Radio Access Technologies Research Articles

Design and Evaluation of Steganographic Channels in Fifth-Generation New Radio

Mobile communication is ubiquitous in everyday life. The fifth generation of mobile networks (5G) introduced 5G New Radio as a radio access technology that meets current bandwidth, quality, and application requirements. Network steganographic channels that hide secret message transfers in an innocent carrier communication are a particular threat in mobile communications as these channels are often used for malware, ransomware, and data leakage. We systematically analyze the protocol stack of the 5G–air interface for its susceptibility to network steganography, addressing both storage and timing channels. To ensure large coverage, we apply hiding patterns that collect the essential ideas used to create steganographic channels. Based on the results of this analysis, we design and implement a network covert storage channel, exploiting reserved bits in the header of the Packet Data Convergence Protocol (PDCP). the covert sender and receiver are located in a 5G base station and mobile device, respectively. Furthermore, we sketch a timing channel based on a recent overshadowing attack. We evaluate our steganographic storage channel both in simulation and real-world experiments with respect to steganographic bandwidth, robustness, and stealthiness. Moreover, we discuss countermeasures. Our implementation demonstrates the feasibility of a covert channel in 5G New Radio and the possibility of achieving large steganographic bandwidth for broadband transmissions. We also demonstrate that the detection of the channel by a network analyzer is possible, limiting its scope to application scenarios where operators are unaware or ignorant of this threat.

Efficient Radio Resource Management in Cell-less Wireless Communication Systems

In this paper, the particle swarm optimization (PSO) method with dynamic generation of biasing factors is used to determine the optimal particle size, maximize cell spectral efficiency (CSE) and balance the load in 5G networks. This work studies two distinct interference scenarios: in the first approach, CSE is calculated with varying numbers of users, when different radio services are used by each tier (when several radio access technologies are used), and when interference is received by the consumer only from the same tier base stations (BSs). In the second approach, interference is created when all levels use the same radio services and interference from BSs belonging to the same tier and other tiers is received by the consumer. Simulation results show that the cell-less network performs better than the cellular network in terms of maximizing CSE and balancing the load.

Combining 5G New Radio, Wi-Fi, and LiFi for Industry 4.0: Performance Evaluation.

Fifth-generation mobile networks (5G) are designed to support enhanced Mobile Broadband, Ultra-Reliable Low-Latency Communications, and massive Machine-Type Communications. To meet these diverse needs, 5G uses technologies like network softwarization, network slicing, and artificial intelligence. Multi-connectivity is crucial for boosting mobile device performance by using different Wireless Access Technologies (WATs) simultaneously, enhancing throughput, reducing latency, and improving reliability. This paper presents a multi-connectivity testbed from the 5G-CLARITY project for performance evaluation. MultiPath TCP (MPTCP) was employed to enable mobile devices to send data through various WATs simultaneously. A new MPTCP scheduler was developed, allowing operators to better control traffic distribution across different technologies and maximize aggregated throughput. Our proposal mitigates the impact of limitations on one path affecting others, avoiding the Head-of-Line blocking problem. Performance was tested with real equipment using 5GNR, Wi-Fi, and LiFi -complementary WATs in the 5G-CLARITY project-in both static and dynamic scenarios. The results demonstrate that the proposed scheduler can manage the traffic distribution across different WATs and achieve the combined capacities of these technologies, approximately 1.4 Gbps in our tests, outperforming the other MPTCP schedulers. Recovery times after interruptions, such as coverage loss in one technology, were also measured, with values ranging from 400 to 500 ms.

