Search Results (5,013)

Micro-scale kinetic energy harvesters are in large demand to function as sustainable power sources for wireless sensor networks and the Internet of Things. However, one of the challenges associated with them is their inability to easily tune the frequency during the manufacturing process, requiring devices to be custom-made for each application. Previous attempts have either used active tuning, which consumes power, or passive devices that increase their energy footprint, thus decreasing power density. This study involved developing a novel passive method that does not alter the device footprint or power density. It involved creating a proof mass with an array of chambers or cavities that can be individually filled with liquid to alter the overall proof mass as well as center of gravity. The resonant frequency of a rectangular cantilever can then be altered by changing the location, density, and volume of the liquid-filled mass. The resolution can be enhanced by increasing the number of chambers, whereas the frequency tuning range can be increased by increasing the amount of liquid or density of the liquids used to fill the cavities. A piezoelectric cantilever with a 340 Hz initial resonant frequency was used as the testing device. Liquids with varying density (silicone oil, liquid sodium polytungstate, and Galinstan) were investigated. The resonant frequencies were measured experimentally by filling various cavities with these liquids to determine the tuning frequency range and resolution. The tuning ranges of the first resonant frequency mode for the device were 142–217 Hz, 108–217 Hz, and 78.4–217 Hz for silicone oil, liquid sodium polytungstate, and Galinstan, respectively, with a sub Hz resolution. Full article

The sustainable reuse of batteries after their first life in electric vehicles requires accurate state-of-health (SoH) estimation to ensure safe and efficient repurposing. This study applies the systematic ProKnow-C methodology to analyze the state of the art in SoH estimation using machine learning (ML). A bibliographic portfolio of 534 papers (from 2018 onward) was constructed, revealing key research trends. Public datasets are increasingly favored, appearing in 60% of the studies and reaching 76% in 2023. Among 12 identified sources covering 20 datasets from different lithium battery technologies, NASA’s Prognostics Center of Excellence contributes 51% of them. Deep learning (DL) dominates the field, comprising 57.5% of the implementations, with LSTM networks used in 22% of the cases. This study also explores hybrid models and the emerging role of transfer learning (TL) in improving SoH prediction accuracy. This study also highlights the potential applications of SoH predictions in energy informatics and smart systems, such as smart grids and Internet-of-Things (IoT) devices. By integrating accurate SoH estimates into real-time monitoring systems and wireless sensor networks, it is possible to enhance energy efficiency, optimize battery management, and promote sustainable energy practices. These applications reinforce the relevance of machine-learning-based SoH predictions in improving the resilience and sustainability of energy systems. Finally, an assessment of implemented algorithms and their performances provides a structured overview of the field, identifying opportunities for future advancements. Full article

Industry 4.0 and digitalization are driving a major transformation in the bakery sector. This systematic review examines the latest advancements in digital technologies and platforms within the bakery industry. Innovations such as robotics, automation, blockchain, and wireless sensor networks are currently revolutionizing bakery operations by enhancing production efficiency, enabling real-time monitoring, and ensuring product traceability. Additionally, digital platforms are improving customer interactions through e-commerce, personalized product offerings, and targeted marketing strategies. Digitalization is also contributing to waste reduction, quality control improvement, and data-driven decision-making, leading to optimized inventory management and more efficient production automation. These advancements are fostering stronger customer engagement, resulting in cost savings and increased profitability. However, the sector faces several challenges, including resistance from companies to adopt new technologies, high implementation costs, a shortage of expertise, and concerns about preserving artisanal quality. This review provides valuable insights for researchers, businesses, and industry experts to deepen their understanding of how digitalization is shaping the future of the bakery sector while highlighting emerging opportunities, challenges, and avenues for future research. Full article

