Search Results (4,451)

Wireless sensor networks using battery-powered, low-cost sensors, due to their non-rechargeability and strictly limited energy resources, are more sensitive to energy efficiency than other networks of this type. Clustered wireless sensor networks address this problem. In these networks, the most energy-intensive communication, i.e., a long-range one, is carried out via designated nodes, called cluster head nodes, while other cluster nodes communicate with their cluster heads. Cluster head node selection is handled by appropriate routing protocols, and newly designed protocols are first tested in simulations. Among the simulators of cluster head selection protocols, those implemented in a MATLAB environment play an important role, and among these, those implementing a first-order radio model to estimate the energy cost of transmission, both at the transmitter and at the receiver, play a particularly important role. This paper presents and discusses the energy aspects of MATLAB-based open-source wireless sensor network environments that employ the first-order radio model for the simulation of cluster head selection protocols. Current MATLAB-based open-source simulators of cluster head selection protocols were inventoried and analyzed. The review results showed that the first-order radio model had been used in its classic form for years, with the same default parameters. Although the simulators were written using different programming paradigms, precluding simple copy-and-paste, the first-order radio model was generally similar. However, there were exceptions to this rule. A hard exception is the simulator for a body-area wireless sensor network, which only implements a version of the first-order radio model specific to that environment. Soft exceptions are two simulators of the popular cluster head selection protocol, which implemented only half the functionality of the classic first-order radio model. On the one hand, this demonstrates both the widespread use of a conservative approach to the model, which ensures relatively easy repeatability of simulation results, and, on the other hand, the flexibility of the model, which allows its extension to other environments. Finally, the limitations of the model are presented and directions for future research are indicated. Full article

WSN node energy forecasting contributes to improving network efficiency, extending network lifespan, and providing energy management strategies. In this study, a deep-learning-based wireless sensor network (WSN) node energy forecasting model based on Long Short-Term Memory (LSTM) and stacked-LSTM was developed across different wireless communication technologies in both static and dynamic WSN setups. The performance of the deep-learning-based models was compared with traditional forecasting techniques such as Exponential Smoothing and Prophet. The results showed the superiority of LSTM and stacked-LSTM in terms of root mean square error and mean absolute error, with consistently lower values compared with the traditional forecasting techniques. The results also show that the models perform best with Long Range technology. The deep learning-based model also demonstrates its ability to perform better in both static and dynamic WSN scenarios. These results demonstrate the potential of deep-learning-based models in WSN node energy management, which can result in an optimal energy efficiency and prolong the network lifetime. Future research is needed to explore hybrid approaches to further improve the prediction performance of deep learning-based models by combining their strengths with statistical or traditional forecasting techniques. Full article

The increasing deployment of underwater vehicles demands accurate and energy-efficient target tracking in sensor networks. However, existing approaches have largely addressed tracking accuracy and energy efficiency in isolation, and a system-level framework that jointly optimizes both remains lacking. To address this gap, this paper proposes a joint optimization framework with two main contributions. First, to improve tracking accuracy under complex maneuvering conditions, we develop an Interactive Multi-Model using Long Short-Term Memory Classification (IMM-LSTM-C) algorithm, which integrates multi-step model likelihoods into an LSTM network for precise motion classification, achieving a 7.1% accuracy improvement over IMM-BP. Second, to reduce network energy consumption while maintaining tracking performance, we introduce an Improved Binary Prairie Dog Optimization (IBPDO) algorithm for node selection, enhanced with Cauchy mutation and opposition-based learning. Simulation results show that IBPDO achieves 6.1–8.2% higher accuracy than BWOA and reduces energy consumption by 12% compared to LNS. Furthermore, the complete joint framework demonstrates synergistic effects, reducing tracking error by 19.3% and energy consumption by 15.4% over the IMM + LNS baseline. The proposed framework provides an effective balance between tracking accuracy and energy efficiency in underwater acoustic sensor networks. Full article

Cross-interface architectures are increasingly central to large-scale ocean observation systems, where underwater sensor nodes transmit data to spatially distributed buoys that relay information to terrestrial networks. In these deployments, the inherent broadcast nature of underwater acoustic (UWA) propagation enables a single node’s signals to be captured by multiple buoys. However, substantial and dynamic propagation delays lead to inherent reception asynchrony and severe multi-user interference. Conventional detection relies on large hydrophone arrays on single platforms and assumes strict synchronization, hindering scalability and elevating costs. This study proposes a distributed asynchronous reception framework for buoy-assisted UWA networks. Under a cloud software-defined acoustic (C-SDA) architecture, spatially separated buoys are treated as a virtual distributed multiple-input multiple-output (MIMO) receiver. We introduce a minimum-delay-based equivalent reconstruction to regularize the asynchronous structure, followed by blind channel identification and pilot-assisted synchronization for robust multi-user detection. By leveraging long-delay broadcast propagation as a source of spatial diversity, the framework facilitates scalable and cost-effective multi-user access. The results demonstrate that the architecture provides a practical paradigm for the underwater Internet of Things and long-term ocean observation. Full article

