i-manager Publications

i-manager's Journal on IoT and Smart Automation (JIOT)

Current Issue Vol. 1 Issue 2

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Volume 1 Issue 1 January - June 2023

Research Paper

IoT-Based Overvoltage, Overcurrent, and Over-Temperature Fault Detector in Industries

Bhimrao Neharkar* , Shubham Patil, Sushmita A. Sarkar*, Shubham Tekam****

*-**** Department of Electrical Engineering, Kolhapur Institute of Technology's College of Engineering (Autonomous), Kolhapur, India.

Neharkar, B., Patil, S., Sarkar, S. A., and Tekam, S. (2023). IoT-Based Overvoltage, Overcurrent, and Over-Temperature Fault Detector in Industries. i-manager’s Journal on IoT and Smart Automation, 1(1), 1-6.

Abstract

Electrical faults such as overvoltage, overcurrent, and over-temperature can lead to serious damage to equipment and pose a safety risk to personnel. To address these issues, the Internet of Things (IoT)-based fault detection system has been proposed. The system utilizes IEEE standards for communication and data transfer and includes sensors for detecting over voltage, over current, and overtemperature, as well as a breaker trip detection mechanism and relay for isolating faulty equipment. The system is designed to continuously monitor the electrical parameters of equipment and provide real-time notifications of any faults detected. It also incorporates a fault management module that can prioritize and alert personnel of critical faults. The proposed IoT-based fault detection system provides a reliable and efficient solution for detecting and managing electrical faults in the industrial sector. It has the potential to reduce equipment damage, increase safety, and improve operational efficiency.

References

[1]. Aditya. (2011). Industrial Fault Indication System with Over Voltage over Temperature.

[2]. Arunthavanathan, R., Khan, F., Ahmed, S., & Imtiaz, S. (2021). An analysis of process fault diagnosis methods from safety perspectives. Computers & Chemical Engineering, 145, 107197.

[3]. Islam, M. N., Rahman, M. A., Ashrafi, K. M. A., & Ahmed, T. (2023). Development of an IoT based industrial fault detection and diagnosis system. Asian Journal for Convergence in Technology (AJCT), 9(1), 18-22.

[4]. Lee, H. (2017). Framework and development of fault detection classification using IoT device and cloud environment. Journal of Manufacturing Systems, 43(2), 257-270.

[5]. Madakam, S., Lake, V., Lake, V., & Lake, V. (2015). Internet of Things (IoT): A literature review. Journal of Computer and Communications, 3(5), 164-173.

[6]. Sattar, M. K., Waseem, M., Fayyaz, S., Kalsoom, R., Hussain, H. A., & Saddique, M. S. (2021). IoT based fault detection and protection of power transformer in the smart grid. Engineering Proceedings, 12(1), 1-7.

[7]. Wang, C., Vo, H. T., & Ni, P. (2015, December). An IoT application for fault diagnosis and prediction. In 2015 IEEE International Conference on Data Science and Data Intensive Systems (pp. 726-731). IEEE.

[8]. Zhang, P., Du, Y., Habetler, T. G., & Lu, B. (2010). A survey of condition monitoring and protection methods for medium-voltage induction motors. IEEE Transactions on Industry Applications, 47(1), 34-46.

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Research Paper

Real-Time Monitoring and Assessment of the Indoor Air Quality Hazard Index using Deep Learning Approach

Rami Alkhalil* , Shatha Ammourah, Kasim Mousa Alwan Al-Aubidy*

*-*** Faculty of Engineering & Technology, Philadelphia University, Jordan.

Alkhalil, R., Ammourah, S., and Al-Aubidy, K. M. A. (2023). Real-Time Monitoring and Assessment of the Indoor Air Quality Hazard Index using Deep Learning Approach. i-manager’s Journal on IoT and Smart Automation, 1(1), 7-16.

