Search Results (96)

The rapid development of the Artificial Intelligence of Things (AIoT) has created unprecedented demands for distributed, long-term, and maintenance-free sensing systems. Conventional battery-powered sensors suffer from inherent drawbacks such as limited lifetime, high maintenance costs, and environmental concerns, which hinder large-scale deployment. Self-powered sensing technologies provide a transformative pathway by integrating energy harvesting and sensing into a single platform, thereby eliminating the reliance on external power supplies. This review systematically summarizes the key components of self-powered wireless sensing systems, with a particular focus on different energy harvesting technologies, self-powered sensing technologies, and the latest advances in low-power intelligent computation for diverse application scenarios. The integration of energy harvesting, self-sensing, and intelligent computation will make self-powered wireless sensing systems an inevitable direction for the evolution of AIoT, enabling sustainable, scalable, and intelligent monitoring networks. Full article

Wireless sensor networks (WSNs) consist of distributed nodes to monitor various physical and environmental parameters. The sensor nodes (SNs) are usually resource constrained such as power source, communication, and computation capacity. In WSN, energy consumption varies depending on the distance between sender and receiver SNs. Communication among SNs having long distance requires significantly additional energy that negatively affects network longevity. To address these issues, WSNs are deployed using multi-hop routing. Using multi-hop routing solves various problems like reduced communication and communication cost but finding an optimal cluster head (CH) and route remain an issue. An optimal CH reduces energy consumption and maintains reliable data transmission throughout the network. To improve the performance of multi-hop routing in WSN, we propose a model that combines Multi-Objective Particle Swarm Optimization (MOPSO) and a Decision Tree for dynamic CH selection. The proposed model consists of two phases, namely, the offline phase and the online phase. In the offline phase, various network scenarios with node densities, initial energy levels, and BS positions are simulated, required features are collected, and MOPSO is applied to the collected features to generate a Pareto front of optimal CH nodes to optimize energy efficiency, coverage, and load balancing. Each node is labeled as selected CH or not by the MOPSO, and the labelled dataset is then used to train a Decision Tree classifier, which generates a lightweight and interpretable model for CH prediction. In the online phase, the trained model is used in the deployed network to quickly and adaptively select CHs using features of each node and classifying them as a CH or non-CH. The predicted nodes broadcast the information and manage the intra-cluster communication, data aggregation, and routing to the base station. CH selection is re-initiated based on residual energy drop below a threshold, load saturation, and coverage degradation. The simulation results demonstrate that the proposed model outperforms protocols such as LEACH, HEED, and standard PSO regarding energy efficiency and network lifetime, making it highly suitable for applications in green computing, environmental monitoring, precision agriculture, healthcare, and industrial IoT. Full article

The rapid expansion of wireless communications has led to increasing demand and interference in the electromagnetic spectrum, raising the question of how to achieve reliable and adaptive monitoring in complex and dynamic environments. This study aims to investigate whether groups of unmanned aerial vehicles (UAVs) can provide an effective alternative to conventional, static spectrum monitoring systems. We propose a cooperative monitoring system in which multiple UAVs, integrated with software-defined radios (SDRs), conduct energy measurements and share their observations with a data fusion center. The fusion process is based on Dempster–Shafer theory (DST), which models uncertainty and combines partial or conflicting data from spatially distributed sensors. A simulation environment developed in MATLAB emulates UAV mobility, communication delays, and propagation effects in various swarm formations and environmental conditions. The results confirm that cooperative spectrum monitoring using UAVs with DST data fusion improves detection robustness and reduces susceptibility to noise and interference compared to single-sensor approaches. Even under challenging propagation conditions, the system maintains reliable performance, and DST fusion provides decision-supporting results. The proposed methodology demonstrates that UAV groups can serve as scalable, adaptive tools for real-time spectrum monitoring and contributes to the development of intelligent monitoring architectures in cognitive radio networks. Full article

