Uninterrupted Communication Research Articles

Multi-UAV trajectory planning for RIS-assisted SWIPT system under connectivity preservation

Effective information transmission and energy supply are essential for low-power Internet-of-things (IoT) devices, particularly in geographically constrained or disaster-affected areas. As an emerging technology, simultaneous wireless information and power transfer (SWIPT) ensures uninterrupted communication through supplying power, thereby eliminating issues related to battery depletion. Owing to the high maneuverability, unmanned aerial vehicle (UAV) has enabled SWIPT a new paradigm by providing flexible and on-demand service. However, maintaining connectivity among UAVs while they execute tasks is a critical challenge, for their powerful online decision-making relies on information sharing. Unfortunately, this issue is often overlooked in existing literature on UAV-aided wireless communication. In this paper, a novel connectivity preserved multi-agent deep reinforcement learning algorithm (CP-MARL) is designed to solve the trajectory planning problem in complex environments, where multi UAVs cooperatively provide SWIPT service for ground users (GUs) while ensuring seamless information exchange. Moreover, fairness of service received among GUs is also considered, as unequal resource allocation can lead to suboptimal system performance. Besides, reconfigurable intelligent surface (RIS) is adopted to redirect signals to achieve line-of-sight (LoS) channel. Under this framework, the fair energy efficiency is maximized by jointly optimizing the UAVs’ trajectory, the phase shift of RIS, power splitting (PS) ratio and the association relationships between UAVs and GUs. The formulated problem is inherently non-convex but the proposed CP-MARL algorithm successfully delivers online near-optimal solutions. Numerical results demonstrate that compared with benchmark solutions, the designed CP-MARL algorithm significantly enhances the energy-efficiency performance of the system, achieving 40.22% performance improvement compared to scenarios where connectivity is not maintained, along with a more stable training process. Additionally, the superiority of incorporating RIS in this system is corroborated by the 27.31% improvement in energy-efficiency.

Modified REL-Based Piecewise Path Loss Modeling Approach for Shore-to-Ship Communication at 5.6 GHz

The need for reliable and uninterrupted communication systems in the marine environment has become critically important with increasing maritime activities, environmental monitoring, and the spread of autonomous systems. However, the complex structure of electromagnetic wave propagation in a sea environment limits the accuracy of the existing propagation models. Thus, the Modified Round Earth Loss (REL) model was first developed in this study to estimate the path loss more accurately in shore-to-ship communication. Subsequently, a piecewise modeling approach based on the principle of two-segment data modeling was proposed. In the Modified REL model, unlike the traditional REL model, the paths and gains of the direct and reflected waves were not considered equal in the calculations. Moreover, unlike in the classical approach, the receiver height was not taken as a fixed value; the estimated best receiver height value for each measurement was included in the calculations as a representation of the effect of roughness in the sea environment. Thus, the model is better adapted to various environmental conditions. In addition, the proposed piecewise model divides the propagation medium into two regions using a break point calculated by Fresnel zone theory. The Modified REL model was used for the first region and the log-distance model was used for the second region. This method allows for more accurate modeling of signal behaviors, especially at different distances. Experimental measurements and performance evaluations conducted using four different shore-to-ship communication scenarios show that the Modified REL model shows an average improvement of up to 3% in Root Mean Square Error (RMSE) values compared to the classical REL model. Additionally, the proposed piecewise model improves the fitting error of the Modified REL model, which models the data as a single whole, by an average of 22.25%. These findings emphasize the necessity of propagation models that are sensitive and adaptable to environmental changes for maritime communication.

MILP-MPC for Planned Maneuver station-keeping and Collision Avoidance of GEO Satellites Using On-Off Chemical Thrusters

GEO satellites require precise station-keeping and ensuring collision avoidance to maintain their desired orbital positions and ensure uninterrupted communication services. However, maneuvering these satellites poses significant challenges due to various geophysical factors and orbital perturbations. This work proposes a model predictive control for planned maneuver station-keeping and collision avoidance of GEO satellites using south, east, and west maneuvers. These maneuvers have been achieved using on/off chemical thrusters with a short firing time. The station-keeping problem is reformulated as an optimization problem using mixed-integer linear programming. MILP-MPC formulation simultaneously considers multiple objectives and constraints such as fuel consumption, maneuver planning, and collision avoidance for mitigating collision risks. Simulation results demonstrate the effectiveness of the proposed MILP-MPC framework compared to the real-time telemetry in achieving accurate station-keeping and collision-free operation while optimizing fuel consumption. The proposed controller increases the satellite lifetime by reducing the thruster firing time.

