Pub Date : 2022-06-30DOI: 10.1109/ISCC55528.2022.9912819
Yichuan Li, M. El-Hajjar
As a low-cost solution for the 5G communication system, centralised radio access network (C- RAN) has been implemented in the ultra-dense environment, where radio over fiber (RoF) technology can enable reduced operational cost as well as coordinated multi-point (CoMP) despite its less-robustness and reduced system performance. On the other hand, machine learning has been recognised as an efficient method for accelerating the fiber-optic communications with the aid of the advancements of the learning algorithms as well as the available high processing capabilities. In this paper, we propose a supervised learning-aided A - RoF system, where the logistic regression classification is invoked for removing the A-RoF module's need for re-customization and for boosting its performance. As a result, we can adaptively select the modulation format according to the optical power and the RF voltage, where we obtain an enhanced spectral efficiency and dynamic range (DR) by a factor of 4/3 and 19/13, respectively, while the learning network can be updated online.
{"title":"Intelligent Analog Radio Over Fiber aided C-RAN for Mitigating Nonlinearity and Improving Robustness","authors":"Yichuan Li, M. El-Hajjar","doi":"10.1109/ISCC55528.2022.9912819","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912819","url":null,"abstract":"As a low-cost solution for the 5G communication system, centralised radio access network (C- RAN) has been implemented in the ultra-dense environment, where radio over fiber (RoF) technology can enable reduced operational cost as well as coordinated multi-point (CoMP) despite its less-robustness and reduced system performance. On the other hand, machine learning has been recognised as an efficient method for accelerating the fiber-optic communications with the aid of the advancements of the learning algorithms as well as the available high processing capabilities. In this paper, we propose a supervised learning-aided A - RoF system, where the logistic regression classification is invoked for removing the A-RoF module's need for re-customization and for boosting its performance. As a result, we can adaptively select the modulation format according to the optical power and the RF voltage, where we obtain an enhanced spectral efficiency and dynamic range (DR) by a factor of 4/3 and 19/13, respectively, while the learning network can be updated online.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130589858","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}
Pub Date : 2022-06-30DOI: 10.1109/ISCC55528.2022.9912799
Francesco Martella, M. Fazio, A. Celesti, Valeria Lukaj, A. Quattrocchi, M. D. Gangi, M. Villari
Nowadays, video surveillance is a very common practice in Smart Cities. There are public and private video surveillance systems, and very often different systems or single devices frame the same area. However, when a target needs to be identified or needs to be tracked in real-time, such solutions typically require human intervention to configure the devices in the best possible way (e.g., choosing the optimal cameras, setting up their focus, and so on). To address such a problem, in this paper, we define a new interrogation method based on a Federated Edge approach. This approach addresses the problem from the point of view of both camera hardware and shooting angle associated with it. According to the presented approach, it is possible to understand which the best camera to identify a target and possibly tracking it in a specific area is. A case study is defined in the context of urban mobility management.
