Traditional model of computing with wireless sensors/devices imposes restrictions on how efficiently these devices can be used due to resource constraints. Newer models for interacting with wireless sensors/devices such as Internet of Things and Sensor Cloud aim to overcome these restrictions. In this seminar, I will discuss sensor cloud architectures, which enable different wireless sensor and IoT networks, spread in a huge geographical area to connect together and be used by multiple users at the same time on demand basis. I will further discuss how virtual sensors assist in creating a multiuser environment on top of resource constrained physical wireless sensors and can help in supporting multiple applications on-demand basis. I will discuss security, privacy and data integrity and other security issues in sensor cloud as well as risk assessment in sensor cloud applications.
{"title":"Sensor Cloud: Sensing-as-a-Service Paradigm","authors":"S. Madria","doi":"10.1109/MDM.2018.00014","DOIUrl":"https://doi.org/10.1109/MDM.2018.00014","url":null,"abstract":"Traditional model of computing with wireless sensors/devices imposes restrictions on how efficiently these devices can be used due to resource constraints. Newer models for interacting with wireless sensors/devices such as Internet of Things and Sensor Cloud aim to overcome these restrictions. In this seminar, I will discuss sensor cloud architectures, which enable different wireless sensor and IoT networks, spread in a huge geographical area to connect together and be used by multiple users at the same time on demand basis. I will further discuss how virtual sensors assist in creating a multiuser environment on top of resource constrained physical wireless sensors and can help in supporting multiple applications on-demand basis. I will discuss security, privacy and data integrity and other security issues in sensor cloud as well as risk assessment in sensor cloud applications.","PeriodicalId":205319,"journal":{"name":"2018 19th IEEE International Conference on Mobile Data Management (MDM)","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127342063","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}
Marileni Angelidou, Constantinos Costa, Artyom Nikitin, D. Zeinalipour-Yazti
In this demonstration paper, we present an integrated indoor signal management studio, coined Fingerprint Management Studio (FMS), which provides a spatio-temporal platform to: (i) manage the collection of location-dependent sensor readings (i.e., fingerprints) in indoor environments; (ii) estimate the localization accuracy based on the collected fingerprints; and (iii) assess Wi-Fi coverage and data rates. The demonstration will present the components comprising FMS, namely CSM (Crowd Signal Map), ACCES (Accuracy Estimation) and WS (Wi-Fi Surveying), through a compelling map-based visual analytic interface implemented on top of our open-source indoor navigation service, coined Anyplace. We will present FMS in two modes: (i) Online Mode, where attendees will be able to collect and analyze real fingerprints at the conference venue; and (ii) Offline Mode, where attendees will be able to interact with measurements of University campus in Cyprus, a Hotel in the US and an Expo in S. Korea.
{"title":"FMS: Managing Crowdsourced Indoor Signals with the Fingerprint Management Studio","authors":"Marileni Angelidou, Constantinos Costa, Artyom Nikitin, D. Zeinalipour-Yazti","doi":"10.1109/MDM.2018.00054","DOIUrl":"https://doi.org/10.1109/MDM.2018.00054","url":null,"abstract":"In this demonstration paper, we present an integrated indoor signal management studio, coined Fingerprint Management Studio (FMS), which provides a spatio-temporal platform to: (i) manage the collection of location-dependent sensor readings (i.e., fingerprints) in indoor environments; (ii) estimate the localization accuracy based on the collected fingerprints; and (iii) assess Wi-Fi coverage and data rates. The demonstration will present the components comprising FMS, namely CSM (Crowd Signal Map), ACCES (Accuracy Estimation) and WS (Wi-Fi Surveying), through a compelling map-based visual analytic interface implemented on top of our open-source indoor navigation service, coined Anyplace. We will present FMS in two modes: (i) Online Mode, where attendees will be able to collect and analyze real fingerprints at the conference venue; and (ii) Offline Mode, where attendees will be able to interact with measurements of University campus in Cyprus, a Hotel in the US and an Expo in S. Korea.","PeriodicalId":205319,"journal":{"name":"2018 19th IEEE International Conference on Mobile Data Management (MDM)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114752836","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}
S. Luo, Y. Ng, Terence Zheng Wei Lim, Cliff Choon Hua Tan, Nannan He, Giuseppe Manai, Y. Li
Localisation of mobile devices has been a topic of academic research and industry practice for solving various application problems, examples can be footfall counting and profiling used for location based digital or physical advertising, crowd monitoring for public security, emergency handling, transport measurement and management, etc. Often the solutions for localisation require large scale networks hardware and/or software upgrades, which can be very costly. However, we note the fact that many commercial use cases actually do not require very high resolution of localisation and satisfactory level of accuracy may be sufficient for attaining business decision quality. A reasonable trade-off between achieving business value and minimising additional costs on network equipment purchase and maintenance is to build solutions that rely only on telco-network data and utilize data mining methods to improve the localisation accuracy of mobile devices that carried by subscribers. In this work, we aim to achieve acceptable accuracy for localisation at the resolution of region of interest (ROI), the exact shape of which is defined according to business requirements. One example is the geographical division of planning sub-zone in Singapore. We make use of the Global Positioning System (GPS) locations extracted from mobile broadband log that contain the longitude and latitude of the subscriber to annotate the telco-network data. We experimented with three learning models: maximum likelihood estimation, dominant serving ROI, and random forest, along with the baseline of localisation based on cellular tower locations. The experiment results demonstrate the effectiveness of the proposed models and demonstrate accuracy improvement from baseline of 37.8% (naive cellular tower localisation) to 78.4% (random forest classification).
