Journal of Sensor and Actuator Networks最新文献_第5页

Reduction in Data Imbalance for Client-Side Training in Federated Learning for the Prediction of Stock Market Prices 在预测股市价格的联合学习中减少客户端训练的数据不平衡

IF 3.5

Journal of Sensor and Actuator Networks Pub Date : 2023-12-21 DOI: 10.3390/jsan13010001

Momina Shaheen, M. Farooq, Tariq Umer

{"title":"Reduction in Data Imbalance for Client-Side Training in Federated Learning for the Prediction of Stock Market Prices","authors":"Momina Shaheen, M. Farooq, Tariq Umer","doi":"10.3390/jsan13010001","DOIUrl":"https://doi.org/10.3390/jsan13010001","url":null,"abstract":"The approach of federated learning (FL) addresses significant challenges, including access rights, privacy, security, and the availability of diverse data. However, edge devices produce and collect data in a non-independent and identically distributed (non-IID) manner. Therefore, it is possible that the number of data samples may vary among the edge devices. This study elucidates an approach for implementing FL to achieve a balance between training accuracy and imbalanced data. This approach entails the implementation of data augmentation in data distribution by utilizing class estimation and by balancing on the client side during local training. Secondly, simple linear regression is utilized for model training at the client side to manage the optimal computation cost to achieve a reduction in computation cost. To validate the proposed approach, the technique was applied to a stock market dataset comprising stocks (AAL, ADBE, ASDK, and BSX) to predict the day-to-day values of stocks. The proposed approach has demonstrated favorable results, exhibiting a strong fit of 0.95 and above with a low error rate. The R-squared values, predominantly ranging from 0.97 to 0.98, indicate the model’s effectiveness in capturing variations in stock prices. Strong fits are observed within 75 to 80 iterations for stocks displaying consistently high R-squared values, signifying accuracy. On the 100th iteration, the declining MSE, MAE, and RMSE (AAL at 122.03, 4.89, 11.04, respectively; ADBE at 457.35, 17.79, and 21.38, respectively; ASDK at 182.78, 5.81, 13.51, respectively; and BSX at 34.50, 4.87, 5.87, respectively) values corroborated the positive results of the proposed approach with minimal data loss.","PeriodicalId":37584,"journal":{"name":"Journal of Sensor and Actuator Networks","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138951408","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}

引用次数: 0

Performance Evaluation of LoRa Communications in Harsh Industrial Environments 恶劣工业环境中 LoRa 通信的性能评估

IF 3.5

Journal of Sensor and Actuator Networks Pub Date : 2023-11-28 DOI: 10.3390/jsan12060080

L. Aarif, Mohamed Tabaa, Hanaa Hachimi

{"title":"Performance Evaluation of LoRa Communications in Harsh Industrial Environments","authors":"L. Aarif, Mohamed Tabaa, Hanaa Hachimi","doi":"10.3390/jsan12060080","DOIUrl":"https://doi.org/10.3390/jsan12060080","url":null,"abstract":"LoRa technology is being integrated into industrial applications as part of Industry 4.0 owing to its longer range and low power consumption. However, noise, interference, and the fading effect all have a negative impact on LoRa performance in an industrial environment, necessitating solutions to ensure reliable communication. This paper evaluates and compares LoRa’s performance in terms of packet error rate (PER) with and without forward error correction (FEC) in an industrial environment. The impact of integrating an infinite impulse response (IIR) or finite impulse response (FIR) filter into the LoRa architecture is also evaluated. Simulations are carried out in MATLAB at 868 MHz with a bandwidth of 125 kHz and two spreading factors of 7 and 12. Many-to-one and one-to-many communication modes are considered, as are line of sight (LOS) and non-line of Sight (NLOS) conditions. Simulation results show that, compared to an environment with additive white Gaussian noise (AWGN), LoRa technology suffers a significant degradation of its PER performance in industrial environments. Nevertheless, the use of forward error correction (FEC) contributes positively to offsetting this decline. Depending on the configuration and architecture examined, the gain in signal-to-noise ratio (SNR) using a 4/8 coding ratio ranges from 7 dB to 11 dB. Integrating IIR or FIR filters also boosts performance, with additional SNR gains ranging from 2 dB to 6 dB, depending on the simulation parameters.","PeriodicalId":37584,"journal":{"name":"Journal of Sensor and Actuator Networks","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139220590","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}

