Mango anthracnose, caused by Colletotrichum gloeosporioides, is a destructive fungal disease that severely reduces fruit quality and yield, threatening farmer livelihoods and the mango industry. Early and reliable detection is crucial, as traditional manual inspection is slow, subjective, and often fails to identify infections at an early stage. To address this challenge, we present LEAF: a Lightweight Explainable AI Framework for mango anthracnose detection and severity estimation. LEAF employs a fine-tuned MobileNetV2 model for binary classification of healthy and infected mango leaves, coupled with severity grading to provide actionable insights. The framework integrates Grad-CAM for explainability, allowing visualization of infection regions and improving model interpretability. Experimental evaluation on a curated dataset achieved over 99% classification accuracy using MobileNetV2 with transfer learning. The lightweight architecture, further optimized through TensorFlow Lite, demonstrates suitability for real-time mobile deployment, while Grad-CAM enhances interpretability by localizing disease-specific regions in mango leaves. The combined use of efficient transfer learning, explainable AI, and severity quantification highlights the novelty of this approach and its practical value in field applications.

Optimization algorithms are crucial for energy-efficient routing in Internet of Things (IoT)-based Wireless Sensor Networks (WSNs) because they help minimize energy consumption, reduce communication overhead, and improve overall network performance. By optimizing the routing paths and scheduling data transmission, these algorithms can prolong network lifetime by efficiently managing the limited energy resources of sensor nodes, ensuring reliable data delivery while conserving energy. In this work, we present Greylag Goose-based Optimized Clustering (GGOC), which aids in selecting the Cluster Head (CH) using the proposed critical fitness parameters. These parameters include residual energy, sensor sensing range, distance of a candidate node from the sink, number of neighboring nodes, and energy consumption rate. Simulation analysis shows that the proposed approach improves various performance metrics, namely network lifetime, stability period, throughput, the network’s remaining energy, and the number of clusters formed.

Wireless sensor networks (WSNs) can be conceptualized as large-scale, dynamic social systems where nodes interact to achieve collective objectives. These networks generate extensive data through interactions, offering opportunities for large-scale knowledge discovery to optimize operations and enhance resilience. However, challenges such as limited resources and susceptibility to distributed denial-of-service (DDoS) attacks necessitate efficient and secure mechanisms for managing these “social” interactions. This article proposes a lightweight trusted framework that applies computational modeling principles to clustering in WSNs. The framework employs bi-directional long short-term memory (Bi-LSTM) networks for malicious node detection, mirroring the role of anomaly detection in social systems, and uses the walrus optimization algorithm (WOA) for optimized cluster head (CH) selection. By considering parameters such as residual energy, proximity to base stations, node density, and trust value, WOA ensures effective “role assignment” within the network, similar to optimizing functional roles in human social systems. The Bi-LSTM model analyzes node behavior to exclude malicious actors, fostering trusted, and efficient clustering. Evaluated in simulated DDoS attack scenarios, the framework significantly reduces the impact of attacks by isolating malicious nodes while improving network performance and resilience. Metrics such as stability period, throughput, network lifetime, energy efficiency, and attack mitigation are analyzed, demonstrating the framework’s effectiveness. This research bridges the domains of social system modeling and WSN operations, providing an energy-efficient and secure solution for managing large-scale dynamic networks.

Unmanned Aerial Vehicle (UAV)-assisted Wireless Sensor Networks (WSN) enhance the effectiveness of smart city applications by providing comprehensive, real-time data collection capabilities that contribute to better urban planning, resource management, and disaster response. The need for energy-optimized routing in UAV-assisted WSNs for smart city applications is a big problem that needs to be solved by creating unique routing algorithms that can balance energy efficiency, real-time adaptability, and scalability to meet the specific needs of cities. In this work, we present an optimized Cluster-Head (CH) selection using Golden Jackal Optimization (GJO) by utilizing parameters, namely, node energy levels, node-to-sink distance, neighboring node density, and energy consumption rate. The network is three-level energy-heterogeneous for enhancing the network lifetime. The proposed work is suitable for smart city applications, namely Intelligent Transportation Systems, Landslide Detection, Wildfire Detection, etc., wherein the sensor nodes need to have optimized routing. The simulation analysis is done in MATLAB software wherein the proposed work i.e., GJO-based Routing Optimized for UAV-assisted WSN (GROW) outperforms the recently proposed routing frameworks on different benchmarks of performance measures (16% and 19% improvement over the stability period and network lifetime).

Industrial cyber–physical systems (ICPSs) can play a crucial role in damage assessment during extreme conditions by leveraging their integration of physical infrastructure, sensing capabilities, and advanced analytics. However, due to the wireless sensing devices that are made to operate in ICPS, there is a dire need to address the green routing (energy-efficient) challenges through an optimized solution. In recent times, artificial intelligence (AI) has had a significant impact on wireless sensor networks (WSNs) designed to operate as ICPS components. In this research work, we present AGRIC: AI-based green routing for ICPS. While following the cluster-based routing, the election of cluster head (CH) is executed using our proposed AI-inspired extended spotted hyena Lévy flight optimization (ESHLFO) algorithm. Furthermore, to address the energy hole problem, four energy-unlimited data collection nodes are used around the periphery of the network. The results of the experiment demonstrate the fact AGRIC delivers network longevity and supreme performance in the context of stability time, throughput, and energy left over in the network as important performance indicators.

