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Location-based services (LBS) offer various functionalities, but ensuring secure access to sensitive user data remains a challenge. Traditional access control methods often need more detail to enforce location-specific restrictions. This paper proposes a new approach that utilizes the Generalized Spatio-Temporal Role-Based Access Control Model (GSTRBAC) within the context of LBS. GSTRBAC grants access based on user credentials, authorized locations, and access times, providing a detailed approach to securing LBS data. We introduce an optimized cloud-based GSTRBAC implementation suitable for deployment in modern LBS architectures. The system uses two secure communication protocols tailored to different security requirements. This allows for efficient communication for less-sensitive data while offering robust protection for highly sensitive information. Additionally, a proof-of-concept mobile application demonstrates the system's functionality and efficiency within an LBS environment. Our evaluation confirms the effectiveness of the cloud-based GSTRBAC implementation in enforcing location-specific access control while maintaining resource and time efficiency. [ABSTRACT FROM AUTHOR]

In the competitive landscape of mobile food ordering applications (MFOA) in India, the primary focus is enhancing the customer experience to mirror or even exceed their offline meal acquisition experiences. Existing research underscores the pivotal role of a superior online experience in driving business success. Against the backdrop of a dearth of studies addressing online customer experience (OCE), our current research seeks to gain insight into its state and its implications for attitudes and intentions. Specifically, we investigate the impact of OCE on the continued usage intentions (CUI) of new MFOA users. This study not only sheds light on the relationship between OCE and CUI but also presents a fresh configuration of OCE, addressing its varied conceptualization. Furthermore, drawing on data collected from over 400 first-time users of MFOA, our findings reveal that e-satisfaction and e-trust act as full mediators in influencing CUI. Finally, the study also suggests that e-trust mediates the effect of e-satisfaction on the CUI of MFOA users. Our research contributes to our understanding of OCE by specifically highlighting the experiences and outcomes of first-time users of MFOAs. Practitioners should employ strategies including personalized orientation and data gathering, location-based services, in-app messaging, push notifications and gamification techniques to increase OCE and drive CUI. [ABSTRACT FROM AUTHOR]

The popularity of cyber-physical systems is fueling the rapid growth of location-based services. This poses the risk of location privacy disclosure. Effective privacy preservation is foremost for various mobile applications. Recently, geo-indistinguishability and expected inference error are proposed for limiting location leakages. In this article, we argue that personalization means regionalization for geo-indistinguishability, and we propose a regionalized location obfuscation mechanism called DPIVE with personalized utility sensitivities. This substantially corrects the differential and distortion privacy problem of the PIVE framework proposed by Yu et al. on NDSS 2017. We develop DPIVE with two phases. In Phase I, we determine disjoint sets by partitioning all possible positions such that different locations in the same set share the Protection Location Set (PLS). In Phase II, we construct a probability distribution matrix in which the rows corresponding to the same PLS have their own sensitivity of utility (PLS diameter). Moreover, by designing a QK-means algorithm for more search space in 2-D space, we improve DPIVE with a refined location partition and present fine-grained personalization, enabling each location to have its own privacy level endowed with a customized privacy budget. Experiments with two public datasets demonstrate that our mechanisms have the superior performance, typically on skewed locations. [ABSTRACT FROM AUTHOR]

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With the development of mobile applications, location-based services (LBSs) have been incorporated into people's daily lives and created huge commercial revenues. However, when using these services, people also face the risk of personal privacy breaches due to the release of location and query content. Many existing location privacy protection schemes with centralized architectures assume that anonymous servers are secure and trustworthy. This assumption is difficult to guarantee in real applications. To solve the problem of relying on the security and trustworthiness of anonymous servers, we propose a Geohash-based location privacy protection scheme for snapshot queries. It is named G L P S . On the user side, G L P S uses Geohash encoding technology to convert the user's location coordinates into a string code representing a rectangular geographic area. G L P S uses the code as the privacy location to send check-ins and queries to the anonymous server and to avoid the anonymous server gaining the user's exact location. On the anonymous server side, the scheme takes advantage of Geohash codes' geospatial gridding capabilities and GL-Tree's effective location retrieval performance to generate a k-anonymous query set based on user-defined minimum and maximum hidden cells, making it harder for adversaries to pinpoint the user's location. We experimentally tested the performance of G L P S and compared it with three schemes: C a s p e r , G C a s p e r , and D L S . The experimental results and analyses demonstrate that G L P S has a good performance and privacy protection capability, which resolves the reliance on the security and trustworthiness of anonymous servers. It also resists attacks involving background knowledge, regional centers, homogenization, distribution density, and identity association. [ABSTRACT FROM AUTHOR]

Digitalization affects media businesses, media marketplaces, and with every single user the entire society, as well. But which of the new technologies, media distribution and usage will last? "Location-based services" (LBS), allowing to localize mobile devices to provide media content tailored to the user's current context, appear to be a sound approach to meet the demands of fast, flexible and individualized communication. However, due to rapid technological developments, their future – still – remains uncertain. This contribution assesses relevant international LBS-experts in a two-round Delphi study. Findings indicate a strong emphasis on future challenges for (potential) LBS providers and developers, mainly addressing an intraorganizational level – especially with regard to workflows of traditionally oriented media enterprises. Further barriers brought up were privacy and security issues. Regarding the motivation to install LBS apps, scientists consider reliability, ease of use, and fulfillment of human needs to be of importance. Regarding hardware components, the indoor-deployment of LBS was – rather surprisingly – deemed much more promising than an outdoor use. Concerning trends in research, user-centered studies integrating psychological perspectives were brought up, as well as research on socio-ethical implications of LBS. [ABSTRACT FROM AUTHOR]

