High Traffic Conditions Research Articles

Detection and Mitigation of DDoS Attacks : A Review of Robust and Scalable Solutions

Distributed Denial-of-Service (DDoS) attacks have emerged as a critical threat to network security, causing significant disruptions by overwhelming systems with malicious traffic. The motivation behind this review is the growing sophistication and frequency of DDoS attacks, which demand more robust and scalable detection and mitigation techniques. While numerous methods have been proposed, limitations such as high false positive rates, resource constraints, and the evolving nature of attacks continue to challenge existing solutions. This review aims to analyze and evaluate various robust detection mechanisms, including machine learning, anomaly detection, and hybrid models, with a focus on scalability and adaptability in real-world applications. The objective is to identify key strengths and weaknesses in current approaches, highlighting future research directions for building more resilient DDoS defense systems capable of operating efficiently under high-traffic conditions.

Evaluating a Multidisciplinary Model for Managing Human Uncertainty in 5G Cyber–Physical–Social Systems

This paper presents a comprehensive evaluation of the previously introduced multidisciplinary model to quantify human uncertainty (MMtQHU) within a realistic 5G-enabled cyber–physical–social systems (CPSS) environment. The MMtQHU, which integrates human, social, and environmental factors into CPSS modeling, is applied to the Ingolstadt traffic scenario (InTAS), a detailed urban simulation reflecting high-traffic conditions. By modeling unpredictable driver behaviors, such as deviations from optimal routes, the study assesses the model’s effectiveness in managing human-induced uncertainties in vehicle-for-hire (VFH) applications. The evaluation shows that human uncertainty significantly impacts 5G network resource allocation and traffic dynamics. A comparative analysis of traditional resource allocation methods reveals their limitations in handling the dynamic nature of human behavior. These findings underscore the necessity for advanced, adaptive strategies, potentially leveraging artificial intelligence and machine learning to enhance the resilience and efficiency of 5G networks in CPSS environments. The study offers valuable insights for future advancements in robust and adaptive 5G infrastructure by highlighting the critical role of integrating human behavior into CPSS models.

Implementation of Load Balancing With PCC Method And Failover Using Mikrotik At PT. Maxpower Indonesia

In connecting to a single Internet Service Provider (ISP), one must consider the possibility of network congestion due to high traffic as well as potential disruptions in the internet connection. Load Balancing Per Connection Classifier (PCC) is a method used in combining two ISP services. In supporting day-to-day operational activities, a reliable internet connection is crucial for any company. Therefore, it is also necessary to have an internet connection that remains standby without interruption when there are disruptions in the ISP. This paper discusses the merging of two internet services and the addition of failover techniques. In the first test, a load test was carried out with 1 ISP service. The second test was carried out using two ISP services with the PCC method in high internet traffic conditions. By using the ISP and PCC method, traffic can be reduced and charged to both ISPs so that the network is not burdened by just one ISP. In the second test, it can also be proven that the use of two ISP services with failover techniques is much better when a problem occurs at one of the ISPs, thereby reducing the company's operational impact.

Optimization of piezoelectric device with both mechanical and electrical properties for power supply of road sensors

Collecting and converting renewable energy from the road environment relieves pressure on energy supply for road-based sensing and monitoring, and simplify the process of road grid installation. Aiming at the characteristic of mechanical vibration energy collection, the internal structure of the piezoelectric device is optimized. The dowel bar structure is optimized by comparing the output power and stress distribution characteristics, while the actuation mode is selected based on the measured electrical output level and output stability. Then, based on the limit distance of single and dynamic loading, the range of actuation distance under different conditions is clarified. Finally, the electrical performance and application of energy harvester with optimized internal structure are systematically evaluated. The results indicate that the force-electric properties of the energy harvester are superior under the action of the cylinder, the actuation mode of the energy harvester driven by the dowel bar is conducive to improving the electrical performance. The upper limit of actuation distance under high traffic conditions is 1.3 mm. Under long-term high-frequency condition, the power density of the energy harvester can reach 14,726.67 W/m3, and the voltage has almost no attenuation after 100,000 continuous loads. The proposed internal structure improves the electrical performance and durability of the energy harvester, which is conducive to the development of self-powered energy supply for road sensing technology.

NEXUS: A 28nm 3.3pJ/SOP 16-Core Spiking Neural Network with a Diamond Topology for Real-Time Data Processing.

