Mobile location services over the next generation IP core network

The widespread use of location-based services (LBS), in which any informative service is provided simply based on the user's present location, has generated serious concerns about the user's location privacy. For example, if a customer wants to know “where is the nearest ATM machine?”, she must provide her exact geographical coordinates in order to receive the location-based informative services she requested. Although location-based services open up a wide range of markets and give remarkable convenience to the end user, they also bring minor privacy threats to the user's location data. The requirement that the user informs the LBS provider of their current location in order to get connected services exposes the system to a risk of privacy invasion. Because the volume of data collected from moving or stationary mobile users utilizing LBS may be large, it is critical to design safe frameworks and systems that keep location information private. The two most common techniques to preserving location privacy in LBS are cloaking and obfuscation. These solutions leverage a trusted third party (TTP) and the K-anonymity principle to make the query issuer indistinguishable from other K 1 additional users. The thesis's initial goal is to investigate the user privacy concerns raised by location-based services, and it presents the VIC-PRO scheme, a TTP-based technique to avoid location-based proximity inference of the user who submits a query. The proposed VIC-PRO technique enhances the privacy of query initiating proximity information and helps to close the holes in the present system's K-anonymity approach. In TTP-based methods, all data (namely location coordinates and query) is available at the central server; hence, the central node has complete awareness of the query (including user ID). This is a fundamental flaw in TTP-based design, and it renders these frameworks vulnerable to various privacy assaults. We observe the need for collaboration-based communication between peer users who belong to the population of mobile users in a decentralized or TTP-free architecture and propose CAST (acronym for CAching with truST), a collaborative P2P communication model employing a cascading series of trust between peers, where peers use cached data to collaborate with one another and the results can be obtained. The method operates effectively and gives results locally with little latency when the peers have similar interests (or data value). Under a pull-based sporadic query situation, the suggested algorithm prioritizes user privacy and performs well. We utilize the benefits and mitigate the flaws of both traditional current techniques since measuring and evaluating peer trust have always been a vital aspect. We offer HYB, a hybrid approach, for achieving location privacy for mobile users that regularly utilize location services. The proposed HYB method is based on collaborative location data preprocessing and makes use of homomorphic encryption techniques. Location privacy may be accomplished on two levels: close proximity and far distance. Under a particular, pull-based, occasional query situation, the suggested privacy algorithms successfully safeguard user location privacy. The privacy issue becomes critical when location-tagged data publications, such as public healthcare data and regional criminal history data, are constructed in reverse order by a challenger to locate the authentic consumer against the location specified in the specific record tuple. In most circumstances, address information is regarded as one of the most sensitive aspects of the public record. Any association of such data with a publicly released identifier that has quasi-property has the potential to reveal a great regarding a user (that would normally remain concealed) or, in the worst-case scenario, ruin the user's social reputation. We determine the current situation in geospatial masking, undertake a thorough study of current masking approaches, as well as design three-layer iterative RDV masking, a viable technique that gives location secrecy without rendering publicly available data useless. The recommended approach is excellent for geo-referenced, consistent, granular point published data.

The conventional global positioning system (GPS) can often fail to provide position determination for a mobile user in indoor and urban environments. To cope with GPS failure in such environments, a new navigation system which utilizes a terrestrial digital multimedia broadcasting (T-DMB) signal to obtain the mobile user's position is presented. Since the T-DMB transmitters in Korea construct a single frequency network (SFN), which forces the transmitters to be synchronized, the mobile user can measure a time difference of arrival (TDOA) for all audible T-DMB transmitter pairs. The time difference between T-DMB transmitters is converted to a distance difference by multiplying the time difference by the speed of light. Using these measurements and a TDOA positioning method, the mobile user position can be estimated. An experiment with a T-DMB receiver and a data acquisition (DAQ) board is performed in Seoul to analyze the error characteristic of TDOA measurements. It is certified that the measurement error is bounded under 300 m and can be used to determine the mobile user's position with a small standard deviation.

Understanding the Design of Mobile Social Networking

The combination of wireless mobile service and spatial information service introduces location-based services (LBS). LBS utilizes GIS, spatial positioning and embedded technologies in mobile computing environment to provide mobile terminal users location relevant information. The purpose of this paper is to design a framework supporting advanced LBS over 4G mobile-IP networks, with the transition of existing LBS services to an all mobile-IP based architecture. The architecture, compositions and challenges for LBS in 4G are discussed. The detailed functions of each component are presented. On the base of this, the research points in implementation including positioning methods, new service management model, mobile spatial data compression and transmission, data cache in mobile terminals are also proposed.

