User Mobility Profiles Research Articles

A Lightweight Approach for Building User Mobility Profiles

Data captured by mobile devices enable us, among other things, learn the places where users go, identify their home and workplace, the places they usually visit (e.g., supermarket, gym, etc.), the different paths they take to move from one place to another and even their routines. In summary, with this information, it is possible to learn a user mobility profile. In this work, we propose a lightweight approach for building mobility profiles from data collected with mobile devices. The mobility profiles of a user consist of the places visited, the visit history and the travel paths. Our approach aims to solve some of the challenges and limitations identified in the literature. Particularly, it considers geographic information to identify certain kinds of places, such as open spaces, big places and small places, that are hard to distinguish with existing approaches. We use different sensors and time frequencies to collect data in order to optimize battery consumption and maximize precision. Finally, it executes entirely on the mobile devices, avoiding the exposure of sensitive user information and then preserving user privacy. The proposal was evaluated in the context of the real usage of the developed prototype applications in two cities of Argentina. The results obtained with our approach outperformed other approaches in the literature, both in precision and recall.

Reinforced Imitative Graph Learning for Mobile User Profiling

Mobile user profiling refers to the efforts of extracting users’ characteristics from mobile activities. In order to capture the dynamic varying of user characteristics for generating effective user profiling, we propose an imitation-based mobile user profiling framework. Considering the objective of teaching an autonomous agent to imitate user mobility based on the user’s profile, the user profile is the most accurate when the agent can perfectly mimic the user behavior patterns. The profiling framework is formulated into a reinforcement learning task, where an agent is a next-visit planner, an action is a POI that a user will visit next, and the state of the environment is a fused representation of a user and spatial entities. An event in which a user visits a POI will construct a new state, which helps the agent predict users’ mobility more accurately. In the framework, we introduce a spatial Knowledge Graph (KG) to characterize the semantics of user visits over connected spatial entities. Additionally, we develop a mutual-updating strategy to quantify the state that evolves over time. Along these lines, we develop a reinforcement imitative graph learning framework for mobile user profiling. Finally, we conduct extensive experiments to demonstrate the superiority of our approach.

Urban Knowledge Graph Aided Mobile User Profiling

Nowadays, the explosive growth of personalized web applications and the rapid development of artificial intelligence technology have flourished the recent research on mobile user profiling, i.e., inferring the user profile from mobile behavioral data. Particularly, existing studies mainly follow the data-driven paradigm to develop feature engineering and representation learning on such data, which however suffer from the robustness issue, i.e., generalizing poorly across datasets and profiles without considering semantic knowledge therein. In comparison, the rising knowledge-driven paradigm built upon the knowledge graph (KG) offers a potential solution to mitigate such weakness. Therefore, in this paper, we propose a Knowledge Graph aided framework for Mobile User Profiling (KG-MUP). Specifically, to distil semantic knowledge among data, we firstly construct an urban knowledge graph (UrbanKG) with domain entities like users, regions, point of interests (POIs), etc. identified, as well as semantic relations for home, workplace, spatiality, etc. extracted. Moreover, we leverage tensor decomposition and graph neural network to obtain knowledgeable user representations from UrbanKG. In addition, we introduce several customized features to quantify individual mobility characteristics for mobile user profiling. Extensive experiments on three real-world mobility datasets demonstrate that KG-MUP achieves state-of-the-art performance on user profile inference tasks. Moreover, further results also reveal the importance of various semantic knowledge to user profile inference, which provides meaningful insights on user modeling with mobile behavioral data.

Characterization of the Mobile User Profile Based on Sentiments and Network Usage Attributes

Providing resources to meet user needs in futuristic mobile networks is still challenging since the network resources like spectrum and base stations do not increase in the same proportion as the accelerated growth of network traffic. Because of this, human/user behavior attributes can assist resource management in dealing with these challenges, which pick up aspects of how the user impacts the usage of mobile networks, such as network usage, the content of interest, urban mobility routines, social networks, and sentiment. A user profile is a combination of user/human behavior attributes. Such profiles are expected to be a knowledge for softwarization enablers to improve the management of future wireless networks fully. Nevertheless, the correlation between human sentiment and wireless and mobile network usage has not been deeply investigated in the literature about the mobile user profile. This work aims to define the user profile using a transfer learning approach for the sentiment classification of WhatsApp messages. A real-life experiment was conducted to collect users' attributes, namely the WhatsApp messages and network usage. A new data analysis methodology is proposed that consists of a frequent item-set pattern mining (FP-Growth) based on Association Rules, the Chi-squared statistical test, and descriptive statistics. This methodology assesses the correlation between sentiment and network usage in a profound way. Results show that the users participating in the experiment form three groups. The first group, with 55.6% of the users, contains users who present a strong relation between negative sentiment and low network usage and also a strong relation between positive sentiment and high network usage. The second group contains 25.9% of the users and is composed of userswho present a strong relation between positive sentiment and high network usage. The third group contains 18.5% of the users for whom the correlation between sentiment and network usage is still statistical significant, but the strength of this relation is much more weak then in the other two groups. Thus, 81.5% of the users (the first two groups) present a strong relation between user sentiment captured from WhatsApp messages and the network traffic generated by them.

