Communication Latency Research Articles

Priority/Demand-Based Resource Management with Intelligent O-RAN for Energy-Aware Industrial Internet of Things

The last decade has witnessed the explosive growth of the internet of things (IoT), demonstrating the utilization of ubiquitous sensing and computation services. Hence, the industrial IoT (IIoT) is integrated into IoT devices. IIoT is concerned with the limitation of computation and battery life. Therefore, mobile edge computing (MEC) is a paradigm that enables the proliferation of resource computing and reduces network communication latency to realize the IIoT perspective. Furthermore, an open radio access network (O-RAN) is a new architecture that adopts a MEC server to offer a provisioning framework to address energy efficiency and reduce the congestion window of IIoT. However, dynamic resource computation and continuity of task generation by IIoT lead to challenges in management and orchestration (MANO) and energy efficiency. In this article, we aim to investigate the dynamic and priority of resource management on demand. Additionally, to minimize the long-term average delay and computation resource-intensive tasks, the Markov decision problem (MDP) is conducted to solve this problem. Hence, deep reinforcement learning (DRL) is conducted to address the optimal handling policy for MEC-enabled O-RAN architectures. In this study, MDP-assisted deep q-network-based priority/demanding resource management, namely DQG-PD, has been investigated in optimizing resource management. The DQG-PD algorithm aims to solve resource management and energy efficiency in IIoT devices, which demonstrates that exploiting the deep Q-network (DQN) jointly optimizes computation and resource utilization of energy for each service request. Hence, DQN is divided into online and target networks to better adapt to a dynamic IIoT environment. Finally, our experiment shows that our work can outperform reference schemes in terms of resources, cost, energy, reliability, and average service completion ratio.

Optimization of D2D emergency communication resources for UAV relay based on DA-STD3

The allocation of communication resources is extremely important for the device-to-device (D2D) communication network with unmanned aerial vehicle (UAV) relays, but this field currently mainly focuses on the communication between UAVs and ground devices. While neglecting the comprehensive optimization of air-to-ground communication and internal ground communication. This paper studies the optimization problem of communication for ground D2D devices based on UAV relays in urban environment. We study the comprehensive optimization considering the air-to-ground communication and the mobile ground communication devices. We construct the D2D communication neighbor table, and the terrestrial D2D devices will sort and select the best D2D link for pairing based on the Euclidean distance and gain set around them. We construct a communication model for this scenario and propose an improved Dual-Actor Network Softmax Twin Delayed Deep Deterministic Policy Gradient algorithm based on deep reinforcement learning. We perform transmit power optimization to address the interference caused by D2D communication using frequency division multiplexing technology. By designing the reward function and training mechanism, we can achieve good power control and reduce communication interference. The results of the simulation analysis performed in this scenario show an effective reduction in communication latency and an increase in D2I (device-to-infrastructure) channel capacity, D2D connectivity success, and energy efficiency. The feasibility and effectiveness of the algorithm is verified by comparing the baseline algorithm.

Modern Kubernetes Ingress Solutions: an In-depth Comparison of Contour and Istio Architectures

This article presents a comprehensive comparative analysis of Kubernetes ingress controllers Contour and Istio, addressing the critical challenge of traffic management in modern cloud-native architectures. With Kubernetes adoption reaching 83% among enterprises in 2023, the selection of appropriate ingress controllers significantly impacts application performance, security, and operational efficiency. Through quantitative analysis of performance metrics across varying load conditions (100-10,000 RPS) and real-world case studies, this article evaluates both solutions across multiple dimensions. Results demonstrate that Contour exhibits superior performance in simple to medium-scale deployments with 30% better latency characteristics (0.5-1.5ms per request) and a 45% smaller memory footprint, while Istio proves more effective for complex deployments, handling up to 75,000 requests per second and reducing service-to-service communication latency by up to 60%. Implementation case studies of an e-commerce platform and a financial services system reveal that Contour achieves 99.99% availability with 47% infrastructure cost reduction, while Istio provides comprehensive security features including automated mTLS implementation and advanced observability tools. The article concludes with a detailed decision framework and implementation guidelines, enabling organizations to select and optimize the most appropriate ingress controller based on their specific requirements, scale of operations, and security needs. The findings demonstrate that while both solutions excel in their targeted use cases, the choice between them should be guided by application complexity, performance requirements, and operational constraints.

