12 research outputs found
Predicting Multimedia Traffic in Wireless Networks: A Performance Evaluation of Cognitive Techniques
Traffic engineering in networking is defined as the process that incorporates sophisticated methods in order to ensure optimization and high network performance. One of the most constructive tools employed by the traffic engineering concept is the traffic prediction. Having in mind the heterogeneous traffic patterns originated by various modern services and network platforms, the need of a robust, cognitive, and error-free prediction technique becomes even more pressing. This work focuses on the prediction concept as an autonomous, functional, and efficient process, where multiple cutting-edge methods are presented, modeled, and thoroughly assessed. To this purpose, real traffic traces have been captured, including multiple multimedia traffic flows, so as to comparatively assess widely used methods in terms of accuracy
Predicting Multimedia Traffic in Wireless Networks: A Performance Evaluation of Cognitive Techniques
Traffic engineering in networking is defined as the process that incorporates sophisticated methods in order to ensure optimization and high network performance. One of the most constructive tools employed by the traffic engineering concept is the traffic prediction. Having in mind the heterogeneous traffic patterns originated by various modern services and network platforms, the need of a robust, cognitive, and error-free prediction technique becomes even more pressing. This work focuses on the prediction concept as an autonomous, functional, and efficient process, where multiple cutting-edge methods are presented, modeled, and thoroughly assessed. To this purpose, real traffic traces have been captured, including multiple multimedia traffic flows, so as to comparatively assess widely used methods in terms of accuracy
Finite Automata Algorithms in Map-Reduce
In this thesis the intersection of several large nondeterministic finite automata (NFA's) as well as minimization of a large deterministic finite automaton (DFA) in map-reduce are studied. We have derived a lower bound on replication rate for computing NFA intersections and provided three concrete algorithms for the problem. Our investigation of the replication rate for each of all three algorithms shows where each algorithm could be applied through detailed experiments on large datasets of finite automata. Denoting n the number of states in DFA A, we propose an algorithm to minimize A in n map-reduce rounds in the worst-case. Our experiments, however, indicate that the number of rounds, in practice, is much smaller than n for all DFA's we examined. In other words, this algorithm converges in d iterations by computing the equivalence classes of each state, where d is the diameter of the input DFA
Clustering Sustainable Destinations: Empirical Evidence from Selected Mediterranean Countries
Within the globalized tourism market, tourism destinations have the option to turn to sustainability as a conceptual and management framework for their unique branding and identity proposition. This research highlights the importance and utility of sustainability branding that stems from clustering tourism destinations based on the similarities of their tourism performance attributes. The study builds on secondary data from 11 coastal destinations in 8 Mediterranean countries. The analysis leads to the formulation of three main sets of evaluation indicators: (a) environmental footprint; (b) destination dependency on tourism; and (c) locals’ prosperity, incorporating elements of social and psychological carrying capacity. Findings identify three to four distinct destination clusters based mainly on the attributes of destinations’ cultural and natural attributes, seasonality of supply, typology of prevailing accommodation and tourist profile. From a theoretical perspective, the research identifies key clustering attributes of sustainable destinations that could inform management interventions around destination branding and competitive sustainability performance positioning
Game-Based Learning, Gamification in Education and Serious Games
The aim of this book is to present and discuss new advances in serious games to show how they could enhance the effectiveness and outreach of education, advertising, social awareness, health, policies, etc. We present their use in structured learning activities, not only with a focus on game-based learning, but also on the use of game elements and game design techniques to gamify the learning process. The published contributions really demonstrate the wide scope of application of game-based approaches in terms of purpose, target groups, technologies and domains and one aspect they have in common is that they provide evidence of how effective serious games, game-based learning and gamification can be
Data and the city – accessibility and openness. a cybersalon paper on open data
This paper showcases examples of bottom–up open data and smart city applications and identifies lessons for future such efforts. Examples include Changify, a neighbourhood-based platform for residents, businesses, and companies; Open Sensors, which provides APIs to help businesses, startups, and individuals develop applications for the Internet of Things; and Cybersalon’s Hackney Treasures. a location-based mobile app that uses Wikipedia entries geolocated in Hackney borough to map notable local residents. Other experiments with sensors and open data by Cybersalon members include Ilze Black and Nanda Khaorapapong's The Breather, a "breathing" balloon that uses high-end, sophisticated sensors to make air quality visible; and James Moulding's AirPublic, which measures pollution levels. Based on Cybersalon's experience to date, getting data to the people is difficult, circuitous, and slow, requiring an intricate process of leadership, public relations, and perseverance. Although there are myriad tools and initiatives, there is no one solution for the actual transfer of that data
Sensor Network Based Collision-Free Navigation and Map Building for Mobile Robots
Safe robot navigation is a fundamental research field for autonomous robots
including ground mobile robots and flying robots. The primary objective of a
safe robot navigation algorithm is to guide an autonomous robot from its
initial position to a target or along a desired path with obstacle avoidance.
With the development of information technology and sensor technology, the
implementations combining robotics with sensor network are focused on in the
recent researches. One of the relevant implementations is the sensor network
based robot navigation. Moreover, another important navigation problem of
robotics is safe area search and map building. In this report, a global
collision-free path planning algorithm for ground mobile robots in dynamic
environments is presented firstly. Considering the advantages of sensor
network, the presented path planning algorithm is developed to a sensor network
based navigation algorithm for ground mobile robots. The 2D range finder sensor
network is used in the presented method to detect static and dynamic obstacles.
The sensor network can guide each ground mobile robot in the detected safe area
to the target. Furthermore, the presented navigation algorithm is extended into
3D environments. With the measurements of the sensor network, any flying robot
in the workspace is navigated by the presented algorithm from the initial
position to the target. Moreover, in this report, another navigation problem,
safe area search and map building for ground mobile robot, is studied and two
algorithms are presented. In the first presented method, we consider a ground
mobile robot equipped with a 2D range finder sensor searching a bounded 2D area
without any collision and building a complete 2D map of the area. Furthermore,
the first presented map building algorithm is extended to another algorithm for
3D map building