Cong Shi, Kaustubh R. Joshi, R. Panta, M. Ammar, E. Zegura
The explosive growth of mobile data traffic poses severe pressure on cellular providers to better manage their finite spectrum. Proposed solutions such as congestion-pricing exist, but they degrade users' ability to use the network when they want. In this paper, we propose a fundamentally different approach - rather than reducing the aggregate busy hour traffic, we seek to smooth the peaks that cause congestion. Our approach is based on two key insights obtained from traffic traces of a large cellular provider. First, mobile traffic demonstrates high short-term variation so that delaying traffic for very short periods of time can significantly reduce peaks. Second, by making collaborative decisions on which traffic gets delayed and by how much across all users of a cell, the delays need not result in any degradation of user experience. We design a system, CoAST, to implement this approach using three key mechanisms: a protocol to allow mobile applications and providers to exchange traffic information, an incentive mechanism to incentivize mobile applications to collaboratively delay traffic at the right time, and mechanisms to delay application traffic. We provide extensive evaluations that show that CoAST reduces traffic peaks by up to 50% even for applications that are not thought to be delay-tolerant, e.g., streaming and web browsing, but which together account for 70% of all cellular traffic.
{"title":"CoAST: collaborative application-aware scheduling of last-mile cellular traffic","authors":"Cong Shi, Kaustubh R. Joshi, R. Panta, M. Ammar, E. Zegura","doi":"10.1145/2594368.2594385","DOIUrl":"https://doi.org/10.1145/2594368.2594385","url":null,"abstract":"The explosive growth of mobile data traffic poses severe pressure on cellular providers to better manage their finite spectrum. Proposed solutions such as congestion-pricing exist, but they degrade users' ability to use the network when they want. In this paper, we propose a fundamentally different approach - rather than reducing the aggregate busy hour traffic, we seek to smooth the peaks that cause congestion. Our approach is based on two key insights obtained from traffic traces of a large cellular provider. First, mobile traffic demonstrates high short-term variation so that delaying traffic for very short periods of time can significantly reduce peaks. Second, by making collaborative decisions on which traffic gets delayed and by how much across all users of a cell, the delays need not result in any degradation of user experience. We design a system, CoAST, to implement this approach using three key mechanisms: a protocol to allow mobile applications and providers to exchange traffic information, an incentive mechanism to incentivize mobile applications to collaboratively delay traffic at the right time, and mechanisms to delay application traffic. We provide extensive evaluations that show that CoAST reduces traffic peaks by up to 50% even for applications that are not thought to be delay-tolerant, e.g., streaming and web browsing, but which together account for 70% of all cellular traffic.","PeriodicalId":131209,"journal":{"name":"Proceedings of the 12th annual international conference on Mobile systems, applications, and services","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132612161","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}
Video traffic surveillance monitors traffic situations such as traffic jams, traffic accidents, or running a red light. Although automatic traffic event detection has been studied for years, current systems often fail to handle various situations and do not fully take advantage of existing video traffic surveillance data. Hence, there is a need for an approach that integrates labor resources with intelligent video analysis to enhance the robustness of video analysis models and fulfill the demands of traffic surveillance. Motivated by the intuition that a driver or pedestrian often needs to know the exact traffic conditions before selecting a particular route, we propose a crowdsourcing [2] surveillance framework to assist existing traffic surveillance systems. In particular, people can use their smartphones to check the detected traffic situation and the corresponding video clips received from the video surveillance system, and make quick judgements about the received results. This finegrained information provided by traffic surveillance system not only shows the detected traffic results but also presents live video clips. Furthermore, smartphone users can provide their feedback to the system for improving the intelligent video surveillance model or correcting errors that may be present in the current traffic event detection.