Abstract

Growing interest in Indoor Air Quality (IAQ) has attracted considerable attention since the COVID19 pandemic. To maintain a good IAQ, both gaseous and particulate contaminants should be maintained below an acceptable limit in an indoor environment. IAQ is not easy to predict, given that most indoor air gas pollutants are present simultaneously within an indoor built environment at variable concentrations. Therefore, it is difficult to assess or classify these mysterious gaseous pollutant concentrations and hazard levels. This paper presents the design of an accurate IAQ index standard based on acceptable limits for the level of multiple indoor air pollutants when designing Heating, Ventilation and Air Conditioning (HVAC) systems. These limits will be used in the deep learning process in the Artificial Neural Network Fuzzy Inference System (ANFIS). Cognitive expertise is used to create the rules of fuzzy inference that generate the final risk level. The ANFIS-based controller uses real-time data obtained by IAQ sensors to generate the final value of the resulting hazard level. The resulting hazard level indicator can be fed to a microcontroller for monitoring, alarms, and control purposes. The microcontroller can generate an operating signal for the HVAC system to maintain an acceptable indoor air level. As a result, a safe IAQ level is maintained, which will reduce the cost of the low throughput of poor ventilation systems.

References

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[2]. Cociorva, S., & Iftene, A. (2017). Indoor air quality evaluation in intelligent building. Energy Procedia, 112, 261-268.

[3]. Dasgupta, S., Huq, M., Khaliquzzaman, M., Pandey, K., & Wheeler, D. (2006). Indoor air quality for poor families: New evidence from Bangladesh. Indoor Air, 16(6), 426-444.

[4]. EPA. (2006, May). Guidelines for Reporting of Daily Air Quality - Air Quality Index (AQI).

[5]. Esquiagola, J., Manini, M., Aikawa, A., Yoshioka, L., & Zuffo, M. (2018, August). Monitoring indoor air quality by using IoT technology. In 2018 IEEE XXV International Conference on Electronics, Electrical Engineering and Computing (INTERCON) (pp. 1-4). IEEE.

[6]. Hapsari, A. A., Hajamydeen, A. I., & Abdullah, M. I. (2018). A review on indoor air quality monitoring using IoT at campus environment. International Journal of Engineering & Technology, 7(4.22), 55-60.

[7]. Jo, J., Jo, B., Kim, J., Kim, S., & Han, W. (2020). Development of an IoT-based indoor air quality monitoring platform. Journal of Sensors, 1-14.

[8]. Kang, J., & Hwang, K. I. (2016). A comprehensive realtime indoor air-quality level indicator. Sustainability, 8(9), 881.

[9]. Lakshmi, M. B., Kumar, N. S., Ilakyaah, B., & Balamurugan, K. (2020). Monitoring indoor air quality in industries. International Research Journal of Engineering and Technology (IRJET), 7(4), 1933-1937.

[10]. Marques, G., Saini, J., Dutta, M., Singh, P. K., & Hong, W. C. (2020). Indoor air quality monitoring systems for enhanced living environments: A review toward sustainable smart cities. Sustainability, 12(10), 4024.

[11]. Masic, A., Kepnik, G., Bektesevic, J., Mehuljic, M., Saric, I., & Hadziabdic, V. (2020, January). The network of smart sensors for indoor air quality monitoring. In Proceedings of the 31st DAAAM International Symposium on Intelligent Manufacturing and Automation (pp. 232-235).

[12]. Putra, J. C. P., Safrilah, S., & Ihsan, M. (2018, June). The prediction of indoor air quality in office room using artificial neural network. In AIP Conference Proceedings, 1977(1), 020040. AIP Publishing.

[13]. Saad, S. M., Shakaff, A. Y. M., Saad, A. R. M., Yusof, A. M., Andrew, A. M., Zakaria, A., & Adom, A. H. (2017, March). Development of indoor environmental index: Air quality index and thermal comfort index. In AIP Conference Proceedings, 1808(1), 020043. AIP Publishing LLC.

[14]. Saini, J., Dutta, M., & Marques, G. (2020). Indoor air quality monitoring systems based on internet of things: A systematic review. International Journal of Environmental Research and Public Health, 17(14), 4942.

[15]. Soni, R., Dhankar, R., & Mor, V. (2013). Indoor air quality index and chronic health disease: A pilot study. International Journal of Research in Engineering and Technology, 2(12), 282-286.

[16]. Srivatsa, P., & Pandhare, A. (2016, March). Indoor air quality: IoT solution. International Journal of Research in Advent Technology (pp. 218-220).

[17]. USGBC. (n.d.). ASHRAE Standard 62.1–2010: Ventilation for Acceptable Indoor Air Quality.

[18]. Wagdi, D., Tarabieh, K., & Zeid, M. N. A. (2018). Indoor air quality index for preoccupancy assessment. Air Quality, Atmosphere & Health, 11, 445-458.

