Mobile Location-based Services Research Articles

The Impact of Contextual Offer on Purchase Intention Through Consumption Value in Mobile Service

Location-based services (LBSs) on the basis of the ubiquity and portability in mobile devices can play a useful role in fulfilling user needs such as convenience, social networking, and digital entertainment effectively and efficiently. Users of mobile LBSs filled out a survey, which produced 200 valid samples. The results of this study demonstrated that contextual offer positively informs utilitarian value and hedonic value. Since contextual offers have more influence on utilitarian value than hedonic value, this means that contextual offers should be based on developing a more utilitarian value. Second, consumption value positively affects purchase intentions. Purchase intention is more effected by hedonic value than utilitarian value. This implies that hedonic value is more important predictor of purchase intention in mobile LBSs. Thus, the service providers should put emphasis on strengthening contextual offer to improve purchase intentions through the consumption value of mobile LBSs. This study may deliver some useful implications to both research and practice.

An interval type 2 hesitant fuzzy MCDM approach and a fuzzy c means clustering for retailer clustering

Owing to the advancements in information and telecommunication technologies, mobile location-based services are able to use previously collected mobile check-in data. This multi-dimensional data can provide new opportunities for research problems such as establishing new platforms for location-based advertising and location-based personalized recommendations. In other words, location data of potential customers indicate personal interests and visiting preferences. In some cases, customers’ preferences could not be easily determined or predicted while considering visiting patterns of mobile users. In this respect, this study provides a novel retailer segmentation approach based on multi-criteria decision-making (MCDM) combined fuzzy data clustering. The proposed model consists of two phases: (1) an interval type 2 hesitant MCDM approach for the determination of location perceived value and (2) retailer (store) clustering via different product sale prices with fuzzy data-based fuzzy c means (FcM) clustering. Proposed approach enables the simplification of FcM clustering adaptation to non-symmetric fuzzy data using dissimilarity measure. Using this integrated approach, advertisers and recommender system suppliers will be able to manage their product-special offerings to customers considering retailer segments and shopping mall characteristics. Additionally, the proposed approach constitutes the infrastructure of location-based recommender systems under imprecise environment.

A Dual Privacy Preserving Approach for Location-Based Services in Mobile Multicast Environment

Privacy preserving is imperative for mobile location-based services of which the location data of the users and their objects of interest are subject to exploitation. A variety of privacy preserving mechanisms for location-based query has been proposed in recent years, but these current methods are generally either focused on the privacy of the users or the desired objects, which are not sufficient in mobile multicast environment. This paper introduces a novel dual privacy preserving approach, encompassing anonymous user’s location and object of interests as a means to minimize privacy risks for location-based services in multicast wireless environment. Experimental evaluation of the proposed method on a simulation platform demonstrated its effectiveness in preserving users’ privacy whilst ensuring minimum response time and power utilization of the users to receive the relevant objects.

A Survey of Contract Theory-Based Incentive Mechanism Design in Wireless Networks

The prevalence of high performance mobile devices such as smartphones and tablets has brought fundamental changes to existing wireless networks. The growth of multimedia and location-based mobile services has exponentially increased network congestion and the demands for more wireless access. This has led to the development of advanced techniques to address the resulting challenges based on the concept of cooperation in various heterogeneous network scenarios. Thus, innovative incentive mechanisms in wireless networks are needed to ensure the participation of third party nodes, such as access points, small cells, and users. In this tutorial, we demonstrate the effectiveness of contract theory to design incentive mechanisms for a wide range of application scenarios in wireless networks. In contract theory, participants are offered properly designed rewards based on their performances to encourage better participation. First, we present an overview of basic concepts and models of contract theory, with comparisons to other related methods from economics. We then discuss incentive mechanisms, with a focus on the design of rewards in a contract. We demonstrate how contract theory can be utilized for developing effective incentive mechanisms for emerging wireless network scenarios such as traffic offloading, mobile crowdsourcing, and spectrum trading.

Next Check-in Location Prediction Combines with Collaborative Filtering

With the thriving of mobile location-based services, location-based social networks (LBSNs) have become a popular form of social media recently. Large numbers of “check-in” records continue to accumulate over time, which contain rich information of social and geographical context and provide a unique opportunity for researchers to study user’s social behavior, which in turn enables a variety of services including place advertisement, intelligent transportation, and Urban Computing. In this paper, we mainly study the problem of user’s location prediction. Firstly we analyzed the regular mobility patterns about the visited locations. However, on account of personality trait of neophilia, people also show propensities of novelty seeking in human mobility, such as exploring unvisited but tailored locations for them to visit. Hence, we not only propose a set of features that aim to describe the regular mobility patterns, but also use the collaborative social knowledge to analysis the users’ mobility patterns on the unvisited locations. Finally, we further extend our study combining all individual features in two supervised learning models, based on linear regression and M5 model trees, to get a higher overall prediction accuracy. And we find that the supervised methodology based on M5 model trees the offers better prediction accuracy.

