Pub Date : 2017-03-13DOI: 10.1109/PERCOMW.2017.7917607
D. Raj, M. Ramesh, S. Duttagupta
Delay tolerant networks (DTN) are characterized by lack of end-to-end communications and stable infrastructures. This paper deals with DTN networks consisting of a number of heterogeneous mobile fishing vessels where some nodes, referred to as adaptive nodes, are capable of communicating through long-range Wi-Fi whereas other nodes are having simple Wi-Fi access network. The nodes form different clusters consisting of adaptive nodes and access nodes. Message routing in this heterogeneous network happens through adaptive nodes if the source and destination nodes belong to different clusters. Real data from field study reflects that mobile nodes in this network follow Gaussian-Markov mobility model and may have high inter-meeting arrival time based on deployment and node density. Our proposed DTN routing protocol incorporates simple encounter-based message forwarding and achieves lower latency and high delivery probability in the range of 90–98% for most of the scenarios. The proposed protocol is verified through a realistic mobile ad-hoc wireless simulator.
{"title":"Delay tolerant routing protocol for heterogeneous marine vehicular mobile ad-hoc network","authors":"D. Raj, M. Ramesh, S. Duttagupta","doi":"10.1109/PERCOMW.2017.7917607","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917607","url":null,"abstract":"Delay tolerant networks (DTN) are characterized by lack of end-to-end communications and stable infrastructures. This paper deals with DTN networks consisting of a number of heterogeneous mobile fishing vessels where some nodes, referred to as adaptive nodes, are capable of communicating through long-range Wi-Fi whereas other nodes are having simple Wi-Fi access network. The nodes form different clusters consisting of adaptive nodes and access nodes. Message routing in this heterogeneous network happens through adaptive nodes if the source and destination nodes belong to different clusters. Real data from field study reflects that mobile nodes in this network follow Gaussian-Markov mobility model and may have high inter-meeting arrival time based on deployment and node density. Our proposed DTN routing protocol incorporates simple encounter-based message forwarding and achieves lower latency and high delivery probability in the range of 90–98% for most of the scenarios. The proposed protocol is verified through a realistic mobile ad-hoc wireless simulator.","PeriodicalId":319638,"journal":{"name":"2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"70 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":"126337841","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.2017.7917617
Masaya Kurahashi, Kazuya Murao, T. Terada, M. Tsukamoto
Biological information can easily be monitored by installing sensors in a lavatory bowl. Lavatories are usually shared by several people, so users need to be identified. Because of the need for privacy, using cameras, microphones, or scales is not appropriate. Though personal identification can be done using a touch panel, the user may forget to use it because the action is not necessary. In this paper, we focus on the differences in the way of pulling a toilet paper roll and propose a system that identifies individuals based on features of rotating of toilet paper rolls with a gyroscope. The evaluation results revealed that 83.9% accuracy was achieved for a five-person group in a laboratory environment, and 69.2% accuracy was achieved for a five-person group in a practical environment.
{"title":"Personal identification system based on rotation of toilet paper rolls","authors":"Masaya Kurahashi, Kazuya Murao, T. Terada, M. Tsukamoto","doi":"10.1109/PERCOMW.2017.7917617","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917617","url":null,"abstract":"Biological information can easily be monitored by installing sensors in a lavatory bowl. Lavatories are usually shared by several people, so users need to be identified. Because of the need for privacy, using cameras, microphones, or scales is not appropriate. Though personal identification can be done using a touch panel, the user may forget to use it because the action is not necessary. In this paper, we focus on the differences in the way of pulling a toilet paper roll and propose a system that identifies individuals based on features of rotating of toilet paper rolls with a gyroscope. The evaluation results revealed that 83.9% accuracy was achieved for a five-person group in a laboratory environment, and 69.2% accuracy was achieved for a five-person group in a practical environment.","PeriodicalId":319638,"journal":{"name":"2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"86 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":"126292061","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.2017.7917585
R. Stock, Moritz Merkle
This research examines human-robot acceptance during service encounters. Based on role theory and the technology acceptance model (TAM), we argue that users draw on various categories of expectations, which in turn, leads to a user's acceptance of frontline service robots (FSR). Results of a qualitative study with 63 participants reveal that users form their expectations toward FSR based on three categories: (1) their ideal imagination of a service, (2) their expectations toward a human frontline employee, and (3) their expectations toward a self-service technology. The theoretically developed Robot- Acceptance-Model (RAM) is tested in an experimental services setting with 82 users and service frontline robots.
