Unsupervised Learning for Human Mobility Behaviors

Learning human mobility behaviors from location-sensing data are crucial to mobility data mining because of its potential to address a range of analytical purposes in mobile context reasoning, including exploration, inference, and prediction. However, existing approaches suffer from two practical problems: temporal and spatial sparsity. To address these shortcomings, we present two unsupervised learning methods to model the mobility behaviors of multiple users (i.e., a population), considering efficiency and accuracy. These methods intelligently overcome the sparsity in individual data by seeking temporal commonality among users’ heterogeneous location behaviors. The advantages of our models are highlighted through experiments on several real-world mobility data sets, which also show how our methods can realize the three analytical purposes in a unified manner.

Digital Identity Challenge: The Security and Convenience Dilemma

This paper argues that the essential pieces of an enduring digital identity should be privacy, security, and convenience. Authentication should be frictionless. In this sense, the core of the digital identity of the future will be created around location sensing techniques. Incognia proposes a solution to secure and frictionless authentication for mobile apps that is composed of five steps. Its proprietary technology called environment fingerprinting can identify location spoofing and precisely determine the devices actual location. Incognia has found that most mobile logins, sensitive transactions, and purchases occur at trusted locations. To date, 90% of mobile logins and 89% of mobile banking sessions happen at a trusted location. Experimental results show false-negative rates below 0.004% and a decrease of over 85% of account takeover attacks.

Automated activity and progress analysis based on non-monotonic reasoning of construction operations

PurposeReal-time location sensing (RTLS) systems offer a significant potential to advance the management of construction processes by potentially providing real-time access to the locations of workers and equipment. Many location-sensing technologies tend to perform poorly for indoor work environments and generate large data sets that are somewhat difficult to process in a meaningful way. Unfortunately, little is still known regarding the practical benefits of converting raw worker tracking data into meaningful information about construction project progress, effectively impeding widespread adoption in construction.Design/methodology/approachThe presented framework is designed to automate as many steps as possible, aiming to avoid manual procedures that significantly increase the time between progress estimation updates. The authors apply simple location tracking sensor data that does not require personal handling, to ensure continuous data acquisition. They use a generic and non-site-specific knowledge base (KB) created through domain expert interviews. The sensor data and KB are analyzed in an abductive reasoning framework implemented in Answer Set Programming (extended to support spatial and temporal reasoning), a logic programming paradigm developed within the artificial intelligence domain.FindingsThis work demonstrates how abductive reasoning can be applied to automatically generate rich and qualitative information about activities that have been carried out on a construction site. These activities are subsequently used for reasoning about the progress of the construction project. Our framework delivers an upper bound on project progress (“optimistic estimates”) within a practical amount of time, in the order of seconds. The target user group is construction management by providing project planning decision support.Research limitations/implicationsThe KB developed for this early-stage research does not encapsulate an exhaustive body of domain expert knowledge. Instead, it consists of excerpts of activities in the analyzed construction site. The KB is developed to be non-site-specific, but it is not validated as the performed experiments were carried out on one single construction site.Practical implicationsThe presented work enables automated processing of simple location tracking sensor data, which provides construction management with detailed insight into construction site progress without performing labor-intensive procedures common nowadays.Originality/valueWhile automated progress estimation and activity recognition in construction have been studied for some time, the authors approach it differently. Instead of expensive equipment, manually acquired, information-rich sensor data, the authors apply simple data, domain knowledge and a logical reasoning system for which the results are promising.

A New Location Sensing Algorithm Based on DV-Hop and Quantum-Behaved Particle Swarm Optimization in WSN

In wireless sensor network, the location sensing of the sensor nodes is practical. If there is no location information of the sensor nodes, the perceived data would have no meaning. In recent years, the range-free location sensing algorithms have got great attention. DV-Hop localization algorithm is one of the important algorithm in range-free location algorithms. It has high efficiency, convenient operation and low energy consumption. However, the localization accuracy cannot meet the requirements in some applications. In this paper, a new localization method is proposed, which is based on DV-Hop and Quantum-behaved Particle Swarm Optimization (QPSO) algorithm. First, it deals with the high influence of average single jumping distance and then modifies the calculation of it in the DV-Hop algorithm. Second, in order to solve the problem of the coordinate optimization in the DV-Hop algorithm, this study chooses QPSO algorithm to optimize the unknown nodes’ coordinates. Simulation results show that the new method can improve the localization accuracy of the unknown nodes obviously in WSN.

Advanced Vehicle Security and Location Sensing System by using a Single Wireless Device RSSI

Existing investigates on the spot pursue following spotlight either altogether on indoor or totally on outside by exploitation of various gadgets and strategies. This paper expects to follow a client position in each indoor and outside conditions by utilizing a solitary remote gadget with insignificant pursue blunder. RSSI (Received Signal Strength Indication) method alongside smoothing calculations is intended to cook this answer. The arranged RSSI-based pursue system is part into 2 principle stages, especially the normalization of RSSI coefficients and accordingly the separation together with position estimation of client area by reiterative trilateration. A low quality RSSI smoothing recipe is authorized to lessen the dynamic change of radio sign got from each reference hub once the objective hub is moving. Test estimations square measure distributed to examine the affectability of RSSI. The outcomes uncover the attainability of those calculations in concocting a great deal of right timeframe position watching framework.