Artificial Neural Network Approach to Mobile Location Estimation in GSM Network

AbstractThe increase in utilisation of mobile location-based services for commercial, safety and security purposes among others are the key drivers for improving location estimation accuracy to better serve those purposes. This paper proposes the application of Levenberg Marquardt training algorithm on new robust multilayered perceptron neural network architecture for mobile positioning fitting for the urban area in the considered GSM network using received signal strength (RSS). The key performance metrics such as accuracy, cost, reliability and coverage are the major points considered in this paper. The technique was evaluated using real data from field measurement and the results obtained proved the proposed model provides a practical positioning that meet Federal Communication Commission (FCC) accuracy requirement.

Location Based Service (LBS): Tracking System

Mobile devices are getting to be progressively public in the meantime, mobile location-based service (MLBS) is considered as a productive open door for service suppliers. However, there has been a moderate development in the MLBS market before few years. The goal for each location based data framework is to support with the precise data, at perfect place progressively with customized setup and location affectability. We have possessed the capacity to build various distinctive applications where we give the client data concerning a location he or she needs to travel. However, this software maybe restricted to computer only. We have to bring them on mobile gadgets. This paper deploys real-time location tracking framework for distinctive cell phones utilizing GPS gadgets. It concentrates on the administration and perception of a greater part of individuals can be anticipated. Our work can service an ongoing location tracking service for a various parties utilizing TCP/IP and GPS software in a mobile framework. The client may obtain and oversee location data of indicated subjects, who need special care progressively. Such client requiring particular defensive measures, elderly and kids. The proposed GPS and remote location based tracking framework may be extensive to different application areas that recognize client location data and use this at anytime, anyplace.

Offloading Method for Efficient Use of Local Computational Resources in Mobile Location-Based Services Using Clouds

With the development of mobile computing, location-based services (LBSs) have been developed to provide services based on location information through communication networks or the global positioning system. In recent years, LBSs have evolved into smart LBSs, which provide many services using only location information. These include basic services such as traffic, logistic, and entertainment services. However, a smart LBS may require relatively complicated operations, which may not be effectively performed by the mobile computing system. To overcome this problem, a computation offloading technique can be used to perform certain tasks on mobile devices in cloud and fog environments. Furthermore, mobile platforms exist that provide smart LBSs. The smart cross-platform is a solution based on a virtual machine (VM) that enables compatibility of content in various mobile and smart device environments. However, owing to the nature of the VM-based execution method, the execution performance is degraded compared to that of the native execution method. In this paper, we introduce a computation offloading technique that utilizes fog computing to improve the performance of VMs running on mobile devices. We applied the proposed method to smart devices with a smart VM (SVM) and HTML5 SVM to compare their performances.

The Influence of Contextual Offer, Utilitarian, and Hedonic Value on Purchase Intention in Mobile Location-Based Services

The Influence of Contextual Offer, Utilitarian, and Hedonic Value on Purchase Intention in Mobile Location-Based Services

Providing location-aware location privacy protection for mobile location-based services

Location privacy has been a serious concern for mobile users who use location-based services provided by third-party providers via mobile networks. Recently, there have been tremendous efforts on developing new anonymity or obfuscation techniques to protect location privacy of mobile users. Though effective in certain scenarios, these existing techniques usually assume that a user has a constant privacy requirement along spatial and/or temporal dimensions, which may be not true in real-life scenarios. In this paper, we introduce a new location privacy problem: Location-aware Location Privacy Protection (L2P2) problem, where users can define dynamic and diverse privacy requirements for different locations. The goal of the L2P2 problem is to find the smallest cloaking area for each location request so that diverse privacy requirements over spatial and/or temporal dimensions are satisfied for each user. In this paper, we formalize two versions of the L2P2 problem, and propose several efficient heuristics to provide such location-aware location privacy protection for mobile users. Through extensive simulations over large synthetic and real-life datasets, we confirm the effectiveness and efficiency of the proposed L2P2 algorithms.

