Bicycles Mobility Prediction

The growth in mobile wireless communication requires sharp solutions in handling mobility problems that encompass poor handover management, interference in access points, excessive load in macrocells, and other relevant mobility issues. With the deployment of small cell networks in 5G mobile systems the problems mentioned intensify thus, mobility prediction schemes arise to surpass and mitigate these issues. Predicting mobility is not a trivial task due to the vastness of different variables that characterize a mobility route translating into unpredictability and randomness. Therefore, the task of this work is to overcome these challenges by building a solid mobility prediction architecture that can analyze big data and find patterns in the mobility aspect to ultimately perform reliable predictions. The models introduced in this dissertation are two deep learning schemes based on an Artificial Neural Network (ANN) architecture and a LSTM Long-Short Term Memory (LSTM) architecture. The prediction was made in two levels: Short-term prediction and Long-term prediction. We verified that in the short-term domain both models performed equivalently with successful results. However, in long-term prediction, the LSTM model surpassed the ANN model. Consequently, the LSTM approach constitutes the stronger model in all prediction aspects. Implementing this model in cellular networks is an important asset in optimizing processes such as routing and caching as the cellular networks can allocate the necessary resources to provide a better user experience. With this optimization impact and with the emergence of the Internet of Things (IoT), the prediction model can support and improve the development of smart applications related to our daily mobility routine.O crescimento da comunicação móvel sem fios exige soluções precisas para lidar com problemas de mobilidade que englobam uma gestão pobre de handover, interferência em pontos de acesso, carga excessiva em macrocélulas e outros problemas relevantes ao aspeto da mobilidade. Com a implantação de redes de pequenas células no sistema móvel 5G, os problemas mencionados intensificam-se. Desta forma, são necessários esquemas de previsão de mobilidade para superar e mitigar esses problemas. Prever a mobilidade não é uma tarefa trivial devido à imensidão de diferentes variáveis que caracterizam uma rota de mobilidade, traduzindo-se em grandes dimensões de imprevisibilidade e aleatoriedade. Portanto, a tarefa deste trabalho é superar esses desafios construindo uma arquitetura sólida de estimação de mobilidade, que possa analisar um grande fluxo de dados e encontrar padrões para, em última análise, realizar previsões credíveis e assertivas. Os modelos apresentados nesta dissertação são dois esquemas de deep learning baseados em uma arquitetura de RNA (Rede Neuronal) e uma arquitetura LSTM (Long-Short Term Memory). A previsão foi feita em dois níveis: previsão de curto prazo e previsão de longo prazo. Verificámos que no curto prazo ambos os modelos tiveram um desempenho equivalente com resultados bem sucedidos. No entanto, na previsão de longo prazo, o modelo LSTM superou o modelo ANN. Consequentemente, a abordagem LSTM constitui o modelo mais forte em todos os aspectos de previsão. A implementação deste modelo, em redes celulares, é uma medida importante na otimização de processos como, routing ou caching, proporcionando uma melhor experiência wireless ao utilizador. Com este impacto de otimização e com o surgimento da Internet of Things (IoT), o modelo de previsão pode apoiar e melhorar o desenvolvimento de aplicações inteligentes relacionadas com a nossa rotina diária de mobilidade

Predicting the temporal activity patterns of new venues.

Estimating revenue and business demand of a newly opened venue is paramount as these early stages often involve critical decisions such as first rounds of staffing and resource allocation. Traditionally, this estimation has been performed through coarse-grained measures such as observing numbers in local venues or venues at similar places (e.g., coffee shops around another station in the same city). The advent of crowdsourced data from devices and services carried by individuals on a daily basis has opened up the possibility of performing better predictions of temporal visitation patterns for locations and venues. In this paper, using mobility data from Foursquare, a location-centric platform, we treat venue categories as proxies for urban activities and analyze how they become popular over time. The main contribution of this work is a prediction framework able to use characteristic temporal signatures of places together with k-nearest neighbor metrics capturing similarities among urban regions, to forecast weekly popularity dynamics of a new venue establishment in a city neighborhood. We further show how we are able to forecast the popularity of the new venue after one month following its opening by using locality and temporal similarity as features. For the evaluation of our approach we focus on London. We show that temporally similar areas of the city can be successfully used as inputs of predictions of the visit patterns of new venues, with an improvement of 41% compared to a random selection of wards as a training set for the prediction task. We apply these concepts of temporally similar areas and locality to the real-time predictions related to new venues and show that these features can effectively be used to predict the future trends of a venue. Our findings have the potential to impact the design of location-based technologies and decisions made by new business owners

Smartphone App Usage Analysis : Datasets, Methods, and Applications

As smartphones have become indispensable personal devices, the number of smartphone users has increased dramatically over the last decade. These personal devices, which are supported by a variety of smartphone apps, allow people to access Internet services in a convenient and ubiquitous manner. App developers and service providers can collect fine-grained app usage traces, revealing connections between users, apps, and smartphones. We present a comprehensive review of the most recent research on smartphone app usage analysis in this survey. Our survey summarizes advanced technologies and key patterns in smartphone app usage behaviors, all of which have significant implications for all relevant stakeholders, including academia and industry. We begin by describing four data collection methods: surveys, monitoring apps, network operators, and app stores, as well as nine publicly available app usage datasets. We then systematically summarize the related studies of app usage analysis in three domains: app domain, user domain, and smartphone domain. We make a detailed taxonomy of the problem studied, the datasets used, the methods used, and the significant results obtained in each domain. Finally, we discuss future directions in this exciting field by highlighting research challenges.Peer reviewe

