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In this study, we present a novel low-frequency magnetic field energy harvester (EH) employing beryllium bronze/Pb(Zr,Ti)O3 ceramic composited dual-beam structures with tip magnets attached to the inner and outer beams. This design incorporates the internal magnetic-coupled (IMC) effect, resulting in significantly enhanced coupling ability and a wide bandwidth. The validity of the IMC mechanism is confirmed through theoretical formulas and numerical simulations. By leveraging the IMC condition, the EH achieves an expanded bandwidth, which increases from 22 to 43 Hz. Moreover, the total output voltages at the inherent resonance and internal resonance are boosted by 15.4% and 32%, respectively. The performance of the IMC-EH can be further improved by increasing the number of the endmost magnets. Experimental investigations reveal that the IMC-EH generates a maximum RMS output power density of 56.25 μW Oe−2 cm−3, surpassing existing magnetically coupled piezoelectric energy harvesters. Remarkably, even under an ambient magnetic field as low as 1 Oe, the proposed IMC-EH still yields a total output power of 185 μW, sufficient to continuously power 26 LEDs in real time. This demonstrates its potential as a promising solution for low-power consumption small electronics. Furthermore, the implications of this work extend beyond its immediate benefits, as it inspires the design of future self-powered wireless sensor networks in the context of the Internet of Things. [ABSTRACT FROM AUTHOR]

Summary: Wireless body sensor network (WBSN) is essential for monitoring patients' health problems and offers a low‐cost option for various healthcare applications. In this manuscript, a Novel Health Monitoring Approach for WBSNs (DIWGAN‐WBSN) is proposed, which uses Dual Interactive Wasserstein Generative Adversarial Network (DIWGAN) optimized with War Strategy Optimization Algorithm (WSOA). After sensing the aforementioned attribute information, it is the responsibility of WBSN nodes to transfer the sensed data to the sink node. The Volcano Eruption Algorithm (VEA) is applied to select the optimum cluster heads in WBSN. The results from VEA are fed to the target node; it consists of DIWGAN to classify the health records and to portray the patient's health status. Generally, DIWGAN does not adopt any optimization methods for measuring the ideal parameters and guaranteeing accurate health monitoring and risk assessment. So the proposed WSOA is considered to enhance the DIWGAN. The proposed method is activated in MATLAB; its efficacy is estimated under performance metrics, like precision, specificity, accuracy, and energy utilization. The proposed approach attains 23.9%, 21.34%, and 51.09% higher accuracy; 21.45%, 13.94%, and 20.6% higher precision; 31.32%, 29.61%, and 11.03% higher specificity; and 20.9%, 19.87%, and 24.6% lower energy utilization for HD classification using the Cleveland database than the existing methods like back propagation neural network‐based risk detection in WBSN for health monitoring, random forest algorithm–based health monitoring in WBSN, and ensemble deep learning and feature fusion for health monitoring using WBSN methods, respectively. [ABSTRACT FROM AUTHOR]

Summary: Ensuring data integrity in wireless sensor networks (WSNs) is crucial for accurate monitoring, yet missing data due to sensor faults present a significant challenge. This research introduces an innovative approach that integrates advanced data recovery techniques with leading‐edge methods to address this issue. The system begins by identifying and isolating fault nodes using a specialized algorithm that analyzes network behavior. By applying fuzzy density‐based spatial clustering of applications with noise (FDBSCAN), potential fault nodes are precisely located based on deviations from expected patterns. Subsequently, an intelligent missing data recovery mechanism powered by bidirectional long short‐term memory (Bi‐LSTM) networks takes action. The Bi‐LSTM model is trained on existing sensor data to capture intricate patterns and dependencies, enabling accurate prediction and reconstruction of missing values caused by identified faults. The synergy between Bi‐LSTM for missing data recovery and FDBSCAN for fault node detection comprehensively addresses the missing data problem in WSNs. In missing data recovery, it demonstrates low mean absolute deviation (MAD) ranging from 0.021 to 0.13 and mean squared deviation (MSD) ranging from 0.0025 to 0.05 across various missing data ratios. Data reliability remains consistently high at 96% to 98%, even with up to 80% missing data. For fault node detection, the approach achieves precision of 95.7%, recall of 96.3%, F1‐score of 96.1%, and accuracy of 97.4%, outperforming existing techniques. The computational cost during training is noted at 5.79 h, presenting a limitation compared to other methods. This research highlights the importance of integrating fault node detection into missing data recovery mechanisms, presenting an innovative solution for the advancement of WSNs. [ABSTRACT FROM AUTHOR]

The very large-scale integration implementation of a unique hardware-oriented image compression technique for wireless sensor networks (WSNs) is presented in this work. Networks of individually owned sensors spread out throughout an area that can detect, measure, and report changes in environmental variables are known as wireless sensor networks (WSNs). Color sampling, block truncation coding (BTC), threshold optimization, sub-sampling, estimation, quantization, and enhanced Golomb-rice coding (EGRC) are all included in the proposed design. A unique improved BTC with an enhanced Golomb-rice coding (IBTC-EGRC) framework has been proposed in this paper. IBTC training framework has been developed using the fuzzy decision-based approach to achieve representative levels and satisfy WSN requirements to accomplish the cost-effective and power-efficient features. Two ideal reconstruction values and bitmap files have been obtained for every block. IBTC divides images into variable block sizes for mathematical translation and inter-pixel redundancy removal. The subsampling, estimation, and quantization stages have minimized redundant data. Finally, EGRC has been used to code the value with the highest likelihood. An EGRC module decreases memory use and computation complexity. The EGRC technique reduces hardware resource utilization by removing the need for the context module, a crucial part of lossless image compressor designs and its memory. Proposed method, Golomb-rice parameter forecasting and managment module is used to preserve pixel connection and improved compression ratio. A UMC 180 nm CMOS technology has been used to implement the suggested framework. This design has 5.8k synthesized gate counts and a core area of 56,000 µm2. 100 MHz and 3.01 mW were the operational frequency and energy consumption, respectively. The proposed method has a 9.37% reduction in gate count compared to the previous fuzzy BTC-based approach. [ABSTRACT FROM AUTHOR]

As neural network-based localization algorithms are becoming popular, there is a need to shorten the training time and the localization time for sustainability and efficiency purposes. To address such issues, the fuzzy transform (or F-transform for short) is employed here for the first time in a neural network-based localization algorithm. The F-transform is a dimensionality reduction method, which has found several applications over the last decade, but it has not been well explored in the form of a prepending layer to a neural network. In this respect, some properties (including the computational cost) of the F-transformed neural scheme are formally discussed here. The performance of the neural network-based approach with and without F-transform, and with a state-of-the-art reduction technique, i.e. the principal component analysis, is evaluated first on simulated data and then on publicly available real-world data. Different neural network architectures have been tried jointly with the above-mentioned reduction techniques. The numerical experiments show the excellent performance of the proposed fuzzy transform-based approach, which can ensure considerable savings in training time and query response time, without significant losses in accuracy. [ABSTRACT FROM AUTHOR]