Privacy-preserving targeted mobile advertising: A Blockchain-based framework for mobile ads

The targeted advertising is based on preference profiles inferred via relationships among individuals, their monitored responses to previous advertising and temporal activity over the Internet, which has raised critical privacy concerns. In this paper, we present a novel proposal for a Blockchain-based advertising platform that provides: a system for privacy preserving user profiling, privately requesting ads from the advertising system, the billing mechanisms for presented and clicked ads, the advertising system that uploads ads to the cloud according to profiling interests, various types of transactions to enable advertising operations in Blockchain-based network, and the method that allows a cloud system to privately compute the access policies for various resources (such as ads, mobile user profiles). Our main goal is to design a decentralised framework for targeted ads, which enables private delivery of ads to users whose behavioural profiles accurately match the presented ads, defined by the ad system. We implement a POC of our proposed framework i.e. a Bespoke Miner and experimentally evaluate various components of Blockchain-based in-app advertising system, implementing various critical components; such as, evaluating user profiles, implementing access policies, encryption and decryption of users’ profiles. We observe that the processing delay for traversing policies of various tree sizes, the encryption/decryption time of user profiling with various key-sizes and user profiles of various interests evaluates to an acceptable amount of processing time as that of the currently implemented ad systems.

Co-learning Graph Convolution Network for Mobile User Profiling

Co-learning Graph Convolution Network for Mobile User Profiling

A survey on analyzing encrypted network traffic of mobile devices

Over the years, use of smartphones has come to dominate several areas, improving our lives, offering us convenience, and reshaping our daily work circumstances. Beyond traditional use for communication, they are used for many peripheral tasks such as gaming, browsing, and shopping. A significant amount of traffic over the Internet belongs to the applications running over mobile devices. Applications encrypt their communication to ensure the privacy and security of the user’s data. However, it has been found that the amount and nature of incoming and outgoing traffic to a mobile device could reveal a significant amount of information that can be used to identify the activities performed and to profile the user. To that end, researchers are trying to develop techniques to classify encrypted mobile traffic at different levels of granularity, with the objectives of performing mobile user profiling, network performance optimization, etc. This paper proposes a framework to categorize the research works on analyzing encrypted network traffic related to mobile devices. After that, we provide an extensive review of the state of the art based on the proposed framework.

Joint Optimization of Privacy and Cost of in-App Mobile User Profiling and Targeted Ads

Online mobile advertising ecosystems provide advertising and analytics services that collect, aggregate, process, and trade a rich amount of consumers’ personal data and carry out interest-based ad targeting, which raised serious privacy risks and growing trends of users feeling uncomfortable while using the internet services. In this paper, we address users’ privacy concerns by developing an optimal dynamic optimisation cost-effective framework for preserving user privacy for profiling, ads-based inferencing, temporal apps usage behavioral patterns, and interest-based ad targeting. A major challenge in solving this dynamic model is the lack of knowledge of time-varying updates during the profiling process. We formulate a mixed-integer optimisation problem and develop an equivalent problem to show that the proposed algorithm does not require knowledge of time-varying updates in user behavior. Following, we develop an online control algorithm to solve the equivalent problem and overcome the difficulty of solving nonlinear programming by decomposing it into various cases and to achieve a trade-off between user privacy, cost, and targeted ads. We carry out extensive experimentations and demonstrate the proposed framework’s applicability by implementing its critical components using POC (Proof Of Concept) ‘System App’. We compare the proposed framework with other privacy-protecting approaches and investigate whether it achieves better privacy and functionality for various performance parameters.

