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In our previous research a Geographic Information System microservice was introduced as part of the SensorHUB framework and CV4SensorHUB framework. The main data sources and architectural background was presented. The purpose of the GIS microservice is also to serve as a research and development subsystem connecting the two frameworks. We implemented services to show the strength of the microservice architecture-based framework ecosystem. For this we also used the terminology of Industrial Data Space architecture model. In this paper we present case studies showing how it connects the frameworks, how the data flow can be controlled; or how it collects location-based data from smartphone environments. Another case study introduces a route planning service using current live data from different data sources. The paper also presents the possibilities of load-based, network traffic-based and code-based performance measurement in the system with the comparison of different data aggregation methods (Java-based and MapReduce-based implementation) towards the cognitive data analysis and human-machine interface.

For archaeologists knowing the provenance of mar-ble artifacts is important. The methodologies are based on finding the boundaries of the marble grains but only a few algorithms are available to do this instead of the expert. In this paper we propose an adaptive algorithm, called live-polyline, which is able to help the experts marking the grain boundaries and it is able to learn from user interactions as well. We investigate two different approaches. The first one is a heuristic based method, however the other one is a machine learning based solution. We define metrics for the performance, identify its key indicators, provide an algorithm to calculate it and determine the required values of the key indicators for sufficient performance. We also examined the heuristic and machine learning methods in terms of these indicators and measured their performance.

We introduce an implementation of a GIS (Geographic Information System) research and development subsystem in the CV4SensorHUB (Computer Vision for SensorHUB) framework. The main concept is the usage and connection of open-access online databases such as OpenStreetMaps, Citybik.es, BKK Futar, MAV Elvira and others. These databases cover different fields in the geographic domain. By connecting these databases with MapReduce paradigm and implementing smart algorithms, we are about to create an online tool where users could parametrize their trips around the town, using the live information and then visualize them on a map. For example, with the OpenStreetMaps it is possible to extract the maps, streets, POIs (point of interest), then with the Citybik.es we can get the live data about public bicycle sharing system, and with the BKK Futar we can access the live public transportation data, vehicles, timetables. In this paper, we focus on the data sources, the architecture with its common data model, and the main implementation steps of the system.

Due to the increasing computational capacity of embedded systems, it has now become possible to monitor a given area efficiently by using a mass of low-cost sensors. However, the collection and analysis of such big data is still an open question from the perspective of energy efficiency. In this article will show a concept which covers the whole lifecycle of such huge scale sensor networks. We introduce a deployment and maintenance solution for uncharted areas. We present a method for gathering the measured data of each sensor and we will show a system for analyzing and visualizing the collected data.

In this paper we deal with (step and crease) edge preserving smoothing and normal estimation in low depth-resolution range images. As a result of short base distance stereo, compression or scene geometry, such range images consist of few discrete range values. We propose a method for smoothing such range images by interpolation while preserving boundary and crease edges, and estimating surface normals. The proposed method is based on rotated weighting sampling matrices and iso-range curves extracted from the range data. Samples and weights are selected using a rotated weight matrix and planar surfaces are fitted. Based on the fitting error, the best fit is selected to estimate the surface normal and interpolate surface points in such sparse dataset. Fitting error distribution is also used for estimating crease edge score. Such low-level understanding of range images can be utilized in further processing steps such as segmentation, localization, mapping, object detection etc. The output of the algorithm is presented and evaluated using both simulation and real range image data. It is shown that such sampling preserves crease edges and provides lower estimation errors for interpolated range image points.

The popularity of smartphones is growing every day. The hardware components of the mobile devices are renewed almost every six months. however the smartphones still cannot stay awake for more than one day. This is one of the biggest problem today both in the mobile industry and among users as well. The engineers try to reduce the energy usage not just through hardware optimization but also software solutions are made. These solutions are mostly unsuitable for everyday usage, but what happens if we optimize the energy consumption based on the most frequent user habits? The users usually use their devices to be continuously notified about the latest messages, activities, phone calls etc. This behavior can be sometimes unnecessary, for example during the night it is not important to synchronize all e-mails of the users. From that point of view, the users and their devices can form a socio-cognitive channel, which can help to create behavior-based technologies. In this paper we introduce a model which represents the most common smartphone usage scenarios using Markov chains. Based on this model we make suggestions how the processes can be modified to improve the energy consumption of the mobile devices.