Synergizing intelligent signal processing with wavelength-division multiplexing for enhanced efficiency and speed in photonic network communications

Abstract The explosive growth of worldwide mobile data traffic seeks innovations in communication technology to cater to the mounting need for rapid connectivity, high-capacity connections. The mainstreaming of 5G technologies for communication is a dramatic step towards meeting the aforementioned goals, with the ability for reshaping IoT (Internet of things), D2D (device-to-Device) communications, and the smart grids. This work conveys an in-depth study of the fundamental innovations that underlie 5G, including full-duplex distribution, huge multiple-input-multiple-output, ultra-dense connections, the phenomenon of beamforming and millimeter-wave approaches. A special emphasis is focused on the integration of photonic technologies, or microwave photonics, which serves as a critical multidisciplinary study topic. Optical fibers, with their tremendous bandwidth and capacity, have been determined as the best medium for backhaul and fronthaul amenities, outpacing conventional copper cables to accommodate tiny cells and next-generation networks. The synergy between optical and wireless access technologies is analyzed with the emphasis on the central role of wavelength-division multiplexing (WDM) for improving network efficiency and speed. The investigation additionally explores the possibility of intelligent signal processing methods combined with WDM to optimize photonic network communications. The mingling of these technologies anticipates producing unrivaled levels of performance, rupturing the path for an additional intelligent, interconnected era.

Adaptive Radio Access Technology Selection Algorithm for Heterogeneous Wireless Networks

In Heterogeneous Wireless Networks (HWNs), Radio Access Technologies (RAT) can only consider the situation of one particular Radio Resource Management (RRM) which is unsuitable for managing multiple RATs. This study deployed an adaptive RAT selection scheme model to allocate users to the best RAT with the use of the cost function variable. The adopted model uses different input criteria like signal strength, network loads, service type and QoS requirement for the best access network selections. The adaptive RAT selection algorithm was executed in different service mixes (voice and data service) to access model suitability for users in Global System for Mobile Communications with Enhanced Data Rates for Global Evolution Radio Access Network (GERAN) and Universal Mobile Telecommunications System Radio Access Network (UTRAN). The proposed algorithm resulted in the call blocking probability reduction by 0.03 for GERAN and 0.14 for UTRAN as validated with the existing algorithm based on load balancing, service-based and priority-based. The drop implied an increased probability of ensuring session stability and high quality of the active service, leading to a high load distribution.

Spatio-temporal adaptive algorithm using Hamming code based on 3D-MIMO wireless channel model

Space-time coding is an effective method for constructing communication systems while saving bandwidth, taking into account the shortage of frequency resources and power in fading channels, which is implemented based on the advantages of using adaptive antenna arrays. Currently, WiMAX radio access technology has become widespread; its use is justified in conditions of direct visibility between the base and mobile stations, but in a city with dense buildings and in the presence of organized interference, its use leads to an increase in the likelihood of bit errors. At the same time, the combination of OFDM (orthogonal frequency division multiplexing) and MIMO (spatial coding) technologies based on adaptive antenna arrays significantly reduces the probability of bit errors, and therefore increases the throughput of the communication system. The use of adaptive space-time devices at the outputs of antenna arrays makes it possible to select a signal path with maximum power and with a minimum error in receiving OFDM pilot subcarriers, even in the absence of direct visibility between the base and mobile stations and in conditions of active interference. In addition, the use of a 3D model of the communication channel will allow us to evaluate the effectiveness of the communication system model in real operating conditions. The goal of the work is to solve the problem of additionally increasing noise immunity in communication systems in conditions of numerous reflections caused by dense buildings in urban environments, using a combination of Hamming code, OFDM and an algorithm for adaptive spatio-temporal signal processing in receivers such as SISO (Singl Input – Singl Output), and MIMO (Multiply Input – Multiply Output), built on adaptive antenna arrays. The work calculated the weight coefficients of the adaptation algorithm using the theory of eigenvalues and eigenvectors of the spatial correlation matrix of signals at the outputs of the adaptive antenna arrays. Modeling of algorithms and a 3D model of the communication channel in Matlab using the Hamming code was carried out, as a result, an increase in the efficiency of error correction for the Hamming code was demonstrated, as well as a significant reduction in the probability of code errors and an increase in the performance of the communication system when using an adaptive algorithm. At the same time, the article uses the term “multipath” channel in contrast to the generally accepted, but incorrect “multibeam” channel, since multibeam can be a characteristic of an antenna system. The presented results confirm that when processing signals in a receiving system in a multipath channel with dense urban development, the use of adaptive spatiotemporal algorithms provides increased noise immunity and radio communication capacity, especially for image transmission when using unmanned aerial vehicles.

