Vincent Cergolj, Simon Stankoski, Matija Pirc, M. Luštrek
Adequate hydration is important for one’s health, but many people do not consume sufficient fluids. By constantly monitoring fluid intake, we gain information that can be extremely useful in dealing with unhealthy drinking habits. This paper deals with the problem of developing a machine learning method for drinking detection, intended for use on an edge device, with a specific focus on power consumption. The proposed approach is based on data from inertial sensors built into a practical, non-invasive wrist-worn device that monitors wrist movement throughout the day and automatically detects drinking events. It ensures low energy consumption by triggering the machine learning only when the probability of drinking is high, as well as by other energy saving measures. To develop and validate our methods, we collected data from 19 participants, which resulted in 135 hours of data, of which 2 hours and 30 minutes correspond to drinking activities. The algorithm was thoroughly assessed through both offline testing and by running the algorithm directly on the wristband in real life. During the offline evaluation, we obtained a precision of 94.5 %, a recall of 84.9 %, and an F1 score of 89.4 %. Testing in real life demonstrated a precision of 74.5 % and a recall of 89.9 %. Additionally, the energy efficiency analysis showed that our proposed technique for triggering the drinking detection method reduced the battery power consumption during the periods of inactivity by a factor of 5.8 compared to continuously monitoring for drinking events.
{"title":"Drinking event detection on a sensing wristband using machine learning","authors":"Vincent Cergolj, Simon Stankoski, Matija Pirc, M. Luštrek","doi":"10.3233/ais-230524","DOIUrl":"https://doi.org/10.3233/ais-230524","url":null,"abstract":"Adequate hydration is important for one’s health, but many people do not consume sufficient fluids. By constantly monitoring fluid intake, we gain information that can be extremely useful in dealing with unhealthy drinking habits. This paper deals with the problem of developing a machine learning method for drinking detection, intended for use on an edge device, with a specific focus on power consumption. The proposed approach is based on data from inertial sensors built into a practical, non-invasive wrist-worn device that monitors wrist movement throughout the day and automatically detects drinking events. It ensures low energy consumption by triggering the machine learning only when the probability of drinking is high, as well as by other energy saving measures. To develop and validate our methods, we collected data from 19 participants, which resulted in 135 hours of data, of which 2 hours and 30 minutes correspond to drinking activities. The algorithm was thoroughly assessed through both offline testing and by running the algorithm directly on the wristband in real life. During the offline evaluation, we obtained a precision of 94.5 %, a recall of 84.9 %, and an F1 score of 89.4 %. Testing in real life demonstrated a precision of 74.5 % and a recall of 89.9 %. Additionally, the energy efficiency analysis showed that our proposed technique for triggering the drinking detection method reduced the battery power consumption during the periods of inactivity by a factor of 5.8 compared to continuously monitoring for drinking events.","PeriodicalId":508128,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"120 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141647106","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}
One of the challenges drivers face in today’s fast-paced urban world is the ability to access parking spaces quickly. The increase in the number of vehicles in cities, especially in urban areas, as well as the growth of network services in smart cities, has created the need to provide easy-to-use smart parking networks. However, the most critical challenge in building such a network is the secure storage of customer and service provider information. Using blockchain to store data securely requires powerful computing resources and high energy consumption. In this research, combining existing ideas in the field of smart parking and blockchain, a solution will be proposed to use blockchain in dynamic nodes with poor computing resources, such as sensors used in smart parking. As one of the layers introduced in smart parking, we simulated the proposed algorithm in a wireless sensor network (WSN). In light of the findings of previous studies demonstrating the efficacy of Zigbee technology as a mesh topology in WSNs with low-cost infrastructure and resource requirements and our simulation results, we employed this technology to evaluate its performance in our proposed smart parking application. On the other hand, functions created with the Solidity language can use online payment services on such a secure network.
