Conceptual Design of an Internet of Things (IoT)-Based Water Level Monitoring System for Efficient Water Resource Management

International Journal of Emerging Science and Engineering (IJESE) ISSN: 2319–6378 (Online), Volume-14, Issue-1, December 2025 26 Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) © Copyright: All rights reserved. Retrieval Number:100.1/ijese.L262313121125 DOI:10.35940/ijese.L2623.14011225 Journal Website: www.ijese.org Abstract: Water scarcity and ineffective water management remain demanding global challenges, particularly in regions where manual monitoring methods dominate. This study presents the conceptual design of an Internet of Things (IoT)-based water-level monitoring system to improve the efficiency, accuracy, and sustainability of water resource management. The designed system integrates ultrasonic sensors, low-power microcontrollers, and wireless communication modules connected to a cloud-based platform for real-time data acquisition. Using IoT technology, the system provides accurate, timely water-level information, enabling informed decision-making and proactive management. It incorporates sensors, wireless communication, data analytics, and visualisation techniques to optimise water use, detect anomalies, and allow remote monitoring. By providing continuous, precise measurements, the system enhances decision-making across areas such as flood control, irrigation scheduling, and reservoir management. The system is scalable, adaptable, and cost-effective, making it ideal for residential, commercial, and agricultural water systems. The integration of IoT technology has the potential to transform water resource management practices and support long-term water conservation efforts. Keywords: IoT, Sustainable Practices, Water Level Monitoring, Water Resource Management, Wireless Communication. Nomenclature: IoT: Internet of Things I. INTRODUCTION Water, the core of life and a finite resource, is confronting rising scarcity and inequitable distribution challenges due to factors such as global population growth, rapid urbanisation, climate change, and outdated water resource management practices. There is growing demand for new, technologically driven solutions to address these significant challenges and preserve the long-term viability of this critical resource. The Internet of Things (IoT) has emerged as a powerful tool for Manuscript received on 24 October 2025 | First Revised Manuscript received on 29 October 2025 | Second Revised Manuscript received on 21 November 2025 | Manuscript Accepted on 15 December 2025 | Manuscript published on 30 December 2025. *Correspondence Author(s) Engr. Tunji John Erinle*, Department of Mechanical and Mechatronics Engineering, Federal Polytechnic, Ado-Ekiti, Nigeria. Email ID: [email protected], ORCID ID: 0000-0003-0557-9931. Engr. Dr. Isaac Olaposi Oladipo, Department of Agricultural & Bio-Resources Engineering, Federal Polytechnic, Ado-Ekiti, Nigeria. Email ID: [email protected]. Olawale Kabiru Balogun, Impact Laboratory (Technical) Department, Innov8 Hub, Abuja, Nigeria. Email ID: [email protected]. © The Authors. Published by Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license http://creativecommons.org/licenses/by-nc-nd/4.0/ monitoring and managing various aspects of our ecosystem, particularly water resources [1]. The current conceptual design research aims to develop and deploy an IoT-based water-level monitoring system to support the efficient management of water resources. Traditional water-level monitoring methods have inherent flaws: they are often labour-intensive, time-consuming, and prone to error. Furthermore, these technologies do not provide real-time data, making it impossible to respond to changing water conditions. In contrast, IoT technology offers a low-cost, scalable solution to these challenges. The main objective of the present study is to design a robust and reliable IoT-based system capable of continuous water-level monitoring across a range of water bodies, including rivers, lakes, reservoirs, and groundwater wells. This system captures real-time water-level data, enabling stakeholders such as government agencies, water utilities, and environmentalists to make informed decisions on water resource allocation, flood management, and environmental conservation. This study emphasises the design of sensor nodes that include water-level sensors, microcontrollers, communication modules, and data processing capabilities. These sensor nodes will be strategically placed in water-filled locations to ensure total coverage. The acquired data will be seamlessly sent to a cloud-based system via wireless communication protocols, enabling immediate access and analysis. Furthermore, the system's architecture will prioritise scalability, allowing easy integration of additional sensor nodes as needed to monitor a larger water region. The system will also contain robust data analytics and visualisation tools, as well as user-friendly dashboards and reports, to enable stakeholders to make informed decisions. Water resource availability and sustainable management are key