Base Station Network Research Articles

Heterogeneous load balancing clustering protocol for Wireless Sensor Networks

Wireless Sensor Networks is one of the most significant area of research where large number of sensor nodes that are distributed in a geographical area operate on limited battery power. As these networks, depending on the application, are sometimes deployed in in-hostile environment, which makes energy conservation one of the major challenge faced in WSN. To reduce energy consumption, clustering is considered to be the most efficient technique. This work proposes a new clustering approach that decreases energy consumption and results in prolonged network lifetime which is an important requirement for networks operating in inaccessible areas. In the proposed approach, heterogeneity is also implemented to increase the stability and energy efficiency of random networks. We have evaluated the efficiency of the proposed protocol through simulations and comparison is performed with well-known existing distributed protocols. Proposed approach shows efficient results in terms of the stability period for different network configurations and Base Station positions. Also, the results are found better in terms of number of alive nodes and network lifetime.

S-CDCA: a semi-cluster directive-congestion protocol for priority-based data in WSNs

The internet of things (IoT) protocols and regulations are being developed forvarious applications includes: habitat monitoring, machinery control, general health-care, smart-homes and more. A great part of I0T comprised of sensors nodes in connected networks (i.e. sensor networks.). A sensor network is a group of nodes with sensory module and computational elements connected through network interfaces. The most interesting type of sensor networks are wireless sensor networks. The nodes here are connected through wirless interfaces. The shared medium between these nodes, creates different challenges. Congestion in such network is ineavitable. Different models andmethods were proposed to alleviate congestion in wireless sensor networks.This paper presents a semi-cluster directive congestion method that allivatenetwork congestion forpriority-baseddata transmission. The method simprove the network performance by implementing temporary cluster forlow level priority data packets while providing a clear link between highpriority data source node and the network base station. Simulation resultsshow that. The proposed method outperformes ad hocOn-demand distance vector (AODV) reactive procotol approach and priority-based congestion control dynamic clustering (PCCDC) a cluster-based methodin network energy consumption and control packets overhead during network operation.The proposed method also shows comparative improvments in end-to-enddelays versus PCCDC.

Intelligent Data Collaboration in Heterogeneous-device IoT Platforms

The merging boundaries between edge computing and deep learning are forging a new blueprint for the Internet of Things (IoT). However, the low-quality of data in many IoT platforms, especially those composed of heterogeneous devices, is hindering the development of high-quality applications for those platforms. The solution presented in this article is intelligent data collaboration, i.e., the concept of deep learning providing IoT with the ability to adaptively collaborate to accomplish a task. Here, we outline the concept of intelligent data collaboration in detail and present a mathematical model in general form. To demonstrate one possible case where intelligent data collaboration would be useful, we prepared an implementation called adaptive data cleaning (ADC), designed to filter noisy data out of temperature readings in an IoT base station network. ADC primarily consists of a denoising autoencoder LSTM for predictions and a four-level data processing mechanism to perform the filtering. Comparisons between ADC and a maximum slop method show ADC with the lowest false error and the best filtering rates.

A Hybrid Algorithm for Changepoint Aware Long-Term Seasonality Detection of Mobile Network Base Stations

Automated capacity planning for mobile networks requires long-term forecasting of traffic demand by using historical patterns. To decide the correct time of investment and correct capacity expansion size or to improve the accuracy of forecasting algorithms with exogenous features, both seasonal decomposition, and seasonal period identification improves decision accuracy. We design a hybrid algorithm to calculate these features on live network data with improved accuracy which uses piecewise Seasonality Trend Decomposition with Loess (STL) decomposition and Prophet library’s regression with Laplace prior under the hood. Combining both methods with the awareness of their weak and strong parts and leveraging overall output with changepoint and similarity analysis help us to improve our accuracy around 18.6% comparing the average of single usage of these methods. We also provide and present some special cases that increase problem complexity and decrease decomposition accuracy.

