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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 pressure sensor placement in the water distribution networks (WDNs) is a typical NP‐Hard problem. The goal is to monitor the running state of the whole network area by placing a small number of pressure sensors. This letter proposes an enhanced Rafflesia optimization algorithm (EROA) to solve this problem. The simulation results show that compared with the original ROA algorithm and the traditional intelligent optimization algorithms, the proposed EROA algorithm exhibits superior optimization capabilities and solution efficiency, and can provide a more reasonable sensor placement scheme. [ABSTRACT FROM AUTHOR]

Wearable sensor devices for continuous patient monitoring produce a large volume of data, necessitating scalable infrastructures for efficient data processing, management and security, especially concerning Patient Health Information (PHI). Adherence to the Health Insurance Portability and Accountability Act (HIPAA), a legislation that mandates developers and healthcare providers to uphold a set of standards for safeguarding patients' health information and privacy, further complicates the development of remote patient monitoring within healthcare ecosystems. This paper presents an Internet of Things (IoT) architecture designed for the healthcare sector, utilizing commercial cloud platforms like Microsoft Azure and Amazon Web Services (AWS) to develop HIPAA-compliant health monitoring systems. By leveraging cloud functionalities such as scalability, security, and load balancing, the architecture simplifies the creation of infrastructures adhering to HIPAA standards. The study includes a cost analysis of Azure and AWS infrastructures and evaluates data processing speeds and database query latencies, offering insights into their performance for healthcare applications. [ABSTRACT FROM AUTHOR]

ABSTRACT The constant demand to secure the location of an asset in wireless sensor networks (WSNs) has led to the need for the development of location privacy preservation mechanisms. Source location privacy preservation, particularly for a single source has received tantamount attention in the last few years, whereas very few schemes have been discussed for securing the location privacy of multiple sources. To address this issue, in this paper, we have proposed a red‐zone‐based angular routing technique to provide location privacy for multiple sources against local adversaries who continuously eavesdrop and backtrack the packets in the network. The proposed technique termed as red‐zone‐based randomized angular routing (Red‐RAR) establishes a red zone around all the source nodes exists in the network to divert the routing path of a source node away from other source nodes. The Red‐RAR algorithm consists of four different phases that contributes in moving the packets far away from the source and maximizes the path diversity to mislead the adversary. The performance of the proposed algorithm is evaluated through extensive experiments. The experimental results reveal that Red‐RAR outperforms the current state of the art methods in terms of efficiency measured in safety period, capture percentage, and network lifetime for four source nodes. [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]

Cyber-physical system is one of the essential components of wireless sensor network (WSN). WSN conjointly senses, collects, analyzes and communicates data on recognized items at service part before transmitting this data to the owner of the network. Blackhole, Grayhole, Flooding, Scheduling are the common WSN attacks that rapidly damage the system. Intrusion detection systems, such as WSN have drawbacks, like less detection rates, high computation costs and high percentage of false alarms, because sensor nodes have limited resources. Therefore, a Self Attention Generative Adversarial Capsule Network as an intrusion detection scheme for WSN (SAGACN-ID-WSN) is proposed in this paper to address the aforementioned issues. Initially, the data are amassed from WSN-DS dataset. Then, the data are sent for pre-processing. The input data are preprocessed using the Guided Box Filtering (GBF) method. Then, the preprocessed data are provided to the Atomic Orbital Search (AOS), Tasmanian Devil Optimization (TDO) and Ebola Optimization Search (EOS) algorithms for feature selection. The attacks are divided into four categories: normal, Grayhole attack, Black hole attack, flooding attack, and scheduling attack. The proposed ID-WSN is implemented in Network simulator 2 (NS-2) utilizing the dataset of WSN-DS. The metrics, like accuracy, precision, recall, F-measure, ROC and computational time are analyzed. The proposed SAGACN-ID-WSN technique attains 25.5%, 20.12% and 20.7% high accuracy, 51.136%, 59.04% and 32.81%; higher precision, 2.292%, 1.51% and 3.915% lower error rate compared to the existing models, like Dataset for Intrusion Detection Systems in WSN (SLGBM-ID-WSN), multi-layer machine learning-based ID-WSN (NB-RF-ID-WSN), deep learning and entity embedding-base intrusion detection systems in WSN, respectively. [ABSTRACT FROM AUTHOR]

In wireless multihop networks, such as wireless sensor networks, link quality (LQ) is one of the most important metrics and is widely used in higher‐layer applications such as routing protocols. An accurate LQ prediction may greatly improve the performance of wireless multihop networks. Researchers have proposed a lot of LQ prediction models in recent years. However, due to the dynamic and stochastic nature of wireless transmission, the performance of LQ prediction is limited. In this article, we mainly analyze the influence of stochastic nature of wireless transmission on LQ prediction models and study the limitation of such models. Then, we discuss the benefits in the application of wireless multihop networks with the performance‐limited LQ prediction models. [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]