Summary: Location‐based underwater communication applications such as strategic surveillance, disaster prevention, marine research, and mine detection have given the field of underwater wireless sensor networks (UWSN) a head start. Node localization is a prerequisite for accurate data collection, target monitoring, and network management in UWSNs. However, the unique characteristics of the underwater environment, such as signal attenuation, multipath propagation, and variable acoustic properties, pose a major challenge to effective node localization. Accurate sensor node location data is essential for successful underwater data collection, but difficult to achieve as the GPS system cannot be used in an underwater environment. In this paper, existing node localization techniques such as ALS, SLUM, MASL, SLMP, UDB, USP, etc., and recent advances such as the fusion of range‐based and range‐free techniques, the fusion of RSSI and AoA to improve localization accuracy by using directional information in addition to signal strength, and the use of optimization techniques such as PSO, COA, and WOA algorithms to improve the accuracy of the applied node localization algorithm, e.g., TP‐TSFLA, and challenges related to UWSN are discussed. Also, different localization algorithms that affect the accuracy of UWSN localization techniques have been evaluated and compared with NS2 in terms of localization error, localization coverage, energy consumption, and average communication cost metrics. In addition, this paper also provides an up‐to‐date investigation of localization techniques. Finally, the tools available for simulation are presented, followed by open research questions that need to be addressed in the localization of nodes. [ABSTRACT FROM AUTHOR]

Summary: In recent years, the use of wireless sensor devices in several applications, for example, monitoring in dangerous geographical spaces and the Internet of Things, has dramatically increased. Though sensor nodes (SNs) have limited power, battery replacement is not feasible in most cases. Therefore, energy saving in wireless sensor networks (WSN) is the major concern in the design of effective transmission protocol. Clustering might lower energy usage and increase network lifetime. Routing protocol for WSN represents an engineering area that has gained considerable interest among researchers due to its rapid evolution and development. Among them, the clustering routing protocol corresponds to the most effective technique to manage the energy consumption of each SN. In this manuscript, we focus on the design of a new metaheuristic optimization‐based energy‐aware clustering with routing protocol for lifetime maximization (MOEACR‐LM) method in WSN. The purpose of the MOEACR‐LM method is to improve network efficiency via proper selection of cluster heads (CHs) and effective data transmission. Initially, a hunter–prey optimization (HPO) method‐based clustering technique is used for cluster construction and the CH selection process. Next, the clouded leopard optimization (CLO) model is used for the route selection process in WSN. The HPO and CLO models derive a fitness function involving multiple parameters for clustering and routing processes. A comprehensive experimental analysis is carried out to demonstrate the enhanced performance of the MOEACR‐LM technique. The overall comparison study pointed out the improved energy efficiency results of the MOEACR‐LM technique over other existing approaches. [ABSTRACT FROM AUTHOR]

Summary: Underwater wireless sensor networks (UWSNs) based on autonomous underwater vehicles (AUVs) have become the standard technology for underwater search tasks with the advent of new underwater information and communication technologies (ICTs). It is also vulnerable to threats and malicious attacks because of its inherent characteristics, open acoustic channel and hostile underwater environment. The purpose of this research is to develop a secure data transfer (SDF) system based on software‐defined networking (SDN) architecture with cooperative searching scheme (CSS) (SDN‐SDF‐CSS) for AUV‐based UWSN. This part employs data, local control and the primary control layer to provide a scalable SDN‐based architecture for the AUV‐based UWSN. To accomplish the underwater search operation, the data layer is primarily deployed to gather AUVs (G‐AUVs) and store AUVs (S‐AUVs). In order to schedule the AUV, the clustering process takes place based on priority ranking with respect to the average power of each cluster. Then, the CSS is developed, and it is performed in the data layer. The hierarchical localization framework (HLF) can be used to locate each AUV's location within the data layer, which is a necessary step in deploying the cooperative searching model. Finally, for an efficient data transfer, the communication model is deployed in the data layer. UWSNs are vulnerable to various malicious attacks (such as bad‐mouthing attacks, on–off attacks, blackhole attacks and wormhole attacks) because of the high bit error rate and unstable optical/acoustic channels in the underwater environment. To overcome this, an SDF technique is used, which involves isolating the attacker node from the data layer. The suggested SDN‐SDF‐CSS model is implemented on the MATLAB platform, and its performance is evaluated using various evaluation metrics, both with and without attacks. As a result, the proposed SDN‐SDF‐CSS model has achieved better performance and proved its superiority in the UWSN environment. [ABSTRACT FROM AUTHOR]

Summary: For the sensor network, greedy forwarding is thought to be the best energy‐efficient routing method. There are two restrictions on greedy forwarding: a minimum local scenario and an intersection problem in which it is unable to locate an existing route. The proposed independent gradient forward (IGF) proves that the Euclidean equation used by greedy forwarding is less capable of dealing with the problems mentioned above. IGF considers each dimension independently to enhance the efficiency of greedy forwarding, sustaining its simplicity. It contributes to strengthening the suggested IGF's ability to manage holes and get around obstructions in the wireless sensor network. We have demonstrated that it travels a shorter potential path and avoids stuck nodes well in advance. It is more energy‐efficient since it can predict the void node and never visits a stalled node. We evaluated the suggested IGF's performance against other traditional boundary traversal‐based methods on both rare and dense networks. In comparison with other boundary traversal‐based algorithms, the simulation results indicate that it generates a 10% smaller route to bypass. The IGF average energy consumption increases slowly, rising by just 7% and 12.5% for dense and rare networks, respectively, with a fivefold increase in void size (from 100 to 500 m). [ABSTRACT FROM AUTHOR]

Summary: Wireless sensor networks (WSNs) are becoming increasingly important and well liked for delivering pervasive computing environments for a range of applications. Extending the networking life lifetime in WSNs is an important issue that must be addressed. Effective techniques for conserving the WSN's limited energy resources must be developed. Cross‐layer protocols are employed in WSNs to solve network lifespan difficulties. This paper proposes a new cross‐layer–cross‐domain routing scheme with stages such as "(1) network association stage, (2) nearer node detection phase, and (3) consistent state phase." In the consistent stage, the optimal cluster head selection (CHS) is carried out by taking into account risk, delay, energy, trust, and distance. A new model called manta ray collided dwarf mongoose optimization (MRC‐DMO) is introduced to help with this. Furthermore, the routing is accomplished by dependable data communication. The results obtained establish the effectiveness of the MRC‐DMO scheme for SEACRCLCD in WSN over varied methods. [ABSTRACT FROM AUTHOR]

Non-cooperative scenarios in cognitive wireless sensor network (CWSN) are often encountered with shadowing and hidden terminal issues. Cooperative spectrum sensing (CSS) can solve these issues but at the expense of large overhead. It is necessary to optimise the energy of battery-operated unlicensed users, also known as secondary users (SUs). The effect of fading and noise uncertainty is often overlooked when determining CSS performance. Energy efficiency (EE) is a comprehensive parameter that gives a complete picture of the overall performance of the CSS. There are several parameters that affect the EE of CSS. In this paper, the detector threshold is optimised to maximise the EE of the system. A system model is proposed to determine the EE of centralised CSS over different fading channels in noisy reporting conditions. An iterative algorithm is presented which determines the optimum detector threshold for which the EE is maximum. Results show that the optimum value of the detector threshold from the analytical model matches with simulation data. [ABSTRACT FROM AUTHOR]