The throughput of a cellular network depends on a number of factors such as radio technology, limitations of device hardware (e.g., chipsets, antennae), physical layer effects (interference, fading, etc.), node density and demand, user mobility, and the infrastructure of Mobile Network Operators (MNO). Therefore, understanding and identifying the key factors of cellular network performance that affect end-users experience is a challenging task. We use a dataset collected using netradar, a platform that measures cellular network performance crowd- sourced from mobile user devices. Using this dataset we develop a methodology (a classifier using a machine learning approach) for understanding cellular network performance. We examine key characteristics of cellular networks related to throughput from the perspective of mobile user activity, MNO, smartphone models, link stability, location and time of day. We perform a network-wide correlation and statistical analysis to obtain a basic understanding of the influence of individual factors. We use a machine learning approach to identify the important features and to understand the relationship between different ones. These features are then used to build a model to classify the stability of cellular network based on the data reception characteristics of the user. We show that it is possible to classify reasons for network instability using minimal cellular network metrics with up to 90% of accuracy.

The conference call search problem in wireless networks

Combine federated learning with the mobile network a good opportunity for the introduction of AI technology research and application in the mobile communication network.This article proposes a kind of the mobile network architecture based on FL to promote the integration of FL technologies with mobile communication network and to meet the joint modelling needs of users, operators, and industries. And points out some key technologies of the mobile network to illustrate how the mobile network to support FL working. Meanwhile, proposes some use cases to illustrate what possible application scenarios and values, if the FL technology is introduced into the mobile communication network.

Location-based service (LBS) is a kind of mobile valueadded services in mobile communication systems. The development of mobile positioning techniques promotes application of LBS. This paper presents a type of architecture of LBS. In this architecture, TDOA positioning modules are appended into the mobile basestations in order to implement LBS. LBS gateway and application platform are two vital parts in this architecture. LBS gateway modules are added into MSC of the mobile communication networks. LBS application platform is similar to a mobile location server. TDOA positioning modules deal with rough location testing and simple data computing. LBS gateway is a comprehensive management device which processes location information related to the mobile users. LBS application platform incepts location information processed in LBS gateway, cooperates with the mobile terminal and sends location information to the mobile users by proper data format. LBS application platform is developed based on ArcIMS map server which provides a lot of APIs. The application programming flowchart for LBS is implemented in detail in this paper. The real testing in the mobile communication systems shows that the proposed LBS can provide exact location services for mobile users.

An increasing number of mobile users (MUs) share common interests in general information, such as traffic information, weather forecast, and domestic/international news. However, the centralized infrastructure-based system has not been designed for efficiently disseminating the information of common interest (IoCI) to numerous requesters. Thanks to the rapid development of mobile devices equipped with large storage and multiple communication modes, opportunistic communication between a pair of MUs can be readily realized. With the aid of opportunistic networks formed by MUs, we can improve the connectivity of cellular networks in the rural areas, we can offload the tele-traffic from the overloaded cellular networks, and we can efficiently disseminate the IoCI in the densely populated areas. We commence with a detailed survey on the cross-disciplinary research area of social network analysis aided telecommunication networking. We continue by focusing our attention on the family of integrated cellular and large-scale opportunistic networks, whose performance is dominated by the inter-contact duration as well as the contact duration between any pairs of MUs. A continuous-time-pure-birth Markov chain is invoked for analyzing the relevant performance. We demonstrate that the delivery ratio of the IoCI before it expires becomes higher than 90% with the aid of opportunistic networks consisting of 20 MUs. Moreover, our experiments based on the InfoCom 2006 mobility trace show that the opportunistic networks are capable of offloading 58% of the tele-traffic from the cellular networks. Thereafter, we propose a hybrid information dissemination scheme for the integrated cellular and small-scale opportunistic networks, which is comprised of two main stages: 1) the base station-aided single-hop multicast (BSSHM) stage and 2) the cooperative multicast aided spontaneous dissemination stage. In contrast to their large-scale counterparts, in small-scale opportunistic networks, the information dissemination is mainly affected by the mobility of the MUs, by the wireless channel attenuation and by the resource scheduling, where a discrete-time-pure-birth Markov chain is utilized for characterizing the relevant performance. We demonstrate that our hybrid information dissemination scheme outperforms the traditional BSSHM in terms of various delay metrics, despite consuming less energy.

