Location Information Of Mobile Users Research Articles

Proactive UAV Network Slicing for URLLC and Mobile Broadband Service Multiplexing

The unmanned aerial vehicle (UAV) network that is convinced as a significant component of 5G and emerging 6G wireless networks is desired to accommodate multiple types of service requirements simultaneously. However, how to converge different types of services onto a common UAV network without deploying an individual network solution for each type of service is challenging. We tackle this challenge in this paper through slicing the UAV network, i.e., creating logical UAV networks customized for specific requirements. To this end, we formulate the UAV network slicing problem as a sequential decision problem to provide mobile broadband (MBB) services for ground mobile users while satisfying ultra-reliable and low-latency requirements of UAV control and non-payload signal delivery. This problem, however, is difficult to be directly solved mainly due to the sequence-dependent characteristic and the lack of accurate location information of mobile users and accurate and tractable channel gain models in practice. To overcome these difficulties, we propose a novel solution approach based on learning and optimization methods. Particularly, we develop a distributed learning method to predict mobile users’ locations, where partial user location information stored on each UAV is utilized to train user location prediction networks. To achieve accurate channel gain models, we design deep neural networks (DNNs) that are trained by signal measurements at each UAV. To cope with the challenging sequence-dependent characteristic of the problem, we develop a Lyapunov-based optimization framework with provable performance guarantees to decompose the original problem into a sequence of separate optimization subproblems based on the learned results. Finally, an iterative optimization scheme joint with a successive convex approximation technique is exploited to solve these subproblems. Simulation results demonstrate the accuracy of the learning methods as well as the effectiveness of the Lyapunov-based optimization framework.

Location-based clustering and collaborative filtering for mobile learning

This paper presents a new m-learning model described as location-based collaborative M-learning (LCM). This new model exploits location information of mobile users and implements two main operations; k-means for clustering mobile users, and location-based collaborative filtering (CF) to provide learning items recommendations to clustered users. A comprehensive evaluation methodology was utilised to validate the proposed model in terms of complexity, performance and learning items' recommendations quality. Results have confirmed successful implementation of the proposed LCM model during different mobile users' settings. It was shown that LCM structure efficiently reduces processing overload and time required for users' clustering and learning content authoring, and improves learning items recommendations' accuracy.

State of the art on road traffic sensing and learning based on mobile user network log data

Abstract With the improvement of the storage and big data processing technology, mobile operators are able to extract and store a large amount of mobile network generated user behavior data, in order to develop various intelligent applications. One interesting application based on these data is traffic sensing, which uses techniques of learning human mobility patterns from updated location information in network interaction log data. Mobile networks, under which a huge amount of frequently updated location information of mobile users are tracked,can provide complete coverage to estimate traffic condition on roads and highways. This paper studies potential challenges and opportunities in intelligent traffic sensing from the data science point of view with mobile network generated data. Firstly, we classify the data resources available in the commercial radio network according to different taxonomy criteria. Then we outline the broken-down problems that fit in the framework of traffic sensing based on mobile user network log data. We study the existing data processing and learning algorithms on extracting traffic condition information from a large amount of mobile network log data. Finally we make suggestion on potential future work for traffic sensing on data from mobile networks. We believe the techniques and insights provided here will inspire the research community in data science to develop the machine learning models of traffic sensing on the widely collected mobile user behavior data.

Protecting Multi-Lateral Localization Privacy in Pervasive Environments

Location-based services (LBSs) have raised serious privacy concerns in the society, due to the possibility of leaking a mobile user's location information in enabling location-dependent services. While existing location-privacy studies are mainly focused on preventing the leakage of a user's location in accessing the LBS server, the possible privacy leakage in the calculation of the user's location, i.e., the localization, has been largely ignored. Such a privacy leakage stems from the fact that a localization algorithm typically takes the location of anchors (reference points for localization) as input, and generates the target's location as output. As such, the location of anchors and target could be leaked to others. An adversary could further utilize the leakage of anchor's locations to attack the localization infrastructure and undermine the accurate estimation of the target's location. To address this issue, in this paper, we study the multi-lateral privacy-preserving localization problem, whereby the location of a target is calculated without the need of revealing anchors' location, and the knowledge of the localization outcome, i.e., the target's location, is strictly limited to the target itself. To fully protect the user's privacy, our study protects not only the user's exact location information (the geo-coordinates), but also any side information that may lead to a coarse estimate of the location. We formulate the problem as a secure least-squared-error (LSE) estimation for an overdetermined linear system and develop three privacy-preserving solutions by leveraging combinations of information-hiding and homomorphic encryption. These solutions provide different levels of protection for location-side information and resilience to node collusion and have the advantage of being able to trade a user's privacy requirements for better computation and communication efficiency. Through numerical results, we verify the significant efficiency improvement of the proposed schemes over existing multiparty secure LSE algorithms.