The internet of things (IoT) represents a transformable breakthrough in the domain of intelligent devices, enabling essential functions such as data collection, management, and efficient storage. It has the potential to drive significant progress in various aspects of daily life, particularly within industrial environments where it can facilitate real-time product monitoring, information management, and status evaluation. Securing the authentication, authorization, and confidentiality of data within interconnected industrial IoT networks is of paramount importance. This study presents a secure lightweight IoT architecture that incorporates wireless sensor networks (WSN). Performance evaluation involves comparing the conventional security solutions with the proposed approach. The results demonstrate a 3% reduction in CPU cycles, 9% less execution time, a 3% decrease in memory capacity requirements, and a 9% improvement in security effectiveness. Additionally, the proposed model achieved enhanced cryptanalysis accuracy, registering at 97.34%. [ABSTRACT FROM AUTHOR]

Ongoing research is focused on underwater wireless sensor networks (UWSNs), which hold great promise for various applications to improve human lives. Many sensor nodes have been strategically positioned in rivers and oceans to closely monitor and analyze the underwater ecosystem. UWSNs face significant challenges due to energy limitations, unreliable communication channels, hole occurrence, and low packet delivery ratio. This paper introduces effective energy routing, hole detection and mitigation, and reliable delivery schemes to address these issues. In ERR-UWSN, a single-path routing strategy optimizes energy consumption by selecting the best-sending node through a unique weight function. However, the reliability of this approach is compromised in the harsh and unpredictable submarine environment. To overcome this limitation, co-ERR-UWSN combines cooperative routing with relay nodes between source–destination pairs. A carefully selected weight function ensures low energy consumption, reliability, and optimal node selection for data transmission. The simulation results show that ERR-UWSN is better than CEER, Co-EPRR, and FB-DBR in terms of residual energy, end-to-end delay, energy consumption, and total number of alive nodes. Also, Co-ERR-UWSN has a special advantage in the PDR criterion compared to other schemes. [ABSTRACT FROM AUTHOR]

Wireless rechargeable sensor network (WRSN) is one of the most important networks in today's world for extending network lifetime. To ensure a collision-free contention in WRSN, this paper proposes the Contention Window Optimization using the Cuttlefish Algorithm (CWO-CA) and addresses existing issues such as the capture effect, the fairness problem, and the queue hike. For safe channel access, the fitness function of CWO-CA is proposed using the retransmission count, queue size, and residual energy of the sensor node. The proposed algorithm divides the Contention Window interval into two halves based on the fitness function, and the nodes are assigned in either half to guarantee service differentiation. The fitness function among groups of the proposed algorithm constitutes the best fitness value. By selecting the best fitness value, the proposed algorithm facilitates adaptive contention management by dynamically balancing resource utilization of the network with the optimal result. The nodal behavior of CWO-CA has been modeled using a discrete Markov model and also simulated to measure throughput, packet delivery ratio, packet loss ratio, average queue size, residual energy, and delay. These results confirm that the proposed CWO-CA algorithm attained better performance than other existing algorithms. [ABSTRACT FROM AUTHOR]

In wireless sensor networks (WSNs), data aggregation techniques are used to reduce energy consumption of the sensors and extend the network lifetime. Recent work in this area considers the utilization of external mobile agents (elements) in data aggregation to further improve the WSN lifespan. The sensitivity of data collected by WSNs varies based on the application environment, necessitating data aggregation that adheres to specific conditions and constraints. One of the challenges in using external mobile elements is establishing trust between these elements and WSNs to ensure that data aggregation conditions set by the WSNs are met. In this paper, we propose a fog-cloud framework for a distributed trust service that enables WSNs to recruit trusted mobile elements for data aggregation. Our proposed trust framework provides WSNs with the flexibility to align their trust policy with the conditions of their data aggregation processes, facilitating efficient access to trust services. Simulation results demonstrate that our framework reduces energy loss while ensuring consistent and reliable data aggregation in WSNs. [ABSTRACT FROM AUTHOR]

The paper investigates the effect of controlling the transmission power used for communication of data packets at physical layer to prolong longevity of network and adaptive learning rates in a reinforcement-learning algorithm working at network layer for dynamic and quick decision making. A routing protocol is proposed for data communication, which works in tandem with physical layer, to improve performance of Wireless Sensor Networks used in IoT applications. The proposed methodology employs Q-learning, a form of reinforcement learning algorithm at network layer. Here, an agent at each sensor node employs the Q-learning algorithm to decide on an agent which is to be used as packet forwarder and also helps in mitigating energy-hole problem. On the other hand, the transmission power control method saves agents' battery energy by determining the appropriate power level to be used for packet transmission, and also achieving reduction in overhearing among neighboring agents. An agent derives its learning rate from its environment comprising of its neighboring agents. Each agents determines its own learning rate by using the hop distance to sink, and the residual energy (RE) of neighboring agents. The proposed method uses a higher learning rate at first, which is gradually decreased with the reduction in energy levels of agents over time. The proposed protocol is simulated to work in high-traffic scenarios with multiple source-sink pairs, which is a common feature of IoT applications in the monitoring and surveillance domain. Based on the NS3 simulation results, the proposed strategy significantly improved network performance in comparison with other routing protocols using Q-learning. [ABSTRACT FROM AUTHOR]

The present study analyzes the microstrip antenna design for wireless power transfer in smart buildings, harnessing the ambient electromagnetic radiation due to common electronic gadgets that energize wireless sensor networks, computing devices, and connected appliances. With the increased number of these devices, so does the potential for health problems caused by electromagnetic radiation. However, these devices also provide a renewable energy source through their emissions. This study suggests the creation of a 5G Microstrip antenna that enhances the absorption of this radiation for the purpose of recharging batteries in smart buildings. The design capitalizes on the inherent low-profile and cost-effective features of microstrip antennas, making them well-suited for incorporation into building infrastructure and 5G wireless technologies. Although each individual device emits a little amount of energy, the combined effect achieved by advanced antenna design and power converters is anticipated to result in a substantial energy production. The antenna designer tool from MATLAB was used to carry out a conceptual simulation of the microstrip antenna. This has set up the framework of a feasible way of predicting the performance with high efficiency and sustainability for a wireless power transfer (WPT) system. [ABSTRACT FROM AUTHOR]

