High Error Probability Research Articles

Symbol-level scheme for combating eavesdropping: Symbol conversion and constellation adjustment

In the non-orthogonal multiple access scenario, users may suffer inter-multiuser eavesdropping due to the feature of successive interference cancellation, and the conditions of eavesdropping suppression methods in the traditional schemes may not be satisfied. To combat this eavesdropping, we consider physical layer security and propose a novel scheme by specially designing symbol conversion and constellation adjustment methods. Based on these methods, the amplitudes and phases of symbols are properly changed. When each user intercepts information as an eavesdropper, he/she has to accept high error probability, or he/she has to undergo exorbitant overhead. Analytical and numerical results demonstrate that the proposed scheme can protect the privacy of information, and this protection does not destruct the execution of successive interference cancellation and symbol transmission.

Sensitivity analysis of human error in the steel industry: exploring the effects of psychosocial and mental health risk factors and burnout using Bayesian networks.

Human error and the high rates of fatalities and other occupational accidents in the steel industry are of significant global relevance. The aim of this study was to investigate the effect of psychosocial, mental health, and burnout risk factors on human error probabilities in an industrial environment using Bayesian networks. This cross-sectional study was conducted in 2023. The participants were 252 employees of a steel company. Error probabilities related to the tasks of participants were estimated using the Human Error Assessment and Reduction Technique (HEART). Other data was collected using a survey that consisted of demographic information, the Maslach Burnout Inventory, Depression Anxiety Stress Scales, and a short version of the Copenhagen Psychosocial Questionnaire. A theoretical model was drawn in GeNIe academic software (version 2.3). The results showed that all the studied variables were able to significantly affect the distribution of human error probabilities. Considering a distribution of 100% for the high state of these variables, the results showed that the greatest increases in error probability were related to two burnout dimensions: emotional exhaustion (29%) and depersonalization (28%). All the variables, with a probability of 100%, increased the probability of high human error probabilities by 46%. The most important variables in terms of their effect on human error probabilities were burnout dimensions, and these variables also had a mediation effect on the psychosocial and mental health variables. Therefore, preventive measures to control human error should first focus on managing the risks of burnout in workers. This, in turn, can also reduce the effect of psychosocial risk factors and mental health problems on human error in the workplace.

A robust temporal map of speech monitoring from planning to articulation

Speakers continuously monitor their own speech to optimize fluent production, but the precise timing and underlying variables influencing speech monitoring remain insufficiently understood. Through two EEG experiments, this study aimed to provide a comprehensive temporal map of monitoring processes ranging from speech planning to articulation.In both experiments, participants were primed to switch the consonant onsets of target word pairs read aloud, eliciting speech errors of either lexical or articulatory-phonetic (AP) origin. Experiment I used pairs of the same stimuli words, creating lexical or non-lexical errors when switching initial consonants, with the degree of shared AP features not fully balanced but considered in the analysis. Experiment II followed a similar methodology but used different words in pairs for the lexical and non-lexical conditions, fully orthogonalizing the number of shared AP features.As error probability is higher in trials primed to result in lexical versus non-lexical errors and AP-close compared to AP-distant errors, more monitoring is required for these conditions. Similarly, error trials require more monitoring compared to correct trials. We used high versus low error probability on correct trials and errors versus correct trials as indices of monitoring.Across both experiments, we observed that on correct trials, lexical error probability effects were present during initial stages of speech planning, while AP error probability effects emerged during speech motor preparation. In contrast, error trials showed differences from correct utterances in both early and late speech motor preparation and during articulation. These findings suggest that (a) response conflict on ultimately correct trials does not persist during articulation; (b) the timecourse of response conflict is restricted to the time window during which a given linguistic level is task-relevant (early on for response appropriateness-related variables and later for articulation-relevant variables); and (c) monitoring during the response is primarily triggered by pre-response monitoring failure. These results support that monitoring in language production is temporally distributed and rely on multiple mechanisms.

Prediction of human error probability in helicopter to ship transfer operation under an evidential reasoning extended CREAM approach.

