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Go to Editorial ManagerIn the past few years, all over the world, crime against children has been on the rise, and parents always worry about their children whenever they are outside. For this reason, tracking and monitoring children have become a considerable necessity. This paper presents an outdoor IoT tracking system which consists of a child module and a parent module. The child module monitors the child location in real time and sends the information to a database in the cloud which forwards it to the parent module (represented as a mobile application). This information is shown in the application as a location on Google maps. The mobile application is designed for this purpose in addition to a number of extra functions. A Raspberry Pi Zero Wireless is used with a GSM/GPS module on shield to provide mobile communication, internet and to determine location. Implementation results for the suggested system are provided which shows that when the child leaves a pre-set safe area, a warring message pops up on the parent’s mobile and a path from the current parent location to the child location is shown on a map.
In the field of engineering, 3D printers are indispensable due to their high precision. This study focuses on the construction and optimization of a 3D printer using aluminum T-slotted bars for the frame, Raspberry Pi 4 for control, and Lightburn software for image printing and machine control. After assembling the main components and programming with Marlin firmware, the machine was tested for vibration and noise reduction. The research compared the vibration of a diode laser and spindle during printing, revealing significantly lower vibration with the laser compared to the spindle. These findings demonstrate the effectiveness of the constructed 3D printer in reducing vibration and noise during operation.
Women’s safety remains an urgent challenge, particularly in moments when conventional panic button devices fail due to a victim’s inability to act or poor network coverage. To overcome these shortcomings, TRIAD-Lite is introduced as an IoT-enabled wearable framework that unites multimodal physiological sensing with lightweight deep learning for proactive distress identification. The system captures heart rate, blood pressure, galvanic skin response, and motion patterns, while incorporating a triple-tap gesture to confirm user intent, all processed locally on a Raspberry Pi for real-time inference. Unlike reactive mechanisms, this design anticipates danger by analyzing variations in physiological signals that often precede visible distress. Communication reliability is reinforced through a hybrid strategy: alerts are transmitted via GSM or Wi-Fi under normal conditions, but in the event of limited connectivity, a LoRa-based backup ensures long-range transmission. Experimental analysis using simulated datasets yielded an AUC of 1.000 with flawless precision and recall, highlighting the model’s reliability and calibration. Further field evaluation demonstrated that LoRa maintained connectivity across 5.7 kilometers with complete packet delivery, proving effective for both rural and urban environments. By combining predictive analytics, gesture-based confirmation, and dual communication layers, TRIAD-Lite offers a scalable, privacy-conscious, and highly resilient framework that strengthens women’s safety and extends protective technology into regions where conventional systems often fail.