Mobility management is an essential feature of cellular networks. High accuracy of mobile user positioning is needed to handle mobility efficiently enough and bad cell data can harm this feature significantly. Inaccuracy of cell shapes, lack of cell data measurements, and inaccurate coordination in a geographical area are major shortcomings when it comes to positioning of mobile users in cellular networks. This paper describes a tool that visualizes and analyzes cell data based on XML log files. The tool evaluates a mathematical expression to identify bad cells from the log file and successfully fixes most of the bad cells identified. The tool repairs bad cell shapes in order to achieve better positioning of mobile users.

Emerging social media applications are expected to cause severe congestion to mobile networks, both mobile core network and mobile radio access network. These social media applications are characterized by the fact that they involve sessions with frequently and dynamically updated content, shared with a potential number of mobile users sharing the same location, or being dispersed over a wide area. A method to dynamically identify such applications/sessions and initiate multicast based delivery of the relevant content is proposed. The performance of the proposed method is evaluated through computer simulations, taking Twitter as an example. Encouraging results are obtained.

As use of location-based services (LBSs) is becoming increasingly prevalent, mobile users are more and more enticed to reveal their locations, which may be exploited by attackers to infer the points of interest (POIs) the users visit and then their privacy information. We propose a novel approach to the protection of a user's location privacy based on unobservability, preventing the attackers from relating any particular POI to the user's current location. We design, implement, and evaluate a privacy-protection system, called the Location Information ScrAmbler (LISA) which protects the user's location privacy by adjusting the location noise and hence, the uncertainty of associating his location with any POI, while conserving resources (especially battery energy) on mobile devices. By protecting location privacy locally on each mobile user's device, LISA eliminates the reliance on the trusted third-party servers required by most existing approaches. Therefore, it not only avoids the vulnerability of a single point of failure, but also facilitates the deployment of LBSs. Our evaluation of LISA using real-world users' traces demonstrates its efficacy and efficiency.

Personal mobility (PM) is one of the key issues in realizing a personal communications service (PCS) in emerging third-generation mobile communication networks such as IMT-2000. It can be realized through the universal personal telecommunication (UPT) service. Two PM management schemes are proposed to manage PM information related to UPT users for incoming call (incall) registration/deregistration, incall registration reset by a mobile terminal (MT) owner, and incall delivery to UPT users in mobile communication networks based on the location information managed by the user's home network. The relative cost, i.e., the ratio of cost per unit time for supporting both terminal mobility (TM) and PM management to that of TM management, is derived, and the effect of PM management is analyzed from the aspects of update or query cost, signaling delivery cost, and processing cost. These results can be utilized in the implementation of PM management in IMT-2000.

With 5G networks on the horizon, providing immense radio access rate, mobile core networks will face an extremely heavy load to accommodate the users' requests. Employing Content Delivery Networks inside Mobile Networks (Telco-CDNs) is one of the promising solutions to alleviate the extra load and avoid congestion in the mobile core networks. Our goal is to exploit the users' profiles in cache replacement policies in order to improve their Quality of Experience (QoE). By using the information readily available in Mobile Network Operators (MNOs) such as user locations and their content preference, we propose a novel cache replacement strategy incorporating the users' profile information. We evaluate the proposed method compared to de-facto policies. Furthermore, we demonstrate that in scenarios involving moving users, our approach shows better performance with up to 23% more traffic saving relative to traditional methods. Finally, we investigate our method's sensitivity to profile accuracy and demonstrate its capabilities despite possible errors in profile estimations.

The ubiquity of mobile devices with global positioning functionality (e.g., GPS and Assisted GPS) and Internet connectivity (e.g., 3G and Wi-Fi) has resulted in widespread development of location-based services (LBS). Typical examples of LBS include local business search, e-marketing, social networking, and automotive traffic monitoring. Although LBS provide valuable services for mobile users, revealing their private locations to potentially untrusted LBS service providers pose privacy concerns. In general, there are two types of LBS, namely, snapshot and continuous LBS. For snapshot LBS, a mobile user only needs to report its current location to a service provider once to get its desired information. On the other hand, a mobile user has to report its location to a service provider in a periodic or on-demand manner to obtain its desired continuous LBS. Protecting user location privacy for continuous LBS is more challenging than snapshot LBS because adversaries may use the spatial and temporal correlations in the user's a sequence of location samples to infer the user's location information with a higher degree of certainty. Such user spatial trajectories are also very important for many applications, e.g., business analysis, city planning, and intelligent transportation. However, publishing original spatial trajectories to the public or a third party for data analysis could pose serious privacy concerns. Privacy protection in continuous LBS and trajectory data publication has increasingly drawn attention from the research community and industry. In this chapter, we describe the state-of-the-art privacy-preserving techniques for continuous LBS and trajectory publication.