Proceedings of the 9th ACM international symposium on Mobile ad hoc networking and computing - MobiHoc '08, 2008
Constructing sensor barriers to detect intruders crossing a randomly-deployed sensor network is a... more Constructing sensor barriers to detect intruders crossing a randomly-deployed sensor network is an important problem. Early results have shown how to construct sensor barriers to detect intruders moving along restricted crossing paths in rectangular areas. We present a complete solution to this problem for sensors that are distributed according to a Poisson point process. In particular, we present an efficient distributed algorithm to construct sensor barriers on long strip areas of irregular shape without any constraint on crossing paths. Our approach is as follows: We first show that in a rectangular area of width w and length with w = Ω(log ), if the sensor density reaches a certain value, then there exist, with high probability, multiple disjoint sensor barriers across the entire length of the area such that intruders cannot cross the area undetected. On the other hand, if w = o(log ), then with high probability there is a crossing path not covered by any sensor regardless of the sensor density. We then devise, based on this result, an efficient distributed algorithm to construct multiple disjoint barriers in a large sensor network to cover a long boundary area of an irregular shape. Our algorithm approximates the area by dividing it into horizontal rectangular segments interleaved by vertical thin strips. Each segment and vertical strip independently computes the barriers in its own area. Constructing "horizontal" barriers in each segment connected by "vertical" barriers in neighboring vertical strips, we achieve continuous barrier coverage for the whole region. Our approach significantly reduces delay, communication overhead, and computation costs compared to centralized approaches. Finally, we implement our algorithm and carry out a number of experiments to demonstrate the effectiveness of constructing barrier coverage.
Proceedings of the 6th ACM international symposium on Mobile ad hoc networking and computing - MobiHoc '05, 2005
Previous work on the coverage of mobile sensor networks focuses on algorithms to reposition senso... more Previous work on the coverage of mobile sensor networks focuses on algorithms to reposition sensors in order to achieve a static configuration with an enlarged covered area. In this paper, we study the dynamic aspects of the coverage of a mobile sensor network that depend on the process of sensor movement. As time goes by, a position is more likely to be covered; targets that might never be detected in a stationary sensor network can now be detected by moving sensors. We characterize the area coverage at specific time instants and during time intervals, as well as the time it takes to detect a randomly located stationary target. Our results show that sensor mobility can be exploited to compensate for the lack of sensors and improve network coverage. For mobile targets, we take a game theoretic approach and derive optimal mobility strategies for sensors and targets from their own perspectives.
2012 IEEE 13th International Conference on Mobile Data Management, 2012
ABSTRACT Most current mobile devices are able to determine their location, which has become part ... more ABSTRACT Most current mobile devices are able to determine their location, which has become part of the contextual information available to applications. However, in many cases, the exact position of the device in terms of longitude and latitude is not necessary. On the contrary, applications might benefit more from a discrete context variable that indicates the "place" in which the device currently is. To realize this, the continuous device's trajectory needs to be clustered into discrete locations. Besides, the device's location is often not measured directly, but rather inferred from other measurements, such as the list of available WiFi access points. Since similar WiFi measurements lead to similar estimates of the position, it appears that the conversion into geographical coordinates is an unnecessary step in the identification of places. In this paper, we describe a density-based clustering approach that allows to learn significant places directly from a set of raw WiFi measurements.
2011 IEEE International Symposium on Information Theory Proceedings, 2011
We conduct an experiment where ten attendees of an open-air music festival are acting as Bluetoot... more We conduct an experiment where ten attendees of an open-air music festival are acting as Bluetooth probes. We then construct a parametric statistical model to estimate the total number of visible Bluetooth devices in the festival area. By comparing our estimate with ground truth information provided by probes at the entrances of the festival, we show that the total population can be estimated with a surprisingly low error (1.26% in our experiment), given the small number of agents compared to the area of the festival and the fact that they are regular attendees who move randomly. Also, our statistical model can easily be adapted to obtain more detailed estimates, such as the evolution of the population size over time.
Proceedings of the seventh ACM international symposium on Mobile ad hoc networking and computing - MobiHoc '06, 2006
... delay than the time of first contact with any node, in networks with less than 10% of nodes w... more ... delay than the time of first contact with any node, in networks with less than 10% of nodes without a path to the sink, which means that even a small percentage of node failures may have a drastic impact on the performance of intrusion detection by a wireless sensor network. ...
