Pub Date : 2017-03-13DOI: 10.1109/PERCOMW.2017.7917616
R. Mihailescu, P. Davidsson
Pervasive technologies permeating our immediate surroundings provide a wide variety of low-cost means of sensing and actuating in our environment. This paper presents an approach for leveraging insights onto the lifestyle and routines of the users in order to control heating in a smart home through the use of individual climate zones, while ensuring system efficiency at a grid-level scale. Organizing smart living spaces into controllable individual climate zones allows us to exert a more fine-grained level of control. Thus, the system can benefit from a higher degree of freedom to adjust the heat demand according to the system objectives. Whereas district heating planing is only concerned with balancing heat demand among buildings, we extend the reach of these systems inside the home through the use of pervasive sensing and actuation. That is to say, we bridge the gap between traditional district heating systems and pervasive technologies in the home designed to maintain the thermal comfort of the user, in order to increase efficiency. The objective is to automate heating based on the user's preferences and behavioral patterns. The control scheme proposed applies a learning algorithm to take advantage of the sensing data inside the home in combination with an optimization procedure designed to trade-off the discomfort undertaken by the user and heating supply costs. We report on preliminary simulation results showing the effectiveness of our approach and describe the setup of our forthcoming field study.
{"title":"Integration of Smart Home technologies for district heating control in Pervasive Smart Grids","authors":"R. Mihailescu, P. Davidsson","doi":"10.1109/PERCOMW.2017.7917616","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917616","url":null,"abstract":"Pervasive technologies permeating our immediate surroundings provide a wide variety of low-cost means of sensing and actuating in our environment. This paper presents an approach for leveraging insights onto the lifestyle and routines of the users in order to control heating in a smart home through the use of individual climate zones, while ensuring system efficiency at a grid-level scale. Organizing smart living spaces into controllable individual climate zones allows us to exert a more fine-grained level of control. Thus, the system can benefit from a higher degree of freedom to adjust the heat demand according to the system objectives. Whereas district heating planing is only concerned with balancing heat demand among buildings, we extend the reach of these systems inside the home through the use of pervasive sensing and actuation. That is to say, we bridge the gap between traditional district heating systems and pervasive technologies in the home designed to maintain the thermal comfort of the user, in order to increase efficiency. The objective is to automate heating based on the user's preferences and behavioral patterns. The control scheme proposed applies a learning algorithm to take advantage of the sensing data inside the home in combination with an optimization procedure designed to trade-off the discomfort undertaken by the user and heating supply costs. We report on preliminary simulation results showing the effectiveness of our approach and describe the setup of our forthcoming field study.","PeriodicalId":319638,"journal":{"name":"2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123436615","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 : 2017-03-13DOI: 10.1109/PERCOMW.2017.7917643
J. Dahmen, Alyssa La Fleur, Gina Sprint, D. Cook, D. Weeks
Wrist-worn sensors have increased in popularity in health care settings. As the use of wrist-worn sensors increases, a better understanding is needed of how to detect changes in behavior as well as an ability to quantify such changes. We introduce a statistical method to address this need. In this study, we used Fitbit Charge Heart Rate devices with two separate populations to continuously record data. There were eight participants in the healthy control group and nine in the hospitalized inpatient rehabilitation group. We performed comparisons both within the groups and between groups on the gathered step count and heart rate data. The inpatient rehabilitation group showed improved step count changes between the first half of the study participation and the second half. Heart rate did not show significant changes for either the healthy control group or inpatient rehabilitation group across time. We conclude that our statistical change analysis applied to wrist-worn sensors can effectively detect changes in physical activity that provides valuable information to patients as well as their healthcare care providers.
