S. Marrone, L. Campanile, Roberta De Fazio, Michele Di Giovanni, U. Gentile, F. Marulli, Laura Verde
Sustainability is one of the main goals to pursue in several aspects of everyday life; the recent energy shortage and the price raise worsen this problem, especially in the management of energy in buildings. As the Internet of Things (IoT) is an assessed computing paradigm able to capture meaningful data from the field and send them to cloud infrastructures, other approaches are also enabled, namely model-based approaches. These methods can be used to predict functional and non-functional properties of Building Energy Management Systems (BEMS) before setting up them. This paper aims at bridging the gap between model-based approaches and physical realizations of sensing and small computing devices. Through an integrated approach, able to exploit the power of different dialects of Petri Nets, this paper proposes a methodology for the early evaluation of BEMS properties as well as the automatic generation of IoT controllers.
{"title":"A Petri net oriented approach for advanced building energy management systems","authors":"S. Marrone, L. Campanile, Roberta De Fazio, Michele Di Giovanni, U. Gentile, F. Marulli, Laura Verde","doi":"10.3233/ais-230065","DOIUrl":"https://doi.org/10.3233/ais-230065","url":null,"abstract":"Sustainability is one of the main goals to pursue in several aspects of everyday life; the recent energy shortage and the price raise worsen this problem, especially in the management of energy in buildings. As the Internet of Things (IoT) is an assessed computing paradigm able to capture meaningful data from the field and send them to cloud infrastructures, other approaches are also enabled, namely model-based approaches. These methods can be used to predict functional and non-functional properties of Building Energy Management Systems (BEMS) before setting up them. This paper aims at bridging the gap between model-based approaches and physical realizations of sensing and small computing devices. Through an integrated approach, able to exploit the power of different dialects of Petri Nets, this paper proposes a methodology for the early evaluation of BEMS properties as well as the automatic generation of IoT controllers.","PeriodicalId":49316,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"15 1","pages":"211-233"},"PeriodicalIF":1.7,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69735580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The need for remote healthcare monitoring systems that utilize limited resources’ biosensors is growing. These biosensors increase the amount of transmitted data across the Internet of Healthcare Things (IoHT) network. Therefore, it is necessary to decrease the transmitted data and make a decision at the edge gateway to save the energy of the biosensors and produce a quick response for the medical staff. This paper proposes an energy-efficient multisensor adaptive sampling and aggregation (EMASA) for patient monitoring in edge computing-based IoHT networks. In the edge-based IoHT network, EMASA operates on two levels: biosensors and the edge gateway. Each biosensor removes the redundant sensed data using the local emergency detection and sampling rate adaptation algorithms. In the edge gateway, it implements a machine learning-based Support Vector Machine (SVM) model to provide a suitable decision about the status of the monitored patient. We accomplished various examinations using real data from the patients’ biosensors. According to the simulation results, EMASA reduced the size of transmitted data from 93.5% to 99% and saved 78.35% of energy when compared to a previous study. It keeps the whole score with a good representation at the Edge gateway and provides accurate and fast decisions based on the patient’s condition.