Self-sufficient sensors scattered over different regions of the world comprise distributed wireless sensor networks (DWSNs), which track a range of environmental and physical factors such as pressure, temperature, vibration, sound, motion, and pollution. The use of data fusion becomes essential for combining information from various sensors and system performance. In this study, we suggested the multi-class support vector machine (SDF-MCSVM) with synthetic minority over-sampling techniques (SMOTE) data fusion for wireless sensor network (WSN) performance. The dataset includes 1,334 instances of hourly averaged answers for 12 variables from an AIR quality chemical multisensor device. To create a balanced dataset, the unbalanced data was first pre-processed using the SMOTE. The grey wolf optimization (GWO) approach is then used to reduce features in an effort to improve the efficacy and efficiency of feature selection procedures. This method is applied to classify the fused feature vectors into multiple categories at once to improve classification performance in WSNs and address unbalance datasets. The result shows the proposed method reaches high precision, accuracy, F1-score, recall, and specificity. The computational complexity and processing time were decreased in the study by using the proposed method. This is great potential for accurate and timely data fusion in dispersed WSNs with the successful integration of data fusion technologies. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are of significant importance in many applications; nevertheless, their operational efficiency and longevity might be impeded by energy limitations. The low energy adaptive clustering hierarchy (LEACH) protocol has been specifically developed with the objective of achieving energy consumption equilibrium and regularly rotating cluster heads (CHs). This study presents a novel technique, namely the hierarchical fuzzy logic controller (HFLC), which is integrated with the LEACH protocol to enhance the process of CH selection and effectively prolong the network's operational lifespan. The HFLC system employs fuzzy logic as a means to address the challenges posed by uncertainty and imprecision. It assesses many aspects, including residual energy, node proximity, and network density, in order to make informed decisions. The combination of HFLC with LEACH demonstrates superior performance compared to the conventional LEACH protocol in terms of energy efficiency, stability, and network durability. This study emphasizes the potential of intelligent and adaptive mechanisms in improving the performance of WSNs by improving the survivability of nodes by reducing the energy consumption of the nodes during the communication of network process. It also paves the way for future research that integrates soft computing approaches into network protocols. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) have revolutionized data collection in diverse environments, from industrial settings to natural ecosystems. However, their decentralized nature and energy constraints pose unique security and operational challenges. Previous research provided foundational insights into WSN security but lacked comprehensive strategies for real-time intrusion prevention and efficient energy utilization. Our work employs a multi-layered approach, integrating network intrusion prevention systems (NIPS) with WSNs and leveraging machine learning for threat detection. We developed MinE-DT (minimum energy-direct transmission) hybrid routing an integrated WSN model that not only identifies and mitigates distributed denial-of-service (DDoS) attack but also optimizes energy consumption, ensuring prolonged network longevity without compromising security. The proposed model's distinctiveness lies in its fusion of NIPS with energy-saving algorithms, offering a dual advantage of enhanced security and energy efficiency. Utilizing a combination of simulations and theoretical analysis, our methodology yielded promising results, showcasing significant improvements in threat detection rates and energy conservation. [ABSTRACT FROM AUTHOR]

Summary: UAVs are emerging as a critical asset in the field of data collection from extensive wireless sensor networks (WSNs) on a large scale. UAVs can be used to deploy energy‐efficient nodes or recharge nodes, but it should not compromise the network's coverage and connectivity. This paper proposes a comprehensive approach to optimize UAV trajectories within large‐scale WSNs, utilizing Multi‐Objective Reinforcement Learning (MORL) to balance critical objectives such as coverage, connectivity, and energy efficiency. This research investigates the configuration of a Wireless Sensor Network (WSN) assisted by a pen_spark UAV. In this network, Cluster Heads (CHs) act as central points for collecting data from their assigned sensor nodes. A predefined path is established for the UAV to efficiently gather data from these CHs. The Hybrid Threshold‐sensitive Energy Efficient Network (Hy‐TEEN) encompasses sophisticated algorithms for CH selection, dynamic A* for 3D trajectory planning and leverages reinforcement learning for multi‐objective optimization. The experimental results and analysis demonstrate the effectiveness and efficiency of the proposed approach in improving UAV performance and energy efficiency. The results demonstrate that the proposed methodology's trajectories are capable of achieving a time savings of 3.52% in mission completion when contrasted with conventional baseline methods. [ABSTRACT FROM AUTHOR]

In most developed countries, the medical healthcare system is experiencing rapid development from the stage of clinical information to the stage of information dissemination. In all of these countries, it is undeniable that the Internet of Medical Things (IoMT) technologies have contributed in order to develop information medical healthcare. In reality, the development of smart medical healthcare has been hindered by the protection of medical privacy, according to research and acceptance. This is especially true as telecommunications systems continue to expand and wireless sensor networks (WSN) develop, as well as ways to penetrate those checks that have become increasingly difficult. In the smart healthcare system, protecting users' information remains an outstanding issue. IoMT features and the protection of privacy and security have led to the development of an extended privacy homomorphism algorithm based on scrambling matrixes, an encryption algorithm enhanced by Modified RSA (mRSA), and a method of encrypted data compression that ensures data confidentiality. For the above purpose, we built a prototype system on a demo temporary domain using both hardware and software. According to the results, the proposed scheme protects E-healthcare from potential threats by providing stakeholders with a secure interface and preventing unauthorized users from accessing the mCloud, thus ensuring privacy. E-healthcare services based on cloud technology are protected by the proposed scheme because it is simple and robust. [ABSTRACT FROM AUTHOR]

In order to solve the problem of accurate and low-cost location of substation personnel in digital cities, a substation personnel location system based on wireless sensor cloud computing network is proposed. ZigBee wireless sensor cloud computing network is introduced into the substation to improve the direct location algorithm of substation personnel, and a fuzzy reasoning algorithm is proposed. The algorithm takes the signal strength received by each reference node and the relative distance between the reference nodes as input. After fuzzy, fuzzy reasoning and deblurring, the reliability of the received signal strength of each reference node is obtained, and then three reference nodes with high reliability are selected for trilateral positioning calculation. The experimental results show that the positioning error after improvement is more stable than that before improvement, and the maximum error before improvement is 1.4115m. Practice has proved that the algorithm can significantly improve the positioning accuracy of substation personnel without adding any hardware and using fewer nodes. [ABSTRACT FROM AUTHOR]

In the twenty-first century, the Internet of Things (IoT) is rapidly becoming one of the most influential technologies that connect billions of devices to the Internet. The importance and rapid growth of IoT technology make it an undeniably significant area of study in the contemporary world. This study evaluates the performance and research trends of East Asian countries in the area of Internet of Things (IoT) using scientometric tools. This paper collected 1146 data from Scopus over a period of 5 years (2016–2020). MS Excel, VOSviewer, and Biblioshiny were used to analyze the data using various Scientometric indicators. Cluster analysis and network visualization techniques are employed to evaluate the data and results are presented in tables and graphs. The finding shows that the topic has been exponential growth in recent years. China continues to top the IoT research rankings. The joint author contributed the most publications, and "Electronics (Switzerland)" is the most preferred journal for publication, followed by IEEE journals. In addition, the study found significant growth in smart environments such as "smart factories," "machine learning," "smart cities," "wireless sensor networks," "blockchains," etc., which pose significant concerns for many researchers. The study concludes with recommendations for future research. [ABSTRACT FROM AUTHOR]

