Mobile Location-based Services Research Articles

FogLBS: Utilizing fog computing for providing mobile Location-Based Services to mobile customers

The growth of Location-Based Services (LBSs) has been made possible by the widespread use of GPS-enabled devices. Some important LBSs require the ability to quickly process moving spatial-keyword queries over moving objects, such as when a moving customer is looking for a nearby mobile fuel delivery service. While there have been solutions proposed for scenarios where either the queries or the objects being queried are moving, there is still a need for solutions that can handle scenarios where both are in motion. This research focuses on the application of fog computing to provide real-time processing of moving spatial-keyword queries for LBSs. Specifically, the research proposes a new model, FogLBS, designed to efficiently process moving continuous top-k spatial-keyword queries over moving objects in a directed streets network, with a particular emphasis on the use case of a mobile service provider. FogLBS computes queries’ answer sets for time intervals and incrementally updates them using novel optimization techniques and indexing structures. By implementing FogLBS in a fog computing architecture, the model is able to meet the real-time requirements of the service provider application and other similar LBSs. The results of extensive experiments demonstrate the effectiveness of the proposed model in terms of efficiency, scalability, and accuracy, making it a valuable contribution to the field of fog computing in LBSs.

Visualization Techniques via MLBS for Personnel Management in Major Events

Mobile location-based services (MLBS) refer to services around geographic location data. Mobile terminals use wireless communication networks (or satellite positioning systems) to obtain users’ geographic location coordinate information based on spatial databases and integrate with other information to provide users with required location-related services. The development of systems based on MLBS has significance and practical value. In this paper a visualization management information system for personnel in major events based on microservices, namely MEPMIS, is designed and implemented by using MLBS. The system consists of a server and a client app, and it has some functions including map search and query, personnel positioning and scheduling, location management, messaging, and location service. Managers of the events can quickly search and locate the staff on the specific area of the map in real-time, and make broadcasting messages to the staff, and manage the staff. The client app is developed on the Android system, by which staff users can send the positions information to the server timely. The client users can search fuzzily near their peers and list their locations, and also call near peers through sending messages or query the history record of staff locations. In the design of the system, several new proposed techniques, including visual annotation method for overlapping locations, correcting trajectory drift algorithm, microservices-based overall system architecture methodology and other new techniques, which are applied to the implementation of the system. Also, HTML5, JQuery, MLBS APIs (Application Program Interfaces) related programming techniques have been used and combined with loading Ajax asynchronously and Json data encapsulation, map marker optimization techniques, that can improve the positioning accuracy and the performance of the system. The developed system with practical functions can enhance the efficiencies of the organization and management of major events.

Refining mobile location-based service adoption: the lens of pull effect- and push effect-related motivations

PurposeMobile location-based service (m-LBS) seems like a new class of personalized service due to location positioning technologies. This work aims to investigate consumer readiness (RED) toward m-LBS based on integrating pull effect- and push effect-related factors into the technology acceptance model (TAM).Design/methodology/approachAn online survey collected data from 423 participants, and the research framework was analyzed using structural equation modeling (SEM).FindingsThe results divulge that consumer RED is determined by TAM antecedents, including usefulness (USE) and ease of use (EOU). EOU motivates USE in m-LBS. Regarding pull effect-related factors, absorptive capacity (ABC) is the strongest positive factor influencing consumer RED to use m-LBS, followed by technology willingness (TWI) and innovativeness (INN). Moreover, INN, trust (TRU) and perceived risk (RIS) significantly influence USE and EOU.Originality/valueThis work endeavors to explicate customer RED toward m-LBS by incorporating some meaningful pull effect-related dimensions (i.e. ABC, TWI and INN) and pushing effect-related dimensions (i.e. RIS) into crucial antecedents rooted in TAM. Thus, the findings assist practitioners in developing marketing strategies by boosting pull effects and controlling push effects on customer engagement in m-LBS.