{"title":"Energy-efficient multisensor adaptive sampling and aggregation for patient monitoring in edge computing based IoHT networks","authors":"A. Idrees, Duaa Abd Alhussein, Hassan Harb","doi":"10.3233/ais-220610","DOIUrl":"https://doi.org/10.3233/ais-220610","url":null,"abstract":"The need for remote healthcare monitoring systems that utilize limited resources’ biosensors is growing. These biosensors increase the amount of transmitted data across the Internet of Healthcare Things (IoHT) network. Therefore, it is necessary to decrease the transmitted data and make a decision at the edge gateway to save the energy of the biosensors and produce a quick response for the medical staff. This paper proposes an energy-efficient multisensor adaptive sampling and aggregation (EMASA) for patient monitoring in edge computing-based IoHT networks. In the edge-based IoHT network, EMASA operates on two levels: biosensors and the edge gateway. Each biosensor removes the redundant sensed data using the local emergency detection and sampling rate adaptation algorithms. In the edge gateway, it implements a machine learning-based Support Vector Machine (SVM) model to provide a suitable decision about the status of the monitored patient. We accomplished various examinations using real data from the patients’ biosensors. According to the simulation results, EMASA reduced the size of transmitted data from 93.5% to 99% and saved 78.35% of energy when compared to a previous study. It keeps the whole score with a good representation at the Edge gateway and provides accurate and fast decisions based on the patient’s condition.","PeriodicalId":49316,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"15 1","pages":"235-253"},"PeriodicalIF":1.7,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69735514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bangrong Wang, Jun Wang, Xiaofeng Xu, Xianglin Bao
Gas masks are essential respiratory protective equipment commonly used by laborers who work in harsh environments. However, respiratory diseases and accidents can occur due to the absence of gas masks. To prevent these accidents, this paper developed an object detector that uses convolutional neural networks (CNNs) to detect whether workers are wearing gas masks. To achieve this goal, a gas mask detection dataset was constructed derived from real industrial scenarios and Faster R-CNN was improved for gas mask wearing detection. Firstly, to address the multi-scale problem in real scenes, the Feature Pyramid Network was introduced into Faster R-CNN to effectively fuse features between different levels and improve the detection ability of small objects. Secondly, the Online Hard Sample Mining algorithm was used to alleviate the class imbalance problems in the dataset. Finally, Mixup and Mosaic were used in the training process to augment the data and make the model better adapt to different scenes and complex backgrounds. After multiple experiments, the combination of the three optimization strategies improved the mAP 0.5 : 0.95 by 23.2%. This work is an initial attempt at gas mask wearing detection and there is still much room for improvement in terms of model and dataset.
{"title":"Gas mask wearing detection based on faster R-CNN","authors":"Bangrong Wang, Jun Wang, Xiaofeng Xu, Xianglin Bao","doi":"10.3233/ais-220460","DOIUrl":"https://doi.org/10.3233/ais-220460","url":null,"abstract":"Gas masks are essential respiratory protective equipment commonly used by laborers who work in harsh environments. However, respiratory diseases and accidents can occur due to the absence of gas masks. To prevent these accidents, this paper developed an object detector that uses convolutional neural networks (CNNs) to detect whether workers are wearing gas masks. To achieve this goal, a gas mask detection dataset was constructed derived from real industrial scenarios and Faster R-CNN was improved for gas mask wearing detection. Firstly, to address the multi-scale problem in real scenes, the Feature Pyramid Network was introduced into Faster R-CNN to effectively fuse features between different levels and improve the detection ability of small objects. Secondly, the Online Hard Sample Mining algorithm was used to alleviate the class imbalance problems in the dataset. Finally, Mixup and Mosaic were used in the training process to augment the data and make the model better adapt to different scenes and complex backgrounds. After multiple experiments, the combination of the three optimization strategies improved the mAP 0.5 : 0.95 by 23.2%. This work is an initial attempt at gas mask wearing detection and there is still much room for improvement in terms of model and dataset.","PeriodicalId":49316,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46614735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
José L. Gómez-Sirvent, Desirée Fernández-Sotos, Francisc López de la Rosa, A. Fernández-Caballero
Building Information Modeling (BIM) is a powerful process for creating and managing data throughout the life cycle of a building. Traditionally, measuring the well-being of building occupants has been addressed solely through objective physical variables such as temperature or relative air humidity. However, recent studies indicate that the built environment influences subjective aspects of human well-being. This article presents a scoping review to find information related to the use of BIM in the assessment of the mental and emotional state of inhabitants. A scoping review has been undertaken following the PRISMA-ScR guidelines by searching in Scopus, ACM, IEEE Xplore and PsycINFO databases. Fourteen articles meeting the inclusion criteria were found after the screening process, all of them published in the last decade, twelve in the last five years. Two ways of using BIM have been identified in relation to the subject matter of this review: (i) for visualization and monitoring of occupant well-being and (ii) for showing building design alternatives to future occupants. The included papers show that BIM has potential for assessing the mental and emotional state of building occupants. However, the results of these studies are still limited and much research in this area remains pending.
