Context-aware Location Research Articles

DATA DRIVEN FRAMEWORK FOR ANALYSIS OF AIR QUALITY LANDSCAPE FOR THE CITY OF LAHORE

Abstract. Several Pakistani cities are among the world’s most polluted. In the previous three years, air pollution in Lahore has been considerably over World Health Organization guideline levels, endangering the lives of the city’s more than 11 million citizens. In this paper, we investigate the city’s capability to combat air pollution by analyzing three essential aspects: (1) Data, (2) Capacity, and (3) Public awareness. Several studies have reported the need for expansion of the current air quality monitoring network. In this work, we also provide a context-aware location recommendation algorithm for installing the new air quality stations in Lahore. Data from four publicly available reference-grade continuous air quality monitoring stations and nine low-cost air quality measuring equipment are also analyzed. Our findings show that in order to measure and mitigate the effects of air pollution in Lahore, there is an urgent need for capacity improvement (installation of reference-grade and low-cost air quality sensors) and public availability of reliable air quality data. We further assessed public awareness by conducting a survey. The questionnaire results showed huge gaps in public awareness about the harms of the air quality conditions. Lastly, we provided a few recommendations for designing data-driven policies for dealing with the current apocalyptic air quality situation in Lahore.

A Systematic Analysis on the Impact of Contextual Information on Point-of-Interest Recommendation

As the popularity of Location-based Social Networks increases, designing accurate models for Point-of-Interest (POI) recommendation receives more attention. POI recommendation is often performed by incorporating contextual information into previously designed recommendation algorithms. Some of the major contextual information that has been considered in POI recommendation are the location attributes (i.e., exact coordinates of a location, category, and check-in time), the user attributes (i.e., comments, reviews, tips, and check-in made to the locations), and other information, such as the distance of the POI from user’s main activity location and the social tie between users. The right selection of such factors can significantly impact the performance of the POI recommendation. However, previous research does not consider the impact of the combination of these different factors. In this article, we propose different contextual models and analyze the fusion of different major contextual information in POI recommendation. The major contributions of this article are as follows: (i) providing an extensive survey of context-aware location recommendation; (ii) quantifying and analyzing the impact of different contextual information (e.g., social, temporal, spatial, and categorical) in the POI recommendation on available baselines and two new linear and non-linear models, which can incorporate all the major contextual information into a single recommendation model; and (iii) evaluating the considered models using two well-known real-world datasets. Our results indicate that while modeling geographical and temporal influences can improve recommendation quality, fusing all other contextual information into a recommendation model is not always the best strategy.

Pre-training Context and Time Aware Location Embeddings from Spatial-Temporal Trajectories for User Next Location Prediction

Pre-training location embeddings from spatial-temporal trajectories is a fundamental procedure and very beneficial for user next location prediction. In the real world, a location usually has variable functionalities under different contextual environments. If the exact functions of a location in the trajectory can be reflected in its embedding, the accuracy of user next location prediction should be improved. Yet, existing location embeddings pre-trained on trajectories are mostly based on distributed word representations, which mix a location's various functionalities into one latent representation vector. To address this problem, we propose a Context and Time aware Location Embedding (CTLE) model, which calculates a location's representation vector with consideration of its specific contextual neighbors in trajectories. In this way, the multi-functional properties of locations can be properly tackled. Furthermore, in order to incorporate temporal information in trajectories into location embeddings, we propose a subtle temporal encoding module and a novel pre-training objective, which further improve the quality of location embeddings. We evaluate our proposed model on two real-world mobile user trajectory datasets. The experimental results demonstrate that, compared with the existing embedding methods, our CTLE model can pre-train higher quality location embeddings and significantly improve the performance of downstream user location prediction models.

Context-aware next location prediction using data mining and metaheuristics

Due to the heavy use of smartphones and other GPS enabled devices, researchers have easy access to substantial mobility data. Many existing techniques predict the next location of the users based on their mobility traces which includes only geographical coordinates in the form of spatio-temporal data. These raw mobility traces possess hidden information known as location context. Contextual information of any location means its name, time spent there, associated activity, preferred visit time and many such parameters. Enriching raw mobility traces with such contextual information adds more value to it and more sense to it's applications. The proposed model performs geographical, contextual and behavioural enrichment of raw trajectories. It also assigns relevant tag to each identified location automatically using metaheuristic approach. This paper proposes a model CANLoc to perform data collection, trajectory enrichment and location prediction. Performance of the proposed model is verified using two datasets: GeoLife and Mobi-India, which shows significant improvement in the location prediction accuracy.

