Futuristic Trends in Network and Communication Technologies

The article proposes the structure of operators of quantum algorithms, its mathematical and schematic representation. The procedure for developing a quantum computing system or algorithm involves the presence of interference, quantum entanglement, and superposition. In a classical computer, the amount of data is measured in bits, and in a quantum computer, in qubits. A qubit is a quantum discharge or the smallest element for storing information in a quantum computer, as well as a quantum object that can be in a superposition of two states, that is, encode both a logical unit and zero at the same time. The general structure of the universal quantum algorithm is implemented as diagrams that reveal the basic elements, their properties, functions and place in the work of the quantum algorithm. The input value for the quantum algorithm is a binary function. A detailed decomposition of each block of the schematic diagram for the description of sequential processes and stages of quantum algorithms has been performed. A quantum block that runs n times is necessary for the sequential use of quantum operators and the subsequent measurement of the result of the entire computing process. Due to the probabilistic nature of quantum computing, the obtained basis vectors will contain some of the information necessary to solve a particular computational problem. Classical and quantum types of errors and methods for their elimination are described and developed. The main obstacles to protecting the channel from noise are the inability to copy data and information, the continuity of error and the destruction of quantum information during measurement (according to the principles of quantum computing, measuring a set of qubits without destroying the information encoded there is impossible). The bit and phase types of errors were corrected by modeling quantum circuits, three-qubit coding, and a set of quantum gates.

This paper presents a discrete model of the quantum fast Fourier transform (QFFT) defined over a quantum mechanical system that includes N basis states (QMS(N)). In this case, the QMS(N) is defined based on a known discrete model described using 2(N − 1) parameters, of which (N − 1) parameter describes the amplitude components of the basic states of the QMS(N), and (N − 1) parameter describes the phase components. QFFT is modelled discretely by specifying the same type of operations, which are described as the known quantum gates: A single-qubit Hadamard gate and a two-qubit gate with a controlled phase. Each of these valves affects the specified parameters of the QMS(N) N/2 times. This representation allows you to display the impact of the QFFT on the QMS(N) by varying only N of 2(N − 1) of its parameters. In addition, simulation of the effects of N/2 Hadamard gates and N/2 controlled-phase gates on the specified parameters of the discrete QMS(N) model can be performed in parallel. This circumstance allows us to perform distributed modeling of the impact of QFFT on the QMS(N) when using multiprocessor computing systems, both existing and prospective, both specialized and general-purpose.

A compact feed poly line slotted rectangular microstrip patch antenna with defected ground surface has been proposed in this paper for the wireless LANs, Bluetooth, and WiFi applications. The patch is designed based on a polyline slot and with defected ground structure. The simulated and fabricated models of demonstrated antenna exhibit better return loss, bandwidth, acceptable gain, and other antenna parameters over the required frequency range and the central frequency of 2.4 GHz is a worldwide free spectrum ISM band. The proposed antenna is fabricated using a highly sensitive PCB technique, and fabricated antenna parameters have been achieved compared to simulated results. The simulated and fabricated results conclude less than 3% error among them and owing to the proposed method, miniaturization of antenna achieved with a gain of 5 dB.

The authors in this paper formulated mathematical equations of Multi-area Economic load dispatch in conventional approach. Multi-area Economic load dispatch problem is a vital issue in power system scheduling, processing, organizing and managing. MAELD issue in power system is explored with the combination of electric utilities of various different regions. The mathematical formulation of multi area dynamic dispatch problem in view of real power and tie-line limits have been explained in this paper. This work of mathematical formulation will be useful for the research work on multi-area economic load dispatch problems with electric vehicles (EVs) and Renewable Energy Sources (RES).

Electric power system problems are amongst the most complex and challenging problems of electric industry market. The main aim of economic load dispatch in power system operation control and planning is to satisfy the energy demand at the least cost while fulfilling all the equality and inequality constraints. This paper presents the mathematical formulation of optimal load dispatch problem by considering the sources of energy generation from conventional power plants and all the important constraints of the realistic power system. In the proposed research the memetic optimizer is developed by hybridizing slime mould algorithm with pattern search algorithm. The proposed hSMA-PS has been tested to obtain the solution of economic load dispatch problem and experimentally it has been observed that the proposed memetic optimizer is providing cost effective solution to complex economic load dispatch problem of electric power system.