{"title":"Building information modeling and affective occupancy evaluation: A scoping review","authors":"José L. Gómez-Sirvent, Desirée Fernández-Sotos, Francisc López de la Rosa, A. Fernández-Caballero","doi":"10.3233/ais-230046","DOIUrl":"https://doi.org/10.3233/ais-230046","url":null,"abstract":"Building Information Modeling (BIM) is a powerful process for creating and managing data throughout the life cycle of a building. Traditionally, measuring the well-being of building occupants has been addressed solely through objective physical variables such as temperature or relative air humidity. However, recent studies indicate that the built environment influences subjective aspects of human well-being. This article presents a scoping review to find information related to the use of BIM in the assessment of the mental and emotional state of inhabitants. A scoping review has been undertaken following the PRISMA-ScR guidelines by searching in Scopus, ACM, IEEE Xplore and PsycINFO databases. Fourteen articles meeting the inclusion criteria were found after the screening process, all of them published in the last decade, twelve in the last five years. Two ways of using BIM have been identified in relation to the subject matter of this review: (i) for visualization and monitoring of occupant well-being and (ii) for showing building design alternatives to future occupants. The included papers show that BIM has potential for assessing the mental and emotional state of building occupants. However, the results of these studies are still limited and much research in this area remains pending.","PeriodicalId":49316,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45043974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The purpose of this paper is to offer a unique adaptive path planning framework to address a new challenge known as the Unknown environment Persistent Monitoring Problem (PMP). To identify the unknown events’ occurrence location and likelihood, an unmanned ground vehicle (UGV) equipped with a Light Detection and Ranging (LIDAR) and camera is used to record such events in agriculture land. A certain level of detecting capability must be the distinct monitoring priority in order to keep track of them to a certain distance. First, to formulate a model, we developed an event-oriented modelling strategy for unknown environment perception and the effect is enumerated by uncertainty, which takes into account the sensor’s detection capabilities, the detection interval, and monitoring weight. A mobile robot scheme utilizing LIDAR on integrative approach was created and experiments were carried out to solve the high equipment budget of Simultaneous Localization and Mapping (SLAM) for robotic systems. To map an unfamiliar location using the robotic operating system (ROS), the 3D visualization tool for Robot Operating System (RVIZ) was utilized, and GMapping software package was used for SLAM usage. The experimental results suggest that the mobile robot design pattern is viable to produce a high-precision map while lowering the cost of the mobile robot SLAM hardware. From a decision-making standpoint, we built a hybrid algorithm HSAStar (Hybrid SLAM & A Star) algorithm for path planning based on the event oriented modelling, allowing a UGV to continually monitor the perspectives of a path. The simulation results and analyses show that the proposed strategy is feasible and superior. The performance of the proposed hyb SLAM-A Star-APP method provides 34.95%, 27.38%, 33.21% and 29.68% lower execution time, 26.36%, 29.64% and 29.67% lower map duration compared with the existing methods, such as ACO-APF-APP, APFA-APP, GWO-APP and PSO-APP.