Preserving privacy in the context of location based services through location hider in mobile-tourism

Location based services (LBS) provide several conveniences for tourists based on the location-context. For example, whenever tourists visit any place of interest, mobile LBS can help locate resources, navigate to a destination, accomplish location-specific services, etc. LBS offer pervasive services to tourists based on the location-context, and could be seen as a ubiquitous tour guide. However, the privacy and security concerns of tourists are considered a major obstacle toward widespread adoption of LBS in the tourism industry. While a significant amount of research and development has focused on user-friendly design and implementation of tourism-based LBS, there has been much fewer efforts toward enhancing privacy during these interactions. To address the privacy related issues, this work focuses on two methodologies to ensure better privacy based on an intermediary location hider between LBS app and service providers that creates a cloaked region when sending the query in both static and dynamic (movement) contexts. The experimental results show the effectiveness of the proposed method.

EPLQ: Efficient Privacy-Preserving Location-Based Query Over Outsourced Encrypted Data

With the pervasiveness of smart phones, location-based services (LBS) have received considerable attention and become more popular and vital recently. However, the use of LBS also poses a potential threat to user's location privacy. In this paper, aiming at spatial range query, a popular LBS providing information about points of interest (POIs) within a given distance, we present an efficient and privacy-preserving location-based query solution, called EPLQ. Specifically, to achieve privacy-preserving spatial range query, we propose the first predicate-only encryption scheme for inner product range (IPRE), which can be used to detect whether a position is within a given circular area in a privacy-preserving way. To reduce query latency, we further design a privacy-preserving tree index structure in EPLQ. Detailed security analysis confirms the security properties of EPLQ. In addition, extensive experiments are conducted, and the results demonstrate that EPLQ is very efficient in privacy-preserving spatial range query over outsourced encrypted data. In particular, for a mobile LBS user using an Android phone, around 0.9 s is needed to generate a query, and it also only requires a commodity workstation, which plays the role of the cloud in our experiments, a few seconds to search POIs.

Development of a scale to measure the quality of mobile location-based services

Mobile location-based service (m-LBS) presents attractive business opportunities for various companies. Recent improvements of technologies have resulted in a dramatic growth of m-LBSs. However, development of scales for evaluating the m-LBS quality has scarcely been addressed. This study aims to develop a new scale applicable to evaluating m-LBS quality. The scale was qualitatively designed at first, and the designed scale was assessed with a total number of 281 responded survey data. As a result, an m-LBS quality scale was developed, which consists of 9 quality dimensions and 29 measurement items. The distinctive characteristic of m-LBS is captured by a newly defined dimension called “localization” and its three measurement items (namely organization, update, and inclusiveness). The proposed scale was shown to be statistically reliable and valid. The results of this study would significantly contribute to providing a valid scale for use in measuring the m-LBS quality.

A Model of Attitude toward Mobile Location-Based Services

At a recent Cornell Hospitality Research summit, Expedia presented its study that more Internet access occurs through mobile devices than by laptop or desktop. This trend is going to increase in the future. Location-based service is the new frontiers for hospitality practitioners to enhance customer engagement and retention. This research studies the attitude to location-based services for hospitality customers, and how it relates to the perceived usefulness of the mobile phone, a latent construct measured by the perceived social functions and perceived usability of mobile phone. A survey was conducted among customers of hotels and restaurants in the San Francisco Bay Area. A structural equation model was developed. Results indicate that usability and social functions are important aspects of perceived usefulness of the mobile phone, and this perceived usefulness affects the attitude to location-based services. Implications of the research for hospitality practitioners are discussed.

Protecting personalized privacy against sensitivity homogeneity attacks over road networks in mobile services

Privacy preservation has recently received considerable attention for location-based mobile services. A lot of location cloaking approaches focus on identity and location protection, but few algorithms pay attention to prevent sensitive information disclosure using query semantics. In terms of personalized privacy requirements, all queries in a cloaking set, from some user's point of view, are sensitive. These users regard the privacy is breached. This attack is called as the sensitivity homogeneity attack. We show that none of the existing location cloaking approaches can effectively resolve this problem over road networks. We propose a (K, L, P)-anonymity model and a personalized privacy protection cloaking algorithm over road networks, aiming at protecting the identity, location and sensitive information for each user. The main idea of our method is first to partition users into different groups as anonymity requirements. Then, unsafe groups are adjusted by inserting relaxed conservative users considering sensitivity requirements. Finally, segments covered by each group are published to protect location information. The efficiency and effectiveness of the method are validated by a series of carefully designed experiments. The experimental results also show that the price paid for defending against sensitivity homogeneity attacks is small.

