Deepak Kumar Sharma, M. Devgan, Gaurav Malik, Prashant Dutt, Aarti Goel, Deepak Gupta, F. Al-turjman
The world of computation has shown wide variety of wonders in the past decade with Internet of Things (IoT) being one of the most promising technology. Emergence of IoT brings a lot of good to the technology pool with its capability to provide intelligent services to the users. With ease to use, IoT is backed by a strong Cloud based infrastructure which allows the sensory IoT devices to perform specific functions. Important features of cloud are its reliability and security where the latter must be dealt with proper care. Cloud centric systems are susceptible to Denial of Service (DoS) attacks wherein the cloud server is subjected to an overwhelming number of incoming requests by a malicious device. If the same attack is carried out by a network of devices such as IoT devices then it becomes a Distributed DoS (DDoS) attack. A DDoS attack may render the server useless for a long period of time causing the services to crash due to extensive load. This paper proposes a lightweight, efficient and robust method for DDoS attack by detecting the compromised node connected to the Fog node or edge devices before it reaches the cloud by taking advantage of the Fog layer and prevent it from harming any information recorded or from increasing the unnecessary traffic in a network. The chosen technology stack consists of languages and frameworks which allow proposed approach to works in real time complexity for faster execution and is flexible enough to work on low level systems such as the Fog nodes. The proposed approach uses mathematical models for forecasting data points and therefore does not rely on a computationally heavy approach such as neural networks for predicting the expected values. This approach can be easily modelled into the firmware of the system and can help make cloud services more reliable by cutting off rogue nodes that try to attack the cloud at any given point of time.
{"title":"DDoS prevention architecture using anomaly detection in fog-empowered networks","authors":"Deepak Kumar Sharma, M. Devgan, Gaurav Malik, Prashant Dutt, Aarti Goel, Deepak Gupta, F. Al-turjman","doi":"10.3233/AIS-210600","DOIUrl":"https://doi.org/10.3233/AIS-210600","url":null,"abstract":"The world of computation has shown wide variety of wonders in the past decade with Internet of Things (IoT) being one of the most promising technology. Emergence of IoT brings a lot of good to the technology pool with its capability to provide intelligent services to the users. With ease to use, IoT is backed by a strong Cloud based infrastructure which allows the sensory IoT devices to perform specific functions. Important features of cloud are its reliability and security where the latter must be dealt with proper care. Cloud centric systems are susceptible to Denial of Service (DoS) attacks wherein the cloud server is subjected to an overwhelming number of incoming requests by a malicious device. If the same attack is carried out by a network of devices such as IoT devices then it becomes a Distributed DoS (DDoS) attack. A DDoS attack may render the server useless for a long period of time causing the services to crash due to extensive load. This paper proposes a lightweight, efficient and robust method for DDoS attack by detecting the compromised node connected to the Fog node or edge devices before it reaches the cloud by taking advantage of the Fog layer and prevent it from harming any information recorded or from increasing the unnecessary traffic in a network. The chosen technology stack consists of languages and frameworks which allow proposed approach to works in real time complexity for faster execution and is flexible enough to work on low level systems such as the Fog nodes. The proposed approach uses mathematical models for forecasting data points and therefore does not rely on a computationally heavy approach such as neural networks for predicting the expected values. This approach can be easily modelled into the firmware of the system and can help make cloud services more reliable by cutting off rogue nodes that try to attack the cloud at any given point of time.","PeriodicalId":49316,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"52 1","pages":"201-217"},"PeriodicalIF":1.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88243308","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}
Smart manufacturing is a challenging trend being fostered by the Industry 4.0 paradigm. In this scenario Multi-Agent Systems (MAS) are particularly elected for modeling such types of intelligent, decentralised processes, thanks to their autonomy in pursuing collective and cooperative goals. From a human perspective, however, increasing the confidence in trustworthiness of MAS based Cyber-physical Production Systems (CPPS) remains a significant challenge. Manufacturing services must comply with strong requirements in terms of reliability, robustness and latency, and solution providers are expected to ensure that agents will operate within certain boundaries of the production, and mitigate unattended behaviours during the execution of manufacturing activities. To address this concern, a Manufacturing Agent Accountability Framework is proposed, a dynamic authorization framework that defines and enforces boundaries in which agents are freely permitted to exploit their intelligence to reach individual and collective objectives. The expected behaviour of agents is to adhere to CPPS workflows which implicitly define acceptable regions of behaviours and production feasibility. Core contributions of the proposed framework are: a manufacturing accountability model, the representation of the Leaf Diagrams for the governance of agent behavioural autonomy, and an ontology of declarative policies for the identification and avoidance of ill-intentioned behaviours in the execution of CPPS services. We outline the application of this enhanced trustworthiness framework to an agent-based manufacturing use-case for the production of a variety of hand tools.