Optimasi Jaringan Sensor Nirkabel untuk Monitoring Suhu dan Kelembaban

The development of Internet of Things (IoT) technology has provided new opportunities to improve monitoring through the integration of temperature and humidity sensors in wireless networks. This study aims to optimize a wireless sensor network for temperature and humidity monitoring. This study combines an experimental approach with network performance analysis and optimization methods. First, the selection of temperature and humidity sensors is carried out. Next, an efficient and reliable wireless sensor network architecture is designed to collect temperature and humidity data from various locations. The process of sending data from sensors to base stations and integration with IoT platforms are also studied. Furthermore, a series of experiments are conducted to test the performance of the wireless sensor network under real conditions. Analysis of the collected data is used to identify potential improvements in network performance and improve system reliability and responsiveness. The performance parameters evaluated include connection stability, data throughput, energy consumption, and communication latency. The results of this study contribute to the development of a monitoring system that is reliable, efficient, and easy to implement.

Causality-Sensitive Scheduling to Reduce Latency in Vehicle-to-Vehicle Interactions.

This paper shows through real-life measurement that bi-directional vehicle-to-vehicle (V2V) communication latency can be dominated by sidelink scheduling delay when causality is not taken into account. Moreover, the large delay persists for a few seconds at a time once it occurs. In applications like maneuver coordination between autonomous vehicles or in platoon, such delay can be highly detrimental to safety and efficiency. We investigate the source of the problem and propose a solution that factors in causality in interactive communication. Specifically, we develop a constraint under which the resource positions are automatically aligned between the communicating vehicles, and the delay spikes are provably eliminated. Through the measurements on commercial V2X devices, we confirm that enforcing the constraint can remove latency spikes so that 5G sidelink can be more easily applied to time-sensitive interactions between vehicles.

A Secure and Decentralized Using Blockchain and Ring‐Based Cryptosystem

ABSTRACTThe advancement of Intelligent Transport Systems (ITSs) and the Internet of Vehicles (IoVs) introduces significant security challenges, particularly in ensuring secure communication between vehicles and vehicles (V2V), vehicles and Road Side Units (V2RSU). These vulnerabilities could potentially compromise the integrity of vehicular networks, making robust security measures essential. This paper aims to develop a secure communication framework for IoV environments that addresses these security concerns by enhancing authentication and encryption processes. The proposed approach combines the Ring‐Based Degree Truncated Polynomial Cryptosystem (RNTPC) with a Blockchain framework incorporating an enhanced proof‐of‐authentication (BEPoAuth) consensus algorithm. The approach is initiated from the registration phase and managed by a Trusted Authority (TA), ensuring secure vehicle and RSU registration. Authentication is carried out in two ways: V2V authentication through vehicle signatures and V2RSU authentication via batch verification of cluster members. Group keys are generated using RNTPC for secure communication, and blockchain technology is integrated to secure transactions within the IoV environment. The proposed methodology is evaluated using various performance metrics, demonstrating a 15%–20% improvement in throughput compared to existing techniques such as Practical Byzantine Fault Tolerance (PBFT), Secure and Highly Efficient Blockchain PBFT Consensus Algorithm (SG‐PBFT), credit‐based PBFT consensus algorithm (CPBFT) and Geographic‐PBFT (G‐PBFT). The system also exhibited minimal computational and communication latency while enhancing V2V and V2RSU communication efficiency. The RNTPC‐BEPoAuth framework offers a robust and secure solution for IoV communications, significantly outperforming existing methods in terms of throughput and efficiency. This approach provides a reliable foundation for secure ITSs, addressing critical security concerns in vehicular networks.

Implementation of a novel secured authentication protocol for cyber security applications

Robust verification protocols are crucial for maintaining the security and reliability of sensitive information due to the increasing complexity of cyber-attacks. This paper introduces a novel 5G Secure Handover Protocol aimed at addressing security and effectiveness issues encountered in existing systems. The proposed protocol is robust against various attacks, including de-synchronization, replay, man-in-the-middle (MITM), denial of services (DoS), and jamming, ensures perfect forward key secrecy, safeguarding communication confidentiality. The proposed protocol utilizes a combination of spiking neural network and fuzzy logic (SNN-FL) techniques that must choose the goal cell as carefully as possible before initiating the transfer process. By combining fuzzy logic and spiking neural networks to reduce handover latency and thwart several types of cyberattacks, the proposed 5G Secure Handover Protocol improves security. Extensive simulations show its efficacy and emphasize its potential for safe communication in large-scale cybersecurity applications. The paper presents a novel secure authentication protocol that significantly reduces handover delays and improves efficiency. Simulations show its resilience against common security threats, protecting sensitive information and maintaining secure communication channels. The protocol, with low communication expenses, complex spatial, and latency for changeover verification, is ideal for large-scale cybersecurity applications, contributing to the development of secure digital authentication mechanisms.