{"title":"Poster: Crowdsourcing for video traffic surveillance","authors":"Hui Wen, Qiang Li, Qi Han, Shiming Ge, Limin Sun","doi":"10.1145/2594368.2601460","DOIUrl":"https://doi.org/10.1145/2594368.2601460","url":null,"abstract":"Video traffic surveillance monitors traffic situations such as traffic jams, traffic accidents, or running a red light. Although automatic traffic event detection has been studied for years, current systems often fail to handle various situations and do not fully take advantage of existing video traffic surveillance data. Hence, there is a need for an approach that integrates labor resources with intelligent video analysis to enhance the robustness of video analysis models and fulfill the demands of traffic surveillance. Motivated by the intuition that a driver or pedestrian often needs to know the exact traffic conditions before selecting a particular route, we propose a crowdsourcing [2] surveillance framework to assist existing traffic surveillance systems. In particular, people can use their smartphones to check the detected traffic situation and the corresponding video clips received from the video surveillance system, and make quick judgements about the received results. This finegrained information provided by traffic surveillance system not only shows the detected traffic results but also presents live video clips. Furthermore, smartphone users can provide their feedback to the system for improving the intelligent video surveillance model or correcting errors that may be present in the current traffic event detection.","PeriodicalId":131209,"journal":{"name":"Proceedings of the 12th annual international conference on Mobile systems, applications, and services","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125610197","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 explore the use of TV whitespace communication systems for providing robust connectivity to vehicles. A key challenge in this setup is the asymmetry in transmit power limits -- the fixed base station is allowed to communicate at up to 4 W, while the mobile gateways in vehicles are limited to 100 mW. This paper presents a specific solution to deal with this asymmetry in which whitespace transceivers are used in the downlink direction while a more traditional cellular path is used in the uplink one. While heterogeneous communication systems have been considered before (e.g., in some satellite networks), our solution explores some unique opportunities that arise in vehicular systems. In particular, we describe a system called Scout that uses a front radio at the head of a vehicle to look ahead and identify the best channel parameters to be used when the rear radio eventually reaches the forward post. We use these channel estimates to adapt a number of transmission mechanisms for improving the performance of flows through this heterogeneous network. We have implemented and deployed this system on moving vehicles in an urban environment, and demonstrated 3 -- 8× performance improvement over simpler alternatives that do not use the scouting technique.
{"title":"Enhancing vehicular internet connectivity using whitespaces, heterogeneity, and a scouting radio","authors":"Tan Zhang, Sayandeep Sen, Suman Banerjee","doi":"10.1145/2594368.2594371","DOIUrl":"https://doi.org/10.1145/2594368.2594371","url":null,"abstract":"We explore the use of TV whitespace communication systems for providing robust connectivity to vehicles. A key challenge in this setup is the asymmetry in transmit power limits -- the fixed base station is allowed to communicate at up to 4 W, while the mobile gateways in vehicles are limited to 100 mW. This paper presents a specific solution to deal with this asymmetry in which whitespace transceivers are used in the downlink direction while a more traditional cellular path is used in the uplink one. While heterogeneous communication systems have been considered before (e.g., in some satellite networks), our solution explores some unique opportunities that arise in vehicular systems. In particular, we describe a system called Scout that uses a front radio at the head of a vehicle to look ahead and identify the best channel parameters to be used when the rear radio eventually reaches the forward post. We use these channel estimates to adapt a number of transmission mechanisms for improving the performance of flows through this heterogeneous network. We have implemented and deployed this system on moving vehicles in an urban environment, and demonstrated 3 -- 8× performance improvement over simpler alternatives that do not use the scouting technique.","PeriodicalId":131209,"journal":{"name":"Proceedings of the 12th annual international conference on Mobile systems, applications, and services","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126113428","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. Mariakakis, Souvik Sen, Jeongkeun Lee, Kyu-Han Kim
This paper presents SAIL, a Single Access Point Based Indoor Localization system. Although there have been advances in WiFi-based positioning techniques, we find that existing solutions either require a dense deployment of access points (APs), manual fingerprinting, energy hungry WiFi scanning, or sophisticated AP hardware. We design SAIL using a single commodity WiFi AP to avoid these restrictions. SAIL computes the distance between the client and an AP using the propagation delay of the signal traversing between the two, combines the distance with smartphone dead-reckoning techniques, and employs geometric methods to ultimately yield the client's location using a single AP. SAIL combines physical layer (PHY) information and human motion to compute the propagation delay of the direct path by itself, eliminating the adverse effect of multipath and yielding sub-meter distance estimation accuracy. Furthermore, SAIL systematically addresses some of the common challenges towards dead-reckoning using smartphone sensors and achieves 2-5x accuracy improvements over existing techniques. We have implemented SAIL on commodity wireless APs and smartphones. Evaluation in a large-scale enterprise environment with 10 mobile users demonstrates that SAIL can capture the user's location with a mean error of 2.3m using just a single AP.