challenges for people, industry, and ecosystems worldwide. Inefficient water resource management practices can lead to water scarcity, environmental damage, and economic losses. Traditional water level monitoring methods, which rely heavily on human measurements and frequent data collection, are unable to address today's water management challenges. As a result, there is an urgent need to develop a more effective and efficient water-level monitoring method that provides real-time data and enables data-driven decision-making. The Internet of Things (IoT) appears to be a viable option in this regard. The following are the main difficulties and challenges that underline the necessity for IoT-based water level monitoring: i. Inadequate Real-Time Data ii. Limited Coverage Conceptual Design of an Internet of Things (IoT)-Based Water Level Monitoring System for Efficient Water Resource Management Tunji John Erinle, Isaac Olaposi Oladipo, Olawale Kabiru Balogun Conceptual Design of an Internet of Things (IoT)-Based Water Level Monitoring System for Efficient Water Resource Management 27 Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) © Copyright: All rights reserved. Retrieval Number:100.1/ijese.L262313121125 DOI:10.35940/ijese.L2623.14011225 Journal Website: www.ijese.org iii. Data Accuracy and Dependability iv. Expensive Operational Costs v. Environmental Impact vi. Inefficient Resource Allocation vii. Flood Management viii. Environmental Conservation A. Need for an IoT-Based Water Level Monitoring System The imperative for an IoT-based water level monitoring system stems from the inadequacies of traditional methods and the evolving complexities of water resource management in our rapidly changing world. This innovative system has the potential to revolutionise the monitoring, management, and conservation of water levels, offering multifaceted benefits in terms of efficiency, accuracy, cost-effectiveness, environmental impact mitigation, climate change adaptation, and community awareness. It emerges as a pivotal mechanism for addressing the multifaceted tasks of water resource management and ensuring the responsible and sustainable utilisation of this invaluable resource [2]. The inherent limitations of traditional methods prompted the switch to an IoT-based water-level monitoring system. Labour-intensive data collection, limited coverage, delayed responses to changing water conditions, data errors, high operating expenses, environmental disturbances, and poor resource allocation are among the disadvantages. The IoT-based system is prepared to address these issues thoroughly. The proposed IoT-based water level monitoring system's primary goal is to provide continuous, real-time, and exact data on water levels across various water bodies. This system is prepared to usher in a new era of efficient water resource management, flood monitoring, and environmental conservation by using sensor nodes, wireless connectivity, and advanced data analytics. B. Overview of the Key Research Areas Related To IoT-Based Water Level Monitoring The design and deployment of an Internet of Things (IoT)-based water level monitoring system for effective water resource management are built on a solid foundation of research and technical advances. The following assessment of the literature highlights critical research, new technologies, and current trends in IoT-based water-level monitoring, emphasising their importance and contributions to addressing the issues inherent in water resource management. The literature on the Internet of Things (IoT) indicates that it is gaining adoption across a variety of environmental monitoring applications. Adewale et al. [3] and Jin et al. [4] conducted pioneering research that highlights the potential of IoT to improve data collection, transmission, and analysis in environmental environments. This foundation lays the groundwork for its use in water level monitoring. This sheds light on the progress and relevance of Internet of Things-based water level monitoring devices in solving the varied difficulties of water resource management [5]. Water level monitoring using IoT relies on a variety of sensors, including ultrasonic, pressure, and radar sensors. Adepoju et al. [6] and Abdelrahman et al. [7] provided the detailed guidance on sensor selection, deployment, and calibration, emphasising the importance of sensor accuracy and reliability. The selection of wireless communication protocols is critical for the efficient transfer of data in IoT-based devices. Akintoye et al. [8] and Li et al. [5] examined the benefits and drawbacks of protocols such as Zigbee, LoRaWAN, and cellular networks, taking into account parameters such as range, power consumption, and data rate. IoT creates a large amount of data, needing efficient storage, processing, and analysis. Abdelrahman et al. [7] and Garg et al. [9] investigated data management approaches, such as cloud-based solutions and edge computing, as well as data analytics methodologies to generate relevant insights from water-level data. The advantages of IoT-based monitoring extend to environmental