{"title":"Federated Edge for Tracking Mobile Targets on Video Surveillance Streams in Smart Cities","authors":"Francesco Martella, M. Fazio, A. Celesti, Valeria Lukaj, A. Quattrocchi, M. D. Gangi, M. Villari","doi":"10.1109/ISCC55528.2022.9912799","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912799","url":null,"abstract":"Nowadays, video surveillance is a very common practice in Smart Cities. There are public and private video surveillance systems, and very often different systems or single devices frame the same area. However, when a target needs to be identified or needs to be tracked in real-time, such solutions typically require human intervention to configure the devices in the best possible way (e.g., choosing the optimal cameras, setting up their focus, and so on). To address such a problem, in this paper, we define a new interrogation method based on a Federated Edge approach. This approach addresses the problem from the point of view of both camera hardware and shooting angle associated with it. According to the presented approach, it is possible to understand which the best camera to identify a target and possibly tracking it in a specific area is. A case study is defined in the context of urban mobility management.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123200335","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}
Pub Date : 2022-06-30DOI: 10.1109/ISCC55528.2022.9912800
L. D. Dominicis, Spyridion Kolovos, Kakia Panagidi, Ralph Hedel, Ilias Mitsoulas, K. Boudergui, Argiro Boziari, S. Hadjiefthymiades
In the framework of the H2020 project INCLUDING (www.including-cluster.eu), the Piraeus port commercial terminal has hosted a field exercise on the identification and recovery of two orphan sources inside a cargo container. The primary objective of the field exercise was to test the integration of Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) in the response plan coordinated by the CBRN experts of the Hellenic Ministry of Defence and the first operational verification of a platform under development in the project for the management of the mobilized resources on the incident scene. The activity marks a solid step ahead in introducing innovation in the exercise and training activities in the nuclear security domain and in improving sharing of resources at EU level. In this article is described the concept of operation of the INCLUDING platform, its architecture and its use in the different phase of the Piraeus port exercise, that is one of the eight Joint Action planned during the INCLUDING project duration.
{"title":"The INCLUDING platform for Radiological and Nuclear exercises and training: the Joint Action at the Piraeus port","authors":"L. D. Dominicis, Spyridion Kolovos, Kakia Panagidi, Ralph Hedel, Ilias Mitsoulas, K. Boudergui, Argiro Boziari, S. Hadjiefthymiades","doi":"10.1109/ISCC55528.2022.9912800","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912800","url":null,"abstract":"In the framework of the H2020 project INCLUDING (www.including-cluster.eu), the Piraeus port commercial terminal has hosted a field exercise on the identification and recovery of two orphan sources inside a cargo container. The primary objective of the field exercise was to test the integration of Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) in the response plan coordinated by the CBRN experts of the Hellenic Ministry of Defence and the first operational verification of a platform under development in the project for the management of the mobilized resources on the incident scene. The activity marks a solid step ahead in introducing innovation in the exercise and training activities in the nuclear security domain and in improving sharing of resources at EU level. In this article is described the concept of operation of the INCLUDING platform, its architecture and its use in the different phase of the Piraeus port exercise, that is one of the eight Joint Action planned during the INCLUDING project duration.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127609694","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}
Pub Date : 2022-06-30DOI: 10.1109/ISCC55528.2022.9912855
Yaqiang Zhang, Ruyang Li, Yaqian Zhao, Rengang Li, Xuelei Li, Tuo Li
In centralized task allocation strategies, real-time status information needs to be collected from distributed edge nodes. Therefore, the overloaded transmission on backbone network appears and leads to devastating decrease in the per-formance of centralized strategies. To address this issue, this paper proposes a multi-agent deep reinforcement learning based online decentralized task allocation mechanism, where each edge node makes task allocation decisions based on local network-state information. A centralized-training distributed-execution method is adopted to decrease data transmission load, and a value decomposition-based technique is applied at training stage for improving long-term performance of task allocation in edge col-laborative networks. Extensive experiments are conducted, and evaluation results demonstrate that our mechanism outperforms other three baseline algorithms in reducing the long-term average system delay and improving request completion rate.
