The routing protocol for low-power and lossy networks (RPL) is a specialized routing protocol designed for optimized data routing, specifically for resource-constrained Internet of Things (IoT) networks with unreliable links and high packet loss. However, RPL is highly vulnerable to significant security challenges, particularly the decrease rank attack (DRA), in which malicious nodes attract child nodes by falsely advertising lower ranks, leading to routing inefficiencies, unnecessary retransmissions, and increased energy consumption. To address this problem, we propose a novel intent-based networking-driven centralized real-time reinforced detection scheme (CRRDS), which translates high-level security intents into policy-driven automated control strategies for DRA detection. In the proposed CRRDS, a resource-rich root node acts as a deep reinforcement learning agent that collects critical information from the child nodes, including the node ID, end-to-end delay, received signal strength indicator, and hop count, to detect suspicious behavior accurately and intelligently. Initially, we implemented a deep Q-network (DQN)-assisted CRRDS in detecting DRA. Subsequently, we utilized double DQN (DDQN) and dueling DDQN due to their enhanced capabilities in value estimation and policy learning. The dueling DDQN performed optimally because of its deeper architecture. Simulation results demonstrate that the proposed dueling DDQN-assisted CRRDS achieves the highest detection accuracy of 98% with notable gains in true positive and false positive rates, even in complex scenarios with up to 30% malicious nodes.

The increasing acceptance of Electric Vehicles (EVs) leads to significant challenges for traditional forecasting methods due to external variables such as weather conditions, availability of renewable energy sources, and real-time traffic data. These factors affect the forecasting accuracy because of the unpredictable nature of renewable energy sources and weather. Conventional methods have limitations in terms of adapting dynamic conditions, leading to problems in allocating power and managing energy in EV Charging Stations (EVCS). To address these challenges, we propose a novel AI-driven approach called Dynamic Allocation and Management Optimization (DYNAMO), which integrates cutting-edge demand forecasting, power allocation, and efficiency-enhanced methods for smart city EV infrastructure. DYNAMO uses a Lite Transformer Gated Recurrent Unit (LT-GRU) for advanced demand prediction by considering critical factors like the number of incoming EVs, session duration, and station usage frequency. In LT-GRU, we integrate the strengths of transformer attention mechanism and sequential data processing of GRU to improve the prediction accuracy by capturing the long-term dependencies and prioritizing the important features even though in dynamic conditions. Additionally, an Intelligent Central Manager (ICM) groups EVCS into high, moderate, and low demand clusters, allowing for dynamic optimization of charging infrastructure. Furthermore, a Game Theory-based Deep Reinforcement Learning (GT-DRL) approach is employed, which considers variables such as vehicle demand, battery capacity, charging speed, and weather conditions, while preventing overloads and outages. Our approach not only enhances the operational efficiency of EVCS but also contributes to the development of more sustainable and reliable EV charging networks. Overall, the proposed framework’s ability to adapt in real-time ensures that it can support the increasing demand for EV infrastructure, minimize inefficiencies, and improve user experience.

Breast cancer remains a major concern for women’s lives worldwide and serves as evidence of the need for better classification strategies according to severity. Computer-aided diagnosis (CADx) powered by explainable artificial intelligence (XAI) offers a promising solution by minimizing diagnostic errors and fostering trust through a more transparent decision-making process. As XAI evolves, it plays a crucial role in increasing the interpretability of AI-driven diagnostics, particularly in distributed healthcare systems. XAI explains the model’s prediction, making clinicians more confident in accepting clinical outcomes. Accordingly, this study provides a comparative analysis of multiple deep learning models for breast cancer identification based on a publicly available dataset of 780 ultrasound images with their masks. Explanation of the classification result is then provided using the Grad-CAM method which improves the interpretability and tractability of the models. The proposed method lets the models explain their decisions visually, using heatmaps that show what part of an image contributes to the predictions in a valuable way when studying medical images. Obtained results demonstrate the XAI’s transformative potential in medical imaging, paving the way for more reliable, scalable, and efficient diagnostic tools. Also, providing a critical comparison of various types of deep learning models for breast cancer identification, the study underlines the advantages and limitations of the different architectures in solving the task.

Due to the increasing need for accurate location-based services, indoor positioning systems (IPSs) have evolved rapidly. This study reviews the literature published from 2010 to 2024, employing the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology for the identification, screening, validation, and inclusion of research literature. By exploring the complexities of IPS methodologies, algorithms, technologies, and challenges, this study offers a comprehensive introduction for researchers, scholars, and specialists. Additionally, the scope of this study expands its focus to encompass mapping techniques, such as crowdsourcing, geographic information systems (GISs), remote sensing, LiDAR, cartography, and augmented reality (AR). The findings of this study will contribute to the increased applicability of these methods and techniques in the field of urban planning and environmental management. Contributing to the expanding pool of knowledge on indoor positioning, this work is a valuable resource for those exploring the ever-changing field of IPS. This work not only contributes to the academic but also bridges the gap between scientific discoveries and practical, real-world applications.

