Pub Date : 2022-09-26DOI: 10.1109/ISC255366.2022.9922572
M. Bessho, Ken Sakamura
Social distancing plays an important role in the control of the spread of infectious diseases. This study proposes a service that forecasts street-level crowd density in the near future. We collected street-level crowd density levels for months during the COVID-19 pandemic by observing public Bluetooth Low Energy advertisements from popular contact tracing applications. We then designed a model to predict crowd density level from other factors such as calendars, weather, and recent trends of crowd density level using Random Forest Regressor. Based on the model, we implemented a crowd density forecast service by incorporating an external weather forecast service, and we published the forecast on our website and a Japanese television program. The experimental results indicate that the model can predict the crowd density for the following week with a coefficient of determination of 0.85 or higher on average, which demonstrates that a practical crowd density forecast can be realized with our method.
{"title":"Design and Implementation of Street-level Crowd Density Forecast using Contact Tracing Applications","authors":"M. Bessho, Ken Sakamura","doi":"10.1109/ISC255366.2022.9922572","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9922572","url":null,"abstract":"Social distancing plays an important role in the control of the spread of infectious diseases. This study proposes a service that forecasts street-level crowd density in the near future. We collected street-level crowd density levels for months during the COVID-19 pandemic by observing public Bluetooth Low Energy advertisements from popular contact tracing applications. We then designed a model to predict crowd density level from other factors such as calendars, weather, and recent trends of crowd density level using Random Forest Regressor. Based on the model, we implemented a crowd density forecast service by incorporating an external weather forecast service, and we published the forecast on our website and a Japanese television program. The experimental results indicate that the model can predict the crowd density for the following week with a coefficient of determination of 0.85 or higher on average, which demonstrates that a practical crowd density forecast can be realized with our method.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116514110","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 : 2022-09-26DOI: 10.1109/ISC255366.2022.9921985
Zahraa Khais Shahid, S. Saguna, C. Åhlund
The need for smart healthcare tools and techniques has increased due to the availability of low-cost IoT sensors and devices and the growing aging population in the world. Early detection of health conditions such as dementia and Parkinsons are important for treatment and medication. Out of the many symptoms of such health conditions, one critical behavior is sleep activity changes. In this paper, we evaluate and apply an unsupervised machine learning: K-Means, to detect changes in long-term sleep behavior in the bedroom using smart-home motion sensors installed in 6 real-life single resident elderly homes for approximately three years. Our method analyses the transformation of clusters for a participant over three years, 2019, 2020, and 2021. This is done using three features: duration of stay, the hour of the day, and duration frequency. Data clustering is used to group durations of being in the bedroom at different hours of the day. This is done to see if there is a shift in these clusters for elderly persons with healthy aging and those developing health conditions like dementia and Parkinsons. We foresee that such methods to detect long-term behavior changes can support caregivers in carrying out their assessment for discovering the early onset of health conditions, thereby preventing further progression and providing timely treatment.