The realization of brain-scale spiking neural networks (SNNs) is impeded by power constraints and low integration density. To address these challenges, multi-core SNNs are utilized to emulate numerous neurons with high energy efficiency, where spike packets are routed through a network-on-chip (NoC). However, the information can be lost in the NoC under high spike traffic conditions, leading to performance degradation. This work presents NEXUS, a 16-core SNN with a diamond-shaped NoC topology fabricated in 28-nm CMOS technology. It integrates 4096 leaky integrate-and-fire (LIF) neurons with 1M 4-bit synaptic weights, occupying an area of 2.16 mm2. The proposed NoC architecture is scalable to any network size, ensuring no data loss due to contending packets with a maximum routing latency of 5.1μs for 16 cores. The proposed congestion management method eliminates the need for FIFO in routers, resulting in a compact router footprint of 0.001 mm2. The proposed neurosynaptic core allows for increasing the processing speed by up to 8.5× depending on input sparsity. The SNN achieves a peak throughput of 4.7 GSOP/s at 0.9 V, consuming a minimum energy per synaptic operation (SOP) of 3.3 pJ at 0.55 V. A 4-layer feed-forward network is mapped onto the chip, classifying MNIST digits with 92.3% accuracy at 8.4Kclassification/ s and consuming 2.7-μJ/classification. Additionally, an audio recognition task mapped onto the chip achieves 87.4% accuracy at 215-μJ/classification.

Navigating the future: Evaluating two-level transport solutions for sustainable urban development

Purpose: The purpose of this paper is to propose and evaluate two options for two-level transport solutions at the intersection of the bypass road and the Pidvolochyske Highway in Ternopil, Ukraine. The aim is to address the issues associated with the current road network passing through residential zones and present an optimal resolution for the intersection. Methodology: The methodology involves using the PTV Vissim software to conduct simulation modelling. The transport and operational indicators of the two options for two-level transport solutions, an elongated loop and two interconnected rings, are compared across different traffic intensities. Results: The results show that the transport solution with two roundabouts exhibits superior characteristics, particularly under high traffic flow conditions. The strengths and limitations of each solution are comprehensively delineated, encompassing factors like efficiency, cost-effectiveness, safety measures, and ecological impacts. Theoretical contribution: The paper contributes to advancing knowledge and practice in two-level transport solutions. It provides valuable insights for developing the transportation system in Ternopil and other post-conflict cities. The advantages and disadvantages of public-private partnerships (PPP) as a tool for attracting investments and innovations in transportation infrastructure are also discussed. Practical implications: The findings of this research can be used by professionals in transportation, urban planning, and ecology for infrastructure development. It also serves as a valuable resource for residents of Ternopil interested in fostering improvements to their city’s quality of life.

Re-routable and Low-latency All Optical Switching Algorithm for Next Generation DCN

Digital infrastructure is now indispensable to the function of society and quality of life of the citizen. All over the world there is leveraging of digital infrastructure that comprises use of data, computerized devices, methods and systems. During COVID-19 pandemic it comes more prominently in front. India being the most popular and populated country in the world, comprises nearly half billion internet users expected to transform the digital ecosystem. With the increasing demand of smartphone application, online activities gigantic amount of data has been generated, so evolution and development of data centers be the high importance not only for India but as well as for the world. Thus, it is the need to promote and create robust data center network infrastructure that supports emerging technologies such as 5G, IoT, AI, machine learning, adaptive manufacturing, smart agriculture, and automation and so on. In this paper we propose an all Optical re-routable, low latency Data Center Network (DCN) architecture using Passive Optical Datacenter Switch (PODS). The PODS consists of an Arrayed Waveguide Grating Router (AWGR) in association with a controller unit. An efficient wavelength assignment algorithm is integrated in PODS, to reroute the packet for congestion avoidance and packet loss.. This feature makes the DCN architecture more scalable, proficient with high throughput, low latency, power efficient under high traffic and bursty traffic conditions. The performance of the proposed PODS-based DCN is compared with existing passive optical DCN architectures PODCA and finds improved characteristics in terms of delay, throughput and blocking probability. Simulation of the framework is done in Python platform and executed on google COLAB in the Windows environment and the result shows 46.3% improvement in latency and throughput in terms of 90% network load compared with PODCA architecture.

Comparison of software defined networking with traditional networking using NS2 simulator

The Software Defined Network (SDN) is an emerging concept in network engineering that entails the decoupling of the control plane and data plane. The data plane is executed on discrete nodes, while a centralised control plane is accountable for overseeing network-wide functionalities. In conventional networks, the integrated planes are incorporated into the network suite, and the transmission is determined by the route. In this work, the traditional way of routing using a communication plane is compared with routing using an SDN controller. The present study employs the NS2 simulator to evaluate the quality of service performance under high background traffic conditions. In the event of network congestion, it is imperative to utilize the available bandwidth to its fullest extent in order to increase the rate of bit transmission per second. The results of the traditional network and SDN network based on various parameters like Ambiguous collision, Delay, Jitter, Availability and Latency are compared in this paper. The results show that the SDN network's performance is better with concerning time.

