The majority of the Earth’s surface is covered by water, with oceans holding approximately 97% of this water and serving as the lifeblood of our planet. These oceans are essential for various purposes, including transportation, sustenance, and communication. However, establishing effective communication networks between the numerous sub-islands present in many parts of the world poses significant challenges. Underwater optical wireless communication, or UWOC, can indeed be an excellent solution to provide seamless connectivity underwater. UWOC holds immense significance due to its ability to transmit data at high rates, low latency, and enhanced security. In this work, we propose polarization division multiplexing-based UWOC system under the impact of salinity with an on–off keying (OOK) modulation format. The proposed system aims to establish high-speed network connectivity between underwater divers/submarines in oceans at different salinity levels. The numerical simulation results demonstrate the effectiveness of our proposed system with a 2 Gbps data rate up to 10.5 m range in freshwater and up to 1.8 m in oceanic waters with salinity up to 35 ppt. Successful transmission of high-speed data is reported in underwater optical wireless communication, especially where salinity impact is higher.
{"title":"A Salinity-Impact Analysis of Polarization Division Multiplexing-Based Underwater Optical Wireless Communication System with High-Speed Data Transmission","authors":"Sushank Chaudhary, Abhishek Sharma, Sunita Khichar, Shashi Shah, Rizwan Ullah, Amir Parnianifard, Lunchakorn Wuttisittikulkij","doi":"10.3390/jsan12050072","DOIUrl":"https://doi.org/10.3390/jsan12050072","url":null,"abstract":"The majority of the Earth’s surface is covered by water, with oceans holding approximately 97% of this water and serving as the lifeblood of our planet. These oceans are essential for various purposes, including transportation, sustenance, and communication. However, establishing effective communication networks between the numerous sub-islands present in many parts of the world poses significant challenges. Underwater optical wireless communication, or UWOC, can indeed be an excellent solution to provide seamless connectivity underwater. UWOC holds immense significance due to its ability to transmit data at high rates, low latency, and enhanced security. In this work, we propose polarization division multiplexing-based UWOC system under the impact of salinity with an on–off keying (OOK) modulation format. The proposed system aims to establish high-speed network connectivity between underwater divers/submarines in oceans at different salinity levels. The numerical simulation results demonstrate the effectiveness of our proposed system with a 2 Gbps data rate up to 10.5 m range in freshwater and up to 1.8 m in oceanic waters with salinity up to 35 ppt. Successful transmission of high-speed data is reported in underwater optical wireless communication, especially where salinity impact is higher.","PeriodicalId":37584,"journal":{"name":"Journal of Sensor and Actuator Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135300581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Human activity recognition has been widely used to monitor users during physical activities. By embedding a pre-trained model into wearable devices with an inertial measurement unit, it is possible to identify the activity being executed, count steps and activity duration time, and even predict when the user should hydrate himself. Despite these interesting applications, these approaches are limited by a set of pre-trained activities, making them unable to learn new human activities. In this paper, we introduce a novel approach for generating runtime models to give the users feedback that helps them to correctly perform repetitive physical activities. To perform a distributed analysis, the methodology focuses on applying the proposed method to each specific body segment. The method adopts the Restricted Boltzmann Machine to learn the patterns of repetitive physical activities and, at the same time, provides suggestions for adjustments if the repetition is not consistent with the model. The learning and the suggestions are both based on inertial measurement data mainly considering movement acceleration and amplitude. The results show that by applying the model’s suggestions to the evaluation data, the adjusted output was up to 3.68x more similar to the expected movement than the original data.
