Pub Date : 2015-08-24DOI: 10.1109/HPCC-CSS-ICESS.2015.207
Hiroaki Fukuda, Paul Leger
A Wireless Sensor Network (WSN) is typically deployed on a place in which no electric source is provided, meaning that its battery consumption is crucial. Applications for WSNs require implementations of complex operations like network administration. To simplify the development of these applications, several mobile agent middle wares have been proposed (e.g., Agilla). Applications for these middle wares are executed by communications among agents, therefore, a common operation is to look up agents. Because existing proposals do not have much technical support for efficient approach to look up agents, every lookup consumes a significant amount of battery energy and time. In addition, current approaches can fail their lookup operations if the target agent moves during a lookup operation. This paper proposes CMSN, an efficient and effective lookup for mobile agent middle wares using distributed hash table. We evaluate and compare CMSN in terms of effective lookups and battery consumption.
{"title":"An Efficient Agent Location Management for Wireless Sensor Networks","authors":"Hiroaki Fukuda, Paul Leger","doi":"10.1109/HPCC-CSS-ICESS.2015.207","DOIUrl":"https://doi.org/10.1109/HPCC-CSS-ICESS.2015.207","url":null,"abstract":"A Wireless Sensor Network (WSN) is typically deployed on a place in which no electric source is provided, meaning that its battery consumption is crucial. Applications for WSNs require implementations of complex operations like network administration. To simplify the development of these applications, several mobile agent middle wares have been proposed (e.g., Agilla). Applications for these middle wares are executed by communications among agents, therefore, a common operation is to look up agents. Because existing proposals do not have much technical support for efficient approach to look up agents, every lookup consumes a significant amount of battery energy and time. In addition, current approaches can fail their lookup operations if the target agent moves during a lookup operation. This paper proposes CMSN, an efficient and effective lookup for mobile agent middle wares using distributed hash table. We evaluate and compare CMSN in terms of effective lookups and battery consumption.","PeriodicalId":332746,"journal":{"name":"2015 International Conference on Distributed Computing in Sensor Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121446930","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}
Enamul Hoque, Robert F. Dickerson, S. Preum, M. Hanson, Adam T. Barth, J. Stankovic
Advances in wireless sensor networks have enabled the monitoring of daily activities of elderly people. The goal of these monitoring applications is to learn normal behavior in terms of daily activities and look for any deviation, i.e., Anomalies, so that alerts can be sent to relatives or caregivers. However, human behavior is very complex, and many existing anomaly detection systems are too simplistic which cause many false alarms, resulting in unreliable systems. We present Holmes, a comprehensive anomaly detection system for daily in-home activities. Holmes accurately learns a resident's normal behavior by considering variability in daily activities based not only on a per day basis, but also considering specific days of the week, different time periods such as per week and per month, and collective, temporal, and correlation based features. This approach of learning complicated normal behaviors reduces false alarms. Also, based on resident and expert feedback, Holmes learns semantic rules that explain specific variations of activities in specific scenarios to further reduce false alarms. We evaluate Holmes using data collected from our own deployed system, public data sets, and data collected by a senior safety system provider company from an elderly resident's home. Our evaluation shows that compared to state of the art systems, Holmes reduces false positives and false negatives by at least 46% and 27%, respectively.
{"title":"Holmes: A Comprehensive Anomaly Detection System for Daily In-home Activities","authors":"Enamul Hoque, Robert F. Dickerson, S. Preum, M. Hanson, Adam T. Barth, J. Stankovic","doi":"10.1109/DCOSS.2015.20","DOIUrl":"https://doi.org/10.1109/DCOSS.2015.20","url":null,"abstract":"Advances in wireless sensor networks have enabled the monitoring of daily activities of elderly people. The goal of these monitoring applications is to learn normal behavior in terms of daily activities and look for any deviation, i.e., Anomalies, so that alerts can be sent to relatives or caregivers. However, human behavior is very complex, and many existing anomaly detection systems are too simplistic which cause many false alarms, resulting in unreliable systems. We present Holmes, a comprehensive anomaly detection system for daily in-home activities. Holmes accurately learns a resident's normal behavior by considering variability in daily activities based not only on a per day basis, but also considering specific days of the week, different time periods such as per week and per month, and collective, temporal, and correlation based features. This approach of learning complicated normal behaviors reduces false alarms. Also, based on resident and expert feedback, Holmes learns semantic rules that explain specific variations of activities in specific scenarios to further reduce false alarms. We evaluate Holmes using data collected from our own deployed system, public data sets, and data collected by a senior safety system provider company from an elderly resident's home. Our evaluation shows that compared to state of the art systems, Holmes reduces false positives and false negatives by at least 46% and 27%, respectively.","PeriodicalId":332746,"journal":{"name":"2015 International Conference on Distributed Computing in Sensor Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115254386","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}
In Internet of Things (IoT), there are many future applications in smart environments. This paper first presents a possible scenario of a family making use of the IoT in their daily living, and then work leading to integration of unobtrusive sensors in the smart environment is discussed. In the applications discussed, Bluetooth connections linked and connected the sensors that were used in the wireless sensor network as an intuitive sensor communication platform. The paper presents results of a methodology and experimentation for human identification using sensors and microcontrollers and discusses how this can lead to integration and increased personalization within an IoT smart environment using only unobtrusive sensors.