Mobile Location-Based Services

This study investigated three factors of self-determination theory (SDT) that influence the intrinsic and external motivation of mobile location-based services (M-LBS). This study was conducted on M-LBS users through an online survey, and a total of 308 response data were used for analysis The results showed that the three factors of SDT significantly influenced the extrinsic motivation of M-LBS and only perceived relatedness significantly affected intrinsic motivation. Intrinsic and external motivation had a great influence on attitudes and attitudes. Recognized competency influenced perceived usefulness more than pull type. A positive attitude influenced the intention to continue pulling rather than pushing. According to service type (i.e., push and pull), users are more likely to continue to use the service when users are satisfied with the service requested by M-LBS than when the operator provides the service on its own, so they need to understand how M-LBS affects perceived usefulness and intrinsic motivation.

S-PDR: SBAUPT-Based Pedestrian Dead Reckoning Algorithm for Free-Moving Handheld Devices

Mobile location-based services (MLBS) are attracting attention for their potential public and personal use for a variety of applications such as location-based advertisement, smart shopping, smart cities, health applications, emergency response, and even gaming. Many of these applications rely on Inertial Navigation Systems (INS) due to the degraded GNSS services indoors. INS-based MLBS using smartphones is hindered by the quality of the MEMS sensors provided in smartphones which suffer from high noise and errors resulting in high drift in the navigation solution rapidly. Pedestrian dead reckoning (PDR) is an INS-based navigation technique that exploits human motion to reduce navigation solution errors, but the errors cannot be eliminated without aid from other techniques. The purpose of this study is to enhance and extend the short-term reliability of PDR systems for smartphones as a standalone system through an enhanced step detection algorithm, a periodic attitude correction technique, and a novel PCA-based motion direction estimation technique. Testing shows that the developed system (S-PDR) provides a reliable short-term navigation solution with a final positioning error that is up to 6 m after 3 min runtime. These results were compared to a PDR solution using an Xsens IMU which is known to be a high grade MEMS IMU and was found to be worse than S-PDR. The findings show that S-PDR can be used to aid GNSS in challenging environments and can be a viable option for short-term indoor navigation until aiding is provided by alternative means. Furthermore, the extended reliable solution of S-PDR can help reduce the operational complexity of aiding navigation systems such as RF-based indoor navigation and magnetic map matching as it reduces the frequency by which these aiding techniques are required and applied.

Implementation of an Android Mobile Location-Based Service Application for General Auto Repair Shops

The need of a location-based service more specifically as a mobile application sparked the interest of this paper to develop an Android Mobile Application using Location-based services. Yelp and Zomato are one of the few examples. The application created is specific to locating the nearest auto repair shops used via smartphones that detect the location of the user in order to perform the task. This application will not only benefit the customers in need for a nearby auto shop but as well as the providers of the service. The application was developed using Android Studio and was tested by using five (5) nearest auto shops around the vicinity. The results showed the directions getting to the shops as well as the type of services being provided to customers. An acceptability survey was conducted to validate the applicability of the mobile application. The survey was conducted using a controlled group consisting of the nearest auto shops and thirty (30) automobile enthusiasts. The survey presents an applicable and engaging application in assisting possible customers in case of emergency instances arise. Using the survey as basis, an enhancement of the application was completed. Finally, it was suggested that the application will reach its full potential once additional functions are implemented. Registration or Google verification of shop owners is key as it will add to the service provider directory to enable a broad listing.

CollabLoc: Privacy-Preserving Multi-Modal Collaborative Mobile Phone Localization