The article analyses the methods of increasing the efficiency of the processes of airborne objects’ classification along with the possible solutions to the problems of airspace operating procedure. The methods of formalizing and assessing the information quality are developed to classify airborne objects. Formulated is the verification method of the fuzzy logical system’s software used to classify airborne objects. Particular attention is paid to the analysis of the completeness and reliability of the input information, the properties of the mechanism of logical conclusions having deterministic decision-making branches on airborne objects and decision-making branches based on fuzzy logical inference with the formation of the «confidence» vector towards decision options.

The article is just the first part of a group of methods proposed to the scientific community for the purpose of simplifying the processes of identifying airborne objects illegally located in controlled areas and, in particular, verifying the software used for this purpose. It also continues a series of articles dealing with the problems of air navigation.

The paper considers the main methods of synchronization in quantum cryptography systems. A generalized structure of a quantum key distribution system with phase coding of photon states is described. The results of experimental studies of the synchronization process in a two-pass quantum communication system are presented and the features of optical pulse detection are shown. Analytical expressions are given for calculating the error of detecting the length of a quantum channel during synchronization. The estimation of the security of the synchronization process using a multiphoton pulse is given. An optical signal detection algorithm that reduces the probability of an attack on the quantum key distribution system during synchronization is proposed.

Telephones are one of the most common telecommunication devices in the protected premises. The telephone includes various acoustic transducers (speaker, microphone), as a result of which these devices have a microphone effect and can cause leakage of speech information into the telephone network. This paper describes the model of the piezoelectric speaker of the Gigaset DA210 telephone connected to the buzzer circuit using simulations in COMSOL Multiphysics. The piezoelectric (Lead Zirconate Titanate 8) actuator is investigated, a comparison based on the obtained results of acoustic transducers is made with the parameters specified by the piezoelectric speaker designers. The paper studies the characteristics of this piezoelectric speaker as a sensor under the influence of an acoustic field, as a result of which a technical channel of speech information leakage channel. Based on the data obtained, the speech intelligibility assessment in the telephone network is made using the developed model. The results can be used in the development of secure telephones and the educational process in the training of specialists in the area of information security.

The paper addresses problems of spatial distribution of data and computations, organizing data exchange within pool of parallel processes to perform parallelizing optimization with data locality improvement when compiling affine programs for distributed memory systems with MPI support. The presented method of spatial distribution of data and computations rely on polyhedral framework implementing the idea of reducing construction of affine transformations of the program to multi-objective optimization problem. Data and computation placements are constructed accordingly spatial locality principle and satisfies forward communication only property. There is no single master node orchestrating the computational process and storing all the data to be processed – all the parallel processes are equal. Finally, an MPI-program with MPI_send and MPI_recv invocations is generated. A concept of communication polyhedron is introduced for modeling of information exchange within MPI communicator. Three algorithms of linear algebra are taken for benchmarks: LU decomposition, syr2k, atax. Results of parallelization are compared with Pluto compiler output in the aspect of performance.

This article covers the issue of the data preprocessing when highlighting steady-state visual evoked potentials using preliminary band-pass filtering of the EEG signal. In the introduction part the authors illustrate relevance of the system integration such as human-machine interaction and brain-computer interface. The integration of the above-mentioned systems as well as the ways of the signal preprocessing for highlighting of steady-state visual evoked potentials in electroencephalograms were examined. The article contains researches of the electroencephalogram signals with steady-state visual evoked potentials for photostimulation frequencies of 8 and 14 Hz with sampling frequency of 5 kHz based on the multivariate synchronization index method. Influence of preliminary band-pass filtering on recognition accuracy of the signal frequency under study is considered. Ratio of the correctly recognized states is considered in the function of accuracy metric. Butterworth filters, Chebyshev filters of I and II types, elliptic filters as well as Bessel filters of different orders are considered as bend-pass filters. The result of the authors’ investigation is a number of recommendations on parameters used while signal preprocessing for highlighting of steady-state visual evoked potentials in the multivariate synchronization index method. The results obtained are of considerable practical importance as they can be used for brain-computer interface producing on the basis of steady-state visual evoked potentials and later can be taken for building of control theory of robot systems of different application and for implementation of decisions on human-machine interaction within narrow practical tasks.