{"title":"Recognizing Long-term Sleep Behaviour Change using Clustering for Elderly in Smart Homes","authors":"Zahraa Khais Shahid, S. Saguna, C. Åhlund","doi":"10.1109/ISC255366.2022.9921985","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9921985","url":null,"abstract":"The need for smart healthcare tools and techniques has increased due to the availability of low-cost IoT sensors and devices and the growing aging population in the world. Early detection of health conditions such as dementia and Parkinsons are important for treatment and medication. Out of the many symptoms of such health conditions, one critical behavior is sleep activity changes. In this paper, we evaluate and apply an unsupervised machine learning: K-Means, to detect changes in long-term sleep behavior in the bedroom using smart-home motion sensors installed in 6 real-life single resident elderly homes for approximately three years. Our method analyses the transformation of clusters for a participant over three years, 2019, 2020, and 2021. This is done using three features: duration of stay, the hour of the day, and duration frequency. Data clustering is used to group durations of being in the bedroom at different hours of the day. This is done to see if there is a shift in these clusters for elderly persons with healthy aging and those developing health conditions like dementia and Parkinsons. We foresee that such methods to detect long-term behavior changes can support caregivers in carrying out their assessment for discovering the early onset of health conditions, thereby preventing further progression and providing timely treatment.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128257958","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 : 2022-09-26DOI: 10.1109/ISC255366.2022.9922404
Z. Wang, Shen Wang
Rerouting vehicles for urban congestion avoidance is challenging as the decision has to be undertaken promptly with the consideration of traffic condition changes caused by other vehicles' routing plans. Existing solutions such as the on-board navigation systems (e.g., Google Maps) cannot meet these requirements which is prone to trigger the well-known routing oscillation problem. Though deep reinforcement learning (DRL) approaches are able to provide a high-quality solution and satisfy the real-time requirement, not only do they usually suffer the slow and instability issues for convergence, but the input information, like a picture for each time step, is also teeming with redundant information. In this paper, we propose XRouting model that uses policy-based DRL and the revised Gated Transformer (GTr) architecture to accelerate and stabilize the training convergence in solving dynamic routing problems. Our simulation study validates that compared with existing rerouting solutions, XRouting can achieve higher reductions in travel time, fuel consumption, CO2 emission, and the route length. More importantly, XRouting is capable of determining which features are predominant when vehicles conduct rerouting. This explainable ability of our model can further guide human drivers what features to consider when rerouting manually in real life.
{"title":"XRouting: Explainable Vehicle Rerouting for Urban Road Congestion Avoidance using Deep Reinforcement Learning","authors":"Z. Wang, Shen Wang","doi":"10.1109/ISC255366.2022.9922404","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9922404","url":null,"abstract":"Rerouting vehicles for urban congestion avoidance is challenging as the decision has to be undertaken promptly with the consideration of traffic condition changes caused by other vehicles' routing plans. Existing solutions such as the on-board navigation systems (e.g., Google Maps) cannot meet these requirements which is prone to trigger the well-known routing oscillation problem. Though deep reinforcement learning (DRL) approaches are able to provide a high-quality solution and satisfy the real-time requirement, not only do they usually suffer the slow and instability issues for convergence, but the input information, like a picture for each time step, is also teeming with redundant information. In this paper, we propose XRouting model that uses policy-based DRL and the revised Gated Transformer (GTr) architecture to accelerate and stabilize the training convergence in solving dynamic routing problems. Our simulation study validates that compared with existing rerouting solutions, XRouting can achieve higher reductions in travel time, fuel consumption, CO2 emission, and the route length. More importantly, XRouting is capable of determining which features are predominant when vehicles conduct rerouting. This explainable ability of our model can further guide human drivers what features to consider when rerouting manually in real life.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129788680","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 : 2022-09-26DOI: 10.1109/ISC255366.2022.9922226
Animesh Mehta, Gayatri Doctor, Anita Kane, D. Sawant
The concept of Carbon Neutrality is gaining momentum in recent years due to the rising awareness of climate change. Carbon neutrality means (A) Minimize greenhouse gas emissions to the best extent possible, and (B) Create a sink for the residual GHG emissions. Tree plantation being the most effective way for creating natural carbon sinks. The overall objective of this study is reducing the carbon footprint of an educational and research institution in India. The study starts with the assessment of carbon emissions covering scope 1, scope 2 and scope 3 for the selected site. The emissions are quantified keeping in mind the inclusions and exclusions of the study. It further looks at carbon offsets/sinks and the impact that they have on the campus. The study compares data from different years and recommends the way forward towards the achievement of carbon neutrality. This study aims to act as a framework for similar studies for campuses who take a step towards sustainability.
