Pub Date : 2022-09-26DOI: 10.1109/ISC255366.2022.9922560
Philipp Matthes, T. Springer
Green Light Optimized Speed Advisory (GLOSA) apps provide speed recommendations for drivers to pass traffic lights during their green phases. In this way, the comfort and efficiency of traveling can be significantly improved. Thus, GLOSA apps are a valuable contribution to smart mobility. Mobile GLOSA apps provide an attractive alternative to static info signs, but they need to anticipate upcoming traffic lights that the vehicle will pass. While this imposes no challenge for predominating research within simulation or test track environments, real-world deployments need to correctly match a few from thousands of traffic lights to a route. In this paper, we discuss in a novel approach that MAP topologies, an international ETSI standard for turn geometries of traffic lights, can be used to perform this matching. However, routing is usually performed on public map data, which is not aligned with the MAP topologies. We explore two computational methods, specifically map-matching as preprocessing for adjacency lookup and topologic feature matching, that account for discrepancies between the MAP topologies and the route. We show that the core problem can be addressed using these algorithms to enable large-area deployments of real-world mobile GLOSA apps. In a comparative evaluation, the topologic feature matching technique achieved an F1 score of 89.5%, while the map-matched adjacency lookup method only achieved an F1 score of 48.3%. We analyze this performance gap and conclude further research directions.
{"title":"Matching Traffic Lights to Routes for Real-World Deployments of Mobile GLOSA Apps","authors":"Philipp Matthes, T. Springer","doi":"10.1109/ISC255366.2022.9922560","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9922560","url":null,"abstract":"Green Light Optimized Speed Advisory (GLOSA) apps provide speed recommendations for drivers to pass traffic lights during their green phases. In this way, the comfort and efficiency of traveling can be significantly improved. Thus, GLOSA apps are a valuable contribution to smart mobility. Mobile GLOSA apps provide an attractive alternative to static info signs, but they need to anticipate upcoming traffic lights that the vehicle will pass. While this imposes no challenge for predominating research within simulation or test track environments, real-world deployments need to correctly match a few from thousands of traffic lights to a route. In this paper, we discuss in a novel approach that MAP topologies, an international ETSI standard for turn geometries of traffic lights, can be used to perform this matching. However, routing is usually performed on public map data, which is not aligned with the MAP topologies. We explore two computational methods, specifically map-matching as preprocessing for adjacency lookup and topologic feature matching, that account for discrepancies between the MAP topologies and the route. We show that the core problem can be addressed using these algorithms to enable large-area deployments of real-world mobile GLOSA apps. In a comparative evaluation, the topologic feature matching technique achieved an F1 score of 89.5%, while the map-matched adjacency lookup method only achieved an F1 score of 48.3%. We analyze this performance gap and conclude further research directions.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"124 4 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":"124267478","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/ISC255366.2022.9921984
Louis Closson, C. Cérin, D. Donsez, D. Trystram
The long-term objective of the paper aims to provide decision aid support to a technical smart buildings manager to potentially reduce the emission of data produced by sensors inside a building and, more generally, to acquire knowledge on the data produced in the facility. As the first step, the paper proposes to characterize the smart-building ecosystem's Internet-of-things (IoT) data sets. The description and the construction of learning models over data sets are crucial in engineering studies to advance critical analysis and serve diverse researchers' communities, such as architects or data scientists. We examine two data sets deployed in one location in the Grenoble area in France. We assume that the building is an autonomic computing system. Thus, the underlying model we deal with is the well-known MAPE-K methodology introduced by IBM. The paper mainly addresses the analysis component and the adjacent connector component of the MAPE-K model. The content of this layer, and its organization, constitutes the methodological point we put forward. Consequently, we automatically provide a complete set of practices and methods to pass to the planning component of the MAPE-K model. We also sketch a semi-automatic way of reducing the number of measures done by sensors. In the background of our study, we aim to reduce the operational cost of making measures with a much more sober approach than the current one. We also discuss in profound the main findings of our work. Finally, we provide insights and open questions for future outcomes based on our experience.