Investigation on cellular LTE C-V2X network serving vehicular data traffic in realistic urban scenarios

The need for reliable vehicular communication is an essential precondition of future Cooperative Intelligent Transportation Systems (C-ITS). The C-ITS applications and their diverse requirements necessitate a hybrid approach that combines ad hoc and cellular networks to support seamless and robust connectivity. While the research showed tremendous potential, the successful adaptation of the hybrid approach depends on the optimal understanding of its impact on network and protocol performance in an actual reference scenario. This work, which has practical implications for the field, focuses on creating a realistic representation of the dual-link-enabled Hybrid Vehicular Network (HVN) in the simulator. Several enhancements were implemented into a network simulator, including multiple Radio Access Technologies (multi-RAT) support, models for path loss predictions, static subscribers, background interference, and SINR throughput mapping within a multi-cell environment. We then presented a parameterized data traffic steering algorithm that enables switching between Dedicated Short-Range Communication (DSRC) and Long Term Evolution (LTE) RAT based on channel congestion and cell load conditions. The simulation result shows the high potential of a dual-link-enabled hybrid approach and its implications on both networks under realistic vehicular networking conditions. The investigations reveal that an intelligent decision to steer traffic can target balanced connectivity and highly reliable vehicular communication at a reduced network load.

Joint Optimization of Age of Information and Energy Consumption in NR-V2X System Based on Deep Reinforcement Learning.

As autonomous driving may be the most important application scenario of the next generation, the development of wireless access technologies enabling reliable and low-latency vehicle communication becomes crucial. To address this, 3GPP has developed Vehicle-to-Everything (V2X) specifications based on 5G New Radio (NR) technology, where Mode 2 Side-Link (SL) communication resembles Mode 4 in LTE-V2X, allowing direct communication between vehicles. This supplements SL communication in LTE-V2X and represents the latest advancements in cellular V2X (C-V2X) with the improved performance of NR-V2X. However, in NR-V2X Mode 2, resource collisions still occur and thus degrade the age of information (AOI). Therefore, an interference cancellation method is employed to mitigate this impact by combining NR-V2X with Non-Orthogonal multiple access (NOMA) technology. In NR-V2X, when vehicles select smaller resource reservation intervals (RRIs), higher-frequency transmissions use more energy to reduce AoI. Hence, it is important to jointly considerAoI and communication energy consumption based on NR-V2X communication. Then, we formulate such an optimization problem and employ the Deep Reinforcement Learning (DRL) algorithm to compute the optimal transmission RRI and transmission power for each transmitting vehicle to reduce the energy consumption of each transmitting vehicle and the AoI of each receiving vehicle. Extensive simulations demonstrate the performance of our proposed algorithm.

Heterogeneous wireless network selection using feed forward double hierarchy linguistic neural network

Network selection in heterogeneous wireless networks (HWNs) is a complex issue that requires a thorough understanding of service features and user preferences. This is because the various wireless access technologies have varying capabilities and limitations, and the best network for a voice, video, and data service depends on a variety of factors. For selecting the optimal network in HWNs, varying factors such as the user’s position, accessible network resources, service quality requirements, and user preferences must be considered. The classical decision making procedure is very difficult and uncertain to select the desirable HWNs for voice, video, and data. Therefore, we develop a novel decision making model based on feed-forward neural networks under the double hierarchy linguistic information for the selection of the best HWNs for voice, video, and data. In this article, we introduce a novel feed-forward double hierarchy linguistic neural network using the Hamacher t-norm and t-conorm. Further, the feed-forward double hierarchy linguistic neural network applies to the decision making model for the selection of the best HWNs for voice, video, and data. In this novel decision making model, we first take the given data about HWNs and use the converting function to convert the given data into a double hierarchy linguistic term set. We calculate the hidden layer and output layer information by using Hamacher aggregation operations. Finally, we use the sigmoid activation function on the output layer information to decide on the best HWNs for voice, video, and data according to ranking. The proposed approach is compared with other existing models of decision making and the results of the comparison show that the proposed technique is applicable and reliable for the decision support model.