Mobile location-based services are important context-aware services that are more and more used for enforcing security policies, for supporting indoor room navigation, and for providing personalized assistance. However, a major problem still remains unaddressed-the lack of solutions that work across buildings while not using additional infrastructure and also accounting for privacy and reliability needs. A privacy-preserving, multi-modal, cross-building, collaborative localization platform is proposed based on Wi-Fi Received Signal Strength Indicator (RSSI) (existing infrastructure), Cellular RSSI, sound, light, and geo-magnetic levels, that enables sub-room level localization. The solution is fully based on mobile phones and existing Wi-Fi infrastructure, and has privacy inherently built into it via cryptographically-secured onion routing and perturbation/randomization techniques. It also exploits the idea of weighted collaboration to increase the reliability as well as to limit the effect of noisy devices (due to sensor noise/privacy). The solution has been analyzed in terms of latency overhead due to onion-routing, request load on phones, privacy-accuracy tradeoffs, optimum parameters, granularity, different classification algorithms using real location data collected at multiple indoor and outdoor locations via an Android application. The additional features other than Wi-Fi RSSI values are shown to increase the accuracy to a maximum of 15 percent, while considering Geo-magnetic field is shown to enhance the granularity from 2.5 m to ≈1 m, a 60 percent improvement.

Effects of privacy concern, risk, and information control in a smart tourism destination

Smart tourism destinations are growing, with the use and implementation of technology to make travel more enjoyable. Still, the tourist's information privacy concerns generated by the big data available to the destination and service providers is a significant problem for smart tourism, which can affect the sustainability and economic benefit of the smart tourism destination. This study investigates privacy concern, perceived risk, control of information, service provider trust in a smart tourism destination, and the resulting behavioural intentions. The results were obtained using an online survey of 384 respondents that have visited the smart tourism destination Dubai. The results indicated that use context as an environmental factor helps mitigate the tourists' negative privacy concerns and perceived risk towards trust in a smart tourism destination. Furthermore, previous privacy violation experience as a personal factor was negatively related to trust in the smart tourism destination and the service providers. The study provides meaningful contributions on smart tourism to destination management organizations (DMO's) and service providers on what influences and mitigates the privacy concern of the tourists using mobile application location-based services available in a smart tourism destination, building on the social cognitive theory.

A BIM Based Hybrid 3D Indoor Map Model for Indoor Positioning and Navigation

Accurate and fast indoor Location-Based Services (LBS) is very important for daily life and emergency response. Indoor map is the basis of indoor LBS. The model construction and data organization of indoor map are the key scientific problems that urgently need to be solved in the current indoor LBS application. In recent years, hybrid models have been used widely in the research of indoor map, because they can balance the limitations of single models. However, the current studies about hybrid model pay more attention to the model accuracy and modeling algorithm, while ignoring its relationship between positioning and navigation and its practicality in mobile indoor LBS applications. This paper addresses a new indoor map model, named Building Information Modeling based Positioning and Navigation (BIMPN), which is based on the entity model and the network model. The highlight of BIMPN is that it proposes a concept of Step Node (SN) to assist indoor positioning and navigation function. We developed the Mobile Indoor Positioning and Navigation System (MIPNS) to verify the practicability of BIMPN. Results indicate that the BIMPN can effectively organize the characteristics of indoor spaces and the building features, and assist indoor positioning and navigation. The BIMPN proposed in this paper can be used for the construction of indoor maps and it is suitable for mobile indoor positioning and navigation systems.

Assessing Mobile Location-Based Service (m-LBS) Quality: A Combination of m-LBS Quality Scale and Importance-Performance Analysis

Assessing Mobile Location-Based Service (m-LBS) Quality: A Combination of m-LBS Quality Scale and Importance-Performance Analysis

Mobile Location-Based Services\u2019 Value-in-Use in Inner Cities: Do a\xa0Customer\u2019s Shopping Patterns, Prior User Experience, and Sales Promotions Matter?

