Pub Date : 2017-03-13DOI: 10.1109/PERCOMW.2017.7917629
Utsav Drolia, Katherine Guo, Jiaqi Tan, R. Gandhi, P. Narasimhan
With the available sensors on mobile devices and their improved CPU and storage capability, users expect their devices to recognize the surrounding environment and to provide relevant information and/or content automatically and immediately. For such classes of real-time applications, user perception of performance is key. To enable a truly seamless experience for the user, responses to requests need to be provided with minimal user-perceived latency. Current state-of-the-art systems for these applications require offloading requests and data to the cloud. This paper proposes an approach to allow users' devices and their onboard applications to leverage resources closer to home, i.e., resources at the edge of the network. We propose to use edge-servers as specialized caches for image-recognition applications. We develop a detailed formula for the expected latency for such a cache that incorporates the effects of recognition algorithms' computation time and accuracy. We show that, counter-intuitively, large cache sizes can lead to higher latencies. To the best of our knowledge, this is the first work that models edge-servers as caches for compute-intensive recognition applications.
{"title":"Towards edge-caching for image recognition","authors":"Utsav Drolia, Katherine Guo, Jiaqi Tan, R. Gandhi, P. Narasimhan","doi":"10.1109/PERCOMW.2017.7917629","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917629","url":null,"abstract":"With the available sensors on mobile devices and their improved CPU and storage capability, users expect their devices to recognize the surrounding environment and to provide relevant information and/or content automatically and immediately. For such classes of real-time applications, user perception of performance is key. To enable a truly seamless experience for the user, responses to requests need to be provided with minimal user-perceived latency. Current state-of-the-art systems for these applications require offloading requests and data to the cloud. This paper proposes an approach to allow users' devices and their onboard applications to leverage resources closer to home, i.e., resources at the edge of the network. We propose to use edge-servers as specialized caches for image-recognition applications. We develop a detailed formula for the expected latency for such a cache that incorporates the effects of recognition algorithms' computation time and accuracy. We show that, counter-intuitively, large cache sizes can lead to higher latencies. To the best of our knowledge, this is the first work that models edge-servers as caches for compute-intensive recognition applications.","PeriodicalId":319638,"journal":{"name":"2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"238 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":"115586568","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.2017.7917567
R. B. Hadj, Catherine Hamon, Stéphanie Chollet, Germán Vega, P. Lalanda
Smart Homes aim to improve the daily lives of the inhabitants by integrating a variety of context-aware applications, generally pertaining to multiple fields and provided by different actors. These applications share the same context and may have to compete for the access to resources in their surroundings. Sharing resources leads to conflicts, particularly if these applications act in contradictory ways or have interfering effects on the environment. Such conflicts can lead to critical situations by putting the home's inhabitants at risk. In this paper, we present a context-based approach to manage conflicts among pervasive applications in smart home environments. Our approach is optimistic and aims to address conflicts at runtime before their undesired effects will occur. This approach is developed and integrated in the iCASA platform as iPOJO components.
{"title":"Context-based conflict management in pervasive platforms","authors":"R. B. Hadj, Catherine Hamon, Stéphanie Chollet, Germán Vega, P. Lalanda","doi":"10.1109/PERCOMW.2017.7917567","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917567","url":null,"abstract":"Smart Homes aim to improve the daily lives of the inhabitants by integrating a variety of context-aware applications, generally pertaining to multiple fields and provided by different actors. These applications share the same context and may have to compete for the access to resources in their surroundings. Sharing resources leads to conflicts, particularly if these applications act in contradictory ways or have interfering effects on the environment. Such conflicts can lead to critical situations by putting the home's inhabitants at risk. In this paper, we present a context-based approach to manage conflicts among pervasive applications in smart home environments. Our approach is optimistic and aims to address conflicts at runtime before their undesired effects will occur. This approach is developed and integrated in the iCASA platform as iPOJO components.","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":"115030095","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.2017.7917592
C. Bettini
Pervasive computing is increasing its impact in several areas related to health-care and well-being. Data collected from sensors in smart-homes are being processed to continuously recognize activities, change of habits, and critical events leading to innovative applications in monitoring patients with chronic diseases, elderly at risk of cognitive decline, and enable new opportunities for active aging. Data collected from wireless medical devices, smart-phones and watches complement data from environmental sensors to continuously collect an increasingly rich “personal medical context”.