{"title":"An Online Method for Supporting and Monitoring Repetitive Physical Activities Based on Restricted Boltzmann Machines","authors":"Marcio Alencar, Raimundo Barreto, Eduardo Souto, Horacio Oliveira","doi":"10.3390/jsan12050070","DOIUrl":"https://doi.org/10.3390/jsan12050070","url":null,"abstract":"Human activity recognition has been widely used to monitor users during physical activities. By embedding a pre-trained model into wearable devices with an inertial measurement unit, it is possible to identify the activity being executed, count steps and activity duration time, and even predict when the user should hydrate himself. Despite these interesting applications, these approaches are limited by a set of pre-trained activities, making them unable to learn new human activities. In this paper, we introduce a novel approach for generating runtime models to give the users feedback that helps them to correctly perform repetitive physical activities. To perform a distributed analysis, the methodology focuses on applying the proposed method to each specific body segment. The method adopts the Restricted Boltzmann Machine to learn the patterns of repetitive physical activities and, at the same time, provides suggestions for adjustments if the repetition is not consistent with the model. The learning and the suggestions are both based on inertial measurement data mainly considering movement acceleration and amplitude. The results show that by applying the model’s suggestions to the evaluation data, the adjusted output was up to 3.68x more similar to the expected movement than the original data.","PeriodicalId":37584,"journal":{"name":"Journal of Sensor and Actuator Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136094261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article is aimed at designing an inventive compact-size quad-port antenna that can be operated within terahertz (THz) frequency spectra for a 6G high-speed wireless communication link. The single-element antenna comprises a lotus-petal-like radiating patch and a defected ground structure (DGS) on a 20 × 20 × 2 µm3 polyamide substrate and is designed to operate within the 8.96–13.5 THz frequency range. The THz antenna is deployed for a two-port MIMO configuration having a size of 46 × 20 × 2 µm3 with interelement separation of less than a quarter-wavelength of 0.18λ (λ at 9 THz). The two-port configuration operates in the 9–13.25 THz frequency range, with better than −25 dB isolation. Further, the two-port THz antenna is mirrored vertically with a separation of 0.5λ to form the four-port MIMO configuration. The proposed four-port THz antenna has dimensions of 46 × 46 × 2 µm3 and operates in the frequency range of 9–13 THz. Isolation improvement better than −25 dB is realized by incorporating parasitic elements onto the ground plane. Performance analysis of the proposed antenna in terms of MIMO diversity parameters, viz., envelope correlation coefficient (ECC) < 0.05, diversity gain (DG) ≈ 10, mean effective gain (MEG) < −3 dB, total active reflection coefficient (TARC) < −10 dB, channel capacity loss (CCL) < 0.3 bps/Hz, and multiplexing efficiency (ME) < 0 dB, is performed to justify the appropriateness of the proposed antenna for MIMO applications. The antenna has virtuous radiation properties with good gain, which is crucial for any wireless communication system, especially for the THz communication network.
{"title":"A Quad-Port Nature-Inspired Lotus-Shaped Wideband Terahertz Antenna for Wireless Applications","authors":"Jeenal Raghunathan, Praveen Kumar, Tanweer Ali, Pradeep Kumar, Parveez Shariff Bhadrvathi Ghouse, Sameena Pathan","doi":"10.3390/jsan12050069","DOIUrl":"https://doi.org/10.3390/jsan12050069","url":null,"abstract":"This article is aimed at designing an inventive compact-size quad-port antenna that can be operated within terahertz (THz) frequency spectra for a 6G high-speed wireless communication link. The single-element antenna comprises a lotus-petal-like radiating patch and a defected ground structure (DGS) on a 20 × 20 × 2 µm3 polyamide substrate and is designed to operate within the 8.96–13.5 THz frequency range. The THz antenna is deployed for a two-port MIMO configuration having a size of 46 × 20 × 2 µm3 with interelement separation of less than a quarter-wavelength of 0.18λ (λ at 9 THz). The two-port configuration operates in the 9–13.25 THz frequency range, with better than −25 dB isolation. Further, the two-port THz antenna is mirrored vertically with a separation of 0.5λ to form the four-port MIMO configuration. The proposed four-port THz antenna has dimensions of 46 × 46 × 2 µm3 and operates in the frequency range of 9–13 THz. Isolation improvement better than −25 dB is realized by incorporating parasitic elements onto the ground plane. Performance analysis of the proposed antenna in terms of MIMO diversity parameters, viz., envelope correlation coefficient (ECC) < 0.05, diversity gain (DG) ≈ 10, mean effective gain (MEG) < −3 dB, total active reflection coefficient (TARC) < −10 dB, channel capacity loss (CCL) < 0.3 bps/Hz, and multiplexing efficiency (ME) < 0 dB, is performed to justify the appropriateness of the proposed antenna for MIMO applications. The antenna has virtuous radiation properties with good gain, which is crucial for any wireless communication system, especially for the THz communication network.","PeriodicalId":37584,"journal":{"name":"Journal of Sensor and Actuator Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136129702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Matthieu Mouyart, Guilherme Medeiros Machado, Jae-Yun Jun