{"title":"SAIL: single access point-based indoor localization","authors":"A. Mariakakis, Souvik Sen, Jeongkeun Lee, Kyu-Han Kim","doi":"10.1145/2594368.2594393","DOIUrl":"https://doi.org/10.1145/2594368.2594393","url":null,"abstract":"This paper presents SAIL, a Single Access Point Based Indoor Localization system. Although there have been advances in WiFi-based positioning techniques, we find that existing solutions either require a dense deployment of access points (APs), manual fingerprinting, energy hungry WiFi scanning, or sophisticated AP hardware. We design SAIL using a single commodity WiFi AP to avoid these restrictions. SAIL computes the distance between the client and an AP using the propagation delay of the signal traversing between the two, combines the distance with smartphone dead-reckoning techniques, and employs geometric methods to ultimately yield the client's location using a single AP. SAIL combines physical layer (PHY) information and human motion to compute the propagation delay of the direct path by itself, eliminating the adverse effect of multipath and yielding sub-meter distance estimation accuracy. Furthermore, SAIL systematically addresses some of the common challenges towards dead-reckoning using smartphone sensors and achieves 2-5x accuracy improvements over existing techniques. We have implemented SAIL on commodity wireless APs and smartphones. Evaluation in a large-scale enterprise environment with 10 mobile users demonstrates that SAIL can capture the user's location with a mean error of 2.3m using just a single AP.","PeriodicalId":131209,"journal":{"name":"Proceedings of the 12th annual international conference on Mobile systems, applications, and services","volume":"171 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126217825","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}
Zhanwei Du, Yongjian Yang, Wu Liao, Linlu Liu, Lipeng Liu
Body area network(BAN) technology, as an emerging technology, has a very short history. Initial applications of BANs are expected to appear primarily in the healthcare domain, especially for continuous monitoring and logging vital parameters of patients suffering from chronic diseases such as diabetes, asthma and heart attacks. However, many hospitals acquire electronic patient records systems in these years, with Electronic health record (EHR), which is a systematic collection of electronic health information about an individual patient. While the BAN technology is still in its primitive stage it is being widely researched and once adopted. Considering the incomplete utilization of electronic health record lost in most BAN systems, we propose a technique to Semi-automatic monitor human body using wearable biosensors, with the help of ontology model for EHR knowledge. The system MobiEHR has some distinctive features different from the existing systems [1, 2]: The continuously information will be transmitted wirelessly to users’ mobile phone first. Then this phone will instantly transmit all information in real or delayed time to the backend system. In the backend system, every user’s health status is monitored and computed by EHR ontology model. If an emergency is detected, the system will immediately inform the patients’ family and paramedic through the computer system by sending appropriate messages or alarms.
{"title":"Poster: Semi-automatic monitoring vital parameters of mobile users","authors":"Zhanwei Du, Yongjian Yang, Wu Liao, Linlu Liu, Lipeng Liu","doi":"10.1145/2594368.2601455","DOIUrl":"https://doi.org/10.1145/2594368.2601455","url":null,"abstract":"Body area network(BAN) technology, as an emerging technology, has a very short history. Initial applications of BANs are expected to appear primarily in the healthcare domain, especially for continuous monitoring and logging vital parameters of patients suffering from chronic diseases such as diabetes, asthma and heart attacks. However, many hospitals acquire electronic patient records systems in these years, with Electronic health record (EHR), which is a systematic collection of electronic health information about an individual patient. While the BAN technology is still in its primitive stage it is being widely researched and once adopted. Considering the incomplete utilization of electronic health record lost in most BAN systems, we propose a technique to Semi-automatic monitor human body using wearable biosensors, with the help of ontology model for EHR knowledge. The system MobiEHR has some distinctive features different from the existing systems [1, 2]: The continuously information will be transmitted wirelessly to users’ mobile phone first. Then this phone will instantly transmit all information in real or delayed time to the backend system. In the backend system, every user’s health status is monitored and computed by EHR ontology model. If an emergency is detected, the system will immediately inform the patients’ family and paramedic through the computer system by sending appropriate messages or alarms.","PeriodicalId":131209,"journal":{"name":"Proceedings of the 12th annual international conference on Mobile systems, applications, and services","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125218486","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}
Multiple entities in the smartphone ecosystem employ various methods to provide better web browsing experience. In this paper, we take a first comprehensive examination of the resource usage of mobile web browsing by focusing on two important types of resources: bandwidth and energy. Using a novel traffic collection and analysis tool, we examine a wide spectrum of important factors including protocol overhead, TCP connection management, web page content, traffic timing dynamics, caching efficiency, and compression usage, for the most popular 500 websites. Our findings suggest that that all above factors at different layers can affect resource utilization for web browsing, as they often poorly interact with the underlying cellular networks. Based on our findings, we developed novel recommendations and detailed best practice suggestions for mobile web content, browser, network protocol, and smartphone OS design, to make mobile web browsing more resource efficient.