The existing nodes of IoT networks are very small in size, deployed for long periods, and have very limited resources, which means that an IoT network must be very energy efficient to survive for a long time. Therefore, finding optimal routing techniques that lead to better data sharing without wasting energy can lead to more energy savings. This is an optimization problem, which means that we need to use optimization algorithms to find the optimal path in an IoT network. Some of the optimization algorithms are called meta-heuristic algorithms, these algorithms are inspired by nature, such as Artificial Neural Networks (ANN), which are Gradient methods to find the most suitable solution for a given problem. Our next algorithm is Particle Swarm Optimization (PSO). If the search combination of both algorithms is used in parallel, the search power will increase and better answers will be found in less time. For this reason, we suggest using a combination of the above algorithms. This idea is a combination of two optimization algorithms, PSO (Particle Swarm Optimization) and ANN (Neural Network) to optimize routing and reduce latency when sending information between IoT nodes in an IoT system. The proposed protocol is focused on optimizing energy consumption and execution time with the help of the GA-PSO algorithm based on routing-based clustering. Finally, to evaluate the proposed protocol, it was simulated using C++ software and compared with the method presented in the reference article based on the enhanced Ant Colony Algorithm, and the results show the efficiency of the proposed method in terms of energy consumption and execution time. The results show that in the presented algorithm, the execution time has been reduced to almost a quarter of the execution time in the algorithm of the reference article. Also, the results showed that our proposed method consumed 20 kJ less energy. [ABSTRACT FROM AUTHOR]

The smart city model on multi-agent systems and the Internet of Things using a wireless sensor network is designed to improve the quality of life for citizens, increase resource efficiency, and reduce costs. This model enables the collection, analysis, and sharing of information by connecting and coordinating devices and systems within the smart city. In this model, intelligent agents act as sensors, and the smart gateway plays the role of a base station. The main goal of this model is to reduce energy consumption. To achieve this goal, intelligent agents are divided into clusters, with each cluster having a cluster head. The cluster head’s task is to collect and aggregate information from the intelligent agents within its cluster and send it to the smart gateway. In the proposed method, each intelligent agent selects a cluster in a distributed manner. An intelligent agent may choose another intelligent agent as its cluster head or select itself as a cluster head and directly send the data to the smart gateway. Each intelligent agent chooses the cluster head after calculating the importance level of neighboring intelligent agents. By using this model, cities can experience increased resource efficiency and cost reduction by leveraging innovative technologies. The proposed method has been implemented in different scenarios of smart cities, such as sparse and crowded smart cities with varying message sizes. In all simulations, the proposed method demonstrated good capabilities in optimizing energy consumption management. [ABSTRACT FROM AUTHOR]

ABSTRACT Currently, healthcare systems operate under conventional management practices and entail storing and processing substantial medical data. Integrating the Internet of Things (IoT) and wireless sensor networks (WSNs) technologies has facilitated the development of IoT‐enabled healthcare, which possesses advanced data processing capabilities and extensive data storage. This paper proposes a WSN and IoT framework for patient monitoring in high‐speed 5G communications. Based on an artificial neural network (ANN), an intelligent health monitoring system was developed using IoT technology to monitor a person's blood pressure, heart rate, oxygen level, and temperature. Furthermore, the system helps the elderly being in critical cases in their homes to communicate and update their medical condition with the hospital, especially in critical cases, to be treated as soon as possible, especially in remote areas. The experimental results showed the superiority and effectiveness of the proposed system. Moreover, relying on ANNs to extract the basic features, the accuracy reached 96%. The proposed system was implemented practically, and the results were displayed in real time and compared with commercial medical devices. Maximum relative errors are heart rate (2.19), body temperature (2.94), systolic blood pressure (3.4), diastolic blood pressure (2.89), and SpO2 (1.05). On the other hand, the proposed system is much faster than other wireless communication methods, regardless of the detection quality. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) disorder is caused by the imbalance of inhibitory dopamine and excitatory acetylcholine neurotransmitters, which causes hindrance in locomotion. Freezing of gait (FOG), tremors, and bradykinesia are the most debilitating and disconcerting symptoms of Parkinson's Disease (PD). FOG episodes occur due to neurological control disorder and motorized impairments, which sternly hinder voluntary movement and forward locomotion. The computational intelligence techniques can effectively predict PD from the sensor data. To detect the FOG episodes often experienced by PD patients, this paper utilizes a 5G spectrum that operates at 4.8 GHz. The main idea of the proposed system is to extract the wireless channel characteristics information (containing amplitude variances) that can be integrated into a 5G communication system. This system collects data through wireless devices such as dipole antennas, network interface cards, and RF signal generators. Five human activities were performed: fast walking, slow walking, sitting on a chair, FOG episodes, and voluntary stopping. We proposed a novel deep dual self-attention and Bi-LSTM fusion architecture for PD prediction to train the collected dataset. Two architectures have been proposed, namely Deep Dual Attention Neural Network (D2ANN) and BiLSTM Neural Network (BNN), for the classification of PD using Chanel State Information (CSI) data. After that, the most prominent information is extracted using self-attention activation. A newly informed weighted fusion approach is proposed, and tree growth optimization is employed for the best feature selection. Finally, the selected features are classified using a multilayer full SoftMax neural network. The experimental process was conducted on FOG wireless sensor network data and obtained the highest accuracy of 98.66%. Compared to the results with state-of-the-art techniques, the proposed framework shows improved accuracy, recall, and precision rates. [ABSTRACT FROM AUTHOR]

This paper considers the notion of nonbinary (linear) codes and the notion of hypergraphs and makes a relation between two mathematical tools for real-world applications. We consider a nonbinary (linear) code as an underline set and construct a type of hypergraphs called N.C-hypergraph (N.L.C-hypergraph) and establish any given hypergraph is isomorphic to an N.C-hypergraph (N.L.C-hypergraph). This study introduces an equivalence relation on any given code as a positive equivalence relation based on the weight of codewords, generates quotient N.C-hypergraph, and computes the cardinal of its hyperedges and Hamming distance of any given two code words of quotient N.C-hypergraphs. Also, we introduce a binary relation on quotient N.C-hypergraphs, convert them to simple graphs as code-based graphs, and prove that all graphs are positive code-based graphs. We characterize the minimum distance and maximum distance of codewords in hyperedges of code-based (hyper)graphs concerning linear codes. Finally, we design real problems (such as wireless sensor (hyper)networks and American standard code for information interchange) as complex hypernetworks (such that are based on the code words) as a quotient N.C-hypergraph (N.L.C-hypergraph) and convert them to the code-based graphs (such as wireless sensor networks). [ABSTRACT FROM AUTHOR]