A network controlled vertical handoff mechanism for heterogeneous wireless network using optimized support vector neural network

PurposeVertical handoff mechanism (VHO) becomes very popular because of the improvements in the mobility models. These developments are less to certain circumstances and thus do not provide support in generic mobility, but the vertical handover management providing in the heterogeneous wireless networks (HWNs) is crucial and challenging. Hence, this paper introduces the vertical handoff management approach based on an effective network selection scheme.Design/methodology/approachThis paper aims to improve the working principle of previous methods and make VHO more efficient and reliable for the HWN.Initially, the handover triggering techniques is modelled for identifying an appropriate place to initiate handover based on the computed coverage area of cellular base station or wireless local area network (WLAN) access point. Then, inappropriate networks are eliminated for determining the better network to perform handover. Accordingly, a network selection approach is introduced on the basis ofthe Fractional-dolphin echolocation-based support vector neural network (Fractional-DE-based SVNN). The Fractional-DE is designed by integrating Fractional calculus (FC) in Dolphin echolocation (DE), and thereby, modifying the update rule of the DE algorithm based on the location of the solutions in past iterations. The proposed Fractional-DE algorithm is used to train Support vector neural network (SVNN) for selecting the best weights. Several parameters, like Bit error rate (BER), End to end delay (EED), jitter, packet loss, and energy consumption are considered for choosing the best network.FindingsThe performance of the proposed VHO mechanism based on Fractional-DE is evaluated based on delay, energy consumption, staytime, and throughput. The proposed Fractional-DE method achieves the minimal delay of 0.0100 sec, the minimal energy consumption of 0.348, maximal staytime of 4.373 sec, and the maximal throughput of 109.20 kbps.Originality/valueIn this paper, a network selection approach is introduced on the basis of the Fractional-Dolphin Echolocation-based Support vector neural network (Fractional-DE-based SVNN). The Fractional-DE is designed by integrating Fractional calculus (FC) in Dolphin echolocation (DE), and thereby, modifying the update rule of the DE algorithm based on the location of the solutions in past iterations. The proposed Fractional-DE algorithm is used to train SVNN for selecting the best weights. Several parameters, like Bit error rate (BER), End to end delay (EED), jitter, packet loss, and energy consumption are considered for choosing the best network.The performance of the proposed VHO mechanism based on Fractional-DE is evaluated based on delay, energy consumption, staytime, and throughput, in which the proposed method offers the best performance.

A novel sizing method of a standalone photovoltaic system for powering a mobile network base station using a multi-objective wind driven optimization algorithm

A new multi-objective wind driven optimization algorithm is proposed to size a standalone photovoltaic system’s components to meet the load demand for a mobile network base station at a 1% loss of load probability or less with a minimum annual total life cost. To improve the sized model’s accuracy, a long short-term memory deep learning model is utilized to forecast the hourly performance of a photovoltaic module. The long-term memory model’s performance is compared with those obtained by a linear photovoltaic model and an artificial neural network model. The comparison is carried out based on the values of normalized root mean square error, normalized mean bias error, mean absolute percentage error, and the training and testing time. Accordingly, on the values obtained for these statistical errors, the long short-term memory model outperforms better than the linear model and the artificial neural network model based. In addition, a dynamic battery model is utilized to characterize the dynamic charging and discharging process. The findings show that the optimal number of the photovoltaic array and the capacity of the storage battery required to cover the load demand of a mobile network base station are 5.4 kWp and 2640 Ah/48 V, respectively. Besides, the annual total life cycle cost for the sized photovoltaic/battery configuration is 4028.33 AUD/year. The simulation time for the proposed method is 421.25 s. To generalize the sizing results for the mobile network base stations based on Sydney weather conditions, the photovoltaic array and storage battery ratios are calculated as 0.324 and 0.223, respectively. In addition, the cost of an energy unit generated by the optimized system is 0.254 AUD/kWh. Here, the results of the proposed method have been compared with those obtained by developed and recent benchmark published methods. The comparison outcomes show the effectiveness of the proposed method in terms of providing a high availability sized system at minimum cost within less simulation time than the other considered methods.