本文的目的是提供一个独特的自适应路径规划框架，以应对一个新的挑战，即未知环境持续监测问题（PMP）。为了确定未知事件的发生位置和可能性，使用配备了光探测和测距（LIDAR）和相机的无人地面车辆（UGV）来记录农田中的此类事件。一定水平的检测能力必须是不同的监控优先级，以便在一定距离内跟踪它们。首先，为了建立模型，我们为未知环境感知开发了一种面向事件的建模策略，并通过不确定性来列举影响，其中考虑了传感器的检测能力、检测间隔和监测权重。针对机器人系统同时定位和测绘（SLAM）设备预算高的问题，提出了一种基于LIDAR的一体化移动机器人方案，并进行了实验。为了使用机器人操作系统（ROS）绘制不熟悉的位置，使用了机器人操作系统的3D可视化工具（RVIZ），并使用了GMapping软件包用于SLAM。实验结果表明，该移动机器人设计模式能够在降低移动机器人SLAM硬件成本的同时生成高精度地图。从决策的角度来看，我们基于面向事件的建模，构建了一种用于路径规划的混合算法HSAStar（hybrid SLAM&a Star）算法，允许UGV持续监控路径的视角。仿真结果和分析表明，该策略是可行的、优越的。与现有方法（如ACO-PF-APP、APF-AAPP、GWO-APP和PSO-APP）相比，所提出的hyb SLAM-A Star APP方法的性能分别降低了34.95%、27.38%、33.21%和29.68%的执行时间，26.36%、29.64%和29.67%的映射持续时间。
{"title":"Adaptive path planning for unknown environment monitoring","authors":"Nandhagopal Gomathi, Krishnamoorthi Rajathi","doi":"10.3233/ais-220175","DOIUrl":"https://doi.org/10.3233/ais-220175","url":null,"abstract":"The purpose of this paper is to offer a unique adaptive path planning framework to address a new challenge known as the Unknown environment Persistent Monitoring Problem (PMP). To identify the unknown events’ occurrence location and likelihood, an unmanned ground vehicle (UGV) equipped with a Light Detection and Ranging (LIDAR) and camera is used to record such events in agriculture land. A certain level of detecting capability must be the distinct monitoring priority in order to keep track of them to a certain distance. First, to formulate a model, we developed an event-oriented modelling strategy for unknown environment perception and the effect is enumerated by uncertainty, which takes into account the sensor’s detection capabilities, the detection interval, and monitoring weight. A mobile robot scheme utilizing LIDAR on integrative approach was created and experiments were carried out to solve the high equipment budget of Simultaneous Localization and Mapping (SLAM) for robotic systems. To map an unfamiliar location using the robotic operating system (ROS), the 3D visualization tool for Robot Operating System (RVIZ) was utilized, and GMapping software package was used for SLAM usage. The experimental results suggest that the mobile robot design pattern is viable to produce a high-precision map while lowering the cost of the mobile robot SLAM hardware. From a decision-making standpoint, we built a hybrid algorithm HSAStar (Hybrid SLAM & A Star) algorithm for path planning based on the event oriented modelling, allowing a UGV to continually monitor the perspectives of a path. The simulation results and analyses show that the proposed strategy is feasible and superior. The performance of the proposed hyb SLAM-A Star-APP method provides 34.95%, 27.38%, 33.21% and 29.68% lower execution time, 26.36%, 29.64% and 29.67% lower map duration compared with the existing methods, such as ACO-APF-APP, APFA-APP, GWO-APP and PSO-APP.","PeriodicalId":49316,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47007523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As the world’s population rises, the healthcare system experiences significant changes. Wireless body area network (WBAN) is an emerging technology that has considerable impact on medical and non-medical applications. However, two crucial challenges in WBANs are interference minimization and channel assignment. High interference may increase collision probability, transmission delay, and energy consumption. Multichannel schemes are proposed to reduce the data transmission latency and improve the system throughput by allowing simultaneous transmission of sensors in coexisting WBANs. When WBAN users move, they need to switch the channels frequently to avoid potential channel conflicts and to maintain the Quality of Service (QoS). However, frequent switching may raise energy consumption and aggravate delay. Existing multichannel assignment schemes failed to perform well in highly dynamic and densely deployed WBANs environments. In contrast to existing studies, this paper proposes a Prediction-based Channel Assignment (PCA) algorithm that selects the channels for WBANs to remain valid for future time instances and thus minimizes the delay and number of channel switches for dynamic and coexisting WBANs. When a WBAN needs to switch a channel, the proposed method predicts the future neighbors of that WBAN based on its history. It explores the channel information of present and future neighbors to select a suitable channel with higher resilience in a dynamic environment. Thus, our algorithm minimizes channel interference by avoiding unnecessary channel switching. We have used machine learning algorithms to predict the future neighbors of a WBAN. Experiment results show that the proposed algorithm performs better than an existing algorithm and random channel assignment in delay and throughput.
