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

Improved Fair-Zone technique using Mobility Prediction in WSN

The self-organizational ability of ad-hoc Wireless Sensor Networks (WSNs) has led them to be the most popular choice in ubiquitous computing. Clustering sensor nodes organizing them hierarchically have proven to be an effective method to provide better data aggregation and scalability for the sensor network while conserving limited energy. It has some limitation in energy and mobility of nodes. In this paper we propose a mobility prediction technique which tries overcoming above mentioned problems and improves the life time of the network. The technique used here is Exponential Moving Average for online updates of nodal contact probability in cluster based network.Comment: 10 pages, 7 figures, Published in International Journal Of Advanced Smart Sensor Network Systems (IJASSN

An Intelligent Mobility Prediction Scheme for Location-Based Service over Cellular Communications Network

One of the trickiest challenges introduced by cellular communications networks is mobility prediction for Location Based-Services (LBSs). Hence, an accurate and efficient mobility prediction technique is particularly needed for these networks. The mobility prediction technique incurs overheads on the transmission process. These overheads affect properties of the cellular communications network such as delay, denial of services, manual filtering and bandwidth. The main goal of this research is to enhance a mobility prediction scheme in cellular communications networks through three phases. Firstly, current mobility prediction techniques will be investigated. Secondly, innovation and examination of new mobility prediction techniques will be based on three hypothesises that are suitable for cellular communications network and mobile user (MU) resources with low computation cost and high prediction success rate without using MU resources in the prediction process. Thirdly, a new mobility prediction scheme will be generated that is based on different levels of mobility prediction. In this thesis, a new mobility prediction scheme for LBSs is proposed. It could be considered as a combination of the cell and routing area (RA) prediction levels. For cell level prediction, most of the current location prediction research is focused on generalized location models, where the geographic extent is divided into regular-shape cells. These models are not suitable for certain LBSs where the objectives are to compute and present on-road services. Such techniques are the New Markov-Based Mobility Prediction (NMMP) and Prediction Location Model (PLM) that deal with inner cell structure and different levels of prediction, respectively. The NMMP and PLM techniques suffer from complex computation, accuracy rate regression and insufficient accuracy. In this thesis, Location Prediction based on a Sector Snapshot (LPSS) is introduced, which is based on a Novel Cell Splitting Algorithm (NCPA). This algorithm is implemented in a micro cell in parallel with the new prediction technique. The LPSS technique, compared with two classic prediction techniques and the experimental results, shows the effectiveness and robustness of the new splitting algorithm and prediction technique. In the cell side, the proposed approach reduces the complexity cost and prevents the cell level prediction technique from performing in time slots that are too close. For these reasons, the RA avoids cell-side problems. This research discusses a New Routing Area Displacement Prediction for Location-Based Services (NRADP) which is based on developed Ant Colony Optimization (ACO). The NRADP, compared with Mobility Prediction based on an Ant System (MPAS) and the experimental results, shows the effectiveness, higher prediction rate, reduced search stagnation ratio, and reduced computation cost of the new prediction technique

STAR: A Concise Deep Learning Framework for Citywide Human Mobility Prediction

Human mobility forecasting in a city is of utmost importance to transportation and public safety, but with the process of urbanization and the generation of big data, intensive computing and determination of mobility pattern have become challenging. This study focuses on how to improve the accuracy and efficiency of predicting citywide human mobility via a simpler solution. A spatio-temporal mobility event prediction framework based on a single fully-convolutional residual network (STAR) is proposed. STAR is a highly simple, general and effective method for learning a single tensor representing the mobility event. Residual learning is utilized for training the deep network to derive the detailed result for scenarios of citywide prediction. Extensive benchmark evaluation results on real-world data demonstrate that STAR outperforms state-of-the-art approaches in single- and multi-step prediction while utilizing fewer parameters and achieving higher efficiency.Comment: Accepted by MDM 201

Mobility Prediction for Handover Management in Cellular Networks with Control/Data Separation

In research community, a new radio access network architecture with a logical separation between control plane (CP) and data plane (DP) has been proposed for future cellular systems. It aims to overcome limitations of the conventional architecture by providing high data rate services under the umbrella of a coverage layer in a dual connection mode. This configuration could provide significant savings in signalling overhead. In particular, mobility robustness with minimal handover (HO) signalling is considered as one of the most promising benefits of this architecture. However, the DP mobility remains an issue that needs to be investigated. We consider predictive DP HO management as a solution that could minimise the out-of-band signalling related to the HO procedure. Thus we propose a mobility prediction scheme based on Markov Chains. The developed model predicts the user's trajectory in terms of a HO sequence in order to minimise the interruption time and the associated signalling when the HO is triggered. Depending on the prediction accuracy, numerical results show that the predictive HO management strategy could significantly reduce the signalling cost as compared with the conventional non-predictive mechanism

Human Mobility Prediction Through Twitter.

Abstract Social media, in recent years, have become an invaluable source of information concerning human dynamics within urban context, allowing to enhance the comprehension of people behaviour, including human mobility regularities. The paper presents an approach to predict human mobility by exploiting Twitter data. The prediction approach is based on a novel trajectory pattern similarity measure that allows to identify the more suitable historic patterns to exploit for the prediction of the user next location. The pattern with the highest similarity to the user current trajectory will be used to predict the user next position. The experimental results obtained by using a real-world dataset show that the proposed method is effective in predicting the users next places achieving a remarkable precision