Mobile Social Helping Platform of LBS

Mobile Internet grows with demands of location information of mobile users . In order to overcome the limitation of sociality, we develop Mobile social helping platform (MSH) of LBS, which realize the sharing of sociality. First, we analyze the requirement of MSHP platform, then design its architecture and database, and finally discuss the key technologies of MSHP. The platform makes use of open source technology, implements and optimizes LBS service. The simulation results show the system has good portability and maintainability, which is easy to be commercialized.

Mathematical Analysis of Signaling Overhead in MIPv6 Based N-Layer Architecture

IP based wireless mobile networks are of great importance now-a-days to users on the move. To provide connectivity to mobile users with same IP address despite of their change in location or point of attachment, the location information of mobile users must be kept up to date. Managing location information of such users basically needs information to be exchanged between different network entities. The transmission of such messages consumes bandwidth, incurred processing time and transmission cost. This cost is normally referred as signaling overhead caused by mobile users. The signaling overhead is mainly dependent on the frequency of location change caused by the movement of users and number of messages to be exchange to complete the location update process. Hierarchical arrangement of anchor agents can reduce such signaling overhead but degrades scalability in presence of large number of mobile users. The objective of this work is to analyze mathematically the location update frequency and cost in an N layered hierarchical architecture for IPv6 based network in wireless environment. The intention is to find optimal levels of hierarchy in terms of frequency and cost of location update to suggest a scalable IPv6 based mobile architecture. Result obtained from mathematical calculation shows that three levels architecture may provide optimal values for these two performance parameters. Keyword: HMIPv6, Mathematical Modeling, Performance Analysis, Signaling cost.

이동 무선 센서 네트워크에서 상호배제 기반 위치인식 기법

The localization service which provides the location information of mobile user, is one of important service provided by sensor network. Many methods to obtain the location information of mobile user have been proposed. However, these methods were developed for only one mobile user so that it is hard to extend for multiple mobile users. If multiple mobile users start the localization process concurrently, there could be interference of beacon or ultrasound that each mobile user transmits. In the paper, we propose LME, the localization technique for multiple mobile nodes in mobile wireless sensor networks. In LME, collision of localization between sensor nodes is prevented by forcing the mobile node to get the permission of localization from anchor nodes. For this, we use CTS packet type for localization initiation by mobile node and RTS packet type for localization grant by anchor node. NTS packet type is uevento reject localization by anchor node for interference avoidance.nghe experimental result shows that the number of interference between nodes are increased in proportion to the number of mobile nodes and LME provides efficient localization.