Microcontroller-based embedded systems integrating multiple sensors are increasingly required to support continuous data acquisition, on-board processing, and long-term storage within tightly coupled hardware–software architectures. In such platforms, overall performance is often constrained not by computational capability but by storage I/O behavior, particularly under real-time constraints and concurrent workloads. This study presents a comprehensive empirical evaluation of eMMC storage performance on an STM32U5 microcontroller running the ThreadX RTOS. The proposed methodology combines multi-dimensional stress testing, controlled task concurrency (0–4 tasks), and long-duration aging analysis (90 h), together with timing variability assessment under electrical stress and interrupt-driven preemption. Both synthetic workloads and realistic sensor-node scenarios with heterogeneous and asynchronous access patterns are considered. The results highlight significant performance limitations, including up to 98% throughput degradation under four concurrent tasks and a nonlinear increase in metadata latency as free space decreases below 40% (from 10 ms to over 200 ms for file creation). Additionally, timing jitter increases by 2–5× under voltage variation and interrupt load. Based on these findings, practical firmware-level design guidelines are derived, including sector-aligned buffering, dedicated I/O task architectures, and proactive capacity management, enabling substantial improvements in throughput and latency. This study provides quantitative insights and reproducible methodologies for optimizing storage subsystems in multi-sensor embedded applications. Full article

This paper proposes a multi-strategy hybrid-enhanced Educational Competition Optimizer (MEECO) to improve the performance of swarm-based optimization algorithms in complex search environments. From the perspective of symmetry, population-based optimization algorithms inherently rely on the symmetric distribution and evolution of individuals in the search space, while the imbalance between exploration and exploitation often leads to symmetry breaking, resulting in premature convergence and loss of diversity. Unlike the standard ECO, which suffers from limited information exchange, premature convergence, and boundary stagnation, the proposed method integrates three complementary mechanisms: adaptive differential evolution, vertical crossover, and global-best-guided boundary handling. Specifically, the adaptive differential evolution strategy enhances global exploration and maintains population distribution symmetry through dynamic mutation, the vertical crossover mechanism improves inter-dimensional symmetry and information interaction, and the boundary-handling strategy restores symmetry by guiding infeasible solutions back to promising regions. These strategies jointly improve population diversity, exploration–exploitation balance, and convergence efficiency while preserving structural symmetry in the search process. Extensive experiments on CEC2017 and CEC2022 benchmark suites demonstrate that MEECO consistently achieves superior optimization accuracy, faster convergence speed, and stronger robustness compared with several state-of-the-art algorithms. Statistical analyses further confirm the significance and reliability of the improvements. In addition, the proposed method is applied to a wireless sensor network node deployment problem, where it significantly improves coverage rate and deployment uniformity. The results indicate that MEECO provides an effective, robust, and symmetry-preserving optimization framework for both benchmark problems and real-world engineering applications. Full article

Healthcare Wireless Sensor Networks (HWSNs) have attracted significant attention due to their vital role in diseases’ diagnosis, monitoring, and treatment. By continuously collecting patients’ physiological data and enabling remote medical services, these networks can greatly improve the quality of healthcare. However, the inadequate handling of security and privacy issues poses serious risks to patients. In this context, signcryption schemes are essential cryptographic primitives that simultaneously provide authentication, confidentiality, and data integrity with a low overhead. Recently, Deng et al. proposed a certificateless signcryption (CL-SC) scheme for HWSNs and proved its security in the standard model. In this paper, we demonstrate that their scheme is insecure under an enhanced adversarial model, where a super Type II adversary, which is a malicious key generation center, can replace the system’s master public key using the master secret key under its control, and subsequently forge valid signcryptions on arbitrary messages on behalf of a sensor node. To address this vulnerability, we propose an enhanced CL-SC scheme based on elliptic curve cryptography (ECC). Under the hardness assumptions of the Elliptic Curve Decisional Diffie–Hellman Problem (ECDDHP) and the Computation Attack Algorithm (CAA), the proposed scheme achieves confidentiality and existential unforgeability against both super Type I and super Type II adversaries in the standard model. Performance analysis further shows that our scheme is efficient and well suited for resource-constrained HWSN environments. Full article