Optimizing data transmission in 6G software defined networks using deep reinforcement learning for next generation of virtual environments

Data transmission of Virtual Reality (VR) plays an important role in delivering a powerful VR experience. This increasing demand on both high bandwidth and low latency. 6G emerging technologies like Software Defined Network (SDN) and resource slicing are acting as promising technologies for addressing the transmission requirements of VR users. Efficient resource management becomes dominant to ensure a satisfactory user experience. The integration of Deep Reinforcement Learning (DRL) allows for dynamic network resource balancing, minimizing communication latency and maximizing data transmission rates wirelessly. Employing slicing techniques further aids in managing distributed resources across the network for different services as enhanced Mobile Broadband (eMBB) and Ultra-Reliable and Low Latency Communications (URLLC). The proposed VR-based SDN system model for 6G cellular networks facilitates centralized administration of resources, enhancing communication between VR users. This innovative solution seeks to contribute to the effective and streamlined resource management essential for VR video transmission in 6G cellular networks. The utilization of Deep Reinforcement Learning (DRL) approaches, is presented as an alternative solution, showcasing significant performance and feature distinctions through comparative results. Our results show that implementing strategies based on DRL leads to a considerable improvement in the resource management process as well as in the achievable data rate and a reduction in the necessary latency in dynamic and large scale networks.

Security analysis and provision of authentication protocol, based on peer-to-peer structure in IOT platform

The Internet of Things technology allows you to transfer data to any asset through communication networks such as the Internet or intranet. One of the most important problems is security, which includes confidentiality, authenticity validation, and completeness. Among security problems, the most important ones are ensuring authenticity and protecting privacy. The elliptic curve encryption algorithm is famous protocols for maintaining privacy and verifying authenticity. Despite the capabilities they have and provide good security against network attacks, they face challenges such as response time, delay in the authentication and authentication process, as well as the amount of memory consumed. Two-factor authentication (2FA) is a security process where users provide two different authentication factors to authenticate themselves. It also provides a higher level of security than authentication methods that rely on single-factor authentication (SFA). This article presents two novel methods for 2FA that use encryption techniques, scramble architecture, and chord architecture. These methods allow for the authentication process to be initiated by both parties involved in the communication. Three scenarios and three metrics of reaction time, memory usage, and connection latency were used to assess the efficacy of the suggested approach. The average reaction time is improved and is 0.012 s when the lengths of the finger table in the chord structure are increased to 10, 15, and 20. The findings show that the suggested approach reduces computing complexity, reaction time, and communication latency. It has an average reaction time of 0.016 s, an average memory use of 30,360 kb, and an average connection latency of 0.042 s.

Latency Analysis of Drone-Assisted C-V2X Communications for Basic Safety and Co-Operative Perception Messages

Drone-assisted radio communication is revolutionizing future wireless networks, including sixth-generation (6G) and beyond, by providing unobstructed, line-of-sight links from air to terrestrial vehicles, enabling robust cellular cehicle-to-everything (C-V2X) communication networks. However, addressing communication latency is imperative, especially when considering autonomous vehicles. In this study, we analyze different types of delay and the factors impacting them in drone-assisted C-V2X networks. We specifically investigate C-V2X Mode 4, where multiple vehicles utilize available transmission windows to communicate the frequently collected sensor data with an embedded drone server. Through a discrete-time Markov model, we assess the medium access control (MAC) layer performance, analyzing the trade-off between data rates and communication latency. Furthermore, we compare the delay between cooperative perception messages (CPMs) and periodically transmitted basic safety messages (BSMs). Our simulation results emphasize the significance of optimizing BSM and CPM transmission intervals to achieve lower average delay as well as utilization of drones’ battery power to serve the maximum number of vehicles in a transmission time interval (TTI). The results also reveal that the average delay heavily depends on the packet arrival rate while the processing delay varies with the drone occupancy and state-transition rates for both BSM and CPM packets. Furthermore, an optimal policy approximates a threshold-based policy in which the threshold depends on the drone utilization and energy availability.

