Domain Of Wireless Networks Research Articles

Evaluating Sensitivity and Stability in Secure Deep Learning Based Channel Estimation for NextG Networks with Defensive Distillation

In the domain of wireless networks, including 5G and beyond, envisions a highly interconnected environment, enabling swift data transmission, expanded cell capacity, and minimal latency to support various applications and advanced manufacturing. Next Generation (NextG) networks seek to optimize and enhance network functionalities through Artificial intelligence (AI) integration, although concerns persist regarding the security of AI based models, particularly regarding model poisoning. Protecting these networks against cybersecurity threats, especially adversarial attacks, is vital. Hence, effective mitigation techniques and secure solutions employing AI-based methods are crucial. This study delves into the pivotal aspects of stability and sensitivity in AI-driven models tailored for NextG networks, focusing on Deep learning (DL) based channel estimation models. Utilizing data from MATLAB's 5G toolbox, we scrutinize these models' susceptibility to adversarial attacks and evaluate the efficacy of defensive distillation techniques in mitigating vulnerabilities. The research highlights the urgent necessity to fortify DL-based models against potential threats within NextG networks. Through a comprehensive evaluation of defensive distillation strategies, we showcase their ability to bolster both the stability and sensitivity of channel estimation models. By addressing these critical aspects, the study aims to enhance the resilience of AI-driven solutions within NextG environments, strengthening defenses against cybersecurity risks and safeguarding network integrity. Ultimately, the findings underscore the importance of prioritizing sensitivity and stability in the development and deployment of AI-based models for NextG networks. Through proactive measures and advanced mitigation methods, efforts are made to mitigate the risks posed by adversarial attacks and foster a more secure and resilient network infrastructure for the future.

A study of routing-based distributed mobility management in supporting seamless data transmission in smart cities

<p>The current trend of people having their multimedia-capable wireless and mobile devices roam within highly urbanized areas (i.e., smart cities) has led to the wide deployment of overlapping heterogeneous wireless network technologies. In this regard, various challenges have emerged, such as the number of mobile devices that connect or disconnect a wireless network domain, the diversity of time of their connections, frequent handovers as mobile devices frequently change their locations, the heterogeneity of wireless network technologies, cooperation challenges between the overlapping heterogeneous wireless network technologies, the increasing volume of multimedia traffic, security and privacy issues, and many more. This paper focuses on the deployment of a routing-based distributed mobility management (DMM) scheme to address the constraints and limitations of centralized wireless architectures for smart cities. The comparative analysis with centralized mobility management solutions shows significant alleviation in performance as to handover latency and packet losses, thus providing seamless handovers to maintain quality of service (QoS) for multimedia services.</p>

Analysis of a Fuzzy Based Intrusion Detection System in Wireless Ad Hoc Networks

Technology and its growth is considerably enormous. This massive growth allows the opening of new fields of application in the domain of wireless networking and mobile ad-hoc networks (MANET) is one of its kinds. Mobile ad hoc network is widely used from collaborative computing to time critical applications in indoor and outdoor environment. Mobility of ad hoc network makes very attractive in all areas of mobile applications. Nodes participating in mobile ad hoc networks are autonomous, self-configurable and act as a router as well. These types of networks are dynamic in nature and have sort life time. Dynamic nature and limitations of the wireless transmission medium make MANET unsecured and vulnerable to various attacks. It is very tough to implement security for this networks and it opens up doors for further research work on this area. There is a great scope for designing a system to identify attacks and take countermeasures to minimize it and keep the performance of the network within acceptable limits. Intrusion detection system is one of its kinds and considered as security mechanism for MANET. This thesis explores different types of intrusion detection system like misuse detection and anomaly detection for mobile ad hoc networks. Anomaly detection techniques for ad hoc networks depend on the characterization of normal behavior pattern of wireless nodes. This research work focuses on wireless node behavior based detection technique. Most of anomaly intrusion detection systems are focusing on upper layer traffic to a profile normal behavior of wireless node. This research work focus on media access control (MAC) layer and network layer of wireless node. It is inefficient to use a large feature set of MAC layer and network layer due to energy limitation in ad-hoc network. A minimal feature set from MAC layer and network layer were proposed. This research work proposed an anomaly intrusion detection system for mobile ad hoc network using fuzzy logic and weighted average method. The network performance of mobile ad hoc network with intrusion detection system was analyzed using various network parameters. Further the performance of the performance of the intrusion detection system was analyzed using detection rate and false alarms. Results show good improvement in detection rate and other performance metrics