{"title":"Secure storage of dynamic node information in smart parking using local blockchain","authors":"Saeed Khanjari, Amir Masoud Rahmani","doi":"10.3233/ais-230269","DOIUrl":"https://doi.org/10.3233/ais-230269","url":null,"abstract":"One of the challenges drivers face in today’s fast-paced urban world is the ability to access parking spaces quickly. The increase in the number of vehicles in cities, especially in urban areas, as well as the growth of network services in smart cities, has created the need to provide easy-to-use smart parking networks. However, the most critical challenge in building such a network is the secure storage of customer and service provider information. Using blockchain to store data securely requires powerful computing resources and high energy consumption. In this research, combining existing ideas in the field of smart parking and blockchain, a solution will be proposed to use blockchain in dynamic nodes with poor computing resources, such as sensors used in smart parking. As one of the layers introduced in smart parking, we simulated the proposed algorithm in a wireless sensor network (WSN). In light of the findings of previous studies demonstrating the efficacy of Zigbee technology as a mesh topology in WSNs with low-cost infrastructure and resource requirements and our simulation results, we employed this technology to evaluate its performance in our proposed smart parking application. On the other hand, functions created with the Solidity language can use online payment services on such a secure network.","PeriodicalId":508128,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"49 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141338267","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}
Smart homes integrate several sensors to facilitate information exchange and the execution of tasks. In addition, with the development of the Internet of Things (IoT) platforms, the control of appliances and remote devices has become possible. This sensor collects data in real time to closely monitor the devices of a user’s household. The present study employs a machine learning methodology to perform a global analysis of energy consumption and efficiency in smart homes. In This work we propose two advanced ensemble models to improve the performance of energy consumption in smart homes, the first one is a voting ensemble model based on a ranking weight averaging that combines following basic machine learning techniques: decision tree (DT), random forest (RF), and eXtreme Gradient Boosting (XGB). The second one is the stacking ensemble model in which the basic models (DT-RF-XGB) are combined through stacked generalization, then uses a secondary layer model or meta-learner (RF) to provide output prediction. The findings obtained show that the proposed ensemble model based on DT-RF-XGB using stacking technique surpasses all other basic algorithms with R2 around 0.9825.
{"title":"Forecasting energy demand and efficiency in a smart home environment through advanced ensemble model: Stacking and voting","authors":"Nadia Drir, Younes Kebour","doi":"10.3233/ais-230134","DOIUrl":"https://doi.org/10.3233/ais-230134","url":null,"abstract":"Smart homes integrate several sensors to facilitate information exchange and the execution of tasks. In addition, with the development of the Internet of Things (IoT) platforms, the control of appliances and remote devices has become possible. This sensor collects data in real time to closely monitor the devices of a user’s household. The present study employs a machine learning methodology to perform a global analysis of energy consumption and efficiency in smart homes. In This work we propose two advanced ensemble models to improve the performance of energy consumption in smart homes, the first one is a voting ensemble model based on a ranking weight averaging that combines following basic machine learning techniques: decision tree (DT), random forest (RF), and eXtreme Gradient Boosting (XGB). The second one is the stacking ensemble model in which the basic models (DT-RF-XGB) are combined through stacked generalization, then uses a secondary layer model or meta-learner (RF) to provide output prediction. The findings obtained show that the proposed ensemble model based on DT-RF-XGB using stacking technique surpasses all other basic algorithms with R2 around 0.9825.","PeriodicalId":508128,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"51 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141339310","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}
Juan Morales-García, Diego Padilla-Quimbiulco, Magdalena Cantabella, Belén Ayuso, Andrés Muñoz, José M. Cecilia
Greenhouses constitute intricate systems where numerous variables play a pivotal role in enhancing crop yields within the framework of intensive agriculture. Consequently, real-time monitoring and visualization of these variables are imperative to strike a balance between resource efficiency and production maximization. Furthermore, the ability to make predictive assessments regarding these variables is essential to avert potential greenhouse disasters. In this article, we introduce an intelligent alert system designed to efficiently oversee agricultural operations within a functioning greenhouse, ultimately bolstering productivity through the optimization of crop growth and energy consumption. This system comprises a web application, GreenhouseGuard, which improves the graphical and statistical representation of data collected by a network of sensors strategically positioned throughout the greenhouse, as well as the forecasts generated from this data. These sensors are strategically located to provide more precise real-time data readings, thereby minimizing error margins. Moreover, GreenhouseGuard offers diverse data visualization options and forecasts of greenhouse variables to enable in-depth analysis of the acquired information. Consequently, this alert system empowers greenhouse managers to proactively address abnormal situations that may jeopardize their crop yields.