The use of wireless sensor networks (WSNs) in critical applications such as environmental monitoring, smart agriculture, and industrial automation has created significant security concerns, particularly due to the broadcasting nature of wireless communication. The absence of physical-layer authentication mechanisms exposes these networks to threats like spoofing, compromising data authenticity. This paper introduces a multi-attribute physical layer authentication (PLA) scheme to enhance WSN security by using physical attributes such as received signal strength indicator (RSSI), battery level (BL), and altitude. The LoRaWAN join procedure, a key risk due to plain text transmission without encryption during initial communication, is addressed in this study. To evaluate the proposed approach, a partially synthesized dataset was developed. Real-world RSSI values were sourced from the LoRa at the Edge Dataset, while BL and altitude columns were added to simulate realistic sensor behavior in a forest fire detection scenario. Machine learning (ML) models, including Logistic Regression (LR), Random Forest (RF), and K-Nearest Neighbors (KNN), were compared with deep learning (DL) models, such as Multi-Layer Perceptron (MLP) and Convolutional Neural Networks (CNN). The results showed that RF achieved the highest accuracy among machine learning models, while MLP and CNN delivered competitive performance with higher resource demands. Full article

The global navigation satellite system (GNSS) struggles to deliver the precision and reliability required for positioning, navigation, and timing (PNT) services in environments with severe interference. Fifth-generation (5G) cellular networks, with their low latency, high bandwidth, and large capacity, offer a robust communication infrastructure, enabling 5G base stations (BSs) to extend coverage into regions where traditional GNSSs face significant challenges. However, frequent multi-sensor faults, including missing alarm thresholds, uncontrolled error accumulation, and delayed warnings, hinder the adaptability of navigation systems to the dynamic multi-source information of complex scenarios. This study introduces an advanced, tightly coupled GNSS/5G/IMU integration framework designed for distributed PNT systems, providing all-source fault detection with weighted, robust adaptive filtering. A weighted, robust adaptive filter (MCC-WRAF), grounded in the maximum correntropy criterion, was developed to suppress fault propagation, relax Gaussian noise constraints, and improve the efficiency of observational weight distribution in multi-source fusion scenarios. Moreover, we derived the intrinsic relationships of filtering innovations within wireless measurement models and proposed a time-sequential, observation-driven full-source FDE and sensor recovery validation strategy. This approach employs a sliding window which expands innovation vectors temporally based on source encoding, enabling real-time validation of isolated faulty sensors and adaptive adjustment of observational data in integrated navigation solutions. Additionally, a covariance-optimal, inflation-based integrity protection mechanism was introduced, offering rigorous evaluations of distributed PNT service availability. The experimental validation was carried out in a typical outdoor scenario, and the results highlight the proposed method’s ability to mitigate undetected fault impacts, improve detection sensitivity, and significantly reduce alarm response times across step, ramp, and multi-fault mixed scenarios. Additionally, the dynamic positioning accuracy of the fusion navigation system improved to 0.83 m ($1\sigma$). Compared with standard Kalman filtering (EKF) and advanced multi-rate Kalman filtering (MRAKF), the proposed algorithm achieved 28.3% and 53.1% improvements in its $1\sigma$ error, respectively, significantly enhancing the accuracy and reliability of the multi-source fusion navigation system. Full article

Efficient monitoring of soil moisture is crucial for optimizing water usage and ensuring crop health in agricultural fields, especially under rainfed conditions. This study proposes a high-throughput soil moisture monitoring method that integrates LoRa-based wireless sensor networks with region-specific statistical models. Wireless sensors were deployed in the top 0–0.2 m soil layer to gather real-time moisture data, which were then combined with the Biswas model to estimate soil moisture distribution down to a depth of 2.0 m. The model was calibrated using field capacity and crop wilting coefficients. Results demonstrated a strong correlation between model predictions and actual measured soil moisture storage, with a coefficient of determination (R²) exceeding 0.94. Additionally, 83% of sample points had relative errors below 18.5%, and for depths of 0–1.2 m, 90% of sample points had relative errors under 15%. The system effectively tracked daily soil moisture dynamics during maize growth, with predicted evapotranspiration relative errors under 10.25%. This method provides a cost-effective and scalable tool for soil moisture monitoring, supporting irrigation optimization and improving water use efficiency in dryland agriculture. Full article