{"title":"Towards a Methodology for the Characterization of IoT Data Sets of the Smart Building Sector","authors":"Louis Closson, C. Cérin, D. Donsez, D. Trystram","doi":"10.1109/ISC255366.2022.9921984","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9921984","url":null,"abstract":"The long-term objective of the paper aims to provide decision aid support to a technical smart buildings manager to potentially reduce the emission of data produced by sensors inside a building and, more generally, to acquire knowledge on the data produced in the facility. As the first step, the paper proposes to characterize the smart-building ecosystem's Internet-of-things (IoT) data sets. The description and the construction of learning models over data sets are crucial in engineering studies to advance critical analysis and serve diverse researchers' communities, such as architects or data scientists. We examine two data sets deployed in one location in the Grenoble area in France. We assume that the building is an autonomic computing system. Thus, the underlying model we deal with is the well-known MAPE-K methodology introduced by IBM. The paper mainly addresses the analysis component and the adjacent connector component of the MAPE-K model. The content of this layer, and its organization, constitutes the methodological point we put forward. Consequently, we automatically provide a complete set of practices and methods to pass to the planning component of the MAPE-K model. We also sketch a semi-automatic way of reducing the number of measures done by sensors. In the background of our study, we aim to reduce the operational cost of making measures with a much more sober approach than the current one. We also discuss in profound the main findings of our work. Finally, we provide insights and open questions for future outcomes based on our experience.","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":"129477692","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/ISC255366.2022.9922223
C. Mademlis, N. Jabbour, E. Tsioumas, Markos Kosseoglou, D. Papagiannis
This paper investigates the challenging problem of reducing the cost needed for converting a conventional building to a nearly-zero energy building (nZEB). This can be at-tained by properly selecting the sizing of the domestic renewable energy sources (DRES) and battery storage system (BSS), and improving the performance of the building electric microgrid. Thus, on the one side, a new methodology based on the genetic algorithm (GA) is proposed to properly determine the correct size of the DRES and BSS. On the other side, an integrated control method based on the GA technique too for the energy man-agement in the home microgrid is suggested that is accomplished through a correct balance between the maximum exploitation of the DRES and BSS, comfort of the building residents, and en-ergy saving. Therefore, the problem of reducing the cost for de-veloping an nZEB is addressed by reducing the two cost components, i.e. installation and operating cost. Moreover, the influ-ence of the one cost on the other is considered, and therefore an integrated calculation method is developed that provides a ho-listic solution for the nZEB's cost problem. The proposed calcu-lation strategy has been experimentally validated in a pilot building and several experimental results are presented in this paper to demonstrate the effectiveness, practicality, and functionality of the suggested methodology.
{"title":"Reduction of the Cost Needed for Converting a Conventional Building to a Nearly Zero Energy Building","authors":"C. Mademlis, N. Jabbour, E. Tsioumas, Markos Kosseoglou, D. Papagiannis","doi":"10.1109/ISC255366.2022.9922223","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9922223","url":null,"abstract":"This paper investigates the challenging problem of reducing the cost needed for converting a conventional building to a nearly-zero energy building (nZEB). This can be at-tained by properly selecting the sizing of the domestic renewable energy sources (DRES) and battery storage system (BSS), and improving the performance of the building electric microgrid. Thus, on the one side, a new methodology based on the genetic algorithm (GA) is proposed to properly determine the correct size of the DRES and BSS. On the other side, an integrated control method based on the GA technique too for the energy man-agement in the home microgrid is suggested that is accomplished through a correct balance between the maximum exploitation of the DRES and BSS, comfort of the building residents, and en-ergy saving. Therefore, the problem of reducing the cost for de-veloping an nZEB is addressed by reducing the two cost components, i.e. installation and operating cost. Moreover, the influ-ence of the one cost on the other is considered, and therefore an integrated calculation method is developed that provides a ho-listic solution for the nZEB's cost problem. The proposed calcu-lation strategy has been experimentally validated in a pilot building and several experimental results are presented in this paper to demonstrate the effectiveness, practicality, and functionality of the suggested methodology.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"10 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":"128574076","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/ISC255366.2022.9922461
Chen-Yeou Yu, Wensheng Zhang, Carl K. Chang
Traffic congestion may cause elongated travel time, increased fuel consumption and extra pollution. To mitigate congestion, we propose a new approach based on multi-agent reinforcement learning (RL) to learn policies dictating path selections for vehicles. The algorithm utilizes the interactions between RL agents with Q-Learning and edge servers in monitoring traffic at road intersections. As an important difference between this work and existing approaches, we take human desire and realistic rewards into account. Extensive simulation experiments show that the resulting mechanism is promising and more RL agents can be incentive to follow rerouting directions when congestion is detected. Also, this algorithm has comparable performance as the Dynamic Dijkstra Algorithm.