{"title":"Online Decentralized Task Allocation Optimization for Edge Collaborative Networks","authors":"Yaqiang Zhang, Ruyang Li, Yaqian Zhao, Rengang Li, Xuelei Li, Tuo Li","doi":"10.1109/ISCC55528.2022.9912855","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912855","url":null,"abstract":"In centralized task allocation strategies, real-time status information needs to be collected from distributed edge nodes. Therefore, the overloaded transmission on backbone network appears and leads to devastating decrease in the per-formance of centralized strategies. To address this issue, this paper proposes a multi-agent deep reinforcement learning based online decentralized task allocation mechanism, where each edge node makes task allocation decisions based on local network-state information. A centralized-training distributed-execution method is adopted to decrease data transmission load, and a value decomposition-based technique is applied at training stage for improving long-term performance of task allocation in edge col-laborative networks. Extensive experiments are conducted, and evaluation results demonstrate that our mechanism outperforms other three baseline algorithms in reducing the long-term average system delay and improving request completion rate.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127874264","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}
Pub Date : 2022-06-30DOI: 10.1109/ISCC55528.2022.9912934
J. F. Schmidt, Udo Schilcher, Siddhartha S. Borkotoky, Christian A. Schmidt
We propose a scheme to reduce and balance the energy consumption of nodes in dense IoT use cases implemented with LoRa. We study a multiple gateways network with nodes uniformly distributed around each gateway, and restricted to use only short spreading factors. Relays using longer spreading factors, are added to the network infrastructure to forward the nodes transmissions that cannot reach a gateway directly. We find that the impact of the interference added by the relays on the probability of successful transmissions becomes marginal for high density networks. Furthermore, in such dense networks the consumption at the nodes is lowered one order of magnitude with marginal increase in the overall energy consumption of the network. Also, the consumption spread between nodes close and far from the gateway is effectively reduced.
{"title":"Energy Consumption in LoRa IoT: Benefits of Adding Relays to Dense Networks","authors":"J. F. Schmidt, Udo Schilcher, Siddhartha S. Borkotoky, Christian A. Schmidt","doi":"10.1109/ISCC55528.2022.9912934","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912934","url":null,"abstract":"We propose a scheme to reduce and balance the energy consumption of nodes in dense IoT use cases implemented with LoRa. We study a multiple gateways network with nodes uniformly distributed around each gateway, and restricted to use only short spreading factors. Relays using longer spreading factors, are added to the network infrastructure to forward the nodes transmissions that cannot reach a gateway directly. We find that the impact of the interference added by the relays on the probability of successful transmissions becomes marginal for high density networks. Furthermore, in such dense networks the consumption at the nodes is lowered one order of magnitude with marginal increase in the overall energy consumption of the network. Also, the consumption spread between nodes close and far from the gateway is effectively reduced.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132363288","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}
Pub Date : 2022-06-30DOI: 10.1109/ISCC55528.2022.9912939
Hongyue Kang, W. Li, J. Misic, V. Mišić, Xiaolin Chang
Unmanned aerial vehicle (UAV) communication is easily wiretapped by malignant nodes due to the broadcast nature of line-of-sight (LoS) wireless channels. To tackle this problem, this paper investigates a dual-UAV aided secure dynamic ground-to-UAV (G2U) communication system. By dynamic, we mean UAVs communicate with moving ground devices (GDs). Our objective is maximizing the sum secrecy rate by the joint optimization of UAV trajectory and GDs transmit power. To achieve it, we first formulate this nonconvex optimization problem as a Constrained Markov Decision Process (CMDP) under the constraints of UAV flying speed, initial and final locations, limited energy, and average transmit power. Then, a Deep Deterministic Policy Gradient (DDPG) based deep reinforcement learning algorithm is designed, named SC-TDPC, to learn the optimal transmit power and UAV trajectory. The experiment results demonstrate that, compared to other benchmark schemes, SC-TDPC can efficiently enhance the UAV communication security in terms of sum secrecy rate.
