In this paper, we present Breadcrumbs, a mobility dataset collected in the city of Lausanne (Switzerland) from multiple mobile phone sensors (GPS, WiFi, Bluetooth) from 81 users for a duration of 12 weeks. Currently a...
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In this paper, we present a comprehensive survey of human-mobility modeling based on 1680 articles published between 1999 and 2019, which can serve as a roadmap for research and practice in this area. Mobility modelin...
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Sharing location traces with context-aware service providers has privacy implications. Location-privacy preserving mechanisms, such as obfuscation, anonymization and cryptographic primitives, have been shown to have i...
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Sharing location traces with context-aware service providers has privacy implications. Location-privacy preserving mechanisms, such as obfuscation, anonymization and cryptographic primitives, have been shown to have i...
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Sharing location traces with context-aware service providers has privacy implications. Location-privacy preserving mechanisms, such as obfuscation, anonymization and cryptographic primitives, have been shown to have impractical utility/privacy tradeoff. Another solution for enhancing user privacy is to minimize data sharing by executing the tasks conventionally carried out at the service providers' end on the users' smartphones. Although the data volume shared with the untrusted entities is significantly reduced, executing computationally demanding server-side tasks on resource-constrained smartphones is often impracticable. To this end, we propose a novel perspective on lowering the computational complexity by treating spatiotemporal trajectories as space-time signals. Lowering the data dimensionality facilitates offloading the computational tasks onto the digital-signal processors and the usage of the non-blocking signal-processing pipelines. While focusing on the task of user mobility modeling, we achieve the following results in comparison to the state of the art techniques: (i) mobility models with precision and recall greater than 80%, (ii) reduction in computational complexity by a factor of 2.5, and (iii) reduction in power consumption by a factor of 0.5. Using real-world mobility datasets, we demonstrate the suitability of our technique to function on smartphones.
We examine the non-Markovian nature of human mobility by exposing the inability of Markov models to capture criticality in human mobility. In particular, the assumed Markovian nature of mobility was used to establish ...
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We put forth a system, to predict distant-future positions of multiple moving entities and index the forecasted trajectories, in order to answer predictive queries involving long time horizons. Today, the proliferatio...
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Location-based services today, exceedingly depend on user mobility prediction, in order to push context aware services ahead of time. Existing location forecasting techniques are driven by large volumes of data to tra...
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We introduce a system-level architecture providing fine-grained control over user privacy, in the context of location-based services accessed via mobile devices. In contrast with most mobile platforms today, users onl...
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We introduce a system-level architecture providing fine-grained control over user privacy, in the context of location-based services accessed via mobile devices. In contrast with most mobile platforms today, users only have coarse-grained control over their privacy, either accepting to unconditionally stream their locations in order to use a service, or renouncing the service altogether. However, not all location-based services do require the same level of location accuracy and the same level of privacy renouncement. With this architecture, the user can adapt the tradeoff between location privacy and location accuracy. To achieve this, our architecture relies on three main elements: a trusted module extending the underlying mobile platform, a secure protocol between that module and untrusted applications offering location-based services, and a tree capturing user's zones of interest and organizing them in various accuracy levels. Untrusted mobile applications no longer receive user locations directly: the trusted module intercepts them to compute user's zones of interest and create the tree. The user can then decide what level of accuracy will be disclosed to what application. We evaluate this architecture from a privacy preserving point of view by comparing well-known blurring mechanisms and our tree.
We introduce an algorithm that implements a time-limited neighbor detector service in mobile ad hoc networks. The time-limited neighbor detector enables a mobile device to detect nearby devices in the past, present an...
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We introduce an algorithm that implements a time-limited neighbor detector service in mobile ad hoc networks. The time-limited neighbor detector enables a mobile device to detect nearby devices in the past, present and up to some bounded time interval in the future. In particular, it can be used by a new trend of mobile applications known as proximity-based mobile applications. To implement the neighbor detector, our algorithm uses n = 2 k virtual mobile nodes where k is a non-negative integer. A virtual mobile node is an abstraction that is akin to a mobile node that travels in the network in a predefined trajectory. In practice, it can be implemented by a set of mobile nodes based on a replicated state machine approach. Our algorithm implements the neighbor detector for nodes located in a circular region. We assume that each node can accurately predict its own locations up to some bounded time interval Δ predict in the future. The key idea of the algorithm is that the virtual mobile nodes regularly collect location predictions of nodes from different subregions, meet to share what they have collected with each other and then distribute the collected location predictions to nodes. Thus, each node can use the distributed location predictions for neighbor detection. We show that our algorithm is correct under certain conditions. Compared to a solution that works with a single virtual mobile node, our algorithm has a main advantage: as n grows, it remains correct with smaller values of Δ predict . This feature makes the real world implementation of the neighbor detector more feasible. In fact, although there exist different approaches to predict the future locations of a node, usually predictions tend to become less accurate as Δ predict increases.
We introduce a time-limited neighbor detector service for mobile ad hoc networks, which enables a mobile device to detect other nearby devices in the past, present and up to some bounded time interval in the future. O...
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We introduce a time-limited neighbor detector service for mobile ad hoc networks, which enables a mobile device to detect other nearby devices in the past, present and up to some bounded time interval in the future. Our motivation lies in the emergence of a new trend of mobile applications known as proximity-based mobile applications, which enable a user to communicate with other users within some defined range and for a certain amount of time. Neighbor discovery is a fundamental requirement for these applications and is not restricted to the current neighbors but can include past or future neighbors. To implement the time-limited neighbor detector service, we apply an approach based on virtual mobile nodes. A virtual mobile node is an abstraction that is akin to a mobile node that travels in the network in a predefined trajectory. In practice it can be implemented by a set of mobile nodes based on a replicated state machine approach. In this paper, we assume that each node can accurately predict its own locations up to some bounded time interval in the future. Thus, we present a time-limited neighbor detector algorithm that uses a virtual mobile node that continuously travels in the network, collects the predicted locations of all nodes, performs the neighborhood matching between nodes and sends the list of neighbors to each node. We show that our algorithm correctly implements the time-limited neighbor detector service under a set of conditions.
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