Users of geo-localized applications on mobile devices need protection to avoid threats to their privacy. Such protection should vary in time, to cope with the dynamical nature of mobility data. We present a method to protect the privacy of users of location-based services, based on Model Predictive Control techniques. We employ three different predictors for future movements: an exact predictor, which serves as the baseline for the best expected performance, and two additional predictors allowing for online implementation. One of these predictors assumes the user is moving in a way that minimizes privacy, while the other is a linear predictor. The method has been applied to two datasets, Privamov and Cabspotting, which contain mobility data collected from real users when using a mobile device. The method demonstrated an improvement in privacy compared to a state-of-the-art mechanism by approximately 12% increase for Privamov users and 5% for Cabspotting users, while maintaining the same level of utility. • A Model Predictive Method to protect users of location-based services. • Two datasets with real user movement data were used to evaluate the method. • To assess the method, three predictors of future user positions were utilized. • The method improves privacy values compared to the current state of the art. [ABSTRACT FROM AUTHOR]

Precise position information available from smartphones can play an important role in developing new location-based service (LBS) applications. Starting from 2016, and after the release of Nougat version (Version 7) by Google, developers have had access to the GNSS raw measurements through the new application programming interface (API), namely android.location (API level 24). However, the new API does not provide the typical GNSS observations directly (e.g., pseudorange, carrier-phase and Doppler observations) which have to be generated by the users themselves. Although several Apps have been developed for the GNSS observations generation, various data analyses indicate quality concerns, from biases to observation inconsistency in the generated GNSS observations output from those Apps. The quality concerns would subsequently affect GNSS data processing such as cycle slip detection, code smoothing and ultimately positioning performance. In this study, we first investigate algorithms for GNSS observations generation from the android.location API output. We then evaluate the performances of two widely used Apps (Geo++RINEX logger and GnssLogger Apps), as well as our newly developed one (namely UofC CSV2RINEX tool) which converts the CSV file to a Receiver INdependent Exchange (RINEX) file. Positioning performance analysis is also provided which indicates improved positioning accuracy using our newly developed tool. Future work finding out the potential reasons for the identified misbehavior in the generated GNSS observations is recommended; it will require a joint effort with the App developers. [ABSTRACT FROM AUTHOR]

With the advent of wireless sensor networks and their deep integration with the world have enabled users worldwide to achieve benefits from location-based services through mobile applications, the problems such as low bandwidth, high network traffic, and disconnections issues are normally extracted from mobile services. An efficient database system is required to manage mentioned problems. Our research work finds the probability of user's next locations. A mobile user (query issuer) changes its position when performing a specific mobile search, where these queries change and repeat the search with the issuer position. Moreover, the query issuer can be static and may perform searches with varying conditions of queries. Data is exchanged with mobile devices and questions that are formulated during searching for query issuer locations. An aim of the research work is achieved through effectively processing of queries in terms of location-dependent, originated by mobile users. Significant studies have been performed in this field in the last two decades. In this paper, our novel approach comprise of usage of semantic caches with the Bayesian networks using a prediction algorithm. Our approach is unique and distinct from the traditional query processing system especially in mobile domain for the prediction of future locations of users. Consequently, a better search is analyzed using the response time of data fetch from the cache. [ABSTRACT FROM AUTHOR]

Recently, the study of emotional recognition models has increased in the human-computer interaction field. With high recognition accuracy of emotions' data, we could get immediate feedback from mobile users, get a better perception of human behavior while interacting with mobile apps, and thus make the user experience design more adaptable and intelligent. The harnessing of emotional recognition in mobile apps can dramatically enhance users' experience. Therefore, this paper proposes a visual emotion-aware cloud localization user experience framework based on mobile location services. An important feature of our proposed framework is to provide a personalized mobile app based on the user's visual emotional changes. The framework captures the emotion-aware data, processes them in the cloud server, and analyzes them for an immediate localization process. The first stage in the framework builds a correlation between the app's default language and the user's visual emotional feedback. In the second stage, the localization model loads the appropriate app's resources and adjusts the screen features based on the real-time user's emotion obtained in the first stage and according to the app's location data collected from the mobile device. Our experiments demonstrate the effectiveness of the proposed framework. The results show that our proposed framework can provide a high-quality application experience in terms of a user's emotional levels and deliver an excellent level of usability that was not possible before. [ABSTRACT FROM AUTHOR]

This paper is focused on the usage of location services in mobile applications that were developed for the purpose of reporting different events that are based on their location. The event that is intended to be generic and universal can, as in examples used in this paper, be the reporting of some occurrence to a city’s communal affairs office. Such a generic event can include both multimedia and textual data, in addition to location information obtained using mobile device running the app. The software solution that is described in this paper consists of a mobile application that was developed for the Android operating system and a web application that includes a series of PHP scripts that run on a dedicated server. The web application consists of a backend scripts that facilitate the communication of a smart phone and the server and frontend related scripts used by users and administrators to access and check the data and process the reported events. [ABSTRACT FROM AUTHOR]

Due to the increased availability of locality-based cyber services in mobile applications seclusion protection for users is critical and stands one of the major difficulties of study. The aim of the paper is to propose algorithm to maintain the privacy position of mobile device users on location based cyber services in a region of concern algorithm based on division Privacy Position of Mobile Users on Cyber Services (PPCS). In contrast to existing draw breath methods, our PPCS methodology is dumb places and the semantically information of these places is taken into account. The PPCS algorithm allows the locations created to exclude or decrease exposure to the actual location of a user. We show that PPCS is impervious to conniving and inferential abuse. We also examine the effectiveness of our proposed approach and demonstrate the usefulness of comprehensive simulations. [ABSTRACT FROM AUTHOR]

Despite the fact that city tourism has become increasingly attractive, still the planning authorities undervalue it when developing city infrastructures, especially when extending Wi-Fi covering or other mobile location services. In this paper, we are discussing some problems that occur in existing solutions for mobile applications for city tourism. We check a hypothesis if sharing a real-time location of city visitors may be beneficial for choosing urban attractions. Further, we examine a new type of tourism application that is based on gamification models and location-sharing tools. [ABSTRACT FROM AUTHOR]

