ABSTRACT With increasing focus on sustainable agriculture and AI‐enabled solutions, this work proposes AloeVeraNet, a compact deep learning model designed for the efficient and real‐time detection of aloe vera leaf diseases on edge devices. The model employs depthwise and pointwise convolutions to achieve a significantly reduced parameter count (289 K) and model size (1.10 MB), enabling deployment in low‐resource environments. With 96.09% accuracy, AloeVeraNet sets a new benchmark in classifying aloe vera leaf conditions: healthy, rust‐infected and spot‐affected, outperforming MobileNetV2, EfficientNetV2‐S and VGG16. This sustainable, artificial intelligence (AI)‐based solution supports precision agriculture through optimised computation, energy efficiency and local disease monitoring, all without relying on cloud infrastructure, thereby contributing to environmentally responsible farming practices. This study demonstrates the value of integrating AI with sustainable edge computing in creating resilient and inclusive solutions for the agricultural sector.

Abstract Following the fourth industrial revolution and subsequent developments in information and communication technology, applying intelligent techniques in microgrid is gaining popularity in academia and business worldwide. A significant amount of data is continuously generated by the widespread use of internet of things (IoT) technologies and sensor networks in microgrids. This data includes essential information to progress the performance of microgrids. This paper includes a comprehensive review of IoT, cloud computing, big data, artificial intelligence, machine learning, blockchain in microgrid and the concepts of digital twin and metaverse and their applications. By aiding in the design, operation management, and maintenance of microgrids, these methods offer a potent tool for managing the massive historical data and real‐time data stream in a proficient and protected way. They also facilitate microgrids operation. Contextual awareness, security, and resilient operation are important for microgrids, therefore their possible improvement in light of these intelligent approaches is comprehensively examined. A theoretical implementation for managing robust operation of microgrids is then described. The discussion of recommendations in microgrids concludes.

Abstract The protection of wireless sensor networks (WSN) against malware attacks is crucial. The paper discusses the issue of malware attacks in WSN, which are commonly used for monitoring and surveillance in various applications. Due to resource constraints, sensor nodes in WSN are vulnerable to malware attacks, which can spread rapidly and paralyze the network. The development of new technologies such as IoT, Industry 4.0 has increased the importance of WSN, and it has become essential to address the challenges posed by the resource‐constrained nature of sensor nodes and security concerns. In this paper, a model is considered with two exposed states to investigate the behaviour of malware spreading in WSN, and a SE 1 E 2 IR (Susceptible—Exposed State 1 ‐ Exposed State 2 ‐ Infectious—Recovered) model is proposed. The model is formulated as a system of differential equations, and its equilibrium and stability are examined. The basic reproduction number (R 0 ) is also calculated as a key parameter that characterizes the spread of malware in the network. This parameter helps to identify the conditions under which the network will remain malware‐free or when it will experience an outbreak of malware. The proposed model provides a mechanism for the earlier detection of malware occurrences in WSN, and also discusses the effect of connectivity and coverages on the propagation of malware in the network. The paper also includes a comparative study of the proposed model with existing models; extensive theoretical study and computation analysis are performed to validate the proposed model.

Abstract Solid waste management (SWM) is a crucial management entity in urban cities to handle the waste from its generation to disposal to accomplish a clean environment. The waste management operation mainly encompasses various climatic, demographic, environmental, legislative, technological, and socioeconomic dimensions. The traditional approaches deliver limitations in the process of predict and optimizing such composite non‐linear operations. The integration of the internet of things (IoT) and artificial intelligence (AI) methods have progressively gained attention by delivering potential alternatives for resolving the difficulties in SWM. This article presents a review of the significance of the amalgamation of IoT and machine learning (ML) in the SWM to predict waste generation, waste classification, route optimization, estimation of methane emissions, and so forth. The article covers the application of each ML model for the activities, including SWM, compositing, incineration, pyrolysis, gasification, landfill, and anaerobic digestion. Moreover, it is concluded that the decision tree and random forest (DT‐RF) algorithm is minor implemented, and artificial neural network (ANN) is implemented majorly in the SWM. The large number of data sets covered in the publication are secured and hidden; it limits replicating the AI models; this is also one key constraint of the non‐implementation of AI models in SWM. Scarcity of data, accurate data, rare availability of customized AI models for tackling the activities in SWM are the limitations identified from the previous studies. Implementation of low‐power ML processors, edge and fog computing‐based devices is the future direction for overcoming SWM limitations.

Abstract It has been speculated that by the year 2050, technology will have progressed to the point of complete autonomy. This paper scrolls through patent pathways and intellectual developments throughout the industrial revolutions listing significant products and services that landmarked each revolution up to Industry 4.0. The patent trails and the recent IPR inputs are expected to assist readers in fast‐tracking up to speed on the bleeding edge of the current research pools while having an eagle's eye perspective on the previous developments so far. The research pools of Industry 4.0 are classified and explored. A lack of Human‐machine workforce synergy in Industry 4.0 and the nascent “customized manufacturing” concept is addressed in subsequent sections. The paper classifies two expected phases of Industry 5.0, highlighting the subdomains touted to be its focal areas. Lastly, Industry 5.0's niche research areas are checked and a suitable pathway to achieve the goals set for the sixth revolution is proposed.

Solid waste management (SWM) is a key administrative unit for managing the urban waste to deliver an eco-friendly environment to the citizens residing in urban cities. Generally, many technologies are implemented and developed by researchers for enhancing the mechanism of SWM and minimizing the waste generation. Yet, the management of waste generation is still a concern. So, here, there is requirement of technology that can involve the individuals for achieving the target reducing the waste. At present, the blockchain technology is an appropriate technology for SWM, as it provides the applications of time tracing activities, secure data transactions, and automatic reward system. In this study, a blockchain-based reward system is proposed to generate the rewards based on real-time series data such as quantity of garbage and level of waste. Furthermore, LoRa-range-based customized sensors are developed for bins to obtain real time information. Moreover, the generated information further transferred to cloud by utilizing LoRa wireless enabled gateway. By the use of flask server, a technique is proposed for integrating real-time data with blockchain via a local network application programming interface (API). A real-time implementation is evaluated on the data to the check the performance efficiency of the proposed approach, where the procedure of automatic reward system is presented in detail.