{"title":"Prediction-based channel assignment for minimizing channel switching in mobile WBANs","authors":"P. Pradhan, Sanghita Bhattacharjee","doi":"10.3233/ais-220193","DOIUrl":"https://doi.org/10.3233/ais-220193","url":null,"abstract":"As the world’s population rises, the healthcare system experiences significant changes. Wireless body area network (WBAN) is an emerging technology that has considerable impact on medical and non-medical applications. However, two crucial challenges in WBANs are interference minimization and channel assignment. High interference may increase collision probability, transmission delay, and energy consumption. Multichannel schemes are proposed to reduce the data transmission latency and improve the system throughput by allowing simultaneous transmission of sensors in coexisting WBANs. When WBAN users move, they need to switch the channels frequently to avoid potential channel conflicts and to maintain the Quality of Service (QoS). However, frequent switching may raise energy consumption and aggravate delay. Existing multichannel assignment schemes failed to perform well in highly dynamic and densely deployed WBANs environments. In contrast to existing studies, this paper proposes a Prediction-based Channel Assignment (PCA) algorithm that selects the channels for WBANs to remain valid for future time instances and thus minimizes the delay and number of channel switches for dynamic and coexisting WBANs. When a WBAN needs to switch a channel, the proposed method predicts the future neighbors of that WBAN based on its history. It explores the channel information of present and future neighbors to select a suitable channel with higher resilience in a dynamic environment. Thus, our algorithm minimizes channel interference by avoiding unnecessary channel switching. We have used machine learning algorithms to predict the future neighbors of a WBAN. Experiment results show that the proposed algorithm performs better than an existing algorithm and random channel assignment in delay and throughput.","PeriodicalId":49316,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"1 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42413822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rajendran Sugin Elankavi, D. Dinakaran, A. Doss, R.M. Kuppan Chetty, M. M. Ramya
This paper discusses the development and design of two wheeled-type In-Pipe Inspection Robots (IPIRs), Kuzhali I and Kuzhali II, which were created to address the limitations of traditional human inspection methods and earlier robot designs. Specifically, the robots aim to overcome the motion singularity experienced by IPIRs when navigating through curved pipes. Kuzhali I was developed with wheels mounted at an asymmetric angle, which enables the wheels to maintain contact with the pipe’s surface, preventing motion singularity. However, Kuzhali I had limitations due to its prismatic mechanism, and thus Kuzhali II was developed with a telescopic mechanism to allow it to pass through vertical pipes with obstacles. Motion analysis was conducted on both robots to demonstrate how they overcome motion singularity and navigate through straight and curved pipelines. Simulation results showed that the forces acting on the robots’ wheels fell within 5 N to 12 N, demonstrating stability while navigating pipeline junctions. Experimental tests were conducted on Kuzhali II, and the results were compared to simulation results, showing an error of less than 5%. The results of the experiments indicate that Kuzhali II is safe to use for pipeline inspection, can navigate through vertical pipelines with ease and can overcome motion singularity in curved pipes. These robots offer a faster, more accurate, and safer alternative to human inspection, which can reduce the risk of pipeline failures and associated environmental and safety hazards.