With every new generation of smart devices, new sensors are introduced, such as depth camera or UWB sensors. Combined with the rapidly growing number of smart mobile devices, indoor positioning systems (IPS) have seen increasing interest due to numerous indoor location-based services (ILBS) and mobile applications at large. Wi-Fi Received Signal Strength (RSS) based fingerprinting positioning (WF) techniques are popularly used in many IPS as the widespread deployment of IEEE 802.11 WLAN (Wi-Fi) networks, as this technique requires no line-of-sight to the access points (APs), and it is easy to extract Wi-Fi signal from 802.11 networks with smart devices. However, WF techniques have problems with fingerprint variance, i.e., fluctuation of the sensed signal, and efficient map updating due to the frequently changing environment. To address these problems, we propose a novel framework of IPS which uses particle filter to fuse WF and state-of-the-art CNN-based visual localization method to better adapt to changing indoor environment. The suggested system was tested with real-world crowdsourced data collected by multiple devices in an office hallway. The experimental results demonstrate that the system can achieve robust localization at a 0.3~1.5 m mean error (ME) accuracy, and map updating with a 79% correction rate. [ABSTRACT FROM AUTHOR]

Technology has transformed maps into interactive tools for exploring and understanding the world. In this article, we present the user experience (UX) design process for Flyover Country (http://flyovercountry.io), a mobile mapping application that visualizes information about the Earth's geology and history, allowing users to save and then access the information offline while flying, driving, or hiking. Specifically, the paper presents a UX design study that tracks the conceptualization, implementation, and revision of Flyover Country, and employs scenario-based design to walk through a pair of use case scenarios that informed our UX design: science outreach to the general public and field trip guides for geoscience education. In the paper, we note UX design insights that are potentially transferable to other mobile mapping contexts, organizing insights by mobile representation design (scale, projection, symbolization, and typography) versus mobile interaction design (map entry point, bottom navigation, floating action button, and pull-up information panel). [ABSTRACT FROM AUTHOR]

Geographically explicit Ecological Momentary Assessment (GEMA), an extension of Ecological Momentary Assessment (EMA), allows to record time-stamped geographic location information for behavioral data in the every-day environments of study participants. Considering that GEMA studies are continually gaining the attention of researchers and currently there is no single approach in collecting GEMA data, in this paper, we propose and present a GEMA architecture that can be used to conduct any GEMA study based on our experience developing and maintaining the Postpartum Mothers Mobile Study (PMOMS). Our GEMA client-server architecture can be customized to meet the specific requirements of each GEMA study. Key features of our proposed GEMA architecture include: utilization of widely used smartphones to make GEMA studies practical; alleviation of the burden of activities on participants by designing clients (mobile applications) that are very lightweight and servers that are heavyweight in terms of functionality; utilization of at least one positioning sensor to determine EMA contexts marked with locations; and communication through the Internet. We believe that our proposed GEMA architecture, with the illustrated foundation for GEMA studies in our exemplar study (PMOMS), will help researchers from any field conduct GEMA studies efficiently and effectively. [ABSTRACT FROM AUTHOR]

The increase in the number of social media users and smartphone usage has a positive relationship with the diversity of applications. People use mobile applications that provide location-based service either directly or indirectly to share location with their smartphones. With the increase in the use of applications that determine location information by determining location information on mobile devices, mobile applications have become an important research area for user behavior. These applications are also utilized by users for communication and socialization purposes. The literature has usually focused on popular social media applications and studies on location-based applications (LBAs) have been insufficient. In this study, we investigate the impact of location-based services, such as Swarm and Foursquare. This study uses the technology acceptance model (TAM) and the expectation confirmation model (ECM) to understand why users continue using mobile applications. This article examines the role of hedonic comprised of application aesthetics and perceived enjoyment and utilitarian benefits (comprised of application quality and application utility) for consumer behavior in the development of application markets on satisfaction and application continuance intention. Besides, we show the benefits of the strongest effect on application users. By using the mediation satisfaction effect between hedonic and utilitarian benefits; we test the application continuance intention with regression analyses and the Sobel test. We surveyed young subjects as our sapling frame who regularly use mobile applications. We collected data from 400 users by convenience sampling method to test our hypotheses. Given our findings, we show that utilitarian and hedonic benefits are positively related to the application continuance intention. Besides, we show that satisfaction significantly mediates the relationship between hedonic/utilitarian benefits and application continuance intention. Since the main purpose of the application developers is using the application per se in the long term, they need to focus chiefly on user satisfaction. We also show that determining the relevant factors that affect application continuance intention positively is important for the businesses in a competitive environment. [ABSTRACT FROM AUTHOR]

At the centre of this article is iNakba, a digital navigational application created by a Tel Aviv-based NGO in 2014. The app superimposes a layer of ruined Palestinian localities destroyed following the 1948 war, on top of the established and hegemonic Israeli geographical representation. At the end of this war and with the formation of the State of Israel, the presence of these localities was eradicated from both the territory and Israeli maps. The iNakba app uses Google maps and Waze as platforms for allowing users to find these localities and navigate towards them in order to increase knowledge about the repressed geographical past of the territory and bring about a political change. By examining iNakba from a media practice perspective, and by comparing it to a printed map published almost seven decades earlier and in very different circumstances, we highlight the unique role of layers in constructing and deconstructing cartographical knowledge. Layers, we argue, allow new potentialities for the representation and imagination of space, specifically, problematizing the eradication of Palestinian localities from the map and suggesting to re-imagine the territory as comprising the two histories and the two peoples. More broadly, the article suggests to understand layers as both a technical and political device. [ABSTRACT FROM AUTHOR]