{"title":"Dual-UAV Aided Secure Dynamic G2U Communication","authors":"Hongyue Kang, W. Li, J. Misic, V. Mišić, Xiaolin Chang","doi":"10.1109/ISCC55528.2022.9912939","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912939","url":null,"abstract":"Unmanned aerial vehicle (UAV) communication is easily wiretapped by malignant nodes due to the broadcast nature of line-of-sight (LoS) wireless channels. To tackle this problem, this paper investigates a dual-UAV aided secure dynamic ground-to-UAV (G2U) communication system. By dynamic, we mean UAVs communicate with moving ground devices (GDs). Our objective is maximizing the sum secrecy rate by the joint optimization of UAV trajectory and GDs transmit power. To achieve it, we first formulate this nonconvex optimization problem as a Constrained Markov Decision Process (CMDP) under the constraints of UAV flying speed, initial and final locations, limited energy, and average transmit power. Then, a Deep Deterministic Policy Gradient (DDPG) based deep reinforcement learning algorithm is designed, named SC-TDPC, to learn the optimal transmit power and UAV trajectory. The experiment results demonstrate that, compared to other benchmark schemes, SC-TDPC can efficiently enhance the UAV communication security in terms of sum secrecy rate.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132651727","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}
Pub Date : 2022-06-30DOI: 10.1109/ISCC55528.2022.9913053
Hua Wu, Chaoqun Cui, Guang Cheng, Xiaoyan Hu
Network traffic classification is the basis for network management, Quality of Service and intrusion detection. As the number of Internet applications increases, the variety of unknown protocols grows, posing a significant challenge to network traffic classification. Traditional rule-based traffic classification methods are currently limited by the rise of dynamic ports and encryption protocols. Statistical methods using statistical features have good recognition of protocols with public formats. However, there is no public protocol format for unknown protocols, making it challenging to extract useful features. This paper proposes a practical Probability Statistics and Cluster Merging (PSCM) method to automatically extract encrypted unknown protocol features and map the clustering results to the actual protocols. Experimental results on real-world network traffic show that the method achieves an accuracy of 99.28% and performs well in the sampling scenarios.
{"title":"PSCM: Towards Practical Encrypted Unknown Protocol Classification","authors":"Hua Wu, Chaoqun Cui, Guang Cheng, Xiaoyan Hu","doi":"10.1109/ISCC55528.2022.9913053","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9913053","url":null,"abstract":"Network traffic classification is the basis for network management, Quality of Service and intrusion detection. As the number of Internet applications increases, the variety of unknown protocols grows, posing a significant challenge to network traffic classification. Traditional rule-based traffic classification methods are currently limited by the rise of dynamic ports and encryption protocols. Statistical methods using statistical features have good recognition of protocols with public formats. However, there is no public protocol format for unknown protocols, making it challenging to extract useful features. This paper proposes a practical Probability Statistics and Cluster Merging (PSCM) method to automatically extract encrypted unknown protocol features and map the clustering results to the actual protocols. Experimental results on real-world network traffic show that the method achieves an accuracy of 99.28% and performs well in the sampling scenarios.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134644518","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}
Pub Date : 2022-06-30DOI: 10.1109/ISCC55528.2022.9912822
Diogo Machado, V. S. Costa, Pedro Brandão
Finger-pricking is the traditional procedure for glycaemia monitoring. It is an invasive method where the person with diabetes is required to prick their finger. In recent years, continuous-glucose monitoring (CGM), a new and more convenient method of glycaemia monitoring, has become prevalent. CGM provides continuous access to glycaemic values without the need of finger-pricking. Data mining can be used to understand glycaemic values, and to ideally warn users of abnormal situations. CGM provides significantly more data than finger-pricking. Thus, the amount and value of CGM data ultimately questions the role of finger-pricking for glycaemic studies. In this work we use the OhioTlDM data set in order to study the importance of finger-prick-based data. We use Random Forest as a classification method, a robust method that tends to obtain quality results. Our results indicate that, although more demanding and scarcer, finger-prick-based glycaemic values have a significant role on diabetes management and on data mining.