Smartphone Sensor Based Indoor Positioning: Current Status, Opportunities, and Future Challenges

The last two decades have witnessed a rich variety of indoor positioning and localization research. Starting with Microsoft Research pioneering the fingerprint approach based RADAR, MIT’s Cricket, and then moving towards beacon-based localization are few among many others. In parallel, researchers looked into other appealing and promising technologies like radio frequency identification, ultra-wideband, infrared, and visible light-based systems. However, the proliferation of smartphones over the past few years revolutionized and reshaped indoor localization towards new horizons. The deployment of MEMS sensors in modern smartphones have initiated new opportunities and challenges for the industry and academia alike. Additionally, the demands and potential of location-based services compelled the researchers to look into more robust, accurate, smartphone deployable, and context-aware location sensing. This study presents a comprehensive review of the approaches that make use of data from one or more sensors to estimate the user’s indoor location. By analyzing the approaches leveraged on smartphone sensors, it discusses the associated challenges of such approaches and points out the areas that need considerable research to overcome their limitations.

Soft Computing for Scalability in Context Aware Location based Services

Ubiquitous computing blended with context awareness gives user the facility of “anywhere anytime” computing. Location based services represents a class of context aware computing. Involvement of location as the primary input in location based services triggered concerns for user’s privacy. Most of the privacy work in domain of location based services relies on obfuscation strategy along with K anonymity. The proposed work acknowledges the idea of calculating value of K for K anonymity using context factors in fuzzy format. However, with increasing number of these fuzzy context factors resulting in more fuzzy rules, the system will tend to get slower. In order to address this issue, requirement is to reduce the size of rule base without hampering the performance much. Goal of the proposed work is to attain scalability and high performance for the above said system. Towards this, reduction of number of rules in the rule base, of fuzzy inference system has been done using Fuzzy C Means and Genetic Algorithm. Results of reduced rule base have been compared with the results of exhaustive rule base. It has been identified that number of rules can be reduced up to considerable extent with comparable performances and acceptable level of error.

Socially adaptive electronic partners for improved support of children’s values: An empirical study with a location-sharing mobile app

Abstract Mobile location-sharing technology is increasingly being used by parents to locate their children. Research shows that these technologies may pose risks to important user values such as privacy and responsibility, while they aim to promote others such as family security. As a solution, we proposed the use of Social Commitment (SC) models for governing the sharing and receiving of data. A social commitment represents an agreement between two people about which data should (not) be shared and received in which situation. We hypothesize that the use of SCs in mobile location sharing applications provides improved support for user values since it allows for a more flexible, context-aware location sharing. In this paper, we present a user study to test this hypothesis. The study focuses on primary school children ( n = 34 ) as the main target group, who’s values may be demoted through the use of location-sharing technology. Children were provided with two versions of a mobile location sharing app: one with basic check-in functionality –the basic app –and one augmented with an SC model, which we call a Socially Adaptive Electronic Partner ( saep ). Our findings suggest, among other things that the saep would provide improved support for children’s values compared to the basic app.

GMove: Group-Level Mobility Modeling Using Geo-Tagged Social Media.

Understanding human mobility is of great importance to various applications, such as urban planning, traffic scheduling, and location prediction. While there has been fruitful research on modeling human mobility using tracking data (e.g., GPS traces), the recent growth of geo-tagged social media (GeoSM) brings new opportunities to this task because of its sheer size and multi-dimensional nature. Nevertheless, how to obtain quality mobility models from the highly sparse and complex GeoSM data remains a challenge that cannot be readily addressed by existing techniques. We propose GMove, a group-level mobility modeling method using GeoSM data. Our insight is that the GeoSM data usually contains multiple user groups, where the users within the same group share significant movement regularity. Meanwhile, user grouping and mobility modeling are two intertwined tasks: (1) better user grouping offers better within-group data consistency and thus leads to more reliable mobility models; and (2) better mobility models serve as useful guidance that helps infer the group a user belongs to. GMove thus alternates between user grouping and mobility modeling, and generates an ensemble of Hidden Markov Models (HMMs) to characterize group-level movement regularity. Furthermore, to reduce text sparsity of GeoSM data, GMove also features a text augmenter. The augmenter computes keyword correlations by examining their spatiotemporal distributions. With such correlations as auxiliary knowledge, it performs sampling-based augmentation to alleviate text sparsity and produce high-quality HMMs. Our extensive experiments on two real-life data sets demonstrate that GMove can effectively generate meaningful group-level mobility models. Moreover, with context-aware location prediction as an example application, we find that GMove significantly outperforms baseline mobility models in terms of prediction accuracy.