PERFORMANCE ANALYSIS OF WIRELESS COMPUTER NETWORKS IN CONDITIONS OF HIGH INTERFERENCE INTENSITY

Context. The decrease in the probability of successful frame transmission in the infrastructure domain of IEEE 802.11 DCF wireless network is caused both by the influence of the collision intensity and by the impact of external interference in the radio path. Using the Markov chain approach as a baseline, we explicitly expressed the dependence of the network throughput on the number of operating stations, bit error rate (BER), and the frame fragmentation factor.
 Objective. The purpose of this article is to study the influence of interference intensity on the throughput of a wireless network domain in a wide range of the number of operating stations when transmitting frames of various lengths in the absence and with the use of the fragmentation mechanism.
 Method. The performed mathematical modelling showed, that in the range of increased and high noise intensity (BER = 10–5 – 10–4), a decrease in the length of the frame data field from the standard length of 12000 bits to 3000 bits is accompanied by a decrease in the throughput for all values of the number of competing stations. At the same time, it must be noted that as the amount of the frame data decreases, the throughput becomes less susceptible to an increase in the noise intensity. Qualitatively different results are obtained in the region of very high interference intensity (BER = 2·10–4). A significant increase in the probability of frame transmission in this region observed with a decrease in the standard length of the frame data field by 2–3 times, made it possible to increase the throughput compared to the original one. This effect is especially pronounced when the length is halved.
 Results. The study of the standard frame transmitting process, but with a fragmented data field, showed that if for BER = 5·10–5 and less with an increase in fragmentation factor, the throughput values decrease, in the entire range of the number of stations due to the predominant increase in overhead costs, then in the region of high (BER = 10–4) and very high noise intensity (BER = 2·10–4) we have the opposite effect. To the greatest extent, the throughput increases when the frame data is transmitted in two equal fragments. We have made a comparison of the network throughput determined by simply reducing the length of the frame data field and using fragmentation of a standard frame. The comparison showed that the use of the fragmentation mechanism is more beneficial both when throughput is stabilized under conditions of increased noise intensity and when the throughput is increased under conditions of high and very high noise intensity.
 Conclusions. In this article, a mathematical model has been modified for direct calculation of the wireless network throughput. Using this model, we studied the changes in throughput over a wide range of BER and a number of operation stations for various values of the transmitted frame fragmentation factor. The conditions for increasing the throughput are determined.

Prompt-WNQA: A prompt-based complex question answering for wireless network over knowledge graph

With the rapid development of wireless networks, the scale of network devices is constantly expanding. Experts spend vast amounts of time consulting and dealing with various network problems of the same nature during the course of daily network operation and maintenance service, and there is no time to collect statistics and sort their information, which affects the overall operation and maintenance efficiency. This paper investigates complex Knowledge Graph Question Answering (KGQA) in the wireless network domain in order to improve operation and maintenance efficiency. Accordingly, we propose Wireless Network QA over the KG using Prompt learning (Prompt-WNQA), a novel method that deals with both constraint-based and multi-hop complex questions making use of prompt learning. Our method is also helpful in performing complex KGQA over incomplete KGs, which can complete missing relations between unconnected entities. Compared to state-of-the-art approaches, our Prompt-WNQA achieves significant improvement over extensive experiments on both the wireless network QA dataset and two public complex QA datasets.