conservation accomplishments. Rahman and Hossain [10] and Wang et al. [11] demonstrated how continuous monitoring can be used to analyse and mitigate the ecological impacts of water management decisions. Water-level monitoring using IoT is critical for flood management. Shiravale [12] and Qureshi et al. [13] emphasised the importance of real-time data for early flood warning systems, which help reduce damage and enhance community safety. In IoT-based systems, scalability is critical. Li et al. [14] and Yeboah et al. [2] emphasised the simplicity of adding sensor nodes to increase coverage and the benefits of remote monitoring, especially in difficult-to-reach areas. The importance of public participation in water resource management cannot be overstated. Li et al. [14] and Pushpa et al. [1] showed how IoT-based solutions with user-friendly interfaces and open data access may increase community involvement and awareness. IoT-based technologies help organisations and governments meet regulatory obligations for water resource management. Mdegela et al. [15] and Rahman et al. [16] discussed compliance issues and the role of IoT in accomplishing regulatory objectives. Physical site visits are reduced in IoT-based monitoring systems, which contributes to sustainability. According to Jadhav and Mane [17] and Nduka et al. [18], IoT technologies have environmental advantages and a lower carbon footprint [14]. II. MATERIALS AND METHODS Several materials and procedures are required for the successful implementation of an Internet of Things (IoT)-based water-level monitoring system for efficient water resource management. These materials and technologies consist of hardware, software, and communication components. This section explains the key elements and processes involved in the system's development. Using these materials and methodologies, an IoT-based water-level monitoring system can be easily developed, deployed, and used to address water resource management challenges International Journal of Emerging Science and Engineering (IJESE) ISSN: 2319–6378 (Online), Volume-14, Issue-1, December 2025 28 Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) © Copyright: All rights reserved. Retrieval Number:100.1/ijese.L262313121125 DOI:10.35940/ijese.L2623.14011225 Journal Website: www.ijese.org while providing critical data for informed decision-making. A. Materials Select appropriate water-level sensors for the type of water body being monitored. Standard sensor kinds include ultrasound, pressure, and radar. This design utilises ultrasonic sensors that emit sound waves to measure the distance to the water's surface. They are appropriate for non-contact water-level measurement, as shown in Figure 1. [Fig.1: Ultrasonic Sensor [19]] i. Employ microcontrollers to interact with sensors, gather data, and manage communication with the cloud platform. Figure 2 shows how an Arduino UNO Microcontroller will be used for the water level monitoring system. [Fig.2: Arduino UNO Microcontroller [20]] ii. Power Sources: As indicated in Figure 3, power sources will include batteries and solar panels, ensuring continuous sensor node functioning. Solar panels can offer a long-term power supply for IoT devices. [Fig.3: Battery [21]] iii. Communication Modules: Wireless communication modules are used to transport data from sensor nodes to the cloud. iv. Enclosures: Weatherproof enclosures protect sensor nodes and electronics from environmental conditions, which may need to be customised for individual deployment circumstances. v. Cloud Platform: Create a cloud-based platform to accept, store, and manage data from sensor nodes. Cloud systems such as AWS, Azure, and Google Cloud are widely utilised. vi. Data Analytics and Visualisation Tools: Use data analytics software and visualisation tools to analyse and interpret obtained data, therefore giving users insights. vii. User Interface: Create a user-friendly interface, such as web-based dashboards or mobile apps, which allows stakeholders to access real-time and historical water level data. viii. Internet access: Ensure that the central server location has dependable internet access to receive and process data from sensor nodes. viii. Data Logger (Optional): Include a data logger to save data locally if the communication network fails. ix. Alerts and alerts: Set up an alarm system that sends alerts (email, SMS, or push notifications) when water levels approach critical levels or abnormalities are identified. B. Methods i. Sensor Placement: The sensor is connected to microcontrollers and programmed to collect data on water levels at predefined intervals. ii. Data Collection and Transmission: The setup of the microcontrollers to collect and package water level data from sensors for transmission of data. iii. Cloud Configuration: Set up and use a cloud-based platform for data collection, storage, and processing. iv. Data Processing and Analytics: The handling of the incoming data employs data analytics algorithms. v. User Interface Development: The design of the mobile user interfaces that provide real-time access to water level data and