Mobile location-based services (LBS) represent a promising opportunity for inner-city retailers and service providers to react to changes in customer behavior due to digitalization. To gain competitive advantages, mobile LBS must offer customers high value-in-use and help them reach their shopping goals during their inner-city visits. Shopping goals differ depending on shopping patterns; thus, these patterns may influence customers’ evaluation of mobile LBS during inner-city visits. Since value-in-use is not only a context-specific but also a temporally dynamic construct, customers’ user experience must also be considered. Therefore, this study investigates the influence of customers’ shopping patterns and current user experience on their evaluation of mobile LBS’ value-in-use during inner-city visits. Moreover, the impacts of the offers transmitted through mobile LBS on the value-in-use are examined.Using field test data, we empirically verify a conceptualization of mobile LBS and determine a comprehensive view of mobile LBS’ value-in-use during shopping trips with different shopping patterns and user experience within a mixed-method analysis. Our results identify both utilitarian and hedonic value-in-use components as being empirically relevant for high value-in-use evaluations regarding mobile LBS in inner cities. Furthermore, the relevance of monetary benefits, fun benefits, and irritation on value-in-use vary according to customers’ user experience. A customer’s shopping pattern affects the value-in-use of mobile LBS; however, this effect is not as differentiated as expected. Moreover, the number of relevant monetary and non-monetary offers transmitted during an inner-city visit are shown to represent a potential, albeit limited, management instrument for affecting mobile LBS’ value-in-use.

Privacy Preserving Location-Based Client-Server Service Using Standard Cryptosystem

Location-Based Mobile Services (LBMS) is rapidly gaining ground and becoming increasingly popular, because of the variety of efficient and personalized services it offers. However, if users are not guaranteed their privacy and there is no assurance of genuineness of server's response, the use of these services would be rendered useless and could deter its growth in mobile computing. This paper aims to provide confidentiality and integrity for communication that occurs between users and location service providers. A practical system that guarantees a user's privacy and integrity of server's response, using a cryptographic scheme with no trusted intermediary, is provided. This scheme also employs the use of symmetric and asymmetric encryption algorithms to ensure secure message and key transfer. In order to overcome the problem of computational complexities with these algorithms, AES-256 is used to encrypt the message and user's location. Several researches have been done in this category but there is still no system that checks the integrity of server's response. The proposed scheme is resistant to a range of susceptible attacks, because it provides a detailed security analysis and, when compared with related work, shows that it can actually guarantee privacy and integrity with faster average response time and higher throughput in LBMS.

Understanding Ridesourcing Mobility and the Future of Electrification: A Comparative Study in Beijing

ABSTRACT The development of mobile Internet, smartphones, and location-based services has enabled ridesourcing, which pools vehicles and drivers to provide on-demand travel services. As an alternative transportation option, ridesourcing has significant impacts on urban travel. However, the unique mobility pattern of ridesourcing and its impact on vehicle electrification have not been well studied. To address this gap, this paper presents a comparative, big-data-driven framework to characterize the ridesourcing mobility pattern, and evaluate the acceptance potential of electric vehicles for ridesourcing in comparison with other types of vehicle use. Multi-temporal resolution ridesourcing trips are extracted from raw GPS trajectories. The patterns of three urban travel (household, ridesourcing, and taxis) are extracted from GPS trajectories in Beijing, and compared. The electrification potentials of these types of travel under different charging levels are then evaluated. The results demonstrate that mobility patterns of household, ridesourcing, and taxi drivers are similar when a single trip is considered but differ significantly when total vehicle travel is considered. We show that potential acceptance of electric vehicles decreases significantly from household to ridesourcing and taxi vehicle use. These findings provide useful insights into of the role vehicle electrification can play in sustainability of urban personal transportation across a range of drivers.

Comprehensive Review: Privacy Protection of User in Location-Aware Services of Mobile Cloud Computing

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.

MoveWithMe: Location Privacy Preservation for Smartphone Users

With the prevalence of smartphones, mobile websites have been more and more popular. However, many mobile websites collect the location information which greatly increases users’ risks of being tracked unexpectedly. The current location access control setting is not sufficient since it cannot prevent the service providers which have been granted location-access permissions from tracking the users. In this paper, we propose a novel location privacy preservation mobile app, called MoveWithMe, which automatically generates decoy queries to hide the real users’ locations and intentions when they are using location-based mobile services. Unlike the existing works on dummy trajectories which may be easily discovered by attackers through data analysis, the uniqueness of the MoveWithMe app is that our generated decoys closely behave like real humans. Each decoy in our system has its own moving patterns, daily schedules, and social behaviors, which ensures its movements to be semantically different from the real user’s trace and satisfying geographic constraints. Thus, our decoys can hardly be distinguished even by advanced data mining techniques. Another advantage of the MoveWithMe app is that it guarantees the same level of user experience without affecting the response time or introducing extra control burdens. Decoys move independently in the back end and automatically submit queries to the same service provider whenever the user does so. Our proposed MoveWithMe app has both iOS and Android versions and has been tested on different brands of smartphones against various location-based services, such as Yelp and TripAdvisor. Experimental results demonstrate its practicality, effectiveness, and efficiency.

