Abstract This paper investigates the energy-efficient resource allocation algorithm for a massive multiple input multiple output (MIMO) system, in which each base station adapts the number of antennas to the daily load profile. Our paper examines the effect of two user location distribution (ULD) models, on the energy-efficiency (EE) of load adaptive masive MIMO system. We propose a resource allocation strategy to adapt the number of antennas based on tracking variations of ULD and cell loading maximizing the EE. We also evaluate impact of cell size, available bandwidth and output power level of the BS on EE at different cell loading.

This paper investigates the allocation model, the flexibility, and the scalability of fully distributed communication architectures for metering systems in smart grids. Smart metering infrastructure aggregates data from Smart Meters (SMs) and sends the collected data to the fog or the cloud data centres to be stored and analysed. The system needs to be scalable and reliable and to respond to increased demand with minimal cost. The problem is to find the optimal distribution of application data among devices, data centres or clouds. The need for support computing at marginal resources, which can be hosted within the building itself or shared within the construction of the complex, has become important over recent years. The resource allocation model is presented to optimize the cost of the resources in the communications and relevance parts of computing (the data processing cost). The fog helps cloud computing connectivity on the edge network. This paper explains how calculation/analysis can be performed closer to the data collection site to complement the analysis that would be undertaken at the data centre. Results for a range of typical scenarios are presented to show the effectiveness of the proposed method.

Abstract The security of using applications in cloud services and on the Internet is an important topic in the field of engineering. In this paper, two laboratory tests for data transmission protection, specifically designed for different security analysis techniques, are presented and explained. During lab tests on public Wi-Fi networks from the MIDM (“Man in the Middle”) attacks, various monitoring techniques were applied, using a special lab test scenario with Kali Linux penetration tools by creating an SSH tunnel on an Android mobile device. These test benches allow easy data capturing, and the captured data is processed using available software programs. Expected outcomes, practical improvement and security performance assessment are presented in detail, and considered in terms of their value in security engineering. The aim of this paper is to detect and overcome some of the weaknesses of the application of security protocols in a Wi-Fi network environment.

In this paper we present an overview of massive Multiple-Input Multiple-Output (MIMO) technology and its most important concepts. The concepts analysed in the paper include: beamforming, antenna parameters, pilot contamination and modulation. The paper provides the guidelines for achieving a good design of antenna array and beamforming, and proposals to address the challenges faced by the massive MIMO technology. The main advantages of the massive MIMO technology include significantly improved network throughput and lower latency, which is why the massive MIMO is expected to be used in the architecture of various systems. The paper presents examples of the application of massive MIMO technology.

Cognitive radio (CR) is seen as a new approach to a more efficient utilization of natural resources - the radio electromagnetic spectrum. CR is described as an intelligent wireless communication system that is aware of your surroundings and to which it adapts own radio operating parameters. The most important function of cognitive radio is the detection of the spectrum, or spectral detection of free space. In this paper, the focus is placed on energy detection technique, by which we determine the status of the primary users (i.e. occupancy / availability of the spectrum), and the (centralized) cooperative detection range, within which it applies detection of energy together with the fusion of deciding where the data fusion is the process of combining local data detected, in order to make cooperative decisions. Optimal detection performance in such a co-operative detection of the spectrum is tested using MATLAB software package, with the aim of minimizing the total error of detecting a primary user.

The aim of this paper is to demonstrate, how to communicate the vehicle between themselves in a heterogeneous vehicles network, and show on which way is done the exchange of information with the infrastructure, to overcome the shortcomings of using a single wireless technology. DSRC does not offer enough good coverage and range around intersections in urban areas for specific applications. On the other side, as an alternative to overcoming these deficiencies proposed LTE, advanced mobile communications technology. The evaluation of performance is usually done by means of simulations in particular the programming software, which integrates tools to support Wi-Fi, IEEE 802.11p, mobile technology and feedback mobility. For the realization of heterogeneous networks vehicle that has support for LTE is used open source simulator for communication between vehicles and infrastructure Veins LTE, composed of a network simulator Omnet++ and traffic simulator SUMO. These two simulators are working in parallel, and allow modeling of communication between vehicles.