Context Aware Location Identification in Social Media Environment

Context Aware Location Identification in Social Media Environment

Context-aware location recommendation by using a random walk-based approach

The location-based social networks (LBSN) enable users to check in their current location and share it with other users. The accumulated check-in data can be employed for the benefit of users by providing personalized recommendations. In this paper, we propose a context-aware location recommendation system for LBSNs using a random walk approach. Our proposed approach considers the current context (i.e., current social relations, personal preferences and current location) of the user to provide personalized recommendations. We build a graph model of LBSNs for performing a random walk approach with restart. Random walk is performed to calculate the recommendation probabilities of the nodes. A list of locations are recommended to users after ordering the nodes according to the estimated probabilities. We compare our algorithm, CLoRW, with popularity-based, friend-based and expert-based baselines, user-based collaborative filtering approach and a similar work in the literature. According to experimental results, our algorithm outperforms these approaches in all of the test cases.

Modeling User Activity Preference by Leveraging User Spatial Temporal Characteristics in LBSNs

With the recent surge of location based social networks (LBSNs), activity data of millions of users has become attainable. This data contains not only spatial and temporal stamps of user activity, but also its semantic information. LBSNs can help to understand mobile users' spatial temporal activity preference (STAP), which can enable a wide range of ubiquitous applications, such as personalized context-aware location recommendation and group-oriented advertisement. However, modeling such user-specific STAP needs to tackle high-dimensional data, i.e., user-location-time-activity quadruples, which is complicated and usually suffers from a data sparsity problem. In order to address this problem, we propose a STAP model. It first models the spatial and temporal activity preference separately, and then uses a principle way to combine them for preference inference. In order to characterize the impact of spatial features on user activity preference, we propose the notion of personal functional region and related parameters to model and infer user spatial activity preference. In order to model the user temporal activity preference with sparse user activity data in LBSNs, we propose to exploit the temporal activity similarity among different users and apply nonnegative tensor factorization to collaboratively infer temporal activity preference. Finally, we put forward a context-aware fusion framework to combine the spatial and temporal activity preference models for preference inference. We evaluate our proposed approach on three real-world datasets collected from New York and Tokyo, and show that our STAP model consistently outperforms the baseline approaches in various settings.

Context-aware location discovery exchange in a ubiquitous environment

It becomes challenging in order to represent, discover and exchange location information in a ubiquitous environment due to dynamic movement and interaction between mobile nodes inside. In this paper, a new method is presented in order to make location information context-aware so that organizing the format of location information and maintaining the communication between direct connected nodes in a ubiquitous environment is enabled. The structure of a contextual location information repository and a context information communication protocol is manipulated to implement the proposed features. According to the simulation results in network simulator version2 (NS2), the new method has depicted good discovery success and consumed efficient service discovery bandwidth. Other network traffic, i.e. transmission control protocol, (TCP) has been simulated in the scenarios but the new location-aware method has shown its robustness with continuous context discovery process.

Exploiting Context-Awareness to Enhance Source-Location Privacy in Wireless Sensor Networks

The source-location privacy problem in Wireless Sensor Networks has been traditionally tackled by the creation of random routes for every packet transmitted from the source nodes to the base station. These schemes provide a considerable protection level at a high cost in terms of message delivery time and energy consumption. This overhead is due to the fact that the data routing process is done in a blind way, without knowledge about the location of the attacker. In this work, we propose the Context-Aware Location Privacy (CALP) approach, which takes advantage of the ability of sensor nodes to perceive the presence of a mobile adversary in their vicinity in order to transmit data packets in a more energy-efficient and privacy-preserving manner. In particular, we apply the concepts of CALP to the development of a shortest-path CALP routing algorithm. A permissive and a strict version of the protocol are studied for different adversarial models and the proposed schemes are evaluated through simulation experiments in terms of privacy protection and energy consumption. Finally, we present the conclusions of the paper as well as possible extensions of this work.

Topics in in-how networking - Ubiquitous computing in home networks

In the past decade, there have been numerous efforts in ubiquitous. computing. For home networks, we believe that ubiquitous computing requires a global-scale system that is securable, administered by, multiple independent nonspecialist administrators, and integrates off-the-shelf hardware and software. In this system every home owner acts as an administrator of the network in the home. We are developing such a system based on the Session Initiation Protocol (SIP), with Bluetooth devices for location sensing and the Service Location Protocol (SLP) for service discovery. We also introduce context-aware location information to augment device discovery bind user communication. The system builds on our CINEMA infrastructure.