Intrusion detection systems can defectively perform when they are adjusted with datasets that are unbalanced in terms of attack data and non-attack data. Most datasets contain more non-attack data than attack data, and this circumstance can introduce biases in intrusion detection systems, making them vulnerable to cyberattacks. As an approach to remedy this issue, we considered the Conditional Tabular Generative Adversarial Network (CTGAN), with its hyperparameters optimized using the tree-structured Parzen estimator (TPE), to balance an insider threat tabular dataset called the CMU-CERT, which is formed by discrete-value and continuous-value columns. We showed through this method that the mean absolute errors between the probability mass functions (PMFs) of the actual data and the PMFs of the data generated using the CTGAN can be relatively small. Then, from the optimized CTGAN, we generated synthetic insider threat data and combined them with the actual ones to balance the original dataset. We used the resulting dataset for an intrusion detection system implemented with the Adversarial Environment Reinforcement Learning (AE-RL) algorithm in a multi-agent framework formed by an attacker and a defender. We showed that the performance of detecting intrusions using the framework of the CTGAN and the AE-RL is significantly improved with respect to the case where the dataset is not balanced, giving an F1-score of 0.7617.
{"title":"A Multi-Agent Intrusion Detection System Optimized by a Deep Reinforcement Learning Approach with a Dataset Enlarged Using a Generative Model to Reduce the Bias Effect","authors":"Matthieu Mouyart, Guilherme Medeiros Machado, Jae-Yun Jun","doi":"10.3390/jsan12050068","DOIUrl":"https://doi.org/10.3390/jsan12050068","url":null,"abstract":"Intrusion detection systems can defectively perform when they are adjusted with datasets that are unbalanced in terms of attack data and non-attack data. Most datasets contain more non-attack data than attack data, and this circumstance can introduce biases in intrusion detection systems, making them vulnerable to cyberattacks. As an approach to remedy this issue, we considered the Conditional Tabular Generative Adversarial Network (CTGAN), with its hyperparameters optimized using the tree-structured Parzen estimator (TPE), to balance an insider threat tabular dataset called the CMU-CERT, which is formed by discrete-value and continuous-value columns. We showed through this method that the mean absolute errors between the probability mass functions (PMFs) of the actual data and the PMFs of the data generated using the CTGAN can be relatively small. Then, from the optimized CTGAN, we generated synthetic insider threat data and combined them with the actual ones to balance the original dataset. We used the resulting dataset for an intrusion detection system implemented with the Adversarial Environment Reinforcement Learning (AE-RL) algorithm in a multi-agent framework formed by an attacker and a defender. We showed that the performance of detecting intrusions using the framework of the CTGAN and the AE-RL is significantly improved with respect to the case where the dataset is not balanced, giving an F1-score of 0.7617.","PeriodicalId":37584,"journal":{"name":"Journal of Sensor and Actuator Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135202441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The frequency of cyber-attacks on the Internet of Things (IoT) networks has significantly increased in recent years. Anomaly-based network intrusion detection systems (NIDSs) offer an additional layer of network protection by detecting and reporting the infamous zero-day attacks. However, the efficiency of real-time detection systems relies on several factors, including the number of features utilized to make a prediction. Thus, minimizing them is crucial as it implies faster prediction and lower storage space. This paper utilizes recursive feature elimination with cross-validation using a decision tree model as an estimator (DT-RFECV) to select an optimal subset of 15 of UNSW-NB15’s 42 features and evaluates them using several ML classifiers, including tree-based ones, such as random forest. The proposed NIDS exhibits an accurate prediction model for network flow with a binary classification accuracy of 95.30% compared to 95.56% when using the entire feature set. The reported scores are comparable to those attained by the state-of-the-art systems despite decreasing the number of utilized features by about 65%.