Advancing the efficiency and reliability of wireless sensor networks is a paramount pursuit in modern networking research. In this context, we introduce a groundbreaking approach based on Hidden Markov Chain (HMC) modeling with opportunistic routing, harnessed by the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) mechanism. Our innovative algorithm addresses key challenges in Wireless Body Area Networks, including delay, throughput, and bandwidth utilization. By strategically integrating the HMC model, which captures intricate state transitions and emission probabilities, our proposed method introduces a robust solution for optimizing node connections and routing decisions. This fusion of HMC and opportunistic routing capitalizes on the strengths of both paradigms, enhancing the network's ability to make intelligent decisions in dynamic scenarios. Through rigorous simulations, we showcase the algorithm's prowess in achieving efficient data transmission. The empirical evidence from these simulations underscores the algorithm's superiority when juxtaposed against state-of-the-art mechanisms such as Simple Multi-Packet Access (SMPA) and Priority-based Congestion-avoidance Routing Protocol (PCRP). Our algorithm not only outperforms existing solutions in terms of delay, throughput, and bandwidth utilization, but it also showcases its ability to adapt to varying network conditions, making it a versatile tool for diverse applications. [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]

The current industrial wireless sensor network (IWSN) cluster routing methods suffer from energy inefficiency. Designing efficient cluster-based routing protocols is crucial for improving network performance and energy efficiency. Therefore, this paper first designs a new clustering model to achieve efficient cluster head (CH) selection and data transmission performance by comprehensively considering multiple key factors such as CH energy, base station (BS) distance, packet loss rate, and data delay. Based on this clustering model, a novel cluster routing protocol based on Gaussian mutation adaptive artificial fish swarm algorithm (GAAFSA) is proposed. At the same time, a new Gaussian mutation strategy and an adaptive strategy were introduced to effectively promote the protocol to avoid local optima and prevent premature convergence. The GAAFSA based cluster routing protocol was experimentally compared with five popular schemes, namely CMSTR, D2CRP, EEHCHR, ESCVAD and BAFSA. The results showed that the proposed protocol outperformed the other four schemes in terms of network energy consumption, system lifetime, data transmission reliability, and latency. Specifically, GAAFSA has improved network lifespan by at least 15.68%, BS received packets by at least 7.46%, and reduced packet loss rate by at least 15.28%. Therefore, GAAFSA effectively optimizes network performance and extends network lifespan, greatly reducing energy loss within the network and significantly improving network quality of service (QoS). [ABSTRACT FROM AUTHOR]

This paper presents NN_ILEACH, a novel neural network-based routing protocol designed to enhance the energy efficiency and longevity of Wireless Sensor Networks (WSNs). By integrating the Energy Hole Removing Mechanism (EHORM) with a sophisticated neural network for cluster head selection, NN_ILEACH effectively addresses the energy depletion challenges associated with traditional protocols like LEACH and ILEACH. Our extensive simulations demonstrate that NN_ILEACH significantly outperforms these classical protocols. Specifically, NN_ILEACH extends the network lifetime to an impressive 11,361 rounds, compared to only 505 rounds achieved by LEACH under identical conditions—representing a more than 20-fold improvement. Additionally, NN_ILEACH achieves a 30% increase in throughput and a 25% enhancement in packet delivery ratio, while reducing overall energy consumption by 40%. These results underscore the protocol's potential to optimize energy usage and maintain network stability, paving the way for more resilient IoT systems in dynamic environments. Future work will explore further integration of machine learning techniques to enhance adaptability and performance in WSNs. [ABSTRACT FROM AUTHOR]

The agricultural WSN (wireless sensor network) has the characteristics of long operation cycle and wide coverage area. In order to cover as much area as possible, farms usually deploy multiple monitoring devices in different locations of the same area. Due to different types of equipment, monitoring data will vary greatly, and too many monitoring nodes also reduce the efficiency of the network. Although there have been some studies on data fusion algorithms, they have problems such as ignoring the dynamic changes of time series, weak anti-interference ability, and poor processing of data fluctuations. So in this study, a data fusion algorithm for optimal node tracking in agricultural wireless sensor networks is designed. By introducing the dynamic bending distance in the dynamic time warping algorithm to replace the absolute distance in the fuzzy association algorithm and combine the sensor's own reliability and association degree as the weighted fusion weight, which improved the fuzzy association algorithm. Finally, another three algorithm were tested for multi-temperature sensor data fusion. Compare with the kalman filter, arithmetic mean and fuzzy association algorithm, the average value of the improved data fusion algorithm is 29.5703, which is close to the average value of the other three algorithms, indicating that the data distribution is more even. Its extremely bad value is 8.9767, which is 10.04%, 1.14% and 9.85% smaller than the other three algorithms, indicating that it is more robust when dealing with outliers. Its variance is 2.6438, which is 2.82%, 0.65% and 0.27% smaller than the other three algorithms, indicating that it is more stable and has less data volatility. The results show that the algorithm proposed in this study has higher fusion accuracy and better robustness, which can obtain the fusion value that truly feedbacks the agricultural environment conditions. It reduces production costs by reducing redundant monitoring devices, the energy consumption and improves the data collection efficiency in wireless sensor networks. [ABSTRACT FROM AUTHOR]