ABSTRACT This paper presents an overview of the Mobile Data Challenge (MDC), a large-scale resear... more ABSTRACT This paper presents an overview of the Mobile Data Challenge (MDC), a large-scale research initiative aimed at generating innovations around smartphone-based research, as well as community-based evaluation of mobile data analysis methodologies. First, we review the Lausanne Data Collection Campaign (LDCC), an initiative to collect unique longitudinal smartphone dataset for the MDC. Then, we introduce the Open and Dedicated Tracks of the MDC, describe the specific datasets used in each of them, discuss the key design and implementation aspects introduced in order to generate privacy-preserving and scientifically relevant mobile data resources for wider use by the research community, and summarize the main research trends found among the 100+ challenge submissions. We finalize by discussing the main lessons learned from the participation of several hundred researchers worldwide in the MDC Tracks.
Continuum percolation models in which pairs of points of a two-dimensional Poisson point process ... more Continuum percolation models in which pairs of points of a two-dimensional Poisson point process are connected if they are within some range of each other have been extensively studied. This paper considers a variation in which a connection between two points depends not only on their Euclidean distance, but also on the positions of all other points of the point process. This model has been recently proposed to model interference in radio communications networks. Our main result shows that, despite the infinite-range dependencies, percolation occurs in the model when the density λ of the Poisson point process is greater than the critical density value λ c of the independent model, provided that interference from other nodes can be sufficiently reduced (without vanishing).
IEEE Journal on Selected Areas in Communications, 2000
Wireless networks are fundamentally limited by the intensity of the received signals and by their... more Wireless networks are fundamentally limited by the intensity of the received signals and by their interference. Since both of these quantities depend on the spatial location of the nodes, mathematical techniques have been developed in the last decade to provide communication-theoretic results accounting for the network's geometrical configuration. Often, the location of the nodes in the network can be modeled as random, following for example a Poisson point process. In this case, different techniques based on stochastic geometry and the theory of random geometric graphs -including point process theory, percolation theory, and probabilistic combinatorics -have led to results on the connectivity, the capacity, the outage probability, and other fundamental limits of wireless networks. This tutorial article surveys some of these techniques, discusses their application to model wireless networks, and presents some of the main results that have appeared in the literature. It also serves as an introduction to the field for the other papers in this special issue.
AbstractVehicular Ad Hoc Networks (VANETs) are a pecu-liar subclass of mobile ad hoc networks th... more AbstractVehicular Ad Hoc Networks (VANETs) are a pecu-liar subclass of mobile ad hoc networks that raise a number of technical challenges, notably from the point of view of their mobility models. In this paper, we provide a thorough analysis of the connectivity of such networks ...
Mobile phones have recently been used to collect large-scale continuous data about human behavior... more Mobile phones have recently been used to collect large-scale continuous data about human behavior. In a paradigm known as people centric sensing, users are not only the carriers of sensing devices, but also the sources and consumers of sensed events. This paper describes a data collection campaign wherein Nokia N95 phones are allocated to a heterogeneous sample of nearly 170 participants from Lausanne, a mid-tier city in Switzerland, to be used over a period of one year. The data collection software runs on the background of the phones in a non-intrusive manner, yielding data on modalities such as social interaction and spatial behavior. The main motivations for organizing a new campaign on top of the ones that have been successfully conducted in the past are the following: First, in comparison to the Reality Mining data, generated in 2004-2005, the present data set is expected to provide a richer means to study location attributes, in particular, because today's mobile phones are more powerful and equipped with more sensors. Second, we aim to recruit a heterogeneous set of participants, comprising family and leisure related social networks in addition to organizationally driven ones. This paper provides a methodological description of the project and shows the potential of the resulting data set in terms of illuminating multiple aspects of human behavior.
The supercritical regime of a percolation model refers to the range of probabilities (discrete) o... more The supercritical regime of a percolation model refers to the range of probabilities (discrete) or densities (continuous) above a critical value for which there exists a unique unbounded cluster almost surely. In this paper, we provide an upper bound to the linear distance from the origin to this giant connected component for both the discrete and the continuous (Boolean) model in two-dimensions. By modeling a dense wireless sensor network with a supercritical Boolean model, our result bounds the distance traveled by a target moving in a straight line before it is detected by a node who can relay the alert through a multihop path to the sink. This result incorporates a solidified definition of detection requiring that the intrusion alert successfully reach the central authority.
This paper presents an overview of the Mobile Data Challenge (MDC), a large-scale research initia... more This paper presents an overview of the Mobile Data Challenge (MDC), a large-scale research initiative aimed at generating innovations around smartphone-based research, as well as community-based evaluation of related mobile data analysis methodologies. First we review the Lausanne Data Collection Campaign (LDCC) -an initiative to collect unique, longitudinal smartphone data set for the basis of the MDC. Then, we introduce the Open and Dedicated Tracks of the MDC; describe the specific data sets used in each of them; and discuss some of the key aspects in order to generate privacy-respecting, challenging, and scientifically relevant mobile data resources for wider use of the research community. The concluding remarks will summarize the paper.