Intelligent Transportation Systems rely on understanding, predicting and affecting the interactions between vehicles. The goal of this paper is to choose a small subset from the larger set so that the resulting regression model is simple, yet have good predictive ability for Vehicle agent speed relative to Vehicle intruder. The method of ANFIS (adaptive neuro fuzzy inference system) was applied to the data resulting from these measurements. The ANFIS process for variable selection was implemented in order to detect the predominant variables affecting the prediction of agent speed relative to intruder. This process includes several ways to discover a subset of the total set of recorded parameters, showing good predictive capability. The ANFIS network was used to perform a variable search. Then, it was used to determine how 9 parameters (Intruder Front sensors active (boolean), Intruder Rear sensors active (boolean), Agent Front sensors active (boolean), Agent Rear sensors active (boolean), RSSI signal intensity/strength (integer), Elapsed time (in seconds), Distance between Agent and Intruder (m), Angle of Agent relative to Intruder (angle between vehicles °), Altitude difference between Agent and Intruder (m)) influence prediction of agent speed relative to intruder. The results indicated that distance between Vehicle agent and Vehicle intruder (m) and angle of Vehicle agent relative to Vehicle Intruder (angle between vehicles °) is the most influential parameters to Vehicle agent speed relative to Vehicle intruder.

Introduction of LTE-Advanced network brings a series of new techniques as well as an improvement of the existing ones, which contributes to better performance in relation to LTE network. Enhanced MIMO represents one of LTE-Advanced network’s most significant techniques which enable considerably better performance. Spectral efficiency and throughput are important system performance indicators. The paper aims to estimate LTE-Advanced network performance by applying an enhanced MIMO. The results verify consistency with given the analytical values, as well as which system parameter has the greatest influence on the achieved results. When enhanced MIMO is applied, several performance testing scenarios have been created depending on the parameter which was analysed for the given scenario, namely: LTE-Advanced channel scenario, antenna configuration, number of HARQ retransmissions and the type of subframe mode. DOI: http://dx.doi.org/10.5755/j01.eee.22.1.7698

Accurate prediction of traffic information is important in many applications in relation to Intelligent Transport systems (ITS), since it reduces the uncertainty of future traffic states and improves traffic mobility. There is a lot of research done in the field of traffic information predictions such as speed, flow and travel time. The most important research was done in the domain of cooperative intelligent transport system (C-ITS). The goal of this paper is to introduce the novel cooperation behaviour profile prediction through the example of flexible Road Trains useful road cooperation parameter, which contributes to the improvement of traffic mobility in Intelligent Transportation Systems. This paper presents an approach towards the control and cooperation behaviour modelling of vehicles in the flexible Road Train based on hybrid automaton and neuro-fuzzy (ANFIS) prediction of cooperation profile of the flexible Road Train. Hybrid automaton takes into account complex dynamics of each vehicle as well as discrete cooperation approach. The ANFIS is a particular class of the ANN family with attractive estimation and learning potentials. In order to provide statistical analysis, RMSE (root mean square error), coefficient of determination (R2) and Pearson coefficient (r), were utilized. The study results suggest that ANFIS would be an efficient soft computing methodology, which could offer precise predictions of cooperative interactions between vehicles in Road Train, which is useful for prediction mobility in Intelligent Transport systems.

Regular and systematic public transport is of great importance to all residents in any country, in the city and on commuter routes. In our environment, users of public transport can track the movement of vehicles with great difficulty, given that the current system does not meet the necessary criteria, and does not comply with the functioning of transport system. The aim of the final paper is to show the development of such a system using ZigBee and Arduino platforms. This paper shows an example of use the technologies mentioned above, their main advantages and disadvantages, with the emphasis on communication between the device and its smooth progress. In order to show the way in which the system could function, a simple mesh network was created, consisting of coordinator, routers for data distribution and end devices representing the vehicles. To view the results a web application was developed using open-source tool which is for display of the collected data on the movement of nodes in the network.