Mitigating critical nodes in brain simulations via edge removal

Brain simulation holds promise for advancing our comprehension of brain mechanisms, brain-inspired intelligence, and addressing brain-related disorders. However, during brain simulations on high-performance computing platforms, the sparse and irregular communication patterns within the brain can lead to the emergence of critical nodes in the simulated network, which in turn become bottlenecks for inter-process communication. Therefore, effective moderation of critical nodes is crucial for the smooth conducting of brain simulation. In this paper, we formulate the routing communication problem commonly encountered in brain simulation networks running on supercomputers. To address this issue, we firstly propose the Node-Edge Centrality Addressing Algorithm (NCA) for identifying critical nodes and edges, based on an enhanced closeness centrality metric. Furthermore, drawing on the homology of spikes observed in biological brains, we develop the Edge Removal Transit Algorithm (ERT) to reorganize sparse and unbalanced inter-process communication in brain simulation, thereby diminishing the information centrality of critical nodes. Through extensive simulation experiments, we evaluate the performance of the proposed communication scheme and find that the algorithm accurately identifies critical nodes with a high accuracy. Our simulation experiments on 1600 GPU cards demonstrate that our approach can reduce communication latency by up to 25.4%, significantly shortening simulation time in large-scale brain simulations.

Multi-Layered Unsupervised Learning Driven by Signal-to-Noise Ratio-Based Relaying for Vehicular Ad Hoc Network-Supported Intelligent Transport System in eHealth Monitoring.

Every year, about 1.19 million people are killed in traffic accidents; hence, the United Nations has a goal of halving the number of road traffic deaths and injuries by 2030. In line with this objective, technological innovations in telecommunication, particularly brought about by the rise of 5G networks, have contributed to the development of modern Vehicle-to-Everything (V2X) systems for communication. A New Radio V2X (NR-V2X) was introduced in the latest Third Generation Partnership Project (3GPP) releases which allows user devices to exchange information without relying on roadside infrastructures. This, together with Massive Machine Type Communication (mMTC) and Ultra-Reliable Low Latency Communication (URLLC), has led to the significantly increased reliability, coverage, and efficiency of vehicular communication networks. The use of artificial intelligence (AI), especially K-means clustering, has been very promising in terms of supporting efficient data exchange in vehicular ad hoc networks (VANETs). K-means is an unsupervised machine learning (ML) technique that groups vehicles located near each other geographically so that they can communicate with one another directly within these clusters while also allowing for inter-cluster communication via cluster heads. This paper proposes a multi-layered VANET-enabled Intelligent Transportation System (ITS) framework powered by unsupervised learning to optimize communication efficiency, scalability, and reliability. By leveraging AI in VANET solutions, the proposed framework aims to address road safety challenges and contribute to global efforts to meet the United Nations' 2030 target. Additionally, this framework's robust communication and data processing capabilities can be extended to eHealth monitoring systems, enabling real-time health data transmission and processing for continuous patient monitoring and timely medical interventions. This paper's contributions include exploring AI-driven approaches for enhanced data interaction, improved safety in VANET-based ITS environments, and potential applications in eHealth monitoring.

Optimizing Routing Protocol Design for Long-Range Distributed Multi-Hop Networks

The advancement of communication technologies has facilitated the deployment of numerous sensors, terminal human–machine interfaces, and smart devices in various complex environments for data collection and analysis, providing automated and intelligent services. The increasing urgency of monitoring demands in complex environments necessitates low-cost and efficient network deployment solutions to support various monitoring tasks. Distributed networks offer high stability, reliability, and economic feasibility. Among various Low-Power Wide-Area Network (LPWAN) technologies, Long Range (LoRa) has emerged as the preferred choice due to its openness and flexibility. However, traditional LoRa networks face challenges such as limited coverage range and poor scalability, emphasizing the need for research into distributed routing strategies tailored for LoRa networks. This paper proposes the Optimizing Link-State Routing Based on Load Balancing (LB-OLSR) protocol as an ideal approach for constructing LoRa distributed multi-hop networks. The protocol considers the selection of Multipoint Relay (MPR) nodes to reduce unnecessary network overhead. In addition, route planning integrates factors such as business communication latency, link reliability, node occupancy rate, and node load rate to construct an optimization model and optimize the route establishment decision criteria through a load-balancing approach. The simulation results demonstrate that the improved routing protocol exhibits superior performance in node load balancing, average node load duration, and average business latency.