The paper describes the stages of a smart city's concept from the point of view of transport. Simultaneously, the concept of smart cities is intimately interconnected with the use of unmanned vehicles. The hardware components resolve the unmanned vehicle to see, communicate, and move. The unmanned vehicle software provides the perception of sensor data, planning, and motion control based on the processed data. But the use of the unmanned vehicle is associated with risks and security issues associated with vulnerabilities in both software and hardware. In this regard, the necessity of developing safety management systems for unmanned vehicles is shown, based on identifying risk factors and assessing their degree of danger, as well as examples of sources of the main vulnerabilities of intelligent unmanned vehicles. The necessity of using safety functions is shown to determine how the control system of an unmanned vehicle meets the requirements for this system at all stages of the life cycle.

Analytical expressions are Obtained for calculating the probabilities of correct detection and false alarm of a two-channel inertial detector of pulsed optical radiation in the photon counting mode, containing a receiving optical complex of two-lens antennas, two single-photon photoemission devices and an electronic adder.

Spectrum awareness with energy preservation is a primary requirement in Cognitive Radio Sensor Networks as the sensor nodes are energy constrained and operate in physically unattended areas. Cluster based routing coupled with channel allocation achieves energy efficient path selection as a countermeasure to conserve energy. The proposed protocol in this work implements reliable routing with cluster based channel allocation which contributes in enhancement of network performance. Shortest path is computed for source to destination using Dijkstra’s algorithm and simulation results show that proposed protocol achieves higher packet delivery with minimum delay, optimizing energy consumption than the existing approach.

Partitioning is one of the most important problems at the design stage during the Very Large Scale Integrated (VLSI) manufacture. The article provides a description of this problem and its formal statement as partitioning of a hypergraph into parts. Partitioning belongs to the NP-hard class of optimization problems. A combination of swarm intelligence and genetic search methods made it possible to develop a hybrid approach to partitioning of VLSI circuits. A distinctive feature of this approach is to divide search process in two stages. At the first stage, search space is reduced by allocation of areas with high objective function values on the basis of a bee colony optimization method. As a result, an effective initial population of alternative solutions is generated. At the second stage, optimization of obtained solutions can be implemented with the use of the genetic search method. The suggested approach is supported by a hybrid algorithm which can obtain quazi-optimal solutions in polynomial time and avoid falling into local optima. A new software application has been developed to confirm the effectiveness of the suggested approach and hybrid algorithm. Experiments have been carried out on the basis of IBM benchmarks. The results of experiments show that the quality of solutions obtained by the suggested algorithm exceeds on average of 5% the well-known partitioning algorithm hMetis. Time complexity of the developed algorithm can be represented as \(O\left( {n^{2} } \right)\) in the best case and \(O\left( {n^{3} } \right)\) in the worst case.

Internet of Things (IoT) consists of heterogeneous nodes that consumes more network resources due to this network lifetime is foreshorten. To enhance network lifetime, dynamic cluster head selection algorithm (DCHSA) is propounded in this work. This algorithm combines both tree and cluster based data aggregation that classifies cluster head (CH) into primary cluster head (PCH) and secondary cluster head (SCH) to improve energy efficiency and network lifetime. The proposed work provides fault tolerance whenever primary cluster head fails and secondary cluster head takes over the task of primary cluster head. The data sent from individual node in the cluster is collected and aggregated by the cluster head. Further tree based data aggregation scheme is proposed to send the data from PCH to base station. The results obtained through simulation outperforms with respect to energy efficiency, lifetime of the network and energy dissipation in comparison with existing works.

Many real life problems that play a major role in human lives are mostly optimization problems and need to be solved in order to judiciously utilize vital resources. The vast solution space consists of a large number of feasible solutions. Solving these problems requires finding the most optimal solution while satisfying the constraints imposed (if any). Vehicle Routing is a real life problem originated primarily in the logistics industry where the consignments are to be delivered to the clients in such a way, that there is minimal usage of resources like fuel, and time. In addition, the consignments are to be successfully delivered to the clients through the shortest route and utilizing the maximum capacity of each vehicle. Automation is the key to solve such large problems while reducing effort and complexity of the solution. Literature reveals that nature inspired algorithms have proved their ability for solving such large complex optimization problems. These algorithms are inspired from various natural phenomena and are supported by their successful survival. This paper proposes a hybrid framework to solve Vehicle Routing Problem (VRP) utilizing Differential Evolution and Marine Predators algorithm. We have considered the variant called Capacitated Vehicle Routing Problem (CVRP) to conduct experiments and compared the results to evaluate the performance of proposed hybrid approach DEMPA with Differential Evolution (DE). Results indicate the superiority of the proposed approach over Differential Evolution.

