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Review Article
Smart Monitoring and Control Systems in Bioreactor-Based Aquaculture Water Treatment
Alebachew Molla*
Issue:
Volume 13, Issue 2, December 2025
Pages:
22-32
Received:
7 September 2025
Accepted:
18 September 2025
Published:
10 October 2025
Abstract: This abstract presents a concise overview of smart monitoring and control systems for aquaculture water treatment. It highlights the critical safety and productivity challenges faced by aquaculture due to fluctuations in essential water quality parameters such as temperature, pH, dissolved oxygen, and ammonia. Traditional water quality monitoring methods are often labor-intensive and intermittent, risking suboptimal conditions and economic losses. The advent of Internet of Things based smart systems, integrating diverse sensors, cloud computing, and automated actuators, enables real-time, continuous water quality monitoring and dynamic control. These systems facilitate remote data access, efficient management, and rapid response to environmental changes, enhancing fish health and optimizing bioreactor performance. Furthermore, incorporation of artificial intelligence and machine learning offers predictive analytics that improve decision-making and enable proactive interventions. Practical deployments demonstrate significant benefits such as reduced labor costs, improved resource utilization, and enhanced sustainability. Challenges in sensor robustness, data security, and cost remain, but ongoing advances in low-cost, energy-efficient sensors and integrated biosensing technologies promise wider adoption. Overall, smart monitoring and control technologies represent a transformative step toward fully automated, data-driven aquaculture systems, promoting a sustainable blue economy while meeting the growing global demand for aquatic food resources. This review encompasses current technologies, applications, challenges, case studies, and future directions in this dynamic field, offering valuable insights for researchers, practitioners, and policy makers aiming to advance sustainable aquaculture water management.
Abstract: This abstract presents a concise overview of smart monitoring and control systems for aquaculture water treatment. It highlights the critical safety and productivity challenges faced by aquaculture due to fluctuations in essential water quality parameters such as temperature, pH, dissolved oxygen, and ammonia. Traditional water quality monitoring m...
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Research Article
An FM Broadcast IoT System for Enhancing Highway Safety
Issue:
Volume 13, Issue 2, December 2025
Pages:
33-45
Received:
25 August 2025
Accepted:
10 September 2025
Published:
14 October 2025
Abstract: Land travel on highways in developing countries, such as Cameroon, faces significant safety challenges due to factors like natural disasters, poor road networks, low-quality vehicles, error-prone drivers, and negligent pedestrians, with government investments showing only minor improvements in reducing road accidents. This paper proposes an Internet of Things (IoT) based FM broadcast system to enhance highway safety by providing real-time and timely guides to drivers. The proposed system comprises three main units: a Roadside Unit (RSU), an Onboard Unit (OBU), and a Control Centre (CC). The RSU is an IoT-based smart unit built around a Raspberry Pi 4 Model B, equipped with environmental sensors (DHT11 for temperature/humidity, ROBODO 130008 for rain) and internet connectivity. It integrates a locally designed 5W Phase Locked Loop (PLL) FM transmitter, set to a test frequency of 100.8 MHz, with a broadcast radius of 10 km. Python code on the RSU utilizes APIs like OpenWeatherMap and Google Suite to gather real-time weather forecasts, environmental data, traffic conditions (estimated arrival times, delays), and information on nearby places (parking, gas stations, lodging). It also dynamically suggests speed limits based on real-time weather and generates audio warnings for adverse conditions and road events. The system primarily uses an audio-only broadcast to minimize driver distraction. The OBU functions as a specialized receiver, consisting of a Raspberry Pi 4 Model B with an RTL-SDR dongle that demodulates and outputs FM audio to speakers, with GNU Radio software processing the signals. A remote-Control Centre manages and configures RSUs securely via SSH, enhancing operational efficiency. Experimental results confirm the system's effectiveness. The locally designed FM transmitter demonstrated robust performance with a total RF gain of 43 dB and stable 5W output power, along with excellent frequency stability (±50 ppm crystal oscillator, -90 dBc/Hz VCO phase noise). Performance indicators like Signal-to-Noise Ratio (SNR), Modulation Error Rate (MER), and power spectrum were analysed. SNR showed an inverse relationship with distance, dropping to -40.36 dB at 5000m, which is acceptable for highway radio quality, and tracked closely with a commercial transmitter. MER analysis indicated proper functioning of the transmitter-receiver pair, as demodulated signals exhibited tightly clustered constellation points, implying high SNR and better audio quality. Furthermore, the power spectrum showed minimal variation between original and received audio signals, with an improved gain post-modulation, ensuring clear audio output. This comprehensive system provides a robust and cost-effective solution for real-time highway advisories in developing countries.
