An Efficient Approach Formulation of Social Groups of User Calls of GSM

We are living in a world of wireless technology. The most widely used wireless i.e. mobile computing device today is the Mobile phone, can be used not only for voice and data communications but also as a computing device running context aware applications. In this paper we present a model that based on GSM data base. The objective of this paper identifies social and suspicious groups based on Cell Id, IMSI, IMEI, date and time, Location Area, MCC and MNC. This information can be used by applications for the detection of users, user context, discovering of groups and relation between them using clustering technique of data mining. One of the vital means in dealing with these data is to classify or group them into a set of categories or clusters. We demonstrate that even without knowledge of observed cell tower locations, we can recognize mobility modes that are useful for several application domains. Our mobility detection system was evaluated with GSM traces from the everyday lives of three data collector

Sensing motion using spectral and spatial analysis of WLAN RSSI

In this paper we present how motion sensing can be obtained just by observing the WLAN radio signal strength and its fluctuations. The temporal, spectral and spatial characteristics of WLAN signal are analyzed. Our analysis confirms our claim that ’signal strength from access points appear to jump around more vigorously when the device is moving compared to when it is still and the number of detectable access points vary considerably while the user is on the move’. Using this observation, we present a novel motion detection algorithm, Spectrally Spread Motion Detection (SpecSMD) based on the spectral analysis of WLAN signal’s RSSI. To benchmark the proposed algorithm, we used Spatially Spread Motion Detection (SpatSMD), which is inspired by the recent work of Sohn et al. Both algorithms were evaluated by carrying out extensive measurements in a diverse set of conditions (indoors in different buildings and outdoors - city center, parking lot, university campus etc.,) and tested against the same data sets. The 94% average classification accuracy of the proposed SpecSMD is outperforming the accuracy of SpatSMD (accuracy 87%). The motion detection algorithms presented in this paper provide ubiquitous methods for deriving the state of the user. The algorithms can be implemented and run on a commodity device with WLAN capability without the need of any additional hardware support

Map++: A Crowd-sensing System for Automatic Map Semantics Identification

Digital maps have become a part of our daily life with a number of commercial and free map services. These services have still a huge potential for enhancement with rich semantic information to support a large class of mapping applications. In this paper, we present Map++, a system that leverages standard cell-phone sensors in a crowdsensing approach to automatically enrich digital maps with different road semantics like tunnels, bumps, bridges, footbridges, crosswalks, road capacity, among others. Our analysis shows that cell-phones sensors with humans in vehicles or walking get affected by the different road features, which can be mined to extend the features of both free and commercial mapping services. We present the design and implementation of Map++ and evaluate it in a large city. Our evaluation shows that we can detect the different semantics accurately with at most 3% false positive rate and 6% false negative rate for both vehicle and pedestrian-based features. Moreover, we show that Map++ has a small energy footprint on the cell-phones, highlighting its promise as a ubiquitous digital maps enriching service.Comment: Published in the Eleventh Annual IEEE International Conference on Sensing, Communication, and Networking (IEEE SECON 2014

Scalable Mining of Common Routes in Mobile Communication Network Traffic Data

A probabilistic method for inferring common routes from mobile communication network traffic data is presented. Besides providing mobility information, valuable in a multitude of application areas, the method has the dual purpose of enabling efficient coarse-graining as well as anonymisation by mapping individual sequences onto common routes. The approach is to represent spatial trajectories by Cell ID sequences that are grouped into routes using locality-sensitive hashing and graph clustering. The method is demonstrated to be scalable, and to accurately group sequences using an evaluation set of GPS tagged data

Building a personal symbolic space model from GSM CellID Positioning Data

Série : Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol. 7The context in which a person uses a mobile context-aware application can be described by many dimensions, including the, most popular, location and position. Some of the data used to describe these dimensions can be acquired directly from sensors or computed by reasoning algorithms. In this paper we propose to contextualize the mobile user of context-aware applications by describing his/her location in a symbolic space model as an alternative to the use of a position represented by a pair of coordinates in a geometric absolute referential. By exploiting the ubiquity of GSM networks, we describe a method to progressively create this symbolic and personal space model, and propose an approach to compute the level of familiarity a person has with each of the identified places. The validity of the developed model is evaluated by comparing the identified places and the computed values for the familiarity index with a ground truth represented by GPS data and the detailed agenda of a few persons