{"title":"Recursive Feature Elimination with Cross-Validation with Decision Tree: Feature Selection Method for Machine Learning-Based Intrusion Detection Systems","authors":"Mohammed Awad, Salam Fraihat","doi":"10.3390/jsan12050067","DOIUrl":"https://doi.org/10.3390/jsan12050067","url":null,"abstract":"The frequency of cyber-attacks on the Internet of Things (IoT) networks has significantly increased in recent years. Anomaly-based network intrusion detection systems (NIDSs) offer an additional layer of network protection by detecting and reporting the infamous zero-day attacks. However, the efficiency of real-time detection systems relies on several factors, including the number of features utilized to make a prediction. Thus, minimizing them is crucial as it implies faster prediction and lower storage space. This paper utilizes recursive feature elimination with cross-validation using a decision tree model as an estimator (DT-RFECV) to select an optimal subset of 15 of UNSW-NB15’s 42 features and evaluates them using several ML classifiers, including tree-based ones, such as random forest. The proposed NIDS exhibits an accurate prediction model for network flow with a binary classification accuracy of 95.30% compared to 95.56% when using the entire feature set. The reported scores are comparable to those attained by the state-of-the-art systems despite decreasing the number of utilized features by about 65%.","PeriodicalId":37584,"journal":{"name":"Journal of Sensor and Actuator Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135109997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The integration of communication channels with the feedback loop in a networked control system (NCS) is attractive for many applications. A major challenge in the NCS is to reduce transmissions over the network between the sensors, the controller, and the actuators to avoid network congestion. An efficient approach to achieving this goal is the event-triggered implementation where the control actions are only updated when necessary from stability/performance perspectives. In particular, periodic event-triggered control (PETC) has garnered recent attention because of its practical implementation advantages. This paper focuses on the design of stabilizing PETC for linear time-invariant systems. It is assumed that the plant state is partially known; the feedback signal is sent to the controller at discrete-time instants via a digital channel; and an event-triggered controller is synthesized, solely based on the available plant measurement. The constructed event-triggering law is novel and only verified at periodic time instants; it is more adapted to practical implementations. The proposed approach ensures a global asymptotic stability property for the closed-loop system under mild conditions. The overall model is developed as a hybrid dynamical system to truly describe the mixed continuous-time and discrete-time dynamics. The stability is studied using appropriate Lyapunov functions. The efficiency of the technique is illustrated in the dynamic model of the tunnel diode system.