Bluetooth Low Energy (BLE) is a wireless network technology used for transmitting data over short distances. BLE maintains a data transmission range comparable to the regular Bluetooth transmission, but consumes less energy and cost. iBeacon technology refers to BLE mobile devices, which allow mobile applications to receive signals from iBeacons in both indoor and outdoor environments. It is commonly used nowadays for positioning, location services, navigation and marketing, for the sustainable development of smart cities. The applications, however, can be further enhanced for use in many disciplines, such as education, health sector, and exhibitions for disseminating information. This study performed a set of robustness and performance tests on BLE-based iBeacons in the teaching and learning environments to evaluate the performance of iBeacon signals for positioning. During robustness testing, positioning accuracy, signal availability and stability were assessed under different environmental conditions, and the findings suggested pedestrian traffic blocking the line of sight between iBeacon and receiver, causing the most signal attenuations and variation in RSSI. In performance testing, a series of tests was conducted to evaluate the deployment of the iBeacons for positioning; leading to recommendations of iBeacon deployment location, density, transmission interval, fingerprint space interval and collection time in physical learning spaces for sustainable eLearning environments. [ABSTRACT FROM AUTHOR]

• The relationship among the users, helpers and server, is modeled as a cooperative protection system architecture. • Community structure based trilateral Stackelberg game model is proposed for achieving the collaboration ability. • The optimization method based on proposed model is designed by the backward induction process. • The security and the performances of proposed model and method are analyzed under different situations. Location-based services are widely used in mobile applications, which not only bring convenience, but also cause serious privacy concerns. Based on the characteristics of social network, this work proposes a cooperative protection architecture to model the relationship among users, communities and location-based service. Furthermore, in order to construct K anonymity set, a novel community structure-based trilateral Stackelberg game model is developed for K -anonymity protection. In addition, an optimization method based on the proposed model is designed by the backward induction process. Finally, the security and the performance under different situations such as the anonymity parameter K and the community structure parameter overlapping weights are analyzed. The analysis results indicate that the proposed model and the optimization method are effective for privacy protection and can achieve high secure in location-based services. [ABSTRACT FROM AUTHOR]

In the age of globalisation and high competition among destinations, cities are empowered with new marketing tools to attract new residents and visitors while keeping their existing communities satisfied. Therefore, the main objective of the paper is to understand the role of location-based mobile applications among university students, who as members of the wired generation tend to use technology more than older generations. The secondary objective is to explore the most preferred location-based apps with their benefits and how they are used by the students to link themselves to the city and its services. Two focus groups are conducted with university students in Besiktas, which is claimed to be one of the most student-friendly environments in Istanbul. The findings reveal students' preferences and reasoning for the use of location-based mobile apps. Apart from their satisfaction with the diversity of shopping, wayfinding, and geosocial networking apps, their concerns for digital privacy and personal data protection have become apparent. Further research is recommended for understanding the satisfaction and expectations of different user segments in the city since their evaluations might contribute to further develop and improve location-based services, and promote the city in the local and global markets. [ABSTRACT FROM AUTHOR]

The ranking and recommendation of regions of interest are increasingly important in recent years. In this light, we propose and study a novel and interesting problem of inferring region significance using multi-source spatiotemporal data. In our study, POIs, locations, regions, trajectories, and spatial networks are taken into account. Given a set of regions R and a set of trajectories T, we seek for the top-k most attractive regions to users, i.e., regions with the top-k highest spatial-density correlations to the trajectories of travelers. This study is useful in many mobile applications such as urban computing, region recommendation, and location-based service in general. This problem is challenging due to two reasons: (1) how to model the spatial-density correlation effectively and practically and (2) how to process the problem in interactive time. To overcome the challenges, we design a novel spatial-density correlation function to evaluate the relationship between regions and trajectories, and the density of POIs and network distance are taken into account. Then, we develop a series of optimization techniques to accelerate the query efficiency. Furthermore, we develop a parallel mechanism to support big spatial data. Finally, we conduct extensive experiments on real and synthetic spatial data sets to show the efficiency and effectiveness of developed algorithms. [ABSTRACT FROM AUTHOR]

This paper documents the results of an experiment to provide location-based services information to smart phone apps in an airport terminal from a back-end cloud-based server environment, replacing the need for smart phone app integration with proprietary location-based services software development kits (SDKs). Specifically, the experiment implements a low-energy Bluetooth (BLE) indoor positioning system (IPS) and a mobile application using a Representational State Transfer (REST) application programming interface (API) to a cloud-hosted IPS. A proprietary SDK is not required to be compiled into the native mobile application. The experiment tests a system architecture to determine if indoor positions can be processed by a cloud system instead of by a native mobile application using only Bluetooth radio signal readings. No other sensors and readings from the compass, accelerometer or gyroscope are required by the IPS to determine device location. The IPS's performance is evaluated by measuring location accuracy, time to process location requests, client battery usage while using the API, and scalability to handle simultaneous API requests from multiple devices. [ABSTRACT FROM AUTHOR]

One of the recent trends of networking and mobile technology is mobile cloud computing (MCC) that provides rich computational, storage resources and services in clouds to mobile users. MCC applications provide a variety of services to users and one of them is the location-based services (LBS) applications that are widely spread. By using mobile applications and LBS, mobile devices act as a thin client where the abundant data locations are collected and stored at the mobile cloud to provide corresponding services. Privacy of the user's location has been a renewed research interest and extensively studied in recent years. However, privacy is one of the most important challenges in MCC because the user's location on mobile devices is offloaded from mobile devices to cloud providers which can be utilized by third parties. Since protecting the privacy of the user is the key to maintain the trust on the mobile environment. LBS faces issues in protecting privacy such as, the privacy of user's current location, which may contain private information. In case, if the user's current location is compromised through unauthorized access, it possibly results in severe consequences. Therefore, protecting location privacy of the user while achieving precise location is still a challenge in MCC. This comprehensive research review will provide the challenge of protecting the privacy of user's location in MCC; analyze several related works regarding the issue. In addition, it suggests possible solutions related to the issue, in lighted few shortcomings which still needs attention with few related case studies. [ABSTRACT FROM AUTHOR]