Wireless Sensor Network (WSN) is a promising domain that is gaining more attention because of its applicability and suitability in modern applications that comprise health care, disaster management, and environment monitoring purposes. Energy efficiency is considered an important issue because of the restricted energy of non-rechargeable batteries in the sensor. Cluster based routing is significant in handling the issue of energy efficiency in the WSN. In this paper, the Energy-based Multi Objective Golden Jackal Optimization (EMOGJO) is proposed for enhancing energy efficiency. The EMOGJO is used to select the optimum Cluster Heads (CHs) by using the Mean Node Energy (MNE), Individual Node Neighborhood Count (INNC), the interspace between sensors, the interspace between CH and BS, and node degree. Subsequently, the route through CHs until Base Station (BS) is discovered using the EMOGJO which is optimized by energy, and interspace between CH and BS measures. Therefore, the EMOGJO is used to enhance life expectancy while increasing the throughput. The EMOGJO method is evaluated using alive nodes, energy consumption, data packets received in BS, throughput, packet loss ratio and life expectancy. The EMOGJO is compared with the existing approaches that are, Hybrid Improved Whale Artificial Ecosystem Optimization (HIWAEO), Butterfly Optimization Algorithm (BOA)-Ant Colony Optimization (ACO), and Energy Efficient Cluster Based Routing Protocol using Firefly Algorithm namely EECRAIFA. The life expectancy of EMOGJO for 350 nodes is 17,778 rounds, which is greater than the HIWAEO's 1590 rounds, for the same number of nodes. [ABSTRACT FROM AUTHOR]

This study presents an innovative hexagonal deployment model designed specifically for wireless sensor networks (WSNs) with a primary application in precision agriculture. The proposed protocol integrates advanced features, notably an adaptive frequency-hopping spread spectrum (AFHSS) mechanism and a decentralized real-time adaptation strategy to optimize data transmission in dynamic agricultural environments. The simulation study, conducted in diverse terrains with realistic sensor node distributions, meticulously evaluates the protocol's performance using comprehensive Quality of Service (QoS) metrics. The hexagonal deployment model operates by strategically positioning sensor nodes in a hexagonal grid pattern, ensuring uniform coverage of the agricultural field. The AFHSS mechanism dynamically adjusts frequency channels, mitigating interference and fortifying the network's robustness against external disruptions. Complementing this, the decentralized real-time adaptation empowers individual nodes to autonomously respond to the ever-changing environmental conditions, optimizing data transmission efficiency. Quantitative results from the simulations exhibit outstanding performance metrics. The protocol achieves an average latency of 50 milliseconds, a packet loss rate below 2%, a success rate exceeding 95%, and highly efficient obstacle management, with adjusted nodes accounting for less than 5%. These compelling outcomes underscore the protocol's exceptional ability to deliver responsive and reliable data transmission, positioning it as a promising solution for enhancing environmental monitoring in precision agriculture. This study provides quantitative evidence of the protocol's prowess and delves into the nuanced working mechanisms, offering a deeper understanding of its potential impact. The findings contribute significant insights to the field, serving as a robust foundation for researchers and practitioners engaged in designing and implementing resilient WSNs tailored for precision agriculture applications. [ABSTRACT FROM AUTHOR]

Wireless rechargeable sensor network (WRSN) has become the most effective solution to the energy problem in wireless sensor networks (WSNs) by introducing mobile chargers (MCs) to replenish energy for energy‐starved sensor nodes. In the studies of WRSN, there has been growing interest in using lightweight unmanned aerial vehicles (UAVs) as MCs to overcome geographical constraints. Recently, automatic landing pads (PADs) have been introduced in the UAV‐based WRSN, enabling the UAV to automatically replenish energy during the charging flight. Consequently, the UAV's service range is enlarged, and the UAV can now perform large‐scale charging services. In this paper, we investigate the periodic charging scheduling problem in this novel form of WRSN with the goal of minimizing the charging delay. We present the problem's definition and model and prove it is NP‐hard. To address this problem, we propose two periodic charging schemes: SNBC (sensor‐node‐based charging scheme) and PBC (PAD‐based charging scheme). We extensively simulated our proposed schemes in a UAV‐based WRSN with PADs, evaluating them based on two key metrics: charging delay and UAV replenishment frequency. The results indicate the efficiency of our approaches. SNBC averaged a 7.2 × 104 s charging delay, while PBC outperformed it with a 14.01% reduction in charging delay and a 13.66% decrease in replenishment frequency. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSN) faces numerous problems. The deployment of the sensor nodes in faraway places makes battery replacement a difficult process. As a result, there are energy constraints and security issues. So, the fusion of SDN with WSN is SDWSN, which flexibly brings network management. The main issue here is controller placement, which has an impact on communication dependability, latency, and throughput. The major drawbacks in existing work include high energy consumption, inefficient data collection, increased response time, and poor packet delivery ratio. This paper presents a unique Artificial Intelligence-based method for adaptive quorum-based scheduling and interference-free routing in edge-enabled UAV-assisted Software-Defined Wireless Sensor Networks (SDWSNs). It combines energy-efficient clustering with E-DBSCAN, multiple attribute-based software node authentication, and interference-free routing with the IDEE algorithm. The NS-3 tool was utilized to conduct simulations using a setup consisting of 4 edge-assisted UAVs, 4 SDN controllers, and 100 wireless sensor nodes. The findings show considerable improvements in different performance measures when compared to previous techniques. In particular, this suggested approach reduces latency by 64.77 % when compared to DGRL and 66.30 % when compared to ESRA. Additionally, it uses less energy than DGRL and ESRA by 45.74 % and 51.89 %, respectively. In comparison to DGRL and SRA, the packet delivery ratio is enhanced by 27.27 % and 32.43 %, respectively. Furthermore, the Node Network Lifetime is increased by 24.29 % in comparison to ESRA and 12.99 % in comparison to DGRL. Additionally, our method increases throughput by 22.08 % compared to DGRL and 30.56 % compared to SRA, while reducing the Controller Response Time by 2.39 % compared to ESRA and 6.46 % compared to DGRL. These findings demonstrate how well our method improves the effectiveness and efficiency of SDWSNs, making it a viable option for practical use. [ABSTRACT FROM AUTHOR]

Underwater Wireless Sensor Networks (UWSN) consists of various sensors and other components that communicate with each other. UWSN has several uses such as seismic monitoring, subsurface navigation and catastrophe prediction and avoidance. This could lead to issues with power consumption, battery life, time synchronization, location, and sensor installation, among others. Security is also an important consideration for UWSN, as it is vulnerable to attacks. In order to reduce power consumption and counter security threats, an innovative Energy-aware Routing protocol (EWR) is proposed that uses an Improved Circle Search Algorithm (ICSA). EWR employs cooperative routing to improve network efficiency and performance. Finally, the performance of the suggested technique is evaluated using simulations run on NS2 software and results are compared to state-of-the-art protocols like IWDT, RE-PBR, and SEECR based on several metrics, including detection accuracy, energy consumption, transmission loss, throughput, and latency. It shows that the suggested method outperforms the alternatives with more efficiency and better performance. [ABSTRACT FROM AUTHOR]

Accurate and efficient congested link identification is crucial in wireless sensor networks (WSNs). However, in some networks with a centralized management architecture, it is often not feasible to monitor large numbers of internal links directly or even impossible in some heterogeneous networks. Network tomography, the science of inferring the performance characteristics of a network's interior by correlating sets of end-to-end measurements, was put forward to solve this problem. Nevertheless, a network always contains more links than end-to-end paths, making it problematic to find a determined solution. To solve this problem, most of the current methods try to use some additional prerequisites, such as the link congestion probability. However, most existing studies have not considered the congestion caused by node factors and the case of multiple congested links on one path. In this paper, we initially model the issue of link congestion as a Bayesian network model (BNM). Subsequently, we introduce a congestion link identification method based on graph convolutional networks (GCNs), novelly converting the intricate Bayesian network solving problem into a graph node classification task. The simulation results validate the feasibility of our proposed algorithm in identifying congested links and underscore its advantages in scenarios involving node congestion and multiple congested links. [ABSTRACT FROM AUTHOR]