Internet of things (IoT) basically refers to anything that is connected with the internet. IoT is one of the emerging technologies which acts as a bridge between the physical and cyber world. There are many places where the data collection is important but continuous supply of energy is not possible. Energy consumption is one of the most important issues faced in the IoT devices which are battery operated. This paper addresses such issues and techniques for energy conservation. In this paper we have discussed various sources of energy dissipation, causes for energy dissipation and their solutions. We have also surveyed some of the mile stone papers in the area of energy conservation along with the latest protocols. We have critically analyzed and tabulated our findings.

The article is devoted to the problem of identifying implicit information threats of user’s search activity in the Internet based on the analysis of activity during the interaction process. Application of knowledge stored in the Internet space for the implementation of criminal intentions poses a threat to the whole society. Identifying malicious intents in the users’ actions in the global information network is not always a trivial task. Modern technologies for analyzing the context of users’ interests can fail in terms of cautious and competent actions of malicious users, who do not demonstrate their intentions explicitly. The paper analyzes the threats related to certain scenarios for implementing the search procedures, which are expressed in the search activity. Authors present an approach to classification of the mentioned threats considering the given criteria of estimating different scenarios of the user’s behavior in the global information space. The article describes the developed algorithm of machine learning to identify the problem scenarios by comparing them with the key patterns of behavior. To implement the proposed approach, the authors developed software implementing the subsystem for identifying information threats. The experimental research proves the effectiveness of the developed subsystem.

In the real world, it is essential to establish efficient routes in the Internet of Things (IoT) since sensor nodes operate mainly on battery and have limited energy. In this paper, an energy efficient routing technique is proposed to select an optimal routing path from source to destination for the transmission of data. Genetic algorithm (GA) is an optimization technique which is integrated into the proposed work to select an optimal path among the available paths. Since the proposed method selects an optimal path from source cluster head to the sink, this reduces the energy consumption and improves the lifetime of network. The validity of the proposed algorithm is evaluated in MATLAB simulation tool and results generate superiority while considering parameters such as energy consumption, delay and number of rounds.

A combined algorithm for a time series analysis is considered based on two basic methods: the empirical mode decomposition and kernel regression. The essence of the presented algorithm is the sequential calculation of nuclear regressions and residues, which results in the decomposition of the original series into an additive mixture of the number of regressions and residual series. The illustrative examples for the application of the proposed algorithm (immunology, economics, and other fields of studies) are provided along with their statistical results of numerical simulation. The results obtained would be useful for a smart control system design and real-time decision making support as it concerns the problems of stochastic control over a wide range of poorly formalized objects from various applied areas.

Smart waste management is one of the most important services in smart cities. An efficient waste collection system based on IoT technologies allows having the streets clean and reducing resource consumption by optimizing garbage collecting trucks’ routes. In this situation, the present work proposes a smart management system based on LoRaWAN. This solution seeks to mitigate the presence of containers that are at their maximum storage capacity. This solution will contribute to mitigate the spread of diseases due to it avoids having waste in the open air. Different tests of the proposed solution (i.e. communication distance, speed, and energy consumption) were performed to determinate the possibility of real implementations.

The paper considers the task of building the ontological structure of knowledge in the intelligent assistant systems to reduce the structural and semantic conflicts in the process of searching, accumulation, and processing of information objects in the Internet. The authors develop the semantic net providing the integrated representation of knowledge on user preferences, semantic images of the Internet resource, and search domain in the ontological model. To solve the problem, we propose a method for cluster analysis of the Internet-object structure. This allows us to divide the vector space of the features into semantic clusters with the constraints on the hidden patterns features revealing the content risks. To carry out the experiments on a test set of search queries, we developed a search module for the intelligent assistant system. The estimated relevance coefficient of “query-resource” is 60% higher than manually formed user preferences in popular search systems.

Unmanned Aerial Vehicle is an aircraft that operates and flies without a human pilot. It can reach at places where humans may not reach easily, such as search and rescue operations, earthquake mapping and flood mapping. It is additionally valuable for autonomous tasks such as the delivery of any item and target tracking which requires self-governing navigation. Motivated by the mentioned applications, in this paper we present a deep learning model for self-governing navigation of UAV. Our model exploits transfer learning from a well-known network architecture called MobileNet and it is trained on a dataset of images, collected from the various indoor environments. From an image, the model classifies actions such as either to go forward or to stop. Furthermore, after some experiments and results, we infer that among all Convolution Neural Network (CNN) architectures, the MobileNet architecture is ideal and appropriate for our purposed approach.

