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Artificial intelligence (AI) is an effective and efficient solution to manage and analyse data flow in any network. Internet of things (IoT) has quickly attracted significant global attention as an innovative, progressively growing technology. It has shown a rapid and successful involvement in many fields. Thus, IoT applications evolve exorbitantly and produce vast amounts of data required for intelligent data processing. It is approximately calculated that by 2025, IoT could make significant traffic of 79 zettabytes, and by 2030 around 25 billion active smart gadgets would be linked and woven through a single massive information network. It creates hurdles for the end-user to effectively evaluate and analyse the collected information. Therefore, IoT networks utilise robust and effective AI techniques such as machine learning (ML) and data analytics (DA), which examine large amounts of data and generate meaningful information promptly. ML is a self-learning process, and DA is another effective method for predicting the future behaviour of object or activities, using past data to improve productivity in different industries such as agriculture, transportation, online gaming, eHealth, etc. This paper discussed AI techniques such as ML and DA used in IoT networks and their impacts on productivity. Furthermore, we have discussed the future trends and challenges of IoT networks.

Over the past decade, the global use of renewable energy sources for electricity generation has grown significantly. This rise in the use of renewable energy has mainly been driven by increased awareness of the effects of climate change and the need for a sustainable and cleaner future. Clean energy technologies are essential in reducing poverty, expanding rural development, and protecting health while encouraging sustainability and environmental quality. This research aims to analyse the current technologies being implemented in developed countries and find out the useable matching options for third world countries on lower cost and strong sustainability. Furthermore, a low-cost and sustainable model for a selected third world country is proposed, simulated in Simulink MALTAB to compare, and observe the output and efficiency.

Smart devices are being utilised in industries to enhance their productivity and security. In this paper, we have developed a low-cost vehicle monitoring system using an Arduino UNO microcontroller and GSM module. The primary purpose of this research was to develop a low-cost system to start and track an automobile by integrating with GSM module. GPS and GSM modules were integrated with Arduino UNO microcontroller as a processor and controller for the whole system. An Android application was developed to communicate with the Arduino module and get the instruction from the user. The user can use the Android application to start their vehicle and track the vehicle. Locking and unlocking actions were programmed into the application for the vehicle's security purpose. The system could send the location information to the user's email address as part of the security feature. The system was successfully developed and tested in multiple real-time scenarios.