{"title":"A service Robot Acceptance Model: User acceptance of humanoid robots during service encounters","authors":"R. Stock, Moritz Merkle","doi":"10.1109/PERCOMW.2017.7917585","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917585","url":null,"abstract":"This research examines human-robot acceptance during service encounters. Based on role theory and the technology acceptance model (TAM), we argue that users draw on various categories of expectations, which in turn, leads to a user's acceptance of frontline service robots (FSR). Results of a qualitative study with 63 participants reveal that users form their expectations toward FSR based on three categories: (1) their ideal imagination of a service, (2) their expectations toward a human frontline employee, and (3) their expectations toward a self-service technology. The theoretically developed Robot- Acceptance-Model (RAM) is tested in an experimental services setting with 82 users and service frontline robots.","PeriodicalId":319638,"journal":{"name":"2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"271 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":"121358081","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.2017.7917642
Takashi Hamatani, A. Uchiyama, T. Higashino
In this paper, we present a novel application, HeatWatch, which predicts heatstroke and prevents heatstroke by ensuring users breaking and water intake. The application estimates user's core temperature based on human thermal model and vital sensors equipped with smart watches. We also designed the application tracks user's water intake by assuming to apply existing activity recognition technique to acceleration sensors inside a smart watch. We have discussed how to detect heatstroke sign and evaluated its performance through a real data set over 100 hours. Finally, the result showed that our method is able to instantly notify high temperature states with more than 0.9 recall and 0.53 precision by allowing early/late notification within 6 minutes.
{"title":"HeatWatch: Preventing heatstroke using a smart watch","authors":"Takashi Hamatani, A. Uchiyama, T. Higashino","doi":"10.1109/PERCOMW.2017.7917642","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917642","url":null,"abstract":"In this paper, we present a novel application, HeatWatch, which predicts heatstroke and prevents heatstroke by ensuring users breaking and water intake. The application estimates user's core temperature based on human thermal model and vital sensors equipped with smart watches. We also designed the application tracks user's water intake by assuming to apply existing activity recognition technique to acceleration sensors inside a smart watch. We have discussed how to detect heatstroke sign and evaluated its performance through a real data set over 100 hours. Finally, the result showed that our method is able to instantly notify high temperature states with more than 0.9 recall and 0.53 precision by allowing early/late notification within 6 minutes.","PeriodicalId":319638,"journal":{"name":"2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"77 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":"133439748","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.2017.7917563
Thivya Kandappu, Archan Misra, Shih-Fen Cheng, H. Lau
Mobile crowd-sourcing can become as a strategy to perform time-sensitive urban tasks (such as municipal monitoring and last mile logistics) by effectively coordinating smartphone users. The success of the mobile crowd-sourcing platform depends mainly on its effectiveness in engaging crowd-workers, and recent studies have shown that compared to the pull-based approach, which relies on crowd-workers to browse and commit to tasks they would want to perform, the push-based approach can take into consideration of worker's daily routine, and generate highly effective recommendations. As a result, workers waste less time on detours, plan more in advance, and require much less planning effort. However, the push-based systems are not without drawbacks. The major concern is the potential privacy invasion that could result from the disclosure of individual's mobility traces to the crowd-sourcing platform. In this paper, we first demonstrate specific threats of continuous sharing of users locations in such push-based crowd-sourcing platforms. We then propose a simple yet effective location perturbation technique that obfuscates certain user locations to achieve privacy guarantees while not affecting the quality of the recommendations the system generates.We use the mobility traces data we obtained from our urban campus to show the trade-offs between privacy guarantees and the quality of the recommendations associated with the proposed solution. We show that obfuscating even 75% of the individual trajectories will affect the user to make another extra 1.8 minutes of detour while gaining 62.5% more uncertainty of his location traces.