引用次数: 0

Electric Vehicles Energy Management for Vehicle-to-Grid 6G-Based Smart Grid Networks 基于 6G 的智能电网网络的电动汽车能源管理

IF 3.5

Journal of Sensor and Actuator Networks Pub Date : 2023-11-27 DOI: 10.3390/jsan12060079

Rola Naja, Aakash Soni, Circe Carletti

引用次数: 0

A Federated Learning Approach to Support the Decision-Making Process for ICU Patients in a European Telemedicine Network 欧洲远程医疗网络中支持重症监护室患者决策过程的联合学习方法

IF 3.5

Journal of Sensor and Actuator Networks Pub Date : 2023-11-20 DOI: 10.3390/jsan12060078

Giovanni Paragliola, Patrizia Ribino, Zaib Ullah

{"title":"A Federated Learning Approach to Support the Decision-Making Process for ICU Patients in a European Telemedicine Network","authors":"Giovanni Paragliola, Patrizia Ribino, Zaib Ullah","doi":"10.3390/jsan12060078","DOIUrl":"https://doi.org/10.3390/jsan12060078","url":null,"abstract":"A result of the pandemic is an urgent need for data collaborations that empower the clinical and scientific communities in responding to rapidly evolving global challenges. The ICU4Covid project joined research institutions, medical centers, and hospitals all around Europe in a telemedicine network for sharing capabilities, knowledge, and expertise distributed within the network. However, healthcare data sharing has ethical, regulatory, and legal complexities that pose several restrictions on their access and use. To mitigate this issue, the ICU4Covid project integrates a federated learning architecture, allowing distributed machine learning within a cross-institutional healthcare system without the data being transported or exposed outside their original location. This paper presents the federated learning approach to support the decision-making process for ICU patients in a European telemedicine network. The proposed approach was applied to the early identification of high-risk hypertensive patients. Experimental results show how the knowledge of every single node is spread within the federation, improving the ability of each node to make an early prediction of high-risk hypertensive patients. Moreover, a performance evaluation shows an accuracy and precision of over 90%, confirming a good performance of the FL approach as a prediction test. The FL approach can significantly support the decision-making process for ICU patients in distributed networks of federated healthcare organizations.","PeriodicalId":37584,"journal":{"name":"Journal of Sensor and Actuator Networks","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139256521","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}

引用次数: 0

Enhancing Mental Fatigue Detection through Physiological Signals and Machine Learning Using Contextual Insights and Efficient Modelling 利用上下文洞察和高效建模，通过生理信号和机器学习增强精神疲劳检测

Journal of Sensor and Actuator Networks Pub Date : 2023-11-03 DOI: 10.3390/jsan12060077

Carole-Anne Cos, Alexandre Lambert, Aakash Soni, Haifa Jeridi, Coralie Thieulin, Amine Jaouadi

{"title":"Enhancing Mental Fatigue Detection through Physiological Signals and Machine Learning Using Contextual Insights and Efficient Modelling","authors":"Carole-Anne Cos, Alexandre Lambert, Aakash Soni, Haifa Jeridi, Coralie Thieulin, Amine Jaouadi","doi":"10.3390/jsan12060077","DOIUrl":"https://doi.org/10.3390/jsan12060077","url":null,"abstract":"This research presents a machine learning modeling process for detecting mental fatigue using three physiological signals: electrodermal activity, electrocardiogram, and respiration. It follows the conventional machine learning modeling pipeline, while emphasizing the significant contribution of the feature selection process, resulting in, not only a high-performance model, but also a relevant one. The employed feature selection process considers both statistical and contextual aspects of feature relevance. Statistical relevance was assessed through variance and correlation analyses between independent features and the dependent variable (fatigue state). A contextual analysis was based on insights derived from the experimental design and feature characteristics. Additionally, feature sequencing and set conversion techniques were employed to incorporate the temporal aspects of physiological signals into the training of machine learning models based on random forest, decision tree, support vector machine, k-nearest neighbors, and gradient boosting. An evaluation was conducted using a dataset acquired from a wearable electronic system (in third-party research) with physiological data from three subjects undergoing a series of tests and fatigue stages. A total of 18 tests were performed by the 3 subjects in 3 mental fatigue states. Fatigue assessment was based on subjective measures and reaction time tests, and fatigue induction was performed through mental arithmetic operations. The results showed the highest performance when using random forest, achieving an average accuracy and F1-score of 96% in classifying three levels of mental fatigue.","PeriodicalId":37584,"journal":{"name":"Journal of Sensor and Actuator Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135868234","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}