Wireless sensor networks (WSNs) have widespread applications in vital fields, including environmental surveillance, medical care, and smart urban settings. Nevertheless, the restricted battery capacity of sensor nodes renders energy consumption an ongoing hindrance, limiting both the performance and operational lifespan of a network. To mitigate this challenge, this study proposes an enhanced clustering and routing scheme, the Black-Kite Optimization–Time Of Arrival (BKA-TOA) algorithm, which jointly optimizes cluster head selection and data transmission. The proposed BKA-TOA integrates the bio-inspired Black-Kite Optimization (BKA) with relay selection based on the Time of Arrival (TOA) technique. A multidimensional fitness function is constructed by incorporating inter-node distance, energy variance, and cluster head distribution to achieve robust clustering, balanced energy consumption, and improved scalability. Extensive simulations conducted in Matlab R2024a are used to benchmark the proposed algorithm against Particle Swarm Optimization (PSO), Improved Ant Colony–minimum spanning tree (IACO-MST), and Energy-Efficient Uneven Clustering (EEUC). The experimental results indicate that BKA-TOA significantly reduces node mortality, improves residual energy preservation, and prolongs the operational lifetime of WSNs compared with competing algorithms. Full article

Wireless Sensor Networks (WSNs) have a decisive share in various monitoring and control systems. However, their distributed and resource-constrained nature makes them vulnerable to anomalies caused by factors such as environmental noise, sensor faults, and cyber intrusions. In this paper, HEXADWSN, a hybrid ensemble learning-based explainable anomaly detection framework for anomaly detection to improve reliability and interpretability in WSNs, has been proposed. The proposed framework integrates an ensemble learning approach using Autoencoders, Isolation Forests, and One-Class SVMs to achieve robust detection of time-series-based irregularities in the Intel Lab dataset. The framework uses stack and vote ensemble learning. The stack ensemble achieved the highest overall performance, indicating strong effectiveness in detecting varied anomaly patterns. The voting ensemble demonstrated moderate results and offered a balance between detection rate and computation, whereas LSTM, which is efficient at capturing temporal dependencies, exhibited a relatively low performance in the processed dataset. SHAP, LIME, and Permutation Feature Importance techniques are employed for model explainability. These techniques offer insights into feature relevance and anomalies at global and local levels. The framework also measures the mean energy consumption for anomalous and normal data. The interpretability results identified that temperature, humidity, and voltage are the most influential features. HEXADWSN establishes a scalable and explainable foundation for anomaly detection in WSNs, striking a balance between accuracy, interpretability, and energy management insights. Full article

Monitoring indoor air quality is vital for health, as CO₂ is a major pollutant. An automated system that accurately forecasts CO₂ levels can optimize HVAC management, preventing sudden increases and reducing energy waste while maintaining occupant comfort. Traditionally, such systems require extensive datasets collected over months to train algorithms, making them computational expensive and inefficient. To address this limitation, an adaptive IoT-based platform has been developed, leveraging a limited set of recent data to forecast CO₂ trends. Tested in a real-world setting, the system analyzed parameters such as physical activity, temperature, humidity, and CO₂ to ensure accurate predictions. Data acquisition was performed using the Smartex WWS T-shirt for physical activity data and the UPSense UPAI3-CPVTHA environmental sensor for other measurements. The chosen sensor devices are wireless and minimally invasive, while data processing was carried out on a low-power embedded PC. The proposed forecasting model adopts an innovative approach. After a 5-day training period, a Generative Adversarial Network enhances the dataset by simulating a 10-day training period. The model utilizes a Generative Adversarial Network with a Long Short-Term Memory network as the generator to predict future CO₂ values based on historical data, while the discriminator, also a Long Short-Term Memory network, distinguishes between actual and generated CO₂ values. This approach, based on Conditional Generative Adversarial Networks, effectively captures data distributions, enabling more accurate multi-step probabilistic forecasts. In this way, the framework maintains a Root Mean Square Error of approximately 8 ppm, matching the performance of our previous approach, while reducing the need for real training data from 10 to just 5 days. Furthermore, it achieves accuracy comparable to other state-of-the-art methods that typically requires weeks or even months of training. This advancement significantly enhances computational efficiency and reduces data requirements for model training, improving the system’s practicality for real-world applications. Full article