The increasing interest in the development and integration of navigation and positioning services across a wide range of receivers has exposed them to various security threats, including GNSS jamming and spoofing attacks. Early detection of jamming and spoofing interference is crucial to mitigating these threats and preventing service degradation. This research introduces an interference detection technique leveraging an AI algorithm applied to GNSS data utilizing various methods to enhance detection accuracy and efficiency. The objective was to use modern sensors and AI to develop an effective tool that detects, characterizes, and localizes interference, thereby reducing associated risks. These sensors and algorithms enable continuous GNSS interference monitoring and support real-time Decision-making. A server plays a crucial role in managing the entire system. Its primary function is to process data collected from various sensors referred to as nodes (e.g., static, rover, drone, and space) and from (public) GNSS networks as well as to perform localization using rotating-antenna nodes. Within the interference detection module, various methods were implemented at different points in the software receiver architecture. Each method’s certainty in identifying an interference source depends on its design and capabilities, with outcomes—whether positive or negative—being subject to potential accuracy or errors. To enhance the Decision-making process, an AI-based Decision-making block has been introduced to determine the presence of interference at a given epoch. The proposed interference monitoring methods were evaluated through experiments using GNSS signals under clean, jamming, and spoofing scenarios. The results demonstrate the techniques’ applicability across diverse scenarios, achieving high performance in interference detection, characterization, and localization. Full article

Industrial equipment data often exhibit high dimensionality and class imbalance, which make it difficult to achieve both accurate failure detection and identification of the factors contributing to failures. To address this issue, this study proposes an explainable failure classification framework, Feature-Wise Attention Graph Convolutional Network (FWA-GCN), which combines Feature-Wise Attention (FWA) with a Graph Convolutional Network (GCN) to provide both high classification performance and variable-level interpretability. In the proposed model, tabular sensor records are treated as nodes, and a similarity-based graph is constructed to capture relationships among samples. Feature-Wise Attention learns the importance of each feature and reweights node features accordingly, and the reweighted features are then used as input to the GCN to classify failure occurrences. To alleviate the class imbalance problem, a weighted loss function is applied during training by assigning a higher weight to the failure class. Experiments conducted on the Air Pressure System (APS) dataset demonstrate that the proposed FWA-GCN achieves Precision of 79.95%, Recall of 85.07%, and F1-score of 82.43%, outperforming conventional machine learning models including Random Forest, XGBoost, CatBoost, and Multi-Layer Perceptron, as well as a standard GCN model. Furthermore, an ablation study was conducted by removing the top features selected by the attention mechanism. The results show a significant decrease in recall, confirming the effectiveness of the attention-based feature importance and supporting the interpretability of the proposed framework. Full article

The deployment of Wireless Sensor Networks (WSNs) remains challenging and time consuming due to the manual commissioning, configuration, and maintenance of resource-constrained Internet of Things (IoT) devices. Achieving precise network-wide time synchronization in such systems further increases this deployment complexity. This paper presents a novel Real-Time Operating System (RTOS)-integrated time synchronization method that distributes an absolute Coordinated Universal Time (UTC) reference across the network using a single Global Navigation Satellite System (GNSS)-enabled host. The method extends the semantics of the RTOS tick count by directly linking it to a global time reference. Consequently, sensor nodes obtain a notion of UTC time and can execute time-critical tasks at precisely defined moments without requiring a dedicated Real-Time Clock (RTC) or GNSS module on each sensor node. This design reduces both hardware cost and overall system complexity. Experimental results obtained on custom-developed hardware running FreeRTOS demonstrate a task synchronization error below ±30 μs between the GNSS reference and a sensor node operating at a clock frequency of 32 MHz. Such precise network-wide synchronization enables more efficient channel utilization, reduces power consumption, and improves the accuracy of both local and coordinated task execution across multiple devices in WSNs. It therefore serves as a key enabler for self-deployable WSNs. Full article

The last years have seen the increasing development of innovative railway pantographs based on smart materials and equipped with monitoring features based on wireless sensor nodes. In this scenario, one of the most important challenges is the power supply of pantograph sensors. Energy harvesting systems have been proposed for powering monitoring sensors in a variety of applications, including railway pantographs. These systems convert ambient energy sources into electrical energy. The use of energy harvesting systems coupled with storage devices, such as rechargeable batteries or supercapacitors, can be a very promising solution for making the sensors self-powered, thus avoiding the drawbacks associated with supplying from the main grid or disposable batteries. In this paper, the operating principles of the main technologies used for energy harvesting in railway pantographs are described in detail, together with some examples of laboratory prototypes and commercial devices. The proposed analysis focuses on the perspectives and challenges of various energy harvesting technologies and can help select the most suitable technology for the development of innovative sensorized pantographs. Full article