{"title":"Study for achieving carbon-neutral campus in India","authors":"Animesh Mehta, Gayatri Doctor, Anita Kane, D. Sawant","doi":"10.1109/ISC255366.2022.9922226","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9922226","url":null,"abstract":"The concept of Carbon Neutrality is gaining momentum in recent years due to the rising awareness of climate change. Carbon neutrality means (A) Minimize greenhouse gas emissions to the best extent possible, and (B) Create a sink for the residual GHG emissions. Tree plantation being the most effective way for creating natural carbon sinks. The overall objective of this study is reducing the carbon footprint of an educational and research institution in India. The study starts with the assessment of carbon emissions covering scope 1, scope 2 and scope 3 for the selected site. The emissions are quantified keeping in mind the inclusions and exclusions of the study. It further looks at carbon offsets/sinks and the impact that they have on the campus. The study compares data from different years and recommends the way forward towards the achievement of carbon neutrality. This study aims to act as a framework for similar studies for campuses who take a step towards sustainability.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130068988","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 : 2022-09-26DOI: 10.1109/ISC255366.2022.9922020
Shainen M. Davidson, Kenton White
Origin and Destination (O&D) studies provide invaluable information for planning transportation infrastructure; however, they require very large sample sizes, and thus are becoming increasingly expensive as response rates to traditional surveys fall. At the same time, adoption of social media is on the rise. This study examines using social media data to replace traditional survey data to construct an O&D study. Specifically, with the cooperation of Quebec City's public transit provider, an online based O&D study was conducted of Quebec City. The results are compared with a Quebec City O&D survey conducted in 2011 which used traditional methods.
{"title":"Using Twitter data to conduct an Origin and Destination study of Quebec City","authors":"Shainen M. Davidson, Kenton White","doi":"10.1109/ISC255366.2022.9922020","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9922020","url":null,"abstract":"Origin and Destination (O&D) studies provide invaluable information for planning transportation infrastructure; however, they require very large sample sizes, and thus are becoming increasingly expensive as response rates to traditional surveys fall. At the same time, adoption of social media is on the rise. This study examines using social media data to replace traditional survey data to construct an O&D study. Specifically, with the cooperation of Quebec City's public transit provider, an online based O&D study was conducted of Quebec City. The results are compared with a Quebec City O&D survey conducted in 2011 which used traditional methods.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121458556","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 : 2022-09-26DOI: 10.1109/ISC255366.2022.9922242
Ahmed Idries, J. Krogstie, Jayaprakash Rajasekharan
Distributed ledger technologies (DLTs) have become a game changer in electrical services platformization and digitalization. Therefore, the need for DLTs in electrical energy services must be understood. We present a case study of a European Union (EU) project in the Norwegian city of Trondheim, where a DLT-driven energy marketplace was piloted. We contribute to the literature and field by presenting the factors, challenges, and issues affecting DLT implementation in electrical energy services, which can be helpful for further work in electrical energy services and platform ecosystems. For policy makers and practitioners, this paper presents DLT providers' reflections about their experience in an electrical energy services project in the smart city context. These insights could be useful to ease future adoption of DLTs and to provide a ground for future empirical investigations.
{"title":"Evaluation of Distributed Ledger Technology Implementation in Electrical Energy Service through a Case Study","authors":"Ahmed Idries, J. Krogstie, Jayaprakash Rajasekharan","doi":"10.1109/ISC255366.2022.9922242","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9922242","url":null,"abstract":"Distributed ledger technologies (DLTs) have become a game changer in electrical services platformization and digitalization. Therefore, the need for DLTs in electrical energy services must be understood. We present a case study of a European Union (EU) project in the Norwegian city of Trondheim, where a DLT-driven energy marketplace was piloted. We contribute to the literature and field by presenting the factors, challenges, and issues affecting DLT implementation in electrical energy services, which can be helpful for further work in electrical energy services and platform ecosystems. For policy makers and practitioners, this paper presents DLT providers' reflections about their experience in an electrical energy services project in the smart city context. These insights could be useful to ease future adoption of DLTs and to provide a ground for future empirical investigations.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124524181","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 : 2022-09-26DOI: 10.1109/ISC255366.2022.9921933
S. Demirel, T. Alskaif, J. Pennings, M. Verhulst, P. Debie, B. Tekinerdogan
This paper proposes a novel framework for energy utility companies to anticipate their customers' energy usage based on their historical consumption data. The proposed framework comprises three major stages: (i) it detects and removes anomalies in consumers' energy consumption data by employing the isolation forest (iForest); (ii) it forms clusters of distinct consumer groups based on similarities in their consumption behavior via the k-means clustering algorithm; and (iii) it predicts electricity consumption by using deep learning algorithms. To this end, two different deep learning algorithms are designed: a long short-term memory (LSTM) network and the combination of convolutional neural network (CNN) and LSTM (referred to as CNN-LSTM) with multiple inputs. Since the latter is a combination of CNN and LSTM models, we apply a 2-D discrete wavelet transform (DWT) based feature extraction to the Gramian angular field (GAF) transformation of the time series to improve the accuracy of predictions. Various evaluation metrics are utilized for 1-hour- and 24-hours-ahead predictions with two different sliding-window sizes, i.e., 24 hours and 36 hours. The results demonstrate that the CNN-LSTM performs significantly better in predicting 24-hours-ahead electricity consumption.