Since the advent of networked systems, fuzzy graph theory has surfaced as a fertile paradigm for handling uncertainties and ambiguities. Among the different modes of handling challenges created by the uncertainties and ambiguities of current networked systems, integrating fuzzy graph theory with cryptography has emerged as the most promising approach. In this regard, this review paper elaborates on potentially studying fuzzy graph-based cryptographic techniques, application perspectives, and future research directions. Since the expressive power of fuzzy graphs allows the cryptographic schemes to handle imprecise information and to enhance security in many domains, several domains have benefited, such as image encryption, key management, and attribute-based encryption. The paper analyzes in depth the research landscape, mainly by focusing on the varied techniques used, such as fuzzy logic for key generation and fuzzy attribute representation for access control policies. A comparison with performance metrics unveils the trade-offs and advantages of different fuzzy graph-based approaches in efficiency, security strength, and computational overhead. Additionally, the survey explores the security applications of fuzzy graph-based cryptography and underpins potential development for secure communication in wireless sensor networks, privacy-preserving data mining, fine-grained access control in cloud computing, and blockchain security. Some challenges and research directions, such as the standardization of fuzzy logic operators, algorithmic optimization, integration with emerging technologies, and exploitation of post-quantum cryptography applications, are also brought out. This review will thus bring insight into this interdisciplinary domain and stimulate further research for the design of more robust, adaptive, and secure cryptographic systems in the wake of rising complexities and uncertainties. [ABSTRACT FROM AUTHOR]

ABSTRACT It has been observed that the agriculture sector has picked exponential growth with the help of the integration of advanced technology like wireless sensor networks (WSNs), the Internet of Things (IoT), and machine learning (ML). They have not only created new opportunities but also boosted the efficiency and productivity in the sector. Out of so many significant hurdles, accurately pinpointing the positions of the sensor nodes and agricultural assets like crops, livestock, and machinery within the field is one major challenge. Merging ML with a WSN‐assisted IoT (WIoT) network has offered a promising solution to overcome the localization challenge for smart agriculture. This integration will greatly benefit agricultural practices, such as precision agriculture, fast decision‐making, accurate positioning of sensor nodes, monitoring, and managing real‐time data effectively. This paper comprehensively discusses state‐of‐the‐art techniques and methodologies employed in localization within WIoT networks for smart agriculture. It explores various ML algorithms, which include reinforcement learning, supervised learning, and unsupervised learning, to perform the localization activity precisely. Moreover, it explores how fusion technologies like WSN, IoT, ML, and sensors will enhance localization accuracy and reliability in various agriculture activities. Additionally, the paper discusses the application of localization in the agriculture sector, such as crop monitoring, livestock management, precision agriculture, environmental conditions, and crop monitoring. It also discusses the challenges and obstacles in the path domain of various evaluation parameters like energy efficiency, scalability, and robustness, along with research and societal implications. Overall, the paper provides valuable guidance and outlines potential directions for future research in ML‐driven localization in WIoT for smart agriculture, offering a clear roadmap for researchers. [ABSTRACT FROM AUTHOR]

AbstractOrthogonal arrays (OAs) are applied in the statistical design of experiments, coding theory, cryptography, various types of software testing and quality control and have many connections to other combinatorial designs. The Hamming distances of OAs play a critical role in many areas such as algebraic combinatorics, quantum information theory, and wireless sensor networks. However, the calculation method of Hamming distances is difficult, especially for mixed orthogonal arrays (MOAs), since they rely on constructions or structures of OAs in many cases. In this article, by using the number of coincidences, we find some OAs with high strength and MOAs with strength 2 and 3, whose Hamming distances do not depend on their structures. We also calculate Hamming distances of a series of unsaturated asymmetrical OAs via their construction methods such as deleting columns, the expansive replacement method, and difference schemes. [ABSTRACT FROM AUTHOR]