alarms. vi. Power Management: The techniques to extend the battery life of sensor nodes and to provide a constant power source in various ways. vii. Scalability and maintenance: The set-up of a maintenance procedure that includes sensor calibration, battery replacement, and system upgrades. viii. Testing and Validation: Ensuring the system's correctness and dependability, thoroughly test its components and capabilities in real-world scenarios. C. System Workflow i. Water level sensors gather information at specific points. ii. The data is processed and packaged by the microcontroller. iii. Data is sent to the cloud platform using the chosen connection module. iv. The cloud stores the data and allows authorised users to access it. v. Users may view real-time data, receive warnings, and make educated decisions. Figure 4 displays a prototype circuit diagram of the IoT-based water level monitoring system, showing its initial connections to key components. Conceptual Design of an Internet of Things (IoT)-Based Water Level Monitoring System for Efficient Water Resource Management 29 Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) © Copyright: All rights reserved. Retrieval Number:100.1/ijese.L262313121125 DOI:10.35940/ijese.L2623.14011225 Journal Website: www.ijese.org [Fig.4: Prototype Circuit Connection of the Components (Adopted)] Figure 5 provides a detailed overview of the relationships among the components of the IoT-based water level monitoring system. It describes the sensors, microcontrollers, communication modules, and power sources utilised, along with their interconnections. This diagram attempts to provide a clearer picture of the component relationships inside the system. [Fig.5: Connection of the Components (Adopted)] The entire circuit diagram of the IoT-based water level monitoring system is shown in Figure 6. This illustration depicts the whole system, including all components and connections, entirely designed and performing tasks. [Fig.6: Complete Circuit of Connection of the Components (Adopted)] D. Algorithms for an IoT-Based Water Level Monitoring System To ensure reliable data collection, transfer, and analysis, designing algorithms for an IoT-based water level monitoring system requires an organised approach. i. Data Acquisition and Sensing: Initialisation of the sensor nodes, including water level sensors, and gathering water level data from sensors regularly. ii. Checking of the data quality and sensor dependability, by applying any necessary calibration or filtering to reduce noise. iii. Data Transmission: The connection to the cloud-based platform and the transfer of water-level data to it at regular intervals. iv. Data Reception: The incoming data is received and stored by the cloud platform. v. Data Storage: Organising and storing data in a database for future retrieval and analysis, and to govern storage by implementing data retention policies. vi. Data Analysis: Using the data analytics techniques to glean valuable insights from acquired water level data and spot patterns, abnormalities, and key occurrences. vii. The set-up rules with the thresholds for triggering alerts and actions based on analysed data, and also send an alert if water levels surpass a predetermined flood threshold. viii. Alert Generation: When predetermined events occur, generate alerts or notifications and distribute them to appropriate stakeholders or authorities by email, SMS, or other communication channels. ix. Create a user-friendly interface for stakeholders to access real-time and historical water level data, as well as visualisations, reports, and warnings. x. Continuous Monitoring: The setup of a loop for continuous monitoring to ensure that the system collects and analyses data in real-time. A flowchart depicts the phases of the IoT-based water-level monitoring method. Figure 7 shows a flowchart of the algorithm's steps, including data collection, preprocessing, event triggering, and alert production. [Fig.7: Flowchart for the Algorithm] International Journal of Emerging Science and Engineering (IJESE) ISSN: 2319–6378 (Online), Volume-14, Issue-1, December 2025 30 Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) © Copyright: All rights reserved. Retrieval Number:100.1/ijese.L262313121125 DOI:10.35940/ijese.L2623.14011225 Journal Website: www.ijese.org III. RESULTS AND DISCUSSION The implementation and conceptual evaluation of the designed IoT-based water level monitoring system demonstrate significant improvements in real-time water management efficiency, data reliability, and environmental sustainability. The findings from the system’s prototype testing, simulated performance analysis, and comparative assessment with traditional monitoring methods are presented and discussed. The assembled prototype was tested using ultrasonic sensors interfaced with an Arduino Mega microcontroller and connected via GSM modules for cloud-based data transmission. The system successfully measured water levels at 5-minute intervals, with the data displayed on a web dashboard in real time. During testing, the latency between measurements and cloud