Optimal Guard Channel Reservation and its Comparison with Fractional Channel Policies in Mobile Cellular Network

In the current communication era, 2G voice services coexist with higher generation mobile network voice services like Voice over Long Term Evolution (VoLTE) in 4G and Voice over New Radio (VoNR) in 5G. Subscribers registered in the 5G network, but their voice option is in 4G so voice calls can be handled through the Evolved Packet System (EPS) Fallback or Radio access technology (RAT) Fallback. Similarly, subscribers register in a 4G network, but their voice option is in 3G or 2G so voice calls can be handled through Circuit Switched Fallback (CSFB). To provide uninterrupted voice service in such wireless networks usually handoff calls (HCs) receive higher priority than new calls (NCs). So, some guard radio resources (i.e., channels in 2G or RBs in 4G or 5G) may be reserved in each cell to handle this type of HCs. If more channels are reserved for HCs, then the handoff call-dropping probability (HCDP) will be decreased and at that time the new call-blocking probability (NCBP) will be increased. So, by considering the target HCDP if channels are reserved for HC, then HCDP will remain below the target and NCBP will be minimum. In this paper first, we proposed an optimal channel reservation (OCR) policy that reserves guard channels according to the given target HCDP. Second, we compared our OCR policy with fractional channel (FC), limited fractional channel (LFC), and uniform fractional channel (UFC) policies. Finally, we observed the performance of all these policies considering the Global System for the Mobile Communication (GSM) network.

Machine learning approach of multi‐RAT selection for travelling users in 5G NSA networks

AbstractThe rapid increment of mobile device usage and the corresponding huge data volume generated afterwards, necessitated the utilisation of the 5G network spectrum. This is deployed today in terrestrial communication in a non‐stand‐alone (NSA) architectural mode; where 5G networks are supported by 4G LTE networks. Hence, the current 5G implementation with the gargantuan number of mobile subscribers, poses challenges to the choice of network Radio Access Technology (RAT) selection between 4G and 5G networks, among available multiple base‐stations to mobile (travelling) users, with respect to their location, bandwidth requirement, and mobility style. Hence, to address the scenario presented above, the authors record live signal measurements of 4G and 5G networks by a travelling user, that transversed multiple 5G NSA base stations. RAT selection implementations were carried out with support vector machine (SVM), deep neural network (DNN), and eXtreme Gradient Boosting (XGBoost) algorithms to select an appropriate RAT between 4G and 5G RATs, for effective resource allocation for travelling users’ requirements. Evaluation of results with standard classification metrics shows XGBoost with overall outstanding accuracy performance at 99.64%.

Evaluation of Radio Access Protocols for V2X in 6G Scenario-Based Models

The expansion of mobile connectivity with the arrival of 6G paves the way for the new Internet of Verticals (6G-IoV), benefiting autonomous driving. This article highlights the importance of vehicle-to-everything (V2X) and vehicle-to-vehicle (V2V) communication in improving road safety. Current technologies such as IEEE 802.11p and LTE-V2X are being improved, while new radio access technologies promise more reliable, lower-latency communications. Moreover, 3GPP is developing NR-V2X to improve the performance of communications between vehicles, while IEEE proposes the 802.11bd protocol, aiming for the greater interoperability and detection of transmissions between vehicles. Both new protocols are being developed and improved to make autonomous driving more efficient. This study analyzes and compares the performance of the protocols mentioned, namely 802.11p, 802.11bd, LTE-V2X, and NR-V2X. The contribution of this study is to identify the most suitable protocol that meets the requirements of V2V communications in autonomous driving. The relevance of V2V communication has driven intense research in the scientific community. Among the various applications of V2V communication are Cooperative Awareness, V2V Unicast Exchange, and V2V Decentralized Environmental Notification, among others. To this end, the performance of the Link Layer of these protocols is evaluated and compared. Based on the analysis of the results, it can be concluded that NR-V2X outperforms IEEE 802.11bd in terms of transmission latency (L) and data rate (DR). In terms of the packet error rate (PER), it is shown that both LTE-V2X and NR-V2X exhibit a lower PER compared to IEEE protocols, especially as the distance between the vehicles increases. This advantage becomes even more significant in scenarios with greater congestion and network interference.