Pengaruh Pengerolan Alur Pelat Heksagonal Terhadap Frekuensi Pribadinya Dalam Kondisi Bebas-Bebas

Public interest in pick-up cars as a means of transportation for transporting goods has continued to increase lately because they are considered more flexible for transporting raw materials, workshop products and agricultural products and are capable of being operated on urban and rural roads that have different road roughness, as well as in very high traffic conditions. Plate panels that must be used in pick-up vehicles need to be designed as thin as possible but have high stiffness and natural frequency so that they are more efficient in fuel use and able to withstand vibrations that occur during driving. This study aims to determine the natural frequency that occurs in the plate panel profile for vehicle bodies resulting from the cold rolling process. The galvanized steel plates used are 0.6 and 0.8 mm thick. The natural frequency test method is used with free boundary conditions, where the conditioning plate is suspended by a rope. The natural frequency test tool used is the vibroport 80 at an existential pressure of 10 grams, with the hammer hitting the centre of the panel and the sensors being positioned at 0 and 90 degrees. Based on the test results that have been carried out, the panel specimen with a thickness of 0.8 mm obtained the highest natural frequency value of 922.5 Hz, while for a plate thickness of 0.6 mm, the highest value was only 816.25 Hz. However, the two groups of specimens with plate panel thicknesses of 0.6 and 0.8 mm because of rolling experienced an average increase in natural frequency, reaching 2.3 – 2.5 times when compared to the flat plates without the rolling process. The conclusion is that as the natural frequency value increases, the plate panel stiffness also increases.

Wheel-tracking performance of recycled concrete aggregate with recycled glass and brick in unbound pavements under elevated loads

As natural quarry materials become increasingly scarce and uneconomical, the construction industry has turned to sustainable alternatives such as construction and demolition (C&D) wastes and recycled glass for road construction. The aim of this study was to evaluate the performance of mixtures consisting of recycled glass (RG), crushed brick (CB) and recycled concrete aggregate (RCA) under varied traffic conditions. This assessment was conducted through wheel-tracking (WT) tests under simulated high-traffic conditions, which involved subjecting the mixtures to elevated vertical loads and an increased number of load cycles compared to previous studies. The study revealed that both RCA + 20%RG and RCA + 20%CB blends displayed comparable or slightly greater mean surface deformations than natural crushed rock under default conditions. The default conditions specified by the local road authority include an 8 kN wheel load and 40,000 loading cycles. The study further revealed that both blends displayed a consistent increase in rut depth as the number of cycles increased up to 100,000 while being subjected to a 20 kN wheel load. The maximum rut depth of RCA + 20%RG was close to the lower end of the maximum allowable rut depth range specified by road authorities. This suggests that these blends are at the limits of carrying heavier loads on highly trafficked roads.

Bi-level ramp merging coordination for dense mixed traffic conditions

Connected and Autonomous Vehicles (CAVs) hold great potential to improve traffic efficiency, emissions and safety in freeway on-ramp bottlenecks through coordination between mainstream and on-ramp vehicles. This study proposes a bi-level coordination strategy for freeway on-ramp merging of mixed traffic consisting of CAVs and human-driven vehicles (HDVs) to optimize the overall traffic efficiency and safety in congested traffic scenarios at the traffic flow level instead of platoon levels. The macro level employs an optimization model based on fundamental diagrams and shock wave theories to make optimal coordination decisions, including optimal minimum merging platoon size to trigger merging coordination and optimal coordination speed, based on macroscopic traffic state in mainline and ramp (i.e., traffic volume and penetration rates of CAVs). Furthermore, the micro level determines the real platoon size in each merging cycle as per random arrival patterns and designs the coordinated trajectories of the mainline facilitating vehicle and ramp platoon. A receding horizon scheme is implemented to accommodate human drivers’ stochastics as well. The developed bi-level strategy is tested in terms of improving efficiency and safety in a simulation-based case study under various traffic volumes and CAV penetration rates. The results show the proposed coordination addresses the uncertainties in mixed traffic as expected and substantially improves ramp merging operation in terms of merging efficiency and traffic robustness, and reducing collision risk and emissions, especially under high traffic volume conditions.