{"title":"Design of a wheeled type in-pipe inspection robot to overcome motion singularity in curved pipes","authors":"Rajendran Sugin Elankavi, D. Dinakaran, A. Doss, R.M. Kuppan Chetty, M. M. Ramya","doi":"10.3233/ais-220247","DOIUrl":"https://doi.org/10.3233/ais-220247","url":null,"abstract":"This paper discusses the development and design of two wheeled-type In-Pipe Inspection Robots (IPIRs), Kuzhali I and Kuzhali II, which were created to address the limitations of traditional human inspection methods and earlier robot designs. Specifically, the robots aim to overcome the motion singularity experienced by IPIRs when navigating through curved pipes. Kuzhali I was developed with wheels mounted at an asymmetric angle, which enables the wheels to maintain contact with the pipe’s surface, preventing motion singularity. However, Kuzhali I had limitations due to its prismatic mechanism, and thus Kuzhali II was developed with a telescopic mechanism to allow it to pass through vertical pipes with obstacles. Motion analysis was conducted on both robots to demonstrate how they overcome motion singularity and navigate through straight and curved pipelines. Simulation results showed that the forces acting on the robots’ wheels fell within 5 N to 12 N, demonstrating stability while navigating pipeline junctions. Experimental tests were conducted on Kuzhali II, and the results were compared to simulation results, showing an error of less than 5%. The results of the experiments indicate that Kuzhali II is safe to use for pipeline inspection, can navigate through vertical pipelines with ease and can overcome motion singularity in curved pipes. These robots offer a faster, more accurate, and safer alternative to human inspection, which can reduce the risk of pipeline failures and associated environmental and safety hazards.","PeriodicalId":49316,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44727636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Preface to JAISE 15(2)","authors":"Juan Carlos Augusto, Hamid Aghajan, Andrés Muñoz","doi":"10.3233/ais-235002","DOIUrl":"https://doi.org/10.3233/ais-235002","url":null,"abstract":"","PeriodicalId":49316,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135363736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hamid Aghajan, Juan Carlos Augusto, Andrés Muñoz Ortega
Over the past fourteen years of its life, our Journal has been supported by a large number of colleagues who contributed with their time and expertise to assess the quality of the submissions to JAISE and helped decide which papers qualify to be published. These reviewers are an important part of the JAISE community and we would like to explicitly thank all of them for their valuable contributions. The effort of reviewers often remains unnoticed in the community served by a journal, especially in a blind review system. Since nine years ago, we have been acknowledging the participation of our reviewers in the making of JAISE. As a second step towards making our gratitude explicit and highlighting the importance of the contributions made by our reviewers, we have also implemented the practice of selecting two reviewers each year who have consistently provided detailed and quality reviews and inviting them to serve as part of the Editorial Board of JAISE. The list of reviewers in 2022 includes:1 Edgard Benitez-Guerrero, Zhiwen Xiao, Florentin Thullier, IndraRaj Upadhyaya, Qinghe Zheng, Michaelraj Kingston Roberts, J Logeshwaran, Raquel Martinez, Chellaswamy C., S. Rubin Bose, Mario Quinde, Shahzad Ali, Thien T.T. Le, Godwin Okechukwu Ogbuabor, Guadalupe Ortiz, Honghao Gao, Zhenglong Li, Filippo Palumbo, Awais Khan Jumani, Noorayisahbe binti mohd yaacob, Mohanraj Murugesan, Shanthini A., Sowmipriya Rajendiran, Gabriel A., Karthik Chandran, Mohamed Shakeel P., Suganiya Murugan, Velammal