On the Power of Microwave Communication Data to Monitor Rain for Agricultural Needs in Africa

Over the last two decades, prevalent technologies and Internet of Things (IoT) systems have been found to have potential for carrying out environmental monitoring. The data generated from these infrastructures are readily available and have the potential to provide massive spatial coverage. The costs involved in using these data are minimal since the records are already generated for the original uses of these systems. Commercial microwave links, which provide the underlying framework for data transfer between cellular network base stations, are one example of such a system and have been found useful for monitoring rainfall. Wireless infrastructure of this kind is deployed widely by communication providers across Africa and can thus be used as a rainfall monitoring device to complement the sparse proprietary resources that currently exist or to substitute for them where alternatives do not exist. Here we focus this approach’s potential to acquire valuable information required for agricultural needs across Africa using Kenya as an example.

Development of algorithms for data transmission in sensor networks based on fully homomorphic encryption using symmetric Kuznyechik algorithm

The article presents the development of algorithms for data transmission in wireless sensor networks based on fully homomorphic encryption (FHE) using a symmetric encryption algorithm Kuznyechik. During the study, the main points related to the use of modern sensor networks were considered: data transfer protocols, the most common vulnerabilities and various emulators of sensor networks. Approaches to the use of homomorphic encryption for data exchange are considered. An algorithm for the interaction of the base station and sensor nodes of the sensor network for data exchange based on FHE using the Kuznyechik algorithm has been developed. To use the encryption algorithm Kuznyechik as part of the FHE, an algorithm for constructing Zhegalkin polynomials for nonlinear cipher elements has been developed and implemented. For the encryption algorithm Kuznyechik, for the first time, Zhegalkin polynomials are obtained, which describe the operation of the replacement S-block. The use of the obtained Zhegalkin polynomials makes it possible to decrypt the data encrypted by the Kuznyechik algorithm and at the same time leave them in the encrypted form of the FHE.

Integrated Management System for Coal Mine Locomotive Transportation

<p>Combined with the reconstruction project of underground locomotive transportation system in Guqiao Coal Mine of Huainan Mining Group, the dissertation studies the comprehensive management system of coal mine locomotive transportation. The accuracy of ZigBee wireless positioning technology is improved through Gaussian filtering to make it applicable to more complex underground coal mine environment. Frequency conversion control is used in the motor control circuit to realize real-time calculation of the running speed of the locomotive. By analyzing the control scheme of the turnout and traffic lights, the explosion isolation and essential safety control sub-station suitable for gas mine are designed. Communication platform construction of the system is realized through wireless base station of Benan mine and industrial network of coal mine. System relevant data can be uploaded to the ground dispatching center in real time, so that the scheduling staff can reasonably arrange the scheduling tasks in a timely manner. The optimized logistics management and storage management system makes the material dispatching and transportation more reasonable and scientific. Through the analysis of the running data, the construction and personnel management of the locomotives are realized.</p>