{"title":"Impact of the glycaemic sampling method in diabetes data mining","authors":"Diogo Machado, V. S. Costa, Pedro Brandão","doi":"10.1109/ISCC55528.2022.9912822","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912822","url":null,"abstract":"Finger-pricking is the traditional procedure for glycaemia monitoring. It is an invasive method where the person with diabetes is required to prick their finger. In recent years, continuous-glucose monitoring (CGM), a new and more convenient method of glycaemia monitoring, has become prevalent. CGM provides continuous access to glycaemic values without the need of finger-pricking. Data mining can be used to understand glycaemic values, and to ideally warn users of abnormal situations. CGM provides significantly more data than finger-pricking. Thus, the amount and value of CGM data ultimately questions the role of finger-pricking for glycaemic studies. In this work we use the OhioTlDM data set in order to study the importance of finger-prick-based data. We use Random Forest as a classification method, a robust method that tends to obtain quality results. Our results indicate that, although more demanding and scarcer, finger-prick-based glycaemic values have a significant role on diabetes management and on data mining.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115478379","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}
Pub Date : 2022-06-30DOI: 10.1109/ISCC55528.2022.9912918
I. D. Falco, Antonio Della Cioppa, T. Koutny, U. Scafuri, E. Tarantino, Martin Ubl
Within this paper a Grammatical Evolution al-gorithm is exploited to induce personalized and interpretable glucose forecasting models for diabetic patients based on the historical measurements of the glucose, the carbohydrates, and the injected insulin. A real-world data set of Type 1 diabetic patients is used to assess the induced models. The experimental trials show that the performance of extracted models is compara-ble with that obtained by other state-of-the-art techniques that require a more significant computational effort.
{"title":"An Evolution-based Machine Learning Approach for Inducing Glucose Prediction Models","authors":"I. D. Falco, Antonio Della Cioppa, T. Koutny, U. Scafuri, E. Tarantino, Martin Ubl","doi":"10.1109/ISCC55528.2022.9912918","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912918","url":null,"abstract":"Within this paper a Grammatical Evolution al-gorithm is exploited to induce personalized and interpretable glucose forecasting models for diabetic patients based on the historical measurements of the glucose, the carbohydrates, and the injected insulin. A real-world data set of Type 1 diabetic patients is used to assess the induced models. The experimental trials show that the performance of extracted models is compara-ble with that obtained by other state-of-the-art techniques that require a more significant computational effort.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115781843","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}
Pub Date : 2022-06-30DOI: 10.1109/ISCC55528.2022.9912914
Matteo Mendula, Armir Bujari, L. Foschini, P. Bellavista
The increasing pace of sensing and communication technology rollout is paving the way for concrete deployments of smart city applications, enabling a data-driven modeling of processes and the environment. In particular, the Urban Facility Management (UFM) process is growing in importance, recognized to have a direct impact on the sustainability and the development of our cities. In [1] we presented a system's view of a Digital Twin solution for the UFM process. The solution relies on (near)real-time data to quantify the activity index in an area of interest, used as a basis for planning decisions. In this study, we focus on the predictive subsystem, tasked with computing near-to-mid term predictions of the activity index, equipping UFM operators with a flexible decision-support system. Without loss of generality, we present an analysis of the vehicular traffic component, part of the activity index, assessing the accuracy of different predictive schemes, discussing some operational implications.
{"title":"A Data-Driven Digital Twin for Urban Activity Monitoring","authors":"Matteo Mendula, Armir Bujari, L. Foschini, P. Bellavista","doi":"10.1109/ISCC55528.2022.9912914","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912914","url":null,"abstract":"The increasing pace of sensing and communication technology rollout is paving the way for concrete deployments of smart city applications, enabling a data-driven modeling of processes and the environment. In particular, the Urban Facility Management (UFM) process is growing in importance, recognized to have a direct impact on the sustainability and the development of our cities. In [1] we presented a system's view of a Digital Twin solution for the UFM process. The solution relies on (near)real-time data to quantify the activity index in an area of interest, used as a basis for planning decisions. In this study, we focus on the predictive subsystem, tasked with computing near-to-mid term predictions of the activity index, equipping UFM operators with a flexible decision-support system. Without loss of generality, we present an analysis of the vehicular traffic component, part of the activity index, assessing the accuracy of different predictive schemes, discussing some operational implications.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117291757","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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