Water sustainability will be scarce in the coming decades because of global warming, an alarming situation for irrigation systems. The key requirement for crop production is water, and it also needs to fulfill the requirements of the ever-increasing population around the globe. The changing climate significantly impacts agriculture production due to the extreme weather conditions that prevail in various regions. Since urbanization is increasing worldwide, smart cities must find innovative ways to grow food sustainably within built environments. This paper explores how precision agriculture powered by artificial intelligence (AI) can transform crop farms (CF) to enhance food security, nutrition, and environmental sustainability. We developed a robotic CF prototype that uses deep reinforcement learning to optimize seeding, watering, and crop maintenance in response to real-time sensor data. The system was tested in a simulated CF setting and benchmarked. The results revealed a 26% increase in crop yield, a 41% reduction in water utilization, and a 33% decrease in chemical use. We employed AI-enabled precision farming to improve agriculture’s efficiency, sustainability, and productivity within smart cities. The widespread adoption of such technologies makes food supplies resilient, reduces land, and minimizes agriculture’s environmental footprint. This study also qualitatively assessed the broader implications of AI-enabled precision farming. Interviews with farmers and stakeholders were conducted, which revealed the benefits of the proposed approach. The multidimensional impacts of precision crop farming beyond measurable outcomes emphasize its potential to foster social cohesion and well-being in urban communities.

The looming energy crisis is affecting every sector of the world. The dire need to conserve energy has compelled researchers to bring automation to the power sector. The conservation of energy is one of the biggest challenges Third-World countries are facing in general and in Europe due to the Russian–Ukrainian war. There is a need to introduce such systems that can prevent energy loss and let users buy and sell excessive electricity they have. In the field of power and electricity, the Internet of Things (IoT) plays an active role in the conservation of energy. The new concept of smart grids is widely used for efficient transmission. The technique of blockchain can further reduce the wastage of energy and efficient consumption if it is used with smart grids. This article proposes a smart energy meter based on smart grids and blockchain. The proposed implementation is a demonstration containing a few microgrids, each with its very own blockchain. The users will use energy by making transactions, following the smart contracts. The focus is on the peer-to-peer transactions in a microgrid controlled by blockchain. The architectural design outcomes are a smart energy meter, a smart contract on the Ethereum blockchain, and an android application to monitor and control transactions and energy trade via smart contracts with other consumers.

Free space optical (FSO) communication offers huge bandwidth, license-free spectrum and a more secure channel. PIN diodes are normally used for detection, but avalanche photodiodes (APD) are preferred for detecting high-speed FSO signals in many applications. In the case of APD, the noise distribution is input-dependent Gaussian noise (IDGN) rather than input-independent Gaussian noise (IIGN). We investigate the error analysis using on-off keying (OOK) for various detection approaches. This paper proposes a machine learning approach and compares its performance with soft and hard decisions. Soft values in the case of IDGN and IIGN are derived, and the optimum and sub-optimum detection thresholds are evaluated. The proposed novel ML approach shows better performance gains than the other approaches. It is also demonstrated that the IDGN model should have an optimum detection and achieve a gain of 2.5[dB] and about 1[dB] at λ= 0[dB] and λ= 10[dB], respectively. Experimental results are plotted for the FSO channel data, and a model fit curve is plotted using the ML approach.

Integrating telepresence robots with gamification opens up new directions in which higher-education governance could translate into higher levels of student engagement in the AI-driven digital era. Drawing on both constructivism and the self-determination theory (SDT), this study will review evidence on how these technologies enhance autonomy and motivation, boosting effective participation in diverse learning environments. The experiments were carried out across various subjects with socio-economically varied groups of students by deploying gamified learning modules on telepresence robots. Primary metrics under consideration involved participation rates, task completion times, and other qualitative feedback measures about impacts created by such technologies. Indeed, the participation rates of the robot group were 40% higher, task completion times were reduced by 30%, and module completion rates for the robot group were 30% improved. The students reported positive emotions and showed more engagement, with the low-income students showing an 80% engagement rate compared to 40% in the control group. The results from both robots and gamification show promising potential for reshaping traditional learning paradigms, especially for students from geographically distant and underserved areas. The study guides further research on applying advanced tools in higher-education governance to foster sustainable practices in the AI era.

Automation in the modern world has become a necessity for humans. Intelligent mobile robots have become necessary to perform various complex tasks in healthcare and industry environments. Mobile robots have gained attention during the pandemic; human–robot interaction has become vibrant. However, there are many challenges in obtaining human–robot interactions regarding maneuverability, controllability, stability, drive layout and autonomy. In this paper, we proposed a stability and control design for a telepresence robot called auto-MERLIN. The proposed design simulated and experimentally verified self-localization and maneuverability in a hazardous environment. A model from Rieckert and Schunck was initially considered to design the control system parameters. The system identification approach was then used to derive the mathematical relationship between the manipulated variable of robot orientation control. The theoretical model of the robot mechanics and associated control were developed. A design model was successfully implemented, analyzed mathematically, used to build the hardware and tested experimentally. Each level takes on excellent tasks for the development of auto-MERLIN. A higher level always uses the services of lower levels to carry out its functions. The proposed approach is comparatively simple, less expensive and easily deployable compared to previous methods. The experimental results showed that the robot is functionally complete in all aspects. A test drive was performed over a given path to evaluate the hardware, and the results were presented. Simulation and experimental results showed that the target path is maintained quite well.

In the Internet of Things (IoT) era, telepresence robots (TRs) are increasingly a part of healthcare, academia, and industry due to their enormous benefits. IoT provides a sensor-based environment in which robots receive more precise information about their surroundings. The researchers work day and night to reduce cost, duration, and complexity in all application areas. It provides tremendous benefits, such as sustainability, welfare improvement, cost-effectiveness, user-friendliness, and adaptability. However, it faces many challenges in making critical decisions during motion, which requires a long training period and intelligent motion planning. These include obstacle avoidance during movement, intelligent control in hazardous situations, and ensuring the right measurements. Following up on these issues requires a sophisticated control design and a secure communication link. This paper proposes a control design to normalize the integration process and offer an auto-MERLIN robot with cognitive and sustainable architecture. A control design is proposed through system identification and modeling of the robot. The robot control design was evaluated, and a prototype was prepared for testing in a hazardous environment. The robot was tested by considering various parameters: driving straight ahead, turning right, self-localizing, and receiving commands from a remote location. The maneuverability, controllability, and stability results show that the proposed design is well-developed and cost-efficient, with a fast response time. The experimental results show that the proposed method significantly minimizes the obstacle collisions. The results confirm the employability and sustainability of the proposed design and demonstrate auto-MERLIN’s capabilities as a sustainable robot ready to be deployed in highly interactive scenarios.