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Research Paper

Security Challenges and Measures of IoT Devices and its Networks

Srinivasa Ravi Kiran T.* , Shanthi Priya Dasari, Salma Begum*

*-*** Department of Computer Science, P. B. Siddhartha College of Arts & Science, Vijayawada, Andhra Pradesh, India.

Kiran, T. S. R., Dasari, S. P., and Begum, S. (2023). Security Challenges and Measures of IoT Devices and its Networks. i-manager’s Journal on IoT and Smart Automation, 1(1), 17-22.

Abstract

The Internet of Things (IoT) illustrates physical objects with sensors, processing capabilities, software, and other technologies that attach and swap data with other devices and systems over the Internet or other communication networks. The use of IoT devices is widespread across all domains. In this paper, various types of attacks on IoT devices by intruders or hackers to gain access to IoT devices were discussed. In addition, various measures have been formulated to minimize attacks on IoT Devices. In-depth analysis of the likelihood of security threats and various possibilities to minimize security threat hacking were analyzed in detail, and possible measures are stated to overcome security threats.

References

[1]. Assiri, A., & Almagwashi, H. (2018, April). IoT security and privacy issues. In 2018 1st International Conference on Computer Applications & Information Security (ICCAIS) (pp. 1-5). IEEE.

[2]. Huang, X., Craig, P., Lin, H., & Yan, Z. (2016). SecIoT: a security framework for the Internet of Things. Security and Communication Networks, 9(16), 3083-3094.

[3]. Husamuddin, M., & Qayyum, M. (2017, March). Internet of Things: A study on security and privacy threats. In 2017 2nd International Conference on Anti-Cyber Crimes (ICACC) (pp. 93-97). IEEE.

[4]. Li, Z., & Xin, T. (2013). Threat modeling and countermeasures study for the Internet of Things. Journal of Convergence Information Technology, 8(5), 1163-1171.

[5]. Nguyen, K. T., Laurent, M., & Oualha, N. (2015). Survey on secure communication protocols for the Internet of Things. Ad Hoc Networks, 32, 17-31.

[6]. Siddiqui, S. T., Alam, S., Ahmad, R., & Shuaib, M. (2020). Security threats, attacks, and possible countermeasures in internet of things. In Advances in Data and Information Sciences: Proceedings of ICDIS 2019 (pp. 35-46). Springer Singapore.

[7]. Suo, H., Wan, J., Zou, C., & Liu, J. (2012, March). Security in the internet of things: A review. In 2012 International Conference on Computer Science and Electronics Engineering 3, 648-651. IEEE.

[8]. Vasilomanolakis, E., Daubert, J., Luthra, M., Gazis, V., Wiesmaier, A., & Kikiras, P. (2015, September). On the security and privacy of Internet of Things architectures and systems. In 2015 International Workshop on Secure Internet of Things (SIoT) (pp. 49-57). IEEE.

[9]. Vignesh, R., & Samydurai, A. (2017). Security on Internet of Things (IoT) with challenges and countermeasures. International Journal of Engineering Development and Research, 5(1), 417-423.

[10]. Zhou, W., Jia, Y., Peng, A., Zhang, Y., & Liu, P. (2018). The effect of iot new features on security and privacy: New threats, existing solutions, and challenges yet to be solved. IEEE Internet of Things Journal, 6(2), 1606-1616.

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Review Paper

Smart Innovations in IoT Technology

Viju Prakash Maria John*

School of Computing and Innovative Technologies (Major in Cloud Technologies), British University Vietnam, Hanoi.

John, V. P. M. (2023). Smart Innovations in IoT Technology. i-manager’s Journal on IoT and Smart Automation, 1(1), 23-33.

Abstract

The rapid advancement of Internet of Things (IoT) technology has paved the way for a new era of interconnected and intelligent devices with standards emerging primarily for wireless communication between sensors, actuators, and gadgets in day-to-day human life, in general being referred to as things. This research sheds light on the integration of Artificial Intelligence (AI) and IoT in smart devices and how they communicate in our daily lives. The critical aspects of data security and privacy in the IoT ecosystem, emphasizing the need for robust cyber security measures to protect against potential threats are addressed.

References

[1]. Alshamrani, M. (2022). IoT and artificial intelligence implementations for remote healthcare monitoring systems: A survey. Journal of King Saud UniversityComputer and Information Sciences, 34(8), 4687-4701.

[2]. Breivold, H. P., & Sandström, K. (2015, December). Internet of things for industrial automation-Challenges and technical solutions. In 2015 IEEE International Conference on Data Science and Data Intensive Systems (pp. 532-539). IEEE.