Abstract: Land travel on highways in developing countries, such as Cameroon, faces significant safety challenges due to factors like natural disasters, poor road networks, low-quality vehicles, error-prone drivers, and negligent pedestrians, with government investments showing only minor improvements in reducing road accidents. This paper proposes an Interne...
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Research Article
Internet of Things Based Intravenous Fluid Level Monitoring and Alert System for Nigeria Tertiary Healthcare Centers Using Esp32 Microcontroller
Issue:
Volume 13, Issue 2, December 2025
Pages:
46-55
Received:
23 July 2025
Accepted:
1 September 2025
Published:
30 October 2025
Abstract: The application of internet of things (IoT) in critical sectors of human endeavours has extended greatly to healthcare services where IoT technologies has been used to monitor several patient’s vital signs such as heartbeat, glucose level, blood pressure among others and provide timely report for immediate attention to enhance patient’s outcomes. In Nigeria tertiary healthcare centers, the ratio of nurses to patients is very low and most patient needs intravenous (IV) therapy as there are always several critical cases to handle hence a need for automated intravenous fluid level monitoring in our tertiary healthcare centers. Intravenous therapy is a critical component of medical care, yet most Nigeria tertiary healthcare centers rely on traditional monitoring methods that are prone to human error that could compromise patient safety. This paper aims at implementing an internet of things (IoT) based IV fluid level monitoring and alert system in Nigeria tertiary healthcare centers. The system was developed using ESP32 microcontroller, a 5kg load cell with HX711 amplifier, and a multi-channel alert mechanism (LEDs, buzzer, and 16x2 I2C liquid-crystal display (LCD), coupled with cloud connectivity via ThingSpeak and notification services (Mailjet and Twilio). It continuously tracks IV fluid levels, converting weight data into volume measurements, and triggers real-time alerts at warning (50%) and critical (15%) thresholds. The system implemented several Security features, including Transport Layer Security. (TLS) encryption and multi-tier authentication to ensure data integrity. The Arduino Integrated Development Environment (IDE) was used as the programming environment due to its cross-platform compatibility, simplicity, and robust support for ESP32 development. Its intuitive interface accelerated prototyping, enabling rapid deployment of test code for sensor calibration. It has an extensive community-driven documentation and troubleshooting resources, which simplified resolving hardware-specific challenges, such as I2C address conflicts between the HX711 and LCD. Additionally, the IDE’s serial plotter tool proved invaluable for visualizing real-time weight data during load cell calibration, ensuring the accuracy of the weight-to-volume conversion algorithm. The system was tested using use case and it satisfied all test conditions making it very suitable for intravenous fluid level monitoring in our tertiary healthcare centers.
Abstract: The application of internet of things (IoT) in critical sectors of human endeavours has extended greatly to healthcare services where IoT technologies has been used to monitor several patient’s vital signs such as heartbeat, glucose level, blood pressure among others and provide timely report for immediate attention to enhance patient’s outcomes. I...
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Methodology Article
Design of a Framework for Switch Power Control Using Voice Signal
Issue:
Volume 13, Issue 2, December 2025
Pages:
56-64
Received:
1 September 2025
Accepted:
10 September 2025
Published:
22 November 2025
DOI:
10.11648/j.ijssn.20251302.14
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Abstract: Establishing systems that specifically control electric power switches based on the practical implementation of Artificial Intelligence in everyday life reduces the likelihood of accidental switch activation and potentially increases security by ensuring it responds only to authorised users. Individuals with physical disabilities also require systems devoid of direct human interventions and physical interactions to control electrical and power switches. Existing methods for achieving these tasks include smart objects, the Internet of Things, and biometric technologies, with their attendant strengths and weaknesses. This paper presents the design of a voice signal framework for remote control of power switches. The framework uses a voice sensor connected to an Arduino microcontroller to amplify the volume of the user’s voice, while a voice sensor connected to a power switch relay is used to capture the voice signal for registration, training, verification and processing. The Arduino Nano 33 BLE Sense Rev 2 microcontroller sensor combines a tiny form factor with the capability to operate TinyML and TensorFlow Lite environment sensors while running at reconfigurable operating voltage. The switch relay regulates a high voltage to a minimum acceptable level based on integration with the Arduino microcontrollers. The framework also requires an external ESP8266/ESP32 Wi-Fi module to establish a connection between the microcontroller and the network as well as simple TCP/IP connections using Hayes-style commands. The system requires a power switch, an electromechanical device that uses the flow of electric current to open or close an electrical circuit. The user voice recognition is based on Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) networks. The combination of these two models guarantees an effective capturing of temporal dependencies in sequential data typical of audio signals.
Abstract: Establishing systems that specifically control electric power switches based on the practical implementation of Artificial Intelligence in everyday life reduces the likelihood of accidental switch activation and potentially increases security by ensuring it responds only to authorised users. Individuals with physical disabilities also require syste...
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