Proceedings of the 9th ACM international symposium on Mobile ad hoc networking and computing - MobiHoc '08, 2008
Constructing sensor barriers to detect intruders crossing a randomly-deployed sensor network is a... more Constructing sensor barriers to detect intruders crossing a randomly-deployed sensor network is an important problem. Early results have shown how to construct sensor barriers to detect intruders moving along restricted crossing paths in rectangular areas. We present a complete solution to this problem for sensors that are distributed according to a Poisson point process. In particular, we present an efficient distributed algorithm to construct sensor barriers on long strip areas of irregular shape without any constraint on crossing paths. Our approach is as follows: We first show that in a rectangular area of width w and length with w = Ω(log ), if the sensor density reaches a certain value, then there exist, with high probability, multiple disjoint sensor barriers across the entire length of the area such that intruders cannot cross the area undetected. On the other hand, if w = o(log ), then with high probability there is a crossing path not covered by any sensor regardless of the sensor density. We then devise, based on this result, an efficient distributed algorithm to construct multiple disjoint barriers in a large sensor network to cover a long boundary area of an irregular shape. Our algorithm approximates the area by dividing it into horizontal rectangular segments interleaved by vertical thin strips. Each segment and vertical strip independently computes the barriers in its own area. Constructing "horizontal" barriers in each segment connected by "vertical" barriers in neighboring vertical strips, we achieve continuous barrier coverage for the whole region. Our approach significantly reduces delay, communication overhead, and computation costs compared to centralized approaches. Finally, we implement our algorithm and carry out a number of experiments to demonstrate the effectiveness of constructing barrier coverage.
Proceedings of the 6th ACM international symposium on Mobile ad hoc networking and computing - MobiHoc '05, 2005
Previous work on the coverage of mobile sensor networks focuses on algorithms to reposition senso... more Previous work on the coverage of mobile sensor networks focuses on algorithms to reposition sensors in order to achieve a static configuration with an enlarged covered area. In this paper, we study the dynamic aspects of the coverage of a mobile sensor network that depend on the process of sensor movement. As time goes by, a position is more likely to be covered; targets that might never be detected in a stationary sensor network can now be detected by moving sensors. We characterize the area coverage at specific time instants and during time intervals, as well as the time it takes to detect a randomly located stationary target. Our results show that sensor mobility can be exploited to compensate for the lack of sensors and improve network coverage. For mobile targets, we take a game theoretic approach and derive optimal mobility strategies for sensors and targets from their own perspectives.
2012 IEEE 13th International Conference on Mobile Data Management, 2012
ABSTRACT Most current mobile devices are able to determine their location, which has become part ... more ABSTRACT Most current mobile devices are able to determine their location, which has become part of the contextual information available to applications. However, in many cases, the exact position of the device in terms of longitude and latitude is not necessary. On the contrary, applications might benefit more from a discrete context variable that indicates the "place" in which the device currently is. To realize this, the continuous device's trajectory needs to be clustered into discrete locations. Besides, the device's location is often not measured directly, but rather inferred from other measurements, such as the list of available WiFi access points. Since similar WiFi measurements lead to similar estimates of the position, it appears that the conversion into geographical coordinates is an unnecessary step in the identification of places. In this paper, we describe a density-based clustering approach that allows to learn significant places directly from a set of raw WiFi measurements.
2011 IEEE International Symposium on Information Theory Proceedings, 2011
We conduct an experiment where ten attendees of an open-air music festival are acting as Bluetoot... more We conduct an experiment where ten attendees of an open-air music festival are acting as Bluetooth probes. We then construct a parametric statistical model to estimate the total number of visible Bluetooth devices in the festival area. By comparing our estimate with ground truth information provided by probes at the entrances of the festival, we show that the total population can be estimated with a surprisingly low error (1.26% in our experiment), given the small number of agents compared to the area of the festival and the fact that they are regular attendees who move randomly. Also, our statistical model can easily be adapted to obtain more detailed estimates, such as the evolution of the population size over time.
Proceedings of the seventh ACM international symposium on Mobile ad hoc networking and computing - MobiHoc '06, 2006
... delay than the time of first contact with any node, in networks with less than 10% of nodes w... more ... delay than the time of first contact with any node, in networks with less than 10% of nodes without a path to the sink, which means that even a small percentage of node failures may have a drastic impact on the performance of intrusion detection by a wireless sensor network. ...