[3]. Cui, L., Xie, G., Qu, Y., Gao, L., & Yang, Y. (2018). Security and privacy in smart cities: Challenges and opportunities. IEEE Access, 6, 46134-46145.

[4]. Drepaul, N. A. (2020). Sustainable cities and the Internet of Things (IoT) technology: IoT technology improves the development of smart cities' infrastructures and reduces over-population stresses. Consilience, (22), 39-47.

[5]. Friess, P., & Riemenschneider, R. (2022). IoT ecosystems implementing smart technologies to drive innovation for future growth and development. In Digitising the Industry Internet of Things Connecting the Physical, Digital and VirtualWorlds (pp. 5-13). River Publishers.

[6]. Gagliardi, G., Lupia, M., Cario, G., Cicchello Gaccio, F., D'Angelo, V., Cosma, A. I. M., & Casavola, A. (2021). An internet of things solution for smart agriculture. Agronomy, 11(11), 2140.

[7]. Hashmi, S. A., Ali, C. F., & Zafar, S. (2021). Internet of things and cloud computing based energy management system for demand side management in smart grid. International Journal of Energy Research, 45(1), 1007-1022.

[8]. Hengstler, M., Enkel, E., & Duelli, S. (2016). Applied artificial intelligence and trust-the case of autonomous vehicles and medical assistance devices. Technological Forecasting and Social Change, 105, 105-120.

[9]. Lynggaard, P., & Skouby, K. E. (2016). Complex IoT systems as enablers for smart homes in a smart city vision. Sensors, 16(11), 1840.

[10]. Mohammadi, F. G., Shenavarmasouleh, F., & Arabnia, H. R. (2022). Applications of machine learning in healthcare and Internet of Things (IoT): A comprehensive review. arXiv preprint arXiv:2202.02868.

[11]. Parpala, R. C., & Iacob, R. (2017). Application of IoT concept on predictive maintenance of industrial equipment. In MATEC Web of Conferences, 121, 02008. EDP Sciences.

[12]. Pradhan, B., Bhattacharyya, S., & Pal, K. (2021). IoTbased applications in healthcare devices. Journal of Healthcare Engineering, 2021, 1-18.

[13]. Shah, S. F. A., Iqbal, M., Aziz, Z., Rana, T. A., Khalid, A., Cheah, Y. N., & Arif, M. (2022). The role of machine learning and the internet of things in smart buildings for energy efficiency. Applied Sciences, 12(15), 7882.

[14]. Stoian, N. A. (2020). Machine Learning For Anomaly Detection In Iot Networks: Malware Analysis on the Iot-23 Data Set (Bachelor's thesis, University of Twente).

[15]. Sunny, A. I., Zhao, A., Li, L., & Sakiliba, S. K. (2020). Low-cost IoT-based sensor system: A case study on harsh environmental monitoring. Sensors, 21(1), 214.

[16]. Zaidan, A. A., Zaidan, B. B., Qahtan, M. Y., Albahri, O. S., Albahri, A. S., Alaa, M., ... & Lim, C. K. (2018). A survey on communication components for IoT-based technologies in smart homes. Telecommunication Systems, 69, 1-25.

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Review Paper

Integrating IoT, ML and Cloud Computing for Sustainable Agriculture: Opportunities and Challenges

Ushaa Eswaran* , Vishal Eswaran**

* Indira Institute of Technology & Sciences, Markapur, India.

** CVS Health, Dallas, Texas, United States.

Eswaran, U., and Eswaran, V. (2023). Integrating IoT, ML and Cloud Computing for Sustainable Agriculture: Opportunities and Challenges. i-manager’s Journal on IoT and Smart Automation, 1(1), 34-47.

Abstract

The integration of the Internet of Things (IoT), Machine Learning (ML), and Cloud Computing has immense potential to revolutionize the agricultural industry and promote sustainability. By leveraging IoT sensors to capture real-time data on environmental conditions and ML algorithms, farmers can obtain valuable insights and predictions. Cloud computing provides a scalable and cost-effective platform to store and process data. This research presents a comprehensive overview of the opportunities and challenges involved in integrating the IoT, ML, and cloud computing for sustainable agriculture. Various applications of this integration have explored the challenges that need to be addressed and outlined future research directions. This highlights how the integration of these technologies can lead to more efficient and sustainable agricultural practices by providing farmers with real-time data and insights for making data-driven decisions.