{"title":"Improved Localisation Using Spatio-Temporal Data from Cellular Network","authors":"S. Luo, Y. Ng, Terence Zheng Wei Lim, Cliff Choon Hua Tan, Nannan He, Giuseppe Manai, Y. Li","doi":"10.1109/MDM.2018.00022","DOIUrl":"https://doi.org/10.1109/MDM.2018.00022","url":null,"abstract":"Localisation of mobile devices has been a topic of academic research and industry practice for solving various application problems, examples can be footfall counting and profiling used for location based digital or physical advertising, crowd monitoring for public security, emergency handling, transport measurement and management, etc. Often the solutions for localisation require large scale networks hardware and/or software upgrades, which can be very costly. However, we note the fact that many commercial use cases actually do not require very high resolution of localisation and satisfactory level of accuracy may be sufficient for attaining business decision quality. A reasonable trade-off between achieving business value and minimising additional costs on network equipment purchase and maintenance is to build solutions that rely only on telco-network data and utilize data mining methods to improve the localisation accuracy of mobile devices that carried by subscribers. In this work, we aim to achieve acceptable accuracy for localisation at the resolution of region of interest (ROI), the exact shape of which is defined according to business requirements. One example is the geographical division of planning sub-zone in Singapore. We make use of the Global Positioning System (GPS) locations extracted from mobile broadband log that contain the longitude and latitude of the subscriber to annotate the telco-network data. We experimented with three learning models: maximum likelihood estimation, dominant serving ROI, and random forest, along with the baseline of localisation based on cellular tower locations. The experiment results demonstrate the effectiveness of the proposed models and demonstrate accuracy improvement from baseline of 37.8% (naive cellular tower localisation) to 78.4% (random forest classification).","PeriodicalId":205319,"journal":{"name":"2018 19th IEEE International Conference on Mobile Data Management (MDM)","volume":"a3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130723096","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}
With the thriving of location-based social networks, a large number of user check-in data have been accumulated. Tasks such as the prediction of the next check-in location can be addressed through the usage of LBSN data. Previous work mainly uses the historical trajectories of users to analyze users' check-in behavior, while the social information of users was rarely used. In this paper, we propose a unified location prediction framework to integrate the effect of history check-in and the influence of social circles. We first employ the most frequent check-in model (MFC) and the user-based collaborative filtering model (UCF) to capture users' historical trajectories and users' implicit preference, respectively. Then we use the multi-social circle model (MSC) to model the influence of three social circles. Finally, we evaluate our location prediction framework in the real-world data sets, and the experimental results show that our model performs better than the state-of-the-art approaches in predicting the next check-in location.