CITIZEN PARTICIPATION & DIGITAL TOOLS TO IMPROVE PEDESTRIAN MOBILITY IN CITIES

Abstract. In this work, we present a framework supported by mobile and web apps and able to propose personalized pedestrian routes that match user mobility profile considering mobility impediments factors. We explain how these later have been defined using a pedestrian-centric approach based on travel experiences as perceived in the field by senior citizens. Through workshops, six main factors that may influence pedestrian route choices were revealed: passability, obstacle in path, surface problem, security, sidewalk width, slope. These categories were used to build digital tools and guide a citizen participatory approach to collect geolocated points of obstacle documented with walkability information (picture, category, impact score, free comment). We also involved citizens to evaluate these information and especially senior referents for validation. Finally we present how we connect these points of obstacle with a pedestrian network based on OpenStreetMap to configure a routing cost function. The framework has been partially deployed in 2020 with limited people due to the pandemic. Nonetheless, we share lessons learned from interaction with citizens in the design of such a framework whose underlying workflow is reproducible. We plan to further assess its relevance and sustainability in the future.

Performance of Uplink NOMA with User Mobility Under Short Packet Transmission

The scenario of an uplink two-user non-orthogonal multiple access (NOMA) communication system is analytically studied when it operates in the short packet transmission regime. The considered users support mobility and each is equipped with a single antenna, while they directly communicate with a multi-antenna base station. Power-domain NOMA is adopted for the signal transmission as well as the successive interference cancellation approach is performed at the receiver for decoding. The packet error rate (PER) is obtained in simple closed formulae under independent Rayleigh faded channels and for arbitrary user mobility profiles. The practical time variation and correlation of the channels is also considered. Moreover, useful engineering insights are manifested in short transmission time intervals, which define a suitable setup for the forthcoming ultra-reliable and low latency communication systems. Finally, it turns out that the optimal NOMA power allocation can be computed in a straightforward cost-effective basis, capitalizing on the derived PER expressions.

Joint beamforming and user association with reduced CSI signaling in mobile environments: A Deep Q-learning approach

Heterogeneous Cloud Radio Access Network (H-CRAN) is considered as a cost-efficient network solution to meet 5G data traffic requirements. In this paper, we consider the problem of beamforming and user clustering (user-to-Remote Radio Head (RRH) association) in the downlink of a H-CRAN where users have heterogeneous mobility profiles. Given the rapidly time-varying nature of the mobile wireless environment, it is challenging to offer an optimal beamforming and user association performance during a long-term allocation process without incurring large Channel State Information (CSI) and signaling overheads. For that purpose, we proposed in Ha et al. (2019) an Adaptive Beamforming and User Clustering (ABUC) algorithm which resolves the joint beamforming and user clustering problem when considering CSI cost and imperfectness under user mobility assumptions. In this paper, we design a deep reinforcement-learning framework which enables the proposed ABUC algorithm to select on-the-fly its best feedback scheduling parameters, namely the period and type of CSI feedback, given each user mobility profile. The proposed ABUC-DQL approach can overcome the scalability limitation of the Q-learning approach (Ha et al., 2019) and better handle the problem when formulated using a POMDP (Partially Observable Markov Decision Process) model. The simulation results show that the convergence time is mainly impacted by the number of users in the network, and the online-learning ability of the framework can quickly adapt to the changes of users mobility.

Reinforced Imitative Graph Representation Learning for Mobile User Profiling: An Adversarial Training Perspective

In this paper, we study the problem of mobile user profiling, which is a critical component for quantifying users' characteristics in the human mobility modeling pipeline. Human mobility is a sequential decision-making process dependent on the users' dynamic interests. With accurate user profiles, the predictive model can perfectly reproduce users' mobility trajectories. In the reverse direction, once the predictive model can imitate users' mobility patterns, the learned user profiles are also optimal. Such intuition motivates us to propose an imitation-based mobile user profiling framework by exploiting reinforcement learning, in which the agent is trained to precisely imitate users' mobility patterns for optimal user profiles. Specifically, the proposed framework includes two modules: (1) representation module, that produces state combining user profiles and spatio-temporal context in real-time; (2) imitation module, where Deep Q-network (DQN) imitates the user behavior (action) based on the state that is produced by the representation module. However, there are two challenges in running the framework effectively. First, epsilon-greedy strategy in DQN makes use of the exploration-exploitation trade-off by randomly pick actions with the epsilon probability. Such randomness feeds back to the representation module, causing the learned user profiles unstable. To solve the problem, we propose an adversarial training strategy to guarantee the robustness of the representation module. Second, the representation module updates users' profiles in an incremental manner, requiring integrating the temporal effects of user profiles. Inspired by Long-short Term Memory (LSTM), we introduce a gated mechanism to incorporate new and old user characteristics into the user profile. In the experiment, we evaluate our proposed framework on real-world datasets. The extensive experimental results validate the superiority of our method comparing to baseline algorithms.