Effect of On-Street Parking and Pedestrian Crossing on Through Traffic in Jordan

Effect of On-Street Parking and Pedestrian Crossing on Through Traffic in Jordan

Investigating the CoAP Congestion Control Strategies for 6TiSCH-Based IoT Networks

The Constrained Application Protocol (CoAP) is a RESTful protocol standardized by the IETF and widely used for IoT applications. CoAP includes a default congestion control algorithm to ensure efficient operation under high traffic conditions. Other congestion control algorithms for CoAP have been proposed and evaluated in the literature, including the very popular CoCoA algorithm. All these algorithms assume that the underlying wireless communication is regulated through the 802.15.4 CSMA-CA protocol. Today, many IoT systems are based on the 6TiSCH architecture that, instead, leverages the TSCH (Time Slotted Channel Hopping) mode of IEEE 802.15.4, i.e., a synchronous and time-slotted access protocol. In this paper we investigate, by simulation, the suitability of existing CoAP congestion-control algorithms to the 6TiSCH architecture. Our results show that the performance of the considered algorithms are strongly influenced by the Scheduling Function used to allocate communication resources to nodes. In addition, our analysis emphasizes that CoCoA does not provide a significant advantage over the default algorithm in 6TiSCH networks. We investigate the motivations for such a behavior and propose an optimized version of CoCoA, namely 6CoCoA, specifically tailored for 6TiSCH networks. 6CoCoA is able to provide up to a 15% improvement of the Transaction Delivery Ratio and up to a 25% reduction of the end-to-end Transaction Delay, when the network is congested.

Arrival flight efficiency in pre- and post-Covid-19 pandemics

Covid-19 pandemic affected aviation severely, resulting in unprecedented reduction of air traffic. While aviation is slowly re-gaining traffic volumes, we use the opportunity to study the arrival performance in the Terminal Maneuvering Area (TMA) in non-congested scenarios.Applying flight efficiency and environmental performance indicators (PIs) to the historical data of arrivals to Stockholm Arlanda and Gothenburg Landvetter airports, we discover noticeable inefficiencies, despite significant reduction of traffic intensity. We analyze the impact of such factors as weather and traffic intensity on arrival efficiency in isolated scenarios when only one factor dominates: isolated scenario with low traffic and isolated scenario with good weather conditions. Our analysis uncovers that weather has a stronger influence than traffic intensity on the vertical efficiency, while traffic intensity has stronger effect on the lateral efficiency. Impact of traffic intensity on the lateral efficiency might be explained by frequent hold-on patterns and flight trajectory extensions due to vectoring in high traffic conditions. Further investigation is needed to explain weather and vertical/lateral efficiency correlations, the conclusions might be country-specific.

Design of Transit Signal Priority at Signalized Intersections with Queue Jumper Lanes

A queue jumper lane is a special bus preferential treatment that combines a short stretch of a special lane with a transit signal priority (TSP) to allow buses to bypass waiting queues of traffic and then to cut out in front of the queue by getting an early green signal. This paper first proposes a signal control design for queue jumper lanes with actuated TSP strategies and then compares its performance with that of the general actuated mixed-lane TSP. Different design alternatives were evaluated in the VISSIM microscopic simulation. The results show that the proposed TSP with queue jumper lanes can reduce more bus delays than can the commonly-used mixed-lane TSP, especially under high traffic volume conditions. It was also found that a nearside bus stop is superior to the far-side counterpart in terms of both bus delay and overall intersection delay for the proposed design.

The Role of Gender Differences in Distracted Driving Behavior: A Psychophysiological Approach

The present study was designed to empirically examine the role of gender differences in driving by investigating performance and physiological responses. Thirty-six participants consisting of 14 females and 22 males in a medium fidelity GE-ISIM driving simulator completed simulated driving scenarios involving high and low traffic conditions, while completing a 20-Question secondary task, either by cell phone text or call. It was hypothesized gender differences, cell phone distraction type, and traffic density would affect driving behavior measured by driving performance and HRV. Results indicated female participants averaged significantly higher acceleration and speed than their male counterparts. Female participants averaged significantly lower Heart Rate Variability (HRV) reflecting high stress being associated with poor driving outcomes. This study provides further evidence of the negative impact cellphone use has on driving behavior that can be moderated by gender differences. Implications and future research are presented to help alleviate the impact of distracted driving.

Analysis of ADAS Radars with Electronic Warfare Perspective.