{"title":"Next Check-in Location Prediction via Footprints and Friendship on Location-Based Social Networks","authors":"Yijun Su, Xiang Li, Wei Tang, Ji Xiang, Yuanye He","doi":"10.1109/MDM.2018.00044","DOIUrl":"https://doi.org/10.1109/MDM.2018.00044","url":null,"abstract":"With the thriving of location-based social networks, a large number of user check-in data have been accumulated. Tasks such as the prediction of the next check-in location can be addressed through the usage of LBSN data. Previous work mainly uses the historical trajectories of users to analyze users' check-in behavior, while the social information of users was rarely used. In this paper, we propose a unified location prediction framework to integrate the effect of history check-in and the influence of social circles. We first employ the most frequent check-in model (MFC) and the user-based collaborative filtering model (UCF) to capture users' historical trajectories and users' implicit preference, respectively. Then we use the multi-social circle model (MSC) to model the influence of three social circles. Finally, we evaluate our location prediction framework in the real-world data sets, and the experimental results show that our model performs better than the state-of-the-art approaches in predicting the next check-in location.","PeriodicalId":205319,"journal":{"name":"2018 19th IEEE International Conference on Mobile Data Management (MDM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130127433","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}
Mobile CrowdSensing (MCS) applications rely on sensor data collected from a number of mobile participant devices; the participant devices need to sustain in the system for longer duration in order to services multiple requests. In this work, we propose two online load-balanced algorithms, that use available resources on mobile devices, to efficiently allocate tasks to a subset of participants. We have conducted extensive simulations to compare our algorithms with three baseline approaches and observed significant improvements in the system lifetime and the total number of tasks serviced. To further validate our results, we also conduct real-world experiments on 8 smartphones. We achieve 29.3% increase in the number of tasks serviced, with drastic improvements in system lifetime (in resource constrained cases) over the state-of-the-art approaches.
{"title":"Load-Balanced Task Allocation for Improved System Lifetime in Mobile Crowdsensing","authors":"Garvita Bajaj, Pushpendra Singh","doi":"10.1109/MDM.2018.00040","DOIUrl":"https://doi.org/10.1109/MDM.2018.00040","url":null,"abstract":"Mobile CrowdSensing (MCS) applications rely on sensor data collected from a number of mobile participant devices; the participant devices need to sustain in the system for longer duration in order to services multiple requests. In this work, we propose two online load-balanced algorithms, that use available resources on mobile devices, to efficiently allocate tasks to a subset of participants. We have conducted extensive simulations to compare our algorithms with three baseline approaches and observed significant improvements in the system lifetime and the total number of tasks serviced. To further validate our results, we also conduct real-world experiments on 8 smartphones. We achieve 29.3% increase in the number of tasks serviced, with drastic improvements in system lifetime (in resource constrained cases) over the state-of-the-art approaches.","PeriodicalId":205319,"journal":{"name":"2018 19th IEEE International Conference on Mobile Data Management (MDM)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124779345","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}
Ellen Mitsopoulou, Ioannis Boutsis, V. Kalogeraki, Jia Yuan Yu
The rapid growth of ubiquitous mobile smart devices has led to the creation of a new era of mobile crowdsourcing applications, where human workers participate and perform tasks in exchange of a monetary reward. Such crowdsourcing systems can play a vital role during emergency events, where fast and accurate responses are needed. However, a commonly ignored aspect is how the price (i.e. the reward paid to workers) must be set in order for the system to meet two important requirements: (i) to timely receive an adequate number of responses which is crucial during emergencies, and (ii) to meet budget constraints. In the majority of the existing systems, the price per task is set up-front and remains unchanged for all upcoming tasks, leading to either higher monetary cost than necessary or to significantly larger latency than expected. In this work, we provide a formulation based on Kalman Filters that enables the system to estimate the user/worker behavior, i.e., the likelihood over time for a user to provide answers for a specific reward. Specifically, we focus on the problem of developing an adaptive pricing policy to incentivize the users to rapidly provide their responses. Our mechanism can be adjusted dynamically to bridge the gap among the users' behavior and the system's needs so as to maximize the overall utility of the system. We simulate our model and through extensive experimental evaluation we show how our system performs and provides benefits to both the users and the system operator.