{"title":"Personal data protection in pervasive health systems","authors":"C. Bettini","doi":"10.1109/PERCOMW.2017.7917592","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917592","url":null,"abstract":"Pervasive computing is increasing its impact in several areas related to health-care and well-being. Data collected from sensors in smart-homes are being processed to continuously recognize activities, change of habits, and critical events leading to innovative applications in monitoring patients with chronic diseases, elderly at risk of cognitive decline, and enable new opportunities for active aging. Data collected from wireless medical devices, smart-phones and watches complement data from environmental sensors to continuously collect an increasingly rich “personal medical context”.","PeriodicalId":319638,"journal":{"name":"2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"32 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":"121953527","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.2017.7917535
T. Sztyler
Supporting people in everyday life, be it lifestyle improvement or health care, requires the recognition of their activities. For that purpose, researches typically focus on wearable devices to recognize physical human activities like walking whereas smart environments are commonly the base for the recognition of activities of daily living. However, in many interesting scenarios the recognition of physical activities is often insufficient whereas most smart environment works are restricted to a specific area or one single person. Moreover, the recognition of outdoor activities of daily living gets significantly less attention. In our work, we focus on a real world activity recognition scenario, thus, practical application including environmental impact. In this context, we rely on wearable devices to recognize the physical activities but want to deduce the actual task, i.e., activity of daily living by relying on background and context related information using Markov logic as a probabilistic model. This should enable that the recognition is not restricted to a specific area and that even a smart environment could be more flexible concerning the number of sensors and people. Consequently, a more complete recognition of the daily routine is possible which in turn allows to perform behavior analyses.
{"title":"Towards real world activity recognition from wearable devices","authors":"T. Sztyler","doi":"10.1109/PERCOMW.2017.7917535","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917535","url":null,"abstract":"Supporting people in everyday life, be it lifestyle improvement or health care, requires the recognition of their activities. For that purpose, researches typically focus on wearable devices to recognize physical human activities like walking whereas smart environments are commonly the base for the recognition of activities of daily living. However, in many interesting scenarios the recognition of physical activities is often insufficient whereas most smart environment works are restricted to a specific area or one single person. Moreover, the recognition of outdoor activities of daily living gets significantly less attention. In our work, we focus on a real world activity recognition scenario, thus, practical application including environmental impact. In this context, we rely on wearable devices to recognize the physical activities but want to deduce the actual task, i.e., activity of daily living by relying on background and context related information using Markov logic as a probabilistic model. This should enable that the recognition is not restricted to a specific area and that even a smart environment could be more flexible concerning the number of sensors and people. Consequently, a more complete recognition of the daily routine is possible which in turn allows to perform behavior analyses.","PeriodicalId":319638,"journal":{"name":"2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"34 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":"117222939","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.2017.7917588
Mikio Obuchi, T. Okoshi, Takuro Yonezawa, J. Nakazawa, H. Tokuda
Investigating users' interruptibility as an indicator of his/her attention status has been essential in recent pervasive computing where the users' attention resources get scarce against ever increasing amounts of information. In this paper, we address research problems related to the users' available interruptibility, their physical activities, and their current locations and situations. We propose the “Interruptibility Map”, a geographical tool for analyzing and visualizing the user's local interruptibility status in the context of smart city research. Our map describes where citizens are expected to feel more or less interruptive against notifications produced by computing devices, which are known to have negative effects on work productivity, emotion, and psychological state. We conducted a continuous analysis from our previous research and a new additional in-the-wild user study for 2 weeks with 29 participants to investigate the relationship between one's interruptibility and their locations and situations. As a highlight of our findings, we found certain pairs of user activity change and a location that showed better interruptibility to users, such as an activity change of “when user's riding car(bus) stops” in the bus commute situation.
{"title":"Interruptibility Map: Geographical analysis of users' interruptibility in smart cities","authors":"Mikio Obuchi, T. Okoshi, Takuro Yonezawa, J. Nakazawa, H. Tokuda","doi":"10.1109/PERCOMW.2017.7917588","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917588","url":null,"abstract":"Investigating users' interruptibility as an indicator of his/her attention status has been essential in recent pervasive computing where the users' attention resources get scarce against ever increasing amounts of information. In this paper, we address research problems related to the users' available interruptibility, their physical activities, and their current locations and situations. We propose the “Interruptibility Map”, a geographical tool for analyzing and visualizing the user's local interruptibility status in the context of smart city research. Our map describes where citizens are expected to feel more or less interruptive against notifications produced by computing devices, which are known to have negative effects on work productivity, emotion, and psychological state. We conducted a continuous analysis from our previous research and a new additional in-the-wild user study for 2 weeks with 29 participants to investigate the relationship between one's interruptibility and their locations and situations. As a highlight of our findings, we found certain pairs of user activity change and a location that showed better interruptibility to users, such as an activity change of “when user's riding car(bus) stops” in the bus commute situation.","PeriodicalId":319638,"journal":{"name":"2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"23 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":"124460737","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.2017.7917553
S. Taie, Sanaa Taha
There is a growing need for Vehicular Ad-hoc Networks (VANETs), in which vehicles communicate with each other (i. e., Vehicle to Vehicle, V2V) or with the infrastructure (i. e., Vehicle to Infrastructure, V2I) on a wireless basis. This paper presents an improved traffic monitoring system for VANET applications via a proposed security scheme. Specifically, the proposed model analyzes the monitored scene, and automatically generates monitoring reports, which contain the current time, current location, and traffic event type (which may be an accident, crowd, demonstration or protest events). Additionally, two schemes have been proposed: one is detecting vehicle accident using image processing techniques, and the other is detecting both transmitted fake reports about the road and the malicious car's driver, who transmits those fake reports. The security scheme achieves source authentication, data confidentiality, driver anonymity, and non-repudiation security services. Also the monitoring system achieves 85.41% average accuracy and 84.093 msec. average execution time with only 0.011% increase in computation overhead for applying the security scheme.