The Identification of Variables of Quality Influence Mobile Location-Based Service (m-LBS) (A Case Study: Go-Food Services in Semarang City)

Mobile Location-Based Service (m-LBS) is a rapid growing increasingly business type in many countries including Indonesia. One of the large companies in m-LBS business is Go-Jek, in which one of the most widely used service features is Go-Food. In its development, there are many competitors of Go-Food, so Go-Food has to improve its service quality in order not to lose the customers. Based on the preliminary studies, they show that the customers are less satisfied with the quality of Go-Food services. The purpose of this study is to determine the variables affecting the quality of Go-Food services as a whole. The data collection is obtained through the distribution of online questionnaires with the respondents of Go-Food customers in Semarang City. The data collected in this study is 306 respondents, in which this data is then processed by Partial Least Square (PLS) by using SmartPLS 2.0 software. The result of this study indicates that the predicted variables affect significantly to the quality as a whole that the customers feel toward Go-Food services in Semarang City, which are Information Quality with the influence of 14.04%, Driver Reliability 11.04%, Assurance 11.93%, Localization 10.57%, and Design Quality 10.53%. The better quality of the variables, so the better quality level of customers feeling toward Go-Food services in Semarang City. Therefore, the variables are analyzed to give the recommendations to improve the quality of Go-Food services in Semarang City.

Mobile Geographic Visualization Service for Marine Safety Information based on Geo-Location Based Augmented Reality

Abstract. Marine leisure sports activities such as sea fishing, skin scuba diving and yacht sailing are affected by sea conditions such as waves and tides, and weather conditions, as well as by marine traffic. Timely provision of such high accuracy maritime information is very important because that affects the safety of people engaged in leisure activities. Safety information for marine leisure activities includes obstacle information such as the position of other ships and sea reefs and rocks in the vicinity, weather conditions. If marine leisure activists can identify and use various safety-related information around them, it will be a great help to carry out safe marine leisure activities. The geographic visualization environment provides users with the ability to intuitively and easily convey complex information. Therefore, mobile location-based services based on geographical visualization utilizing complex maritime safety information have the advantage of effectively providing the information necessary for people engaged in safe marine leisure activities.In this study, we developed a location-based mobile application based on geographic visualization that support safe marine leisure activities by integrating ship navigation information, marine chart(map), and ocean weather information. The mobile geographical visualization service has a function to effectively integrate various type of data related to marine safety and transmit the fused information to users in order to support safe leisure activities. First, location of ships around the user's location is transmitted from AIS server and displayed on the map. Since the ship type, speed, and direction information are displayed on the map, the user can check the position of the ships operating in the vicinity. Second, since the ocean weather information such as wave, current, temperature, wind speed is received from the server and displayed on the map, the safety can be grasped by using the ocean condition and weather information in real time. The integrated marine safety information is visualized in mobile devicess in two ways. The first method is in which graphs, icons, and images are displayed on a two-dimensional marine chart(map). The second scheme utilizes a location based Augmented Reality(AR) technique. If the user directs the camera of the mobile device to a certain direction or point, safety information of the point can be expressed on the device. For example, when the user directs the camera of the mobile phone to a ship in the vicinity, information on the speed, moving direction, and type of the ship can be displayed on the mobile phone.The contributions of this study are as follows. First, we proposed a novel method to visualize various location-based maritime safety information on mobile devices. Map information of marine charts, weather information expressed in graphic format, and vessel information represented by icons are effectively fused and visualized on the limited display of mobile devices. Second, information transmission channel is diversified by integrating marine safety information by 2D maps and location based AR. The user can utilize either of the 2D maps or 3D AR by the purpose and the kind of the safety information. To build an environment that can consistently convey the same marine safety information in 2D maps and 3D AR remains as a challenge to extend the application of this study for the future.