{"title":"A Cost-Aware Incentive Mechanism in Mobile Crowdsourcing Systems","authors":"Ellen Mitsopoulou, Ioannis Boutsis, V. Kalogeraki, Jia Yuan Yu","doi":"10.1109/MDM.2018.00042","DOIUrl":"https://doi.org/10.1109/MDM.2018.00042","url":null,"abstract":"The rapid growth of ubiquitous mobile smart devices has led to the creation of a new era of mobile crowdsourcing applications, where human workers participate and perform tasks in exchange of a monetary reward. Such crowdsourcing systems can play a vital role during emergency events, where fast and accurate responses are needed. However, a commonly ignored aspect is how the price (i.e. the reward paid to workers) must be set in order for the system to meet two important requirements: (i) to timely receive an adequate number of responses which is crucial during emergencies, and (ii) to meet budget constraints. In the majority of the existing systems, the price per task is set up-front and remains unchanged for all upcoming tasks, leading to either higher monetary cost than necessary or to significantly larger latency than expected. In this work, we provide a formulation based on Kalman Filters that enables the system to estimate the user/worker behavior, i.e., the likelihood over time for a user to provide answers for a specific reward. Specifically, we focus on the problem of developing an adaptive pricing policy to incentivize the users to rapidly provide their responses. Our mechanism can be adjusted dynamically to bridge the gap among the users' behavior and the system's needs so as to maximize the overall utility of the system. We simulate our model and through extensive experimental evaluation we show how our system performs and provides benefits to both the users and the system operator.","PeriodicalId":205319,"journal":{"name":"2018 19th IEEE International Conference on Mobile Data Management (MDM)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124442142","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}
A Telecommunication company (Telco) is traditionally only perceived as the entity that provides telecommunication services, such as telephony and data communication access to users. However, the radio and backbone infrastructure of such entities spanning densely most urban spaces and widely most rural areas, provides nowadays a unique opportunity to collect immense amounts of data that capture a variety of natural phenomena on an ongoing basis, e.g., traffic, commerce and mobility patterns and user service experience. The ability to perform analytics on the generated big data within tolerable elapsed time and share it with key smart city enablers (e.g., municipalities, public services, startups, authorities, and companies), elevates the role of Telcos in the realm of future smart cities from pure network access providers to information providers. In this talk, we overview the state-of-the-art in Telco big data analytics by focusing on a set of basic principles, namely: (i) real-time analytics and detection; (ii) experience, behavior and retention analytics; (iii) privacy; and (iv) storage. We also present experiences from developing an innovative such architecture and conclude with open problems and future directions.
{"title":"Telco Big Data: Current State & Future Directions","authors":"Constantinos Costa, D. Zeinalipour-Yazti","doi":"10.1109/MDM.2018.00016","DOIUrl":"https://doi.org/10.1109/MDM.2018.00016","url":null,"abstract":"A Telecommunication company (Telco) is traditionally only perceived as the entity that provides telecommunication services, such as telephony and data communication access to users. However, the radio and backbone infrastructure of such entities spanning densely most urban spaces and widely most rural areas, provides nowadays a unique opportunity to collect immense amounts of data that capture a variety of natural phenomena on an ongoing basis, e.g., traffic, commerce and mobility patterns and user service experience. The ability to perform analytics on the generated big data within tolerable elapsed time and share it with key smart city enablers (e.g., municipalities, public services, startups, authorities, and companies), elevates the role of Telcos in the realm of future smart cities from pure network access providers to information providers. In this talk, we overview the state-of-the-art in Telco big data analytics by focusing on a set of basic principles, namely: (i) real-time analytics and detection; (ii) experience, behavior and retention analytics; (iii) privacy; and (iv) storage. We also present experiences from developing an innovative such architecture and conclude with open problems and future directions.","PeriodicalId":205319,"journal":{"name":"2018 19th IEEE International Conference on Mobile Data Management (MDM)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126570147","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}
Mining crowd-sourced movement trajectories is a useful tool in urban computing. Common mobility patterns of the visitors or residents of a city can be exploited in applications such as disaster management, transportation planning and ad placement. In recommendation systems, individual behaviour is of special interest. To extract the visiting behaviour of individuals, the trajectories need to be semantically annotated. We describe how hierarchical regions of interest (ROIs) can be used for semantic annotation. By combining multiple layers of smaller and larger regions we can flexibly detect both visits to dense hotspots and trajectory segments visiting larger areas, such as an old town, a park or an island. Extending the annotation beyond common hotspots captures more information about the behaviour.
{"title":"Hierarchical Regions of Interest","authors":"P. Järv, T. Tammet, Marten Tall","doi":"10.1109/MDM.2018.00025","DOIUrl":"https://doi.org/10.1109/MDM.2018.00025","url":null,"abstract":"Mining crowd-sourced movement trajectories is a useful tool in urban computing. Common mobility patterns of the visitors or residents of a city can be exploited in applications such as disaster management, transportation planning and ad placement. In recommendation systems, individual behaviour is of special interest. To extract the visiting behaviour of individuals, the trajectories need to be semantically annotated. We describe how hierarchical regions of interest (ROIs) can be used for semantic annotation. By combining multiple layers of smaller and larger regions we can flexibly detect both visits to dense hotspots and trajectory segments visiting larger areas, such as an old town, a park or an island. Extending the annotation beyond common hotspots captures more information about the behaviour.","PeriodicalId":205319,"journal":{"name":"2018 19th IEEE International Conference on Mobile Data Management (MDM)","volume":"268 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134052972","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}
The focus on reducing CO2 emissions from the transport sector is larger than ever. Increasingly stricter reductions on fuel consumption and emissions are being introduced by the EU, e.g., to reduce the air pollution in many larger cities. Large sets of high-frequent GPS data from vehicles already exist. However, fuel consumption data is still rarely collected even though it is possible to measure the fuel consumption with high accuracy, e.g., using an OBD-II device and a smartphone. This paper, presents a method for comparing fuel-consumption estimates using the SIDRA TRIP model with real fuel measures to determine if the fuel-consumption model is sufficiently accurate. The model is implemented using a 2D, a simple 3D, and a high-precision (H3D) road map of Denmark. The original 2D map is lifted to a 3D map using a Digital Elevation Model (DEM). Results show that introducing a 3D map improves the accuracy of fuel-consumption estimates with up to 40% on hilly roads. There is only very little improvement of the high-precision (H3D) map over the simple 3D map. The fuel consumption estimates are most accurate on flat terrain with average fuel estimates of up to 99% accuracy. The fuel estimates are most inaccurate uphill/downhill and when the vehicles accelerate at speeds above 50 km/h.