{"title":"Privacy in context-aware mobile crowdsourcing systems","authors":"Thivya Kandappu, Archan Misra, Shih-Fen Cheng, H. Lau","doi":"10.1109/PERCOMW.2017.7917563","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917563","url":null,"abstract":"Mobile crowd-sourcing can become as a strategy to perform time-sensitive urban tasks (such as municipal monitoring and last mile logistics) by effectively coordinating smartphone users. The success of the mobile crowd-sourcing platform depends mainly on its effectiveness in engaging crowd-workers, and recent studies have shown that compared to the pull-based approach, which relies on crowd-workers to browse and commit to tasks they would want to perform, the push-based approach can take into consideration of worker's daily routine, and generate highly effective recommendations. As a result, workers waste less time on detours, plan more in advance, and require much less planning effort. However, the push-based systems are not without drawbacks. The major concern is the potential privacy invasion that could result from the disclosure of individual's mobility traces to the crowd-sourcing platform. In this paper, we first demonstrate specific threats of continuous sharing of users locations in such push-based crowd-sourcing platforms. We then propose a simple yet effective location perturbation technique that obfuscates certain user locations to achieve privacy guarantees while not affecting the quality of the recommendations the system generates.We use the mobility traces data we obtained from our urban campus to show the trade-offs between privacy guarantees and the quality of the recommendations associated with the proposed solution. We show that obfuscating even 75% of the individual trajectories will affect the user to make another extra 1.8 minutes of detour while gaining 62.5% more uncertainty of his location traces.","PeriodicalId":319638,"journal":{"name":"2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"97 16","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133455233","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.2017.7917636
Anindya Maiti, Murtuza Jadliwala, Chase Weber
Shoulder surfing or adversarial eavesdropping to infer users' keystrokes on physical QWERTY keyboards continues to be a serious privacy threat. Despite this, practical and efficient countermeasures against such attacks are still lacking. In this paper, we propose keyboard randomization as a simple, yet effective, countermeasure against various types of keystroke inference attacks. Our proposal consists of several keyboard randomization strategies which randomizes or changes the position of keys on the keyboard. The randomized keyboard is then projected to the typing user by means of an augmented reality wearable device. As the randomized keyboard is visually superimposed over the actual physical keyboard, and is visible only to the typing user through the augmented reality device, it acts as an effective countermeasure against both side-channel and visual-channel based keystroke inference attacks. We implement our proposed solution on a commercially available augmented reality device and conduct preliminary evaluations to validate its performance and effectiveness.
{"title":"Preventing shoulder surfing using randomized augmented reality keyboards","authors":"Anindya Maiti, Murtuza Jadliwala, Chase Weber","doi":"10.1109/PERCOMW.2017.7917636","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917636","url":null,"abstract":"Shoulder surfing or adversarial eavesdropping to infer users' keystrokes on physical QWERTY keyboards continues to be a serious privacy threat. Despite this, practical and efficient countermeasures against such attacks are still lacking. In this paper, we propose keyboard randomization as a simple, yet effective, countermeasure against various types of keystroke inference attacks. Our proposal consists of several keyboard randomization strategies which randomizes or changes the position of keys on the keyboard. The randomized keyboard is then projected to the typing user by means of an augmented reality wearable device. As the randomized keyboard is visually superimposed over the actual physical keyboard, and is visible only to the typing user through the augmented reality device, it acts as an effective countermeasure against both side-channel and visual-channel based keystroke inference attacks. We implement our proposed solution on a commercially available augmented reality device and conduct preliminary evaluations to validate its performance and effectiveness.","PeriodicalId":319638,"journal":{"name":"2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"7 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":"124474815","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.2017.7917537
Guohao Lan
With the ubiquity of microphone-enabled pervasive device, the use of speaker-microphone to transfer small piece of information has become a hot area in both industry and research communities. Unfortunately, however, microphone-based acoustic communication systems rely on power-consuming digital signal processing (DSP) to decode the modulated information in the sound. Given the battery lifetime of today's mobile devices is limited, microphone-based systems are facing challenges in achieving long-term computing and communication. In this proposal, we aim to investigates the possibility of using a vibration energy harvesting (VEH) device as an receiver for energy-efficient acoustic communication. By modulating the ambient vibration energy using a transmitting speaker, and demodulating the harvested power at the receiving VEH, our current system prototype [1] is able to transmit small amounts of data at reasonable rates between two proximate devices. The key advantage of using VEH as a receiver is that the modulated sound waves can be successfully demodulated directly from the harvested power without employing the power-consuming DSP, which makes a VEH receiver more power efficient than a conventional microphone-based decoder. As part of our future work, we will further improve and optimize the performance of our prototype system while ensure better user experience and system security.