With the rapid development of the oil and gas industry, monitoring the safety and efficiency of pipeline networks has become particularly important. In this context, Wireless Sensor Networks (WSNs) are widely used for monitoring oil and gas pipelines due to their flexible deployment and cost-effectiveness. However, since sensor nodes typically rely on limited battery power, extending the network's lifecycle and improving energy utilization efficiency have become focal points of research. Therefore, this paper proposes an energy-saving scheduling algorithm based on transformer networks, aimed at optimizing energy consumption and data transmission efficiency of wireless monitoring sensors in oil and gas pipelines. Firstly, this study designs a deep learning-based Transformer model that learns from historical data on energy consumption patterns and environmental variables to predict the energy and data transmission needs of each sensor node. Secondly, based on the prediction results, this algorithm employs a dynamic scheduling strategy that automatically adjusts the sensor's operational mode and communication frequency according to the node's energy status and task urgency. Additionally, we have validated the effectiveness of the proposed algorithm through field tests and simulation experiments. According to the experimental results, our model has higher efficiency in energy saving. Compared with Convolutional Neural Networks, Recurrent Neural Networks and Graph Neural Networks, the total energy consumption of sensor networks under the model scheduling in this paper was reduced by 6.7%, 33.4% and 26.3%, respectively. Our algorithms improve the energy efficiency and stability of the monitoring system and provide important technical support for future intelligent pipeline monitoring systems. We hope this paper will inspire future scientific research in this field. [ABSTRACT FROM AUTHOR]

In this study, we aim to conduct real-time sensing of indoor dust concentration distribution to mitigate the airborne transmission of coronaviruses. Airborne infection, facilitated by viruses present in particulate matter, emphasizes the importance of monitoring dust concentration as an indicator of virus spread. Our approach involves the implementation of a time-synchronized wireless sensor network for real-time sensing of dust concentration distribution. The time-synchronized wireless sensor network relies on a proposed time-synchronization algorithm, ensuring a time error of less than ± 1.27 ms. This precision enables the measurement of even fast-moving dust particles. To validate the feasibility of the wireless time-synchronized sensor network, we utilized a dust ejector (air cannon) and positioned time-synchronized sensors in a row. Dust particles released from the ejector passed through each time-synchronized sensor terminal. Simultaneously, a video recording (60 frames) was conducted, and the measured times of the time-synchronized sensor terminals were compared with the lap times of the video. The results of this comparison revealed identical lap times between the time-synchronized sensor data and the video, affirming the successful operation of the time-synchronized wireless distributed sensor node as designed. [ABSTRACT FROM AUTHOR]

Optimization problem involving interval parameters and multiple conflicting objectives are called multi-objective optimization problems with interval parameters (IMOPs), which are common and hard to be solved effectively in practical applications. An interval multi-objective honey badger algorithm (IMOHBA) is proposed to address the IMOPs in this paper. Firstly, the μ metric is employed to assess the Pareto dominance relationship among interval individuals, which reflects the quality of the optimal solutions. Secondly, the crowding distance suitable for the interval objective is utilized to reflect the distribution of the optimal solution. Finally, the candidate solutions are ranked and selected by the non-dominated sorting method. To validate the performance of IMOHBA, it is tested on 19 benchmark IMOPs as well as an interval multi-objective scheduling problem for underwater wireless sensor networks and compared with three state-of-the-art algorithms. The experimental results demonstrate the superiority and strong competitiveness of IMOHBA in addressing IMOPs, exhibiting improved convergence and broader exploration capabilities of the solution space. These findings further validate the effectiveness and feasibility of IMOHBA, highlighting its unique advantage in solving IMOPs. [ABSTRACT FROM AUTHOR]

For secure earth observation, we need an optimised routing protocol that reduces security breaches by improving the coordination of the base with the observatory using a wireless sensor network, thereby improving the earth observation systems for secure communication. One of the technologies that are used for networking is known as a wireless sensor network. In this type of network, sensor nodes are linked together using wireless communication methods. A random and haphazard dispersal of nodes is carried out during the deployment procedure inside the network region. This distribution is carried out throughout the network. The consequence of this is that the distances that separate the nodes do not stay the same. However, the nodes have a severely limited battery life and a restricted range of perception, despite the fact that they have a great sensing capacity. All nodes inside their respective sensing regions may sense the wireless data. A unique routing protocol is essential to increase throughput and Quality of Service (QoS), which will help resolve these problems and improve WSN performance. The Dijkstra method was most frequently used by researchers to discover the shortest route. However, it is not more flexible in terms of WSN routing. The Floyd-Warshall (FW) technique is also employed to choose the shortest path. However, in the current WSN, varied sensors are dispersed throughout several sites and convey data to various coordinators, who are likewise dispersed across countless sites. As a result, the FW technique must be modified to discover the shortest path among any two nodes. In this study, the Improved Floyd-warshall (IFW) algorithm is proposed. Data structures like adjacency lists and sparse vectors are used by the IFW method to describe the WSN graph. When sparse WSNs need less memory, this helps make that possible. The Floyd-Warshall method, which figures out distances between nodes in a network based on their actual lengths, can get even better by adding location information. Using the LEACH method to split the network into smaller groups makes the network more manageable. The LEACH method is then used directly in each cluster to improve both scaling and processing speed. This is done so that the goals can be met. The computer can also handle situations where there is not a legal way to connect two nodes, and it can handle information going both ways between nodes. [ABSTRACT FROM AUTHOR]

A remarkably high growth has been observed in the uses of wireless sensor networks (WSNs), due to their momentous potential in various applications, namely the health sector, smart agriculture, safety systems, environmental monitoring, military operations, and many more. It is quite important that a WSN must have high reliability along with the least MTTF. This paper introduces a continuous-time Markov process, which is a special case of stochastic process, based on modeling of a wireless sensor network for analyzing the various reliability indices of the same. The modeling has been conducted by considering the different components, including the sensing unit, transceiver, microcontroller, power supply, standby power supply unit, and their failures/repairs, which may occur during their functioning. The study uncovered different important assessment parameters like reliability, components-wise reliability, MTTF, and sensitivity analysis. The critical components of a WSN are identified by incorporating the concept of sensitivity analysis. The outcomes emphasize that the proposed model will be ideal for understanding different reliability indices of WSNs and guiding researchers and potential users in developing a more robust wireless sensor network system. [ABSTRACT FROM AUTHOR]