Cross-layer detection and defence mechanism against DDoS and DRDoS attacks in software-defined networks using P4 switches

This paper presents a comprehensive system for detecting and defending against distributed denial-of-service (DDoS) and distributed reflective denial-of-service (DRDoS) flood attacks in software-defined networking (SDN) environments using Programming Protocol-Independent Packet Processors (P4). The proposed system introduces a hybrid intrusion statistical threshold algorithm (HISTA) that analyzes intrusion data statistics to identify potential malicious attacks. Upon detection, the system utilises P4 programmability to implement a custom defence mechanism on the P4 switch. A novel Uruk protocol header collaborates with HISTA for enhanced cross-layer attack detection and categorisation. The HISTA-Uruk mechanism selectively discards malicious packets while ensuring uninterrupted communication for legitimate packets, effectively preventing DDoS/DRDoS attacks. Extensive experiments validate the system’s ability to efficiently detect and thwart various DDoS, DRDoS, and internal attacks, demonstrating its effectiveness in identifying threats and restoring impacted network connections across diverse attack scenarios.

Next-Generation Dual Transceiver FSO Communication System for High-Speed Trains in Neom Smart City

Smart cities like Neom require efficient and reliable transportation systems to support their vision of sustainable and interconnected urban environments. High-speed trains (HSTs) play a crucial role in connecting different areas of the city and facilitating seamless mobility. However, to ensure uninterrupted communication along the rail lines, advanced communication systems are essential to expand the coverage range of each base station (BS) while reducing the handover frequency. This paper presents the dual transceiver free space optical (FSO) communication system as a solution to achieve these objectives in the operational environment of HSTs in Neom city. Our channel model incorporates log-normal (LN) and gamma–gamma (GG) distributions to represent channel impairments and atmospheric turbulence in the city. Furthermore, we integrated the siding loop model, providing valuable insights into the system in real-world scenarios. To assess the system’s performance, we formulated the received signal-to-noise ratio (SNR) of the network under assumed fading conditions. Additionally, we analyzed the system’s bit error rate (BER) analytically and through Monte Carlo simulation. A comparative analysis with reconfigurable intelligent surfaces (RIS) and relay-assisted FSO communications shows the superior coverage area and efficiency of the dual transceiver model. A significant reduction of up to 76% and 99% in the number of required BSs compared to RIS and relay, respectively, is observed. This reduction leads to fewer handovers and lower capital expenditure (CAPEX) costs.

Translating Gestures: Utilizing CNN to Enhance ASL Communication and Understanding

This research introduces a system for translating American Sign Language (ASL) utilising Convolutional Neural Networks. (CNNs). The system under consideration has the capability to identify manual gestures executed by persons communicating in American Sign Language (ASL) and subsequently convert them into written language, thereby facilitating uninterrupted communication between the deaf and hearing populations. The CNN model was validated and trained using a set of data made up of 78,300 pictures of movements in the ASL Alphabet written in American Sign Language (ASL). In order to improve the performance of the model, the pre-processing of the data included a number of stages, such as converting the photos to grayscale, normalising the pixel values, and enhancing performance of the model. In order to ease classification, fully connected layers were added after a succession of pooling and convolutional layers in CNN's design. After 15 epochs of training, the model attained a validation accuracy of 99.85%. The findings of this research demonstrate the viability of employing Convolutional Neural Networks (CNNs) in the creation of precise and effective American Sign Language (ASL) translation systems, which can serve as a means of communication for people with auditory impairments.

Digital platforms, Hindutva, and disinformation: Communicative strategies and the Leicester violence

ABSTRACT The digital infrastructure of Hindutva seeds, circulates and amplifies Islamophobic hate, interacting bidirectionally with brick-and-mortar violence. This paper examines the circulation of Hindutva on digital platforms (Twitter, Instagram, YouTube, and Telegram) around the intercommunal violence that emerged in Leicester in September 2022. Based on a digital ethnography of Twitter, interconnected digital platforms, and Hindutva media (Hindutva-related digital video channels such as Citti Media on YouTube, mainstream broadcast media such as NewsX, and text-based digital platforms such as OpIndia), the analysis theorizes the global flow of Hindutva across geographically dispersed contexts, connecting the diaspora with India, creating an uninterrupted communication infrastructure around the frame of the “Hindu in danger,” simultaneously intersecting with white supremacy in producing and amplifying Islamophobic hate.