{"title":"Using wrist-worn sensors to measure and compare physical activity changes for patients undergoing rehabilitation","authors":"J. Dahmen, Alyssa La Fleur, Gina Sprint, D. Cook, D. Weeks","doi":"10.1109/PERCOMW.2017.7917643","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917643","url":null,"abstract":"Wrist-worn sensors have increased in popularity in health care settings. As the use of wrist-worn sensors increases, a better understanding is needed of how to detect changes in behavior as well as an ability to quantify such changes. We introduce a statistical method to address this need. In this study, we used Fitbit Charge Heart Rate devices with two separate populations to continuously record data. There were eight participants in the healthy control group and nine in the hospitalized inpatient rehabilitation group. We performed comparisons both within the groups and between groups on the gathered step count and heart rate data. The inpatient rehabilitation group showed improved step count changes between the first half of the study participation and the second half. Heart rate did not show significant changes for either the healthy control group or inpatient rehabilitation group across time. We conclude that our statistical change analysis applied to wrist-worn sensors can effectively detect changes in physical activity that provides valuable information to patients as well as their healthcare care providers.","PeriodicalId":319638,"journal":{"name":"2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"1962 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129612297","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 : 2017-03-13DOI: 10.1109/PERCOMW.2017.7917627
Nima Mousavi, Baris Aksanli, A. S. Akyurek, T. Simunic
Modern power grid has evolved from a passive network into an application of Internet of Things with numerous interconnected elements and users. In this environment, household users greatly benefit from a prediction algorithm that estimates their future power demand to help them control off-grid generation, battery storage, and power consumption. In particular, household power consumption prediction plays a pivotal role in optimal utilization of batteries used alongside photovoltaic generation, creating saving opportunities for users. Since edge devices in Internet of Things offer limited capabilities, the computational complexity and memory and energy consumption of the prediction algorithms are capped. In this paper we forecast 24-hour demand from power consumption, weather, and time data, using Support Vector Regression models, and compare it to state-of-the-art prediction methods such as Linear Regression and persistence. We use power consumption traces from real datasets and a Raspberry Pi 3 embedded computer as testbed to evaluate the resource-accuracy trade-off. Our study reveals that Support Vector Regression is able to achieve 21% less prediction error on average compared to Linear Regression, which translates into 16% more cost savings for users when using residential batteries with photovoltaic generation.
{"title":"Accuracy-resource tradeoff for edge devices in Internet of Things","authors":"Nima Mousavi, Baris Aksanli, A. S. Akyurek, T. Simunic","doi":"10.1109/PERCOMW.2017.7917627","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917627","url":null,"abstract":"Modern power grid has evolved from a passive network into an application of Internet of Things with numerous interconnected elements and users. In this environment, household users greatly benefit from a prediction algorithm that estimates their future power demand to help them control off-grid generation, battery storage, and power consumption. In particular, household power consumption prediction plays a pivotal role in optimal utilization of batteries used alongside photovoltaic generation, creating saving opportunities for users. Since edge devices in Internet of Things offer limited capabilities, the computational complexity and memory and energy consumption of the prediction algorithms are capped. In this paper we forecast 24-hour demand from power consumption, weather, and time data, using Support Vector Regression models, and compare it to state-of-the-art prediction methods such as Linear Regression and persistence. We use power consumption traces from real datasets and a Raspberry Pi 3 embedded computer as testbed to evaluate the resource-accuracy trade-off. Our study reveals that Support Vector Regression is able to achieve 21% less prediction error on average compared to Linear Regression, which translates into 16% more cost savings for users when using residential batteries with photovoltaic generation.","PeriodicalId":319638,"journal":{"name":"2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130360876","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 : 2017-03-13DOI: 10.1109/PERCOMW.2017.7917630
Glenn Ricart
Multiple economic and technology trends suggest a rapidly growing edge infrastructure at the city level to complement the traditional cloud infrastructure. This paper sets forth the economic and technical issues that have motivated the architecture chosen and describes the city edge cloud architecture now being deployed by the US Ignite nonprofit.