{"title":"Edge-based Situ-aware Reinforcement Learning for Traffic Congestion Mitigation","authors":"Chen-Yeou Yu, Wensheng Zhang, Carl K. Chang","doi":"10.1109/ISC255366.2022.9922461","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9922461","url":null,"abstract":"Traffic congestion may cause elongated travel time, increased fuel consumption and extra pollution. To mitigate congestion, we propose a new approach based on multi-agent reinforcement learning (RL) to learn policies dictating path selections for vehicles. The algorithm utilizes the interactions between RL agents with Q-Learning and edge servers in monitoring traffic at road intersections. As an important difference between this work and existing approaches, we take human desire and realistic rewards into account. Extensive simulation experiments show that the resulting mechanism is promising and more RL agents can be incentive to follow rerouting directions when congestion is detected. Also, this algorithm has comparable performance as the Dynamic Dijkstra Algorithm.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"24 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":"121047203","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/ISC255366.2022.9922559
Nathan Puryear, Patrick J. Martin, M. Kuzlu, Özgür Güler, V. Jovanovic, S. Abdelwahed
This paper presents an experiment orchestration platform, called VirtualLab@OpenCyberCity, that supports the research and education of smart city technologies. This platform will allow researchers and students to provision distributed experiments across the cyber-physical agents within OpenCyberCity. These new capabilities will support building a cyber-physical systems workforce with hands-on-experience using technologies that will be incorporated into smart city solutions. Virtual-Lab@OpenCyberCity will (a) provide a learning ecosystem of advanced CPS technologies, (b) inform the employment of advanced technologies and intelligent management systems for smart city planners, and (c) foster fruitful collaboration among academia, industry, and government stakeholders to build a smart city innovation workforce.
{"title":"An Experiment Orchestration Platform to Support Smart City Experiential Learning","authors":"Nathan Puryear, Patrick J. Martin, M. Kuzlu, Özgür Güler, V. Jovanovic, S. Abdelwahed","doi":"10.1109/ISC255366.2022.9922559","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9922559","url":null,"abstract":"This paper presents an experiment orchestration platform, called VirtualLab@OpenCyberCity, that supports the research and education of smart city technologies. This platform will allow researchers and students to provision distributed experiments across the cyber-physical agents within OpenCyberCity. These new capabilities will support building a cyber-physical systems workforce with hands-on-experience using technologies that will be incorporated into smart city solutions. Virtual-Lab@OpenCyberCity will (a) provide a learning ecosystem of advanced CPS technologies, (b) inform the employment of advanced technologies and intelligent management systems for smart city planners, and (c) foster fruitful collaboration among academia, industry, and government stakeholders to build a smart city innovation workforce.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"157 4 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":"130458970","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/ISC255366.2022.9921853
Evangelos Antypas, Georgios Spanos, Antonios Lalas, K. Votis, D. Tzovaras
Autonomous Vehicles (AVs) are expected to revolutionise the methods of transportation. Research and innovation in this field is making huge leaps in the last few years, whether it considers vehicles used for private or public transport. Predicting the Estimated Time of Arrival (ETA) is a very important attribute associated with Public Transport (PT). Especially with the rise of AVs' adoption, PT is expected to follow this trend. Therefore, ETA prediction is deemed to be a service that interests the majority of PT stakeholders. PT is a field that automation benefits both stakeholders and commuters, and this research aims to provide a benchmark considering AVs in PT. Within this work, Gradient Boosting (GB) techniques for ETA prediction were employed, namely eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost) and Light Gradient Boosting Machines (LightGBM). This study proposes competitive ETA prediction methods in Autonomous Buses, while the results of this research are very encouraging and aim to contribute to the overall investigations in the field of autonomous and automated PT.
