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, an efficient method based on genetic algorithms is developed to solve the multiprocessor scheduling problem. To efficiently execute programs in parallel on multiprocessor scheduling problem must be solved to determine the assignment of tasks to the processors and the execution order of the tasks so that the execution time is minimized. Even when the target processors is fully connected and no communication delay is considered among tasks in the task graph the scheduling problem is NP-complete. Complexity of scheduling problems dependent of number of processors (P), task processing time Ti and precedence constraints. This problem has been known as strong NP-hard intractable optimisation problem when it assumes arbitrary number of processors, arbitrary task processing time and arbitrary precedence constraints. We assumed fixed number of processors and tasks are represented by a directed acyclic graph (DAG) called “task graph”.

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.

To accurately predict traffic information is of great importance in a large number of applications in connection with Intelligent Transport systems (ITS), since it reduces the uncertainty of future traffic states and improves traffic mobility. The most important research is done in the domain of cooperative intelligent transport system (C-ITS). Only minor attention has been given to coordinated maneuvering, since testing with real vehicles which can drive autonomously requires a large-scale infrastructure with important security measures. In this paper, we propose hybrid automaton modelling in Matlab/Simulink/ Stateflow to emulate flexible platooning conditions, analysing how cooperation interactions can be accomplished using inter-vehicle communication and certain control of the vehicles. Such analysis reveals to be necessary in order to establish the improvement of traffic mobility in Intelligent Transportation Systems through cooperation behaviour profile prediction. This study presents an approach towards NARX neural network prediction of flexible Platooning maneuvers profile. In order to estimate prediction, MSE and R were utilized. The study results suggest that in the case of noise in test data, NARX neural network would be an efficient prediction tool, and useful for the prediction mobility in Intelligent Transport systems.

Driving simulators are used to analyze and validate the driver's behavior. These simulators are an essential tool in the research of human factor related to car driving. Advantages of using a driving simulator are safety (there are no traffic accidents during driving) and simple collecting of data related to the driver's behavior. The goal of this paper is development of an interactive driving simulator capable of simulating and testing driver's behavior based on lane changing with selected traffic conditions. The overall system is controlled and supervised using the Finite State Machine modeling approach realized in C# programming language, data input/output processing is implemented using the microcontroller user-based interface as well as the virtual driving scenarios is created using Microsoft XNA platform. The usage of video game technology is a central design principle of virtual driving scenarios. The concept of driver physical reaction testing using the software simulator based on configurable parameters is presented too.

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.