{"title":"Characterizing resource usage for mobile web browsing","authors":"Feng Qian, S. Sen, O. Spatscheck","doi":"10.1145/2594368.2594372","DOIUrl":"https://doi.org/10.1145/2594368.2594372","url":null,"abstract":"Multiple entities in the smartphone ecosystem employ various methods to provide better web browsing experience. In this paper, we take a first comprehensive examination of the resource usage of mobile web browsing by focusing on two important types of resources: bandwidth and energy. Using a novel traffic collection and analysis tool, we examine a wide spectrum of important factors including protocol overhead, TCP connection management, web page content, traffic timing dynamics, caching efficiency, and compression usage, for the most popular 500 websites. Our findings suggest that that all above factors at different layers can affect resource utilization for web browsing, as they often poorly interact with the underlying cellular networks. Based on our findings, we developed novel recommendations and detailed best practice suggestions for mobile web content, browser, network protocol, and smartphone OS design, to make mobile web browsing more resource efficient.","PeriodicalId":131209,"journal":{"name":"Proceedings of the 12th annual international conference on Mobile systems, applications, and services","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114294335","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}
Roberto Yus, Primal Pappachan, Prajit Kumar Das, E. Mena, A. Joshi, Timothy W. Finin
Proceedings of the 12th Annual International Conference on Mobile Systems, Applications, and Services
第12届移动系统、应用和服务国际年会论文集
{"title":"Demo: FaceBlock: privacy-aware pictures for google glass","authors":"Roberto Yus, Primal Pappachan, Prajit Kumar Das, E. Mena, A. Joshi, Timothy W. Finin","doi":"10.1145/2594368.2601473","DOIUrl":"https://doi.org/10.1145/2594368.2601473","url":null,"abstract":"Proceedings of the 12th Annual International Conference on Mobile Systems, Applications, and Services","PeriodicalId":131209,"journal":{"name":"Proceedings of the 12th annual international conference on Mobile systems, applications, and services","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127623042","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 fast increase of mobile traffic from smartphone-like devices has created a huge pressure for the cellular operators to manage the network infrastructure and resources. Offloading the mobile traffic to alternative networks such as WiFi is sought as a promising direction to solve this problem cost-effectively. According to our study and experimental findings, existing research proposals are lack of concern for the complexity of network deployment and device limitations, which impedes the solution deployment. To overcome such challenge, we propose SoftOffload, a programmable framework for collaborative mobile traffic offloading. SoftOffload takes the advantage of software defined networking (SDN) paradigm in terms of openness and extensibility. We have implemented the first prototype utilising the open source Floodlight platform.
{"title":"Poster: SoftOffload: a programmable approach toward collaborative mobile traffic offloading","authors":"A. Ding, J. Crowcroft, S. Tarkoma","doi":"10.1145/2594368.2601462","DOIUrl":"https://doi.org/10.1145/2594368.2601462","url":null,"abstract":"The fast increase of mobile traffic from smartphone-like devices has created a huge pressure for the cellular operators to manage the network infrastructure and resources. Offloading the mobile traffic to alternative networks such as WiFi is sought as a promising direction to solve this problem cost-effectively. According to our study and experimental findings, existing research proposals are lack of concern for the complexity of network deployment and device limitations, which impedes the solution deployment. To overcome such challenge, we propose SoftOffload, a programmable framework for collaborative mobile traffic offloading. SoftOffload takes the advantage of software defined networking (SDN) paradigm in terms of openness and extensibility. We have implemented the first prototype utilising the open source Floodlight platform.","PeriodicalId":131209,"journal":{"name":"Proceedings of the 12th annual international conference on Mobile systems, applications, and services","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130046519","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}
All printed documents and credentials are potentially subject to counterfeiting and forgery. Conventional counterfeiting solutions such as watermarking or printing with special-quality paper are not cost-effective. Certification via authorized chops/ stamps is low-cost but only provides a false sense of security/ authenticity. While embedding a serial number in the document for online verification is low-cost and secure, it is not applicable without Internet connection. We demonstrate AuthPaper (Authenticated Paper) to solve these problems by: 1) Digitally sign on the document to be protected; 2) Put the original content, digital signature and optionally the signer's certificate in a self-describing encapsulation; 3) Generate a 2D barcode (e.g. QR code) to carry the encapsulation and embed it as an integral part of the paper document. Note that the information carried in Authenticated QR Code is 40 to 50 times more than a typical one (≈50 Bytes). The biggest technical challenge is to scan and decode such densely packed codes in a robust manner. We have developed an Android application to address the challenge. In short, AuthPaper provides a secure, low-cost and offline method for document authentication.