ABSTRACT Energy efficiency and secure data transmission are considered as the main design targets of wireless sensor network (WSN). Previous energy‐aware cluster head (CH) selection approaches could not provide secured data transmission. To address this problem, a self‐attention–based cycle‐consistent generative adversarial network (SaCyCsGAN) with honey badger algorithm (HyBA)–adopted energy‐aware routing (EgAR) in WSN is proposed. Initially, the proposed system uses a CH to carry out the routing practice. Accordingly, SaCyCsGAN classifier is exploited for CH selection under firm fitness function: delay, distance, energy, cluster density, and traffic rate. Afterward CH selection, a spiteful node may enter the cluster and acts as a CH. Hence, best path selection is needed. For that, HyBA is utilized, it selects the best path depending upon triplet parameter: trust, connectivity, and service degree. Finally, data are conveying into base station (BS) and vice versa under best path. Finally, the proposed EgAR‐WSN‐SaCyCsGAN‐HyBA method attains 24.13%, 28.57%, 5.88%, and 20.37% higher throughput and 18.50%, 21.67%, 13.33%, and 22.22% lesser energy consumption evaluated to the existing methods such as multiple‐objective CH utilizing self‐attention basis progressive generative adversarial network for safe data aggregation (EgAR‐WSN‐SAPrGAN‐ArVOA), reinforcement learning–based energy‐efficient optimized routing protocol in WSN (EgAR‐WSN‐RLGT‐BCSA), data aggregation including clustering protocol in WSN under machine learning (EgAR‐WSN‐AfNN), and optimum cluster along trusted path for routing formation with categorization of intrusion under machine learning categorization (EgAR‐WSN‐RsBPDT‐HoCSOA). [ABSTRACT FROM AUTHOR]

Security is the primary concern when creating security protocols for wireless sensor networks (WSN), which motivates many academics to find security solutions that effectively provide a few benefits, such as low consumption of electricity, flexible communication, and low cost. A few restrictions still remain, including the inability to exactly select the expected cluster, the sensor nodes’ constrained functionality, and their poor efficiency. Thus, Egret-Harris optimization for hybrid secure routing and monitoring mechanisms in IoT-based WSNs (EHO optimized routing protocol in WSN) was introduced in this research. The created EHO algorithm combines the search, fitness function, and hunting phase features from Harris hawks and egrets to determine the best solution among all practical solutions while ensuring safe data transfer from the cluster heads (CHs) to the Base station (BS). Specifically, the EHO-enabled clustering is applied to the suggested model to efficiently choose the ideal group of CHs. Additionally, the EHO algorithm assists in choosing the best possible routes with minimal distance and less delay for facilitating energy-efficient transmission. With 100 nodes analyzed, the suggested EHO-WSN approach without any attacks achieved 22 alive nodes, a delay of 0.10 ms, a normalized energy of 0.346J, and a throughput of 0.64 bps, respectively. Additionally, in the presence of a Sybil attack, the suggested EHO-WSN technique achieves 14, 0.214 J, 0.010 ms, and 0.512 bps for an analysis involving 100 nodes. Compared to previous methods, the suggested EHO-WSN model without attack achieves a delay of 0.07 ms, a throughput of 0.30 bps, an energy of 0.374 J, and 37 alive nodes for 200 node evaluation. For 200 nodes under examination, the EHO-WSN technique yields superior results of attaining 12 alive nodes, a delay of 0.072 ms, a throughput of 0.181 bps, and a normalized energy of 0.330 J even in the presence of the Sybil attack and exceeded other traditional techniques. [ABSTRACT FROM AUTHOR]

Given the resource limitations of wireless sensor networks (WSNs), energy conservation is of utmost importance. Moreover, minimizing data collection delays is crucial to maintaining data freshness. Additionally, it is desirable to increase the number of collected data samples to enhance accuracy and robustness in data collection. For this purpose, this research article proposes a clustering-based routing protocol aimed at maximizing the delivery of data samples while minimizing energy consumption and data collection delays. The protocol employs a scattered search algorithm and fuzzy logic to cluster the sensor nodes. By considering the distance to the sink and the remaining energy level of the battery, the network is dynamically divided into clusters using a lightweight clustering approach. To evaluate the effectiveness of the proposed method, simulations were conducted in OPNET using the AFSRP protocol. The results demonstrate superior performance of the proposed method in terms of end-to-end delay by 13.44%, media access delay by 75.2%, throughput rate by 20.55%, energy consumption by 13.52%, signal-to-noise ratio by 43.40% and delivery rate of successfully sending data to the sink is 0.21% higher than the well-known AFSRP method. [ABSTRACT FROM AUTHOR]