Radon exposure is the second leading cause of lung cancer worldwide, yet monitoring strategies remain limited, expensive, and unevenly applied. Recent advances in the Internet of Things (IoT) offer the potential to change radon surveillance through low-cost, real-time, distributed sensing networks. This review consolidates emerging research on IoT-based radon monitoring, drawing from both primary radon studies and analogous applications in environmental IoT. A search across six major databases and relevant grey literature yielded only five radon-specific IoT studies, underscoring how new this research field is rather than reflecting a shortcoming of the review. To enhance the analysis, we delve into sensor physics, embedded system design, wireless protocols, and calibration techniques, incorporating lessons from established IoT sectors like indoor air quality, industrial safety, and volcanic gas monitoring. This interdisciplinary approach reveals that many technical and logistical challenges, such as calibration drift, power autonomy, connectivity, and scalability, have been addressed in related fields and can be adapted for radon monitoring. By uniting pioneering efforts within the broader context of IoT-enabled environmental sensing, this review provides a reference point and a future roadmap. It outlines key research priorities, including large-scale validation, standardized calibration methods, AI-driven analytics integration, and equitable deployment strategies. Although radon-focused IoT research is still at an early stage, current progress suggests it could make continuous exposure assessment more reliable, affordable, and widely accessible with clear public health benefits. Full article

The research work presents an artificial intelligence-driven, energy-aware data aggregation and routing protocol for wireless sensor networks (WSNs) with the primary objective of extending overall network lifetime. The proposed scheme leverages reinforcement learning in conjunction with deep Q-networks (DQNs) to adaptively optimize both Cluster Head (CH) selection and routing decisions. An adaptive clustering mechanism is introduced wherein factors such as residual node energy, spatial proximity, and traffic load are jointly considered to elect suitable CHs. This approach mitigates premature energy depletion at individual nodes and promotes balanced energy consumption across the network, thereby enhancing node sustainability. For data forwarding, the routing component employs a DQN-based strategy to dynamically identify energy-efficient transmission paths, ensuring reduced communication overhead and reliable sink connectivity. Performance evaluation, conducted through extensive simulations, utilizes key metrics including network lifetime, total energy consumption, packet delivery ratio (PDR), latency, and load distribution. Comparative analysis with baseline protocols such as LEACH, PEGASIS, and HEED demonstrates that the proposed protocol achieves superior energy efficiency, higher packet delivery reliability, and lower packet losses, while adapting effectively to varying network dynamics. The experimental outcomes highlight the scalability and robustness of the protocol, underscoring its suitability for diverse WSN applications including environmental monitoring, surveillance, and Internet of Things (IoT)-oriented deployments. Full article

Wireless sensor networks (WSNs) are fundamental to applications in the Internet of Things, smart cities, and environmental monitoring, where coverage optimization is critical for maximizing monitoring efficacy under constrained resources. Conventional approaches often suffer from low global coverage efficiency, high computational overhead, and a tendency to converge to local optima. To address these challenges, this study proposes the fusion multi-strategy gray wolf optimizer (FMGWO), an advanced variant of the Gray Wolf Optimizer (GWO). FMGWO integrates various strategies: electrostatic field initialization for uniform population distribution, dynamic parameter adjustment with nonlinear convergence and differential evolution scaling, an elder council mechanism to preserve historical elite solutions, alpha wolf tenure inspection and rotation to maintain population vitality, and a hybrid mutation strategy combining differential evolution and Cauchy perturbations to enhance diversity and global search capability. Ablation studies validate the efficacy of each strategy, while simulation experiments demonstrate FMGWO’s superior performance in WSN coverage optimization. Compared to established algorithms such as PSO, GWO, CSA, DE, GA, FA, OGWO, DGWO1, and DGWO2, FMGWO achieves higher coverage rates with fewer nodes—up to 98.63% with 30 nodes—alongside improved convergence speed and stability. These results underscore FMGWO’s potential as an effective solution for efficient WSN deployment, offering significant implications for resource-constrained optimization in IoT and edge computing systems. Full article