모바일 무선 센서 네트워크에서 적응적 파워 조절 기반 효율적인 위치인식 기법

이동 사용자의 위치 정보를 제공하는 위치인식서비스는 센서네트워크가 제공하는 대표적인 서비스이다. 그동안 이동 사용자의 위치정보를 획득하기 위한 다양한 기법들이 제시되어 왔다. 하지만 대부분의 기법들은 단일 사용자인 경우만 고려하여 연구되어 이를 다중 사용자인 경우로 확장하기에는 문제가 있다. 여러 이동 사용자들이 한 지역에서 동시에 위치인식 작업을 수행하는 경우에 센서노드들이 발생시키는 라디오 주파수나 초음파 등이 서로 간에 간섭을 발생시킬 수 있다. 본 논문에서는 여러 이동 사용자들이 동시 다발적으로 위치확인 작업을 수행하고자 하는 경우에 발생 가능한 간섭을 최소화 하는 적응적 파워 조절에 기반한 위치인식 기법인 APL(Adaptive Power Control based Resource Allocation Technique for Efficient Localization Technique)기법을 제안한다. APL기법은 센서노드가 위치인식 작업을 수행하기 전에 주위에 있는 앵커노드를 선점하여 점유함으로써 노드간의 간섭을 방지한다. 이를 위해 IEEE 802.11에 정의된 RTS(Ready To Send)패킷을 자원 선점을 요청하는데 사용하며 CTS(Clear To Send)패킷을 자원 선점을 확인하는데 사용한다. 반대로 이미 앵커노드가 다른 노드에 의해 선점하여 위치인식 작업을 방해할 가능성이 있는 경우 해당 앵커노드들을 임계영역으로 정의한다. 임계영역 처리를 위한 NTS(Not To Send) 패킷을 새로이 정의하여 노드 간에 간섭이 발생하지 않도록 한다. 추가적으로 거리측정 작업시 앵커노드간 동기화를 위한 STS(Start to Send)패킷을 새로이 정의한다. 최종적으로 센서노드의 전송 파워를 적응적으로 조절하여 작업이 영향을 미치는 영역을 최소화하도록 한다. 실험을 통하여 이동 사용자가 다수일 경우 노드 간에 간섭이 많이 발생함을 보이며 제안하는 APL기법이 위치인식 작업시 간섭을 방지함을 보인다. Given the increased interest in ubiquitous computing, wireless sensor network has been researched widely. The localization service which provides the location information of mobile user, is one of important service provided by sensor network. Many methods to obtain the location information of mobile user have been proposed. However, these methods were developed for only one mobile user so that it is hard to extend for multiple mobile users. If multiple mobile users start the localization process concurrently, there could be interference of beacon or ultrasound that each mobile user transmits. In the paper, we propose APL(Adaptive Power Control based Resource Allocation Technique for Efficient Localization Technique), the localization technique for multiple mobile nodes based on adaptive power control in mobile wireless sensor networks. In APL, collision of localization between sensor nodes is prevented by forcing the mobile node to get the permission of localization from anchor nodes. For this, we use RTS(Ready To Send) packet type for localization initiation by mobile node and CTS(Clear To Send) packet type for localization grant by anchor node. NTS(Not To Send) packet type is used to reject localization by anchor node for interference avoidance and STS(Start To Send) for synchronization between 모anchor nodes. At last, the power level of sensor node is controled adaptively to minimize the affected area. The experimental result shows that the number of interference between nodes are increased in proportion to the number of mobile nodes and APL provides efficient localization.

Mining periodic patterns in spatio-temporal sequences at different time granularities1

With the advancement of technology, it is now easy to collect the location information of mobile users over time. Spatio-temporal data mining techniques were proposed in the literature for the extraction of patterns from spatio-temporal data. However, current techniques can only extract patterns of the finest time granularity, and therefore overlooks potential patterns available at coarser time granularities. In this work, we propose two techniques to allow mining at different time granularities. Experimental results show that the proposed techniques are indeed effective and efficient for mining periodic spatio-temporal patterns at different time granularities.

모바일 센서네트워크에서 다중 이동 노드를 위한 간섭회피 기반 위치인식

Given the increased interest in ubiquitous computing, wireless sensor network has been researched widely. The localization service which provides the location information of mobile user, is one of important service provided by sensor network. Many methods to obtain the location information of mobile user have been proposed. However, these methods were developed for only one mobile user so that it is hard to extend for multiple mobile users. If multiple mobile users start the localization process concurrently, there could be interference of beacon or ultrasound that each mobile user transmits. In the paper, we propose IAL, the localization technique with interference avoidance for multiple mobile nodes in mobile wireless sensor networks. In IAL, interference is prevented by forcing the mobile node to get the permission of localization from anchor nodes. For this, we define LIP packet type for localization initiation by mobile node and LGP packet type for localization grant by anchor node. LRP packet type is used to reject localization by anchor node for interference avoidance. The experimental result shows that the number of interference between nodes are increased in proportion to the number of mobile nodes and IAL provides efficient localization.

A System for Locating Mobile Terminals with Tunable Privacy

A number of approaches for capturing and processing location information of mobile users have been proposed in the past; however, only with the latest advances in the handset technology, a terminal-based positioning approach, using overlay SIP signaling on top of a packet switched bearer and area notification as basic functionality becomes feasible for mass applications. Especially in electronic commerce scenarios, in which users often interact with non-trusted services and shops, any location-based solution has to consider privacy aspects as well. The terminal-centric model presented in the paper leads to a simple and efficient way to achieve tunable privacy: mobile users define own "zones" and selectively disclose them to their buddies and to external services. As a result, localization can be performed only in the allowed places and by the allowed watchers, both parameters being configured by the user herself on her mobile terminal. We describe the system architecture, protocols and present representative technical scenarios.