{"title":"A city edge cloud with its economic and technical considerations","authors":"Glenn Ricart","doi":"10.1109/PERCOMW.2017.7917630","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917630","url":null,"abstract":"Multiple economic and technology trends suggest a rapidly growing edge infrastructure at the city level to complement the traditional cloud infrastructure. This paper sets forth the economic and technical issues that have motivated the architecture chosen and describes the city edge cloud architecture now being deployed by the US Ignite nonprofit.","PeriodicalId":319638,"journal":{"name":"2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129348610","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 : 2017-03-13DOI: 10.1109/PERCOMW.2017.7917590
D. V. Le, C. Tham, Yanmin Zhu
Recently, the vehicular sensor network (VSN) is emerging as an efficient solution for executing different sensing tasks in urban environments. However, due to the heterogeneity of vehicles in sensing capability and uncontrollable movement trajectory, it is a challenge to best provide the required quality of information (QoI) of the sensing task in VSNs. In this paper, we introduce a VSN architecture, in which multiple vehicles cooperatively sense a particular urban area of interest, and process the sensed data to achieve the QoI requirements while considering incentives for environment sensing, data processing and communication. Furthermore, we formulate and solve an optimization problem for determining the optimal sampling rates for vehicles with the objective of minimizing the total incentive under the constraints related to QoI requirements. Various numerical results based on realistic vehicular traces are presented to justify the effectiveness of proposed approach in the vehicles' QoI-aware cooperative sensing operations.
{"title":"Quality of Information (QoI)-aware cooperative sensing in vehicular sensor networks","authors":"D. V. Le, C. Tham, Yanmin Zhu","doi":"10.1109/PERCOMW.2017.7917590","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917590","url":null,"abstract":"Recently, the vehicular sensor network (VSN) is emerging as an efficient solution for executing different sensing tasks in urban environments. However, due to the heterogeneity of vehicles in sensing capability and uncontrollable movement trajectory, it is a challenge to best provide the required quality of information (QoI) of the sensing task in VSNs. In this paper, we introduce a VSN architecture, in which multiple vehicles cooperatively sense a particular urban area of interest, and process the sensed data to achieve the QoI requirements while considering incentives for environment sensing, data processing and communication. Furthermore, we formulate and solve an optimization problem for determining the optimal sampling rates for vehicles with the objective of minimizing the total incentive under the constraints related to QoI requirements. Various numerical results based on realistic vehicular traces are presented to justify the effectiveness of proposed approach in the vehicles' QoI-aware cooperative sensing operations.","PeriodicalId":319638,"journal":{"name":"2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130784339","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 : 2017-03-13DOI: 10.1109/PERCOMW.2017.7917635
S. Sathyadevan, V. Vejesh, R. Doss, Lei Pan
Internet of Things is a connected ecosystem where everyday objects have network connectivity allowing them to do data transfers. This scenario often involves a gateway for enabling the communication between things which are connected to the internet. Gateways run multiple network based services in them which are often visible to any device in the same network. It acts as the entry point for the traffic to and from the local IoT network. Usually security of such devices is neglected and left unguarded making them prime targets for hackers. Portguard is an authentication tool developed with the intention of hiding those services that are up and running in the gateway from any external attackers. Any application/middleware servers or edge/sensor nodes attempting to connect to the services in the gateway will be authenticated prior to granting access to the gateway services. We demonstrate the effectiveness and efficiency of portguard against popular attacks.
{"title":"Portguard - an authentication tool for securing ports in an IoT gateway","authors":"S. Sathyadevan, V. Vejesh, R. Doss, Lei Pan","doi":"10.1109/PERCOMW.2017.7917635","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917635","url":null,"abstract":"Internet of Things is a connected ecosystem where everyday objects have network connectivity allowing them to do data transfers. This scenario often involves a gateway for enabling the communication between things which are connected to the internet. Gateways run multiple network based services in them which are often visible to any device in the same network. It acts as the entry point for the traffic to and from the local IoT network. Usually security of such devices is neglected and left unguarded making them prime targets for hackers. Portguard is an authentication tool developed with the intention of hiding those services that are up and running in the gateway from any external attackers. Any application/middleware servers or edge/sensor nodes attempting to connect to the services in the gateway will be authenticated prior to granting access to the gateway services. We demonstrate the effectiveness and efficiency of portguard against popular attacks.","PeriodicalId":319638,"journal":{"name":"2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121426200","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 : 2017-03-13DOI: 10.1109/PERCOMW.2017.7917529
Vidyasagar Sadhu, Gabriel Salles-Loustau, D. Pompili, S. Zonouz, Vincent Sritapan
Argus exploits a Multi-Agent Reinforcement Learning (MARL) framework to create a 3D mapping of the disaster scene using agents present around the incident zone to facilitate the rescue operations. The agents can be both human bystanders at the disaster scene as well as drones or robots that can assist the humans. The agents are involved in capturing the images of the scene using their smartphones (or on-board cameras in case of drones) as directed by the MARL algorithm. These images are used to build real time a 3D map of the disaster scene. In this paper, we present a demo of our approach.
