² of 89.9%) under standard evaluation and achieves an average R ² of 85.4% under time-series validation, demonstrating generalization. To provide real-time visualization of air quality parameters, we have designed a user-friendly dashboard, allowing stakeholders to monitor real-time conditions and derive actionable insights. The developed AQMS finds promising application in industries, houses, office and residential buildings, allowing predictive environment quality monitoring and triggering alarm in case of any anomaly.

In contrast to the existing Wi-Fi-based systems, we have developed a GSM-based, cellular-Internet of Things (C-IoT)-enabled, portable air quality monitoring system that collects critical air quality parameters through advanced sensors in addition to the location of the device using a Global Positioning System (GPS). Our system utilizes sensors to monitor temperature, humidity, carbon dioxide, and Particulate Matter concentrations along with the location information. These sensors are integrated with an ESP32 microcontroller which is interfaced with the GPS module for location information as well as with the GSM module to transmit the sensor data and location information to a central IoT gateway using an existing cellular infrastructure. The developed C-IoT sensor node is powered through a portable power bank, allowing complete mobility of the developed sensor node within the cellular coverage in an entire city. The data collected through the mobile C-IoT system is displayed in a live dashboard as well as over a live map for location-aware air quality monitoring. As a pilot run, we collected localized environmental data through a developed node by moving around a pre-defined urban area to create a pollution map of said area.

In this paper, we present the design, prototyping, and working of a cost-effective, energy-efficient, and scalable air quality monitoring system (AQMS), enabled by a Low-power, long-Range Wide-Area Network (LoRaWAN), an Internet of Things (IoT) technology designed to provide connectivity for massive machine-type communication applications. The growing threat of air pollution necessitates outdoor and mobile environmental monitoring systems to provide real-time, location-specific data, which is unfortunately not possible using fixed monitoring devices. For our AQMS, we have developed two custom-built sensor nodes. The first node is equipped with a Nucleo-WL55JC1 microcontroller and sensors to measure temperature, humidity, and carbon dioxide (CO₂), while the other node is equipped with an Arduino MKR WAN 1310 controller with sensors to measure carbon monoxide (CO), ammonia (NH₃), and particulate matter (PM2.5 and PM10). These sensor nodes connect to a WisGate Edge LoRaWAN gateway, which aggregates and forwards the sensor data to The Things Network (TTN) for processing and cloud storage. The final visualization is handled via the Ubidots IoT platform, allowing for real-time visualization of environmental data. Besides environmental data, we were able to acquire a received signal strength indicator, signal-to-noise ratio, as well as a frame counter, which shows the number of packets received by the gateway. We performed laboratory testing, which confirmed reliable communication, with a packet delivery rate of 98% and a minimal average latency of 2.5 s. Both nodes operated efficiently on battery power, with the Nucleo-WL55JC1 consuming an average of 20 mA in active mode, while the Arduino MKR WAN 1310 operated at 15 mA. These values ensured extended operation for remote deployment. The system’s low power consumption and modular architecture make it viable for smart city applications and large-scale deployments in resource-constrained areas.

Diabetes Mellitus is a significant global health issue, affecting over half a billion people worldwide. Current glucose monitoring methods are invasive, painful, and require skilled application, highlighting the need for the development of effective, non-invasive, and easy to use methods. This paper presents our work on the design, development, and evaluation of a non-invasive blood glucose monitoring system, utilizing Near-Infrared Spectroscopy technique for glucose monitoring. The proposed system comprises a MAX30102 biosensor connected to an ESP32 microcontroller. The biosensor captures the photoplethysmogram signals, which are then processed by a microcontroller to evaluate blood glucose level. In order to increase the accuracy of the results, we have incorporated linear regression with Clarke Error Grid Analysis to calibrate our system. The linear regression model is trained by comparing the results obtained through the developed system with that of a commercial off-the-self invasive device. The glucose levels obtained through the developed system are displayed in real-time on an Organic LED (OLED) screen and simultaneously uploaded to a cloud server via Internet of Things for remote monitoring. To validate the performance of the proposed system, we have compared the performance metrics of our system against existing solutions published in the literature. Performance comparisons show that our system achieves a reasonably good accuracy with a root mean square error of 13.8 mg/dL and a mean absolute relative difference of 12%. The proposed system offers a painless and convenient solution, potentially improving glucose monitoring for patients.

This paper proposes a robust generalised dynamic inversion (GDI) control system design with adaptive neural network (NN) estimation for spacecraft attitude tracking under the absence of knowledge of the spacecraft inertia parameters. The robust GDI control system works to enforce attitude tracking, and the adaptive NN augmentation compensates for the lack of knowledge of the spacecraft inertia parameters. The baseline GDI control law consists of a particular part and an auxiliary part. The particular part of the GDI control law works to realise a desired attitude dynamics of the spacecraft, and the auxiliary part works for finite-time stabilisation of the spacecraft angular velocity. Robustness against modeling uncertainties and external disturbances is provided by augmenting a siding mode control element within the particular part of the GDI control law. The singularity that accompanies GDI control is avoided by modifying the Moore-Penrose generalised inverse by means of a dynamic scaling factor. The NN weighting matrices are updated adaptively through a control Lyapunov function. A detailed stability analysis shows that the closed loop system is semi-global practically stable. For performance assessment, a spacecraft model is developed, and GDI-NN control is investigated for its attitude control problem through numerical simulations. Simulation results reveal the efficacy, robustness and adaptive attributes of proposed GDI-NN control for its application to spacecraft attitude control.

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