Multi-channel Scheduling with Interference Avoidance and Precedence Based Algorithm in Wireless Mesh Networks

Wireless Mesh Networks (WMNs) is the wireless networking domain that has been preferentially attracted by many researchers. Multi radio and multichannel WMN has limited channels due to the constraints given by interferences caused internally and externally within the channels. The research is focused on maximizing the performance in multi radio and multi channel WMNs. The channel overlapping that happens partially can overcome this limitation of resources. Interference Avoidance and Precedence Based Algorithm(IAPB) is proposed to reduce the interferences that occur in WMNs and improve the performance.The interference involved during the usage of channels can be reduced by using the channels with less interference. The interference involved is reduced by consideration of precedence level of interference during assignment of channel. The proposed algorithm results in performance improvement in terms of network throughput, the packet loss, delay involved in the reduction of interference.

Toward Safe and Accelerated Deep Reinforcement Learning for Next-Generation Wireless Networks

Deep reinforcement learning (DRL) algorithms have recently gained wide attention in the wireless networks domain. They are considered promising approaches for solving dynamic radio resource management (RRM) problems in next-generation networks. Given their capabilities to build an approximate and continuously updated model of the wireless network environments, DRL algorithms can deal with the multifaceted complexity of such environments. Nevertheless, several challenges hinder the practical adoption of DRL in commercial networks. In this article, we first discuss two key practical challenges that are faced but rarely tackled when developing DRL-based RRM solutions. We argue that it is inevitable to address these DRL-related challenges for DRL to find its way to RRM commercial solutions. In particular, we discuss the need to have safe and accelerated DRL-based RRM solutions that mitigate the slow convergence and performance instability exhibited by DRL algorithms. We then review and categorize the main approaches used in the RRM domain to develop safe and accelerated DRL-based solutions. Finally, a case study is conducted to demonstrate the importance of having safe and accelerated DRL-based RRM solutions. We employ multiple variants of transfer learning (TL) techniques to accelerate the convergence of intelligent radio access network (RAN) slicing DRL-based controllers. We also propose a hybrid TL-based approach and sigmoid function-based rewards as examples of safe exploration in DRL-based RAN slicing.

WBAN-based remote monitoring system utilising machine learning for healthcare services

The field of wireless sensor networks has grown significantly due to notable research contributions in the domain of wireless networks and semiconductors. It has been noted in the recent past that WBANs significantly support a range of pervasive medical and healthcare applications. A wireless body area network (WBAN) is a task-oriented, self-organising sensor network and is an interconnection of a multitude of biomarker sensors and medical diagnostic gadgets. These backbone nodes may be embedded in the human body or planted on the human body. In this paper, we proposed a secured WBAN-based system for healthcare facilities. This model will help patients and medical professionals monitor the health of patients at any time despite their physical presence. It also discusses the utilisation of a machine learning based cloud system which keeps learning from new data sets and remains updated about new symptoms.

Simulation Test Research on Typical Simulator NS-2 in Urban Vehicle Ad Hoc Network Based on Big Data

Abstract Vehicle ad hoc network (VANET) has gradually become a prominent research topic in the fields of wireless networks and intelligent vehicles. VANETs are unique mobile ad hoc networks with vehicles as their mobile nodes, presenting distinctive performance characteristics compared to traditional wireless self-organizing networks. In recent years, VANETs have gained significant attention in the wireless network and intelligent transportation domain. As an integral aspect of autonomous driving technology, vehicle-to-everything (V2X) communication spans multiple disciplines and is closely related to intelligent transportation, assisted driving, active safety, and smart vehicles. Evaluating VANET protocols and applications in real-world settings can be challenging. Therefore, utilizing simulation tools for VANET research is an effective approach. In this paper, we have designed and developed an optimized platform that uses IEEE 802.11a and IEEE 802.11p protocols for communication within a simulated urban traffic environment created with NS-2. The simulation results confirm the feasibility and rationale of applying the IEEE 802.11p protocol to wireless vehicular ad hoc networks. Within a distance of 300 m, at 0.0000 s, 14 key packets have not arrived in IEEE 802.11a, and 8 packets have not arrived in IEEE 802.11p; at 8.0000 s, 38 key packets have not arrived in IEEE802.11a, and 6 packets have not arrived in IEEE802.11p. Comparing the performance of IEEE 802.11a and IEEE 802.11p, the study concluded that the use of the 802.11p protocol in urban mobile environments can improve reliability and reduce average packet latency.