Convolutional Long Short-Term Memory Predictor for Collaborative Remotely Operated Vehicle Trajectory Tracking in a Leader–Follower Formation Subject to Communication and Sensor Latency in the Presence of External Disturbances

Nowadays, collaborative operations between Remotely Operated Vehicles (ROVs) face considerable challenges, particularly in leader–follower schemes. The underwater environment imposes limitations on acoustic modems, leading to reduced transmission speeds and increased latency in ROV position and speed transmission. This complicates effective communication between the ROVs. Traditional methods, such as Recursive Least Squares (RLS) predictors and the Kalman filter, have been employed to address these issues. However, these approaches have limitations in handling non-linear patterns and disturbances in underwater environments. This paper introduces a Convolutional Long Short-Term Memory (ConvLSTM) predictor designed to enhance communication and trajectory tracking between ROVs in a leader–follower scheme. The proposed ConvLSTM aims to address the shortcomings of previous methods by adapting effectively to varying conditions, including ocean currents, communication delays, and signal interruptions. Simulations were conducted to evaluate ConvLSTM’s performance and compare it with other advanced predictors, such as Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM), under different conditions. The results demonstrated that ConvLSTM achieved a 13.9% improvement in trajectory tracking, surpassing other predictors in scenarios that replicate real underwater conditions and multi-vehicle communication. These results highlight ConvLSTM’s potential to significantly enhance the performance and stability of collaborative ROV operations in dynamic underwater environments.

Federated learning data protection scheme based on personalized differential privacy in psychological evaluation

Federated learning enables multi-party model training by utilizing shared models instead of raw data, allowing for effective use of user data while ensuring privacy protection. However, the training process still has potential threats. Guided by the safe sharing and analysis of data in psychological evaluation, a federated learning data protection scheme based on differential privacy FL-PDP is proposed. First, local model parameters are perturbed using personalized differential privacy techniques to provide varying levels of privacy protection. Second, the privacy configuration selected by the user is divided into multiple levels and uploaded to the server after encryption to avoid targeted attacks in the uplink channel. Third, a probability-based selection method is employed for model aggregation to mitigate the impact of high privacy protection requirements on the model aggregation process. Finally, after aggregation is completed on the server side, noise that meets the global differential privacy threshold is added to quantify the level of global privacy protection. Experimental results indicate that this algorithm achieves high model accuracy, low communication latency, and scalability while ensuring privacy protection.

Investigation of Optical Interconnects for nano-scale VLSI applications

The relentless trend toward miniaturization has brought interconnects to the brink of communication bottlenecks, operating at or near their ampacity (current carrying capacity) limits. This degradation in performance necessitates novel technologies with increased ampacity. This study explores optical interconnect (OI) as a potential future technology, offering high-bandwidth communication and low latency. This study models and simulates OI, integrating recent optical device advancements with modest modulator and detector capacitance (50 fF). Performance metrics including signal delay, power dissipation, and power-delay-product (PDP) are compared across copper (Cu) and single-wall carbon nanotube bundle (SWCNT-B) interconnects at 22 nm and 14 nm technology nodes. Results consistently show OI, with an active voltage current feedback (AVCF) based regulated gain cascode (RGC) transimpedance amplifier (TIA) receiver, outperforms Cu and SWCNT-B interconnects at global lengths and beyond. For instance, at 1000 μm and 22 nm, OI exhibits 88.47 % and 62.15 % delay improvements over Cu and SWCNT-B, respectively. This trend persists at 14 nm, with OI showing 93.68 % and 84.29 % delay improvements, respectively. Additionally, OI surpasses Cu and SWCNT-B in power efficiency beyond a critical length. As interconnects extend to global scales and beyond, the differences in delay, power, and PDP between OI and electrical interconnect increases, highlighting OI's advantages for future VLSI IC applications.

Improved Cell Allocation Strategies Using K-Means Clustering in Congested 6TiSCH Environments.

The 6TiSCH protocol (IEEE 802.15.4e) is crucial for the Industrial Internet of Things (IIoT), utilizing a time-slotted channel hopping (TSCH) mode based on node distribution. In this study, we propose an innovative cell allocation strategy based on node position clustering using the K-means algorithm, specifically designed to address congestion and optimize resource distribution in the 6TiSCH network. Our mechanism effectively groups nodes into clusters, allowing for dynamic adjustment of cell capacities in congested areas by analyzing traffic patterns and the spatial distribution of nodes. This clustering approach enhances the efficiency of slot frame utilization and minimizes communication delays by reducing interference and improving routing stability. The proposed strategy leverages the clustering results to improve cell usage efficiency and reduce communication latency between nodes. By tailoring cell allocation to the specific traffic needs of each cluster, we significantly reduce packet loss, manage congestion more effectively, and enhance data transmission reliability. We evaluated the clustering method using the K-means algorithm through experiments with the 6TiSCH simulator. Additionally, we considered using objective functions in Routing Protocol for Low-Power and Lossy Networks (RPL), such as OF0 and MRHOF, to assess clustering results and their impact on throughput and packet delivery. Our method resulted in significantly improved average performance metrics. Under the OF0 routing protocol, we achieved a 30.01% latency reduction, a 15.95% faster joining time, an 8% higher packet delivery ratio, and a 13.82% throughput increase. Similarly, we observed a 12.34% improvement in packet delivery ratio, 21.06% latency reduction, 12.68% faster joining time, and 25.97% higher throughput speed with the MRHOF routing protocol. These findings highlight the effectiveness of the improved cell allocation strategy in congested 6TiSCH environments, offering a better solution for enhancing network performance in IIoT applications.