{"title":"Estimated Time of Arrival in Autonomous Vehicles Using Gradient Boosting: Real-life case study in public transportation","authors":"Evangelos Antypas, Georgios Spanos, Antonios Lalas, K. Votis, D. Tzovaras","doi":"10.1109/ISC255366.2022.9921853","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9921853","url":null,"abstract":"Autonomous Vehicles (AVs) are expected to revolutionise the methods of transportation. Research and innovation in this field is making huge leaps in the last few years, whether it considers vehicles used for private or public transport. Predicting the Estimated Time of Arrival (ETA) is a very important attribute associated with Public Transport (PT). Especially with the rise of AVs' adoption, PT is expected to follow this trend. Therefore, ETA prediction is deemed to be a service that interests the majority of PT stakeholders. PT is a field that automation benefits both stakeholders and commuters, and this research aims to provide a benchmark considering AVs in PT. Within this work, Gradient Boosting (GB) techniques for ETA prediction were employed, namely eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost) and Light Gradient Boosting Machines (LightGBM). This study proposes competitive ETA prediction methods in Autonomous Buses, while the results of this research are very encouraging and aim to contribute to the overall investigations in the field of autonomous and automated PT.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"80 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":"133391723","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/ISC255366.2022.9921867
Madiha Bencekri, Adnane Founoun, A. Haqiq, A. Hayar
Sustainable development commitments are of concern to the city's decision-makers as well as significantly impacting the existing transportation systems. The concept of the smart city and precisely the component of smart mobility centered on the user and soft transport comes to support this approach of transformation which aims at the low carbon city. Similarly, reducing carbon emissions is one of the main objectives of a smart city, thereby comes the focus on enhancing eco-friendly and active transportation means, for instance, the shared-bike system. The mode benefits from the technology implemented within the smart city concept. Seoul Government has implemented a shared-bike program “Ttareungyi” in 2015, within the big vision of “low carbon green growth”. However, the program struggles to achieve the targeted demand. Therefore, this study is using data analytics to help enlighten decision-makers about the shared-bike system and provide insights for future development. The research was conducted to investigate the influence of the built environment, including slope, land use mix, and centrality parameters, the influence of transport infrastructure, including bike and transit infrastructure, and the influence of the socio-economic characteristics, including population, retail number, car ownership, and job offers on bike demand. And to predict bike demand based on the mentioned variables using the ridge regression method. Results revealed that dock number, population density, and car ownership have a significant positive impact on biking demand, while slope has a significant negative impact. In contradiction to the research hypothesis, land use mix revealed a weak impact on biking demand using random forest, and a negative influence using ridge regression.
{"title":"Investigation of Shared-Bike Demand Using Data Analytics","authors":"Madiha Bencekri, Adnane Founoun, A. Haqiq, A. Hayar","doi":"10.1109/ISC255366.2022.9921867","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9921867","url":null,"abstract":"Sustainable development commitments are of concern to the city's decision-makers as well as significantly impacting the existing transportation systems. The concept of the smart city and precisely the component of smart mobility centered on the user and soft transport comes to support this approach of transformation which aims at the low carbon city. Similarly, reducing carbon emissions is one of the main objectives of a smart city, thereby comes the focus on enhancing eco-friendly and active transportation means, for instance, the shared-bike system. The mode benefits from the technology implemented within the smart city concept. Seoul Government has implemented a shared-bike program “Ttareungyi” in 2015, within the big vision of “low carbon green growth”. However, the program struggles to achieve the targeted demand. Therefore, this study is using data analytics to help enlighten decision-makers about the shared-bike system and provide insights for future development. The research was conducted to investigate the influence of the built environment, including slope, land use mix, and centrality parameters, the influence of transport infrastructure, including bike and transit infrastructure, and the influence of the socio-economic characteristics, including population, retail number, car ownership, and job offers on bike demand. And to predict bike demand based on the mentioned variables using the ridge regression method. Results revealed that dock number, population density, and car ownership have a significant positive impact on biking demand, while slope has a significant negative impact. In contradiction to the research hypothesis, land use mix revealed a weak impact on biking demand using random forest, and a negative influence using ridge regression.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"69 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":"131786237","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/ISC255366.2022.9921836
Marie Njaime, Fahed Abdallah Olivier, H. Snoussi, Judy Akl, C. Chahla, H. Omrani
Air pollution is a serious environmental danger to people, specifically those who live in urbanised regions. Air pollution is also responsible for the climate crisis. Latest researches have shown the efficiency of early alert procedures that permits citizens to decrease their exposure to air pollution. Hence, monitoring air quality has turned into an essential need in most cities. Circulation, electricity, combustible uses, and various factors contribute to air pollution. Air quality ground stations are placed across most countries to record diverse air pollutants (including NO2), but they have a limited number, constraining therefore the accuracy of ground-level NO2 at high temporal and spatial resolutions. Conversely, satellite remote sensing data measures NO2 densities at a global scale. This paper presents a Data Cleaning technique for satellite images so Transfer Learning could be applied in a further step to estimate NO2 concentrations at Luxembourg with high spatial resolutions based on a pretrained Residual Network 50 (ResNet-50).