Scope of machine learning applications for addressing the challenges in next‐generation wireless networks

The convenience of availing quality services at affordable costs anytime and anywhere makes mobile technology very popular among users. Due to this popularity, there has been a huge rise in mobile data volume, applications, types of services, and number of customers. Furthermore, due to the COVID-19 pandemic, the worldwide lockdown has added fuel to this increase as most of our professional and commercial activities are being done online from home. This massive increase in demand for multi-class services has posed numerous challenges to wireless network frameworks. The services offered through wireless networks are required to support this huge volume of data and multiple types of traffic, such as real-time live streaming of videos, audios, text, images etc., at a very high bit rate with a negligible delay in transmission and permissible vehicular speed of the customers. Next-generation wireless networks (NGWNs, i.e. 5G networks and beyond) are being developed to accommodate the service qualities mentioned above and many more. However, achieving all the desired service qualities to be incorporated into the design of the 5G network infrastructure imposes large challenges for designers and engineers. It requires the analysis of a huge volume of network data (structured and unstructured) received or collected from heterogeneous devices, applications, services, and customers and the effective and dynamic management of network parameters based on this analysis in real time. In the ever-increasing network heterogeneity and complexity, machine learning (ML) techniques may become an efficient tool for effectively managing these issues. In recent days, the progress of artificial intelligence and ML techniques has grown interest in their application in the networking domain. This study discusses current wireless network research, brief discussions on ML methods that can be effectively applied to the wireless networking domain, some tools available to support and customise efficient mobile system design, and some unresolved issues for future research directions.

Recent Advances in Mobile Ad Hoc Networks

Mobile ad hoc networks (MANETs) represent a featured domain of wireless networks, which are well-differentiated from cellular network infrastructure and have attracted decades of research effort [...]

A Review on Ultra Reliable and Low Latency Communications (PHY and MAC Layer Perspectives)

Fifth Generation (5G) wireless network plays major attention in the communication scenario due to the advanced features in data transmission. 5G is designed to overcome the issues, such as data rate, reliability, throughput, latency and capacity in the wireless network domains. The 5G wire-less services aim to increase the data rate through massive communication services. In this survey, well known research papers based on low latency communication at the Physical layer (PHY) and Medium Access Control layer (MAC) are reviewed based on the Ultra-Reliable Low Latency Communication (URLLC) perspective. Here, the communication techniques are categorized as, structure-based model, resource reuse-based techniques, radio resource management techniques, scheduling based approach, diversity-based techniques and network slicing model based on the purpose of data communication. The issues and challenges faced by the existing research works are reviewed and discussed in the research gaps and issues section. Moreover, the analysis carried out in this survey is based on the evaluation parameter, network layer, network parameters, system, purpose and simulation toolsets. The future work of the URLLC research is based on the issues collected from the recent research papers reviewed in this study.