To address the problems of unclear node activation strategy and redundant feasible solutions in solving the target coverage of wireless sensor networks, a target coverage algorithm based on deep Q-learning is proposed to learn the scheduling strategy of nodes for wireless sensor networks. First, the algorithm abstracts the construction of feasible solutions into a Markov decision process, and the smart body selects the activated sensor nodes as discrete actions according to the network environment. Second, the reward function evaluates the merit of the smart body’s choice of actions in terms of the coverage capacity of the activated nodes and their residual energy. The simulation results show that the proposed algorithm intelligences are able to stabilize their gains after 2500 rounds of learning and training under the specific designed states, actions and reward mechanisms, corresponding to the convergence of the proposed algorithm. It can also be seen that the proposed algorithm is effective under different network sizes, and its network lifetime outperforms the three greedy algorithms, the maximum lifetime coverage algorithm and the self-adaptive learning automata algorithm. Moreover, this advantage becomes more and more obvious with the increase in network size, node sensing radius and carrying initial energy. Full article

Wireless rechargeable sensor networks (WRSNs) address the energy scarcity problem in wireless sensor networks by introducing mobile chargers (MCs) to recharge energy-hungry sensor nodes. Scheduling MCs to charge the recharge nodes is the primary focus of the energy replenishment scheme in WRSNs. The performance of the energy replenishment scheme is significantly influenced by the energy level of each node, which is depends on the data collection scheme employed by the network. Consequently, integrating energy replenishment and data collection has become a new concern in WRSN research. However, the MCs’ workload and travel time increase when data collection and energy replenishment are performed simultaneously, leading to an increase in both the node’s charging delay and data collection delay. In this work, our goal is to reduce the delays in data collection and node charging by proposing a new joint energy replenishment and data collection approach. In the proposed approach, certain nodes in the network are selected as data storage nodes to temporarily store all the collected data based on their geographical locations. A special class of MCs, called MCDs (mobile charger and data collectors), is then assigned the responsibility of charging these data storage nodes and collecting the data stored. Afterwards, the task of recharging the remaining network nodes falls to another type of MC. By combining the capabilities of two distinct MC types, the workload and the travel distance of MCs are reduced. When compared to the conventional joint algorithms, the simulation results demonstrate that the proposed approach successfully decreases the delay it takes to gather data and recharge nodes. Full article

In the Internet of Everything era of Web 3.0, smart grid (SG) technology is also developing towards intelligent interconnection of terminal devices. However, in the smart grid scenario, security issues are particularly prominent, especially the openness of wireless sensor networks. Sensor nodes are vulnerable to attacks and other security threats, which makes confirming the legitimacy of access identity and ensuring the secure transmission of data an urgent problem to be solved. At present, although a variety of authentication schemes for smart grid nodes have been proposed, most of them have problems. For example, some cannot achieve forward security. Therefore, this paper aims to solve this problem and proposes a lightweight anonymous authentication protocol based on physical unclonable functions (PUFs), which can implement mutual authentication and session key agreement between gateway nodes and sensor nodes. Compared to five state-of-the-art schemes in security and performance, the proposed scheme achieves all eight of the listed security requirements with lightweight calculation overhead, communication overhead, and storage overhead. Full article