引用次数: 0

Enhancing the Fault Tolerance of a Multi-Layered IoT Network through Rectangular and Interstitial Mesh in the Gateway Layer 通过网关层的矩形和间隙网格增强多层物联网网络的容错性

Journal of Sensor and Actuator Networks Pub Date : 2023-10-16 DOI: 10.3390/jsan12050076

Sastry Kodanda Rama Jammalamadaka, Bhupati Chokara, Sasi Bhanu Jammalamadaka, Balakrishna Kamesh Duvvuri, Rajarao Budaraju

{"title":"Enhancing the Fault Tolerance of a Multi-Layered IoT Network through Rectangular and Interstitial Mesh in the Gateway Layer","authors":"Sastry Kodanda Rama Jammalamadaka, Bhupati Chokara, Sasi Bhanu Jammalamadaka, Balakrishna Kamesh Duvvuri, Rajarao Budaraju","doi":"10.3390/jsan12050076","DOIUrl":"https://doi.org/10.3390/jsan12050076","url":null,"abstract":"Most IoT systems designed for the implementation of mission-critical systems are multi-layered. Much of the computing is done in the service and gateway layers. The gateway layer connects the internal section of the IoT to the cloud through the Internet. The failure of any node between the servers and the gateways will isolate the entire network, leading to zero tolerance. The service and gateway layers must be connected using networking topologies to yield 100% fault tolerance. The empirical formulation of the model chosen to connect the service’s servers to the gateways through routers is required to compute the fault tolerance of the network. A rectangular and interstitial mesh have been proposed in this paper to connect the service servers to the gateways through the servers, which yields 0.999 fault tolerance of the IoT network. Also provided is an empirical approach to computing the IoT network’s fault tolerance. A rectangular and interstitial mesh have been implemented in the network’s gateway layer, increasing the IoT network’s ability to tolerate faults by 11%.","PeriodicalId":37584,"journal":{"name":"Journal of Sensor and Actuator Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136112985","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}

引用次数: 0

Short-Range Localization via Bluetooth Using Machine Learning Techniques for Industrial Production Monitoring 工业生产监控中基于蓝牙的机器学习技术的短程定位

Journal of Sensor and Actuator Networks Pub Date : 2023-10-15 DOI: 10.3390/jsan12050075

Francesco Di Rienzo, Alessandro Madonna, Nicola Carbonaro, Alessandro Tognetti, Antonio Virdis, Carlo Vallati

{"title":"Short-Range Localization via Bluetooth Using Machine Learning Techniques for Industrial Production Monitoring","authors":"Francesco Di Rienzo, Alessandro Madonna, Nicola Carbonaro, Alessandro Tognetti, Antonio Virdis, Carlo Vallati","doi":"10.3390/jsan12050075","DOIUrl":"https://doi.org/10.3390/jsan12050075","url":null,"abstract":"Indoor short-range localization is crucial in many Industry 4.0 applications. Production monitoring for assembly lines, for instance, requires fine-grained positioning for parts or goods in order to keep track of the production process and the stations traversed by each product. Due to the unavailability of the Global Positioning System (GPS) for indoor positioning, a different approach is required. In this paper, we propose a specific design for short-range indoor positioning based on the analysis of the Received Signal Strength Indicator (RSSI) of Bluetooth beacons. To this aim, different machine learning techniques are considered and assessed: regressors, Convolution Neural Network (CNN) and Recurrent Neural Network (RNN). A realistic testbed is created to collect data for the training of the models and to assess the performance of each technique. Our analysis highlights the best models and the most convenient and suitable configuration for indoor localization. Finally, the localization accuracy is calculated in the considered use case, i.e., production monitoring. Our results show that the best performance is obtained using the K-Nearest Neighbors technique, which results in a good performance for general localization and in a high level of accuracy, 99%, for industrial production monitoring.","PeriodicalId":37584,"journal":{"name":"Journal of Sensor and Actuator Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136184704","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}