Despite the benefits of the Internet of Things (IoT), the growing influx of IoT-specific malware coordinating large-scale cyberattacks via infected IoT devices has created a substantial threat to the Internet ecosystem. Assessing IoT systems’ security and developing mitigation measures to prevent the spread of IoT malware is therefore critical. Furthermore, for training and testing the fidelity of cyber security-based Machine Learning (ML) and Deep Learning (DL) approaches, the collection and exploration of information from multiple sources from the IoT are crucial. In this regard, we propose a multitask DL model for detecting IoT malware. Our proposed Long Short-Term Memory (LSTM) based model efficiently performs two tasks: 1) determination of whether the provided traffic is benign or malicious, and 2) determination of the malware type for identifying malicious network traffic. We used large-scale traffic data of 145.pcapfiles of benign and malicious traffic collected from 18 different IoT devices. We performed a time-series analysis on the packets of traffic flows, which were then used to train the proposed model. The features extracted from the dataset were categorized into three modalities: flow-related, traffic flag-related, and packet payload-related features. A feature selection approach was employed at the feature and modality levels, and the best modalities and features were utilized for performance enhancement. For tasks 1 and 2 and multitask classification, the flow-related and flag-related modalities showed the best testing accuracies of 92.63%, 88.45%, and 95.83%, respectively.

In a hospital, accurate and rapid mortality prediction of Length of Stay (LOS) is essential since it is one of the essential measures in treating patients with severe diseases. When predictions of patient mortality and readmission are combined, these models gain a new level of significance. Therefore, the most expensive components of patient care are LOS and readmission rates. Several studies have assessed readmission to the hospital as a single-task issue. The performance, robustness, and stability of the model increase when many correlated tasks are optimized. This study develops multimodal multitasking Long Short-Term Memory (LSTM) Deep Learning (DL) model that can predict both LOS and readmission for patients using multi-sensory data from 47 patients. Continuous sensory data is divided into eight sections, each of which is recorded for an hour. The time steps are constructed using a dual 10-second window-based technique, resulting in six steps per hour. The 30 statistical features are computed by transforming the sensory input into the resulting vector. The proposed multitasking model predicts 30-day readmission as a binary classification problem and LOS as a regression task by constructing discrete time-step data based on the length of physical activity during a hospital stay. The proposed model is compared to a random forest for a single-task problem (classification or regression) because typical machine learning algorithms are unable to handle the multitasking challenge. In addition, sensory data combined with other cost-effective modalities such as demographics, laboratory tests, and comorbidities to construct reliable models for personalized, cost-effective, and medically acceptable prediction. With a high accuracy of 94.84%, the proposed multitask multimodal DL model classifies the patient's readmission status and determines the patient's LOS in hospital with a minimal Mean Square Error (MSE) of 0.025 and Root Mean Square Error (RMSE) of 0.077, which is promising, effective, and trustworthy.

In this article, we investigate the wireless power transfer and energy-efficiency (EE) optimization problem for nano-empowered vehicular networks operating over the terahertz band. The nano-sensors in air can harvest energy from a power station and then can transmit the trace information to the micro-device under reconnaissance vehicular scenarios. Hence, by considering the properties of the terahertz band, we develop a long-term EE optimization problem. Furthermore, with the help of the equivalent transformation method, we converted the EE optimization problem into a series of energy-efficient resource allocation problems over the time slots. Each reformulated optimization problem becomes a mixed integer nonlinear programming (MINLP) over a time slot. Hence, to obtain the sub-optimal solution of the reformulated optimization problem, we developed a Quantum-behaved Particle swarm-based EE Optimization (QPEEO) algorithm. Furthermore, by exploiting the special structure of the reformulated problem, we propose an Improved Discrete Particle swarm-based EE Optimization (IDPEEO) algorithm. The proposed IDPEEO algorithm handles the problem's constraints effectively, and greatly reduces the search space and the convergence time. Our simulation results validate the theoretical analysis of the proposed scheme.

In wireless multimedia cognitive radio networks (WMCRNs), to optimize multimedia transmissions and scarce wireless spectrum utilization, a multimedia secondary user (MSU) needs to estimate and/or identify the achievable quality of service (QoS)-levels over the available licensed channels. However, due to the lack of signaling information among MSUs and the primary users (PUs) in uncoordinated environments, identification of the achievable QoS-levels on the available licensed channels is a challenging problem and has not yet been fully explored. To address this challenge, we propose a novel NOn-parametric Bayesian channEls cLustering (NOBEL) scheme. In NOBEL, an infinite Gaussian mixture model-based collapsed Gibbs sampler is adopted to identify the achievable QoS-levels over the feature space, i.e., bitrate, packet delay variation, and packet delivery ratio on the PUs' licensed channels. Real trace-driven evaluation results demonstrate that NOBEL outperforms other baseline clustering techniques and guarantee high accuracy from 98% to 99.5%.