Mobile agent and ontology approach for web service discovery using QoS

Web services are meaningful only if potential users may find and execute them. Universal Description Discovery and Integration (UDDI) help businesses, organisations, and other web services providers to discover and reach to the service(s) by providing the URI of the WSDL file. However, it does not offer a mechanism to choose a web service based on its quality. The standard also lacks sufficient semantic description in the content of web services, this lack makes it difficult to find and compose suitable web services during analysis, search, and matching processes. In addition, a central UDDI suffers from one centralised point problem and the high cost of maintenance. To get around these problems, the authors propose in this paper a novel framework based on mobile agent, metadata catalogue and user profile for web services discovery in order to reduce the search space and increase the number of relevant services.

Protecting Private Attributes in App Based Mobile User Profiling

The Analytics companies enable successful targeted advertising via user profiles, derived from the mobile apps installed by specific users, and hence have become an integral part of the mobile advertising industry. This threatens the users' privacy, when profiling is based on apps representing sensitive information, e.g., gambling problems indicated by a game app. In this work, we propose an app-based profile obfuscation mechanism, ProfileGuard, with the objective of eliminating the dominance of private interest categories (i.e. the prevailing private interest categories present in a user profile). We demonstrate, based on wide-range experimental evaluation of Android apps in a nine month test campaign, that the proposed obfuscation mechanism based on similarity with user's existing apps (ensuring that selected obfuscating apps belong to non-private categories) can achieve a good trade-off between efforts required by the obfuscating system and the resulting privacy protection. We also show how the bespoke (customised to profile obfuscation) and bespoke++ (resource-aware) strategies can deliver significant improvements in the level of obfuscation and (particularly bespoke++) in the use of mobile resources, making the latter a good candidate strategy in resource-constrained scenarios e.g., for fixed data use mobile plans. We also implement a POC ProfileGuard app to demonstrate the feasibility of an automated obfuscation mechanism. Furthermore, we provide insights to Google AdMob profiling rules, such as showing how individual apps map to user's interests within their profile in a deterministic way and that AdMob requires a certain level of activity to build a stable user profile.

PRIMULE: Privacy risk mitigation for user profiles

The availability of mobile phone data has encouraged the development of different data-driven tools, supporting social science studies and providing new data sources to the standard official statistics. However, this particular kind of data are subject to privacy concerns because they can enable the inference of personal and private information. In this paper, we address the privacy issues related to the sharing of user profiles, derived from mobile phone data, by proposing PRIMULE, a privacy risk mitigation strategy. Such a method relies on PRUDEnce (Pratesi et al., 2018), a privacy risk assessment framework that provides a methodology for systematically identifying risky-users in a set of data. An extensive experimentation on real-world data shows the effectiveness of PRIMULE strategy in terms of both quality of mobile user profiles and utility of these profiles for analytical services such as the Sociometer (Furletti et al., 2013), a data mining tool for city users classification.

Modeling and implement of mobile phone user location discrimination based on heuristic strategy

With the all-pervading mobile devices and continuing advancement of big data technologies, mobile phone data research has been gaining widespread popularity in the past few years. Dealing with the implausible location caused by cell handover phenomenon in the communication system is one key problem of user mobility profile building based on mobile phone call detail records (CDRs) data. In this paper, we propose a location discrimination model aiming at CDRs data, where heuristic strategies for the characteristic of the oscillation phenomenon from practical CDRs and handover categories are added to distinguish the stay points, passing points, and oscillating points. A whole month of CDRs data from one communication operator is employed to select parameters and validate the model on the Spark platform. The experiment results betray that the proposed model can identify the false locations effectively. Compared with the threshold models, the result of the proposed model is more reasonable both in the population aggregate level and individual level. Besides, the model can retain more user’s trajectory points than clustering algorithm, so it can improve the quality of user mobility modeling.