Underwater acoustic sensor networks (UASNs) have emerged as a pivotal technology for ocean exploration, tactical surveillance, and environmental monitoring. However, the underwater acoustic channel poses severe challenges, including high propagation delay, limited bandwidth, and rapid time-varying multipath fading, which significantly degrade communication reliability. Cooperative communication, which exploits spatial diversity via relay nodes, offers a promising solution to these impairments. In this paper, we investigate the joint optimization of relay selection and power allocation in UASNs to maximize the long-term system energy efficiency and throughput. This problem is inherently complex due to the hybrid action space, which couples the discrete selection of relay nodes with the continuous allocation of transmission power, and the absence of real-time, perfect channel state information (CSI). To address these challenges, we propose a novel deep hybrid reinforcement learning (DHRL) framework utilizing a parameterized deep Q-Network (P-DQN) architecture. Unlike traditional approaches that discretize power levels or relax discrete constraints, our approach seamlessly integrates a deterministic policy network for continuous power control and a value-based network for discrete relay evaluation. Furthermore, we incorporate a prioritized experience replay (PER) mechanism to improve sample efficiency by focusing on rare but significant channel transition events. We provide a comprehensive theoretical analysis of the algorithm’s complexity and convergence properties. Extensive simulation results demonstrate that the proposed DHRL algorithm outperforms state-of-the-art combinatorial bandit algorithms and conventional deep reinforcement learning baselines in terms of system energy efficiency, and also exhibits superior robustness against channel estimation errors. Full article

Unlike terrestrial environments, the inhomogeneity distribution of underwater sound speed poses significant challenges for underwater ranging and target localization. In the presence of sound ray bending and sensor node position errors in underwater acoustic sensor networks (UASNs), this paper proposes a joint TOA-AOA deep-sea target localization framework based on sound ray bending correction. From the perspective of information theory and time series analysis, the TOA measurements are time series signals carrying target position information, and the entropy-based analysis quantifies the fundamental limit on localization uncertainty. First, based on the TOA time series measurements and combined with the acoustic propagation characteristics of the deep sea, a sound ray bending correction method is adopted to improve the accuracy of slant range measurement. To enhance target localization accuracy, this paper proposes a two-step WLS closed-form solution based on TOA-AOA. To further reduce localization bias, a maximum likelihood estimation (MLE) method based on the Gauss-Newton is also derived. Subsequently, the paper derives and analyzes the Cramér-Rao lower bound (CRLB) for target localization, proving theoretically that jointly using TOA-AOA can improve localization accuracy. Simulations verify the performance of the proposed methods. The slant range estimation method based on sound ray bending correction effectively improves range measurement accuracy. The proposed closed-form solution enhances target localization accuracy, achieving the CRLB accuracy. The Gauss-Newton MLE solution can attain the CRLB accuracy under certain localization geometries and further reduces localization bias. Full article

Continuous monitoring of pathological motor features is vital for post-stroke rehabilitation but remains challenged by power reliance and low sensitivity of wearable sensors. Here, we develop a high-sensitivity, self-powered breathable nanogenerator (BN-TENG) utilizing fish-scale-derived biological hydroxyapatite/carbon (Bio-HAp/C) fillers within electrospun polyvinylidene fluoride (PVDF) nanofibers. The Bio-HAp/C enhances electron-trapping capability, while a high-resilience ethylene-vinyl acetate (EVA) spacer optimizes contact-separation dynamics. The BN-TENG achieves a superior sensitivity of 16.28 V·N⁻¹ and remarkable stability over 10,000 cycles. By implementing a multi-node sensing strategy, the sensor successfully captures complex hemiplegic patterns, including compensatory shoulder hiking, distal muscle spasticity, and postural asymmetry. By resolving subtle micro-vibrations missed by traditional electronics, this work provides a sustainable, autonomous interface for characterizing pathological motor features and assessing rehabilitation progress in hemiplegic patients. Full article

Smart sensors are increasingly used in agriculture to monitor environmental conditions and support data-driven decision-making. However, traditional sensor implementations face critical challenges related to power consumption, especially in remote farms—such as pitaya plantations—where access to electricity and ongoing maintenance is limited. This paper presents a smart energy management system for agricultural sensor nodes integrating a machine learning model for adaptive sampling and a batching strategy to optimize energy usage. A lightweight Stochastic Gradient Descent (SGD) regressor trained on temperature dynamics runs on-device to predict the sampling interval ($T_s$). In parallel, the node adjusts the number of buffered samples as the battery state of charge ($SOC$) decreases, reducing Long Range (LoRa) transmissions. Field experiments show that the proposed approach reduces energy consumption by 77.8% compared with fixed-interval sampling, while maintaining good temperature fidelity with Mean Absolute Error (MAE) of 0.537 °C for temperature reconstruction. Full article