{"title":"Personalization Using Sensors for Preliminary Human Detection in an IoT Environment","authors":"L. Russell, R. Goubran, F. Kwamena","doi":"10.1109/DCOSS.2015.40","DOIUrl":"https://doi.org/10.1109/DCOSS.2015.40","url":null,"abstract":"In Internet of Things (IoT), there are many future applications in smart environments. This paper first presents a possible scenario of a family making use of the IoT in their daily living, and then work leading to integration of unobtrusive sensors in the smart environment is discussed. In the applications discussed, Bluetooth connections linked and connected the sensors that were used in the wireless sensor network as an intuitive sensor communication platform. The paper presents results of a methodology and experimentation for human identification using sensors and microcontrollers and discusses how this can lead to integration and increased personalization within an IoT smart environment using only unobtrusive sensors.","PeriodicalId":332746,"journal":{"name":"2015 International Conference on Distributed Computing in Sensor Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130112186","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}
P. Giridhar, Shiguang Wang, T. Abdelzaher, Jemin George, Lance M. Kaplan, R. Ganti
Recent sensor network literature investigated the use of social networks as sensor networks, and formulated a physical event localization problem from social network data. This paper improves on the above results by formulating a joint localization problem of events and sources, leveraging the fact that sources on social networks often have a location affinity: They tend to comment more on events in their locations of interest. While social networks, such as Twitter, do not offer source location information for the majority of sources, we show that our algorithms for jointly inferring source and event location significantly improve localization quality by mutually enhancing location estimation of both events and sources. We evaluate the performance of our algorithm both in simulation and using Twitter data about current events. The results show that joint inference of source and event location allows us to localize many more of the events identified in real-world datasets.
{"title":"Joint Localization of Events and Sources in Social Networks","authors":"P. Giridhar, Shiguang Wang, T. Abdelzaher, Jemin George, Lance M. Kaplan, R. Ganti","doi":"10.1109/DCOSS.2015.14","DOIUrl":"https://doi.org/10.1109/DCOSS.2015.14","url":null,"abstract":"Recent sensor network literature investigated the use of social networks as sensor networks, and formulated a physical event localization problem from social network data. This paper improves on the above results by formulating a joint localization problem of events and sources, leveraging the fact that sources on social networks often have a location affinity: They tend to comment more on events in their locations of interest. While social networks, such as Twitter, do not offer source location information for the majority of sources, we show that our algorithms for jointly inferring source and event location significantly improve localization quality by mutually enhancing location estimation of both events and sources. We evaluate the performance of our algorithm both in simulation and using Twitter data about current events. The results show that joint inference of source and event location allows us to localize many more of the events identified in real-world datasets.","PeriodicalId":332746,"journal":{"name":"2015 International Conference on Distributed Computing in Sensor Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124703642","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}
Imran Khan, Fatima Zahra Errounda, Sami Yangui, R. Glitho, N. Crespi
With the recent advances in sensor hardware and software, architectures for virtualized Wireless Sensor Networks (vWSNs) are now emerging. Through node- and network-level virtualization, vWSNs can be offered as Infrastructure-as-a-Service (IaaS) which can aid in realizing the true potential of Internet-of-Things (IoT). Cloud computing offers elastic provisioning of large-scale infrastructures to multiple concurrent users where Platform-as-a-Service (PaaS) interacts with IaaS in order to efficiently host and execute applications over these infrastructures. Amalgamating IoT with cloud computing potentially allows rapid application and service provisioning in an efficient, scalable and robust manner. However, interactions between vWSNs and PaaS are largely an unexplored area. Indeed, existing vWSN IaaS are not yet ready for PaaS. This paper proposes a vWSN IaaS architecture which is ready for interactions with PaaS. The proposed architecture is based on our previous works and is rooted in the fundamental differences between traditional IaaS and vWSN IaaS. We built a prototype using Java Sunspot as the WSN tool kit and made early performance measurements.
