Location-Aware Information Delivery with ComMotion

By N. Marmasse, C. Schmandt. HUC, 2000

493 citations recorded by the source corpus.

Subfields: Automotive Engineering, Computer Vision and Pattern Recognition, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/20ed0efbd060f48217839c2ac21d28e543dec70e

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In Automotive Engineering

In Computer Vision and Pattern Recognition

In Human-Computer Interaction

Citations in the corpus

Identifying Important Places in People's Lives from Cellular Network Data (2011, unknown): “Previous work [1, 16–18] has also explored how semantically meaningful places can be discovered based on series of GPS coordinates.”
A Conceptual Framework and a Toolkit for Supporting the Rapid Prototyping of Context-Aware Applications (2001, background): “... the ensuing meeting (Brotherton, Abowd & Truong, 1999); mobile devices enhanced with sensors that determine their context of use to change their settings and actions (Harrison et al., 1998, Hinckley et al., 2000, Schmidt et al., 1999); a camera that captures an image when the user is startled as”
Using GPS to learn significant locations and predict movement across multiple users (2003, methodology): “In their paper on the comMotion system [14], Marmasse and Schmandt explore the idea of an agent that learns frequented locations.”
Inference Attacks on Location Tracks (2007, background): “Marmasse and Schmandt’s comMotion[24] system designated as significant those places where the GPS signal was lost three or more times within a given radius, normally due to a building blocking the signal, after which the user was”
Extracting places from traces of locations (2004, background): “Marmasse and Schmandt [9] identify a place as a region bounded by a certain fixed radius around a point, within which GPS disappears and then reappears (as in when a user enters and leaves a building).”
NextPlace: A Spatio-temporal Prediction Framework for Pervasive Systems (2011, background): “The ability to predict future locations of people allows for a rich set of novel pervasive applications and systems: accurate content dissemination of location related information such as advertisement, leisure events reports and notifications [1, 20] could be implemented in a more effective way, av”
User needs for location-aware mobile services (2003, background): “Marmasse and Schmandt [22] suggest that problems with privacy could be avoided by performing location tracking and analysis solely on the client device.”
SensLoc: sensing everyday places and paths using less energy (2010, background): “defines a place as a Euclidian ball with a fixed radius where GPS is unavailable [23].”
GeoNotes: Social and Navigational Aspects of Location-Based Information Systems (2001, background): “Marmasse & Schmandt’s [18] GPS enabled ComMotion system and Rekimoto’s [21] goggle system focus on user’s annotations, but these are mainly directed to oneself as reminders and documents.”
Learning Significant Locations and Predicting User Movement with GPS (2002, methodology): “Potential applications for a location–modeling system fall into two main categories: single–user, or non–collaborative, and multi–user, or collaborative.”
A survey of computational location privacy (2009, background): “Marmasse and Schmandt’s commotion [44] system designated as significant those places where the GPS signal was lost three or more times within a given radius, normally due to a building blocking the signal, after which the user was prompted for a place name.”
Cooperative Localization in Wireless Networks (2009, background): “A next phase to the development of this field involves the interaction of SPAWN with higher level communication applications, such as location-aware routing [128], location-aware cryptography [129], locationaware information delivery [130], and location-aware computing [131], to name but a few.”
Moving on from Weiser's Vision of Calm Computing: Engaging UbiComp Experiences (2006, background): “An example of an early context-sensitive application was comMotion that used location information and a speech output system to inform people when they were driving or cycling past a store to buy the groceries they needed [30].”
Project Lachesis: Parsing and Modeling Location Histories (2004, background): “Consumer-oriented applications use similar predictive algorithms to help form personal to-do lists [10] or to give trusted friends and co-workers a better sense of one’s current location [8].”
A Survey of Context-Aware Mobile Computing Research (2000, unknown): “Location-aware Information Delivery [ MS00 ] Group: MIT Media Laboratory.”
A Formal Model of Obfuscation and Negotiation for Location Privacy (2005, unknown): “[21]).”
Location-Aware Communications for 5G Networks: How location information can improve scalability, latency, and robustness of 5G (2014, background): “First, we have classical context awareness, which finds natural applications in location-aware information delivery [27] (e.”