The advancements in the field of the Internet of Things (IoT) have fueled technological advancements in remotely handling agricultural operations, as well as the successful implementation of Precision Agriculture (PA) around the world. In this paper, we present Cluster-based Routing using an Optimized framework for Precision agricultural monitoring (CROP) that uses the recently developed Sooty Tern Optimization Algorithm (STOA). CROP specifically aims to detect unauthenticated entry into the agricultural field and various other factors related to PA. We perform a simulation analysis of CROP, and the performance of CROP is overwhelming, as it not only enhances stability period and network longevity by 58.9% and 65.5% respectively, but also proves to be scalable as compared to the Fuzzy-C-Means (FCM) algorithm and other routing protocols pertaining to PA.

Abstract The exponential growth in the smart cities and the massive deployment of wireless sensor network‐based Internet of Things (IoT) has resulted in generating the humongous data, which needs to be orchestrated. Further, it is observed that the resource‐constrained IoT devices act as stumbling block in the successful realization of cognitive smart cities. Hence, there is a high need to manage the data transmission from massive IoT devices and also to enhance the productivity of such devices. To address this issue, in this article, we present energy‐efficient routing paradigm for resource‐constrained IoT‐based cognitive smart city (EI‐CSC). We adapt Sooty tern optimization algorithm (STOA) due to its faster convergence and high ‘exploration and exploitation’ capabilities to perform energy efficient cluster‐based routing. We focus primarily on rendering the optimized solution to the cluster head selection problem through STOA. The outcomes of simulation analysis of EI‐CSC promises enhanced performance in the context of stability period and network lifetime by 83.4% and 107.7%, ‘respectively’ as compared to recently proposed ‘genetic algorithm and PSO based hybrid clustering algorithm’.

The seamless integration of medical sensors and the Internet of Things (IoT) in smart healthcare has leveraged an intelligent Internet of Medical Things (IoMT) framework to detect the criticality of the patients. However, due to the limited storage capacity and computation power of the local IoT devices, patient's health data needs to transfer to remote computing devices for analysis, which can easily result in privacy leakage due to lack of control over the patient's health data and the vulnerability of the network for various types of attacks. Motivated by this, in this paper, an Empirical Intelligent Agent (EIA) based on a unique Swarm-Neural Network (Swarm-NN) method is proposed to identify attackers in the edge-centric IoMT framework. The major outcome of the proposed strategy is to identify the attacks during data transmission through a network and analyze the health data efficiently at the edge of the network with higher accuracy. The proposed Swarm-NN strategy is evaluated with a real-time secured dataset, namely the ToN-IoT dataset that collected Telemetry, Operating systems, and Network data for IoT applications and compares the performance over the standard classification models using various performance metrics. The test results demonstrate that the proposed Swarm-NN strategy achieves 99.5% accuracy over the ToN-IoT dataset.

Wireless Sensor Network (WSN) plays a vital role in dealing with the challenging tasks of information management in Intelligent Transportation Systems (ITS). Current research has shown that the ever growing number of vehicles on the roads is making congestion worse, and many safety concerns are being addressed comprehensively by the WSN-based ITS. However, the energy consumed by sensor nodes, their operational period, and ‘security compromise’ are ever growing concerns. Cluster-based routing strategies have potentially contributed in reducing the energy expenditure of sensor nodes, besides the selection of energy-efficient and secure Cluster Head (CH) is still seeking an optimized approach for acquiring the proliferated performance of WSN. To address these concerns, we propose an Intelligent Clustering approach for ITS (ICITS) which selects CHs based on a hybrid optimization method called GABAT that integrates the strengths of Genetic Algorithm (GA) and BAT Algorithm (BA). The proposed framework (ICITS) is targeted primarily to road transport in military areas due to their stringent requirements in terms of security and reliability while collecting the data from the deployed sensor nodes. The simulation results obtained with ICITS demonstrate that it performs well for various performance metrics that include stability period, network survival period and ‘number of packets sent’, which are improved by 54.7%, 19.6%, and 40.5%, respectively as compared to recently proposed Cluster-based Intelligent Routing Protocol (CIRP).

The sixth generation (6G) is envisioned to be a spawned key technology that will support the ubiquitous and seamless connection of a massive number of Internet-of-Things (IoT) devices. The extremely high data rate, low end-to-end delay, high mobility of IoT devices propel the desideratum of extenuating the concern of reducing the energy consumption, i.e., green communication. Hence, in this article, we address the concern of green communication in 6G-enabled massive IoT devices by following the cluster-based data dissemination in the network. We propose a novel hybrid whale spotted hyena optimization (HWSHO) algorithm by synthesizing the whale optimizer algorithm (WOA) with exploitation capabilities of spotted hyena optimizer (SHO). We perform a simulation experimental study that shows the supreme performance of our proposed technique over the most recent proposed energy-efficient data dissemination methods. The proposed technique is an exemplary solution that could be pertinent to various hostile applications seeking green communication of 6G-enabled IoT devices.

Massive machine-type Internet of Things (IoT) communication (mMTIC) has the potential for high impact in the anticipated future industry 4.0 sensor networking applications. However, the energy limitation and battery life of the IoT nodes have always been one of the long-standing problems. Clustering routing protocol (CRP) being the most efficient existing approach often suffers when nodes closer to the sink depletes their energy, thereby producing an unwanted energy hole, where packets in flight toward the sink often get interrupted. Considering mMTIC covering a large geographical area, such as monitoring bush fires, the multihop communication among the nodes often causes such an energy hole problem. In this article, we develop an artificial-intelligence-based CRP framework for incorporating a small periphery of a fixed shaped area to ameliorate such energy holes. Our proposed framework is not only energy-optimized but also acts as a robust approach for massive communication and informed data collection.