The increasing demand in the development of autonomous driving systems makes the employment of automotive radars unavoidable. Such a motivation for the demonstration of fully-autonomous vehicles brings the challenge of secure driving under high traffic jam conditions. In this paper, we present the investigation of Advanced Driver Assistance Systems (ADAS) radars from the perspective of electronic warfare (EW). Close to real life, four ADAS jamming scenarios have been defined. Considering these scenarios, the necessary jamming power to jam ADAS radars is calculated. The required jamming Effective Radiated Power (ERP) is −2 dBm to 40 dBm depending on the jamming scenario. These ERP values are very low and easily realizable. Moreover, the effect of the jamming has been investigated on the radar detection at radar Range Doppler Map (RDM) and 2-Dimensional Constant False Alarm Rate (2D-CFAR). Furthermore, the possible jamming system requirements have been investigated. It is noted that the required jamming system will not require high-end technology. It is concluded that for the security of automotive driving, the ADAS radar manufacturer should consider the intentional jamming and related Electronic Counter Countermeasures (ECCM) features in the design of ADAS radars.

A fuzzy-based QoS Maximization protocol for WiFi Multimedia (IEEE 802.11e) Ad hoc Networks

The Quality of Service (QoS) management within a multiple-traffic Wi-Fi MultiMedia (WMM) ad hoc network is a tedious task, since each traffic type requires a well determined QoS-level. For this reason, the IEEE Working Group has proposed the IEEE 802.11e Enhanced Distributed Channel Access (EDCA) protocol at the MAC layer of WMM ad hoc networks. However, several studies have shown that EDCA must be further improved for three main reasons. The first reason is the poor performance of EDCA under high traffic conditions due to the high collision rate. The second reason is the need to maximize the traffic performance (delay, throughput, etc.) guaranteed by EDCA, seen the rapid evolution of the applications (multimedia, real time, etc.). The third reason is the need to maximize the energy efficiency of the EDCA, seen its use in battery constrained devices (e.g. Laptop, Smart phone, Tablet computers, etc.). For these three reasons, we propose in this paper a Three-in-One solution MAC protocol called QoS Maximization of EDCA (QM-EDCA), which is an enhanced version of EDCA. Based on the fuzzy logic mathematic theory, QM-EDCA incorporates a dynamic MAC parameters fuzzy logic system, in order to adapt dynamically the Arbitration inter frame Spaces according to the network state and remaining energy. Simulation results show that QM-EDCA outperforms EDCA by reducing significantly the collision rate, and maximizing traffic performance and energy-efficiency. In addition our solution is fully distributed.

Flow-level coordination of connected and autonomous vehicles in multilane freeway ramp merging areas

On-ramp merging areas are deemed to be typical bottlenecks for freeway networks due to the intensive disturbances induced by the frequent merging, weaving, and lane-changing behaviors. The Connected and Autonomous Vehicles (CAVs), benefited from their capabilities of real-time communication and precise motion control, hold an opportunity to promote ramp merging operation through enhanced cooperation. The existing CAV cooperation strategies are mainly designed for single-lane freeways, although multilane configurations are more prevailing in the real-world. In this paper, we present a flow-level CAV coordination strategy to facilitate merging operation in multilane freeways. The coordination integrates lane-change rules between mainstream lanes, proactive creation of large merging gaps, and platooning of ramp vehicles for enhanced benefits in traffic flow stability and efficiency. The strategy is formulated under an optimization framework, where the optimal control plan is determined based on real-time traffic conditions. The impacts of tunable model parameters on the produced control plan are discussed in detail. The efficiency of the proposed multilane strategy is demonstrated in a micro-simulation environment. The results show that the coordination can substantially improve the overall ramp merging efficiency and prevent recurrent traffic congestions, especially under high traffic volume conditions.

DeepNR: An adaptive deep reinforcement learning based NoC routing algorithm

Network-on-Chip (NoC) has become a cost-effective communication interconnect for Tiled Chip Multicore Processor systems. The communication between cores is done through packet exchange. As the computational intensity of applications increases, the amount of packet exchange between cores will also increase. The improper routing of these packets will result in high congestion thereby degrading the system performance. This marks the need for congestion-aware routing in NoC. In the real world, the applications running in NoC create diverse traffic, which in turn creates challenges in routing. Such challenges have resulted in more researchers relying on machine learning algorithms to tackle them. However, the issues pertaining to storage overhead and packet latency prevail in such methodologies. This paper presents an adaptive routing algorithm DeepNR, which uses a deep reinforcement learning approach. The proposed approach considers network information for state representation, routing directions for actions, and queuing delay for reward function. Experiments carried out on synthetic as well as real-time traffics to demonstrate the effectiveness and efficiency of DeepNR using the Gem5 simulator. The results obtained for DeepNR indicate a reduction of up to 21.25% and 44% in overall packet latency under high traffic conditions on real and synthetic traffic respectively, as compared to the existing approaches. Also, DeepNR achieves a throughput of above 90% in both the traffic scenarios.