Dynamic Byzantine Fault-Tolerant Consensus Algorithm with Supervised Feedback Mechanisms

Among the existing consensus algorithms, there are very few low-latency consensus algorithms that can simultaneously take into account node dynamics, fault tolerance, and scalability, and which are applicable to large-scale open scenarios where low latency is required. Therefore, this paper proposes a low-latency scalable dynamic consensus algorithm with high fault tolerance utilizing a combination of layered and threshold signature technologies, known briefly as LTSBFT. Firstly, LTSBFT achieves linear communication complexity through the utilization of threshold signature technology and a two-layer structure. Secondly, the mutual supervision feedback mechanism among nodes enables the attainment of linear complexity for reaching consensus on the faulty upper-layer nodes during the view-change. Lastly, a node dynamic protocol was proposed for the first time to support dynamic changes in the number of nodes during the consensus process. That is to say, consensus can still be reached when the number of nodes dynamically changes without interrupting the client’s request for consensus in the network. The experimental results indicate that LTSBFT exhibits lower communication latency and higher throughput compared to the classic HotStuff and PBFT algorithms. Furthermore, in comparison to double-layer PBFT, the LTSBFT has been demonstrated to have improved fault tolerance.

Implementation of Raspberry Pi As A2dp Module On The Quality of Song Reception of Humanoid Robot

This research discusses comparing the performance of Bluetooth communication on Raspberry Pi in controlling dance humanoid robots using external modules and direct communication. This research aims to understand the difference in performance between the two methods, by analyzing data transmission speed, signal stability, and communication latency that affect the quality of robot movement. In addition, this research also aims to evaluate the effectiveness of using simple filters such as low-pass filter circuits to improve the stability of Bluetooth communication on Raspberry Pi. It is hoped that this research can lead to a better understanding of the influence of both communication methods on the quality of dance humanoid robot movements.

Adaptive quorum based scheduling and interference-free routing for edge enabled UAV assisted software-define WSN using AI

Wireless Sensor Networks (WSN) faces numerous problems. The deployment of the sensor nodes in faraway places makes battery replacement a difficult process. As a result, there are energy constraints and security issues. So, the fusion of SDN with WSN is SDWSN, which flexibly brings network management. The main issue here is controller placement, which has an impact on communication dependability, latency, and throughput. The major drawbacks in existing work include high energy consumption, inefficient data collection, increased response time, and poor packet delivery ratio. This paper presents a unique Artificial Intelligence-based method for adaptive quorum-based scheduling and interference-free routing in edge-enabled UAV-assisted Software-Defined Wireless Sensor Networks (SDWSNs). It combines energy-efficient clustering with E-DBSCAN, multiple attribute-based software node authentication, and interference-free routing with the IDEE algorithm. The NS-3 tool was utilized to conduct simulations using a setup consisting of 4 edge-assisted UAVs, 4 SDN controllers, and 100 wireless sensor nodes. The findings show considerable improvements in different performance measures when compared to previous techniques. In particular, this suggested approach reduces latency by 64.77 % when compared to DGRL and 66.30 % when compared to ESRA. Additionally, it uses less energy than DGRL and ESRA by 45.74 % and 51.89 %, respectively. In comparison to DGRL and SRA, the packet delivery ratio is enhanced by 27.27 % and 32.43 %, respectively. Furthermore, the Node Network Lifetime is increased by 24.29 % in comparison to ESRA and 12.99 % in comparison to DGRL. Additionally, our method increases throughput by 22.08 % compared to DGRL and 30.56 % compared to SRA, while reducing the Controller Response Time by 2.39 % compared to ESRA and 6.46 % compared to DGRL. These findings demonstrate how well our method improves the effectiveness and efficiency of SDWSNs, making it a viable option for practical use.