Nowadays the mobile devices energy consumption has become a very serious issue. Due to the fast growing mobile industry the current devices usually contain wireless (Wi-Fi, 3G, 4G and Bluetooth), GPS and other heavy network sensitive technologies. The cell battery manufacturers usually cannot keep the pace with this fast altering environment and demands, hence the devices get inappropriate battery. Recently many researchers deal with this topic trying to find out a reasonable solution for energy optimization without compromising the functionality of the devices. A possible approach to deal with the problem today and achieve the desired result is the code side optimization. The market based mobile application distribution model makes this challenge harder. Sometimes the code quality of the uploaded applications are poor and the market owners are not able to force the developers to write clear and energy efficient code, they can only give them recommendations. The question is coming up: how could we still encourage the individual developers and companies to write optimized and a bit more standardized code? The answer could be delivering them tools to facilitate the refactoring and code quality managing processes. One way we can deal with energy problem is to reorganize the heavy computational tasks out of the device to the cloud or to just another machine. This technique is called “offloading”. In this paper we are going to introduce a new code generation extension for software engineers which is aiming to automate the offload to web services. Those services can then be easily deployed to any desired location to ease the mobile devices computational task. We think this tool would be really helpful to the community and the industrial users also.

Using a smartphone become the part of our everyday life in the last few years. These devices can help us in many areas of life (sport, job, weather etc.), but sometimes can be also very annoying because of the battery life time. That is why it is very important to find solutions, which can reduce the energy consumption of the smartphones. One possible method is the “computation offloading” where a part of the processes are executed on a remote device (e.g. in the cloud). A lot of example has already shown, that computation offloading can reduce the energy usage of the mobile devices. However, the amount of energy saving may differ, as the decision making of the offloading process can be controlled with several techniques. Our decision making theory is based on the scheduling theory. In this paper, we are going to introduce a new system called ECGM (Energy efficient Code Generator for Mobile phones). ECGM decides automatically at compile time, which task should run on the smartphone and which task can be offloaded. The benefit of our system will be demonstrated through measurements based on our energy-efficiency scheduling technique.

In this paper we analyze the anonymous reports provided by our Android based BitTorrent client called DrTorrent. This application is available for free and is a full featured torrent client. The reports allow us to analyze the usage patterns typical for mobile torrent clients. As the mobile devices have some special properties like processing power, energy, and bandwidth limitations, the behavior differs from the traditional desktop clients. Beside the several statistics on for instance the sizes of accessed torrents and download times, we present parametric estimation models for the number of peers for a given torrent, and for the rate of peers which can be successfully connected to. This research can be considered as a pioneer work in the field of analyzing large peer-to-peer networks in modern mobile environments. The described measurement results enable to understand the mobile BitTorrent behavior in details and the proposed models can be used also as a base model for designing other mobile peer-to-peer services.

This paper presents the optimization of a camera placement for improved localization accuracy. It is the basis of a localization system aiming for high localization accuracy. This goal is achieved using several cameras with very redundant field-of-views. The paper presents the calculation of the localization accuracy - which depends on the camera model and the pixel quantizing error - at one specific point. A method is introduced for handling areas in a probabilistic sense instead of examining only one point. The accuracy can be improved by adding a new camera to the system. The calculation of the optimal position of the new camera at compliance to some restrictions is demonstrated. The Smart Mobile Eyes for Localization (SMEyeL) project is open-source: the source code, all measurement input data and documentation are public available.

With the developments of the last years, smart phones became suitable to be used as smart cameras in a visual sensor network. They have the necessary cameras with an acceptable quality of lenses and CCD, the required processing power, and WiFi or Bluetooth interfaces for communication. This paper describes the architecture of the SMEyeL (Smart Mobile Eyes for Localization) system. It is designed to utilize both smart phones and PC-s as processing nodes of a visual sensor network. It aims to achieve high spatial and temporal resolution, energy consumption optimization via computation off-loading and distributed processing. The SMEyeL project is open source: source code and measurement data sets are available on the web. We present the detailed description of the architecture and experimental results related to timing accuracy and localization precision.