{"title":"Output-Based Dynamic Periodic Event-Triggered Control with Application to the Tunnel Diode System","authors":"Mahmoud Abdelrahim, Dhafer Almakhles","doi":"10.3390/jsan12050066","DOIUrl":"https://doi.org/10.3390/jsan12050066","url":null,"abstract":"The integration of communication channels with the feedback loop in a networked control system (NCS) is attractive for many applications. A major challenge in the NCS is to reduce transmissions over the network between the sensors, the controller, and the actuators to avoid network congestion. An efficient approach to achieving this goal is the event-triggered implementation where the control actions are only updated when necessary from stability/performance perspectives. In particular, periodic event-triggered control (PETC) has garnered recent attention because of its practical implementation advantages. This paper focuses on the design of stabilizing PETC for linear time-invariant systems. It is assumed that the plant state is partially known; the feedback signal is sent to the controller at discrete-time instants via a digital channel; and an event-triggered controller is synthesized, solely based on the available plant measurement. The constructed event-triggering law is novel and only verified at periodic time instants; it is more adapted to practical implementations. The proposed approach ensures a global asymptotic stability property for the closed-loop system under mild conditions. The overall model is developed as a hybrid dynamical system to truly describe the mixed continuous-time and discrete-time dynamics. The stability is studied using appropriate Lyapunov functions. The efficiency of the technique is illustrated in the dynamic model of the tunnel diode system.","PeriodicalId":37584,"journal":{"name":"Journal of Sensor and Actuator Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134914136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Thiruvenkadam Srinivasan, Sujitha Venkatapathy, Han-Gue Jo, In-Ho Ra
Network slicing is widely regarded as the most critical technique for allocating network resources to varied user needs in 5G networks. A Software Defined Networking (SDN) and Network Function Virtualization (NFV) are two extensively used strategies for slicing the physical infrastructure according to use cases. The most efficient use of virtual networks is realized by the application of optimal resource allocation algorithms. Numerous research papers on 5G network resource allocation focus on network slicing or on the best resource allocation for the sliced network. This study uses network slicing and optimal resource allocation to achieve performance optimization using requirement-based network slicing. The proposed approach includes three phases: (1) Slice Creation by Machine Learning methods (SCML), (2) Slice Isolation through Resource Allocation (SIRA) of requests via a multi-criteria decision-making approach, and (3) Slice Management through Resource Transfer (SMART). We receive a set of Network Service Requests (NSRs) from users. After receiving the NSRs, the SCML is used to form slices, and SIRA and SMART are used to allocate resources to these slices. Accurately measuring the acceptance ratio and resource efficiency helps to enhance overall performance. The simulation results show that the SMART scheme can dynamically change the resource allocation according to the test conditions. For a range of network situations and Network Service Requests (NSRs), the performance benefit is studied. The findings of the simulation are compared to those of the literature in order to illustrate the usefulness of the proposed work.
{"title":"VNF-Enabled 5G Network Orchestration Framework for Slice Creation, Isolation and Management","authors":"Thiruvenkadam Srinivasan, Sujitha Venkatapathy, Han-Gue Jo, In-Ho Ra","doi":"10.3390/jsan12050065","DOIUrl":"https://doi.org/10.3390/jsan12050065","url":null,"abstract":"Network slicing is widely regarded as the most critical technique for allocating network resources to varied user needs in 5G networks. A Software Defined Networking (SDN) and Network Function Virtualization (NFV) are two extensively used strategies for slicing the physical infrastructure according to use cases. The most efficient use of virtual networks is realized by the application of optimal resource allocation algorithms. Numerous research papers on 5G network resource allocation focus on network slicing or on the best resource allocation for the sliced network. This study uses network slicing and optimal resource allocation to achieve performance optimization using requirement-based network slicing. The proposed approach includes three phases: (1) Slice Creation by Machine Learning methods (SCML), (2) Slice Isolation through Resource Allocation (SIRA) of requests via a multi-criteria decision-making approach, and (3) Slice Management through Resource Transfer (SMART). We receive a set of Network Service Requests (NSRs) from users. After receiving the NSRs, the SCML is used to form slices, and SIRA and SMART are used to allocate resources to these slices. Accurately measuring the acceptance ratio and resource efficiency helps to enhance overall performance. The simulation results show that the SMART scheme can dynamically change the resource allocation according to the test conditions. For a range of network situations and Network Service Requests (NSRs), the performance benefit is studied. The findings of the simulation are compared to those of the literature in order to illustrate the usefulness of the proposed work.","PeriodicalId":37584,"journal":{"name":"Journal of Sensor and Actuator Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135742074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The robust stabilization of doubly fed induction generators in wind turbines against external disturbances is considered in this study. It is assumed that the angular speeds of wind turbines can only be measured and sent to the controller in a discrete-time fashion over a network. To generate the sampling times, three different triggering schemes were developed: time-triggering, static event-triggering, and dynamic event-triggering mechanisms; moreover, performance comparisons were conducted between such approaches. The design methodology is based on emulation, such that the plant is first stabilized in continuous-time where a robust feedback law is constructed based on the linear quadratic Gaussian regulator (LQG) approach. Then, the impact of the network is taken into account, and an event-triggering mechanism is built so that closed-loop stability is maintained and the Zeno phenomenon is avoided by using temporal regularization. The necessary stability constraints are framed as a linear matrix inequality, and the whole system is modeled as a hybrid dynamical system. A numerical simulation is used to demonstrate the effectiveness of the control strategy. The results show that the event-triggering mechanisms achieve a significant reduction of around 50% in transmissions compared to periodic sampling. Moreover, numerical comparisons with existing approaches show that the proposed approach provides better performance in terms of the stability guarantee and number of transmissions.