{"title":"Getting Virtualized Wireless Sensor Networks' IaaS Ready for PaaS","authors":"Imran Khan, Fatima Zahra Errounda, Sami Yangui, R. Glitho, N. Crespi","doi":"10.1109/DCOSS.2015.39","DOIUrl":"https://doi.org/10.1109/DCOSS.2015.39","url":null,"abstract":"With the recent advances in sensor hardware and software, architectures for virtualized Wireless Sensor Networks (vWSNs) are now emerging. Through node- and network-level virtualization, vWSNs can be offered as Infrastructure-as-a-Service (IaaS) which can aid in realizing the true potential of Internet-of-Things (IoT). Cloud computing offers elastic provisioning of large-scale infrastructures to multiple concurrent users where Platform-as-a-Service (PaaS) interacts with IaaS in order to efficiently host and execute applications over these infrastructures. Amalgamating IoT with cloud computing potentially allows rapid application and service provisioning in an efficient, scalable and robust manner. However, interactions between vWSNs and PaaS are largely an unexplored area. Indeed, existing vWSN IaaS are not yet ready for PaaS. This paper proposes a vWSN IaaS architecture which is ready for interactions with PaaS. The proposed architecture is based on our previous works and is rooted in the fundamental differences between traditional IaaS and vWSN IaaS. We built a prototype using Java Sunspot as the WSN tool kit and made early performance measurements.","PeriodicalId":332746,"journal":{"name":"2015 International Conference on Distributed Computing in Sensor Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129119991","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}
This paper's main goal is to structure an initial research on the Software Ecosystem (SECO) field, regarding the so called CAMSS (Cloud, Analytics, Mobile, Social and Security) and IoT (Internet of Things) technologies. Our main goal is to share our first ideas and some observations we have been conducting in an empirical way. As such, Cloud SECOs bring the ecosystem environment to another level, with new challenges in all three dimensions: technology, social and business. Besides that, we have been observing a possible shift in the center of some SECOs, i.e., Those "things" connected to the Internet have introduced new players - at least in the sense they interact with the environment by providing data. As new software architectures and technologies emerge to support the new scenario being drawn, our research tries to characterize this new ecosystem and identify challenges, providing a roadmap that could help new entrants.
{"title":"A Roadmap for Cloud SECO: EcoData and the New Actors in IoT Era","authors":"Marcelo França, R. Santos, C. Werner","doi":"10.1109/DCOSS.2015.35","DOIUrl":"https://doi.org/10.1109/DCOSS.2015.35","url":null,"abstract":"This paper's main goal is to structure an initial research on the Software Ecosystem (SECO) field, regarding the so called CAMSS (Cloud, Analytics, Mobile, Social and Security) and IoT (Internet of Things) technologies. Our main goal is to share our first ideas and some observations we have been conducting in an empirical way. As such, Cloud SECOs bring the ecosystem environment to another level, with new challenges in all three dimensions: technology, social and business. Besides that, we have been observing a possible shift in the center of some SECOs, i.e., Those \"things\" connected to the Internet have introduced new players - at least in the sense they interact with the environment by providing data. As new software architectures and technologies emerge to support the new scenario being drawn, our research tries to characterize this new ecosystem and identify challenges, providing a roadmap that could help new entrants.","PeriodicalId":332746,"journal":{"name":"2015 International Conference on Distributed Computing in Sensor Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132478428","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 propose three modeling methods using a mobile sensor network to generate high spatio-temporal resolution air pollution maps for urban environments. In our deployment in Lausanne (Switzerland), dedicated sensing nodes are anchored to the public buses and measure multiple air quality parameters including the Lung Deposited Surface Area (LDSA), a state of the art metric for quantifying human exposure to ultra fine particles. In this paper, our focus is on generating LDSA maps. In particular, since the sensor network coverage is spatially and temporally dynamic, we leverage models to estimate the values for the locations and times where the data are not available. We first discretize the area topologically based on the street segments in the city and we then propose the following three prediction models: i) a log-linear regression model based on nine meteorological (e.g., Temperature and precipitations) and gaseous (e.g., NO 2 and CO) explanatory variables measured at two static stations in the city, ii) a novel network-based log-linear regression model that takes into account the LDSA values of the most correlated streets and also the nine explanatory variables mentioned above, iii) a novel Probabilistic Graphical Model (PGM) in which each street segment is considered as one node of the graph, and inference on conditional joint probability distributions of the nodes results in estimating the values in the nodes of interest. More than 44 millions of geo- and time-stamped LDSA measurements (i.e., More than 14 months of real data) are used in this paper to evaluate the proposed modeling approaches in various time resolutions (hourly, daily, weekly and monthly). The results show that the three approaches bring significant improvements in R2, RMSE and FAC metrics compared to a baseline K-Nearest Neighbor method.