ABSTRACT This paper presents an overview of the Mobile Data Challenge (MDC), a large-scale resear... more ABSTRACT This paper presents an overview of the Mobile Data Challenge (MDC), a large-scale research initiative aimed at generating innovations around smartphone-based research, as well as community-based evaluation of mobile data analysis methodologies. First, we review the Lausanne Data Collection Campaign (LDCC), an initiative to collect unique longitudinal smartphone dataset for the MDC. Then, we introduce the Open and Dedicated Tracks of the MDC, describe the specific datasets used in each of them, discuss the key design and implementation aspects introduced in order to generate privacy-preserving and scientifically relevant mobile data resources for wider use by the research community, and summarize the main research trends found among the 100+ challenge submissions. We finalize by discussing the main lessons learned from the participation of several hundred researchers worldwide in the MDC Tracks.
Continuum percolation models in which pairs of points of a two-dimensional Poisson point process ... more Continuum percolation models in which pairs of points of a two-dimensional Poisson point process are connected if they are within some range of each other have been extensively studied. This paper considers a variation in which a connection between two points depends not only on their Euclidean distance, but also on the positions of all other points of the point process. This model has been recently proposed to model interference in radio communications networks. Our main result shows that, despite the infinite-range dependencies, percolation occurs in the model when the density λ of the Poisson point process is greater than the critical density value λ c of the independent model, provided that interference from other nodes can be sufficiently reduced (without vanishing).
IEEE Journal on Selected Areas in Communications, 2000
Wireless networks are fundamentally limited by the intensity of the received signals and by their... more Wireless networks are fundamentally limited by the intensity of the received signals and by their interference. Since both of these quantities depend on the spatial location of the nodes, mathematical techniques have been developed in the last decade to provide communication-theoretic results accounting for the network's geometrical configuration. Often, the location of the nodes in the network can be modeled as random, following for example a Poisson point process. In this case, different techniques based on stochastic geometry and the theory of random geometric graphs -including point process theory, percolation theory, and probabilistic combinatorics -have led to results on the connectivity, the capacity, the outage probability, and other fundamental limits of wireless networks. This tutorial article surveys some of these techniques, discusses their application to model wireless networks, and presents some of the main results that have appeared in the literature. It also serves as an introduction to the field for the other papers in this special issue.
AbstractVehicular Ad Hoc Networks (VANETs) are a pecu-liar subclass of mobile ad hoc networks th... more AbstractVehicular Ad Hoc Networks (VANETs) are a pecu-liar subclass of mobile ad hoc networks that raise a number of technical challenges, notably from the point of view of their mobility models. In this paper, we provide a thorough analysis of the connectivity of such networks ...
Mobile phones have recently been used to collect large-scale continuous data about human behavior... more Mobile phones have recently been used to collect large-scale continuous data about human behavior. In a paradigm known as people centric sensing, users are not only the carriers of sensing devices, but also the sources and consumers of sensed events. This paper describes a data collection campaign wherein Nokia N95 phones are allocated to a heterogeneous sample of nearly 170 participants from Lausanne, a mid-tier city in Switzerland, to be used over a period of one year. The data collection software runs on the background of the phones in a non-intrusive manner, yielding data on modalities such as social interaction and spatial behavior. The main motivations for organizing a new campaign on top of the ones that have been successfully conducted in the past are the following: First, in comparison to the Reality Mining data, generated in 2004-2005, the present data set is expected to provide a richer means to study location attributes, in particular, because today's mobile phones are more powerful and equipped with more sensors. Second, we aim to recruit a heterogeneous set of participants, comprising family and leisure related social networks in addition to organizationally driven ones. This paper provides a methodological description of the project and shows the potential of the resulting data set in terms of illuminating multiple aspects of human behavior.
The supercritical regime of a percolation model refers to the range of probabilities (discrete) o... more The supercritical regime of a percolation model refers to the range of probabilities (discrete) or densities (continuous) above a critical value for which there exists a unique unbounded cluster almost surely. In this paper, we provide an upper bound to the linear distance from the origin to this giant connected component for both the discrete and the continuous (Boolean) model in two-dimensions. By modeling a dense wireless sensor network with a supercritical Boolean model, our result bounds the distance traveled by a target moving in a straight line before it is detected by a node who can relay the alert through a multihop path to the sink. This result incorporates a solidified definition of detection requiring that the intrusion alert successfully reach the central authority.
This paper presents an overview of the Mobile Data Challenge (MDC), a large-scale research initia... more This paper presents an overview of the Mobile Data Challenge (MDC), a large-scale research initiative aimed at generating innovations around smartphone-based research, as well as community-based evaluation of related mobile data analysis methodologies. First we review the Lausanne Data Collection Campaign (LDCC) -an initiative to collect unique, longitudinal smartphone data set for the basis of the MDC. Then, we introduce the Open and Dedicated Tracks of the MDC; describe the specific data sets used in each of them; and discuss some of the key aspects in order to generate privacy-respecting, challenging, and scientifically relevant mobile data resources for wider use of the research community. The concluding remarks will summarize the paper.
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