{"title":"GreenhouseGuard: Enabling real-time warning prediction for smart greenhouse management","authors":"Juan Morales-García, Diego Padilla-Quimbiulco, Magdalena Cantabella, Belén Ayuso, Andrés Muñoz, José M. Cecilia","doi":"10.3233/ais-230359","DOIUrl":"https://doi.org/10.3233/ais-230359","url":null,"abstract":"Greenhouses constitute intricate systems where numerous variables play a pivotal role in enhancing crop yields within the framework of intensive agriculture. Consequently, real-time monitoring and visualization of these variables are imperative to strike a balance between resource efficiency and production maximization. Furthermore, the ability to make predictive assessments regarding these variables is essential to avert potential greenhouse disasters. In this article, we introduce an intelligent alert system designed to efficiently oversee agricultural operations within a functioning greenhouse, ultimately bolstering productivity through the optimization of crop growth and energy consumption. This system comprises a web application, GreenhouseGuard, which improves the graphical and statistical representation of data collected by a network of sensors strategically positioned throughout the greenhouse, as well as the forecasts generated from this data. These sensors are strategically located to provide more precise real-time data readings, thereby minimizing error margins. Moreover, GreenhouseGuard offers diverse data visualization options and forecasts of greenhouse variables to enable in-depth analysis of the acquired information. Consequently, this alert system empowers greenhouse managers to proactively address abnormal situations that may jeopardize their crop yields.","PeriodicalId":508128,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"48 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141338367","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}
Asha Ayyappan, Rajesh Arunachalam, Manivel Lenin Kumar
The “Wireless Sensor Networks (WSN)” has gained a lot of interest among research scholars and has been utilized in various advanced applications in distinct fields. Along with the load balancing techniques, the clustering scheme also prolongs the network’s overall lifespan. The “Cluster Head (CH)” performs the task of load balancing between the nodes in the “Clustering algorithm”; hence, the CH selection procedure is regarded as a critical task in the case of the clustering algorithms. Depending on the CH selection and cluster nodes, the rate of energy consumed by these CHs will be reduced in the wireless sensor. CH selection is a promising solution for the transmission of information within various parameters. Thus, CH selection leads to an increase in the duration of the system and a reduction in the energy utilization by the nodes. Therefore, an “optimization-based CH selection” mechanism in WSN is developed in this paper along with an enhanced node communication performance prediction strategy to provide better communication between the “Sensor Nodes (SNs)” with limited energy expenditure. The node’s communication performance is predicted using the Adaptive Fuzzy, in which metrics such as bit rate, latency, throughput, loss, and packet delivery ratio are specified as the input to the network. Here, the parameters within the fuzzy network are tuned with the help of the recommended “Hybrid Position of Heap and African Buffalo Optimization (HP-HABO)”. Then, to perform efficient node clustering, the “Optimal K-Means Clustering (OKMC)” approach is executed and the CHs are formed using the developed HP-HABO. The objective function depends on the constraints like energy, distance, and predicted communication performance attained by forming these CHs. The performance of the developed CH selection mechanism is verified by analyzing the experimental outcome of the proposed technique with different optimization algorithms and previous works concerning the objective constraints.