{"title":"A framework for multi-stage ML-based electricity demand forecasting","authors":"S. Demirel, T. Alskaif, J. Pennings, M. Verhulst, P. Debie, B. Tekinerdogan","doi":"10.1109/ISC255366.2022.9921933","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9921933","url":null,"abstract":"This paper proposes a novel framework for energy utility companies to anticipate their customers' energy usage based on their historical consumption data. The proposed framework comprises three major stages: (i) it detects and removes anomalies in consumers' energy consumption data by employing the isolation forest (iForest); (ii) it forms clusters of distinct consumer groups based on similarities in their consumption behavior via the k-means clustering algorithm; and (iii) it predicts electricity consumption by using deep learning algorithms. To this end, two different deep learning algorithms are designed: a long short-term memory (LSTM) network and the combination of convolutional neural network (CNN) and LSTM (referred to as CNN-LSTM) with multiple inputs. Since the latter is a combination of CNN and LSTM models, we apply a 2-D discrete wavelet transform (DWT) based feature extraction to the Gramian angular field (GAF) transformation of the time series to improve the accuracy of predictions. Various evaluation metrics are utilized for 1-hour- and 24-hours-ahead predictions with two different sliding-window sizes, i.e., 24 hours and 36 hours. The results demonstrate that the CNN-LSTM performs significantly better in predicting 24-hours-ahead electricity consumption.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"88 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120987740","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 : 2022-09-26DOI: 10.1109/ISC255366.2022.9922506
Gustavo F. Silva, D. G. Costa, Thiago C. Jesus
Emergency detection solutions will be employed to early identify one or more critical situations and trigger proper actions in smart cities, potentially preventing the occurrence of disasters. When such systems are constructed around multi-sensor emergencies detection units, the heterogeneity of monitoring scenarios and eventual requisites changes may demand their reconfiguration to attend new sensing requirements. In this context, this paper proposes a new development framework to guide the programming and operation of multi-sensor detection units that are able to be reconfigured in real time. Moreover, supportive networked elements and interaction messages are proposed within this framework to allow flexible reconfiguration requests in a distributed and scalable way. The required specifications and expected evaluation results are also discussed in this paper.