ПРИМЕНЕНИЕ ЦИФРОВОЙ АЭРОФОТОСЪЕМКИ И ВОЗДУШНОГО ЛАЗЕРНОГО СКАНИРОВАНИЯ ДЛЯ ОЦЕНКИ СОСТОЯНИЯ РИСОВЫХ СИСТЕМ

The article discusses the use of digital aerial photography and aircraft laser scanning to assess the state of rice systems. An analysis of the state of affairs in rice growing shows that today there is not enough financial resources not only for the reconstruction and technical re-equipment of outdated elements of rice systems, updating technologies and equipment in accordance with the achievements of science and best practices, but also to maintain the existing technical level of the systems. The article presents the results of a study of planning technology options and the prospects for its improvement based on the introduction of laser control systems. Rational use of water, material and labor resources is the main reserve for increasing the efficiency of rice systems. The resource-saving potential contributes to the intensification of the production activities of water users. Rice is the most productive and most water-intensive crop. Rice cultivation in Russia was most widespread in the Kuban, occupying a technologically advanced position. Rice irrigation systems occupy once unproductive lands with a thin humus horizon, with a low humus content and unfavorable water-physical properties, prone to swimming and the formation of a surface soil crust. Research has shown that significant improvements in planning quality in the construction of rice paddies can be achieved with laser control systems. As part of the modern development of aerial survey work, a network of base stations, reference and control points is being created to conduct a technical survey of the structures of the rice system. Measurements are made using GNSS satellite measurements. The distance between base stations is no more than 60 km, between control points – no more than 20 km. At each 20-kilometer aerial survey site, it is necessary to provide four planned altitude landmarks and one control point. Identification marks and control points should be applied on the ground, the coordinates of these points should be determined by instrumental geodetic methods for conducting a technical survey of the structures of rice systems.

Моделирование процесса позиционирования источников радиосигналов в геоинформационных системах

The application of the tools of the Anylogic simulation model development environment is described, which makes it possible to clearly understand the basic principles of identifying mobile objects that are sources of radio emission. The article presents the main research results of various approaches to solving the problem of positioning radio signal sources based on measuring the level of its power by base stations of wireless data transmission networks. The positioning problem was solved under conditions of high mobility of the radio signal source. To solve the problem, a set of methods was used: trilateration, fuzzy logic, statistical test method (Monte Carlo), splitting method, as well as heuristic. The results obtained were tested in the developed prototypes in the form of simulation models in the Anylogic environment. Field measurements made in real operating conditions of positioning systems were used as initial data. Within the framework of this work, the following main results were obtained: implementation of a complex of modeling algorithms that carry out a number of random processes: motion of radio signal sources and propagation of radio waves; algorithms that implement the analytical calculation of the estimated location area based on various methods. A complex of simulation models of positioning systems has been developed, which makes it possible to visualize the determination of the location of a radio signal source in an urban environment, taking into account its mobility. Conclusions are formulated regarding the results of the conducted research. The scientific significance of the work lies in the development of a heuristic positioning algorithm based on sector partitioning and fuzzy logic. The practical value of the work lies in the development of a set of algorithms and their practical implementation in the form of applications in the Java language for the Anylogic modeling environment, which allow investigating the effectiveness of various predictive algorithms for positioning accuracy.

ATOMIC: Adaptive Transmission Power and Message Interval Control for C-V2X Mode 4

To support the emerging vehicle-to-everything (V2X) communication for autonomous vehicles and smart transportation services, 3rd Generation Partnership Project (3GPP) has recently introduced cellular V2X (C-V2X) standards. In C-V2X, radio resources can be managed not only centrally by the cellular network base station, but also in a completely distributed manner ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Mode 4</i> ) without any cellular support. However, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Mode 4</i> may suffer significant collisions and interference in a dense environment since each vehicle selects its own resources to transmit V2X messages autonomously without adapting appropriately to vehicle density. To address this challenge, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ATOMIC</i> , an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Adaptive Transmission pOwer and Message Interval Control scheme for C-V2X Mode 4</i> , in which each vehicle utilizes real-time channel sensing and neighbor information to reduce channel contention for improved reliability and latency. Through analysis and extensive simulations, we show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ATOMIC</i> outperforms the standard <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Mode 4</i> in both urban and highway scenarios especially in highly dense environments.

