This study introduces an integrated IoT-based framework for machine learning of agricultural crop yield prediction without rivaling in the accuracy levels. After an intensive survey of 16 machine learning algorithms on multiyear data from the principle agricultural zones of Pakistan, we show that ensemble models are capable of achieving close to perfect prediction accuracy. The Random Forest method had excellent performance with R² = 0.99, RMSE = 538.37 kg/ha and MAPE = 0.2944% which are a new benchmark in the field of agricultural yield prediction. Analysis of 400 agricultural records across seven districts showed evident outperformance of the tree-based ensemble methods against conventional ones. The proposed end-to-end framework integrates IoT sensor networks and cutting-edge ML models to monitor in real-time, and with high accuracy, the yields of major crops viz. wheat, cotton, sugarcane and rice. Here, we present an important leapfrogging in precision agriculture technology development that provides disruptive potential to the agricultural planning, resource allocation and food security improvement of developing countries by fusing IoT and machine learning.

This study discusses the challenges and recent developments in the field of the internet of things (IoT), including privacy and security issues as well as limitations for industrial purposes. To solve security and privacy issues in remote access, it presents a safe and collaborative solution for industrial use in the form of a platform called NBPIIoT. It includes a cloud layer, a fog layer with necessary parts such as nbVF and nbGW, and a new cloud layer. NBPIIoT provides security through point-to-point access, user profiles, and encrypted data transport. It presents private software interfaces (PSIs) and open systems, which are suitable for smart homes, industrial applications, and research training areas. The chapter also discusses the applications of NBPIIoT for smart homes, remote training, and research labs. It also addresses the challenges of security, interoperability, scalability, reliability, data management, privacy, prices, and talent shortage. The implementation of platforms includes modules such as nbVF, nbGW, nbInteg, and nbUI, which provide comfortable and scalable solutions for a variety of deployments. It helps in enhancing the security and privacy of IoT devices, improving integration in smart factories, as well as supporting education settings that make remote access without interruption feasible. The chapter also highlights the importance of IoT in industrial automation and suggests that NBPIIoT is a comprehensive solution to addressing issues, and provides secure, collaborated, and regulated remote access through many applications. <!----> <!----> <!----> <!---->

The reliability of power distribution systems critically depends on the rapid detection and location of faults. Conventional approaches often lack real-time capability, leading to prolonged downtime. This paper presents a low-cost, intelligent wireless system for monitoring faults on power distribution lines, based on the Internet of Things (IoT) concept. The system utilizes an ARM7 microcontroller interfaced with current and voltage sensors, a GPS module, and a GSM communication module. The developed prototype monitors electrical parameters in real time; upon detecting an abnormal condition such as a line-to-ground (LG) or line-to-line (LL) fault, it immediately interrupts the load and automatically classifies and reports the fault via SMS to a control station. The alert message includes the fault type and precise GPS coordinates. Through rigorous testing, the system demonstrated consistent and reliable fault detection across all tested fault scenarios. The achieved average response time of 1.5-2 seconds for fault detection, coupled with a minimal data transmission delay, underscores the system's effectiveness. Results confirm that the proposed system can significantly improve fault management by enabling faster crew dispatch, reducing outage durations, enhancing worker safety, and bolstering overall network reliability.

The emergence of AI all over the world has significant environmental expenses. The CO2 emission of training big models is the same as five cars operating throughout their lives, and the computational needs are doubling after every 3.4 months. This chapter promotes federated learning (FL), which is capable of reducing data transfer energy by 95-99% with no performance and privacy losses. We would suggest a Green AI Score as a standard of sustainability metrics. Cases demonstrate that FL saves computational energy (70-85% in areas such as healthcare). AI will consume 8 percent of the world power without taking action by 2030. Our roadmap to sustainable, responsible development of AI is plotted with the help of efficient algorithms, renewables, and policy.