Urban areas face a significant challenge with water pipeline leaks, resulting in resource wastage and economic consequences. The application of noise logger sensors, integrated with ensemble machine learning, emerges as a promising real-time monitoring solution, enhancing efficiency in Water Distribution Networks (WDNs) and mitigating environmental impacts. The paper investigates the integrated use of Noise Loggers with machine learning models, including Support Vector Machines (SVMs), Random Forest (RF), Naïve Bayes (NB), K-Nearest Neighbors (KNN), Decision Tree (DT), Logistic Regression (LogR), Multi-Layer Perceptron (MLP), and YamNet, along with ensemble models, for effective leak detection. The study utilizes a dataset comprising 2110 sound signals collected from various locations in Hong Kong through wireless acoustic Noise Loggers. RF model stands out with 93.68% accuracy, followed closely by KNN at 93.40%, and MLP with 92.15%, demonstrating machine learning’s potential in scrutinizing acoustic signals. The ensemble model, combining these diverse models, achieves an impressive 94.40% accuracy, surpassing individual models and YamNet. The comparison of various machine learning models provides researchers with valuable insights into the use of machine learning for leak detection applications. Additionally, this paper introduces a novel method to develop a robust ensemble leak detection model by selecting the most performing machine learning models. Full article

Wireless sensor networks (WSNs) are widely used in environmental monitoring, industrial automation, and smart cities. The Hybrid Energy-Efficient Distributed (HEED) protocol is a popular clustering algorithm designed to prolong network lifetime by balancing energy consumption among sensor nodes. However, HEED relies on simple heuristics for cluster-head (CH) selection, which may not fully exploit the complex spatiotemporal patterns in node energy and topology. This paper introduces a novel protocol, Deep Learning–Hybrid Energy-Efficient Distributed (DL-HEED), which, for the first time, integrates a Graph Neural Network (GNN) into the clustering process. By leveraging the relational structure of WSNs and a comprehensive set of node and network features—including residual energy, node degree, spatial position, and signal strength—DL-HEED enables intelligent, context-aware, and adaptive CH selection. DL-HEED leverages the relational structure of WSNs through deep learning, enabling more adaptive and energy-efficient cluster head selection than traditional heuristic-based protocols. Extensive simulations demonstrate that DL-HEED significantly outperforms classic HEED achieving up to 60% improvement in the network lifetime and energy efficiency as the network size increases. This work establishes DL-HEED as a robust, scalable, and practical solution for next-generation WSN deployments, marking a substantial advancement in the application of deep learning to resource-constrained IoT environments. Full article

Wireless Sensor Networks (WSNs) consist of distributed sensor nodes that collect and transmit environmental data, often in resource-constrained and unsecured environments. These characteristics make WSNs highly vulnerable to various security threats. To address this, the objective of this research is to design and evaluate a deep learning-based Intrusion Detection System (IDS) that is both accurate and efficient for real-time threat detection in WSNs. This study proposes a hybrid IDS model combining one-dimensional Convolutional Neural Networks (Conv1Ds), Gated Recurrent Units (GRUs), and Self-Attention mechanisms. A Conv1D extracts spatial features from network traffic, GRU captures temporal dependencies, and Self-Attention emphasizes critical sequence components, collectively enhancing detection of subtle and complex intrusion patterns. The model was evaluated using the WSN-DS dataset and demonstrated superior performance compared to traditional machine learning and simpler deep learning models. It achieved an accuracy of 98.6%, precision of 98.63%, recall of 98.6%, F1-score of 98.6%, and an ROC-AUC of 0.9994, indicating strong predictive capability even with imbalanced data. In addition to centralized training, the model was tested under cooperative, node-based learning conditions, where each node independently detects anomalies and contributes to a collective decision-making framework. This distributed approach improves detection efficiency and robustness. The proposed IDS offers a scalable and resilient solution tailored to the unique challenges of WSN security. Full article