Although the helicopter to ship transfer operation is rarely applied in ports around the world, some of the hub ports have encouraged it. The operational process poses challenges to safety due to the nature of the work to be completed by the ship crew. This article performs a systematic human error prediction to ensure safe helicopter to ship transfer operations under different circumstances since the human factor is becoming a significant contributor to maritime accidents. To accomplish this, the article addresses a cognitive reliability and error analysis method (CREAM) under the evidential reasoning (ER) approach. In the proposed approach, the CREAM provides a comprehensive human error prediction tool and ER is capable of supporting experts' judgment in decision-making. The findings of the research show that 'Secure the loose objects within or adjacent to the operating area' poses a high human error probability (4.50E-02) during helicopter to ship transfer operations.

Investigation of block interleavers in OFDM radio communication systems with convolutional encoding in multipath channels with fast fading

Noise immunity codecs are good at correcting single errors. Group errors often occur in multipath radio channels with fading. Interleaving is used to convert group errors into single errors. Matching the interleaver structure with the signal and code parameters allows the interleaver to work efficiently. Optimization of the interleaver parameters depending on the characteristics of the communication channel increases the efficiency of the interleaver. The purpose of the work is to determine the parameters of block interleavers for an OFDM radio communication system with convolutional coding in conditions of signal scattering in time and frequency. A model of a channel with two beams and Rayleigh fades is considered. The influence of interleaver parameters on the noise immunity of the OFDM radio communication system depending on the channel parameters (fading rate, multipath interval) has been analyzed. Block deterministic and pseudorandom interleavers are considered. With magnification lengths the interleaver increases noise immunity of the communication system. But at the same time, the delay in transmitting the message increases. The optimal length has been determined, providing a compromise between delay and noise immunity. This length is determined depending on the coherence time of the channel. The number of rows of the matrix interleaver is depending on the decoding depth. This is the number of rows provides better noise immunity at optimal length. But sometimes the optimal length is too long. Therefore a function has been introduced that characterizes the minimum distance between bits in the “time-frequency-code” space at the input of the interleaver. The distance between the bits in the time domain is normalized for the coherence time of the channel. The distance between the bits in the frequency domain is calculated as a normalized deviation from the oscillation period in the signal power spectrum at the output of a channel with two beams. The distance between the bits in the “code space” is calculated as the distance normalized the effective decoding depth of the code used. Given the function describes optimal and critical values well parameters of deterministic interleavers of any length. Deterministic interleavers have a higher probability of error per bit than random interleavers. The definition of parameters using the proposed function allows the deterministic interleaver to work with the same low the probability of error per bit as well as random interleavers.

Evaluation of Scikit-Learn Machine Learning Algorithms for Improving CMA-WSP v2.0 Solar Radiation Prediction

This article aims to evaluate the performance of solar radiation forecasts produced by CMA-WSP v2.0 (version 2 of the China Meteorological Administration Wind and Solar Energy Prediction System) and to explore the application of machine learning algorithms from the scikit-learn Python library to improve the solar radiation prediction made by the CMA-WSP v2.0. It is found that the performance of the solar radiation forecasting from the CMA-WSP v2.0 is closely related to the weather conditions, with notable diurnal fluctuations. The mean absolute percentage error (MAPE) produced by the CMA-WSP v2.0 is approximately 74% between 11:00 and 13:00. However, the MAPE ranges from 193% to 242% at 07:00–08:00 and 17:00–18:00, which is greater than that observed at other daytime periods. The MAPE is relatively low (high) for both sunny and cloudy (overcast and rainy) conditions, with a high probability of an absolute percentage error below 25% (above 100%). The forecasts tend to underestimate (overestimate) the observed solar radiation in sunny and cloudy (overcast and rainy) conditions. By applying machine learning models (such as linear regression, decision trees, K-nearest neighbors, random forests regression, adaptive boosting, and gradient boosting regression) to revise the solar radiation forecasts, the MAPE produced by the CMA-WSP v2.0 is significantly reduced. The reduction in the MAPE is closely connected to the weather conditions. The models of K-nearest neighbors, random forests regression, and decision trees can reduce the MAPE in all weather conditions. The K-nearest neighbor model exhibits the most optimal performance among these models, particularly in rainy conditions. The random forest regression model demonstrates the second-best performance compared to that of the K-nearest neighbor model. The gradient boosting regression model has been observed to reduce the MAPE of the CMA-WSP v2.0 in all weather conditions except rainy. In contrast, the adaptive boosting (linear regression) model exhibited a diminished capacity to improve the CMA-WSP v2.0 solar radiation prediction, with a slight reduction in MAPE observed only in sunny (sunny and cloudy) conditions. In addition, the input feature selection has a considerable influence on the performance of the machine learning model. The incorporation of the time series data associated with the diurnal variation of solar radiation as an input feature can further improve the model’s performance.

