The integration of Internet of Things (IoT) technologies into smart agriculture has significantly enhanced automation, monitoring, and productivity. However, these systems introduce critical cybersecurity vulnerabilities and generate heterogeneous data, including structured network traffic and image-based inputs. This requires an intrusion detection system (IDS) capable of handling multimodal data, particularly since adversarial attacks can manipulate inputs to evade traditional detection models. To address these challenges, this paper proposes a novel image-based IDS for smart agriculture environments. The system transforms network traffic into images and employs VGG16 for feature extraction, Binary Greylag Goose Optimization for feature selection, and a random forest for classification. It further integrates a dual defense strategy that combines a Convolutional Autoencoder Denoising (CAED) module with Adversarial Training (AT) to improve robustness against adversarial perturbations. The proposed solution is evaluated on the CICIoT2023 dataset in eight traffic classes under three white-box adversarial attacks. The IDS demonstrates strong resilience across all perturbation levels. For weak perturbations (ε=0.01), the dual defense achieves accuracies of at least 99.46%. Under moderate perturbations (ε=0.1), it maintains high performance with macro-averaged accuracies of at least 99.39%. Even under strong perturbations (ε=0.3), the system remains robust, attaining an accuracy of at least 96.50%. To assess generalization to real agricultural settings, the IDS is also tested using native crop images from the agricultural dataset. Under severe adversarial distortion (ε=0.3), the system maintains robustness, achieving a macro-averaged accuracy of at least 96.71%. These results confirm that the proposed multimodal IDS provides a resilient, adaptive security solution for smart agriculture networks facing advanced adversarial threats.

The Internet of Things (IoT) represents a pervasive system that continuously demonstrates an expanded application in various domains. The energy-efficiency problem has always been a crucial issue linked to this type of network where the system lifetime strongly depends on devices’ batteries. Numerous energy-efficient networking protocols have been proposed in the literature to increase the system lifetime. However, most of the proposed approaches deal with the short-term vision of energy consumption and omit to consider the rechargeable battery degradation when evaluating the network lifetime. Indeed, the major parts of the network devices use rechargeable batteries that age and degrade over time due to several factors (temperature, voltage, charging/discharging cycle, etc.). Therefore, it is essential to promptly detect these internal and environmental degradation factors to avoid network failures. Clustering represents one of the main wireless network protocols and plays an essential role in network self organizing. In this work, we propose a novel long-term energy optimization clustering approach based on battery State of Health (SoH) prediction, called LECA_SOH. The objective is to predict the impact of cluster heads election on the rechargeable batteries SoH before applying the clustering. LECA_SOH fosters the selection of the nodes, which will less suffer from battery degradation during the future rounds, leading to extend the system lifetime. The obtained results demonstrate that the proposed clustering approach improves the network lifetime in the long term and extends the number of recharging cycles compared to the conventional energy-efficient approaches.

Lifetime improvement is a major concern for energy constrained wireless networks. Clustering the network topology is widely utilized for managing and enhancing the system duration. With conventional clustering mechanism Cluster Heads (CHs) close to the Base Station (BS) utilize higher power resource for relaying data packets of the other network CHs. This scenario obstruct the network performance as nodes close to the BS attend an earlier death than their desired durability due to the overloaded routing task. This scenario unbalanced energy consumption and is designated as the hot spot problem. The interest in this work is to carry the intra clustering topology in a vast scale contexts to support the network rising and fairly power balance the energy consuming. In this context, we present an Improved Lifetime Optimization Clustering (ILCK) approach that uses the Kruskal minimal spanning tree heuristic (MST) and consider the state of health (SOH) of devices batteries for the network life maximization. ILCK appeal the Kruskal algorithm in a distributed trend to achieve a minimal MST tree inside wide cluster to consolidate the intra cluster routing topology and mitigate the energy allocated to wireless communications. To the best of our awareness, this is a primary solution that merge the Kruskal approach within an uneven clustering to prolong the objects battery endurance and ease the energy hot spot routing issues. The complexity proof of the proposed approach is provided and simulation results denote that ILCK can adequately scale down the power consumption and lengthen the execution time of the deployed network.

Energy optimization is an imminent worldwide issues for green computing, it constitutes a major concern and a critical aspect especially for energy constrained wireless networks. To overcome this issue, clustering techniques were introduced as a prominent method that arranges the system operation in correlated manner to attend the energy preservation and prolong the network lifespan. However, existing clustering works only focus on preserving the battery charge to operate until it drains out. This approach is most appropriate for non-rechargeable batteries. However, rechargeable batteries become commonly used and need to be considered. The full discharge of rechargeable battery does not mean the device obsolescence. Therefore, the system lifetime optimization should take into consideration the degradation of the rechargeable batteries performances. In this context, we proposed an improved long-term energy efficient unequal clustering approach based on the battery state of health for IoT networks (ILEC_SOH). This work represents an initial step in the integration of the battery health degradation into the unequal network clustering. The obtained results show that the consideration of battery state of health (SOH) significantly improve the network lifespan in the long term compared to the conventional energy efficient approaches.

Energy optimization is one of the major issues in the telecommunication field and particularly in wireless networks. This optimization is an essential condition for the ubiquity of wireless and mobile networks. Recent studies show that the Information and Communications Technology sector (ICT) (production, distribution, and use) amounts to 1.4% of overall global CO2e emissions.

The Internet of Things (IoT) enables the smart devices to be inter-connected. They share information with each other, with us and cloud based applications. These devices combine the physical and digital world and produce a huge amount of data to enhance the productivity of life, industries and society by providing smart services. IoT applications based on smart sensors open a new challenge which is the need of big data storage and huge computation power to provide real time data processing. IoT-Cloud solves such a problem since it provides a huge storage capacity. It also provides users on-demand access to resources at any place and any time.This work is designed to support any system where a huge data is generated and processed in real time such as a traffic monitoring system, a health system for obesity management using sensory and social data. We propose in this paper a new Infrastructure as a Service (IaaS) that provides an intelligent data storage to minimize the latency of any input and output data requests in a massive data storage and a huge number of servers. To ensure a high critical data availability, our IaaS supplies Cloud servers with high monitoring, backup and recovery services in case of a server failure. The proposed approach is termed Reliable lOad Balancing Using Specialization for ioT critical application (ROBUST). We compared the latency of an output file request and the complexity of searching the replicated version of a critical data of ROBUST to a recent IaaS architecture called Load Balancing in the Cloud Using Specialization (LBCS) and the classic one. The results shows a remarkable enhancement in terms of the complexity and the latency of an output file request.