{"title":"Towards trustworthy Cyber-physical Production Systems: A dynamic agent accountability approach","authors":"Richárd Beregi, G. Pedone, D. Preuveneers","doi":"10.3233/AIS-210593","DOIUrl":"https://doi.org/10.3233/AIS-210593","url":null,"abstract":"Smart manufacturing is a challenging trend being fostered by the Industry 4.0 paradigm. In this scenario Multi-Agent Systems (MAS) are particularly elected for modeling such types of intelligent, decentralised processes, thanks to their autonomy in pursuing collective and cooperative goals. From a human perspective, however, increasing the confidence in trustworthiness of MAS based Cyber-physical Production Systems (CPPS) remains a significant challenge. Manufacturing services must comply with strong requirements in terms of reliability, robustness and latency, and solution providers are expected to ensure that agents will operate within certain boundaries of the production, and mitigate unattended behaviours during the execution of manufacturing activities. To address this concern, a Manufacturing Agent Accountability Framework is proposed, a dynamic authorization framework that defines and enforces boundaries in which agents are freely permitted to exploit their intelligence to reach individual and collective objectives. The expected behaviour of agents is to adhere to CPPS workflows which implicitly define acceptable regions of behaviours and production feasibility. Core contributions of the proposed framework are: a manufacturing accountability model, the representation of the Leaf Diagrams for the governance of agent behavioural autonomy, and an ontology of declarative policies for the identification and avoidance of ill-intentioned behaviours in the execution of CPPS services. We outline the application of this enhanced trustworthiness framework to an agent-based manufacturing use-case for the production of a variety of hand tools.","PeriodicalId":49316,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"1 1","pages":"157-180"},"PeriodicalIF":1.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79767973","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 development of Road Information Acquisition Systems (RIASs) based on the Mobile Crowdsensing (MCS) paradigm has been widely studied for the last years. In that sense, most of the existing MCS-based RIASs focus on urban road networks and assume a car-based scenario. However, there exist a scarcity of approaches that pay attention to rural and country road networks. In that sense, forest paths are used for a wide range of recreational and sport activities by many different people and they can be also affected by different problems or obstacles blocking them. As a result, this work introduces SAMARITAN, a framework for rural-road network monitoring based on MCS. SAMARITAN analyzes the spatio-temporal trajectories from cyclists extracted from the fitness application Strava so as to uncover potential obstacles in a target road network. The framework has been evaluated in a real-world network of forest paths in the city of Cieza (Spain) showing quite promising results.
{"title":"Forest path condition monitoring based on crowd-based trajectory data analysis","authors":"Francisco Arcas-Túnez, Fernando Terroso-Sáenz","doi":"10.3233/ais-200586","DOIUrl":"https://doi.org/10.3233/ais-200586","url":null,"abstract":"The development of Road Information Acquisition Systems (RIASs) based on the Mobile Crowdsensing (MCS) paradigm has been widely studied for the last years. In that sense, most of the existing MCS-based RIASs focus on urban road networks and assume a car-based scenario. However, there exist a scarcity of approaches that pay attention to rural and country road networks. In that sense, forest paths are used for a wide range of recreational and sport activities by many different people and they can be also affected by different problems or obstacles blocking them. As a result, this work introduces SAMARITAN, a framework for rural-road network monitoring based on MCS. SAMARITAN analyzes the spatio-temporal trajectories from cyclists extracted from the fitness application Strava so as to uncover potential obstacles in a target road network. The framework has been evaluated in a real-world network of forest paths in the city of Cieza (Spain) showing quite promising results.","PeriodicalId":49316,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"29 1","pages":"37-54"},"PeriodicalIF":1.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80972958","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}
Raman Singh, M. Singh, Sheetal Garg, I. Perl, Olga Kalyonova, A. Penskoi
In the popular field of cloud computing, millions of job requests arrive at the data centre for execution. The job of the data centre is to optimally allocate virtual machines (VMs) to these job requests in order to use resources efficiently. In the future smart cities, huge amount of job requests and data will be generated by the Internet of Things (IoT) devices which will influence the designing of optimum resource management of smart cloud environments. The present paper analyses the performance efficiency of the data centre with and without job request consolidation. First, the work load performance of the data centre was analysed without job request consolidation, exhibiting that the job requests to VM assignment was highly imbalanced, and only 5% of VMs were running with a load factor of more than 70%. Then, the technique for order of preference by similarity to ideal solution-based VM selection algorithm was applied, which was able to select the best VM using parameters such as the provisioned or available central processing unit capacity, provisioned or available memory capacity, and state of machine (running, hibernated, or available). The Bitbrains dataset consisting of 1750 VMs was used to analyse the performance of the proposed methodology. The analysis concluded that the proposed methodology was capable of serving all job requests using less than 24% VMs with improved load efficiency. The fewer number of VMs with an improved load factor guarantees energy saving and an increase in the overall running efficiency of the smart data centre environment.