引用次数: 0

Self-Configuration Management towards Fix-Distributed Byzantine Sensors for Clustering Schemes in Wireless Sensor Networks 面向固定分布拜占庭传感器的无线传感器网络聚类自配置管理

Journal of Sensor and Actuator Networks Pub Date : 2023-10-13 DOI: 10.3390/jsan12050074

Walaa M. Elsayed, Engy El-Shafeiy, Mohamed Elhoseny, Mohammed K. Hassan

{"title":"Self-Configuration Management towards Fix-Distributed Byzantine Sensors for Clustering Schemes in Wireless Sensor Networks","authors":"Walaa M. Elsayed, Engy El-Shafeiy, Mohamed Elhoseny, Mohammed K. Hassan","doi":"10.3390/jsan12050074","DOIUrl":"https://doi.org/10.3390/jsan12050074","url":null,"abstract":"To avoid overloading a network, it is critical to continuously monitor the natural environment and disseminate data streams in synchronization. Based on self-maintaining technology, this study presents a technique called self-configuration management (SCM). The purpose is to ensure consistency in the performance, functionality, and physical attributes of a wireless sensor network (WSN) over its lifetime. During device communication, the SCM approach delivers an operational software package for the radio board of system problematic nodes. We offered two techniques to help cluster heads manage autonomous configuration. First, we created a separate capability to determine which defective devices require the operating system (OS) replica. The software package was then delivered from the head node to the network’s malfunctioning device via communication roles. Second, we built an autonomous capability to automatically install software packages and arrange the time. The simulations revealed that the suggested technique was quick in transfers and used less energy. It also provided better coverage of system fault peaks than competitors. We used the proposed SCM approach to distribute homogenous sensor networks, and it increased system fault tolerance to 93.2%.","PeriodicalId":37584,"journal":{"name":"Journal of Sensor and Actuator Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135918537","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}

引用次数: 0

Cryptographic Grade Chaotic Random Number Generator Based on Tent-Map 基于Tent-Map的密码级混沌随机数发生器

Journal of Sensor and Actuator Networks Pub Date : 2023-10-10 DOI: 10.3390/jsan12050073

Ahmad Al-Daraiseh, Yousef Sanjalawe, Salam Al-E’mari, Salam Fraihat, Mohammad Bany Taha, Muhammed Al-Muhammed

{"title":"Cryptographic Grade Chaotic Random Number Generator Based on Tent-Map","authors":"Ahmad Al-Daraiseh, Yousef Sanjalawe, Salam Al-E’mari, Salam Fraihat, Mohammad Bany Taha, Muhammed Al-Muhammed","doi":"10.3390/jsan12050073","DOIUrl":"https://doi.org/10.3390/jsan12050073","url":null,"abstract":"In recent years, there has been an increasing interest in employing chaotic-based random number generators for cryptographic purposes. However, many of these generators produce sequences that lack the necessary strength for cryptographic systems, such as Tent-Map. However, these generators still suffer from common issues when generating random numbers, including issues related to speed, randomness, lack of statistical properties, and lack of uniformity. Therefore, this paper introduces an efficient pseudo-random number generator, called State-Based Tent-Map (SBTM), based on a modified Tent-Map, which addresses this and other limitations by providing highly robust sequences suitable for cryptographic applications. The proposed generator is specifically designed to generate sequences with exceptional statistical properties and a high degree of security. It utilizes a modified 1D chaotic Tent-Map with enhanced attributes to produce the chaotic sequences. Rigorous randomness testing using the Dieharder test suite confirmed the promising results of the generated keystream bits. The comprehensive evaluation demonstrated that approximately 97.4% of the tests passed successfully, providing further evidence of the SBTM’s capability to produce sequences with sufficient randomness and statistical properties.","PeriodicalId":37584,"journal":{"name":"Journal of Sensor and Actuator Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136295273","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}

引用次数: 0

Applying an Adaptive Neuro-Fuzzy Inference System to Path Loss Prediction in a Ruby Mango Plantation 自适应神经模糊推理系统在红宝石芒果种植园路径损失预测中的应用

Journal of Sensor and Actuator Networks Pub Date : 2023-10-07 DOI: 10.3390/jsan12050071

Supachai Phaiboon, Pisit Phokharatkul

引用次数: 0