The widespread adoption of Internet of Things (IoT) applications has driven the demand for obtaining sensor data. Using unmanned aerial vehicles (UAVs) to collect sensor data is an effective means in scenarios with no ground communication facilities. In this paper, we innovatively consider an indeterministic data collection task in a UAV-assisted wide and sparse wireless sensor network, where the wireless sensor nodes (SNs) obtain effective data randomly, and the UAV has no pre-knowledge about which sensor has effective data. The UAV trajectories, SN serve scheduling and UAV-SN association are jointly optimized to maximize the amount of collected effective sensing data. We model the optimization problem and address the indeterministic effective indicator by introducing an effectiveness probability prediction model. The reformulated problem remains challenging to solve due to the number of constraints varying with the variable, i.e., the serve scheduling strategy. To tackle this issue, we propose a two-layer modified knapsack algorithm, within which a feasibility problem is resolved iteratively to find the optimal packing strategy. Numerical results demonstrate that the proposed scheme has remarkable advantages in the sum of effective data blocks, reducing the completion time for collecting the same ratio of effective data by nearly 30%. [ABSTRACT FROM AUTHOR]

In this paper, we propose a wireless sensor network for pavement health monitoring exploiting the Zigbee technology. Accelerometers are adopted to measure local accelerations linked to pavement vibrations, which are then converted into displacements by a signal processing algorithm. Each device consists of an on-board unit buried in the roadway and a roadside unit. The on-board unit comprises a microcontroller, an accelerometer and a Zigbee module that transfers acceleration data wirelessly to the roadside unit. The roadside unit consists of a Raspberry Pi, a Zigbee module and a USB Zigbee adapter. Laboratory tests were conducted using a vibration table and with three different accelerometers, to assess the system capability. A typical displacement signal from a five-axle truck was applied to the vibration table with two different displacement peaks, allowing for two different vehicle speeds. The prototyped system was then encapsulated in PVC packaging, deployed and tested in a real-life road situation with a fatigue carousel featuring rotating truck axles. The laboratory and on-road measurements show that displacements can be estimated with an accuracy equivalent to that of a reference sensor. [ABSTRACT FROM AUTHOR]

This study provides a thorough examination of the important intersection of Wireless Sensor Networks (WSNs) with machine learning (ML) for improving security. WSNs play critical roles in a wide range of applications, but their inherent constraints create unique security challenges. To address these problems, numerous ML algorithms have been used to improve WSN security, with a special emphasis on their advantages and disadvantages. Notable difficulties include localisation, coverage, anomaly detection, congestion control, and Quality of Service (QoS), emphasising the need for innovation. This study provides insights into the beneficial potential of ML in bolstering WSN security through a comprehensive review of existing experiments. This study emphasises the need to use ML's potential while expertly resolving subtle nuances to preserve the integrity and dependability of WSNs in the increasingly interconnected environment. [ABSTRACT FROM AUTHOR]

This paper presents a passive Envelope Detector (ED) to be used for reception of OOK-modulated signals, such as in Wake-Up Receivers employed within Wireless Sensor Networks, widely used in the IoT. The main goal is implementing a temperature compensation mechanism in order to keep the passive ED input resistance roughly constant over temperature, making it a constant load for the preceding matching network and ultimately keeping the overall receiving chain sensitivity constant over temperature. The proposed ED was designed using STMicroelectronics 90 nm CMOS technology to receive 1 kbps OOK-modulated packets with a 433 MHz carrier frequency and a 0.6 V supply. The use of a block featuring a Proportional-to-Absolute Temperature (PTAT) current yields a 5 dB reduction in sensitivity temperature variation across the −40 °C to 120 °C range. Moreover, two different implementations were compared, one targeting minimal mismatch and the other one targeting minimal area. The minimal area version appears to be better in terms of estimated overall chain sensitivity at all temperatures despite a higher sensitivity spread. [ABSTRACT FROM AUTHOR]

In order to express the energy flow, motion flow, and control flow in wireless rechargeable sensor networks accurately and intuitively, and to maximize the charging benefit of MVs (mobile vehicles), a type of MTS-HACO (Mobile Transition Sequence Hybrid Ant Colony Optimization) is proposed. Firstly, node places are grouped according to the firing time of node's energy consumption transition to ensure that in each time slot, MV places only enable charging transitions for the node places with lower remaining lifetimes. Then, the FSOMCT (Firing Sequence Optimization of Mobile Charging Transition) problem is formulated under the constraints of MV places capacity, travelling arc weight, charging arc weight, and so on. The elite strategy and the Max–Min Ant Colony system are further introduced to improve the ant colony algorithm, while the improved FWA (fireworks algorithm) optimizes the path constructed by each ant. Finally, the optimal mobile charging transition firing sequence and charging times are obtained, ensuring that MVs have sufficient energy to return to the base station. Simulation results indicate that, compared with the periodic algorithm and the PE-FWA algorithm, the proposed method can improve charging benefit by approximately 48.7% and 26.3%, respectively. [ABSTRACT FROM AUTHOR]

Localization is one of the most challenging problems in wireless sensor networks (WSNs), primarily driven by the need to develop an accurate and cost-effective localization system for Internet of Things (IoT) applications. While machine learning (ML) algorithms have been widely applied in various WSN-based tasks, their effectiveness is often compromised by limited training data, leading to issues such as overfitting and reduced accuracy, especially when the number of sensor nodes is low. A key strategy to mitigate overfitting involves increasing both the quantity and diversity of the training data. To address the limitations posed by small datasets, this paper proposes an intelligent data augmentation strategy (DAS)-based deep neural network (DNN) that enhances the localization accuracy of WSNs. The proposed DAS replicates the estimated positions of unknown nodes generated by the Dv-hop algorithm and introduces Gaussian noise to these replicated positions, creating multiple modified datasets. By combining the modified datasets with the original training data, we significantly increase the dataset size, which leads to a substantial reduction in normalized root mean square error (NRMSE). The experimental results demonstrate that this data augmentation technique significantly improves the performance of DNNs compared to the traditional Dv-hop algorithm at a low number of nodes while maintaining an efficient computational cost for data augmentation. Therefore, the proposed method provides a scalable and effective solution for enhancing the localization accuracy of WSNs. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSN) have found more and more applications in remote control and monitoring systems. Energy management in the network is crucial because all nodes in the WSN are energy constrained. Therefore, the design and implementation of WSN protocols that reduce energy depletion in the network is still an open scientific problem. In this paper, we propose a new clustering protocol that combines DEC (deterministic energy-efficient clustering) protocol with K-means clustering, called DEC-KM (deterministic energy-efficient clustering protocol with K-means). DEC is a very energy-efficient clustering protocol that outperforms its predecessors, such as LEACH and SEP. K-means ensures more effective clustering and shorter data transmission distances within the network. The shorter distances improve the network's lifetime and stability and reduce power consumption. Additional heuristic rules in DEC-KM ensure improved cluster head selection, taking into account node energy level and position and minimising the risk of premature cluster head exhaustion. The simulation results for the DEC-KM protocol using MATLAB show that cluster heads have shorter distances to nodes in cluster areas than for the original DEC protocol. The proposed protocol ensures reduced energy consumption, outperforms the standard DEC, and extends the stability period and lifetime of the network. [ABSTRACT FROM AUTHOR]