This paper presents a marker localization system consisting of multiple cameras. It is the basis of a real-time robotic motion tracking system aiming for high localization accuracy and high time resolution. These goals are achieved using several cameras with very redundant field-of-views. The paper presents the theoretical background of 3D camera calibration and localization, the localization accuracy measurement setup, and its results. The measurements are obtained using a color marker and an industrial robotic arm for measurement ground truth generation. The Smart Mobile Eyes for Localization (SMEyeL) project is open-source: the source code, all measurement input data and documentation are public available.

No one disputes that mobile phones have become part of everyday life. Besides phone calling we use them for browsing, messaging, playing games and many other things which were possible only on a desktop computer several years ago. Although these devices are even more “smart”, but due to the growing resource requirements their batteries are discharging in a very short period. This is a very important phenomenon, which forces to find alternative ways to reduce the energy consumption of the smartphones. One of them is the method of “computation offloading” where a part of the processes are executed on a remote device (e.g. in the cloud). The creation of an energy-efficiency model consists of several steps. One of the hardest part is to understand how a smartphone behaves in different circumstances. During the execution of an application every step has its own energy cost. To make the execution energy-efficiency, we need first to measure and analyse these costs. In this paper we are going to present a measurement system which is used to analyse the energy consumption of smartphones. The measurements were made in different scenarios and the goal was to save energy through offloading some tasks. The offloading process is based on scheduling theory.

The popularity of smartphones is growing every day. Thanks to the more powerful hardware the applications can run more tasks and use broadband network connection, however there are several known issues. For example, under typical usage (messaging, browsing, and gaming) a smartphone can be discharged in one day. This makes the battery life one of the biggest problems of the mobile devices. That is a good motivation to find energy-efficient solutions. One of the possible methods is the “computation offloading” mechanism, which means that some of the tasks are uploaded to the cloud. In this paper we are going to present a new energy-efficient job scheduling model and a measurement infrastructure which is used to analyze the energy consumption of smartphones. Our results are going to be demonstrated through some scenarios where the goal is to save energy. The offloading task is based on LP and scheduling problems.

Despite the increasing capabilities and effectiveness of highly autonomous robots, there are still situations where one entity is not sufficient. Escorting and tracking are typical situations, where more robots can be more efficient.

Nowadays the capabilities of autonomous robots have increased rapidly. However there are situations where one robot is not sufficient. In this paper we will introduce a completely distributed algorithm for tracing and surrounding moving objects or targets using homogenous, medium scaled swarm robots based on local sensing. Our new approach is based on the already known concept of basis behaviors as ubiquitous general building blocks for synthesizing artificial group behavior. Our goal was to generate an algorithm based on simple rules in order to protect a territory from a human intruder. In contrast with the common solution which is employing potential fields, we tried to keep our method as simple as possible by utilizing rule-based control mechanism. We will also present our virtual simulation environment and experimental results.

Learning patterns of human movement is a complex and hard task, including several computationally expensive algorithms. This issue has even higher emphasis in mobile environment, since handheld devices contain significantly less memory and computing power than a usual PC does. In this paper we are going to compare novel, mobile-optimized methods for separating trajectories in a human routine recognition framework.

Nowadays the need for autonomous robots has increased rapidly. These robots should have different types of sensors, thus their functionality can be extended. In order to process the data coming from the sensors, the robots require a proper processing hardware. However, the cost of a unique on-board controller system, which is able to handle advanced algorithms, is relatively expensive. Our proposal is to attach an inexpensive commercial mobile device to the robot, which has a high-resolution camera and advanced processing unit for different types of operations. In this paper we will present a vision-based model for environment discovery using homogeneous, medium scaled swarm of robots by using the on-board camera of the controller-smart phone. We introduce an extended axiom system based on bio-inspired swarm models, which optimizes the energy consumption of the swarm.

In this paper we analyze the lifecycle of content shared via the BitTorrent network by focusing on torrents retrieved by mobile phone clients using the MobTorrent application. MobTorrent is a full-feature BitTorrent client for feature phones. It has a profiling mechanism which was used to collect anonymous usage statistics over the last two years. Based on these statistics, we analyze how mobile BitTorrent clients are being used. We also investigate the successfulness of single client sessions by performing additional measurements of peer connection success ratios and download statistics. This research can be considered as a pioneer work in the field of mobile content sharing solutions, as MobTorrent is one of the first and most popular BitTorrent client for features phones.