Gappy POD model for Structural Compliance of the Wing of a Civil Tilt-rotor

The selection of the most critical load conditions is an extremely important topic as it allows the design and structural verification to be limited to a limited subset of conditions that envelop the operational thousands of the Design Limit Load (DLL) book without necessarily having to apply a direct approach. Often, thousands of analyses are performed by a Detailed Finite Element Model (DFEM, including model loading, post-processing and full stress and buckling analysis) taking a long time with a higher probability of error. This work describes the effort to set up a surrogate model of a composite wing, which aims to supply, accurately and efficiently, the structural response of the wing to several solicitation loads that populate the DLL. The surrogate model is defined by employing the Proper Orthogonal Decomposition technique (POD), trained on partial knowledge of data. The snapshots collected are composed of load solicitations and, limited to a subset of wing elements, stress and strain states resulting from the structural response to the relative load solicitation in a numerical analysis. Gappy-POD algorithm is set up to deal with this lack of data in the querying stage when only the solicitations are known. The surrogate model training starts with a selection of loads from the DLL, while its reliability is increased by iteratively adding further FEM analyses based on error maps opportunely defined.

A robust and machine learning-driven identification scheme for malicious nodes in UASNs

Underwater Acoustic Sensor Networks (UASNs) have garnered widespread adoption across maritime domains and national security sectors. Their deployment, often in remote and challenging conditions, necessitates robust trust models to assess node integrity and to pinpoint compromised units, particularly under adversarial assault. Prevailing trust models, however, predominantly focus on nodes’ communicative actions and energy consumption, neglecting the influence of ambient aquatic noise on trust assessments. Moreover, these models typically falter amidst the UASN-specific impediments of high packet collision rates and elevated error probabilities inherent to underwater signal transmission, leading to consequential inaccuracies in node evaluation. To address these limitations, this paper proposes a robust and machine learning-driven malicious node identification scheme (RMIS) for UASNs. The proposed scheme first models and quantifies the impact of packet collisions and the underwater environment on node communication, utilizing existing environment models and MAC protocols. Second, communication traffic is exploited as effective and reliable evidence of trust. The traffic evidence can reflect multiple types of attacks by analyzing changes in traffic in a UASN. Third, the evaluation models are trained using support vector machine and K-means++ algorithms. Finally, two types of trust update mechanisms are proposed to handle the dynamic underwater environment and on–off attack. The simulation results indicate that compared to the other three identification schemes, RMIS is more effective in identifying malicious nodes. The effectiveness of the RMIS becomes particularly noticeable as the ratio of malicious nodes increases. Furthermore, the trust update mechanisms effectively limit the impact of the on–off attack and improve the robustness of the RMIS.

