Efficient irrigation scheduling is essential to optimize crop yield while conserving water in precision agriculture. Traditional static or rule-based methods often fail to capture the dynamic, nonlinear nature of agro-environmental conditions. To overcome these limitations, we propose a hybrid PSO-BiLSTM Attention model that combines Particle Swarm Optimization for hyperparameter tuning with Bidirectional Long Short-Term Memory networks for bidirectional temporal sequence learning and an attention mechanism for feature importance weighting. This approach improves the modeling of irrigation demand based on variables such as crop stage, soil moisture, temperature, and humidity. Compared to baseline models such as LSTM, BiLSTM, PSO-LSTM, and SSA-LSTM, the proposed model demonstrates improved predictive accuracy, enhanced precision, and significantly greater training efficiency per epoch. These results underscore its effectiveness in delivering accurate, efficient, and robust predictions, making it well-suited for resource-aware irrigation control in smart agriculture environments.

The rapid expansion of the Internet of Things (IoT) in sectors such as agriculture, healthcare, smart cities, and industrial automation has increased demand for low-energy, highprecision devices for real-time localization. However, accurate localization in large-scale, resource-constrained IoT networks remains challenging due to increased data communication, energy consumption, and network dynamics. Traditional methods such as GPS, triangulation, and RSSI often fail in obstructed or indoor environments. Although machine learning techniques such as Convolutional Neural Networks (CNNs) offer potential, their data and computational requirements limit their use in IoT settings. Similarly, stochastic models and swarm intelligence algorithms face issues such as slow convergence and difficulty modeling complex environments. This paper proposes hybrid models that integrate CNNs, stochastic models, and swarm intelligence to tackle localization challenges in dynamic IoT networks. We introduce two novel energy-efficient hybrid frameworks, MarkovCNN and SSA-Markov-CNN, which combine CNNs for feature extraction, Markov models for uncertainty management, and swarm intelligence for global optimization. Experiments show that these models significantly improve localization accuracy while reducing energy consumption and latency, offering a promising solution for energy-conscious IoT deployments. Our findings provide insights for selecting optimal localization models for low-latency, energy-efficient IoT applications.

Accurate and secure localization is critical for mission-critical IoT applications, yet networks remain vulnerable to attacks such as Neutralization-Inspired Fake Signal (NIFS) attacks. We address accurate positioning under adversarial conditions while respecting latency and energy constraints. We formulate a secure localization problem optimizing accuracy, cryptographic overhead (Ed25519), and robustness against malicious anchors, and propose a Hybrid Quantum-Convolutional Neural Network (HQC-NN) with K-Means clustering for region-specific specialization. The framework integrates cryptographic verification and monitors performance, energy, and security metrics. Simulations show that HQC-NN and HQC-NN-KMeans outperform CNN and CNN-KMeans baselines in accuracy, efficiency, latency, and attack detection, demonstrating their potential for resilient IoT localization.

In the midst of the rapid proliferation of the Internet of Things (IoT), precise location of distributed IoT devices is paramount for a multitude of applications, ranging from emergency services to environmental monitoring. However, achieving such precision while maintaining energy efficiency and minimizing latency remains a persistent challenge, especially within distributed IoT sensing environments. This paper introduces innovative hybrid localization models tailored to address these requirements by amalgamating the virtues of Salp Swarm Algorithms (SSA) with stochastic methodologies, thereby attaining accuracy, energy efficiency, and low latency, vital in resourceconstrained IoT settings. By combining SSA with the Markov model and harnessing a fusion of SSA with the Gauss model, our models ensure enhanced accuracy while curtailing energy consumption. Extensive simulations validate the efficacy of these hybrid models, showcasing their superiority over conventional baseline approaches. Noteworthy reductions in latency, coupled with enhancements in IoT localization accuracy and energy efficiency relative to traditional baseline models, underscore their potential to fortify distributed sensing applications and nurture a more sustainable IoT ecosystem.

The electric vehicle (EV) market is experiencing substantial growth, and it is anticipated to play a major role as a replacement for fossil fuel-powered vehicles in transportation automation systems. Nevertheless, as a rule of thumb, EVs depend on electric charges, where appropriate usage, charging, and energy management are vital requirements. Examining the work that was done before gave us a reason and a basis for making a system that forecasts the real-time availability of electric vehicle charging stations that uses a scalable prediction engine built into a server-side software application that can be used by many people. The implementation process involved scraping data from various sources, creating datasets, and applying feature engineering to the data model. We then applied fundamental models of machine learning to the pre-processed dataset, and subsequently, we proceeded to construct and train an artificial neural network model as the prediction engine. Notably, the results of our research demonstrate that, in terms of precision, recall, and F1-scores, our approach surpasses existing solutions in the literature. These findings underscore the significance of our approach in enhancing the efficiency and usability of EVs, thereby significantly contributing to the acceleration of their adoption in the transportation sector.