Detection, Traceability, and Propagation of Mobile Malware Threats

In recent years, the application of smartphones, Android operating systems and mobile applications have become more prevalent worldwide. To study the traceability, propagation, and detection of the threats, we perform research on all aspects of the end-to-end environment. With machine learning based on the mobile malware detection algorithms that integrate the dynamic and static research of the identification algorithm, application software samples are collected to study sentences. Through knowledge labeling and knowledge construction, the association relationship of knowledge is extracted to realize the research of knowledge map construction. Flooding is closely correlated with the complexity of the Android mobile version of the kernel and malicious programs. A static dynamic analysis of the mobile malicious program is carried out, and the social network social diagram is constructed to model the propagation of the mobile malicious program. We extended the approach of deriving common malware behavior through graph clustering. On this basis, Android behavior analysis is performed through our virtual machine execution engine. We extend the family characteristics to the concept of DNA race genes. By studying SMS/MMS, Bluetooth, 5G base station networks, metropolitan area networks, social networks, homogeneous communities, telecommunication networks, and application market ecosystem propagation scenarios, we discovered the law of propagation. In addition, we studied the construction of the mobile Internet big data knowledge graph. Quantitative data for the main family chronology of mobile malware are obtained. We conducted detailed research and comprehensive analysis of Android application package (APK) details and behavior, relationship, resource-centric, and syntactic aspects. Furthermore, we summarized the architecture of mobile malware security analysis. We also discuss encryption of malware traffic discrimination. These precise modeling and quantified research results constitute the architecture of mobile malware analysis.

Cоздание высокоточных цифровых моделей высот с использованием ГНСС-БПЛА

Unmanned Aerial Vehicles (UAVs) are increasingly involved in surveying work, becoming a reliable basis for information on three-dimensional terrain features. Until now, ground reference points have been used to provide reliable planimetric evidence for measurements from drones. Their placement and coordinates measurement takes quite a long time, which increases proportionally to the area under study. In addition, the use of these marks produces model distortions (especially over large areas of 1 sq. km or more), which then lead to the appearance of areas of local depressions and uplifts where they are not present in the terrain. These distortions arise due to camera position optimization error minimization algorithms and cannot be corrected. Refusing to use ground control points leads to other geometric distortions associated with characteristics of survey system lenses mounted on UAVs (so-called “saddle-shaped” models). This paper presents the results of high-precision digital elevation models creation using built-in UAV Global Navigation Satellite System (GNSS) receivers. The methodology has been tested at ten sites in the Zakamye region of the Republic of Tatarstan (Russia) with an area ranging from 4 to 58 hectares. Correction of GNSS rover position was performed in post-processing from a virtual base station network located at a 6 to 70 km range from the surveyed sites. For all objects, DEM errors did not exceed 0.05 m on axes X, Y, and Z. At the same time, the dependence of the error value on the study area size was not revealed. The received results of the analysis of errors can indicate the prospect of the use of low-cost GNSS-UAVs without the necessity of organizing a network of ground reference points, being limited only to the necessity of installing control points will repeatedly reduce the time of the field works, in particular on hardly accessible objects.

SPATIAL CHARACTERIZATION OF TELECOMMUNICATION SATELLITES VISIBLE ABOVE THE REPUBLIC OF SERBIA

This paper deals with the general technical, spatial, and temporal characteristics of satellite telecommunications systems. Particular attention was paid to the peculiarities of the territory of the Republic of Serbia in terms of implementation and use of modern satellite telecommunication infrastructure. The overview shows how and on which way to use various satellite telecommunication systems to configure and exploit next-generation networks, especially modern communications such as 5G technology and IoT. Their work cannot be imagined without the high speeds and high frequencies that allow us to transmit a wealth of information - from short messages/news to HD video on a mobile phone. We need either a base station network or in the case of the high seas or areas where the base station system is difficult to imagine, we need satellite communication. The paper presents data that give a numerical and graphical overview of geostationary satellites visible from the territory of the city of Belgrade, depending on the orbital position and the associated angle of azimuth and elevation, which would also be valid for the leading territory of Serbia.