In modern (5G) and future Multi-User (MU) wireless communication systems Beyond 5G (B5G) using Multiple-Input Multiple-Output (MIMO) technology, base stations with a large number of antennas communicate with many mobile stations. This technology is becoming especially relevant in modern multi-user wireless sensor networks in various application scenarios. The problem of organizing an MU mode on the downlink has arisen, which can be solved by precoding at the Base Station (BS) without using additional channel frequency–time resources. In order to utilize an efficient precoding algorithm at the base station, full Channel State Information (CSI) is needed for each mobile station. Transmitting this information for massive MIMO systems normally requires the allocation of high-speed channel resources for the feedback. With limited feedback, reduced information (partial CSI) is used, for example, the codeword from the codebook that is closest to the estimated channel vector (or matrix). Incomplete (or inaccurate) CSI causes interference from the signals, transmitted to neighboring mobile stations, that ultimately results in a decrease in the number of active users served. In this paper, we propose a new downlink precoding approach for MU-MIMO systems that also uses codebooks to reduce the information transmitted over a feedback channel. A key aspect of the proposed approach, in contrast to the existing ones, is the transmission of new, uncorrelated information in each cycle, which allows for accumulating CSI with higher accuracy without increasing the feedback overhead. The proposed approach is most effective in systems with dynamic user selection. In such systems, increasing the accuracy of CSI leads to an increase in the number of active users served, which after a few cycles, can reach a maximum value determined by the number of transmit antennas at the BS side. This approach appears to be promising for addressing the challenges associated with current and future massive MIMO systems, as evidenced by our statistical simulation results. Various methods for extracting and transmitting such uncorrelated information over a feedback channel are considered. In many known publications, the precoder, codebooks, CSI estimation methods and other aspects of CSI transmission over a feedback channel are separately optimized, but a comprehensive approach to jointly solving these problems has not yet been developed. In our paper, we propose to fill this gap by combining a new approach of precoding and CSI estimation with CSI accumulation and transmission over a feedback channel. Full article

Wireless sensor networks (WSNs) are vulnerable to several challenges. Congestion control, the utilization of trust to ensure security, and the incorporation of clustering schemes demand much attention. Algorithms designed to deal with congestion control fail to ensure security and address challenges faced due to congestion in the network. To resolve this issue, a Hybrid Trust-based Congestion-aware Cluster Routing (HTCCR) protocol is proposed to effectively detect attacker nodes and reduce congestion via optimal routing through clustering. In the proposed HTCCR protocol, node probability is determined based on the trust factor, queue congestion status, residual energy (RE), and distance from the mobile base station (BS) by using hybrid K-Harmonic Means (KHM) and the Enhanced Gravitational Search Algorithm (EGSA). Sensor nodes select cluster heads (CHs) with better fitness values and transmit data through them. The CH forwards data to a mobile sink once the sink comes into the range of CH. Priority-based data delivery is incorporated to effectively control packet forwarding based on priority level, thus decreasing congestion. It is evident that the propounded HTCCR protocol offers better performance in contrast to the benchmarked TBSEER, CTRF, and TAGA based on the average delay, packet delivery ratio (PDR), throughput, detection ratio, packet loss ratio (PLR), overheads, and energy through simulations. The proposed HTCCR protocol involves 2.5, 2.3, and 1.7 times less delay; an 18.1%, 12.5%, and 5.5% better detection ratio; 2.9, 2.6, and 1.8 times less energy; a 2.2, 1.9, and 1.5 times lower PLR; a 14.5%, 10.5%, and 5.2% better PDR; a 30.7%, 28.5%, and 18.4% better throughput; and 2.27, 1.91, and 1.66 times lower routing overheads in contrast to the TBSEER, CTRF, and TAGA protocols, respectively. The HTCCR protocol involves 4.1% less delay for the ‘$C_1$’ and ‘$C_2$’ RT packets, and the average throughput of RT is 10.4% better when compared with NRT. Full article