This paper presents a novel document topic similarity measurement technique for mobile device environments. The goal is to help mobile device users to search for documents in a peer-to-peer environment which have similar topic to the documents on the users own device. The method is designed for slower processors, few memory and smaller data traffic between the devices to adapt to the environment of mobile devices like phones or PDA-s. The designed system employs a main server which helps the devices in the topic identification and representation. The keyword lists based topic comparison is enhanced with a taxonomic extension of the topic representations which allows comparison of documents with similar topics but few or no common keywords. The architecture, the employed algorithms and experimental results are presented in this paper.

In our work, we investigate efficient usage of TTCN-3 (Testing and Test Control Notation version 3) based test components in the emerging field of distributed performance testing of communicating systems. TTCN-3 is a high level formal specification language used for the specification of abstract test suites (ATSs) suitable for various testing purposes mainly in telecommunication systems testing. A TTCN-3 based test system may consist of several distributed parallel test components (PTCs) and can be executed utilizing diverse hardware platforms. Our aim is to aid development of load tests by establishing a framework, while collecting best practices at the same time, that can be used to execute tests over mixed hardware resources efficiently. We, mainly focus on automated and dynamic deployment of load test components implemented purely in TTCN-3. Also, the deployment method takes test system load into account by monitoring the participating workstations' behavior on-line. A potential test method is shown that can be used for testing server applications using low-cost or even legacy hardware as the platform for distributed lest components. A basic scenario involving SMTP (simple mail transfer protocol) service testing is presented as an example.

This paper deals with the examination and mathematical modeling of rule-based systems and list topologies applied in software architectures as well as in hardware solutions. In our investigation we focus on telecommunication applications utilizing list topologies. We introduce quasi birth-death processes as the mathematical formalism behind our models. Besides, we extend the simple model of list topologies with respect to the input and output interfaces of the implementation. Furthermore, we present our model on the example of access control lists applied in IP routers and we show results that can be derived from the model

Several approaches have been introduced to derive performance indices of software architectures automatically. Most of them apply the theory of queuing network models or Petri nets. We identify Unified Modeling Language (UML) state charts as a starting point for evaluation, and apply annotations and extensions on them before transforming them into a mathematical model. The mathematical formalism behind our investigations is based on discrete time quasi birth-death processes

The ant search is usually used in (ANTS 2002) for solving problems like the traveling salesman problem (TSP), where all the points of the search graph can be destinations and the edges between them represent the ways with the appropriate weights. In this article we describe a modification of the ant search for another goal: we search for "wide paths", where most of the graph points won't be targets, only small parts of the path we are looking for. The paths found by the algorithm consist of multiple alternative routes near each other without relevant difference in length (or cost). (The result looks like a wide path on a topographical map.) This leads to a loosely defined path, which is very useful, because obstacle avoidance maneuvers can be completed without leaving the path itself. This means that the AI doesn't have to worry about getting back on the route, which would need additional searches. Using the method of the ants this way leads to a very different search type (in search space handling and in result as well), than the approaches mentioned above. A further advantage of the algorithm is that it provides further information about the search space in connection with traffic load. In this aspect, (Marc Reimann) simulates transportation and traffic load, which is partly similar to this case, but still differs in the concept of the search space.

Although several experiments have been conducted in the field of robot navigation, most methods and algorithms still seem to have problems, like not taking the size of the robot into account. We present a global navigation method which can be used with only a little a priori knowledge, assuming that the robot knows its precise position and is only equipped with range sensors, The search only requires implicit knowledge of the environment (the narrowest corridor's width) to determine some parameters.

This paper examines the problem of automatically constructing a map of an unknown environment from a vehicle whose location is also unknown. The application of the Kalman filter to this problem is briefly described and the practical limitation of the filter in this context is discussed. A suboptimal algorithm, the relative filter, is introduced that avoids many of the computational and practical problems of the direct Kalman filter approach to this problem. The performance of the full Kalman filter and the relative filter is compared in a real map building scenario.