Frequency migration challenges and strategic decisions-making in broadband wireless access networks

Purpose: The impetus for this study lies in the pressing need for telecommunication companies to adapt to the rapidly evolving technological landscape and regulatory environment to maintain competitiveness and meet increasing consumer demands. Method: This study conducted a comprehensive analysis using SWOT analysis, Qualitative Data Analysis, and the Analytical Hierarchy Process (AHP) to determine the best course of action. Results: This study navigates through the initial stages of identifying the strategic, operational, and financial implications of PT. XYZ Frequency Migration. It delves into the background of Broadband Wireless Access (BWA) technology, the significance of the 3.3 GHz and 10 GHz bands in telecommunications, and the broader context of industry trends necessitating this shift. Methodologically, this research integrates SWOT, stakeholder, qualitative data analysis (QDA), and the Analytical Hierarchy Process (AHP) to provide a holistic understanding of the complexities of migration. This multi-faceted approach facilitates a comprehensive evaluation of the technological, operational, and strategic aspects of migration and offers nuanced insights into the challenges and opportunities inherent in the transition to the 10 GHz band. The study proposes that the convergence of various factors necessitates a strategic realignment that encompasses operational efficiency, technical excellence, financial prudence, regulatory compliance, and customer focus to effectively adapt to the 10 GHz spectrum. Contributions: This study contributes to the academic and practical understanding of network migration within the telecommunications sector, offering nuanced insights into the complexities of transitioning to higher frequency bands. It provides a framework for other telecommunications entities considering similar migrations, emphasizing the importance of a multifaceted strategic approach to navigating challenges and leveraging opportunities presented by such technological advancements.

Artificial intelligence driven cognitive optimization and predictive analysis using blockchain privacy-based machine learning model

According to the cognitive radio paradigm, spectrum sensing, decision-making, sharing, and mobility phases can be integrated to enable both authorised and unauthorised users to coexist in the radio-electric spectrum to the fullest extent possible. Using a blockchain privacy model and ML approaches in a B5G network, this study suggests a new approach to cognitive network optimisation and predictive analysis which stands for blockchain privacy-based transfer encoder neural networks. One further step is to optimise the network using binary swarm optimisation (BSMO). Bandwidth efficiency, throughput, forecast accuracy, and quality of service are the experimentally measured variables for a given number of channels and users. An innovative QoS-based optimisation phase and two separate decision-making procedures are proposed for usage in a proactive approach. One use of artificial neural networks (ANNs) is the prediction of future traffic loads for different radio access technologies (RATs), which employ different bands of the electromagnetic spectrum.

An intelligent load balancing algorithm for 5G‐satellite networks

SummaryCellular networks are projected to deal with an immense rise in data traffic, as well as an enormous and diverse device, plus advanced use cases, in the nearest future; hence, future 5G networks are being developed to consist of not only 5G but also different radio access technologies (RATs) integrated. In addition to 5G, the user's device (UD) will be able to connect to the network via LTE, WiMAX, WiFi, Satellite and other technologies. On the other hand, Satellite has been suggested as a preferred network to support 5G use cases. However, achieving load balancing is essential to guarantee an equal amount of traffic distributed between different RATs in a heterogeneous wireless network; this would enable optimal utilisation of the radio resources and lower the likelihood of call blocking/dropping. This study presented an artificial intelligent‐based application in heterogeneous wireless networks and proposed an enhanced particle optimisation (EPSO) algorithm to solve the load balancing problem in 5G‐Satellite networks. The algorithm uses a call admission control strategy to admit users into the network to ensure that users are evenly distributed on the network. The proposed algorithm was compared with the Artificial Bee Colony and Simulated Annealing algorithm using three performance metrics: throughput, call blocking and fairness. Finally, based on the experimental findings, results outcomes were analysed and discussed.

