1,720,967 research outputs found
On the Relationship Between Speed and Mobility Sampling Frequency in Dynamic Urban Networks
No full textIn the current era of mobile communications and next-generation networks, mobility analysis has a key role in guaranteeing the quality of service/experience in the available services. Although a vast amount of work has analyzed mobility from both analytic and stochastic points of view, much of it has focused on a time-based analysis and disregarded spectral features. In this article, we propose a method of analyzing the main features of mobility traces in the frequency domain and determining the possible relationships between typical mobility grades (in terms of average and maximum speed) and the required sampling frequencies. The collection and storage of mobility pattern samples when they are not required is impractical, and therefore, we attempt to demonstrate how mobility can be sampled to avoid information loss or oversampling (many works in the literature are based on a default sampling period of 1 s). The work also contributes with the proposal of a closed form for relating the sampling period and average moving speed with the spectral components. We conducted numerous simulations to confirm that, compared with classical sampling approaches that provide static behavior, it is possible to obtain a gain of about 35%–65% in the collected samples, with a negligible loss of accuracy in the reconstructed signal
A deep stochastical and predictive analysis of users mobility based on Auto-Regressive processes and pairing functions
Get PDFWith the proliferation of connected vehicles, new coverage technologies and colossal bandwidth availability, the quality of service and experience in mobile computing play an important role for user satisfaction (in terms of comfort, security and overall performance). Unfortunately, in mobile environments, signal degradations very often affect the perceived service quality, and predictive approaches become necessary or helpful, to handle, for example, future node locations, future network topology or future system performance. In this paper, our attention is focused on an in-depth stochastic micro-mobility analysis in terms of nodes coordinates. Many existing works focused on different approaches for realizing accurate mobility predictions. Still, none of them analyzed the way mobility should be collected and/or observed, how the granularity of mobility samples collection should be set and/or how to interpret the collected samples to derive some stochastic properties based on the mobility type (pedestrian, vehicular, etc.). The main work has been carried out by observing the characteristics of vehicular mobility, from real traces. At the same time, other environments have also been considered to compare the changes in the collected statistics. Several analyses and simulation campaigns have been carried out and proposed, verifying the effectiveness of the introduced concepts
Usability of Destination-Sequenced Distance Vector Routing Protocol Routes
Get PDFThe success of fundamental network tasks of traffic delivery from a source to a destination node is mainly dependent on the efficiency of the routing protocol. In mobile ad hoc networks, the effectiveness of routing protocols is additionally demanding due to the dynamic nature of network nodes. In this paper, we dealt with the exploitation of the routes generated using DSDV bellman-ford routing protocol. Through a total of 3960 network simulations with different topologies, network loads and mobility nodes, various parameters of the DSDV were considered. Our results show that there are a large number of unused routes, and techniques for improving the efficiency of routing and reducing routing overhead can be implemented
Next-cell and mobility prediction in new generation cellular systems based on convolutional neural networks and encoding mobility data as images
No full textMobility prediction has been a popular research topic for many decades. With the advent of new generation technologies (5G and beyond) and smaller coverage cells, hand-over operations have become more frequent. Cellular system companies are therefore taking increasing interest in using the available predictive information on node movements to optimize and manage their bandwidth resources. In particular, the main challenging scope of our contribution consists in solving the issue of reliable next-cell prediction, aimed to call dropping probability minimization. In addition, our proposal is based on the innovative concept of mobility data to image encoding. The scheme is able to a-priori determine the next visited cells during host movements by applying a convolutional neural approach to mobility images. The power of machine learning is used to advantage, and highly accurate image classification is achieved for mobility prediction. We performed numerous simulation campaigns related to next-cell prediction in mobile cellular environments, obtaining very satisfactory results by the application of convolutional neural networks, which have an impressive history of effectiveness with image classification problems. The trained network has been associated to each coverage cell and the prediction accuracy has been evaluated
A New Mobility Samples Encoding Scheme Based on Pairing Functions and Data Analytics
No full textIn the modern telecommunication systems, mobility is one of the key advantage of wireless communications, given that it is possible to transmit/receive data, without caring of having a static position into the network. Of course, mobility poses special issues such as degradations, channel quality fluctuations, fast topology changes, and so on. Modern researches focus their attention on predicting mobile future node positions, in order to a-priori know, for example, what the evolution of the network topology will be or which level of stability each node will reach. Each prediction scheme is based on the storage and analysis of several historical mobility trajectories, in order to train the proper prediction algorithm. In this paper, we focus our attention on the optimization of the space needed to store historical mobility samples, encoding their values and evaluating the conversion error, comparing different encoding functions. Several simulation campaigns have been carried out in order to evaluate the goodness and feasibility of our proposal
Virtual Quantum Key Distribution Network Ecosystem: The National Czech QKD Network
No full textNetwork emulators are essential in testing network systems, applications, and protocols. Emulators bridge the gap between simulation setups that lack realism in results and real-world trials that are accurate but often expensive, non-reproducible, and uncontrollable. This paper describes the simulations and emulations of the national Czech QKD network. Using emulation techniques, a unique ecosystem is formed that includes the processes of generating, processing, storing, and consuming cryptographic keys. The presented tool will undoubtedly spur future development, understanding, and teaching, and it is critical for testing novel applications and protocols applied to QKD networks
Toward designing a quantum key distribution network simulation model
No full textAs research in quantum key distribution network technologies grows larger and more complex, the need for highly accurate and scalable simulation technologies becomes important to assess the practical feasibility and foresee difficulties in the practical implementation of theoretical achievements. In this paper, we described the design of simplified simulation environment of the quantum key distribution network with multiple links and nodes. In such simulation environment, we analyzed several routing protocols in terms of the number of sent routing packets, goodput and Packet Delivery Ratio of data traffic flow using NS-3 simulator
A novel urban mobility classification approach based on convolutional neural networks and mobility-to-image encoding
Get PDFOver the last few decades, the classification and prediction of mobility trajectories in dynamic networks have become major research topics. Switching of mobility areas (hand-over) in modern cellular networks is frequent due to restricted coverage area and node speeds (urban, highway, etc.). Accurate management of hand-over events is highly desirable to improve the system’s quality of service. We have exploited the high accuracy of machine learning to classify user mobility from mobility traces which we encoded into images. The method delivers high performance in mobility classification/prediction (exceeding 95%) and avoids the need to study and implement a dedicated neural network structure. The technique requires the conversion of mobility traces into image structures and the subsequent application of a convolutional neural network. We propose a novel approach to classifying mobility that involves data-to-image encoding and machine learning for image classification. Numerous simulations were performed to demonstrate the benefits of the proposed technique and to illustrate the variance in the accuracy of the functions of many encoding/classification parameters. The work represents a first preliminary step towards a new mobility prediction approach. We demonstrate that it is possible to achieve a very high level of prediction accuracy with low computational complexity, exploiting the strength of neural networks in image recognition
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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