When LoRa meets distributed machine learning to optimize the network connectivity for green and intelligent transportation system

LoRa technology contributes to green energy by enabling efficient, long-range communication for the Internet of Things (IoT). This paper addresses the challenges related to coverage range in outdoor monitoring systems utilizing LoRa, where the network performance is affected by the density of gateways (GWs) and end devices (EDs), as well as environmental conditions. To mitigate interference, data throughput losses, and high-power consumption, the proposed spreading factor (SF) and hybrid (data rate|SF) models dynamically adjust the transmission parameters. The orchestration of concurrent data modifications within the network server (NS) is crucial for uninterrupted communication between GWs and EDs, especially in monitoring electric vehicle (EV) stations to reduce traffic congestion and pollution. Employing K-means and density-based spatial clustering of applications with noise (DBSCAN) algorithms optimizes ED allocation, averts data congestion, and improves the signal-to-interference noise ratio (SINR). These methods ensure seamless information reception by meticulously allocated EDs across various GW combinations. To estimate the free-space losses (FSL), a log-distance path loss model (log-PL) is used. Exploring various bandwidths (BWs), bidirectional communications, and duty cycles (DCs) helps to prevent saturation, thus prolonging the operational lifespan of EDs. Empirical findings reveal a notable packet rejection rate (PRR) of 0% for the DBSCAN (hybrid model). In contrast, the K-means exhibits a PRR ranging from 5% (hybrid model) to 35.29% (SF model) for the ten GWs combination. Notably, the network saturation is reduced to 10.185% and 9.503%, respectively, highlighting an improvement in the average efficiency of slotted ALOHA (91.1%) and pure ALOHA (90.7%). These enhancements increase the lifespan of EDs to 15,465.27 days.

Quantum aided efficient resource control for connected support in IRS assisted networks

Context:To achieve the vision of all connected world with uninterrupted communication support, 6G technology plays an important role. But the scarce radio spectrum and limited network resources is the main challenge in delivering its promised performance. Objectives:This paper presents an IRS-aided cell free NOMA network model that aims to provide uniform network coverage. The future 6G technology envisions for serving billions of interconnected devices with seamless communication support, data handling capabilities and computational accuracy. But the scarcity of network resources is the main limitation. Thus, the need is to design quantum enabled intelligent and dynamic networks capable of offering extended network capabilities. Proposed work is on intelligent network framework that provides uniform network coverage through efficient resource management for a 6G enabled expanded IoT network. Methods:To enable efficient resource management, a quantum enabled resource control algorithm is proposed that creates user clusters and associates each AP-IRS pair to each cluster. Each AP transmits the superimposed signals of its intended cluster against all the user clusters as in conventional NOMA system. The nodes in each cluster have been assigned unique pilots so as to avoid intracluster interference. The use of IRS enables desired NOMA beamforming such that the effect of unfavourable wireless environment is mitigated. Results:The performance of the IRS-aided cell-free NOMA network is evaluated for average sum rate with different AP transmit power, cluster sizes, IRS reflecting elements and IRS phase shifts. It is shown that at transmit power per AP of 30dBm, the average sum rate of the system improves by 7.52% with the proposed algorithm using equal power allocation scheme. Further, the comparative performance analysis of three different communication systems is carried out to validate the proposed communication model. Conclusion:It is observed that with more number of users per cluster, the average sum rate of the system initially increases for small cluster sizes and then it becomes constant for large cluster sizes. The proposed clustering method outperforms the random clustering approach achieving sum rate of 12.9 bits/s/Hz with N =300 and M= 8. The comparison of different communication scenarios reveals that the maximum sum rate of 12.2 bits/s/Hz is achieved with the proposed model incorporating proposed clustering mechanism. Further, the energy efficiency analysis suggests that energy efficiency improves with N and Pc with proposed clustering approach. The use case scenarios for the integration of quantum computing with IRS technology are also presented.