{"title":"A novel secured traffic monitoring system for VANET","authors":"S. Taie, Sanaa Taha","doi":"10.1109/PERCOMW.2017.7917553","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917553","url":null,"abstract":"There is a growing need for Vehicular Ad-hoc Networks (VANETs), in which vehicles communicate with each other (i. e., Vehicle to Vehicle, V2V) or with the infrastructure (i. e., Vehicle to Infrastructure, V2I) on a wireless basis. This paper presents an improved traffic monitoring system for VANET applications via a proposed security scheme. Specifically, the proposed model analyzes the monitored scene, and automatically generates monitoring reports, which contain the current time, current location, and traffic event type (which may be an accident, crowd, demonstration or protest events). Additionally, two schemes have been proposed: one is detecting vehicle accident using image processing techniques, and the other is detecting both transmitted fake reports about the road and the malicious car's driver, who transmits those fake reports. The security scheme achieves source authentication, data confidentiality, driver anonymity, and non-repudiation security services. Also the monitoring system achieves 85.41% average accuracy and 84.093 msec. average execution time with only 0.011% increase in computation overhead for applying the security scheme.","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":"124827635","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.2017.7917571
G. Mokhtari, Qing Zhang, Amir Fazlollahi
This paper proposes the use of Ultra-Wide Band (UWB) technology to detect falls in smart home environment. A bi-static setup is proposed in which the UWB sensor including both transmitter and receiver is mounted over the ceiling to monitor and detect falls among other types of inertial movements such as slow/fast walking and lying down. The experimental results show that the proposed approach can monitor and detect falls efficiently through a real-time analyses of the streaming data generated by the UWB sensor. It also demonstrates that the ambient UWB sensor can be used as a promising fall detection solution in monitoring certain areas of high risks of falls in smart home environments.
{"title":"Non-wearable UWB sensor to detect falls in smart home environment","authors":"G. Mokhtari, Qing Zhang, Amir Fazlollahi","doi":"10.1109/PERCOMW.2017.7917571","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917571","url":null,"abstract":"This paper proposes the use of Ultra-Wide Band (UWB) technology to detect falls in smart home environment. A bi-static setup is proposed in which the UWB sensor including both transmitter and receiver is mounted over the ceiling to monitor and detect falls among other types of inertial movements such as slow/fast walking and lying down. The experimental results show that the proposed approach can monitor and detect falls efficiently through a real-time analyses of the streaming data generated by the UWB sensor. It also demonstrates that the ambient UWB sensor can be used as a promising fall detection solution in monitoring certain areas of high risks of falls in smart home environments.","PeriodicalId":319638,"journal":{"name":"2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"1 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":"129771389","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.2017.7917542
Alexander Diete, T. Sztyler, H. Stuckenschmidt
Annotation of multimodal data sets is often a time consuming and a challenging task as many approaches require an accurate labeling. This includes in particular video recordings as often labeling exact to a frame is required. For that purpose, we created an annotation tool that enables to annotate data sets of video and inertial sensor data. However, in contrast to the most existing approaches, we focus on semi-supervised labeling support to infer labels for the whole dataset. More precisely, after labeling a small set of instances our system is able to provide labeling recommendations and in turn it makes learning of image features more feasible by speeding up the labeling time for single frames. We aim to rely on the inertial sensors of our wristband to support the labeling of video recordings. For that purpose, we apply template matching in context of dynamic time warping to identify time intervals of certain actions. To investigate the feasibility of our approach we focus on a real world scenario, i.e., we gathered a data set which describes an order picking scenario of a logistic company. In this context, we focus on the picking process as the selection of the correct items can be prone to errors. Preliminary results show that we are able to identify 69% of the grabbing motion periods of time.