{"title":"Energy-efficient acoustic communication using vibration energy harvesting","authors":"Guohao Lan","doi":"10.1109/PERCOMW.2017.7917537","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917537","url":null,"abstract":"With the ubiquity of microphone-enabled pervasive device, the use of speaker-microphone to transfer small piece of information has become a hot area in both industry and research communities. Unfortunately, however, microphone-based acoustic communication systems rely on power-consuming digital signal processing (DSP) to decode the modulated information in the sound. Given the battery lifetime of today's mobile devices is limited, microphone-based systems are facing challenges in achieving long-term computing and communication. In this proposal, we aim to investigates the possibility of using a vibration energy harvesting (VEH) device as an receiver for energy-efficient acoustic communication. By modulating the ambient vibration energy using a transmitting speaker, and demodulating the harvested power at the receiving VEH, our current system prototype [1] is able to transmit small amounts of data at reasonable rates between two proximate devices. The key advantage of using VEH as a receiver is that the modulated sound waves can be successfully demodulated directly from the harvested power without employing the power-consuming DSP, which makes a VEH receiver more power efficient than a conventional microphone-based decoder. As part of our future work, we will further improve and optimize the performance of our prototype system while ensure better user experience and system security.","PeriodicalId":319638,"journal":{"name":"2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"59 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":"124943096","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.2017.7917648
Vivek Chandel, D. Jaiswal, Avik Ghose
In this work, an end-to-end indoor routing and guiding solution, ‘InLocW’ (InLoc - Wearable) is discussed. The initial location fix of the user is obtained using an infrastructure of BLE beacons. Primary focus is on continuous calculation of instantaneous walking direction (a primary arrow displayed on the smartwatch) based on user's current location estimated by a particle filter which runs in the back end. A solution is devised to synchronize these directions to user's walking, thereby solving the problem of inherent latency between estimated and actual user's location in a PDR system. Another major contribution of this work is a preventive measure against the deviation of user from the path, for which a secondary arrow is displayed on the smartwatch to inform the user of any impending turn well before the turn itself. Also a correction module is implemented which re-routes the user back to the correct path in case any deviation from the optimum path is detected, which constitutes another major contribution. The overall system works without any dependence on a smartphone, or any involvement of user with the smartwatch during routing, thereby allowing them to walk without any distractions, and just follow the displayed directions. The system has been tested on paths with multiple turns and has proved to be efficient in preventing deviations from the routing path, and ensuring a smooth movement of user from source to the selected destination with high reliability.
{"title":"InLocW: A reliable indoor tracking and guiding system for smartwatches with path re-routing","authors":"Vivek Chandel, D. Jaiswal, Avik Ghose","doi":"10.1109/PERCOMW.2017.7917648","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917648","url":null,"abstract":"In this work, an end-to-end indoor routing and guiding solution, ‘InLocW’ (InLoc - Wearable) is discussed. The initial location fix of the user is obtained using an infrastructure of BLE beacons. Primary focus is on continuous calculation of instantaneous walking direction (a primary arrow displayed on the smartwatch) based on user's current location estimated by a particle filter which runs in the back end. A solution is devised to synchronize these directions to user's walking, thereby solving the problem of inherent latency between estimated and actual user's location in a PDR system. Another major contribution of this work is a preventive measure against the deviation of user from the path, for which a secondary arrow is displayed on the smartwatch to inform the user of any impending turn well before the turn itself. Also a correction module is implemented which re-routes the user back to the correct path in case any deviation from the optimum path is detected, which constitutes another major contribution. The overall system works without any dependence on a smartphone, or any involvement of user with the smartwatch during routing, thereby allowing them to walk without any distractions, and just follow the displayed directions. The system has been tested on paths with multiple turns and has proved to be efficient in preventing deviations from the routing path, and ensuring a smooth movement of user from source to the selected destination with high reliability.","PeriodicalId":319638,"journal":{"name":"2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"18 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":"125022955","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.2017.7917604
Danila Chenchik, Jia Chen, S. Yan, S. Nirjon
We devise an inexpensive and intuitive system for bus route navigation for locales where public transportation may serve as a prevalent mode of commute but where technologies that make arrival predictions through tracking vehicles in transit through GPS or other means do not exist. These systems typically require real-time monitoring of traffic variations. We provide a personalized approach where in a world of pervasive smart phone use, users may take advantage of sensor data to learn and personalize their bus routes, and alert them on time when a bus stop is approaching. We accomplish this through the development and implementation of two algorithms: 1) turn detection using on-board compass sensor of a smart phone, and 2) characterizing road segments in terms of turns and thereby predicting approaching bus stops. We conduct field experiments on a route with four selected bus stops in the town of Chapel Hill. Results show that the accuracy of turn detection and detection of approaching bus stops are 95.7% and 83%, respectively.