Big data analytics for large‐scale UAV‐MBN in quantum networks using efficient hybrid GKM

SummaryA primary important aspect of wireless network domain is known to be secured communication as the signals are emphatically available and broadcasted through the air. Quantum wireless networks are a hot topic of research in recent years that made researchers to avail new techniques in this field. Big data is one of the major aspects that support large‐scale quantum network applications. As sensor nodes have resource constraints, it is more complex when using the traditional techniques of key establishment that only used for stable communication networks. Group‐related applications are also one of the important aspects in wireless network for sending and receiving messages from multiple users. Unfortunately, maintenance of security for the group‐oriented protocols is major issue due to the frequent changes in membership. In this paper, a hybrid GKM with re‐keying procedure is proposed to obtain a secure communication for the remote system of unmanned autonomous vehicle (UAV) with mobile backbone network (MBN). Here, the group controller is selected based on the simplified hybrid energy efficient distributed (HEED) protocol and honey key encryption algorithm is used by the key management center (KMC) for generating and distribution of key for the group's controller. The secure communication is explored using the key exchange mechanism between the nodes. The joining and leaving of a node from the group are initiated by the re‐keying process. The simulation results explain the improved performance of the proposed hybrid method when compared with other existing techniques in terms of privacy level, energy, memory, and time consumption.

Hybrid Bee Colony and Cuckoo Search based centroid initialization for fuzzy c-means clustering in bio-medical image segmentation

In current years, the grouping has become well identified for numerous investigators due to several application fields like communication, wireless networking, and biomedical domain and so on. So, much different research has already been made by the investigators to progress an improved system for grouping. One of the familiar investigations is an optimization that has been efficiently applied for grouping. In this paper, propose a method of Hybrid Bee Colony and Cuckoo Search (HBCCS) based centroid initialization for fuzzy c-means clustering (FCM) in bio-medical image segmentation (HBCC-KFCM-BIM). For MRI brain tissue segmentation, KFCM is most preferable technique because of its performance. The major limitation of the conventional KFCM is random centroids initialization because it leads to rising the execution time to reach the best resolution. In order to accelerate the segmentation process, HBCCS is used to adjust the centroids of required clusters. The quantitative measures of results were compared using the metrics are the number of iterations and processing time. The number of iterations and processing of HBCC-KFCM-BIM method take minimum value while compared to conventional KFCM. The HBCC-KFCM-BIM method is very efficient and faster than conventional KFCM for brain tissue segmentation.

Performance analysis of a GI/D- $$MSP/1/\infty$$ M S P / 1 / ∞ queueing system under different service phase initiations

The main objective of this paper is to derive some analytic results concerning GI/D- $$MSP/1/\infty$$ queueing model in which customers arrive according to a discrete-time renewal process. We assume that the service process is correlated and its configuration is designed through discrete-time Markovian service process under different service phase initiations to start a busy period. The system-length distributions at various time epochs have been obtained by employing the matrix-geometric approach. An explicit closed form expression for the waiting-time distribution in the queue measured in slots has also been derived. Computational experiences with a variety of numerical results are discussed to show the effect of system parameters on the performance measures. This queueing model has potential applications in the domain of computer networks, telecommunication systems, and wireless local communication networks for evaluating the various system performances. An application of this type of queueing model in the domain of network coding in wireless communications is also discussed. This provides relevant information to the system analysts, researchers, and industry people who are interested to investigate congestion problems using queueing theory.

Misbehavior Detection in Industrial Wireless Networks: Challenges and Directions

In communications, there has been a paradigm shift toward the widespread adoption of wireless technologies in recent years. This evolution to—often ad-hoc—wireless communication has led to significant benefits in terms of flexibility and mobility. However, alongside these benefits, arise new attack vectors, which cannot be mitigated by traditional security measures. Especially in scenarios where traditional, proactive cryptographic techniques cannot be deployed or have been compromised, reactive mechanisms are necessary to detect intrusions. In this paper, we discuss new directions and future challenges in detecting insider attacks for the exemplary application domain of industrial wireless networks, an enabling technology for current smart factory trends. First, we review existing work on intrusion detection in mobile ad-hoc networks with a focus on physical-layer-based detection mechanisms. Second, we conduct a proof-of-concept study of insider detection in industrial wireless networks using real-world measurements from an industrial facility. Based on the study, we point out new directions for future research.