{"title":"Data Cleaning to fine-tune a Transfer Learning approach for Air Quality Prediction","authors":"Marie Njaime, Fahed Abdallah Olivier, H. Snoussi, Judy Akl, C. Chahla, H. Omrani","doi":"10.1109/ISC255366.2022.9921836","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9921836","url":null,"abstract":"Air pollution is a serious environmental danger to people, specifically those who live in urbanised regions. Air pollution is also responsible for the climate crisis. Latest researches have shown the efficiency of early alert procedures that permits citizens to decrease their exposure to air pollution. Hence, monitoring air quality has turned into an essential need in most cities. Circulation, electricity, combustible uses, and various factors contribute to air pollution. Air quality ground stations are placed across most countries to record diverse air pollutants (including NO2), but they have a limited number, constraining therefore the accuracy of ground-level NO2 at high temporal and spatial resolutions. Conversely, satellite remote sensing data measures NO2 densities at a global scale. This paper presents a Data Cleaning technique for satellite images so Transfer Learning could be applied in a further step to estimate NO2 concentrations at Luxembourg with high spatial resolutions based on a pretrained Residual Network 50 (ResNet-50).","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"48 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":"128948736","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/ISC255366.2022.9922038
Risa Kimura, Tatsuoki Nakajima
This paper summarizes our ongoing project to develop two smart city platforms based on the sharing economy concept for collectively sharing human eyes and ears. After presenting an overview of our platforms, we describe diverse smart city services developed on the platforms and discuss some promising opportunities of the platforms. Finally, we show two suggestions for developing future innovative smart city services.
{"title":"Collectively Sharing Human Eyes and Ears as Smart City Digital Platforms","authors":"Risa Kimura, Tatsuoki Nakajima","doi":"10.1109/ISC255366.2022.9922038","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9922038","url":null,"abstract":"This paper summarizes our ongoing project to develop two smart city platforms based on the sharing economy concept for collectively sharing human eyes and ears. After presenting an overview of our platforms, we describe diverse smart city services developed on the platforms and discuss some promising opportunities of the platforms. Finally, we show two suggestions for developing future innovative smart city services.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"17 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":"114545554","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/ISC255366.2022.9922207
Huzeyfe Kocabas, Christopher Allred, Mario Harper
Deploying autonomous drone systems in smart cities to identify unexpected events and adapt rapidly to crises has a great potential for optimizing city operations and increasing city-wide situational awareness. This work presents an algorithmic technique, Postman Moving Voronoi Coverage (PMVC), which effectively distributes and plans coverage routes for each drone agent. PMVC divides city roadways into similarly sized subregions based on system limitations for many types of unmanned aerial vehicle (UAV). The findings describe trade-offs a city must make between drone types, number of systems, and the desired speed of city-wide road network traversal. Often, employing more low capacity drones are more cost and time effective for city coverage.
{"title":"Divide and Survey: Observability Through Multi-Drone City Roadway Coverage","authors":"Huzeyfe Kocabas, Christopher Allred, Mario Harper","doi":"10.1109/ISC255366.2022.9922207","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9922207","url":null,"abstract":"Deploying autonomous drone systems in smart cities to identify unexpected events and adapt rapidly to crises has a great potential for optimizing city operations and increasing city-wide situational awareness. This work presents an algorithmic technique, Postman Moving Voronoi Coverage (PMVC), which effectively distributes and plans coverage routes for each drone agent. PMVC divides city roadways into similarly sized subregions based on system limitations for many types of unmanned aerial vehicle (UAV). The findings describe trade-offs a city must make between drone types, number of systems, and the desired speed of city-wide road network traversal. Often, employing more low capacity drones are more cost and time effective for city coverage.","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":"126627436","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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