Resolving the non-uniformity in the feature space of age estimation: A deep learning model based on feature clusters of panoramic images

Age estimation is important in forensics, and numerous techniques have been investigated to estimate age based on various parts of the body. Among them, dental tissue is considered reliable for estimating age as it is less influenced by external factors. The advancement in deep learning has led to the development of automatic estimation of age using dental panoramic images. Typically, most of the medical datasets used for model learning are non-uniform in the feature space. This causes the model to be highly influenced by dense feature areas, resulting in adequate estimations; however, relatively poor estimations are observed in other areas. An effective solution to address this issue can be pre-dividing the data by age feature and training each regressor to estimate the age for individual features. In this study, we divide the data based on feature clusters obtained from unsupervised learning. The developed model comprises a classification head and multi-regression head, wherein the former predicts the cluster to which the data belong and the latter estimates the age within the predicted cluster. The visualization results show that the model can focus on a clinically meaningful area in each cluster for estimating age. The proposed model outperforms the models without feature clusters by focusing on the differences within the area. The performance improvement is particularly noticeable in the growth and aging periods. Furthermore, the model can adequately estimate the age even for samples with a high probability of classification error as they are located at the border of two feature clusters.

On the issue of software for environmental information systems

The increase in the number of sources of pollutants into the atmospheric air is one of the largest environmental problems in the modern world. In order to control emissions of harmful substances, calculations of maximum permissible concentrations of pollutants are used. Calculations of emission sources and maximum permissible concentrations are quite complex, and it is impractical to carry them out manually, since the calculation process takes a lot of time, while there is a high probability of errors. To simplify such a task, special software tools are used – unified programs for calculating atmospheric pollution (UPRZA). The article is devoted to the issues of increasing the level of automation of calculations of emission sources and maximum permissible concentrations using UPRZA. The issues of the standard structure of the UPRSA are considered, input parameters, algorithms for calculating the parameters of the emission source and maximum permissible concentrations are determined in accordance with the methods used. Methods of increasing the level of automation are proposed. The analysis of possible ways of development of these software complexes is carried out. The article describes the process of developing a unified program for calculating atmospheric pollution, the main advantages of its application.

Autonomous Weapon Systems, Errors and Breaches of International Humanitarian Law

Abstract An error in the operation of an autonomous weapon system (AWS) results in civilians or civilian objects being attacked. In such situations, have civilians or civilian objects been ‘made the object of attack’, such that there is a breach of the rule prohibiting attacks against civilians or civilian objects? This question — which is important because of the high probability of such errors — forms the subject of this article. It argues that the rule prohibiting attacks against civilians or civilian objects requires due diligence — contextually reasonable efforts — across the targeting process, to ensure that civilians or civilian objects are not attacked. This implies that AWS errors breach this rule if the errors are unreasonable, i.e., if they originate in a failure of due diligence at any point in the process of development and deployment of AWS. Moreover, the risk-sensitivity of due diligence obligations suggests that the higher degree of risk involved in the development and use of an AWS leads to a corresponding increase in what constitutes contextually reasonable efforts to ensure that civilians or civilian objects are not attacked.

Clustering-based dragonfly optimization algorithm for underwater wireless sensor networks

Communication and monitoring of the undersea environment have numerous applications, including marine environment studies, offshore research, and mineral extraction. However, factors like as the presence of an underwater current, a limited bandwidth, high water pressure, propagation lag, and error probability make these applications more difficult to implement. Underwater communication might be challenging due to the surroundings. As a clustering approach for sensor nodes in underwater wireless sensor networks (UWSNs), we present a clustering-based dragonfly optimisation (CDFO) algorithm with decentralised forwarding over wireless networks. The CDFO-UWSN approach is used to determine how many clusters should be used for routing. Analogous algorithms have been found to be connected to the proposed method, including the Ant Colony Optimizer (ACO), Adaptive Node Clustering for UWSN (ANC-UWSN), Grey Wolf Optimizer (GWO), and Moth Flame Optimizer (MFO). During the simulation, a performance matrix will be used to account for a variety of parameters such as grid size, transmission range, and node density. The results show that CDFO-UWSN outperforms all other algorithms tested. By establishing more clusters than competing systems, it improves overall routing and boosts network lifetime. This enables a network to run for an extended amount of time. The proposed CDFO-UWSN algorithm outperforms the GWO, ACO, MFO, and DFO algorithms in terms of number of nodes, with a percentage improvement of 78.13 % for 1500*1500 m grid size, and a percentage improvement of 64.71 % in terms of transmission range for 1500*1500 m grid size.