Location-based services (LBSs) have become popular in recent years due to the ever-increasing usage of smart mobile devices and mobile applications through networks. Although LBS application provides great benefits to mobile users, it also raises a sever privacy concern of users due to the untrusted service providers. In the lack of privacy enhancing mechanisms, most applications of the LBS may discourage the user's acceptance of location services in general, and endanger the user's privacy in particular. Therefore, it is a great interest to discuss on the recent privacy-preserving mechanisms in LBSs. Many existing location-privacy protection-mechanisms (LPPMs) make great efforts to increase the attacker's uncertainty on the user's actual whereabouts by generating a multiple of fake-locations together with user's actual positions. In this survey, we present a study and analysis of existing LPPMs and the state-of-art privacy measures in service quality aware LBS applications. We first study the general architecture of privacy qualification system for LBSs by surveying the existing framework and outlining its main feature components. We then give an overview of the basic privacy requirements to be considered in the design and evaluation of LPPMs. Furthermore, we discuss the classification and countermeasure solutions of existing LPPMs for mitigating the current LBS privacy protection challenges. These classifications include anonymization, obfuscation, and an encryption-based technique, as well as the combination of them is called a hybrid mechanism. Finally, we discuss several open issues and research challenges based on the latest progresses for on-going LBS and location privacy research. [ABSTRACT FROM AUTHOR]

Nowadays, when services suggesting the shortest path installed in every smartphone, people use them even if it is not necessary. In most cases when people use vehicles, the shortest path may really be the most relevant, but in case of pedestrians, we are less protected from environment and the factors like noise level, air quality, beauty and safety of route are coming to the fore. Moreover, in most cases we consider several factors deciding which route to choose: we need silent and green at the same time, beautiful as well as illuminated and etc. In most studies this factors are taking into account separately. In our research, using an example of Saint-Petersburg, we developed the prototype application suggesting attractive walking route for pedestrians considering the combination of different factors besides it's length. To estimate true score of places from the point of walking attractiveness we conducted a questionnaire, and collected scores of attractiveness in the different places of the city. The acquired samples were used to create model defining attractiveness of place by combination of different factors such as noise, architecture, density of photos and proximity of water bodies as well as green and industrial zones. The model created by Random Forest algorithm classifies places in three groups: attractive, mid-attractive and non-attractive with f 1 - scores about 0.6; Using this model, we determined the attractiveness index of all of the footways in the city. To create navigation algorithm and design mobile application we applied classic Dijkstra's shortest path finding algorithm with cost weighted by attractiveness index of footways. The preliminary estimation of algorithm shows that provided routes are 14% more attractive and just 6% longer than the shortest ones. [ABSTRACT FROM AUTHOR]

Abstract The current study presents Location-based application connectedness (LBS app connectedness) as an indicator of LBS application usage behavior. This paper develops and empirically cross-validates a model to explain the use of LBS apps by integrating the Technology Acceptance Model with the Innovation Resistance Model. The current study recruited a sample of 241 participants for study 1 and 202 for study 2. Results indicated that, as hypothesized, perceived usefulness was positively related to LBS app connectedness and perceived ease of use was positively related to perceived usefulness. Innovation resistance was negatively associated with LBS app connectedness. Surprisingly, perceived privacy risk displayed an unexpected positive association with perceived usefulness. A negative link between perceived usefulness and innovation resistance was newly added in the revised model. On the other hand, a path between perceived ease of use and LBS app connectedness as well as a link between perceived privacy risk and innovation resistance were excluded from the model. Theoretical and practical implications of the proposed model are further discussed, and suggestions for future research are provided. Highlights • Perceived ease of use was positively related to perceived usefulness. • Perceived usefulness was positively related to LBS app connectedness. • Innovation resistance was negatively associated with LBS app connectedness. • Perceived privacy risk showed an unexpected association with perceived usefulness. • A negative link between perceived usefulness and innovation resistance was found. [ABSTRACT FROM AUTHOR]

Mobile crowd-sensing (MCS) enables development of context-aware applications by mining relevant information from a large set of devices selected in an ad hoc manner. For example, MCS has been used for real-time monitoring such as Vehicle ad hoc Networks-based traffic updates as well as offline data mining and tagging for future use in applications with location-based services. However, MCS could be potentially used for much more demanding applications such as real-time perpetrator tracking by online mining of images from nearby mobile users. A recent example is tracking the miscreant responsible for the Boston bombing. We present a new design approach for tracking using MCS for such complex processing in real time. Since MCS applications assume an unreliable underlying computational platform, most typically sample size for recruited devices is guided by concerns such as fault tolerance and reliability of information. As the real-time requirements get stricter coupled with increasing complexity of data-mining approaches, the communication and computation overheads can impose a very tight constraint on the sample size of devices needed for realizing real-time operation. This results in trade-off in acquiring context-relevant data and resource usage incurred while the real-time operation requirements get updated dynamically. Such effects have not been properly studied and optimized to enable real-time MCS applications such as perpetrator tracking. In this article, we propose ContextAiDe architecture, a combination of API, middleware, and optimization engine. The key innovation in ContextAiDe is context-optimized recruitment for execution of computationand communication-heavy MCS applications in edge environment. ContextAiDe uses a notion of two types of contexts, exact (hard constraints), which have to be satisfied, and preferred (soft constraints), which may be satisfied to a certain degree. By adjusting the preferred contexts, ContextAiDe can optimize the operational overheads to enable real-time operation. ContextAiDe provides an API to specify contexts requirements and the code of MCS app, offload execution environment, a middleware that enables context-optimized and a fault-tolerant distributed execution. ContextAiDe evaluation using a real-time perpetrator tracking application shows reduced energy consumption of 37.8%, decrease in data transfer of 24.8%, and 43% less time compared to existing strategy. In spite of a small increase in the minimum distance from the perpetrator, iterations of optimization tracks the perpetrator successfully. Proactively learning the context and using stochastic optimization strategy minimizes the performance degradation caused due to uncertainty (<20%) in usage-dependent contexts. [ABSTRACT FROM AUTHOR]