{"title":"Argus: Smartphone-enabled human cooperation for disaster situational awareness via MARL","authors":"Vidyasagar Sadhu, Gabriel Salles-Loustau, D. Pompili, S. Zonouz, Vincent Sritapan","doi":"10.1109/PERCOMW.2017.7917529","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917529","url":null,"abstract":"Argus exploits a Multi-Agent Reinforcement Learning (MARL) framework to create a 3D mapping of the disaster scene using agents present around the incident zone to facilitate the rescue operations. The agents can be both human bystanders at the disaster scene as well as drones or robots that can assist the humans. The agents are involved in capturing the images of the scene using their smartphones (or on-board cameras in case of drones) as directed by the MARL algorithm. These images are used to build real time a 3D map of the disaster scene. In this paper, we present a demo of our approach.","PeriodicalId":319638,"journal":{"name":"2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121519308","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 : 2017-03-13DOI: 10.1109/PERCOMW.2017.7917619
Akane Ishida, Kazuya Murao, T. Terada, M. Tsukamoto
Refrigerators are commonly used by multiple users in the home and office. However, expired food is sometimes left in the refrigerator, and users may eat food belonging to others since food is often arranged in the refrigerator in a disorderly manner. This happens because food is not organized by owner. If food can be linked with its owner, users will not eat food belonging to others, and food in the refrigerator will be consumed prior to expiration by informing the owner of the expiration date. The simplest way to link food with its owner is to input the name of the owner manually every time he or she puts food in the refrigerator, which, however, is tedious and impractical. We propose a method that identifies who put food in the refrigerator by using pressure sensors, an accelerometer, and a gyroscope attached to the refrigerator door. The method analyzes the motions of opening/closing a refrigerator door and the pressure distribution of gripping the door-handle. In the future, we aim to link food with its owner. From the experiment, we confirmed that the method achieves 90.3% accuracy in user identification for a group of four.
{"title":"A user identification method based on features of opening/closing a refrigerator door","authors":"Akane Ishida, Kazuya Murao, T. Terada, M. Tsukamoto","doi":"10.1109/PERCOMW.2017.7917619","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917619","url":null,"abstract":"Refrigerators are commonly used by multiple users in the home and office. However, expired food is sometimes left in the refrigerator, and users may eat food belonging to others since food is often arranged in the refrigerator in a disorderly manner. This happens because food is not organized by owner. If food can be linked with its owner, users will not eat food belonging to others, and food in the refrigerator will be consumed prior to expiration by informing the owner of the expiration date. The simplest way to link food with its owner is to input the name of the owner manually every time he or she puts food in the refrigerator, which, however, is tedious and impractical. We propose a method that identifies who put food in the refrigerator by using pressure sensors, an accelerometer, and a gyroscope attached to the refrigerator door. The method analyzes the motions of opening/closing a refrigerator door and the pressure distribution of gripping the door-handle. In the future, we aim to link food with its owner. From the experiment, we confirmed that the method achieves 90.3% accuracy in user identification for a group of four.","PeriodicalId":319638,"journal":{"name":"2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126267502","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 : 2017-03-13DOI: 10.1109/PERCOMW.2017.7917550
Samuel Sungmin Cho, C. Julien
The Internet of Things (IoT) connects smart objects so they can share information in a network to provide context-sensitive services. The amount of shared information will increase, likely dramatically, as more and more smart objects join the network and disseminate their contextual information. In this paper, we explain how smart IoT devices can share a large amount of context information using much less storage space and communication bandwidth. We use the versatile and simple JSON format at the application interface to allow applications to define their context descriptions, but we convert this JSON format into size-efficient yet equivalent probabilistic data structures for storage or communication. The loss of schema information in the conversion is compensated for by introducing a schema summary, which incorporates a hierarchical structure, and a state machine that recovers the schema information from the relationship among elements in a summary.
