Journal of Sensor and Actuator Networks最新文献

Dynamic and Distributed Intelligence over Smart Devices, Internet of Things Edges, and Cloud Computing for Human Activity Recognition Using Wearable Sensors 通过智能设备、物联网边缘和云计算实现动态分布式智能，利用可穿戴传感器识别人类活动

IF 3.5

Journal of Sensor and Actuator Networks Pub Date : 2024-01-02 DOI: 10.3390/jsan13010005

Ayman Wazwaz, Khalid Amin, Noura Semary, Tamer Ghanem

{"title":"Dynamic and Distributed Intelligence over Smart Devices, Internet of Things Edges, and Cloud Computing for Human Activity Recognition Using Wearable Sensors","authors":"Ayman Wazwaz, Khalid Amin, Noura Semary, Tamer Ghanem","doi":"10.3390/jsan13010005","DOIUrl":"https://doi.org/10.3390/jsan13010005","url":null,"abstract":"A wide range of applications, including sports and healthcare, use human activity recognition (HAR). The Internet of Things (IoT), using cloud systems, offers enormous resources but produces high delays and huge amounts of traffic. This study proposes a distributed intelligence and dynamic HAR architecture using smart IoT devices, edge devices, and cloud computing. These systems were used to train models, store results, and process real-time predictions. Wearable sensors and smartphones were deployed on the human body to detect activities from three positions; accelerometer and gyroscope parameters were utilized to recognize activities. A dynamic selection of models was used, depending on the availability of the data and the mobility of the users. The results showed that this system could handle different scenarios dynamically according to the available features; its prediction accuracy was 99.23% using the LightGBM algorithm during the training stage, when 18 features were used. The prediction time was around 6.4 milliseconds per prediction on the smart end device and 1.6 milliseconds on the Raspberry Pi edge, which can serve more than 30 end devices simultaneously and reduce the need for the cloud. The cloud was used for storing users’ profiles and can be used for real-time prediction in 391 milliseconds per request.","PeriodicalId":37584,"journal":{"name":"Journal of Sensor and Actuator Networks","volume":"65 12","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139390065","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

Output Stream from the AQM Queue with BMAP Arrivals AQM 队列的输出流与 BMAP 抵达量

IF 3.5

Journal of Sensor and Actuator Networks Pub Date : 2024-01-02 DOI: 10.3390/jsan13010004

A. Chydzinski

引用次数: 0

Multi-Objective Optimization of Gateway Location Selection in Long-Range Wide Area Networks: A Tradeoff Analysis between System Costs and Bitrate Maximization 远距离广域网中网关位置选择的多目标优化：系统成本与比特率最大化之间的权衡分析

IF 3.5

Journal of Sensor and Actuator Networks Pub Date : 2024-01-02 DOI: 10.3390/jsan13010003

Charuay Savithi, Chutchai Kaewta

{"title":"Multi-Objective Optimization of Gateway Location Selection in Long-Range Wide Area Networks: A Tradeoff Analysis between System Costs and Bitrate Maximization","authors":"Charuay Savithi, Chutchai Kaewta","doi":"10.3390/jsan13010003","DOIUrl":"https://doi.org/10.3390/jsan13010003","url":null,"abstract":"LoRaWANs play a critical role in various applications such as smart farming, industrial IoT, and smart cities. The strategic placement of gateways significantly influences network performance optimization. This study presents a comprehensive analysis of the tradeoffs between system costs and bitrate maximization for selecting optimal gateway locations in LoRaWANs. To address this challenge, a rigorous mathematical model is formulated to incorporate essential factors and constraints related to gateway selection. Furthermore, we propose an innovative metaheuristic algorithm known as the M-VaNSAS algorithm, which effectively explores the solution space and identifies favorable gateway locations. The Pareto front and TOPSIS methods are employed to evaluate and rank the generated solutions, providing a robust assessment framework. Our research findings highlight the suitability of a network model comprising 144 gateways tailored for the Ubon Ratchathani province. Among the evaluated algorithms, the M-VaNSAS method demonstrates exceptional efficiency in gateway location selection, outperforming the PSO, DE, and GA methods.","PeriodicalId":37584,"journal":{"name":"Journal of Sensor and Actuator Networks","volume":"106 6","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139391262","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

Robust ISAC Localization in Smart Cities: A Hybrid Network Approach for NLOS Challenges with Uncertain Parameters 智能城市中稳健的 ISAC 定位：针对参数不确定的 NLOS 挑战的混合网络方法

IF 3.5

Journal of Sensor and Actuator Networks Pub Date : 2023-12-29 DOI: 10.3390/jsan13010002

Turke Althobaiti, R. A. Khalil, Nasir Saeed

{"title":"Robust ISAC Localization in Smart Cities: A Hybrid Network Approach for NLOS Challenges with Uncertain Parameters","authors":"Turke Althobaiti, R. A. Khalil, Nasir Saeed","doi":"10.3390/jsan13010002","DOIUrl":"https://doi.org/10.3390/jsan13010002","url":null,"abstract":"Accurate localization holds paramount importance across many applications within the context of smart cities, particularly in vehicular communication systems, the Internet of Things, and Integrated Sensing and Communication (ISAC) technologies. Nonetheless, achieving precise localization remains a persistent challenge, primarily attributed to the prevalence of non-line-of-sight (NLOS) conditions and the presence of uncertainties surrounding key wireless transmission parameters. This paper presents a comprehensive framework tailored to address the intricate task of localizing multiple nodes within ISAC systems significantly impacted by pervasive NLOS conditions and the ambiguity of transmission parameters. The proposed methodology integrates received signal strength (RSS) and time-of-arrival (TOA) measurements as a strategic response to effectively overcome these substantial challenges, even in situations where the precise values of transmitting power and temporal information remain elusive. An approximation approach is judiciously employed to facilitate the inherent non-convex and NP-hard nature of the original estimation problem, resulting in a notable transformation, rendering the problem amenable to a convex optimization paradigm. The comprehensive array of simulations conducted within this study corroborates the efficacy of the proposed hybrid cooperative localization method by underscoring its superior performance relative to conventional approaches relying solely on RSS or TOA measurements. This enhancement in localization accuracy in ISAC systems holds promise in the intricate urban landscape of smart cities, offering substantial contributions to infrastructure optimization and service efficiency.","PeriodicalId":37584,"journal":{"name":"Journal of Sensor and Actuator Networks","volume":"131 5","pages":""},"PeriodicalIF":3.5,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139146418","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

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":"77 6","pages":""},"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":"49 1","pages":""},"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":"41 5","pages":""},"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":"34 15","pages":"0"},"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":"34 1","pages":"0"},"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