{"title":"Multicriteria decision making based optimum virtual machine selection technique for smart cloud environment","authors":"Raman Singh, M. Singh, Sheetal Garg, I. Perl, Olga Kalyonova, A. Penskoi","doi":"10.3233/AIS-210599","DOIUrl":"https://doi.org/10.3233/AIS-210599","url":null,"abstract":"In the popular field of cloud computing, millions of job requests arrive at the data centre for execution. The job of the data centre is to optimally allocate virtual machines (VMs) to these job requests in order to use resources efficiently. In the future smart cities, huge amount of job requests and data will be generated by the Internet of Things (IoT) devices which will influence the designing of optimum resource management of smart cloud environments. The present paper analyses the performance efficiency of the data centre with and without job request consolidation. First, the work load performance of the data centre was analysed without job request consolidation, exhibiting that the job requests to VM assignment was highly imbalanced, and only 5% of VMs were running with a load factor of more than 70%. Then, the technique for order of preference by similarity to ideal solution-based VM selection algorithm was applied, which was able to select the best VM using parameters such as the provisioned or available central processing unit capacity, provisioned or available memory capacity, and state of machine (running, hibernated, or available). The Bitbrains dataset consisting of 1750 VMs was used to analyse the performance of the proposed methodology. The analysis concluded that the proposed methodology was capable of serving all job requests using less than 24% VMs with improved load efficiency. The fewer number of VMs with an improved load factor guarantees energy saving and an increase in the overall running efficiency of the smart data centre environment.","PeriodicalId":49316,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"1 1","pages":"185-199"},"PeriodicalIF":1.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83507592","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":"Urban management image classification approach based on deep learning","authors":"Qinqing Kang, Xiong Ding","doi":"10.3233/AIS-210609","DOIUrl":"https://doi.org/10.3233/AIS-210609","url":null,"abstract":"","PeriodicalId":49316,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"15 5","pages":"347-360"},"PeriodicalIF":1.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72402082","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 13(1)","authors":"V. W. L. Tam, H. Aghajan, J. Augusto","doi":"10.3233/AIS-200589","DOIUrl":"https://doi.org/10.3233/AIS-200589","url":null,"abstract":"","PeriodicalId":49316,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"21 1","pages":"1"},"PeriodicalIF":1.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84496748","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}
Although spatial and temporal information has often been considered to improve recommendation performances, existing multi-criteria recommender systems often neglect to leverage spatial and temporal information. Also, it is a non-trivial task to simultaneously apply such information to recommendation services since the factors have interrelations to each other. In this paper, we propose a multi-criteria tensor model combining spatial and temporal information. The auxiliary information is categorized by several features and applied into the model. In particular, the spatial information of users’ countries is grouped into seven continents to reduce response times for learning the model. The single model enables to us keep the inherent structure of and the interrelations between multi-criteria and spatial/temporal information. To predict user preferences, tensor factorization based on Higher Order Singular Value Decomposition is exploited. Experimental results with a TripAdvisor dataset show that the proposed method outperforms other baseline methods based on a 2-dimensional rating matrix, tensor model, and other multi-criteria recommendation, in terms of RMSE and MAE. Furthermore, several experiments reveal the influences of the individual factors (i.e., multi-criteria, spatial and temporal information) and their consolidations, on restaurant recommendation. A comparative analysis of the multi-criteria elements shows that their influences relate to their correlations.