Task offloading optimization in mobile edge computing under uncertain processing cycles and intermittent communications

Mobile edge computing (MEC) has emerged as a promising solution for addressing the growing computational demands by enabling cloud computing capabilities at the network edge. However, existing MEC models typically make assumptions of known processing cycles and uninterrupted communications, which are not practical in real-world scenarios. This paper aims to tackle the challenges posed by uncertainties and intermittent communications in MEC systems in the context of task offloading. We first derive a closed-form expression for the average offloading success probability in a device-to-device (D2D) assisted MEC system. This expression accurately accounts for uncertain computation processing cycles and intermittent communications. Then, we formulate the task offloading maximization problem (TOMP) and prove its NP-hardness. For problem-solving, we propose two algorithms tailored to different scenarios. In instances exhibiting a uniform structure, we introduce a task scheduling algorithm based on dynamic programming (TSDP). When dealing with general scenarios, we reformulate the problem and propose a repeated matching algorithm (RMA). We use Monte Carlo simulations to validate the closed-form expression. The results demonstrate that gap between them is less than 0.55%, confirming the accuracy of the closed-form expression. Furthermore, the proposed algorithms outperform other algorithms by performance comparison.

A High-Capacity Optical Metro Access Network: Efficiently Recovering Fiber Failures with Robust Switching and Centralized Optical Line Terminal.

This study proposes and presents a new central office (CO) for the optical metro access network (OMAN) with an affordable and distinctive switching system. The CO's foundation is built upon a novel optical multicarrier (OMC) generation technique. This technique provides numerous frequency carriers that are characterized by a high tone-to-noise ratio (TNR) of 40 dB and minimal amplitude excursions. The purpose is to accommodate multiple users at the optical network unit side in the optical metropolitan area network (OMAN). The OMC generation is achieved through a cascaded configuration involving a single phase and two Mach Zehnder modulators without incorporating optical or electrical amplifiers or filters. The proposed OMC is installed in the CO of the OMAN to support the 1.2 Tbps downlink and 600 Gbps uplink transmission, with practical bit error rate (BER) ranges from 10-3 to 10-13 for the downlink and 10-6 to 10-14 for the uplink transmission. Furthermore, in the OMAN's context, optical fiber failure is a main issue. Therefore, we have proposed a possible solution for ensuring uninterrupted communication without any disturbance in various scenarios of main optical fiber failures. This demonstrates how this novel CO can rapidly recover transmission failures through robust switching a and centralized OLT. The proposed system is intended to provide users with a reliable and affordable service while maintaining high-quality transmission rates.

Secure group consensus of nonlinear multi‐agent systems with multiple attacks under periodic event‐triggered control

SummaryIn this paper, the secure group consensus is investigated via periodic event‐triggered output feedback control for nonlinear multi‐agent systems subject to multiple attacks, where replay attacks and denial‐of‐service attacks are considered simultaneously. To avoid uninterrupted communication between agents, a novel fixed/switching continuous‐discrete compensator is first put forward by introducing a discrete‐time triggering mechanism in relative output signals. Then, with the help of the gain control technique, a compensator‐based periodic event‐triggered secure control protocol is established, which includes a triggering mechanism and an output feedback controller that both are discrete‐time. Particularly, Zeno behavior and continuous monitoring of the triggering function are naturally avoided compared with existing event‐triggered control protocols. Further, by combining the Lyapunov‐Krasovskii functional approach with switching topology theory, the secure group consensus is successfully achieved under the designed secure control protocol, meanwhile, the negative effect of multiple attacks on the system is compensated. Finally, two simulation examples are applied to verify the effectiveness of proposed control protocols.

Efficient Deployment of Wired Intercom Systems

This paper delves into the planning and execution of a wired intercom system with the intention of enhancing communication efficiency in residential and commercial environments. A wired intercom system provides a dependable and efficient means of communication between people located in different rooms or areas of a building or property, allowing for uninterrupted communication. Substations are placed in designated rooms or zones, while master stations are strategically placed throughout the paper. The careful selection of appropriate wiring, the availability of alternative power supply options, and, if necessary, the incorporation of the existing infrastructure are all factors that are taken into consideration. The main goal of the wired intercom system is to provide users with a dependable and user-friendly way of communicating that is customized to their individual requirements through meticulous planning, execution, and validation. The above abstract demonstrates the usefulness and adaptability of wired intercom systems.