Cortico-Cerebellar Monitoring of Speech Sequence Production.

In a functional magnetic resonance imaging study, we examined speech error monitoring in a cortico-cerebellar network for two contrasts: (a) correct trials with high versus low articulatory error probability and (b) overtly committed errors versus correct trials. Engagement of the cognitive cerebellar region Crus I in both contrasts suggests that this region is involved in overarching performance monitoring. The activation of cerebellar motor regions (superior medial cerebellum, lobules VI and VIII) indicates the additional presence of a sensorimotor driven implementation of control. The combined pattern of pre-supplementary motor area (active across contrasts) and anterior cingulate cortex (only active in the contrast involving overt errors) activations suggests sensorimotor driven feedback monitoring in the medial frontal cortex, making use of proprioception and auditory feedback through overt errors. Differential temporal and parietal cortex activation across contrasts indicates involvement beyond sensorimotor driven feedback in line with speech production models that link these regions to auditory target processing and internal modeling-like mechanisms. These results highlight the presence of multiple, possibly hierarchically interdependent, mechanisms that support the optimizing of speech production.

Defending edge computing based metaverse AI against adversarial attacks

The metaverse, as an impressive technology, can achieve outstanding performance in many fields. With the development of deep neural networks (DNNs), more and more metaverse artificial intelligence (AI) applications are emerging, which have low tolerance for delay. The edge computing based metaverse AI can overcome this problem. However, these AI applications bring new challenges of high reliability, especially DNNs are subject to the security risk of adversarial attacks that generate small and imperceptible noise and cause classifier to make predictions with a high error probability. To meet the reliability requirements of AI applications, in this paper, we propose a security mechanism against adversarial attacks in edge computing based metaverse AI applications. This mechanism utilizes a modified ResNet model that defends against adversarial attacks. We deploy it on the edge cloud to preprocess the data uploaded by metaverse AI applications. In order to achieve a better model performance, we use multiple residual network blocks to build this neural network model on the basis of autoencoder. To assist the model to produce defensively high-quality images, we utilize several convolution layers with various step sizes to process multi-scale images and design to use multiple types of samples with different properties together for the model training. We evaluate our method with experiments on MNIST, CIFAR-10, and CIFAR-100 datasets, and against five attacks such as FGSM and DeepFool attacks. We show that our method achieves 96% defence success rate in MNIST and significantly outperforms existing denoising methods in terms of residual network block, multi-scale image, and training methods.

HW/SW Co-Design for Reliable TCAM- Based In-Memory Brain-Inspired Hyperdimensional Computing

Brain-inspired hyperdimensional computing (HDC) is continuously gaining remarkable attention. It is a promising alternative to traditional machine-learning approaches due to its ability to learn from little data, lightweight implementation, and resiliency against errors. However, HDC is overwhelmingly data-centric similar to traditional machine-learning algorithms. In-memory computing is rapidly emerging to overcome the von Neumann bottleneck by eliminating data movements between compute and storage units. In this work, we investigate and model the impact of imprecise in-memory computing hardware on the inference accuracy of HDC. Our modeling is based on 14nm FinFET technology fully calibrated with Intel measurement data. We accurately model, for the first time, the voltage-dependent error probability in SRAM-based and FeFET-based in-memory computing. Thanks to HDC's resiliency against errors, the complexity of the underlying hardware can be reduced, providing large energy savings of up to 6x. Experimental results for SRAM reveal that variability-induced errors have a probability of up to 39 percent. Despite such a high error probability, the inference accuracy is only marginally impacted. This opens doors to explore new tradeoffs. We also demonstrate that the resiliency against errors is application-dependent. In addition, we investigate the robustness of HDC against errors when the underlying in-memory hardware is realized using emerging non-volatile FeFET devices instead of mature CMOS-based SRAMs. We demonstrate that inference accuracy does remain high despite the larger error probability, while large area and power savings can be obtained. All in all, HW/SW co-design is the key for efficient yet reliable in-memory hyperdimensional computing for both conventional CMOS technology and upcoming emerging technologies.

