Integrating IoT, ML and Cloud Computing for Sustainable Agriculture: Opportunities and Challenges
Smart Innovations in IoT Technology
Real-Time Monitoring and Assessment of the Indoor Air Quality Hazard Index using Deep Learning Approach
A Comprehensive Review of Internet of Things (IoT) in the Automobile Industry and its Diverse Applications
Security Challenges and Measures of IoT Devices and its Networks
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.
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.
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.
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.
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.