B.L., Mohamed P.S., Mimma Nardelli, Rajalakshmi Sivanaiah, Tülay Korkusuz Polat, pushpa gothwal, Pushpendu Kar, Sumit Kumar Jindal, Ganesh Babu R., Raviraja Holla, Siva Sankari Subbiah, Stephen Czarnuch, Xingqun Qi, Waheb A. Jabbar, Lavanya S., Fouziya Sulthana S., Michele Girolami, Mohammed B., Anbarasan M., Dinesh Samuel, Dhanasekar Kesavan, Bharat Subedi, Abdulsattar Abdullah Hamad, Sabri Barbaria, Hamad Absullah Hamad, Angelin Sophy, Ramesh Kumar S., Stan Curtis, Hui Yie Teh, David Lattanzi, Theodor Panagiotakopoulos, Claudio Vairo, Rosen Ivanov, Davoli Luca, Alexander Kröner, Davide La Rosa, Seonghun Lee, Mohammad Reza Ebrahimi Dishabi, Tong Wang, Sathishkumar V. E., Ihsane Gryech, Ali Araabi, Shabih Fatima, Yuan Roger Luo, David Susic, Balasubramaniyan Divager, Nawa Sakanga, Mervin R., Emna Ben Abdallah, Kenan Meng, S. Padmavathi, Sergio Aguilar Romero, Peer Stelldinger, Sivasankar Ganesan, Martin Stommel, Zijian Wang, Zhisheng Yan, Jose Gines Gimenez, Cherifa Nakkach, Marcel Voelschow, Yilin Kang, Marina Andric, Kumar Gaurav, Ziga Kolar, Lukas Hedegaard, Ali Kadhum M. Al-Qurabat, Bouneb Zine el Abidine, Amine Dahane, Ionel-Bujorel Pavaloiu, Wilfred Pinfold, Antonio Caruso, Ashad Kabir, Cho Doxuan, Ricardo Serafin Alonso, Israa Mohamed, Peyman Najafi, Liyakathunisa Syed.
{"title":"Acknowledgment of JAISE reviewers in 2022","authors":"Hamid Aghajan, Juan Carlos Augusto, Andrés Muñoz Ortega","doi":"10.3233/ais-235001","DOIUrl":"https://doi.org/10.3233/ais-235001","url":null,"abstract":"Over the past fourteen years of its life, our Journal has been supported by a large number of colleagues who contributed with their time and expertise to assess the quality of the submissions to JAISE and helped decide which papers qualify to be published. These reviewers are an important part of the JAISE community and we would like to explicitly thank all of them for their valuable contributions. The effort of reviewers often remains unnoticed in the community served by a journal, especially in a blind review system. Since nine years ago, we have been acknowledging the participation of our reviewers in the making of JAISE. As a second step towards making our gratitude explicit and highlighting the importance of the contributions made by our reviewers, we have also implemented the practice of selecting two reviewers each year who have consistently provided detailed and quality reviews and inviting them to serve as part of the Editorial Board of JAISE. The list of reviewers in 2022 includes:1 Edgard Benitez-Guerrero, Zhiwen Xiao, Florentin Thullier, IndraRaj Upadhyaya, Qinghe Zheng, Michaelraj Kingston Roberts, J Logeshwaran, Raquel Martinez, Chellaswamy C., S. Rubin Bose, Mario Quinde, Shahzad Ali, Thien T.T. Le, Godwin Okechukwu Ogbuabor, Guadalupe Ortiz, Honghao Gao, Zhenglong Li, Filippo Palumbo, Awais Khan Jumani, Noorayisahbe binti mohd yaacob, Mohanraj Murugesan, Shanthini A., Sowmipriya Rajendiran, Gabriel A., Karthik Chandran, Mohamed Shakeel P., Suganiya Murugan, Velammal B.L., Mohamed P.S., Mimma Nardelli, Rajalakshmi Sivanaiah, Tülay Korkusuz Polat, pushpa gothwal, Pushpendu Kar, Sumit Kumar Jindal, Ganesh Babu R., Raviraja Holla, Siva Sankari Subbiah, Stephen Czarnuch, Xingqun Qi, Waheb A. Jabbar, Lavanya S., Fouziya Sulthana S., Michele Girolami, Mohammed B., Anbarasan M., Dinesh Samuel, Dhanasekar Kesavan, Bharat Subedi, Abdulsattar Abdullah Hamad, Sabri Barbaria, Hamad Absullah Hamad, Angelin Sophy, Ramesh Kumar S., Stan Curtis, Hui Yie Teh, David Lattanzi, Theodor Panagiotakopoulos, Claudio Vairo, Rosen Ivanov, Davoli Luca, Alexander Kröner, Davide La Rosa, Seonghun Lee, Mohammad Reza Ebrahimi Dishabi, Tong Wang, Sathishkumar V. E., Ihsane Gryech, Ali Araabi, Shabih Fatima, Yuan Roger Luo, David Susic, Balasubramaniyan Divager, Nawa Sakanga, Mervin R., Emna Ben Abdallah, Kenan Meng, S. Padmavathi, Sergio