{"title":"High Resolution Air Pollution Maps in Urban Environments Using Mobile Sensor Networks","authors":"A. Marjovi, A. Arfire, A. Martinoli","doi":"10.1109/DCOSS.2015.32","DOIUrl":"https://doi.org/10.1109/DCOSS.2015.32","url":null,"abstract":"We propose three modeling methods using a mobile sensor network to generate high spatio-temporal resolution air pollution maps for urban environments. In our deployment in Lausanne (Switzerland), dedicated sensing nodes are anchored to the public buses and measure multiple air quality parameters including the Lung Deposited Surface Area (LDSA), a state of the art metric for quantifying human exposure to ultra fine particles. In this paper, our focus is on generating LDSA maps. In particular, since the sensor network coverage is spatially and temporally dynamic, we leverage models to estimate the values for the locations and times where the data are not available. We first discretize the area topologically based on the street segments in the city and we then propose the following three prediction models: i) a log-linear regression model based on nine meteorological (e.g., Temperature and precipitations) and gaseous (e.g., NO 2 and CO) explanatory variables measured at two static stations in the city, ii) a novel network-based log-linear regression model that takes into account the LDSA values of the most correlated streets and also the nine explanatory variables mentioned above, iii) a novel Probabilistic Graphical Model (PGM) in which each street segment is considered as one node of the graph, and inference on conditional joint probability distributions of the nodes results in estimating the values in the nodes of interest. More than 44 millions of geo- and time-stamped LDSA measurements (i.e., More than 14 months of real data) are used in this paper to evaluate the proposed modeling approaches in various time resolutions (hourly, daily, weekly and monthly). The results show that the three approaches bring significant improvements in R2, RMSE and FAC metrics compared to a baseline K-Nearest Neighbor method.","PeriodicalId":332746,"journal":{"name":"2015 International Conference on Distributed Computing in Sensor Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129017983","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}
As the number of interacting devices and the complexity of cyber-physical systems increases, self-adaptation is a natural solution to address challenges faced by software developers. To provide a systematic and unified solution to support the development and execution of cyber-physical systems, this doctoral thesis proposes the creation of an environment that offers mechanisms to facilitate the technology-independent communication and uncoupled interoperable coordination between interacting entities of the system, as well as the flexible and adaptable execution of the functionalities specified for each application. The outcome is a set of modules to help developers to face the challenges of cyber-physical systems.
{"title":"System Support for Self-Adaptive Cyber-Physical Systems","authors":"Marcio E. F. Maia, R. Andrade","doi":"10.1109/DCOSS.2015.33","DOIUrl":"https://doi.org/10.1109/DCOSS.2015.33","url":null,"abstract":"As the number of interacting devices and the complexity of cyber-physical systems increases, self-adaptation is a natural solution to address challenges faced by software developers. To provide a systematic and unified solution to support the development and execution of cyber-physical systems, this doctoral thesis proposes the creation of an environment that offers mechanisms to facilitate the technology-independent communication and uncoupled interoperable coordination between interacting entities of the system, as well as the flexible and adaptable execution of the functionalities specified for each application. The outcome is a set of modules to help developers to face the challenges of cyber-physical systems.","PeriodicalId":332746,"journal":{"name":"2015 International Conference on Distributed Computing in Sensor Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126282038","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}
Niklas Wirström, A. Behboodi, Filip Lemic, T. Voigt, A. Wolisz
Range-based IEEE 802.15.4 localization systems currently require relatively high anchor density for indoor deployments. It can therefore be beneficial to use external sources of transmission as additional anchors. We present methods for using WiFi beacons to improve localization accuracy of a range-based IEEE 802.15.4 localization system in cases where only two IEEE 802.15.4 anchor nodes are available. We do this by identifying WiFi beacons from RSSI traces that we dynamically sample online, and applying fingerprinting and range-based methods using the RSSI values of the identified beacons. However, because the data of the WiFi traffic is not decodable by the IEEE 802.15.4 devices, these RSSI measurements lack identifiers that can associate them to specific WiFi Access Points (APs). Therefore, novel methods are required for both fingerprinting and range-based approaches to allow for these additional WiFi APs to be used as anchors. We show by using real-world measurements that our beacon identification method gives a false-positive rate of only 3%, and that if the range measurements to the IEEE 802.15.4 anchors are relatively accurate, with a standard deviation of 1 and 3 m, a localization accuracy improvement of 47% and 24% can be gained, respectively.