Centralized Spectrum Sharing and Coordination Between Terrestrial and Aerial Base Stations of 3GPP-Based 5G Networks

Centralized Spectrum Sharing and Coordination Between Terrestrial and Aerial Base Stations of 3GPP-Based 5G Networks

Heuristic Edge Server Placement in Industrial Internet of Things and Cellular Networks

Rapid developments in industry 4.0, machine learning, and digital twins have introduced new latency, reliability, and processing restrictions in Industrial Internet of Things (IIoT) and mobile devices. However, using current information and communications technology (ICT), it is difficult to optimally provide services that require high computing power and low latency. To meet these requirements, mobile-edge computing is emerging as a ubiquitous computing paradigm that enables the use of network infrastructure components such as cluster heads/sink nodes in IIoT and cellular network base stations to provide local data storage and computation servers at the edge of the network. However, optimal location selection for edge servers within a network out of a very large number of possibilities, such as to balance workload and minimize access delay, is a challenging problem. In this article, the edge server placement problem is addressed within an existing network infrastructure obtained from Shanghai Telecom's base station data set that includes a significant amount of call data records and locations of actual base stations. The problem of edge server placement is formulated as a multiobjective constraint optimization problem that places edge servers strategically to balance between the workloads of edge servers and reduce access delay between the industrial control center/cellular base stations and edge servers. To search randomly through a large number of possible solutions and selecting those that are most descriptive of optimal solution can be a very time-consuming process, therefore, we apply the genetic algorithm and local search algorithms (hill climbing and simulated annealing) to find the best solution in the least number of solution space explorations. Experimental results are obtained to compare the performance of the genetic algorithm against the above-mentioned local search algorithms. The results show that the genetic algorithm can quickly search through the large solution space as compared to local search optimization algorithms to find an edge placement strategy that minimizes the cost function.

Socially Aware Device-to-multi-device User Grouping for Popular Content Distribution

The distribution of popular videos incurs a large amount of traffic at the base stations (BS) of networks. Device-to-multi-device (D2MD) communication has emerged an efficient radio access technology for offloading BS traffic in recent years. However, traditional studies have focused on synchronous user requests whereas asynchronous user requests are more common. Hence, offloading BS traffic in case of asynchronous user requests while considering their time-varying characteristics and the quality of experience (QoE) of video request users (VRUs) is a pressing problem. This paper uses social stability (SS) and video loading duration (VLD)-tolerant property to group VRUs and seed users (SUs) to offload BS traffic. We define the average amount of data transmission (AADT) to measure the network’s capacity for offloading BS traffic. Based on this, we formulate a time-varying bipartite graph matching optimization problem. We decouple the problem into two subproblems which can be solved separately in terms of time and space. Then, we propose the socially aware D2MD user selection (SA-D2MD-S) algorithm based on finite horizon optimal stopping theory, and propose the SA-D2MD user matching (SA-D2MD-M) algorithm to solve the two subproblems. The results of simulations show that our algorithms outperform prevalent algorithms.

Hygienic assessment of mobile communication base stations electromagnetic field exposure

The mobile networks base stations electromagnetic field exposure is the important subject of hygienic assessment, control, monitoring and significant concern in modern society. The Russian assessment approach needs electromagnetic field of multiple sources exposure estimation improvement by application of selective methods for the worst possible case. The general scheme for maximal electromagnetic field exposure assessment from GSM, UMTS, LTE and 5G/NR modern base stations on the basis of international approach are considered. The results of selective electromagnetic field measurements performed for actual and maximal possible exposure assessment in the vicinity of LTE base station are presented.

LTE Signal Detector for LSA Spectrum Sharing Model in Portugal

This paper focuses on the development of an RF sensor, essential to the deployment of the Licensed Shared Access (LSA) concept, in Portugal. This device, the “LSA warner”, which receives and processes RF signals, is used to detect the presence of long-term evolution (LTE) signals in the 2.3–2.4 GHz band. A field-programmable gate array (FPGA) alongside with a software-defined radio (SDR) are the foundation for the implementation of the sensor. By using a partial LTE cell search and the received signal power, the system is able to detect the presence of LTE signals and send that information, together with the location of the device, to a repository. A communication protocol between the LSA warner and the LSA repository was developed and tested to control the mobile network base stations through the sensor. The LSA warner was tested in the field under realistic operating conditions in a pilot network that reproduced an LSA network environment.