The Brain Networks Basis for Deductive and Inductive Reasoning: A Functional Magnetic Resonance Imaging Study.

Frontoparietal (FPN) and cingulo-opercular network (CON) control cognitive functions needed in deductive and inductive reasoning via different functional frameworks. The FPN is a fast intuitive system while the CON is slow and analytical. The default-interventionist model presents a serial view of the interaction between intuitive and analytic cognitive systems. This study aims to examine the activity pattern of the FPN and CON from the perspective of the default-interventionist model via reasoning. We employed functional magnetic resonance imaging (fMRI) to investigate cingulo-opercular and frontoparietal network activities in 24 healthy university students during Raven and Wason reasoning tasks. Due to the different operation times of the CON and FPN, the reaction time was assessed as a behavioral factor. During Raven's advanced progressive matrices (RAPM) test, both the CON and FPN were activated. Also, with the increase in the difficulty level of the Raven test, a linear increase in response time was observed. In contrast, during the Wason's selection task (WST) test, only the activity of FPN was observed. The results of the study support the hypothesis that the default-interventionist model of dual-process theory provides an accurate explanation of the cognitive mechanisms involved in reasoning. Thus, the response method (intuitive/analytical) determines which cognitive skills and brain regions are involved in responding. The cingulo-opercular and fronto-parietal networks (FPNs) control cognitive functions and processes.The frontoparietal network is a fast intuitive system that utilizes short-time attention which is compatible with type 1 processing. In contrast, the cingulo-opercular network (CON) is an analytical time-consuming system that utilizes attention and working memory for a longer time, compatible with type 2 processing.The default-interventionist model of a dual-process theory states that our behaviors are controlled by type 1 processing unless we are confronted with novel and complex problems in which we have no prior experiences. The present study examined the activity of two task-based brain networks through performing diffrent type of reasoning tasks. Fronto-parietal and Cingulo-opercular are the two task-based brain networks that are responsible for cognitive control. These two brain networks direct the way to use cognitive skills and executive functions which are necessary to perform cognitive tasks especially higher-order ones as reasoning tasks. Since the two types of inductive and deductive reasoning tasks requier two different bottom-up and top-down cognitive control respectively, different cognitive skills would be needed which affect the activity of fronto-parietal and cingulo-opercular brain networks. Our results showed that through inductive reasoning task which examined by RAVEN, both of the fronto-parietal and cingulo-opercular brain networks were activated but deductive reasoning task which examined by Wason Selection Card test, just the fronto-parietal brain network was activated. It seems that in the case of deductive reasoninf task, there is a higher probability of errors which lead to giving less correct responses. Based on our results, subjects paid not enough attention to details, so had failure to update informations that leaded to responding with errors. Inactivity of cingulo-opercular network through dedeuctive reasoning task clearly showed that the bottom-up cognitive control did not happen successfully. As a result of that, information processing did not proceed properly.

Throughput and error probability improvement in downlink communication based on MIMO‐NOMA using reconfigurable intelligent surface (RIS)

SummaryThe user far from the base station (BS) can decode the signal via direct transmission or a relay network, but it cannot achieve higher throughput because of its high error probability. The MIMO‐NOMA‐Relay networks are replaced by MIMO‐NOMA‐RIS to achieve higher throughput in the proposal. The MIMO‐NOMA‐RIS sends a superimposed signal to the receiver using a multiple RIS beam scanning algorithm. They can improve the strength of the signal as well as improve the throughput and reduce the probability of error. As more reflecting surface area increases, the strength of the beam‐forming signal also increases, which means that the user could receive more signal strength than the direct transmission, amplify and forward (A&F) relay network. In multi‐user case, use the max–min power control algorithm (MMPCA) to allocate optimum power to a weaker channel strength user and achieve an optimum result. Method 1 used a single user with RIS to find the system equations. In both single‐user and multi‐user scenarios, we use MIMO‐NOMA‐RIS. Our proposed method's complexity is low because of its simplicity. In our proposed method, the MMPCA is used for optimum power allocation, and the resulting non‐convex function is converted into a convex one by using successive convex approximations. The result section shows that the RIS achieved the highest throughput and the lowest probability of error.