ІНТЕГРАЦІЯ ЦИФРОВИХ ТЕХНОЛОГІЙ ТА ЦИФРОВИХ ДЕРЖАВНИХ ПОСЛУГ У МОДЕЛІ ЦИФРОВОЇ ЕКОНОМІКИ

The article explores the relationship between digital technologies, digital public services and the digital economy model. Key aspects of integrating these elements are analysed, such as the development of infrastructure, the creation of digital platforms, and the development of the Ukrainian IT sector. Special attention is paid to the impact of digital transformation on the efficiency of public administration, the lives of citizens and the development of the national economy. The study predicts both positive aspects of digital transformation and pessimistic scenarios related to uneven access to digital technologies, the digital divide and the need to develop digital public services. Based on the analysis, recommendations are formulated for further developing the digital economy and optimising the interaction between the state, businesses and citizens in the digital environment. Digital transformations of Ukraine's economy can ensure the stability and growth of financial revenues necessary for post-war reconstruction. Digital solutions are of financial and strategic interest, as they can increase the efficiency of the industries into which they are integrated into the government and military sectors. According to the study results, the development scenarios of each component of the descriptive model of the digital economy of Ukraine were determined. The Ukrainian IT industry is the engine of the digital economy and has demonstrated its resilience even in wartime. The industry continues to fulfil contracts and export services, provide foreign exchange earnings, and support the economy. Today's performance in IT is much better than in other industries, largely due to its mobility, flexibility and ability to work remotely. While the country is rebuilding, the Ukrainian IT industry will be able to ensure its economic stability largely. But for this, you need a concrete plan for supporting the industry, which it needs for development. They mean economic levers and incentives for investment, development of education, etc. The government directs efforts to integrate Ukraine into the EU’s Single Digital Market, build digital infrastructure, attract investments, and provide citizens with critical services and data, thus ensuring uninterrupted communication for the safety of citizens, which is the key to the digital stability of the economy. Despite the state of war, it pays attention to supporting the IT industry.

Anomaly detection system for network transport with machine learning approach

The rapid growth of network infrastructures and the increasing volume of data transmitted through them have led to a critical need for efficient and accurate anomaly detection systems in network transport. This paper proposes a novel anomaly detection system that utilizes machine learning techniques to identify abnormal patterns and deviations in network traffic. The proposed system follows a multi-layered approach, starting with the collection of network traffic data from various sources, including routers, switches, and gateways. The data is then preprocessed to extract relevant features and eliminate noise. Feature extraction is carried out using statistical, time-series, and flow-based analysis to capture the inherent characteristics of network communication. Machine learning algorithms, such as neural networks, or in this case, auto-encoders, will be trained to learn the patterns of normal network behavior and subsequently detect deviations from these patterns as anomalies. The system provides alerts and notifications to network administrators, allowing prompt investigation and response to potential security threats or network performance issues. It effectively differentiates between benign and malicious network activities, enabling network administrators to take proactive measures to secure their infrastructure and ensure uninterrupted communication.

A digital solution for determining the severity of paroxysmal sympathetic hyperactivity syndrome in patients with brain injury

Paroxysmal sympathetic hyperactivity syndrome is a frequent complication of acute severe brain injury, so with severe traumatic brain injury, it develops in 30 % of patients. Despite a large number of publications, the issues of diagnosis, prevention and treatment of this syndrome remain unresolved. To date, the assessment of the syndrome of paroxysmal sympathetic hyperactivity takes, with proper training of the doctor, from 8 to 15 minutes, taking into account the calculation of points using a calculator. With the digitalization of this process, a single assessment will take about 2–3 minutes to enter the indicators. In this case, the calculation will occur automatically. Subsequently, when using «wearable gadgets for measurement», the process of filling in indicators can also occur automatically. The application allows to register and store basic data about users who can have three roles: patients, specialist doctors, administrators. The creation of a database of patients with sympathetic hyperactivity syndrome will significantly improve diagnostic methods and assess the adequacy of therapy. Medical data is only useful if it can be turned into meaningful information. This requires high-quality data sets, uninterrupted communication between IT systems, and standard data formats that can be processed by humans and machines.

Supervisory Monitoring and Control Solution on Android Mobile Devices for the Water Industry 4.0

The capacity for using mobile devices for monitoring and controlling local processes has seen rapid growth in industry for maintenance operations before and after deployment. This is especially important in the case of geographically widely-dispersed locations, such as in the case of the water sector, where processes, technologies, and local automation solutions are widely spread. Usually, the available mobile solutions are dependent on Supervisory Control and Data Acquisition (SCADA) software installed in the control rooms of water and wastewater facilities, usually without configuration possibilities. Considering the various SCADA control rooms, each focusing on a specific system, and hundreds of smaller locations accessible only with PLC and eventually a small touch screen, the dependence on local SCADA software is proving increasingly impractical. This paper presents the implementation of an easy-to-use SCADA system for the Android operating system, conceived following Industry 4.0 concepts. An OPC UA client-based architecture is proposed to cope with current interoperability standards, mobility and security across industrial processes in various domains. The design relies on a foreground service for uninterrupted communication between the application and the OPC UA client. The system is envisaged to provide notifications to alert the user when alarms are triggered, including both an independent application level alarming module and a new Alarms and Conditions based protocol level module, increasing visibility and response time for technical issues or faults, and being adaptable to both legacy and modern OPC UA specifications. The solution was tested first in the laboratory to validate the communication system with as many OPC UA structures as possible, and then in real scenarios with drinking water and wastewater systems interfacing PLC, HMI and SCADA level OPC UA servers. The tests in the real scenarios included a second-level test for water operators and engineers which accessed and monitored various processes with the developed solution, and all results proved to be satisfactory.