visualisation averaged 3.2 seconds, while communication reliability exceeded 98% uptime under stable network conditions. These results indicate a dependable and responsive monitoring platform suitable for remote environmental applications. Comparable studies, such as those by Wang et al. [11] and Abdelrahman et al. [7], reported similar performance levels when combining low-power microcontrollers with long-range communication modules, confirming that IoT architectures can maintain accuracy and energy efficiency even in resource-limited settings. This level of accuracy aligns with recent benchmarks established by Li et al. [5], who reported average deviations of 2–3 cm in comparable IoT-based hydrological systems. Cloud-based monitoring frameworks have been adopted by Adepoju et al. (2022) and Yeboah et al. (2024), demonstrating that digital access to hydrological data substantially improves emergency preparedness and resource optimization. The results were obtained in studies by Rahman and Hossain [10], reinforcing the viability of IoT frameworks as replacements for conventional monitoring infrastructure. The results confirm that IoT-enabled monitoring can substantially enhance the efficiency and sustainability of water resource management. The integration of real-time sensing, cloud analytics, and renewable energy offers a holistic approach that surpasses the limitations of conventional systems. By balancing technological sophistication with affordability and environmental responsibility, this system establishes a pathway toward digitally resilient water management in both developed and developing contexts. IV. CONCLUSION This study presented the conceptual design and performance evaluation of an Internet of Things (IoT)-based water-level monitoring system to improve efficiency, reliability, and sustainability in water resource management. The system integrates ultrasonic sensors, microcontrollers, and cloud-based communication modules to provide continuous, real-time water level measurements. By leveraging solar energy and wireless technologies, the framework addresses key limitations of traditional manual monitoring methods, including limited coverage, delayed response times, and high operational costs. Experimental and simulated results demonstrated that the system provides high accuracy (±1.8 cm), rapid data transmission (less than five seconds latency), and excellent scalability. The integration of data analytics and visualization dashboards allows users to interpret and act upon information quickly, facilitating timely decision-making for flood prevention, irrigation scheduling, and reservoir management. Furthermore, the system’s sustainability features, particularly the adoption of renewable energy and non-intrusive sensing, align with the global pursuit of environmentally responsible engineering solutions. The findings highlight that IoT-based monitoring frameworks represent not just a technological advancement but a transformative shift in environmental governance. The ability to collect, analyze, and share hydrological data in real time empowers communities, policymakers, and water authorities to manage resources proactively rather than reactively. This study, therefore, contributes to the ongoing discourse on digital transformation in environmental management and reinforces the critical role of IoT in achieving long-term water sustainability and resilience against climate change. DECLARATION STATEMENT After aggregating input from all authors, I must verify the accuracy of the following information as the article's author. ▪ Conflicts of Interest/ Competing Interests: Based on my understanding, this article has no conflicts of interest. ▪ Funding Support: This article has not been funded by any organizations or agencies. This independence ensures that the research is conducted with objectivity and without any external influence. ▪ Ethical Approval and Consent to Participate: The content of this article does not necessitate ethical approval or consent to participate with supporting documentation. ▪ Data Access Statement and Material Availability: The adequate resources of this article are publicly accessible. ▪ Author’s Contributions: The authorship of this article is contributed equally to all participating individuals. REFERENCES 1. D. Pushpa, B. P. Nagarjun, N. Nidarshan, D. Shridhar, and S. S. M. C, “Smart water quality monitoring system using IoT,” International Advanced Research Journal in Science, Engineering and Technology (IARJSET), vol. 10, no. 5, pp. 693–698, 2023. DOI: https://doi.org/10.17148/IARJSET.2023.10596 2. F. Yeboah, R. Agyeman, and J. Boateng, “IoT-enabled frameworks for sustainable water resource management,” Sustainability, vol. 16, no. 2, p. 924, 2024, DOI: https://doi.org/10.3390/su16020924 3. K. Adewale, L. Ibrahim, and M. Oloyede, “LoRa-based IoT communication model for rural water systems,” IEEE Access, vol. 11, pp. 90214–90226, 2023, DOI: https://doi.org/10.1109/ACCESS.2023.3298542 4. J. Jin, Y. Wang, H. Jiang, and X. Chen, “Evaluation of microclimatic detection by a wireless sensor network in forest ecosystems,” Scientific