As mobile devices are becoming ubiquitous nowadays, the geographical activities and interactions of human beings can be easily recorded and accessed. Each mobile individual can belong to an online social network. Unfortunately, the underlying online social relationships are hidden and only available to service providers. Acquiring the social network of mobile users would enrich lots of mobile applications, such as friend recommendation and energy-saving mobile database management. In this work, we propose to infer online social ties using purely offline geographical activities of users, such as check-in records and spatial meeting events. To tackle the problem, we devise a novel inference framework, O2O-Inf, which consists of two components, Feature Modeling and Link Inference. Feature modeling is to characterize both direct and indirect geographical interactions between nodes from co-location and graph features. Link inference aims to infer the social ties based on a small set of observed social links, and the idea is that pairs of nodes sharing similar geographical behaviors have the same tendency of linkage (i.e., either being friends or non-friends). Experiments conducted on a Gowalla location-based social network and a Meetup event-based social network exhibit a satisfying performance in comparison to state-of-the-art prediction methods under the settings of offline-to-online network inference and geo-link prediction. [ABSTRACT FROM AUTHOR]

Introduction: The use of mobile applications has increased in the last years. Most of them require the knowledge of the user location, either for their core service or for marketing purposes. Location -based services (LBS) offer context-based assistance to users based on their location. Although these applications ask the user for permission to use their location and even explain in detail how this information will be used in its terms and conditions, most users are not aware or even interested in the fact that their location information is stored in databases and monetized by selling it to third-party companies. Regarding this situation, we developed a study with the aim to assess perception, concerns and awareness from users about their location information. Methods: This work is based on an exploratory survey applied to the university community, mainly from the North Coast of Colombia, to measure the perception of location privacy of users with mobile devices. The questionnaire was applied using Google Forms. The survey has nineteen questions organized in three sections: personal information, identification of privacy and privacy management. These questions were designed to know the users ' perceptions of privacy concerns in LBS and any actions they take to preserve it. Results: The results show that, in general, the respondents do not have a real concern regarding the privacy of their geolocation data, and the majority is not willing to pay to protect their privacy. Conclusions: This type of surveys can generate awareness among participants about the use of their private information. The results expose in this paper can be used to create government policies and regulations by technology companies about the privacy management. [ABSTRACT FROM AUTHOR]

Conventional paper-based tourist maps are still created and provided by many local tourism organizations, as they have advantages in representing highly detailed and contextual information, although the static medium limits the functions of such maps. To benefit these analog maps from location-aware environments, interactivity, and data feedback from mobile devices, a sustainable ecosystem for local tourist maps is proposed. A low-cost solution for developing mobile tourism applications by providing tools to georeference analog maps and integrate multimedia sightseeing information owned by local tourism organizations is implemented. The processes for analog map georeferencing and content integration, as well as the design principles for mobile tourism applications, are introduced and discussed in detail. In cooperation with college students, university campuses, and local tourism organizations, experimental applications were implemented to evaluate the functionality and usability of the proposed mobile mapping application and the feasibility of the proposed solution. Two published applications are introduced briefly, with preliminary analyses using data donated by application users, which have shown positive contributions to understanding users' behavior and requirements. [ABSTRACT FROM AUTHOR]

Background Dispatch of lay volunteers trained in cardiopulmonary resuscitation (CPR) and equipped with automated external defibrillators (AEDs) may improve survival in cases of out-of-hospital cardiac arrest (OHCA). The aim of this study was to investigate the functionality and performance of a smartphone application for locating and alerting nearby trained laymen/women in cases of OHCA. Methods A system using a smartphone application activated by Emergency Dispatch Centres was used to locate and alert laymen/women to nearby suspected OHCAs. Lay responders were instructed either to perform CPR or collect a nearby AED. An online survey was carried out among the responders. Results From February to August 2016, the system was activated in 685 cases of suspected OHCA. Among these, 224 cases were Emergency Medical Services (EMSs)-treated OHCAs (33%). EMS-witnessed cases (n = 11) and cases with missing survey data (n = 15) were excluded. In the remaining 198 OHCAs, lay responders arrived at the scene in 116 cases (58%), and prior to EMSs in 51 cases (26%). An AED was attached in 17 cases (9%) and 4 (2%) were defibrillated. Lay responders performed CPR in 54 cases (27%). Median distance to the OHCA was 560 m (IQR 332–860 m), and 1280 m (IQR 748–1776 m) via AED pick-up. The survey-answering rate was 82%. Conclusion A smartphone application can be used to alert CPR-trained lay volunteers to OHCAs for CPR. Further improvements are needed to shorten the time to defibrillation before EMS arrival. [ABSTRACT FROM AUTHOR]