{"title":"Characterizing road segments using compass sensors to predict approaching bus stops","authors":"Danila Chenchik, Jia Chen, S. Yan, S. Nirjon","doi":"10.1109/PERCOMW.2017.7917604","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917604","url":null,"abstract":"We devise an inexpensive and intuitive system for bus route navigation for locales where public transportation may serve as a prevalent mode of commute but where technologies that make arrival predictions through tracking vehicles in transit through GPS or other means do not exist. These systems typically require real-time monitoring of traffic variations. We provide a personalized approach where in a world of pervasive smart phone use, users may take advantage of sensor data to learn and personalize their bus routes, and alert them on time when a bus stop is approaching. We accomplish this through the development and implementation of two algorithms: 1) turn detection using on-board compass sensor of a smart phone, and 2) characterizing road segments in terms of turns and thereby predicting approaching bus stops. We conduct field experiments on a route with four selected bus stops in the town of Chapel Hill. Results show that the accuracy of turn detection and detection of approaching bus stops are 95.7% and 83%, respectively.","PeriodicalId":319638,"journal":{"name":"2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"5 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":"121721186","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.2017.7917519
Yuanyi Chen, Jinyu Zhang, M. Guo, Jiannong Cao
Traditional ways of understanding customer behaviour are mainly based on predominantly field surveys, which are not effective as they require labor-intensive survey. As mobile devices and ubiquitous sensing technologies are becoming more and more pervasive, user-generated data from these platforms are providing rich information to uncover customer preference. In this study, we propose a shop recommendation model for urban shopping mall by exploiting user-generated WiFi logs to learn customer preference. Specifically, the proposed model consists of two phases: 1) offline learning customer's preference from their check-in activities; 2) online recommendation by fusing the learnt preference and temporal influence. We have performed a comprehensive experiment evaluation on a real dataset collected by over 39,000 customers during 7 months, and the experiment results show the proposed recommendation model outperforms state-of-the-art methods.
{"title":"Understanding customer behaviour in urban shopping mall from WiFi logs","authors":"Yuanyi Chen, Jinyu Zhang, M. Guo, Jiannong Cao","doi":"10.1109/PERCOMW.2017.7917519","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917519","url":null,"abstract":"Traditional ways of understanding customer behaviour are mainly based on predominantly field surveys, which are not effective as they require labor-intensive survey. As mobile devices and ubiquitous sensing technologies are becoming more and more pervasive, user-generated data from these platforms are providing rich information to uncover customer preference. In this study, we propose a shop recommendation model for urban shopping mall by exploiting user-generated WiFi logs to learn customer preference. Specifically, the proposed model consists of two phases: 1) offline learning customer's preference from their check-in activities; 2) online recommendation by fusing the learnt preference and temporal influence. We have performed a comprehensive experiment evaluation on a real dataset collected by over 39,000 customers during 7 months, and the experiment results show the proposed recommendation model outperforms state-of-the-art methods.","PeriodicalId":319638,"journal":{"name":"2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"56 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":"116886487","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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