CRISPR Teknolojisi ve SARS-CoV-2 Tedavisindeki Önemi

Coronavirus disease-2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) predominantly affects the respiratory system. The COVID-19 pandemic has had devastating effects on the health system and the global economy worldwide. To reduce the worsening impact of the pandemic, various treatment options and vaccines have been developed. Despite these efforts the pandemic could not be stopped because of the single-stranded nature of the virus combined with the lack of proof-reading abilities of the RNA-dependent RNA polymerase (RdRp). This results in a high probability of error in the copying process and consequently, mutations occur. The increase in mutations in SARS-CoV-2 reduced the efficacy of antiviral medicines and vaccines. To fight this problem, studies were conducted on the efficacy and safety of using Clustered Regularly Interspersed Short Palindromic Repeats (CRISPR) in the diagnosis and treatment of COVID-19. Initially, discovered in archaea, CRISPR is a gene-editing tool that works by altering specific parts of the genome. In this review, we focused on the efficacy and safety of CRISPR technology in the treatment of COVID-19.

Review of Localization and Clustering in USV and AUV for Underwater Wireless Sensor Networks

Oceanographic data collection, disaster prevention, aided navigation, critical observation sub-missions, contaminant screening, and seaward scanning are just a few of the submissions that use underwater sensor hubs. Unmanned submerged vehicles (USVs) or autonomous acoustic underwater vehicles (AUVs) through sensors would similarly be able to explore unique underwater resources and gather data when utilized in conjunction with integrated screen operations. The most advanced technological method of oceanic observation is wireless information routing beneath the ocean or generally underwater. Water bottoms are typically observed using oceanographic sensors that collect data at certain ocean zones. Most research on UWSNs focuses on physical levels, even though the localization level, such as guiding processes, is a more recent zone. Analyzing the presenting metrics of the current direction conventions for UWSNs is crucial for considering additional enhancements in a procedure employing underwater wireless sensor networks for locating sensors (UWSNs). Due to their severely constrained propagation, radio frequency (RF) transmissions are inappropriate for underwater environments. This makes it difficult to maintain network connectivity and localization. This provided a plan for employing adequate reliability and improved communication and is used to locate the node exactly using a variety of methods. In order to minimize inaccuracies, specific techniques are utilized to calculate the distance to the destination. It has a variety of qualities, such as limited bandwidth, high latency, low energy, and a high error probability. Both nodes enable technical professionals stationed on land to communicate data from the chosen oceanic zones rapidly. This study investigates the significance, uses, network architecture, requirements, and difficulties of undersea sensors.

Status Prediction and Data Aggregation for AoI-Oriented Short-Packet Transmission in Industrial IoT

Age of information (AoI) is an effective performance metric for time-critical industrial Internet of things (IIoT) applications. We investigate status prediction and data aggregation with prediction error awareness, to enhance the AoI performance for short-packet transmission (SPT) in time-critical IIoT. A predict-compare (PredComp) transmission scheme is proposed, where proactive transmission termination is employed in case of prediction error, by comparing the predicted and real updates at source. It is proved to achieve a significant average AoI performance gain over the case without prediction, even under high prediction error probability. In addition, a predict-aggregate-compare (PredAggComp) transmission scheme is proposed, where two status updates are predicted with different prediction horizons and aggregated by utilizing their time correlation. That allows a good tradeoff between the prediction accuracy and the transmission error probability. A closed-form threshold that the PredAggComp scheme outperforms the PredComp scheme is derived. Moreover, prediction horizon adaptation is conducted to minimize the average AoI of the proposed transmission schemes. Simulation results verify the analytical results and show the superiority of the proposed PredComp and PredAggComp schemes, with an average AoI reduction of up to 64% over the case without prediction.