{"title":"Adaptive fuzzy-based node communication performance prediction with hybrid heuristic Cluster Head selection framework in WSN using enhanced K-means clustering mechanism","authors":"Asha Ayyappan, Rajesh Arunachalam, Manivel Lenin Kumar","doi":"10.3233/ais-230408","DOIUrl":"https://doi.org/10.3233/ais-230408","url":null,"abstract":"The “Wireless Sensor Networks (WSN)” has gained a lot of interest among research scholars and has been utilized in various advanced applications in distinct fields. Along with the load balancing techniques, the clustering scheme also prolongs the network’s overall lifespan. The “Cluster Head (CH)” performs the task of load balancing between the nodes in the “Clustering algorithm”; hence, the CH selection procedure is regarded as a critical task in the case of the clustering algorithms. Depending on the CH selection and cluster nodes, the rate of energy consumed by these CHs will be reduced in the wireless sensor. CH selection is a promising solution for the transmission of information within various parameters. Thus, CH selection leads to an increase in the duration of the system and a reduction in the energy utilization by the nodes. Therefore, an “optimization-based CH selection” mechanism in WSN is developed in this paper along with an enhanced node communication performance prediction strategy to provide better communication between the “Sensor Nodes (SNs)” with limited energy expenditure. The node’s communication performance is predicted using the Adaptive Fuzzy, in which metrics such as bit rate, latency, throughput, loss, and packet delivery ratio are specified as the input to the network. Here, the parameters within the fuzzy network are tuned with the help of the recommended “Hybrid Position of Heap and African Buffalo Optimization (HP-HABO)”. Then, to perform efficient node clustering, the “Optimal K-Means Clustering (OKMC)” approach is executed and the CHs are formed using the developed HP-HABO. The objective function depends on the constraints like energy, distance, and predicted communication performance attained by forming these CHs. The performance of the developed CH selection mechanism is verified by analyzing the experimental outcome of the proposed technique with different optimization algorithms and previous works concerning the objective constraints.","PeriodicalId":508128,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"18 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141346334","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}
{"title":"Preface to JAISE 16(2)","authors":"J. Augusto, H. Aghajan","doi":"10.3233/ais-246002","DOIUrl":"https://doi.org/10.3233/ais-246002","url":null,"abstract":"","PeriodicalId":508128,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":" 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141373442","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}
Connected and Automated Vehicles (CAVs) are rapidly evolving technology with great benefits such as reducing gas emissions and decreasing traffic congestion. They have the potential to change the traditional transportation industry due to their benefits. However, the implementation phase for CAVs decelerates with the uncertainties of legislation on privacy-preserving and public concerns. Perception of people needs to be understood beforehand. Main concern points like possible attacks and mitigation techniques, and privacy protection should be addressed. Certain regulation system should be implemented, and transportation habits should be considered. After thinking over those points, adaption of CAVs can be achieved more smoothly. In this survey paper, we aim to shed light on the obstacles to the widespread use of CAVs by collecting existing literature and creating a sophisticated bouquet of the issues. Public perception, common attacks and mitigation techniques, privacy protection, regulations, and possible transportation habit shifts related to CAVs are examined. With the information gathered from this survey, manufacturers and policymakers can determine an influential pathway for the development of CAVs.
{"title":"A survey on obstacles to the widespread use of connected and automated vehicles","authors":"Serra Uysal, Mehmet Tahir Sandikkaya","doi":"10.3233/ais-230232","DOIUrl":"https://doi.org/10.3233/ais-230232","url":null,"abstract":"Connected and Automated Vehicles (CAVs) are rapidly evolving technology with great benefits such as reducing gas emissions and decreasing traffic congestion. They have the potential to change the traditional transportation industry due to their benefits. However, the implementation phase for CAVs decelerates with the uncertainties of legislation on privacy-preserving and public concerns. Perception of people needs to be understood beforehand. Main concern points like possible attacks and mitigation techniques, and privacy protection should be addressed. Certain regulation system should be implemented, and transportation habits should be considered. After thinking over those points, adaption of CAVs can be achieved more smoothly. In this survey paper, we aim to shed light on the obstacles to the widespread use of CAVs by collecting existing literature and creating a sophisticated bouquet of the issues. Public perception, common attacks and mitigation techniques, privacy protection, regulations, and possible transportation habit shifts related to CAVs are examined. With the information gathered from this survey, manufacturers and policymakers can determine an influential pathway for the development of CAVs.","PeriodicalId":508128,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"120 31","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140381159","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}
Naoto Takeda, Roberto Legaspi, Yasutaka Nishimura, Kazushi Ikeda, A. Minamikawa, Thomas Plötz, Sonia Chernova
We propose a framework for predicting sensor event sequences (SES) in smart homes, which can proactively support residents’ activities and alert them if activities are not completed as intended. We leverage ongoing activity recognition to enhance the prediction performance, employing a GPT2-based model typically used for sentence generation. We hypothesize that the relationship between ongoing activities and SES patterns is akin to the relationship between topics and word sequence patterns in natural language processing (NLP), enabling us to apply the GPT2-based model to SES prediction. We empirically evaluated our method using two real-world datasets in which residents performed their usual daily activities. Our experimental results demonstrates that the use of the GPT2-based model significantly improves the F1 value of SES prediction from 0.461 to 0.708 compared to the state-of-the-art method, and that leveraging knowledge on ongoing activity can further improve performance to 0.837. Achieving these SES predictions using the ongoing activity recognition model required simple feature engineering and modeling, yielding a performance rate of approximately 80%.