Component analysis for energy-efficient multimedia networks utilizing 5G radio access technologies

The object of the research is the energy-efficient multimedia networks based on 5G wireless access technologies. The research is aimed at developing energy-efficient multimedia networks based on 5G wireless access technologies, considering the increasing importance of information and communication technologies (ICT) in modern society. With the proliferation of wireless access networks and the advancement of fifth-generation mobile networks (5G), there is a need to assess and reduce the environmental impact of ICT. The article specifically focuses on the challenges related to energy consumption and CO2 emissions in radio access networks, highlighting the responsibility to balance technological advancements with environmental concerns. The paper examines various components and technologies necessary for enhancing energy efficiency in multimedia networks. It discusses the concept of multimedia, including digital storage, data processing, and interactive elements. Statistical data is provided to underscore the significant energy consumption and carbon footprint of the ICT industry, with an emphasis on radio access networks. Heterogeneous networks, non-orthogonal multiple access (NOMA) technologies, and multiple-input multiple-output (MIMO) technologies are identified as key components for achieving energy efficiency. The importance of reducing the distance between transmitters and receivers in heterogeneous networks is emphasized, as well as the use of energy-saving strategies such as putting small base stations into sleep mode during low network loads. Special attention is given to the role of green data centers in reducing CO2 emissions and optimizing the use of green energy in high-performance networks. Proposed methods include leveraging renewable energy sources, improving hardware energy efficiency, and implementing energy-efficient routing. The findings offer valuable insights for the development and implementation of energy-efficient multimedia networks, particularly in the context of 5G networks. The interdisciplinary approach advocated in the conclusion emphasizes the collective efforts needed to address environmental challenges in the field of information and communication technologies. The combination of these technologies ensures efficient resource utilization and reduced environmental impact compared to similar known approaches.

AN OVERVIEW OF COMPONENTS FOR ENERGY EFFICIENT MULTIMEDIA NETWORKS BASED ON 5G RADIO ACCESS TECHNOLOGIES

Modern society is actively transitioning into an information-based one, and multimedia technologies have become an integral part of this process. Thanks to the proliferation of wireless access networks, users are becoming more mobile, and the development of the fifth generation of mobile networks, known as 5G, is a significant step in the advancement of information and communication technologies. 5G networks offer low latency and reliable connectivity, expanding the capabilities of mobile internet and machine communication. However, along with the opportunities provided by multimedia communication, there is a responsibility to consider the impact of associated technologies on the environment, as well as to address new challenges and the need for prudent resource utilization.
 The article defines the concept of "multimedia" and discusses various aspects of this concept, including digital storage and processing of information, components (text, photos, audio, and video), interactivity, and hypertextuality. It is noted that the transmission of multimedia data and the use of information technologies are closely linked to wireless access networks.
 The authors discuss challenges and solutions in the field of energy efficiency for networks. They provide statistics indicating that the information and communication technology (ICT) industry is responsible for a significant portion of global energy consumption and CO2 emissions, with radio access networks being a major contributor. Various components and technologies that can contribute to the development of energy-efficient multimedia networks based on 5G radio access technologies are examined. Specifically, heterogeneous networks, non-orthogonal multiple access (NOMA) technologies, and multiple-input multiple-output (MIMO) technologies are highlighted as key components for achieving energy efficiency.
 The importance of using heterogeneous networks to reduce the distance between transmitters and receivers is emphasized, along with the possibility of putting small base stations into sleep mode when there is no network load. Technologies like NOMA and MIMO are discussed as crucial components for achieving spectral efficiency and energy efficiency.
 Additionally, the article focuses on wireless sensor networks (WSNs) and suggests ways to optimize them for energy efficiency. This includes operating sensor units only when necessary, implementing wireless charging, using energy-efficient optimization methods, and applying efficient routing schemes.
 The authors also highlight the role of green data centers in reducing CO2 emissions and optimizing the use of green energy in high-performance networks. Methods such as using renewable energy sources, increasing the energy efficiency of hardware, and implementing energy-efficient routing are discussed.
 In conclusion, the article underscores the importance of energy efficiency and reduced CO2 emissions in modern multimedia networks, particularly in the context of 5G networks. It calls for interdisciplinary efforts to address these critical challenges in the field of information and communication technologies.