Wireless Sensor Networks (WSNs) are a component of various applications, including environmental monitoring and the Internet of Things (IoT). Energy efficiency is one of the significant issues in WSNs, since sensor nodes are usually battery-powered and have limited energy resources. The Enhanced Distributed Energy-Efficient Clustering (DEEC) protocol is one of the most common methods for improving energy efficiency and network lifespan by selecting cluster heads according to residual energy. Nevertheless, standard DEEC methods are limited in dynamic environments because of their fixed nature. This paper presents a novel modular implementation of the DEEC protocol for Wireless Sensor Networks, addressing the limitations of the standard DEEC in dynamic and heterogeneous environments. Unlike the typical DEEC protocol, the proposed approach incorporates realistic energy models, supports heterogeneous nodes, implements load balancing, and enables dynamic cluster head selection Numerical simulations in MATLAB^® demonstrate that the improved DEEC protocol achieves a 133% longer stability period (first node death at 1166 rounds vs. 472 rounds), nearly doubles the network lifetime (4000 rounds vs. 2111 rounds), and significantly enhances energy efficiency compared to the standard DEEC. These results verify the effectiveness of the proposed enhancements, making the protocol a robust solution for modern WSN and IoT applications. Full article

Modern commercial poultry farming typically occurs indoors, where partial or complete environmental control is employed to enhance production efficiency. Maintaining optimal conditions, such as temperature, relative humidity, carbon dioxide, and ammonia levels, is essential for ensuring bird comfort and maximizing productivity. Monitoring the conditions of poultry houses requires reliable and intelligent management systems. This study introduces a Wireless Monitoring and Control System developed to regulate environmental conditions within poultry facilities. The system continuously monitors key parameters via a network of distributed sensor nodes, which transmit data wirelessly to a centralized control unit using Wi-Fi. The control unit processes the incoming data, stores it in a database, and adjusts actuators accordingly to maintain ideal conditions. A web-based dashboard allows users to monitor and control the environment in real time. Field testing confirmed the system’s effectiveness in keeping conditions optimal, supporting poultry welfare and operational efficiency. Full article

The underwater domain presents unique challenges and opportunities for scientific exploration, resource extraction, and environmental monitoring. Autonomous underwater vehicles (AUVs) rely on simultaneous localization and mapping (SLAM) for real-time navigation and mapping in these complex environments. However, traditional SLAM techniques face significant obstacles, including poor visibility, dynamic lighting conditions, sensor noise, and water-induced distortions, all of which degrade the accuracy and robustness of underwater navigation systems. Recent advances in deep learning (DL) have introduced powerful solutions to overcome these challenges. DL techniques enhance underwater SLAM by improving feature extraction, image denoising, distortion correction, and sensor fusion. This survey provides a comprehensive analysis of the latest developments in DL-enhanced SLAM for underwater applications, categorizing approaches based on their methodologies, sensor dependencies, and integration with deep learning models. We critically evaluate the benefits and limitations of existing techniques, highlighting key innovations and unresolved challenges. In addition, we introduce a novel classification framework for underwater SLAM based on its integration with underwater wireless sensor networks (UWSNs). UWSNs offer a collaborative framework that enhances localization, mapping, and real-time data sharing among AUVs by leveraging acoustic communication and distributed sensing. Our proposed taxonomy provides new insights into how communication-aware SLAM methodologies can improve navigation accuracy and operational efficiency in underwater environments. Furthermore, we discuss emerging research trends, including the use of transformer-based architectures, multi-modal sensor fusion, lightweight neural networks for real-time deployment, and self-supervised learning techniques. By identifying gaps in current research and outlining potential directions for future work, this survey serves as a valuable reference for researchers and engineers striving to develop robust and adaptive underwater SLAM solutions. Our findings aim to inspire further advancements in autonomous underwater exploration, supporting critical applications in marine science, deep-sea resource management, and environmental conservation. Full article