{"title":"Output Feedback Stabilization of Doubly Fed Induction Generator Wind Turbines under Event-Triggered Implementations","authors":"Mahmoud Abdelrahim, Dhafer Almakhles","doi":"10.3390/jsan12050064","DOIUrl":"https://doi.org/10.3390/jsan12050064","url":null,"abstract":"The robust stabilization of doubly fed induction generators in wind turbines against external disturbances is considered in this study. It is assumed that the angular speeds of wind turbines can only be measured and sent to the controller in a discrete-time fashion over a network. To generate the sampling times, three different triggering schemes were developed: time-triggering, static event-triggering, and dynamic event-triggering mechanisms; moreover, performance comparisons were conducted between such approaches. The design methodology is based on emulation, such that the plant is first stabilized in continuous-time where a robust feedback law is constructed based on the linear quadratic Gaussian regulator (LQG) approach. Then, the impact of the network is taken into account, and an event-triggering mechanism is built so that closed-loop stability is maintained and the Zeno phenomenon is avoided by using temporal regularization. The necessary stability constraints are framed as a linear matrix inequality, and the whole system is modeled as a hybrid dynamical system. A numerical simulation is used to demonstrate the effectiveness of the control strategy. The results show that the event-triggering mechanisms achieve a significant reduction of around 50% in transmissions compared to periodic sampling. Moreover, numerical comparisons with existing approaches show that the proposed approach provides better performance in terms of the stability guarantee and number of transmissions.","PeriodicalId":37584,"journal":{"name":"Journal of Sensor and Actuator Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135827675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Oscar Acosta, L. Hermida, Marcelo Herrera, Carlos Montenegro, E. Gaona, Mateo Bejarano, K. Gordillo, I. Pavón, C. Asensio
The recent emergence of advanced information technologies such as cloud computing, artificial intelligence, and data science has improved and optimized various processes in acoustics with potential real-world applications. Noise monitoring tasks on large terrains can be captured using an array of sound level meters. However, current monitoring systems only rely on the knowledge of a singular measured value related to the acoustic energy of the captured signal, leaving aside spatial aspects that complement the perception of noise by the human being. This project presents a system that performs binaural measurements according to subjective human perception. The acoustic characterization in an anechoic chamber is presented, as well as acoustic indicators obtained in the field initially for a short period of time. The main contribution of this work is the construction of a binaural prototype that resembles the human head and which transmits and processes acoustical data on the cloud. The above allows noise level monitoring via binaural hearing rather than a singular capturing device. Likewise, it can be highlighted that the system allows for obtaining spatial acoustic indicators based on the interaural cross-correlation function (IACF), as well as detecting the location of the source on the azimuthal plane.