Aguilar Romero, Peer Stelldinger, Sivasankar Ganesan, Martin Stommel, Zijian Wang, Zhisheng Yan, Jose Gines Gimenez, Cherifa Nakkach, Marcel Voelschow, Yilin Kang, Marina Andric, Kumar Gaurav, Ziga Kolar, Lukas Hedegaard, Ali Kadhum M. Al-Qurabat, Bouneb Zine el Abidine, Amine Dahane, Ionel-Bujorel Pavaloiu, Wilfred Pinfold, Antonio Caruso, Ashad Kabir, Cho Doxuan, Ricardo Serafin Alonso, Israa Mohamed, Peyman Najafi, Liyakathunisa Syed.","PeriodicalId":49316,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135822673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gleiston Guerrero-Ulloa, Alejandra Méndez-García, Valeria Torres-Lindao, Vivian Zamora-Mecías, C. Rodríguez-Domínguez, Miguel J. Hornos
The list of Sustainable Development Goals created by the United Nations include good health and well-being as one of its primary objectives. Pollution is a concern worldwide, and pollution levels inside buildings (homes or workplaces) can be higher than outdoors. To alleviate this problem and improve air quality, ornamental plants can be used. This paper presents the application of Internet of Things (IoT) technologies to develop a system called P4L, an acronym for “Plants for Life”. The objective of P4L is the automated care of potted plants to improve air quality and make the indoor environments of a building healthier. This IoT-based system (IoTS) has been developed through low-cost Arduino-compatible components. In addition, the Test-Driven Development Methodology for IoT-based Systems (TDDM4IoTS) has been used to guide P4L development. In fact, this article shows the result of the application of this methodology (phase by phase), with the help of the Test-Driven Development Tool for IoT-based Systems (TDDT4IoTS), which supports the aforementioned methodology, to develop P4L. To validate the methodology, we conducted a survey among developers that have used it, the results of which show that it is efficient and covers all aspects of IoTS development.
{"title":"Internet of Things (IoT)-based indoor plant care system","authors":"Gleiston Guerrero-Ulloa, Alejandra Méndez-García, Valeria Torres-Lindao, Vivian Zamora-Mecías, C. Rodríguez-Domínguez, Miguel J. Hornos","doi":"10.3233/ais-220483","DOIUrl":"https://doi.org/10.3233/ais-220483","url":null,"abstract":"The list of Sustainable Development Goals created by the United Nations include good health and well-being as one of its primary objectives. Pollution is a concern worldwide, and pollution levels inside buildings (homes or workplaces) can be higher than outdoors. To alleviate this problem and improve air quality, ornamental plants can be used. This paper presents the application of Internet of Things (IoT) technologies to develop a system called P4L, an acronym for “Plants for Life”. The objective of P4L is the automated care of potted plants to improve air quality and make the indoor environments of a building healthier. This IoT-based system (IoTS) has been developed through low-cost Arduino-compatible components. In addition, the Test-Driven Development Methodology for IoT-based Systems (TDDM4IoTS) has been used to guide P4L development. In fact, this article shows the result of the application of this methodology (phase by phase), with the help of the Test-Driven Development Tool for IoT-based Systems (TDDT4IoTS), which supports the aforementioned methodology, to develop P4L. To validate the methodology, we conducted a survey among developers that have used it, the results of which show that it is efficient and covers all aspects of IoTS development.","PeriodicalId":49316,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"36 1","pages":"47-62"},"PeriodicalIF":1.7,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85433267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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