{"title":"Demo: AuthPaper - protecting paper-based documents/credentials using authenticated 2D barcodes","authors":"Chak Man Li, Pili Hu, W. Lau","doi":"10.1145/2594368.2601468","DOIUrl":"https://doi.org/10.1145/2594368.2601468","url":null,"abstract":"All printed documents and credentials are potentially subject to counterfeiting and forgery. Conventional counterfeiting solutions such as watermarking or printing with special-quality paper are not cost-effective. Certification via authorized chops/ stamps is low-cost but only provides a false sense of security/ authenticity. While embedding a serial number in the document for online verification is low-cost and secure, it is not applicable without Internet connection. We demonstrate AuthPaper (Authenticated Paper) to solve these problems by: 1) Digitally sign on the document to be protected; 2) Put the original content, digital signature and optionally the signer's certificate in a self-describing encapsulation; 3) Generate a 2D barcode (e.g. QR code) to carry the encapsulation and embed it as an integral part of the paper document. Note that the information carried in Authenticated QR Code is 40 to 50 times more than a typical one (≈50 Bytes). The biggest technical challenge is to scan and decode such densely packed codes in a robust manner. We have developed an Android application to address the challenge. In short, AuthPaper provides a secure, low-cost and offline method for document authentication.","PeriodicalId":131209,"journal":{"name":"Proceedings of the 12th annual international conference on Mobile systems, applications, and services","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130372796","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}
Yan Wang, J. Yang, Yingying Chen, Hongbo Liu, M. Gruteser, R. Martin
We investigate using smartphone WiFi signals to track human queues, which are common in many business areas such as retail stores, airports, and theme parks. Real-time monitoring of such queues would enable a wealth of new applications, such as bottleneck analysis, shift assignments, and dynamic workflow scheduling. We take a minimum infrastructure approach and thus utilize a single monitor placed close to the service area along with transmitting phones. Our strategy extracts unique features embedded in signal traces to infer the critical time points when a person reaches the head of the queue and finishes service, and from these inferences we derive a person's waiting and service times. We develop two approaches in our system, one is directly feature-driven and the second uses a simple Bayesian network. Extensive experiments conducted both in the laboratory as well as in two public facilities demonstrate that our system is robust to real-world environments. We show that in spite of noisy signal readings, our methods can measure service and waiting times to within a $10$ second resolution.
{"title":"Tracking human queues using single-point signal monitoring","authors":"Yan Wang, J. Yang, Yingying Chen, Hongbo Liu, M. Gruteser, R. Martin","doi":"10.1145/2594368.2594382","DOIUrl":"https://doi.org/10.1145/2594368.2594382","url":null,"abstract":"We investigate using smartphone WiFi signals to track human queues, which are common in many business areas such as retail stores, airports, and theme parks. Real-time monitoring of such queues would enable a wealth of new applications, such as bottleneck analysis, shift assignments, and dynamic workflow scheduling. We take a minimum infrastructure approach and thus utilize a single monitor placed close to the service area along with transmitting phones. Our strategy extracts unique features embedded in signal traces to infer the critical time points when a person reaches the head of the queue and finishes service, and from these inferences we derive a person's waiting and service times. We develop two approaches in our system, one is directly feature-driven and the second uses a simple Bayesian network. Extensive experiments conducted both in the laboratory as well as in two public facilities demonstrate that our system is robust to real-world environments. We show that in spite of noisy signal readings, our methods can measure service and waiting times to within a $10$ second resolution.","PeriodicalId":131209,"journal":{"name":"Proceedings of the 12th annual international conference on Mobile systems, applications, and services","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133111998","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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