Towards green computing for Internet of things: Energy oriented path and message scheduling approach

Recently, energy efficiency in sensor enabled wireless network domain has witnessed significant attention from both academia and industries. It is an enabling technological advancement towards green computing in Internet of things (IoT) eventually supporting sensor generated big data processing for smart cities. Related literature on energy efficiency in sensor enabled wireless network environments focuses on one aspect either energy oriented path selection or energy oriented message scheduling. The definition of path also varies in literature without considering links towards energy efficiency. In this context, this paper proposes an energy oriented path selection and message scheduling framework for sensor enabled wireless network environments. The technical novelty focuses on effective cooperation between path selection and message scheduling considering links on path, location of message sender, and number of processor in sensor towards energy efficiency. Specifically, a path selection strategy is developed based on shortest path and less number of links on path (SPLL). The location of message sender, and number of processor in specific sensor are utilized for developing a longer hops (LH) message scheduling approach. A system model is presented based on M/M/1 queuing analysis to showcase the effective cooperation of SPLL and LH towards energy efficiency. Simulation oriented comparative performance evaluation attest the energy efficiency of the proposed framework as compared to the state-of-the-art techniques considering number of energy oriented metrics.

Providing an optimal QoS using demand-based distributed reservation protocol allocation algorithm for WiMedia medium access control

WiMedia Alliance, an industry association for supporting ultra-wide band plays a major role in personal area network. It handles high volume of data for multimedia applications such as construction of home networks, establishment of virtual multimedia connectivity for video or audio sharing, etc. Since quality of service has its own impact in wireless networking domain, we tried to investigate the effect of allocating medium access slot by considering mobility and link quality. We also present an optimal utilisation of bandwidth in a highly contented environment along with a suitable mechanism for scheduling of medium access slots in an effective manner. The performance analysis of the proposed algorithm was tested using OmNet++. The results were promising in terms of improvement delay, throughput and packet drop ratio.

Mobility Issue on Octagonal Structured ZigBee Network Using Riverbed

Wireless Sensor Network (WSN) is a special type of communication medium through distributed sensor nodes. Popular wireless sensor nodes like ZigBee have splendid interoperability after IEEE 802.15.4 standardization in the domain of wireless personal area network (WPAN). ZigBee has another great feature mobility that makes the ZigBee network more versatile. The mobility feature of ZigBee mobile nodes has a greater impact on network performance than fixed nodes. This impact sometimes turns into more severe because of network structure and mobility model. This study mainly focuses on the performance analysis of the ZigBee mobile node under Random and Octagonal mobility management model with the Tree routing method. The Riverbed academic modeler is used to design, implement and simulate the ZigBee network under certain conditions. This study also presents a competitive performance analysis based on ZigBee mobile nodes transmitter and receiver characteristics under the observation of the mobility model. This indicates that Octagonal mobility model exhibits better performance than the Random mobility model. This study will constitute a new way for further designing and planning a reliable and efficient ZigBee network.

Distributed dynamic load balancing in a heterogeneous network using LTE and TV white spaces

With advances in technology, network operators may need to set up a dynamic spectrum access overlay in heterogeneous networks (HetNets) to increase network coverage, spectrum efficiency, and the capacity of these networks. Uses of TV white space (TVWS) and long term evolution (LTE) are the combination of a new research direction to meet the increasing user demands in the domain of wireless cellular networks. Without the consideration of traffic flow, a network may operate with serious congestion problems that degrade the system performance. Congestion problems can be resolved by either reducing traffic flow or increasing the bandwidth provision. This paper has proposed Distributed dynamic load balancing (DDLB) cellular-based TVWS and LTE technique, such that a cellular-based device can operate on both TVWS and LTE by simply switching its frequency of operation when necessary. The objective of this paper is to resolve the congestion problems in a HetNet through dynamically constructing new clusters to increase the system bandwidth. The simulation results show that the proposed technique solved the bottleneck problem, reduced transmission control overhead and power consumption, and increased the average throughput and load balancing index.