Development of seismic fragilities for a base station steel lattice cellular tower

A large portion of the country of Turkey is located in a very high seismic region known as a first-degree earthquake zone where earthquakes occur frequently. Earthquakes result in damage to infrastructure including base station towers and subsequently failure of mobile communication networks. The loss of functionality of mobile communication networks following an earthquake has a direct impact on emergency response and both short and long-term recovery phases. Understanding both these phases requires systematic modeling of infrastructure components, which rely on fragility curves representing the probabilistic relationship between seismic intensity and damage. This study focuses on the development of damage fragility curves (fragilities) for an archetypical mobile cellular tower, namely a 55 m tall steel lattice tower (SLT) used in base station towers in Turkey. Nonlinear time history analysis was performed using a robust suite of far field ground motion records to model the performance of the archetype tower and develop damage fragilities for five specific damage states which were defined in terms of likely performance: damage state 0 (no damage), damage state 1 (uninterrupted communication), damage state 2 (communication interruption), damage state 3 (communication failure), damage state 4 (structural failure). While fragility curve has been developed for lattice towers of different types and sizes in the literature, there is no specific study for 55 m SLT and the novelty of this study is that the resulting SLT archetype fragilities can serve as a benchmark dataset for use in community-level risk and resilience analyses to improve response and recovery planning following earthquakes.

High traffic communication congestion control for wireless sensor networks based on harmony search optimization

In wireless sensor networks (WSNs), communication between the wireless nodes requires minimum response delay, minimum congestion and communication reliability. A wide variety of sensors produces a mixture of heterogeneous traffics with different reliability requirements. The article focuses on high traffic congestion which affects communication and produces latency. In the existing approaches, the congestion was controlled and the optimization was done during the time of node deployment. In the proposed method, high traffic congestion was controlled by a hop-by-hop approach which was applied in the statically deployed sensor nodes, the optimization was performed at the time of communication. To provide a uninterrupted communication to the WSNs the proposed approach analyses the occupancy ratio of the buffer and evaluates the downstream node congestion level. Here, the Harmony Search Algorithm is considered for design the optimal sensor network with Support Vector Machine (SVM). The experimental result shows the effectiveness and feasibility of the HSA-SVM environment. Also, it significantly enhances communication in diverse traffic conditions, specifically in heavy traffic areas with limited data.

Security Issues in Internet Architecture and Protocols Based on Behavioural Biometric Block Chain-Enhanced Authentication Layer

Internet security is of paramount importance due to the pervasive nature of the network in modern society. As the globe grows increasingly interconnected, issues like data breaches, unauthorized access, and service disruptions become more common. Safeguarding private data, ensuring uninterrupted communication, and protecting vital services are all essential to establishing confidence and stability in the online world. Internet security is a complex problem to solve due to the interconnected nature of the Internet's Architecture and Protocols (IAP). Due to the wide variety of devices and platforms that can access the Internet, cybercriminals can breach a complex ecosystem. Constant monitoring and flexibility are required due to the rapid development of new attack methods and vulnerabilities. The difficulty lies in balancing implementing new security measures and minimizing disruptions to the user experience, which calls for adaptive and novel approaches. In this paper, the Behavioural Biometric Block Chain-Enhanced Authentication layer (BBB-EAL) framework recommends a static authentication mechanism for end-users and edge servers. This authentication creates a secure and encrypted link between parties. Access tokens are produced via a smart contract, removing the requirement for a trusted third party. This work emphasizes the importance of architecture design and sequence diagrams in representing participant interactions and information sharing. Additionally, it examines the construction of the Machine Learning (ML) model used to recognize KMT dynamics. Simulations indicate that the recommended design improves user authentication in an IAP-enabled environment. The findings demonstrate the ability to evaluate confidence in real time, achieve minimal authentication time, and utilize resources efficiently.