Применение физического уровня 5G NR в космических коммуникациях, оценка эффективности.

The propagation channel is significantly modified in the case of 5th Generation New Radio radio access technology (5G NR) in space communication systems. In particular, large delays occur with respect to the terrestrial propagation channel, and signal attenuation increases, resulting in a lower signal-to-noise ratio (SNR). The Doppler shift of the carrier frequency is also significantly increased due to the high velocity of the spacecraft (SC) relative to the user equipment (UE). The aim of the work is to investigate the decoding capability of the physical channels and signals of the 5G NR system in case of its application in satellite communication systems (SCS), as well as to calculate the performance of such a system to assess the applicability of 5G NR in SCS. The ability to correctly decode signals and channels depending on SNR is evaluated using mathematical modelling of 5G NR signal and channel shaping in conjunction with the Quasi Deterministic Radio Channel Generator (QuaDRiGa) model. The research within the paper draws conclusions on the applicability of 5G NR radio access technology in a space communications environment.

Performance evaluation of handover triggering condition estimation using mobility models in heterogeneous mobile networks

AbstractHeterogeneous networks (HetNets) refer to the communication network, consisting of different types of nodes connected through communication networks deploying diverse radio access technologies like LTE, Wi‐Fi, Zigbee, and Z‐wave, and using different communication protocols and operating frequencies. Vertical handover, is the process of switching a mobile device from one network type to another, such as from a cellular network to a Wi‐Fi network, and is critical for ensuring a seamless user experience and optimal network performance, within the handover process handover triggering estimation is one of the crucial step affecting the overall performance. A mathematical analysis is presented for the handover triggering estimation. The performance evaluation shows significant improvement in the probability of successful handover using the proposed handover triggering condition based on speed, distance, and different mobility models. The handover triggering condition is optimised based on the speed of the mobile node, handover completion time, and the coverage range of the current and the target networks of the HetNet node, with due consideration of the mobility model.

Testing the Resilience of MEC-Based IoT Applications Against Resource Exhaustion Attacks

Multi-access Edge Computing (MEC) is an emerging computing model that provides the necessary on-demand resources and services to the edge of the network, ensuring powerful computing, storage capacity, mobility, location, and context awareness support to emerging Internet of Things (IoT) applications. Nonetheless, its complex hierarchical model introduces new architectural interdependencies, which can influence the resilience of IoT applications against cyber attacks. Although application resilience has been investigated in the context of cloud computing, existing studies are not directly applicable to such an extended edge-cloud paradigm. The use of different enabling technologies at the edge of the network, such as various wireless access technologies and virtualization, implies several threats and challenges that make the analysis and deployment of resilience mechanisms a technically challenging problem. In this paper, we first present an overview of the threat model, describing the threats for the different layers of this paradigm. We then study the impact of resource-exhausting attacks – a particularly relevant class for this paradigm - on three different IoT applications exploiting the services offered by the MEC-based architecture. We adopt a testing-based methodology conceived to characterize the resilience of such applications under attack. A set of most important resilience-related indicators are also identified. The characterization's results are useful to support the analyst in planning proper protection means at individual architectural layers.