{"title":"Size efficient big data sharing among Internet of Things devices","authors":"Samuel Sungmin Cho, C. Julien","doi":"10.1109/PERCOMW.2017.7917550","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917550","url":null,"abstract":"The Internet of Things (IoT) connects smart objects so they can share information in a network to provide context-sensitive services. The amount of shared information will increase, likely dramatically, as more and more smart objects join the network and disseminate their contextual information. In this paper, we explain how smart IoT devices can share a large amount of context information using much less storage space and communication bandwidth. We use the versatile and simple JSON format at the application interface to allow applications to define their context descriptions, but we convert this JSON format into size-efficient yet equivalent probabilistic data structures for storage or communication. The loss of schema information in the conversion is compensated for by introducing a schema summary, which incorporates a hierarchical structure, and a state machine that recovers the schema information from the relationship among elements in a summary.","PeriodicalId":319638,"journal":{"name":"2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"369 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121737051","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 : 2017-03-13DOI: 10.1109/PERCOMW.2017.7917613
Samy El-Tawab, Raymond Oram, Michael Garcia, C. Johns, B. Park
The rapid increase of modern wireless technology opens the door for several new applications using the Internet of Things (IoT) technology. In an educational environment, students depend on the transit bus system for their daily routine and there is a high demand of people to be served by buses around university campuses in the United States of America. Often times, the members of university communities find themselves waiting for a significant amount of time for a bus to arrive at the bus station. Universities have numerous bus stops as well as routes on which riders can use for travel. Several of these bus stops are covered by WiFi capabilities, and usually students are checking their smart phones while waiting for bus arrival. In order to monitor the quality of transit buses and passengers' services, we design, develop, and demonstrate a low cost IoT system that detects the majority of the riders on the bus system at each station. In this paper, the IoT devices collect, analyze and archive transit and passenger data (e.g., waiting times) to Cloud Storage from each bus station. The goal is to improve the passenger's experience by refining the current infrastructure in place, focusing on better planning and increasing bus ridership through better scheduling. By collecting such data (e.g., waiting times), the performance of the bus system can be further analyzed and suggest changes to a route in order to achieve a more efficient and sustainable urban transportation system.
{"title":"Data analysis of transit systems using low-cost IoT technology","authors":"Samy El-Tawab, Raymond Oram, Michael Garcia, C. Johns, B. Park","doi":"10.1109/PERCOMW.2017.7917613","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917613","url":null,"abstract":"The rapid increase of modern wireless technology opens the door for several new applications using the Internet of Things (IoT) technology. In an educational environment, students depend on the transit bus system for their daily routine and there is a high demand of people to be served by buses around university campuses in the United States of America. Often times, the members of university communities find themselves waiting for a significant amount of time for a bus to arrive at the bus station. Universities have numerous bus stops as well as routes on which riders can use for travel. Several of these bus stops are covered by WiFi capabilities, and usually students are checking their smart phones while waiting for bus arrival. In order to monitor the quality of transit buses and passengers' services, we design, develop, and demonstrate a low cost IoT system that detects the majority of the riders on the bus system at each station. In this paper, the IoT devices collect, analyze and archive transit and passenger data (e.g., waiting times) to Cloud Storage from each bus station. The goal is to improve the passenger's experience by refining the current infrastructure in place, focusing on better planning and increasing bus ridership through better scheduling. By collecting such data (e.g., waiting times), the performance of the bus system can be further analyzed and suggest changes to a route in order to achieve a more efficient and sustainable urban transportation system.","PeriodicalId":319638,"journal":{"name":"2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121745410","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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