{"title":"A Framework for the Development of Reconfigurable Sensors-based Emergencies Detection Units in Smart Cities","authors":"Gustavo F. Silva, D. G. Costa, Thiago C. Jesus","doi":"10.1109/ISC255366.2022.9922506","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9922506","url":null,"abstract":"Emergency detection solutions will be employed to early identify one or more critical situations and trigger proper actions in smart cities, potentially preventing the occurrence of disasters. When such systems are constructed around multi-sensor emergencies detection units, the heterogeneity of monitoring scenarios and eventual requisites changes may demand their reconfiguration to attend new sensing requirements. In this context, this paper proposes a new development framework to guide the programming and operation of multi-sensor detection units that are able to be reconfigured in real time. Moreover, supportive networked elements and interaction messages are proposed within this framework to allow flexible reconfiguration requests in a distributed and scalable way. The required specifications and expected evaluation results are also discussed in this paper.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133723001","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 : 2022-09-26DOI: 10.1109/ISC255366.2022.9921865
Gábor Kovács, T. Szirányi
Intelligent and autonomous vehicle safety is a rapidly developing field. With the increasing number of electric vehicles as well as following consumer trends, cars are getting quieter and also heavier which may lead to severe traffic accidents. To help avoiding potential dangerous situations leading to accidents, this paper proposes a collision danger model for individual pedestrians that can aid vehicle safety features and help decision making, using only forward facing optical cameras. Multi pedestrian detection and tracking is performed with a fast joint model. Semantic segmentation and classification is used to refine pedestrian contours and find the 3D positions as well as to understand the location context of pedestrians in the environment. Pedestrian position is tracked and orientation is estimated using 2D bounding boxes. The proposed pedestrian danger model is the combination of the awareness estimated from orientation, passing distance estimated from trajectories and location context from the segmentation results.
{"title":"Pedestrian Collision Danger Model using Attention and Location Context","authors":"Gábor Kovács, T. Szirányi","doi":"10.1109/ISC255366.2022.9921865","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9921865","url":null,"abstract":"Intelligent and autonomous vehicle safety is a rapidly developing field. With the increasing number of electric vehicles as well as following consumer trends, cars are getting quieter and also heavier which may lead to severe traffic accidents. To help avoiding potential dangerous situations leading to accidents, this paper proposes a collision danger model for individual pedestrians that can aid vehicle safety features and help decision making, using only forward facing optical cameras. Multi pedestrian detection and tracking is performed with a fast joint model. Semantic segmentation and classification is used to refine pedestrian contours and find the 3D positions as well as to understand the location context of pedestrians in the environment. Pedestrian position is tracked and orientation is estimated using 2D bounding boxes. The proposed pedestrian danger model is the combination of the awareness estimated from orientation, passing distance estimated from trajectories and location context from the segmentation results.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"439 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132894289","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 : 2022-09-26DOI: 10.1109/ISC255366.2022.9922326
M. Asprou, A. Akrytov, L. Hadjidemetriou, C. Charalambous, I. Ciornei, G. Ellinas, C. Panayiotou
The fast deployment of the Phasor Measurement Units (PMUs), especially in the transmission level of the power systems, enables the development of wide area monitoring, protection and control (WAMPC) applications that enhance the situational awareness of the power system operator as well as the stability of the power system. Such applications are dependent on the communication network that supports the transfer of the PMU measurements to a central monitoring application or to a local protection application (situated in a substation). It is therefore of paramount importance to ensure the transfer of measurements with the least delay, while at the same time to ensure the integrity of the PMU measurements. In this work, the impact of using a wireless communication network for transferring the PMU measurements to the WAMPC applications is examined and the advantage of the 5G communication network over 4G and 3G in such real-time monitoring and control applications is demonstrated.
{"title":"The Impact of Wireless Communication Networks on Wide Area Monitoring and Protection Applications","authors":"M. Asprou, A. Akrytov, L. Hadjidemetriou, C. Charalambous, I. Ciornei, G. Ellinas, C. Panayiotou","doi":"10.1109/ISC255366.2022.9922326","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9922326","url":null,"abstract":"The fast deployment of the Phasor Measurement Units (PMUs), especially in the transmission level of the power systems, enables the development of wide area monitoring, protection and control (WAMPC) applications that enhance the situational awareness of the power system operator as well as the stability of the power system. Such applications are dependent on the communication network that supports the transfer of the PMU measurements to a central monitoring application or to a local protection application (situated in a substation). It is therefore of paramount importance to ensure the transfer of measurements with the least delay, while at the same time to ensure the integrity of the PMU measurements. In this work, the impact of using a wireless communication network for transferring the PMU measurements to the WAMPC applications is examined and the advantage of the 5G communication network over 4G and 3G in such real-time monitoring and control applications is demonstrated.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129119398","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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