{"title":"Multi-criteria tensor model consolidating spatial and temporal information for tourism recommendation","authors":"Minsung Hong, Jason J. Jung","doi":"10.3233/ais-200584","DOIUrl":"https://doi.org/10.3233/ais-200584","url":null,"abstract":"Although spatial and temporal information has often been considered to improve recommendation performances, existing multi-criteria recommender systems often neglect to leverage spatial and temporal information. Also, it is a non-trivial task to simultaneously apply such information to recommendation services since the factors have interrelations to each other. In this paper, we propose a multi-criteria tensor model combining spatial and temporal information. The auxiliary information is categorized by several features and applied into the model. In particular, the spatial information of users’ countries is grouped into seven continents to reduce response times for learning the model. The single model enables to us keep the inherent structure of and the interrelations between multi-criteria and spatial/temporal information. To predict user preferences, tensor factorization based on Higher Order Singular Value Decomposition is exploited. Experimental results with a TripAdvisor dataset show that the proposed method outperforms other baseline methods based on a 2-dimensional rating matrix, tensor model, and other multi-criteria recommendation, in terms of RMSE and MAE. Furthermore, several experiments reveal the influences of the individual factors (i.e., multi-criteria, spatial and temporal information) and their consolidations, on restaurant recommendation. A comparative analysis of the multi-criteria elements shows that their influences relate to their correlations.","PeriodicalId":49316,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"5 1","pages":"5-19"},"PeriodicalIF":1.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82125389","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}
Supporting smart services are expected to become the characteristic of all cities in the world. This brings with it a number of security challenges as breaching security might have a devastating impact on citizen and city infras-tructure. This thematic issue on trustworthy computing for secure smart cities attempts to shed light on the latest research trends on the improvement of smart city security using trustworthy computing methods and techniques. We hope that researchers will benefit from the papers in this issue and find more motivation to pay attention to this important need. The paper “ Multi-criteria decision making-based optimum virtual machine selection technique for smart cloud environment ” by Singh et al. analyses the performance efficiency of the data centre with and without job request consolidation. A technique for determining the order of preferences was proposed using similarity to the ideal solution-based virtual machine selection algorithm, which was able to select the best VM using parameters such as the provisioned or available capacity, and memory, as well as the state of the machine. In the paper entitled “ DDoS prevention architecture using anomaly detection in Fog-empowered networks ” by Sharma et al., the authors propose a lightweight and robust framework for DDoS attack detection and prevention using mathematical models for detecting anomalies in the behaviour of Fog devices connected to the Fog node. The proposed approach is an efficient algorithm to identify and handle DDoS causing devices on a network by identifying the rogue node. A mist-assisted Fog computing-based load balancing strategy for smart cities resource allocation on a and reinforcement learning in combination a load balancing procedure. proposed model
{"title":"Trustworthy computing for secure smart cities","authors":"W. Mansoor, V. Vijayakumar","doi":"10.3233/AIS-210597","DOIUrl":"https://doi.org/10.3233/AIS-210597","url":null,"abstract":"Supporting smart services are expected to become the characteristic of all cities in the world. This brings with it a number of security challenges as breaching security might have a devastating impact on citizen and city infras-tructure. This thematic issue on trustworthy computing for secure smart cities attempts to shed light on the latest research trends on the improvement of smart city security using trustworthy computing methods and techniques. We hope that researchers will benefit from the papers in this issue and find more motivation to pay attention to this important need. The paper “ Multi-criteria decision making-based optimum virtual machine selection technique for smart cloud environment ” by Singh et al. analyses the performance efficiency of the data centre with and without job request consolidation. A technique for determining the order of preferences was proposed using similarity to the ideal solution-based virtual machine selection algorithm, which was able to select the best VM using parameters such as the provisioned or available capacity, and memory, as well as the state of the machine. In the paper entitled “ DDoS prevention architecture using anomaly detection in Fog-empowered networks ” by Sharma et al., the authors propose a lightweight and robust framework for DDoS attack detection and prevention using mathematical models for detecting anomalies in the behaviour of Fog devices connected to the Fog node. The proposed approach is an efficient algorithm to identify and handle DDoS causing devices on a network by identifying the rogue node. A mist-assisted Fog computing-based load balancing strategy for smart cities resource allocation on a and reinforcement learning in combination a load balancing procedure. proposed model","PeriodicalId":49316,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"43 1","pages":"183-184"},"PeriodicalIF":1.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84005039","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}
Liyun Gong, Lu Zhang, Ming Zhu, Miao Yu, Ross Clifford, Carol Duff, Xujiong Ye, S. Kollias
In this paper, we propose a novel person specific fall detection system based on a monocular camera, which can be applied for assisting the independent living of an older adult living alone at home. A single camera covering the living area is used for video recordings of an elderly person’s normal daily activities. From the recorded video data, the human silhouette regions in every frame are then extracted based on the codebook background subtraction technique. Low-dimensionality representative features of extracted silhouetted are then extracted by convolutional neural network-based autoencoder (CNN-AE). Features obtained from the CNN-AE are applied to construct an one class support vector machine (OCSVM) model, which is a data driven model based on the video recordings and can be applied for fall detection. From the comprehensive experimental evaluations on different people in a real home environment, it is shown that the proposed fall detection system can successfully detect different types of falls (falls towards different orientations at different positions in a real home environment) with small false alarms.