The role of firewalls and security principles in resource‐limited wireless networks and Wireless Sensor Networks (WSN) is more expected than in any dedicated network environment. The advanced wireless technologies and emerging fashion of autonomous wireless network nodes are mostly expecting resilient firewall services with multi‐level security policies. Wireless networks are classified under sensor networks, Internet of Things (IoT) mobile networks, and so forth. According to the expectations, security frameworks are gradually invented around the communication platform using various ideologies. The novel ideology and respective implementation effort open better solutions against wireless network attacks. Anyhow, the minimal production of time complexity, energy complexity, and computation overhead from any novel security approach is always considered under the best practices. The complexity levels directly affect the wireless node's energy consumption ability and operational spans severely. The energy optimization techniques and load‐balancing techniques integrated into multi‐class firewall rules are extremely useful solutions for resource‐limited wireless networks. This article has been motivated to analyze the recent firewall techniques and secure data communication mechanisms used for securing wireless networks. Consequently, the practice of comparative literature analysis helps to improve the current limitations identified under the classified categories of security mechanisms such as energy‐optimized security principles and load‐balanced security principles. The establishment of secure and energy‐optimized multi‐class firewall rules in each wireless node assures a healthy focus on next‐generation networks. The experiment section shows the benefits of energy‐optimized secure network communication in terms of better accuracy and the benefits of load‐balanced secure network communication through minimal overhead in computing platforms. [ABSTRACT FROM AUTHOR]

Wireless sensor network (WSN) works with a collection of multiple sensor nodes to fetch the data from the deployed environment to fulfill the application whether it is agricultural monitoring, industrial monitoring, etc. The agricultural region can be monitored by deploying sensor nodes to multiple verticals where continuous human presence is not feasible. These devices are equipped with limited resources and are easily vulnerable to various cyber‐attacks. The attacker can hack the sensor nodes to steal critical information from WSN devices. The cluster heads in the WSN play a vital role in the process of routing data packets and attackers launch malicious codes through sender nodes to hack or damage the cluster heads to shut down the entire deployed network of agricultural regions. This research paper proposes a framework to improve the security of WSNs by providing a shield to the cluster heads of the network using machine learning techniques. The experimental study of the paper includes the comparative analysis of three machine learning techniques decision tree classifier, Gaussian Naïve Bayes, and random forest classifier for predicting WSN attacks like flooding, gray hole, blackhole, and TDMA that are deployed to support the proposed WSN security framework on the attack dataset. The random forest classifier achieves an accuracy of 98%, Precision of 97.6%, Recall of 97.6%, and F1 score of 97.8% which is the maximum among the deployed machine learning techniques. [ABSTRACT FROM AUTHOR]

Battery-powered sensor nodes encounter substantial energy constraints, especially in linear wireless sensor network (LWSN) applications like border surveillance and road, bridge, railway, powerline, and pipeline monitoring, where inaccessible locations exacerbate battery replacement challenges. Addressing these issues is crucial for extending a network's lifetime and reducing operational costs. This paper presents a comprehensive analysis of the factors affecting WSN energy consumption at the node and network levels, alongside effective energy management strategies for prolonging the WSN's lifetime. By categorizing existing strategies into node energy reduction, network energy balancing, and energy replenishment, this study assesses their effectiveness when implemented in LWSN applications, providing valuable insights to assist engineers during the design of green and energy-efficient LWSN monitoring systems. [ABSTRACT FROM AUTHOR]

Underground mines are considered one of the riskiest facilities for human activities due to numerous accidents and geotechnical failures recorded worldwide over the last century, which have resulted in unsafe labor conditions, poor health outcomes, injuries, and fatalities. One significant cause of these accidents is the inadequate or nonexistent capacity for the real-time monitoring of safety conditions in underground mines. In this context, new emerging technologies linked to the Industry 4.0 paradigm, such as sensors, the Internet of Things (IoT), and LoRaWAN (Long Range Wide Area Network) wireless connectivity, are being implemented for planning the efficient, safe, and sustainable performance of underground mine labor environments. This paper studies the implementation of an ecosystem composed of IoT sensors and LoRa wireless connectivity in a data-acquisition system, which eliminates the need for expensive cabling and manual monitoring in mining operations. Laying cables in an underground mine necessitates cable support and protection against issues, such as machinery operations, vehicle movements, mine operator activities, and groundwater intrusion. As the underground mine expands, additional sensors typically require costly cable installations unless wireless connectivity is employed. The results of this review indicate that an IoT LoRaWAN-based wireless sensor network (WSN) provides real-time data under complex conditions, effectively transmitting data through physical barriers. This network presents an attractive low-cost solution with reliable, simple, scalable, secure, and competitive characteristics compared to cable installations and manually collected readings, which are more sporadic and prone to human error. Reliable data on the behavior of the underground mine enhances productivity by improving key performance indicators (KPIs), minimizing accident risks, and promoting sustainable environmental conditions for mine operators. Finally, the adoption of IoT sensors and LoRaWAN wireless connectivity technologies provides information of the underground mine in real-time, which supports better decisions by the mining industry managers, by ensuring compliance with safety regulations, improving the productive performance, and fostering a roadmap towards more environmentally friendly labor conditions. [ABSTRACT FROM AUTHOR]

The Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol is a widely used method for managing energy consumption in Wireless Sensor Networks (WSNs). However, it has limitations that affect network longevity and performance. This paper presents an improved version of the LEACH protocol, termed MFG-LEACH, which incorporates the Mean Field Game (MFG) theory to optimize energy efficiency and network lifetime. The proposed MFG-LEACH protocol addresses the imbalances in energy consumption by modeling the interactions among nodes as a game, where each node optimizes its transmission energy based on the collective state of the network. We conducted extensive simulations to compare MFG-LEACH with Enhanced Zonal Stable Election Protocol (EZ-SEP), Energy-Aware Multi-Hop Routing (EAMR), and Balanced Residual Energy routing (BRE) protocols. The results demonstrate that MFG-LEACH significantly reduces energy consumption and increases the number of packets received across different node densities, thereby validating the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

This paper investigates the motion state estimation problem of the unmanned surface vehicle (USV) steering system in wireless sensor networks based on the binary coding scheme (BCS). In response to the presence of bandwidth constraints and mixed cyber-attacks in USV communication networks, this paper proposes an improved set-membership state estimation algorithm based on BCS. This algorithm partially addresses the problem of degraded performance in USV steering motion state estimation caused by mixed cyber-attacks and bandwidth constraints. Furthermore, this paper proposes a robust resilient filtering framework considering the possible occurrence of unknown but bounded (UBB) noises, model parameter uncertainties, and estimator gain perturbations in practical scenarios. The proposed framework can accurately estimate the sway velocity, yaw velocity, and roll velocity of the USV under the concurrent presence situation of mixed cyber-attacks, communication capacity constraints, UBB noises, model parameter uncertainties, and estimator gain perturbations. This paper first utilizes mathematical induction to provide the sufficient conditions for the existence of the desired estimator, and obtains the estimator gain by solving a set of linear matrix inequalities. Then, a recursive optimization algorithm is utilized to achieve optimal estimation performance. Finally, the effectiveness of the proposed estimation algorithm is verified through a simulation experiment. [ABSTRACT FROM AUTHOR]

Coverage control is a fundamental and critical issue in plentiful wireless sensor network (WSN) applications. Aiming at the high-dimensional optimization problem of sensor node deployment and the complexity of the monitoring area, an orthogonal learning multi-strategy bald eagle search (OLMBES) algorithm is proposed to optimize the location deployment of sensor nodes. This paper incorporates three kinds of strategies into the bald eagle search (BES) algorithm, including Lévy flight, quasi-reflection-based learning, and quadratic interpolation, which enhances the global exploration ability of the algorithm and accelerates the convergence speed. Furthermore, orthogonal learning is integrated into BES to improve the algorithm's robustness and premature convergence problem. By this way, population search information is fully utilized to generate a more superior position guidance vector, which helps the algorithm jump out of the local optimal solution. Simulation results on CEC2014 benchmark functions reveal that the optimization performance of the proposed approach is better than that of the existing method. On the WSN coverage optimization problem, the proposed method has greater network coverage ratio, node uniformity, and stronger optimization stability when compared to other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

The size of the Packet Forwarding Nodes (PFNs) is becoming very small as the technology advances in Wireless Sensor Networks (WSNs). The node has an additional parameter with low energy levels. The PFNs are distributed in a square cross-sectional area with each node acting as a Sensing Point (SP) that can be used for various kinds of applications like temperature, atmospheric humidity, acoustic, and pressure measurements. The packet is divided into several fragments where each fragment is considered as fixed or variable length. Each of these packets is sent over multiple PFNs toward the data center using PFNs. The selection of PFNs in the path is picked based on the trust level. In the network even special PFNs are placed which are responsible to deliver the packets toward the data center without losing the data during the transmission. The selection of special PFNs is done by computing the meeting probability, remaining energy computation, computation of data weight, and security value computation. The proposed Incentive Routing Protocol with Virtual Projection (IRPVP) method is compared with the conventional approaches concerning the parameters like delay, link count, resource energy, healthy PFNs, non-healthy PFNs, health ratio computation, remaining energy, control to data ratio, and balancing factor. The simulation outcomes show that the performance of the proposed IRPVP algorithm is better than the other conventional algorithms. [ABSTRACT FROM AUTHOR]