The term «Internet of Medical Things» (IoMT) refers to a set of devices and technologies for remote monitoring of patients’ health using wearable devices. One primary problem with patient’s data is ensuring privacy when it is transmitted over open communication channels and stored in cloud systems. A whole range of different approaches to these issues are available. However, when it comes to millions of IoT devices, technologies that have already become classic for Internet resources are not suitable in many aspects at once. The aim of this work is to develop methods and protocols for secure interaction between portable diagnostic devices and cloud services for the analysis and processing of medical data in the Internet of Medical Things networks. The work considered existing technologies and solutions for ensuring security in IoMT networks and personalized medicine systems; also, it focused on secure machine learning methods. Previous studies have emphasized attribute-based encryption (ABE) as a prospective method for data privacy and security. These algorithms solve many problems for IoMT applications: patient’s data confidentiality, flexible key management, fine-grained access control mechanisms, and user control over data. We have proposed a framework for processing patient data from portable diagnostic devices using ABE methods.

An intelligent autonomous robot is in demand for robotic operations in the fields such as industry, medical, bionics, military. For any machine, designed to follow a precise sequence of instructions, self-positioning, path framing, map architecture, and obstacle prevention are the prerequisites of navigation. This paper presents a survey about the key navigation approaches explored by various authors in the last decade. The survey has a brief insight into the various approaches used for robot navigation concerning to the variable and invariable nature of the vicinity and the obstacle. The comprehensive look-over presented in this paper provides an in-depth analysis and assessment of the discrete classical and heuristic approaches used by the researchers. The research assessment is finally concluded by aggregating the complete knowledge of the various path planning techniques by reviewing the literature.

Wireless self-organizing networks are becoming more popular than usual networks. With the help of this type of network, it is possible to implement special purpose systems of varying complexity and focus, from search missions to automated production in the IIoT systems. One of the main problems in this class of networks is the problem of routing. This paper presents clustering methods for further implementation as part of zone-based protocols. This work will help automate and organize devices into groups with the subsequent automation of routing configuration processes.

The number of systems responsible for the processing and transmission of sensory information is steadily growing, which naturally gives rise to the need for a scalable trusted Platform that provides the formation of end-to-end processes in various priority sectors of the economy and social sphere and is an automated information control system for collecting and processing sensory information.

When designing such a system, it is important to pay sufficient attention to the elaboration of the information security issue, which inevitably is based on the development of a threat model.

The proposed Platform is subdivided into 6 subsystems: micromodule subsystem, end device subsystem, border gateway subsystem, cloud service subsystem, operating system subsystem, and user application subsystem.

This article proposes a threat model for each subsystem of the Platform, as well as lists of threats to the Platform’s subsystems and their operating environments.

Navigation of mobile robot with obstacle avoidance is a successful research area owing to its comprehensive applications. Secure and smooth mobile robot navigation through different (static and dynamic) environments for single and multiple robot system to attain its goal with following secure path and producing a most fulfilling end result is the principal purpose of navigation. Many techniques are developed for mobile robot navigation. This paper proposes the soft computing techniques used in mobile robot navigation namely fuzzy logic, neural network and neuro-fuzzy. This paper concludes with strength, limitations, efficiency and tabular data of each methods.

The article considers a system for monitoring the state of hydrolytospheric processes in the region. The results of pilot filtration work (PFW) at the field under consideration are presented. Based on PFW, a methodology for determining the parameters of a link approximating the static coefficients of mutual influence of producing wells is shown. Using the obtained link, the procedure for determining the optimal number of producing wells located on a given size section is shown. Using the results of PFW, the parameters of the discrete mathematical model of the hydrolyte-sphere processes of the field were verified. The technique of designing a distributed network for controlling the flow rate of producing wells of a given field is shown. Using a verified mathematical model of the hydrolyte-sphere process, the operation of a closed control system was simulated.

The article presents an analysis of factors on which the activation of complex natural processes with catastrophic consequences depends. The model for forecasting catastrophic consequences of natural processes using the Bayesian belief network is proposed. The tops of the Bayesian network have been singled out, the expert estimation of possible values of indicators and training of the Bayesian network based on expert estimations has been carried out. The factor “Investments” was proposed as a managing influence on the network. Modeling and forecasting of possible development scenarios of complex natural processes and their catastrophic consequences were carried out. It is proposed to use Bayesian networks in building a decision support system for forecasting and assessment of risks of catastrophic consequences from damage caused by hazardous natural processes.