Privacy preserving semantic trajectory data publishing for mobile location-based services

The development of wireless technologies and the popularity of mobile devices is responsible for generating large amounts of trajectory data for moving objects. Trajectory datasets have spatiotemporal features and are a rich information source. The mining of trajectory data can reveal interesting patterns of human activities and behaviors. However, trajectory data can also be exploited to disclose users’ privacy information, e.g., the places they live and work, which could be abused by a malicious user. Therefore, it is very important to protect the users’ privacy before publishing any trajectory data. While most previous research on this subject has only considered the privacy protection of stay points, this paper distinguishes itself by modeling and processing semantic trajectories, which not only contain spatiotemporal data but also involve POI information and the users’ motion modes such as walking, running, driving, etc. Accordingly, in this research, semantic trajectory anonymizing based on the k-anonymity model is proposed that can form sensitive areas that contain k − 1 POI points that are similar to the sensitive points. Then, trajectory ambiguity is executed based on the motion modes, road network topologies and road weights in the sensitive area. Finally, a similarity comparison is performed to obtain the recordable and releasable anonymity trajectory sets. Experimental results show that this method performs efficiently and provides high privacy levels.

Using phase shift fingerprints and inertial measurements in support of precise localization in urban areas

Localization is an important primitive that is utilized in a number of important applications such as location-based mobile services, augmented reality, and autonomous mobile robotics. While the GPS technology is considered the de facto standard for outdoor localization, it is known to suffer from significant accuracy limitation in urban areas. In this work, we present a particle filter–based data fusion technique for localization in urban areas. The proposed localization technique provides more accurate location estimation results due to its ability to efficiently fuse together information collected from diverse sensor technologies. The novelty of our proposed approach stems from its ability to fuse data from diverse sources, namely, phase shift fingerprints collected from Low Power AM Radio (LPAM) towers and inertial measurement sensors. Our simulation results indicate that the proposed approach can achieve an accuracy of 0.5 m using a limited number of LPAM towers as low as 5. Also, the proposed approach requires the collection of a low number of LPAM phase shift fingerprints. Our simulations indicate that 30 fingerprints are enough to provide 0.5 m accuracy in a 100 × 100 m2 deployment.

소비자의 자기해석수준, 패션제품유형 및 모바일 위치 기반 광고의 판매촉진유형이 점포방문의도에 미치는 영향 : 슈즈 멀티숍의 비콘 서비스를 중심으로

Mobile location-based services are evaluated as effective sales promotion tools that can influence the purchasing decisions of consumers based on individuality and ubiquity. This study investigates the effects of consumers’ intention to visit a store on their self-construal level, types of fashion products and types of mobile location promotion . The study uses a 2x2x2 factorial design: 2(the self-construal level: independent vs interdependent) x 2(types of fashion product: utilitarian vs hedonic) x 2 (types of mobile location promotion: value-additive vs price discount type). One hundred and sixty-eight women in their twenties and thirties residing in Seoul participated in the study. As part of the study we conduct a frequency analysis, reliability analysis, 3-way ANOVA, and simple interaction analysis using the SPSS 18.0 Statistics package. Results are as follows. It is shown that the type of product and mobile location promotion both have a statistically significant effect on consumers’ intention to visit a store. It is also shown that self-construal level does not have a statistically significant effect on intention to visit a store. The effects of consumers’ self-construal level and the types of products purchased are shown to have an interaction; however there is no interaction between self-construal level and type of sales promotion strategy. Lastly, the study confirms an interaction between the type of products purchased and the type of sales promotion strategy used in influencing intention to visit a store.