{"title":"A novel computer vision-based data driven modelling approach for person specific fall detection","authors":"Liyun Gong, Lu Zhang, Ming Zhu, Miao Yu, Ross Clifford, Carol Duff, Xujiong Ye, S. Kollias","doi":"10.3233/AIS-210611","DOIUrl":"https://doi.org/10.3233/AIS-210611","url":null,"abstract":"In this paper, we propose a novel person specific fall detection system based on a monocular camera, which can be applied for assisting the independent living of an older adult living alone at home. A single camera covering the living area is used for video recordings of an elderly person’s normal daily activities. From the recorded video data, the human silhouette regions in every frame are then extracted based on the codebook background subtraction technique. Low-dimensionality representative features of extracted silhouetted are then extracted by convolutional neural network-based autoencoder (CNN-AE). Features obtained from the CNN-AE are applied to construct an one class support vector machine (OCSVM) model, which is a data driven model based on the video recordings and can be applied for fall detection. From the comprehensive experimental evaluations on different people in a real home environment, it is shown that the proposed fall detection system can successfully detect different types of falls (falls towards different orientations at different positions in a real home environment) with small false alarms.","PeriodicalId":49316,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"167 1","pages":"373-387"},"PeriodicalIF":1.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80495621","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}
M. J. Telles, R. Santos, Juarez Machado da Silva, R. Righi, J. Barbosa
Smart cities emergence has allowed a wide variety of technological services to metropolitan areas. These services can improve life quality, minimize environmental impacts, improve health service, improve security, and bear the increasing number of people in the cities. Life quality encompasses many subjects, and accessibility for People with Disabilities (PwD) is one. In this article, smart cities focused on helping PwD are called Assistive Smart Cities (ASCs). In this sense, the article proposes a Model for Assistive Smart Cities called MASC. Related works do not cover geographically broad areas, such as cities and metropolitan regions. Moreover, they are not generic in terms of disabilities and are usually intended only for one type of disability. Given this scenario, the MASC covers large regions and supports various disabilities, such as hearing, visual impairment, and limitation of lower limb movements. Unlike the related works, MASC uses the interactions of PwD to compose histories of contexts offered as services. MASC proposes an ontology-based on ubiquitous accessibility concepts. The model evaluation focused on performance, functionality, and usability. Performance and functionality evaluations were performed using data generated by a context simulator called Siafu and data from the Open Street Maps (OSM) platform. Usability was evaluated using a smart wheelchair prototype. The results of usability show 96% acceptance regarding ease of use and 98% regarding system utility. The results indicate that the model supports massive applications, managing information to generate trails. Besides, MASC provides services for different types of users, namely PwD, healthcare professionals, and public administration.
{"title":"An intelligent model to assist people with disabilities in smart cities","authors":"M. J. Telles, R. Santos, Juarez Machado da Silva, R. Righi, J. Barbosa","doi":"10.3233/AIS-210606","DOIUrl":"https://doi.org/10.3233/AIS-210606","url":null,"abstract":"Smart cities emergence has allowed a wide variety of technological services to metropolitan areas. These services can improve life quality, minimize environmental impacts, improve health service, improve security, and bear the increasing number of people in the cities. Life quality encompasses many subjects, and accessibility for People with Disabilities (PwD) is one. In this article, smart cities focused on helping PwD are called Assistive Smart Cities (ASCs). In this sense, the article proposes a Model for Assistive Smart Cities called MASC. Related works do not cover geographically broad areas, such as cities and metropolitan regions. Moreover, they are not generic in terms of disabilities and are usually intended only for one type of disability. Given this scenario, the MASC covers large regions and supports various disabilities, such as hearing, visual impairment, and limitation of lower limb movements. Unlike the related works, MASC uses the interactions of PwD to compose histories of contexts offered as services. MASC proposes an ontology-based on ubiquitous accessibility concepts. The model evaluation focused on performance, functionality, and usability. Performance and functionality evaluations were performed using data generated by a context simulator called Siafu and data from the Open Street Maps (OSM) platform. Usability was evaluated using a smart wheelchair prototype. The results of usability show 96% acceptance regarding ease of use and 98% regarding system utility. The results indicate that the model supports massive applications, managing information to generate trails. Besides, MASC provides services for different types of users, namely PwD, healthcare professionals, and public administration.","PeriodicalId":49316,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"41 1","pages":"301-324"},"PeriodicalIF":1.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79371296","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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