The necessity of protecting mobile users' privacy when using location-based services (LBS) is highlighted in recent study, and location cloaking is one of the solutions offered. Location cloaking makes an effort to safeguard users' location privacy by introducing uncertainty while finding out the users' location-context. The existing location cloaking algorithms are cautious about quality of the query service metrics (QoS), which mainly include success rate, anonymity level and spatial resolution. However, performance metric that is time to generate the cloaked regions should be improved. This was particularly important when user density is high. This work provides an efficient location cloaking scheme where a clique based cloaked region is constructed via partitioning of user-graph using graph partitioning algorithm, thereby generates reasonably-sized cloaked regions in less time. Further, to optimize cloaked region generation, multi-depth partitioning algorithm called BLH (Balanced Location Hiding) is proposed. Through concurrent and optimal generations of cloaked regions the proposed location cloaking preserves users' location privacy to a higher extent while retaining both the QoS and performance criteria. Experimental findings demonstrate that the success rate of the proposed algorithms is significantly increased. Even with a higher user density, it is approximately 98% with quicker response time. [ABSTRACT FROM AUTHOR]

Although inter visibility analysis between a GPS receiver and visible satellites is crucial for any real-time route navigation, traditional, mission-planning software does not incorporate it - they simply assume that the earth is flat. This paper seeks to correct the situation by outlining an advanced, semi-global matching technique which derives a Digital Surface Model (DSM), depicting the topography model of the earth surface for any study area. Our approach uses a GIS, combined with a freely available Global Navigation Satellite System (GNSS), to accurately predict pseudo satellite locations before building three-dimensional models of buildings from their footprints. We exploit high-resolution satellite data (Cartosat-1 and Geoeye-1) along with high-density elevation information, and we ultimately reduce the need for full-scale surveys and so minimize costs and time. Moreover, our combining of 3D models with GPS-derived, ephemeris data pertaining to densely built-up areas constitutes a valuable input for real time, mobile applications like car navigation systems, personal navigation, fleet tracking, asset tracking and general, location-based services. [ABSTRACT FROM AUTHOR]

The location-based services provide an interesting combination of cyber and physical worlds. However, they can also threaten the users’ privacy. Existing privacy preserving protocols require trusted nodes, with serious security and computational bottlenecks. In this paper, we propose a novel distributed anonymizing protocol based on peer-to-peer architecture. Each mobile node is responsible for anonymizing a specific zone. The mobile nodes collaborate in anonymizing their queries, without the need of getting access to any information about each other. In the proposed protocol, each request will be sent with a randomly chosen ticket. The encrypted response produced by the server is sent to a particular mobile node (called broker node) over the network, based on the hash value of this ticket. The user will query the broker to get the response. All parts of the messages are encrypted except the fields required for the anonymizer and the broker. This will secure the packet exchange over the P2P network. The proposed protocol was implemented and tested successfully, and the experimental results showed that it could be deployed efficiently to achieve user privacy in location-based services. [ABSTRACT FROM PUBLISHER]

We deploy GPS hacking in conjunction with location-based mobile apps to passively survey users in targeted geographical regions. Specifically, we investigate surveying students at different college campuses with Yik Yak, an anonymous mobile app that is popular on US college campuses. In addition to being campus centric, Yik Yak&#x02019;s anonymity allows students to express themselves candidly without self-censorship. We collect nearly 1.6 million Yik Yak messages (&#x0201C;yaks&#x0201D;) from a diverse set of 45 college campuses in the United States. We use natural language processing to determine the sentiment (positive, negative, or neutral) of all of the yaks. We employ supervised machine learning to predict the gender of the authors of the yaks and then analyze how sentiment differs among the two genders on college campuses. We also use supervised machine learning to classify all the yaks into nine topics and then investigate which topics are most popular throughout the US and how topic popularity varies on the different campuses. The results in this article provide significant insight into how campus culture and student&#x02019;s thinking varies among US colleges and universities. [ABSTRACT FROM AUTHOR]

Location-based services are mobile applications that use a device’s location to provide relevant results. Spatial search applications are a popular subset of location-based services that enable people to search through their surrounding space to find nearby locations. This article examines spatial search applications through a framework that combines critical geography research with research on the power search engines exert over information visibility. The main argument of the article is that popular spatial search applications, such as Yelp, may subtly reproduce existing forms of spatial segregation by rendering certain location invisible through the mobile mapping interface. [ABSTRACT FROM AUTHOR]

Existing Location-based social networks (LBSNs), e.g., Foursquare, depend mainly on GPS or cellular-based localization to infer users’ locations. However, GPS is unavailable indoors and cellular-based localization provides coarse-grained accuracy. This limits the accuracy of current LBSNs in indoor environments, where people spend 89 percent of their time. This in turn affects the user experience, in terms of the accuracy of the ranked list of venues, especially for the small screens of mobile devices, misses business opportunities, and leads to reduced venues coverage. In this paper, we present CheckInside: a system that can provide a fine-grained indoor location-based social network. CheckInside leverages the crowd-sensed data collected from users’ mobile devices during the check-in operation and knowledge extracted from current LBSNs to associate a place with a logical name and a semantic fingerprint. This semantic fingerprint is used to obtain a more accurate list of nearby places as well as to automatically detect new places with similar signature. A novel algorithm for detecting fake check-ins and inferring a semantically-enriched floorplan is proposed as well as an algorithm for enhancing the system performance based on the user implicit feedback. Furthermore, CheckInside encompasses a coverage extender module to automatically predict names of new venues increasing the coverage of current LBSNs. Experimental evaluation of CheckInside in four malls over the course of six weeks with 20 participants shows that it can infer the actual user place within the top five venues 99 percent of the time. This is compared to 17 percent only in the case of current LBSNs. In addition, it increases the coverage of existing LBSNs by more than 37 percent. [ABSTRACT FROM AUTHOR]

Numerous location-based services (LBS) studies have suggested that the risk of disclosing personal privacy hinders consumers from adopting LBS, whereas scant attention has focused on clarifying how to mitigate the perceived privacy risk of using LBS. This quantitative study focuses on the effects of consumer quality perceptions (i.e. information quality, system quality, and service quality) on their trust in LBS, which consequently affects perceived privacy risk and continued usage intention towards LBS. Research data were collected through a market survey website; 1399 valid questionnaires were collected. Structural equation modelling analysis was applied to the data. The results revealed that information quality, system quality, and service quality were positively related to perceived trust. Perceived trust also correlated negatively with perceived privacy risk, but positively with continued usage intention. A managerial implication drawn from the findings is that LBS providers should develop more useful user interfaces or provide timely, personalised services to reduce perceived privacy risk and strengthen LBS continued usage intention. [ABSTRACT FROM PUBLISHER]