{"title":"Localization Using Anonymous Measurements","authors":"Niklas Wirström, A. Behboodi, Filip Lemic, T. Voigt, A. Wolisz","doi":"10.1109/DCOSS.2015.23","DOIUrl":"https://doi.org/10.1109/DCOSS.2015.23","url":null,"abstract":"Range-based IEEE 802.15.4 localization systems currently require relatively high anchor density for indoor deployments. It can therefore be beneficial to use external sources of transmission as additional anchors. We present methods for using WiFi beacons to improve localization accuracy of a range-based IEEE 802.15.4 localization system in cases where only two IEEE 802.15.4 anchor nodes are available. We do this by identifying WiFi beacons from RSSI traces that we dynamically sample online, and applying fingerprinting and range-based methods using the RSSI values of the identified beacons. However, because the data of the WiFi traffic is not decodable by the IEEE 802.15.4 devices, these RSSI measurements lack identifiers that can associate them to specific WiFi Access Points (APs). Therefore, novel methods are required for both fingerprinting and range-based approaches to allow for these additional WiFi APs to be used as anchors. We show by using real-world measurements that our beacon identification method gives a false-positive rate of only 3%, and that if the range measurements to the IEEE 802.15.4 anchors are relatively accurate, with a standard deviation of 1 and 3 m, a localization accuracy improvement of 47% and 24% can be gained, respectively.","PeriodicalId":332746,"journal":{"name":"2015 International Conference on Distributed Computing in Sensor Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133136078","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}
David Nunes, Duarte M. G. Raposo, David Silva, Pedro Carmona, J. Silva
Smartphones have spawned a new generation of people-centric sensing applications, where their sensors gather information from users to achieve a wide range of objectives, from fitness monitoring to the urban-wide management of traffic. The connections between devices are of utmost importance for these systems, in fact, switching between the multitude of network interfaces available to smartphones (3G/4G, WiFi) can be beneficial to improve connectivity, the distribution of network traffic and save battery power. Current solutions in the literature for the management of network interfaces are limited in the sense that they only consider either application requirements or the system's status. In this paper, we propose a new model that considers not only these aspects but also the human-context, that is, the user's position and activity to intelligently manage which interfaces should be used. To support the switching between different interfaces without interrupting existing connections, we also discuss several handoff techniques. Finally, we present an experimental evaluation of our model based on Multipath TCP.
{"title":"Achieving Human-Aware Seamless Handoff","authors":"David Nunes, Duarte M. G. Raposo, David Silva, Pedro Carmona, J. Silva","doi":"10.1109/DCOSS.2015.34","DOIUrl":"https://doi.org/10.1109/DCOSS.2015.34","url":null,"abstract":"Smartphones have spawned a new generation of people-centric sensing applications, where their sensors gather information from users to achieve a wide range of objectives, from fitness monitoring to the urban-wide management of traffic. The connections between devices are of utmost importance for these systems, in fact, switching between the multitude of network interfaces available to smartphones (3G/4G, WiFi) can be beneficial to improve connectivity, the distribution of network traffic and save battery power. Current solutions in the literature for the management of network interfaces are limited in the sense that they only consider either application requirements or the system's status. In this paper, we propose a new model that considers not only these aspects but also the human-context, that is, the user's position and activity to intelligently manage which interfaces should be used. To support the switching between different interfaces without interrupting existing connections, we also discuss several handoff techniques. Finally, we present an experimental evaluation of our model based on Multipath TCP.","PeriodicalId":332746,"journal":{"name":"2015 International Conference on Distributed Computing in Sensor Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115478831","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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