Power-saving localization techniques for mobile devices

Purpose – This paper aims to develop generic strategies for improving energy consumption for location sensing on smartphones and compares the results of iOS and Android implementations. Mobile smartphone applications utilizing localization sensors (e.g. Global Positioning System) collectively face the problem of battery draining. Energy consumption is at a peak when applications permanently and stolidly use those sensors, even if their excessive exploitation is avoidable (e.g. when the user carrying the device is not moving). Design/methodology/approach – Considering contextual parameters affecting localization of mobile devices (i.e. incorporating movement probability, speed, etc.) is the basic idea for developing a strategy capable of reducing energy consumption for location determination on mobile devices. This paper explains the paradigm and draws the architecture for a generic context-based energy saving strategy for mobile location-based services. Findings – The paper reveals the positive implications in terms of energy consumption measured in the course of exhaustive tests for iOS and Android devices and discusses accuracy issues and potential workarounds, especially focusing on Apple’s M7 motion co-processor for consuming accelerometer data on a low energy level. Originality/value – The paper identifies and measures energy issues for location determination on smartphones and presents a generic and heuristic concept for saving energy.

Marketing Spillovers of Location-Based Mobile Services

This paper studies the increasingly prevalent check-in phenomenon on location-based service platforms. We examine the range of spatial correlation among check-ins of restaurants and bars on Foursquare, a location-based mobile service, and empirically test the promotion effect on venues’ check-ins. Our model, built on an inhomogeneous Poisson process, employs an instrumental variable approach to correct for the potential bias induced by a dummy endogenous promotion variable. The model can be used to study a broad array of problems based on spatio-temporal count data from social media. We find significant spatial correlations among neighboring venues’ check-ins. Our estimation results indicate strong spillover effects: venues’ promotions not only significantly increase their own check-ins but also benefit their neighboring venues. More interestingly, the spillover effects between restaurants and bars are asymmetric: Bars’ promotions benefit neighboring restaurants while restaurants’ promotions do not benefit neighboring bars. These results have implications for marketers who are using or considering using location-based service platforms like Foursquare, Facebook, and Yelp.

In-store mobile usage: Downloading and usage intention toward mobile location-based retail apps

The purposes of this study were to examine whether characteristics of mobile location-based services (LBS) retail apps—time convenience, interactivity, compatibility, and effort expectancy were related to consumers’ affective and cognitive involvement, which in turn were related to their intention to download and use mobile LBS retail apps. Also investigated was the moderating effect of experiential orientation on the links between consumers’ affective and cognitive involvements and usage intention. An online survey was administered to mobile Internet users (n=853) from a U.S. consumer panel. Structural equation modeling was employed to test the proposed model. Perceived interactivity and compatibility were influential antecedents that shaped consumers’ affective involvement with mobile LBS retail apps, which in turn influenced their intention to download and use the retail apps. The relationship between affective involvement and usage intention was greater for mobile consumers with high experiential orientation than for those with low experiential orientation.

Creating geo-enabled hand-drawn maps: an experiment of user-generated mobile mapping

ABSTRACTThe current user-generated content (UGC) in mobile location-based services (LBS) is mainly bound to geographic points and represented based on web mappings. The authors have proposed a Human-Centered Mobile Mapping framework to integrate smartphones’ positioning functions with various maps including hand-drawn maps. The framework is able to extend the current extent of UGC to include user-created maps, and to increase the diversity of maps in mobile environments. With the mobile application prototype Manpo, user experiments of creating geo-enabled hand-drawn maps have been conducted to college students who have little background knowledge of cartography. The results of the experiments have proved the functionality and usability of Manpo, but have also revealed that the users’ skills and experiences can strongly influence the quality of the content, especially the accuracy of positioning. Typical mistakes in map drawings and deployments of points of interest (POIs) are analyzed in details. The experiments have prompted improvements of the positioning methods, and raised future research issues including effective guidelines, user interfaces and assisted georeference editing for nonprofessional users.

Positive word-of-mouth for mobile location-based service retail apps usage

Mobile location-based service LBS retail apps, a prominent web 3.0 trend, offer customers reward opportunities when they physically walk into stores or scan the barcodes of certain products using mobile cameras. The purpose of our study was to examine: 1 whether m-internet users' characteristics were related to perceived multidimensional benefits such as conditional epistemic benefit, social self-concept benefit, emotional benefit, and functional benefit; 2 whether perceived multidimensional benefits were related to positive word-of-mouth WOM behaviours relative to mobile LBS retail apps. Data were collected from US female m-internet users n = 901. Structural equation modelling was employed to test the proposed model. Conditional epistemic benefit, social self-concept benefit, emotional benefit, and functional benefit affected positive WOM behaviours relative to mobile LBS retail apps. Economic orientation, mobile affinity, and recreational orientation all had an indirect influence on positive WOM behaviours, mediated by perceived multidimensional benefits.