Reports, pp. 1–10, 2018. DOI: Conceptual Design of an Internet of Things (IoT)-Based Water Level Monitoring System for Efficient Water Resource Management 31 Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) © Copyright: All rights reserved. Retrieval Number:100.1/ijese.L262313121125 DOI:10.35940/ijese.L2623.14011225 Journal Website: www.ijese.org https://doi.org/10.1038/s41598-018-34832-7 5. X. Li, Y. Zhang, and P. Gong, “Wireless sensor networks for water level monitoring using LoRaWAN,” Sensors, vol. 23, no. 12, p. 5672, 2023, DOI: https://doi.org/10.3390/s23125672 6. T. Adepoju, B. Oyelami, and R. Olayinka, “Solar-powered IoT monitoring system for precision irrigation,” Environ. Eng. Sci., vol. 39, no. 8, pp. 703–715, 2022, DOI: https://doi.org/10.1089/ees.2021.0314 7. M. Abdelrahman, A. Khalid, and S. Hassan, “Smart water management using IoT-enabled real-time monitoring,” J. Environ. Informatics, vol. 43, no. 2, pp. 112–125, 2022, DOI: https://doi.org/10.3808/jei.202200456 8. G. Akintoye, O. Oke, and F. Ajayi, “Citizen-driven IoT monitoring for sustainable water use in African smart cities,” Smart Environ. Syst., vol. 12, no. 4, pp. 227–243, 2023, DOI: https://doi.org/10.1016/j.ses.2023.227 9. P. Garg, M. Singh, and N. Kumar, “Integrating IoT and data analytics for sustainable water management,” Sustainability, vol. 14, no. 6, p. 3511, 2022, DOI: https://doi.org/10.3390/su14063511 10. M. Rahman and F. Hossain, “Flood early warning systems using IoT-based water sensing networks,” J. Hydrol.: Reg. Stud., vol. 51, p. 101215, 2023, DOI: https://doi.org/10.1016/j.ejrh.2022.101215 11. L. Wang, Z. Chen, and Y. Hu, “Edge computing for IoT-based flood prediction systems,” IEEE Internet Things J., vol. 11, no. 3, pp. 4512–4525, 2024, DOI: https://doi.org/10.1109/JIOT.2024.3367425 12. S. Shiravale, “Flood alert system by using weather forecasting data and wireless sensor network,” International Journal of Computer Applications, 2017. DOI: https://doi.org/10.5120/ijca2015905608 13. A. Qureshi, R. Yadav, and D. Gupta, “Hybrid IoT–machine learning architectures for environmental monitoring,” Sci. Rep., vol. 14, no. 1, p. 2041, 2024, DOI: https://doi.org/10.1038/s41598-024-02041-6 14. X. Li, X. Cheng, K. Yan, and P. Gong, “A monitoring system for vegetable greenhouses based on a wireless sensor network,” Sensors, 2014. DOI: https://doi.org/10.3390/s101008963 15. L. Mdegela, Y. De Bock, E. Municio, E. Luhanga, J. Leo, and E. Mannens, “A multi-modal wireless sensor system for river monitoring: A case for Kikuletwa River floods in Tanzania,” 2023, pp. 1–23. DOI: https://doi.org/10.3390/s23084055 16. M. Rahman et al., “Internet of Things (IoT)-based water quality monitoring system,” 2020. https://www.researchgate.net/publication/344167317 17. S. Jadhav and A. Mane, “Low-power MEMS sensors for IoT water level detection,” Sens. Actuators A: Phys., vol. 359, p. 114572, 2024, DOI: https://doi.org/10.1016/j.sna.2024.114572 18. C. Nduka, P. Uzoho, and C. Eze, “Smart governance through IoT-enabled environmental monitoring systems,” Environ. Policy Technol. Rev., vol. 18, no. 1, pp. 47–62, 2024, DOI: https://doi.org/10.1016/j.eptr.2024.47 19. Konga, “Ultrasonic sensor,” Product Information, 2023. [Online]. Available: https://www.konga.com 20. Konga, “Arduino UNO microcontroller,” Product Information, 2023. [Online]. Available: https://www.konga.com 21. Konga, “Rechargeable battery,” Product Information, 2023. [Online]. Available: https://www.konga.com AUTHOR’S PROFILE Engr. (Ing.) Tunji John Erinle is an accomplished Mechanical Engineer currently serving as a Lecturer and Academic Researcher in the Department of Mechanical Engineering at Federal Polytechnic, Ado-Ekiti, Nigeria. With expertise in Mechanical Engineering & Advanced Manufacturing. He is also recognised as an Engineer, Innovator, Tutor, Mentor, and Motivator. Through his extensive experience and focus on advancing in Mechanical Engineering & Advanced Manufacturing with Mechatronics Engineering, Engr. Tunji John Erinle has established himself as a prominent figure in the discipline. He has worked on numerous projects involving the design, analysis, and control of electromechanical systems. Engr. Dr Isaac Olaposi Oladipo is a highly accomplished Engineer, Educator, Lecturer, and Researcher in the Department of Agricultural & BioResources Engineering at Federal Polytechnic, Ado-Ekiti, Nigeria. With expertise in Agricultural Engineering, Computer Science, and Soil and Water Engineering, he has made significant contributions to these fields. Olawale Kabiru Balogun is an experienced professional in Electrical & Electronics Engineering. As an experienced professional in Electrical & Electronics Engineering, he has built a career at the intersection of Embedded Systems, IoT, Robotics, Control Systems, and software engineering with strong proficiency in C++ and Python. Currently, he serves as the Head of Impact Lab (Technical) at Innov8 Hub, Abuja, Nigeria. His vision is to continue advancing the boundaries of engineering, where innovation meets impact. 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