Big data analysis of human behavior can provide the basis and support for the application of various scenarios. Using sensors for human behavior analysis is an effective means of identification method, which is very valuable for research. To address the problems of low recognition accuracy, low recognition efficiency of traditional human behavior recognition (HBR) algorithms in complex scenes, in this paper, we propose an HBR algorithm for Mobile Big data analytics in wireless sensor network using improved transfer learning. First, different wireless sensors are fused to obtain human behavior mobile big data, and then by analyzing the importance of human behavior features (HBF), the dynamic change parameters of HBF extraction threshold are calculated. Second, combined with the dynamic change parameters of threshold, the HBF of complex scenes are extracted. Finally, the best classification function of human behavior in complex scenes is obtained by using the classification function of HBF in complex scenes. Human behavior in complex scenes is classified according to the HBF in the feature set. The HBR algorithm is designed by using the improved transfer learning network to realize the recognition of human behavior in complex scenes. The results show that the proposed algorithm can accurately recognize up to 22 HBF points, and can control the HBR time within 2 s. The human behavior false recognition rate of miscellaneous scenes is less than 10%. The recognition speed is above 10/s, and the recall rate can reach more than 98%, which improves the HBR ability of complex scenes. [ABSTRACT FROM AUTHOR]

Owing to uncertainties associated with energy and maintenance of large Wireless Sensor Networks (WSN) during transmission, energy-efficient routing strategies are gaining popularity. A Dynamic Threshold Adaptive Routing Algorithm (DTAR) is proposed for determining the most appropriate node to become a Cluster Head (CH) using adaptive participation criteria. For determining the next Forwarder Node (FN), an adaptive ranking scheme depends on distance (d) and Residual Energy ( R egy ). However, additional parameters such as Delivery Ratio (DR), End-to-End delay ( E 2 delay), and Message Success Rate (MSR) should be considered to achieve the most optimal approach to achieve energy efficiency. The proposed DTAR algorithm is validated on variable clustered networks in order to investigate the effect of opportunistic routing with increasing network size and energy resources. The proposed algorithm shows a substantial decrease in energy consumption during transmission. Energy Consumption (EC), Packet Delivery Ratio (PDR), End-to-End delay ( E 2 delay), and Message Success Rate (MSR) are used to illustrate the effectiveness of the proposed algorithm for energy efficiency. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) have advanced technologically, allowing for a wide range of Internet of Things (IoT) applications. The Internet of Things (IoT) provides a platform for connecting items to achieve a specific goal with minimal human intervention. Various practical sectors, such as environmental monitoring and defense, demand IoT support with maximum object connectivity, and low human participation, yet face security concerns from strong unauthorized entities. As location privacy is one of these major vulnerabilities, continual monitoring of contextual information may lead the attacker to the asset's position. The location privacy of the source node that detects and reports the presence of an event is critical. In this study, we provide a source location privacy (SLP) preservation technique based on confounding domain angular routing (SLP-CDAR) that attempts to improve security while increasing network lifespan. To avoid traffic analysis attacks, packets first undergo a random walk, followed by a perplexing time domain routing that creates additional diversionary pathways. The packets are then routed via angle routing, followed by a random path, and finally delivered to the base station using the forward random walk technique. The experimental results prove that the proposed scheme enhances the location privacy strength while providing an improved network lifetime, and a swift increase in the entropy. [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]

eHealth includes services and systems of healthcare and information technologies. A patient monitoring system is essential to have a medical professional be aware of the patient's condition. On the other hand, telecare medical information system (TMIS) makes a doctor able to make diagnoses and provide treatment advice to a patient via the Internet. How to ensure convenience and user privacy for eHealth at the same time is always an urgent issue. Because sensor networks make obtaining instantaneous information feasible, we first analyze related protocols and makes further discussions with proper modifications to ensure the patient monitoring system's security. Then we propose a biometrics-based mutual authentication scheme to ensure privacy and security for TMIS. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) are rapidly being integrated into various fields, significantly impacting and facilitating many aspects of human life. However, the increasingly prominent security issues associated with WSNs have become a significant challenge. This paper provides an in-depth analysis of the security challenges faced by WSNs in resource constrained and open communication environments. As a key component of the Internet of Things (IoT), a WSN can perceive, collect and transmit physical environmental data in real-time, and is widely used in military, medical, agricultural and other fields. However, the insecurity of communication channels and unauthorized user access pose severe threats to network security and data integrity. To address these challenges, this paper proposes a provably secure two-factor authentication protocol. This protocol utilizes a Chebyshev chaotic map and a two-factor authentication mechanism, which not only enhances security in WSNs but also improves authentication efficiency. The protocol is validated through security proof and performance experiments, demonstrating excellent security, functionality and efficiency. This provides strong support for secure and efficient communication in WSNs across various application scenarios. [ABSTRACT FROM AUTHOR]