{"title":"Remote Binaural System (RBS) for Noise Acoustic Monitoring","authors":"Oscar Acosta, L. Hermida, Marcelo Herrera, Carlos Montenegro, E. Gaona, Mateo Bejarano, K. Gordillo, I. Pavón, C. Asensio","doi":"10.3390/jsan12040063","DOIUrl":"https://doi.org/10.3390/jsan12040063","url":null,"abstract":"The recent emergence of advanced information technologies such as cloud computing, artificial intelligence, and data science has improved and optimized various processes in acoustics with potential real-world applications. Noise monitoring tasks on large terrains can be captured using an array of sound level meters. However, current monitoring systems only rely on the knowledge of a singular measured value related to the acoustic energy of the captured signal, leaving aside spatial aspects that complement the perception of noise by the human being. This project presents a system that performs binaural measurements according to subjective human perception. The acoustic characterization in an anechoic chamber is presented, as well as acoustic indicators obtained in the field initially for a short period of time. The main contribution of this work is the construction of a binaural prototype that resembles the human head and which transmits and processes acoustical data on the cloud. The above allows noise level monitoring via binaural hearing rather than a singular capturing device. Likewise, it can be highlighted that the system allows for obtaining spatial acoustic indicators based on the interaural cross-correlation function (IACF), as well as detecting the location of the source on the azimuthal plane.","PeriodicalId":37584,"journal":{"name":"Journal of Sensor and Actuator Networks","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47940198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Smartphones have emerged as a ubiquitous personal gadget that serve as a repository for individuals’ significant personal data. Consequently, both physiological and behavioral traits, which are classified as biometric technologies, are used in authentication systems in order to safeguard data saved on smartphones from unauthorized access. Numerous authentication techniques have been developed; however, several authentication variables exhibit instability in the face of external influences or physical impairments. The potential failure of the authentication system might be attributed to several unpredictable circumstances. This research suggests that the use of distinctive and consistent elements over an individual’s lifespan may be employed to develop an authentication classification model. This model would be based on prevalent personal behavioral biometrics and could be readily implemented in security authentication systems. The biological biometrics acquired from an individual’s typing abilities during data entry include their name, surname, email, and phone number. Therefore, it is possible to establish and use a biometrics-based security system that can be sustained and employed during an individual’s lifetime without the explicit dependance on the functionality of the smartphone devices. The experimental findings demonstrate that the use of a mobile touchscreen as the foundation for the proposed verification mechanism has promise as a high-precision authentication solution.
{"title":"Extraction of Hidden Authentication Factors from Possessive Information","authors":"Nilobon Nanglae, B. M. Yakubu, P. Bhattarakosol","doi":"10.3390/jsan12040062","DOIUrl":"https://doi.org/10.3390/jsan12040062","url":null,"abstract":"Smartphones have emerged as a ubiquitous personal gadget that serve as a repository for individuals’ significant personal data. Consequently, both physiological and behavioral traits, which are classified as biometric technologies, are used in authentication systems in order to safeguard data saved on smartphones from unauthorized access. Numerous authentication techniques have been developed; however, several authentication variables exhibit instability in the face of external influences or physical impairments. The potential failure of the authentication system might be attributed to several unpredictable circumstances. This research suggests that the use of distinctive and consistent elements over an individual’s lifespan may be employed to develop an authentication classification model. This model would be based on prevalent personal behavioral biometrics and could be readily implemented in security authentication systems. The biological biometrics acquired from an individual’s typing abilities during data entry include their name, surname, email, and phone number. Therefore, it is possible to establish and use a biometrics-based security system that can be sustained and employed during an individual’s lifetime without the explicit dependance on the functionality of the smartphone devices. The experimental findings demonstrate that the use of a mobile touchscreen as the foundation for the proposed verification mechanism has promise as a high-precision authentication solution.","PeriodicalId":37584,"journal":{"name":"Journal of Sensor and Actuator Networks","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47708758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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