{"title":"A smart data annotation tool for multi-sensor activity recognition","authors":"Alexander Diete, T. Sztyler, H. Stuckenschmidt","doi":"10.1109/PERCOMW.2017.7917542","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917542","url":null,"abstract":"Annotation of multimodal data sets is often a time consuming and a challenging task as many approaches require an accurate labeling. This includes in particular video recordings as often labeling exact to a frame is required. For that purpose, we created an annotation tool that enables to annotate data sets of video and inertial sensor data. However, in contrast to the most existing approaches, we focus on semi-supervised labeling support to infer labels for the whole dataset. More precisely, after labeling a small set of instances our system is able to provide labeling recommendations and in turn it makes learning of image features more feasible by speeding up the labeling time for single frames. We aim to rely on the inertial sensors of our wristband to support the labeling of video recordings. For that purpose, we apply template matching in context of dynamic time warping to identify time intervals of certain actions. To investigate the feasibility of our approach we focus on a real world scenario, i.e., we gathered a data set which describes an order picking scenario of a logistic company. In this context, we focus on the picking process as the selection of the correct items can be prone to errors. Preliminary results show that we are able to identify 69% of the grabbing motion periods of time.","PeriodicalId":319638,"journal":{"name":"2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"661 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":"127023854","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.2017.7917623
Joseph Bugeja, A. Jacobsson, P. Davidsson
Smart connected home systems aim to enhance the comfort, convenience, security, entertainment, and health of the householders and their guests. Despite their advantages, their interconnected characteristics make smart home devices and services prone to various cybersecurity and privacy threats. In this paper, we analyze six classes of malicious threat agents for smart connected homes. We also identify four different motives and three distinct capability levels that can be used to group the different intruders. Based on this, we propose a new threat model that can be used for threat profiling. Both hypothetical and real-life examples of attacks are used throughout the paper. In reflecting on this work, we also observe motivations and agents that are not covered in standard agent taxonomies.
{"title":"An analysis of malicious threat agents for the smart connected home","authors":"Joseph Bugeja, A. Jacobsson, P. Davidsson","doi":"10.1109/PERCOMW.2017.7917623","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917623","url":null,"abstract":"Smart connected home systems aim to enhance the comfort, convenience, security, entertainment, and health of the householders and their guests. Despite their advantages, their interconnected characteristics make smart home devices and services prone to various cybersecurity and privacy threats. In this paper, we analyze six classes of malicious threat agents for smart connected homes. We also identify four different motives and three distinct capability levels that can be used to group the different intruders. Based on this, we propose a new threat model that can be used for threat profiling. Both hypothetical and real-life examples of attacks are used throughout the paper. In reflecting on this work, we also observe motivations and agents that are not covered in standard agent taxonomies.","PeriodicalId":319638,"journal":{"name":"2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"21 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":"132084629","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.2017.7917562
Anubhuti Garg, A. Nayak
Mobile phones are equipped with a rich set of sensors which are useful in deploying various sensing activities. We focus on participatory sensing in which every participant carrying smartphone senses its environment and shares it with server. Most of the applications require location information to perform sensing activity. But, GPS drains considerable amount of energy if used for localization. So, a set of devices are chosen as broadcasters which turn on GPS, and the neighbouring devices rely on them to calculate their position. We propose an efficient energy model to minimize the power consumption of such a system. The existing scheme for finding optimal set of broadcasters is based on greedy algorithm. This is time efficient only when the number of participants are small. We propose a sorting based algorithm. This provides better time complexity for moderate and large data sets which is the actual case in real scenarios. We validate our work with extensive experiments on both real and synthetic datasets. Results demonstrate that our proposed approach effectively minimizes energy and saves 12–25% of the time for medium and large data sets.
{"title":"Effective role-assignment for participatory sensing systems","authors":"Anubhuti Garg, A. Nayak","doi":"10.1109/PERCOMW.2017.7917562","DOIUrl":"https://doi.org/10.1109/PERCOMW.2017.7917562","url":null,"abstract":"Mobile phones are equipped with a rich set of sensors which are useful in deploying various sensing activities. We focus on participatory sensing in which every participant carrying smartphone senses its environment and shares it with server. Most of the applications require location information to perform sensing activity. But, GPS drains considerable amount of energy if used for localization. So, a set of devices are chosen as broadcasters which turn on GPS, and the neighbouring devices rely on them to calculate their position. We propose an efficient energy model to minimize the power consumption of such a system. The existing scheme for finding optimal set of broadcasters is based on greedy algorithm. This is time efficient only when the number of participants are small. We propose a sorting based algorithm. This provides better time complexity for moderate and large data sets which is the actual case in real scenarios. We validate our work with extensive experiments on both real and synthetic datasets. Results demonstrate that our proposed approach effectively minimizes energy and saves 12–25% of the time for medium and large data sets.","PeriodicalId":319638,"journal":{"name":"2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"24 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":"130495703","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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