The underground environment and its components are susceptible to various attacks due to their role in monitoring environmental conditions and future predictions. Our review of existing literature reveals that most protocols are vulnerable to impersonation and Man-in-the-Middle attacks, which pose significant risks of data theft from sensors. Additionally, these protocols often impose high computational loads on the sensors. To address these challenges, this article presents an inter-mutual authentication scheme tailored for Wireless Sensor Networks (WSNs). We propose a secure authentication and key agreement protocol using the elliptic curve cryptosystem (ECC), which not only provides session key generation and authentication but also protects against several potential attacks. We conducted comprehensive security analyses, including both formal and informal methods, to validate the robustness of our protocol. The informal analysis, based on a threat model, demonstrates the protocol's effectiveness against various critical attacks. The Random Oracle (RoR) model further establishes that breaking the session key security is challenging. Simulations using Scyther confirm that private parameters remain secure during execution, while AVISPA simulations validate the protocol's resilience against replay attack and Man-in-the-Middle attacks. Furthermore, we assessed the security features, computational and communication costs, and overall performance of the proposed scheme, comparing it with existing studies. Our findings indicate that the protocol offers an average improvement of 36.47% and 48-bits for computation and communication overheads. Additionally, this work also offers enhanced security (secure from all possible attacks), making it an effective solution for securing sensitive data in WSNs. [ABSTRACT FROM AUTHOR]

Data redundancy reduction is one of the efficient power conservation methods in IoT based Wireless Sensor Networks (IoT-WSN). Generally, WSN nodes undergo redundant data transmission both spatially and temporally, which in turn consumes more energy. It also leads to unbalanced big data storage, hence reduces the performance of the database management system. Communicating the aggregate representation of sensed data solves the problems of storage space and the high energy consumption for transmission from the source node to the destination node in the network. In the proposed method, the temporal redundant data is annihilated at the member node level using the Cosine similarity function, the spatial redundant data is obliterated at the cluster head level by the Extreme Learning Machine (ELM) and the data prediction is performed at base station using Long Short-Term Memory networks (LSTM). The proposed Temporal-Spatial Redundancy Reduction and Prediction Algorithm (TSRRPA) aids in enhancing the efficient data transmission and energy utilization in IoT based WSN. This paper provides a detailed discussion about the algorithms at node level, cluster head level, and the base station level in the context of redundancy reduction and the experimental results of the simulation. The TSRRPA exhibits a 38% increase in energy consumption efficiency, also achieves an overall accuracy of 98.7% with an impressively low error rate of 0.013. [ABSTRACT FROM AUTHOR]

Heterogeneous wireless sensor networks (HWSNs) are energy-constrained networks. Data aggregation can conserve the energy of HWSN. Clustering protocols and data processing can be used at individual nodes to reduce the amount of transfers and extend the network's lifespan. Considering these advantages, the proposed research introduces an efficient energy supply and data aggregation using effective techniques. Initially, cluster head (CH) election and data transmission are done using an information entropy based-clustering algorithm (IECA). After successful data transmission, an efficient energy supply scheme is enabled between cluster members (CMs) and sink nodes. Then, data aggregation is performed in CH using Planar Flow-Based Variational Auto-Encoder-based data aggregation (PF-VAE-DA). Before performing data aggregation, the useless and redundant data is compressed using a Long-short-term-memory-based auto-encoder (LSTM-based auto-encoder). The compressed data is aggregated in CHs. Before transferring the aggregated data to the sink, efficient data stream collection is performed to equalize the data size utilizing self-adaptive adjustment of sliding window size (SASWS). Finally, the optimal path is selected to transmit the aggregated data from CH to the sink. The performance of the proposed method is evaluated for various performance metrics. The aim of the proposed study is to enhance the accuracy of sensing data by introducing a novel deep learning-based data aggregation approach. This will extract significant features from vast amounts of data and carry out data aggregation. In addition, to improve the dependability of aggregated data transfer, an effective Energy Supply Policy based on data transmission patterns is implemented. The results show that the proposed method outperforms other methods in terms of network energy consumption, packet delivery ratio (PDR), packet dropping ratio, data aggregation rate, transmission delay, and network lifetime. The proposed approach uses 50% less energy than the other methods. The model's transmission delay ranges from 0.1 to 0.4 s as the number of nodes increases. The proposed network contains 282 active nodes at the 400th round, which is much more than the existing networks. [ABSTRACT FROM AUTHOR]