Location, Information and Coordination

In this paper, we consider K finite populations of boundedly rational agents whose preferences and information differ. Each period agents are randomly paired to play some coordination games. We show that several ``special`` (fixed) agents lead the coordination. In a mistake-free environment, all connected fixed agents have to coordinate on the same strategy. In the long run, as the probability of mistakes goes to zero, all agents coordinate on the same strategy. The long-run outcome is unique, if all fixed agents belong to the same population.

Iconic memory, location information, and partial report

It has been suggested that the systematic decline of partial report as the delay of the partial-report cue increases is due to a time-related loss of location information. Moreover, the backward masking effect is said to be precipitated by the disruption of location information before and after identification. Results from three experiments do not support these claims when new indices of location information and of item information are used. Instead, it was found that (a) the systematic decline in partial report was due to a time-related loss of item information, and (b) location information was affected neither by the delay of the partial-report cue nor by the delay of backward masking. Subjects adopted the "select-then-identify" mode of processing

Processing irrelevant location information: practice and transfer effects in a Simon task.

How humans produce cognitively driven fine motor movements is a question of fundamental importance in how we interact with the world around us. For example, we are exposed to a constant stream of information and we must select the information that is most relevant by which to guide our actions. In the present study, we employed a well-known behavioral assay called the Simon task to better understand how humans are able to learn to filter out irrelevant information. We trained subjects for four days with a visual stimulus presented, alternately, in central and lateral locations. Subjects responded with one hand moving a joystick in either the left or right direction. They were instructed to ignore the irrelevant location information and respond based on color (e.g. red to the right and green to the left). On the fifth day, an additional testing session was conducted where the task changed and the subjects had to respond by shape (e.g. triangle to the right and rectangle to the left). They were instructed to ignore the color and location, and respond based solely on the task relevant shape. We found that the magnitude of the Simon effect decreases with training, however it returns in the first few trials after a break. Furthermore, task-defined associations between response direction and color did not significantly affect the Simon effect based on shape, and no significant associative learning from the specific stimulus-response features was found for the centrally located stimuli. We discuss how these results are consistent with a model involving route suppression/gating of the irrelevant location information. Much of the learning seems to be driven by subjects learning to suppress irrelevant location information, however, this seems to be an active inhibition process that requires a few trials of experience to engage

Machine Learning and Location Verification in Vehicular Networks

Location information will play a very important role in emerging wireless networks such as Intelligent Transportation Systems, 5G, and the Internet of Things. However, wrong location information can result in poor network outcomes. It is therefore critical to verify all location information before further utilization in any network operation. In recent years, a number of information-theoretic Location Verification Systems (LVSs) have been formulated in attempts to optimally verify the location information supplied by network users. Such LVSs, however, are somewhat limited since they rely on knowledge of a number of channel parameters for their operation. To overcome such limitations, in this work we introduce a Machine Learning based LVS (ML-LVS). This new form of LVS can adapt itself to changing environments without knowing the channel parameters. Here, for the first time, we use real-world data to show how our ML-LVS can outperform information-theoretic LVSs. We demonstrate this improved performance within the context of vehicular networks using Received Signal Strength (RSS) measurements at multiple verifying base stations. We also demonstrate the validity of the ML-LVS even in scenarios where a sophisticated adversary optimizes her attack location.Comment: 5 pages, 3 figure

Managing Access to Location Information

This publication describes systems and techniques directed to managing access to location information. A wireless-communication device, such as a smartphone, includes a sequestered location integrated circuit (IC) component that includes a location security manager application stored in memory circuitry of the sequestered location IC component. The wireless-communication device, using the sequestered location IC component, performs a technique that includes generating, from received data, a set of location information corresponding to a determined security-access level, encrypting the set of location information, and generating, for the encrypted set of location information, a security key that corresponds to the determined security-access level. The technique also includes determining that an application is allowed access to the encrypted set of location information corresponding to the determined security-access level and providing, to the application, the security key

Bandlimited Spatial Field Sampling with Mobile Sensors in the Absence of Location Information

Sampling of physical fields with mobile sensor is an emerging area. In this context, this work introduces and proposes solutions to a fundamental question: can a spatial field be estimated from samples taken at unknown sampling locations? Unknown sampling location, sample quantization, unknown bandwidth of the field, and presence of measurement-noise present difficulties in the process of field estimation. In this work, except for quantization, the other three issues will be tackled together in a mobile-sampling framework. Spatially bandlimited fields are considered. It is assumed that measurement-noise affected field samples are collected on spatial locations obtained from an unknown renewal process. That is, the samples are obtained on locations obtained from a renewal process, but the sampling locations and the renewal process distribution are unknown. In this unknown sampling location setup, it is shown that the mean-squared error in field estimation decreases as $O(1/n)$ where $n$ is the average number of samples collected by the mobile sensor. The average number of samples collected is determined by the inter-sample spacing distribution in the renewal process. An algorithm to ascertain spatial field's bandwidth is detailed, which works with high probability as the average number of samples $n$ increases. This algorithm works in the same setup, i.e., in the presence of measurement-noise and unknown sampling locations.Comment: Submitted to IEEE Trans on Signal Processin

SVGOpen Conference Guide: An overview

Context-aware applications are emerging on a daily basis and location information proves to be one of the key components in this domain. This stems from the fact that location information enables and facilitates reasoning about what users are doing (user's behavioural patterns) and what users are interested in. Availability of campus-wide WLAN infrastructure at University of Twente (UT) and the fact that SVGOpen 2005 was scheduled to be held at UT, were two strong driving forces towards building a location-aware conference guide. In this paper, a privacy-sensitive, location-aware service architecture is presented, which utilizes a calibration-free localization technique. The presented architecture uses existing WLAN infrastructure for cost efficiency, and uniquely incorporates the location information into Jini service discovery platform. Vector graphics provide better support for highly dynamic interface. Among all available vector formats, SVG proves to be a better choice to design the dynamic user interface and hence it was used in our implementation