Adaptive beamforming and user association in heterogeneous cloud radio access networks: A mobility-aware performance-cost trade-off

Heterogeneous Cloud Radio Access Network (H-CRAN) is a promising network architecture for the future 5G mobile communication system to address the increasing demand for mobile data traffic. In this work, we consider the design of efficient joint beamforming and user clustering (user-to-Remote Radio Head (RRH) association) in the downlink of a H-CRAN where users have different mobility profiles. Given the rapidly time-varying nature of such wireless environment, it becomes very challenging to enable optimized beamforming and user clustering without incurring large Channel State Information (CSI) and signaling overheads. The main objective of this work is to investigate and evaluate the trade-off between system throughput and the incurred costs in terms of complexity and signaling overhead, including the impact of different CSI feedback strategies given different user mobility profiles. We propose the Adaptive Beamforming and User Clustering (ABUC) algorithm which adapts its feedback parameters, namely the period of dynamic user clustering and the type of CSI feedback, in function of user mobility. Furthermore, we design a reinforcement-learning framework which enables the proposed ABUC algorithm to optimize its scheduling parameters on-the-fly, given each user mobility profile. Based on computer simulations, an analysis of the effect of mobility on system performance metrics is presented and conclusions are drawn regarding the algorithm’s adequate parameter tuning for different mobility scenarios.11This is an extended version of the conference paper presented at IEEE PIMRC 2017 [1].

Improved Trade Framework for Business

In the information modern era because of the high usage of internet on social media and mobile phones, the enormous amounts of data have become available on hand to decision makers. Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. The huge growth of the mobilemarketing and advertizing plays very important role because of the rapid improvements of mobile technologies & huge growth in mobile device adoption. This created the new opportunities in mobile marketing and adverting which include real-time customerengagement, improve customer experience, send notification about the product, Build Brand Royalty, increase revenues, and drive customer satisfaction.The challenges for the marketers and advertisers include how to analyze the large amount of data that mobile devices emit and how to derive customer engagement insights from the mobile data. The project “Improved Trade Framework for Business” addresses the challenge by developing mobile marketing analytics and advertising recommendation framework on Big data. This framework supports the advertising operations in which the data analytics techniques are used to provide advertisement of the vendor products based on collecting Data of users' interest trough mobile user profiles, access behaviors, and the users geographical location.The project “Improved Trade Framework for Business” presents prototyping solution design as well as its application and certain experimental results.

A semantic web services discovery approach based on a mobile agent using metadata

PurposeThe success of web services involved the adoption of this technology by different service providers through the web, which increased the number of web services, as a result making their discovery a tedious task. The UDDI standard has been proposed for web service publication and discovery. However, it lacks sufficient semantic description in the content of web services, which makes it difficult to find and compose suitable web services during the analysis, search, and matching processes. In addition, few works on semantic web services discovery take into account the user’s profile. The purpose of this paper is to optimize the web services discovery by reducing the search space and increasing the number of relevant services.Design/methodology/approachThe authors propose a new approach for the semantic web services discovery based on the mobile agent, user profile and metadata catalog. In the approach, each user can be described by a profile which is represented in two dimensions: personal dimension and preferences dimension. The description of web service is based on two levels: metadata catalog and WSDL.FindingsFirst, the semantic web services discovery reduces the number of relevant services through the application of matching algorithm “semantic match”. The result of this first matching restricts the search space at the level of UDDI registry, which allows the users to have good results for the “functional match”. Second, the use of mobile agents as a communication entity reduces the traffic on the network and the quantity of exchanged information. Finally, the integration of user profile in the service discovery process facilitates the expression of the user needs and makes intelligible the selected service.Originality/valueTo the best knowledge of the authors, this is the first attempt at implementing the mobile agent technology with the semantic web service technology.

Velocity-Aware Handover Management in Two-Tier Cellular Networks

While network densification is considered an important solution to cater the ever-increasing capacity demand, its effect on the handover (HO) rate is overlooked. In dense 5G networks, HO delays may neutralize or even negate the gains offered by network densification. Hence, user mobility imposes a nontrivial challenge to harvest capacity gains via network densification. In this paper, we propose a velocity-aware HO management scheme for two-tier downlink cellular network to mitigate the HO effect on the foreseen densification throughput gains. The proposed HO scheme sacrifices the best base station (BS) connectivity, by skipping HO to some BSs along the user trajectory, to maintain longer connection durations and reduce HO rates. Furthermore, the proposed scheme enables cooperative BS service and strongest interference cancellation to compensate for skipping the best connectivity. To this end, we consider different HO skipping scenarios and develop a velocity-aware mathematical model, via stochastic geometry, to quantify the performance of the proposed HO schemes in terms of the coverage probability and user throughput. The results highlight the HO rate problem in dense cellular environments and show the importance of the proposed HO schemes. Finally, the value of BS cooperation along with handover skipping is quantified for different user mobility profiles.