In response to the current key issues in the field of smart irrigation for farmland, such as the lack of data sources and insufficient integration, a low degree of automation in drive execution and control, and over-reliance on cloud platforms for analyzing and calculating decision making processes, we have developed nodes and gateways for smart irrigation. These developments are based on the EC-IOT edge computing IoT architecture and long range radio (LoRa) communication technology, utilizing STM32 MCU, WH-101-L low-power LoRa modules, 4G modules, high-precision GPS, and other devices. An edge computing analysis and decision model for smart irrigation in farmland has been established by collecting the soil moisture and real-time meteorological information in farmland in a distributed manner, as well as integrating crop growth period and soil properties of field plots. Additionally, a mobile mini-program has been developed using WeChat Developer Tools that interacts with the cloud via the message queuing telemetry transport (MQTT) protocol to realize data visualization on the mobile and web sides and remote precise irrigation control of solenoid valves. The results of the system wireless communication tests indicate that the LoRa-based sensor network has stable data transmission with a maximum communication distance of up to 4 km. At lower communication rates, the signal-to-noise ratio (SNR) and received signal strength indication (RSSI) values measured at long distances are relatively higher, indicating better communication signal quality, but they take longer to transmit. It takes 6 s to transmit 100 bytes at the lowest rate of 0.268 kbps to a distance of 4 km, whereas, at 10.937 kbps, it only takes 0.9 s. The results of field irrigation trials during the wheat grain filling stage have demonstrated that the irrigation amount determined based on the irrigation algorithm can maintain the soil moisture content after irrigation within the suitable range for wheat growth and above 90% of the upper limit of the suitable range, thereby achieving a satisfactory irrigation effect. Notably, the water content in the 40 cm soil layer has the strongest correlation with changes in crop evapotranspiration, and the highest temperature is the most critical factor influencing the water requirements of wheat during the grain-filling period in the test area. Full article

Due to the increasing demand for agricultural production and the depletion of natural resources, the rational and efficient use of resources in agriculture becomes essential. Thus, Agriculture 4.0 or precision agriculture (PA) was born, which leverages advanced technologies such as Geographic Information Systems (GIS), Artificial Intelligence (AI), sensors and remote sensing techniques to optimize agricultural practices. This study focuses on an innovative approach integrating data from different sources, within a GIS platform, including data from an experimental atmospheric simulator and from a wireless sensor network, to identify the most suitable areas for future crops. In addition, we also calculate the optimal path of a drone for crop monitoring and for a farm machine for agricultural operations, improving efficiency and sustainability in relation to agricultural practices and applications. Expected and obtained results of the conducted study in a specific area of Reggio Calabria (Italy) include increased accuracy in agricultural planning, reduced resource and pesticide use, as well as increased yields and more sustainable management of natural resources. Full article

The control of water levels in various environments is very important for predicting flooding and mitigating flood damages. Generally, water level device gauges are used to measure water levels, but the structural setting of reservoirs presents significant challenges for the installation of water level device gauges. Therefore, the solution to this problem is to apply image recognition methods using water level device gauges. In this paper, we present an intelligent water level estimation system considering water level device gauge image recognition and a Wireless Sensor Network (WSN). We carried out experiments in a water reservoir to evaluate the proposed system. From the experimental results, we found that the proposed system can estimate the water level via the object recognition of numbers and symbols on the water level device gauge. Full article

Forests play a critical role in maintaining global ecological balance, regulating climate, and supporting biodiversity. Effective forest management and monitoring relies on the deployment of large-scale wireless sensor networks (WSNs) for real-time data collection, enabling the protection of ecosystems and the early detection of environmental changes. However, such massive deployments pose serious challenges with increasingly scarce radio resources. Sparse code multiple access (SCMA), a non-orthogonal multiple access (NOMA) technique, has been identified as a promising solution for facilitating wireless communications among numerous distributed sensors in large-scale WSNs with improved spectral efficiency. This is essential for application scenarios involving a substantial number of terminal devices, including forest monitoring and management. Codebook design is a critical issue for SCMA systems. It is closely related to the detection performance at the receiver, which in turn has a direct effect on the communication coverage or quality of service (QoS) for the terminal devices. This paper investigates the symbol error rate (SER) performance of SCMA systems over AWGN channels and derives its theoretical upper bound. The optimization objectives for each stage of codebook design are mathematically analyzed for a single resource element (RE), a single device, and multi-device, multi-RE scenarios. On this basis, a lightweight stepwise codebook design scheme is proposed in this paper. Simulation results demonstrate that the proposed codebooks can maintain fairness among devices while guaranteeing detection performance. Full article