The proliferation of the Internet of Things (IoT) has had a significant impact in the improvements of localization techniques in wireless sensor networks (WSNs). However, current positing algorithms have shown their limitation in indoor localization and incurred low convergence to accurately predict sensors localization. Thus, there is a need to develop efficient localization algorithms for WSNs to offer better location accuracy and lower energy footprint. This paper explores well known localization algorithms (i.e., Markov optimization model, Gaussian model, Particle Swarm Optimization (PSO), and Salp Swarm Algorithm (SSA)) to assess their capabilities and provides a comparative study, critical discussion and analysis of these algorithms. It describes their algorithms during the self-localization procedure. Empirical results show the performance of these algorithms in terms of energy consumption, location accuracy, and network latency.

The electric vehicle (EV) market is experiencing substantial growth, and it is anticipated to play a major role as a replacement for fossil fuel-powered vehicles in transportation automation systems. Nevertheless, as a rule of thumb, EVs depend on electric charges, where appropriate usage, charging, and energy management are vital requirements. Examining the work done before gave us a reason and a basis for making a system that forecasts the real-time availability for EV charging stations that uses a scalable prediction engine built into a server-side software application that many people can use. The implementation process involved scraping data from various sources, creating datasets, and applying feature engineering to the data model. We then applied fundamental machine learning models to the pre-processed dataset, and subsequently, we implemented an ensemble model that combines the strengths of both Random Forest (RF) and Artificial Neural Network (ANN). This approach leverages the RF’s resilience to overfitting and its ability to handle diverse data while benefiting from the ANN’s capacity to capture complex non-linear relationships. The resulting ensemble model demonstrates significant improvements in precision, recall, and F1-score compared to individual models, making it a valuable contribution to the transportation sector.

In urban environments, bicycle-sharing emerges as an eco-friendly solution, yet inherent imbalances in bicycle rents and returns necessitate systematic rebalancing, posing a global challenge. This underscores the crucial role of forecasting in optimizing bicycle allocation across diverse docks. Despite the commendable goals of reducing carbon emissions and promoting public health, efficient rebalancing remains elusive, emphasizing the need for forecasting to enhance overall system efficiency. User demand in public bicycle-sharing systems presents a primary challenge, influenced by commuting patterns and topographical conditions, leading to critical spatial incongruities. Integration of robust demand prediction mechanisms becomes essential, strategically overcoming challenges and ensuring seamless bicycle-sharing system operation. Our solution proactively addresses disruptions by forecasting user demand and employing manual redistribution based on Long Short-Term Memory (LSTM) models. Empirical validation attests to its efficiency and accuracy, showcasing versatility. The system seamlessly integrates frameworks for forecast delivery to applications, ensuring robustness and high availability through meticulous dataset consumption.

The growing demand and the diverse traffic patterns coming from various heterogeneous Internet of Things (IoT) systems place an increasing strain on the IoT infrastructure at the network edge. Different edge resources (e.g. servers, routers, controllers, gateways) may illustrate different execution times and energy consumption for the same task. They should be capable of achieving high levels of performance to cope with the variability of task handling. However, edge nodes are often faced with issues to perform optimal resource distribution and energy-awareness policies in a way that makes effective run-time trade-offs to balance response time constraints, model fidelity, inference accuracy, and task schedulability. To address these challenging issues, in this paper we present a SDN-based dynamic task scheduling and resource management Deep Reinforcement Learning (DRL) approach for IoT traffic scheduling at the network edge. First, we introduce the architectural design of our solution, with the specific objective of achieving high network performance. We formulate a task assignment and scheduling problem that strives to minimize the network latency while ensuring energy efficiency. The evaluation of our approach offers better results compared against both deterministic and random task scheduling approaches, and shows significant performances in terms of latency and energy consumption.

Fog computing has recently emerged as a new cyber foraging technique to offload resource-intensive tasks from mobile devices to mobile cloudlets in close proximity to endusers. Since the one-hop communication in the network edge is predominantly wireless, Wireless Mesh Networks (WMNs) are being considered to build wireless fog networks. However, WMNs use distributed hop-by-hop routing protocols to reflect a partial visibility of the network, which limits their ability to perform global network management and monitoring needed by fog networks. Software Defined Networking (SDN) provides a centralized control and management of the entire network, which makes it a good candidate to support fog communication. Unfortunately, the SDN OpenFlow protocol does not support any functionalities for wireless fog networks as it is primarily targeted to wired networks. To address these issues, this paper presents a SDN-enabled wireless fog architecture that combines both OpenFlow and distributed wireless protocols. The proposed solution provides lower latency and efficient load balancing to offload the network load by enabling programmable fog routers.