{"title":"Accurate Fuel Estimates Using CAN Bus Data and 3D Maps","authors":"O. Andersen, K. Torp","doi":"10.1109/MDM.2018.00045","DOIUrl":"https://doi.org/10.1109/MDM.2018.00045","url":null,"abstract":"The focus on reducing CO2 emissions from the transport sector is larger than ever. Increasingly stricter reductions on fuel consumption and emissions are being introduced by the EU, e.g., to reduce the air pollution in many larger cities. Large sets of high-frequent GPS data from vehicles already exist. However, fuel consumption data is still rarely collected even though it is possible to measure the fuel consumption with high accuracy, e.g., using an OBD-II device and a smartphone. This paper, presents a method for comparing fuel-consumption estimates using the SIDRA TRIP model with real fuel measures to determine if the fuel-consumption model is sufficiently accurate. The model is implemented using a 2D, a simple 3D, and a high-precision (H3D) road map of Denmark. The original 2D map is lifted to a 3D map using a Digital Elevation Model (DEM). Results show that introducing a 3D map improves the accuracy of fuel-consumption estimates with up to 40% on hilly roads. There is only very little improvement of the high-precision (H3D) map over the simple 3D map. The fuel consumption estimates are most accurate on flat terrain with average fuel estimates of up to 99% accuracy. The fuel estimates are most inaccurate uphill/downhill and when the vehicles accelerate at speeds above 50 km/h.","PeriodicalId":205319,"journal":{"name":"2018 19th IEEE International Conference on Mobile Data Management (MDM)","volume":"16 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133473753","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}
We study stochastic routing in the PAth-CEntric (PACE) uncertain road network model. In the PACE model, uncertain travel times are associated with not only edges but also some paths. The uncertain travel times associated with paths are able to well capture the travel time dependency among different edges. This significantly improves the accuracy of travel time distribution estimations for arbitrary paths, which is a fundamental functionality in stochastic routing, compared to classic uncertain road network models where uncertain travel times are associated with only edges. Based on the PACE model, we investigate the shortest path with on-time arrival reliability (SPOTAR) problem. Given a source, a destination, and a travel time budget, the SPOTAR problem aims at finding a path that maximizes the on-time arrival probability. We develop a generic algorithm with different speedup strategies to solve the SPOTAR problem under the PACE model. Empirical studies with substantial GPS trajectory data offer insight into the design properties of the proposed algorithm and confirm that the algorithm is effective.
{"title":"Stochastic Shortest Path Finding in Path-Centric Uncertain Road Networks","authors":"Georgi Andonov, B. Yang","doi":"10.1109/MDM.2018.00020","DOIUrl":"https://doi.org/10.1109/MDM.2018.00020","url":null,"abstract":"We study stochastic routing in the PAth-CEntric (PACE) uncertain road network model. In the PACE model, uncertain travel times are associated with not only edges but also some paths. The uncertain travel times associated with paths are able to well capture the travel time dependency among different edges. This significantly improves the accuracy of travel time distribution estimations for arbitrary paths, which is a fundamental functionality in stochastic routing, compared to classic uncertain road network models where uncertain travel times are associated with only edges. Based on the PACE model, we investigate the shortest path with on-time arrival reliability (SPOTAR) problem. Given a source, a destination, and a travel time budget, the SPOTAR problem aims at finding a path that maximizes the on-time arrival probability. We develop a generic algorithm with different speedup strategies to solve the SPOTAR problem under the PACE model. Empirical studies with substantial GPS trajectory data offer insight into the design properties of the proposed algorithm and confirm that the algorithm is effective.","PeriodicalId":205319,"journal":{"name":"2018 19th IEEE International Conference on Mobile Data Management (MDM)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115771250","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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