Predicting the temporal activity patterns of new venues

Estimating revenue and business demand of a newly opened venue is paramount as these early stages often involve critical decisions such as first rounds of staffing and resource allocation. Traditionally, this estimation has been performed through coarse-grained measures such as observing numbers in local venues or venues at similar places (e.g., coffee shops around another station in the same city). The advent of crowdsourced data from devices and services carried by individuals on a daily basis has opened up the possibility of performing better predictions of temporal visitation patterns for locations and venues. In this paper, using mobility data from Foursquare, a location-centric platform, we treat venue categories as proxies for urban activities and analyze how they become popular over time. The main contribution of this work is a prediction framework able to use characteristic temporal signatures of places together with k-nearest neighbor metrics capturing similarities among urban regions, to forecast weekly popularity dynamics of a new venue establishment in a city neighborhood. We further show how we are able to forecast the popularity of the new venue after one month following its opening by using locality and temporal similarity as features. For the evaluation of our approach we focus on London. We show that temporally similar areas of the city can be successfully used as inputs of predictions of the visit patterns of new venues, with an improvement of 41% compared to a random selection of wards as a training set for the prediction task. We apply these concepts of temporally similar areas and locality to the real-time predictions related to new venues and show that these features can effectively be used to predict the future trends of a venue. Our findings have the potential to impact the design of location-based technologies and decisions made by new business owners

D4.3 Final Report on Network-Level Solutions

Research activities in METIS reported in this document focus on proposing solutions to the network-level challenges of future wireless communication networks. Thereby, a large variety of scenarios is considered and a set of technical concepts is proposed to serve the needs envisioned for the 2020 and beyond. This document provides the final findings on several network-level aspects and groups of solutions that are considered essential for designing future 5G solutions. Specifically, it elaborates on: -Interference management and resource allocation schemes -Mobility management and robustness enhancements -Context aware approaches -D2D and V2X mechanisms -Technology components focused on clustering -Dynamic reconfiguration enablers These novel network-level technology concepts are evaluated against requirements defined by METIS for future 5G systems. Moreover, functional enablers which can support the solutions mentioned aboveare proposed. We find that the network level solutions and technology components developed during the course of METIS complement the lower layer technology components and thereby effectively contribute to meeting 5G requirements and targets.Aydin, O.; Valentin, S.; Ren, Z.; Botsov, M.; Lakshmana, TR.; Sui, Y.; Sun, W.... (2015). D4.3 Final Report on Network-Level Solutions. http://hdl.handle.net/10251/7675

The Limits of Location Privacy in Mobile Devices

Mobile phones are widely adopted by users across the world today. However, the privacy implications of persistent connectivity are not well understood. This dissertation focuses on one important concern of mobile phone users: location privacy. I approach this problem from the perspective of three adversaries that users are exposed to via smartphone apps: the mobile advertiser, the app developer, and the cellular service provider. First, I quantify the proportion of mobile users who use location permissive apps and are able to be tracked through their advertising identifier, and demonstrate a mark and recapture attack that allows continued tracking of users who hide these identifiers. Ninety-five percent of the 1500 devices we tested were susceptible to this attack. We successfully identified 49% of unlabelled impressions from iOS devices, and 59% from Android, with a budget of only $5 per day, per user. Next, I evaluate an attack wherein a remote server discovers a user\u27s traveled path without permission, simply by analyzing the throughput of the connection to the user over time. In these experiments, a remote attacker can distinguish a user\u27s route among four paths within a University campus with 77% accuracy, and among eight paths surrounding the campus with 83% accuracy. I then propose a protocol for anonymous cell phone usage, which obviates the need for users to trust telecoms with their location, and I evaluate its efficacy against a passive location profiling attack used to infer identity. According to these simulations, even one day is enough to identify one device from among over a hundred with greater than 50% accuracy. To mitigate location profiling attacks, users should change these identifiers every ten minutes and remain offline for 30 seconds, to reduce their identifiability by up to 45%. I conclude by summarizing the key issues in mobile location privacy today, immediate steps that can be taken to improve them, and the inherent privacy costs of remaining constantly connected

Modeling Urban Venue Dynamics through Spatio-Temporal Metrics and Complex Networks

The ubiquity of GPS-enabled devices, mobile applications, and intelligent transportation systems have enabled opportunities to model the world at an unprecedented scale. Urban environments, in particular, have benefited from new data sources that provide granular representations of activities across space and time. As cities experienced a rise in urbanization, they also faced challenges in managing vehicle levels, congestion, and public transportation systems. Modeling these fast-paced changes through rich data from sources such as taxis, bikes, and trains has enabled prediction models capable of characterizing trends and forecasting future changes. Data-driven studies of urban mobility dynamics have been instrumental in helping deliver more contextual services to cities, support urban policy, and inform business decisions. This dissertation explores how novel algorithmic architectures and techniques reveal and predict business trends and urban development patterns. The research informing this dissertation harnesses principles from network science, modeling cities as connected networks of venues. Building upon a foundation of research in complex network theory, urban computing, and machine learning, we propose algorithms tailored for three computing tasks focused on modeling venue dynamics, characteristics, and trends. First, we predict the demand for newly opened businesses using insights from movement patterns across different regions of the city. Through this analysis we demonstrate how temporally similar areas can be successfully used as inputs to predict the visitation patterns of new venues. Next, we forecast the likelihood of business failure through a supervised learning model. We analyze the value of varying features in predicting business failure and explore their impact across new and established venues and across different cities worldwide. Finally, we present a deep learning architecture which integrates both spatial and topological features to predict the future demand for a venue. These works highlight the power of complex network measures to quantify the structure of a city and inform prediction models. This dissertation leverages vast amounts of data from spatio-temporal networks to model venue dynamics. The research puts forward evidence to support a data-driven study of geographic systems applied to fundamental questions in urban studies, retail development, and social science.Gates Cambridge Trus