我们提出了一个预测智能家居中传感器事件序列（SES）的框架，它可以主动支持居民的活动，并在活动未按预期完成时发出警报。我们采用通常用于句子生成的基于 GPT2 的模型，利用正在进行的活动识别来提高预测性能。我们假设，正在进行的活动与 SES 模式之间的关系类似于自然语言处理 (NLP) 中主题与词序模式之间的关系，这使我们能够将基于 GPT2 的模型应用于 SES 预测。我们使用两个真实世界的数据集对我们的方法进行了实证评估，这些数据集中的居民都在进行日常活动。我们的实验结果表明，与最先进的方法相比，使用基于 GPT2 的模型可显著提高 SES 预测的 F1 值，从 0.461 提高到 0.708，而利用正在进行的活动知识可将性能进一步提高到 0.837。利用正在进行的活动识别模型实现这些 SES 预测只需简单的特征工程和建模，性能比约为 80%。
{"title":"Sensor event sequence prediction for proactive smart home: A GPT2-based autoregressive language model approach","authors":"Naoto Takeda, Roberto Legaspi, Yasutaka Nishimura, Kazushi Ikeda, A. Minamikawa, Thomas Plötz, Sonia Chernova","doi":"10.3233/ais-230429","DOIUrl":"https://doi.org/10.3233/ais-230429","url":null,"abstract":"We propose a framework for predicting sensor event sequences (SES) in smart homes, which can proactively support residents’ activities and alert them if activities are not completed as intended. We leverage ongoing activity recognition to enhance the prediction performance, employing a GPT2-based model typically used for sentence generation. We hypothesize that the relationship between ongoing activities and SES patterns is akin to the relationship between topics and word sequence patterns in natural language processing (NLP), enabling us to apply the GPT2-based model to SES prediction. We empirically evaluated our method using two real-world datasets in which residents performed their usual daily activities. Our experimental results demonstrates that the use of the GPT2-based model significantly improves the F1 value of SES prediction from 0.461 to 0.708 compared to the state-of-the-art method, and that leveraging knowledge on ongoing activity can further improve performance to 0.837. Achieving these SES predictions using the ongoing activity recognition model required simple feature engineering and modeling, yielding a performance rate of approximately 80%.","PeriodicalId":508128,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":" February","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140383396","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}
{"title":"Acknowledgment of JAISE reviewers in 2023","authors":"H. Aghajan, J. Augusto, Andrés Muñoz Ortega","doi":"10.3233/ais-246001","DOIUrl":"https://doi.org/10.3233/ais-246001","url":null,"abstract":"","PeriodicalId":508128,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"40 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140242769","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}
{"title":"Preface to JAISE 16(1)","authors":"H. Aghajan, J. Augusto","doi":"10.3233/ais-246000","DOIUrl":"https://doi.org/10.3233/ais-246000","url":null,"abstract":"","PeriodicalId":508128,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"23 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140242291","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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