With the wide use of mobile sensing application, more and more location-embedded data are collected and stored in mobile clouds, such as iCloud, Samsung cloud, etc. Using these data, the cloud service provider (CSP) can provide location-based service (LBS) for users. However, the mobile cloud is untrustworthy. The privacy concerns force the sensitive locations to be stored on the mobile cloud in an encrypted form. However, this brings a great challenge to utilize these data to provide efficient LBS. To solve this problem, we propose a privacy-preserving LBS scheme for mobile sensing data, based on the RSA (for Rivest, Shamir and Adleman) algorithm and ciphertext policy attribute-based encryption (CP-ABE) scheme. The mobile cloud can perform location distance computing and comparison efficiently for authorized users, without location privacy leakage. In the end, theoretical security analysis and experimental evaluation demonstrate that our scheme is secure against the chosen plaintext attack (CPA) and efficient enough for practical applications in terms of user side computation overhead. [ABSTRACT FROM AUTHOR]

An increasing number of researchers and practitioners are working to develop smart cities. Considerable attention has been paid to the college campus as it is an important component of smart cities. Consequently, the question of how to construct a smart campus has become a topical one. Here, we propose a scheme that can facilitate the construction of a smart and friendly campus. We primarily focus on three aspects of smart campuses. These are: the formation of social circles based on interests mining, the provision of educational guidance based on emotion analysis of information posted on a platform, and development of a secondary trading platform aimed at optimizing the allocation of campus resources. Based on these objectives, we designed and implemented a mobile platform called OnCampus as the first step towards the development of a smart campus that has been introduced in some colleges. We found that OnCampus could successfully accomplish the three above mentioned functions of a smart campus. [ABSTRACT FROM AUTHOR]

Mobile applications (also known as "apps") have rapidly grown into a multibillion-dollar industry. Because they are available through devices that are "always on" and often with the user, users often adopt mobile apps "on the fly" as they need them. As a result, users often base their adoption and disclosure decisions only on the information provided through the mobile app delivery platform (e.g., the Apple App Store? or Google Play?). The fact that using a mobile app often requires one to disclose an unprecedented combination of personal information (e.g., location data, preferences, contacts, calendars, browsing history, music library) means that one makes a complex risk/benefit tradeoff decision based on only the small amount of information that the mobile app delivery platform provides--and all in a short period of time. Hence, this process is much shorter and much riskier than traditional software adoption. Through two experiments involving 1,588 mobile app users, we manipulated three primary sources of information provided by a platform (app quality ratings, network size, and privacy assurances) to understand their effect on perceptions of privacy risks and benefits and, in turn, how they influence consumer adoption intentions and willingness to pay (WTP). We found that network size influenced not only perceived benefits but also the perceived risks of apps in the absence of perfect information. In addition, we found that integrating a third party privacy assurance system into the app platform had a significant influence on app adoption and information disclosure. We also found that a larger network size reduces LBS privacy risk perceptions, which confirms our information cascade hypothesis. We discuss the implications of these findings for research and practice. [ABSTRACT FROM AUTHOR]

With the popularity of mobile devices and smartphones, we have witnessed rapid growth in mobile applications and services, especially in location-based services (LBS). According to a mobile marketing survey, maps/location searches are among the most utilized services on smartphones. Points of interest (POIs), such as stores, shops, gas stations, parking lots, and bus stops, are particularly important for maps/location searches. Existing map services such as Google Maps and Wikimapia are constructed manually either professionally or with crowd sourcing. However, manual annotation is costly and limited in current POI search services. With the abundance of information on the Web, many store POIs can be extracted from the Web. In this paper, we focus on automatically constructing a POI database to enable store POI map searches. We propose techniques that are required to construct a POI database, including focused crawling, information extraction, and information retrieval techniques. We first crawl Yellow Page web sites to obtain vocabularies of store names. These vocabularies are then investigated with search engines to obtain sentences containing these store names from search snippets in order to train a store name recognition model. To extract POIs scattered across the Web, we propose a query-based crawler to find address-bearing pages that might be used to extract addresses and store names. We crawled 1.25 million distinct POI pairs scattered across the Web and implemented a POI search service via Apache Lucent’s search platform, called Solr. The experimental results demonstrate that the proposed geographical information retrieval model outperforms Wikimapia and a commercial app called ‘What’s the Number?’ [ABSTRACT FROM PUBLISHER]

With the increasing availability of real-time traffic information, dynamic spatial networks are pervasive nowadays and path planning in dynamic spatial networks becomes an important issue. In this light, we propose and investigate a novel problem of dynamically monitoring shortest paths in spatial networks (DSPM query). When a traveler aims to a destination, his/her shortest path to the destination may change due to two reasons: 1) the travel costs of some edges have been updated and 2) the traveler deviates from the pre-planned path. Our target is to accelerate the shortest path computing in dynamic spatial networks, and we believe that this study may be useful in many mobile applications, such as route planning and recommendation, car navigation and tracking, and location-based services in general. This problem is challenging due to two reasons: 1) how to maintain and reuse the existing computation results to accelerate the following computations, and 2) how to prune the search space effectively. To overcome these challenges, filter-and-refinement paradigm is adopted. We maintain an expansion tree and define a pair of upper and lower bounds to prune the search space. A series of optimization techniques are developed to accelerate the shortest path computing. The performance of the developed methods is studied in extensive experiments based on real spatial data. [ABSTRACT FROM AUTHOR]