Impact Network Performance Research Articles

Evaluating the performance of water distribution network deterioration using customer-oriented performance indices

ABSTRACT Water distribution networks (WDNs) are an essential urban infrastructure, with their performance directly influencing societal well-being. Our study applied to a real network model that employs a 24-h simulation with 1-h time steps and evaluates the impact of leaks and loss in the pipe cross-sectional area on WDN hydraulic performance as experienced by end users. Adhering to a UK utility's 20-m head pressure requirement as the water main benchmark, we present two new customer-oriented performance indices (CPIs) centred on network reliability and pressure deficit severity. The new CPIs adeptly quantify network performance degradation due to pipe deterioration. This degradation translates directly to a poor customer experience, highlighting the potential for these CPIs to pinpoint areas of the network where performance levels are compromised. Furthermore, the CPIs identify individual pipes within the network where defects would severely impact network performance and the sets of pipes which, when simultaneously experiencing defects, would lead to a more severe loss in network performance. Results show that the CPIs capture relatively small performance declines and identify sensitive pipes impacting network performance, providing insights for optimised inspection and maintenance intervention to provide better customer service.

Enhancing connectivity and coverage in wireless sensor networks: a hybrid comprehensive learning-Fick’s algorithm with particle swarm optimization for router node placement

AbstractWireless Sensor Networks (WSNs) are essential for collecting and transmitting data in modern applications that rely on data, where effective network connectivity and coverage are crucial. The optimal placement of router nodes within WSNs is a fundamental challenge that significantly impacts network performance and reliability. Researchers have explored various approaches using metaheuristic algorithms to address these challenges and optimize WSN performance. This paper introduces a new hybrid algorithm, CFL-PSO, based on combining an enhanced Fick’s Law algorithm with comprehensive learning and Particle Swarm Optimization (PSO). CFL-PSO exploits the strengths of these techniques to strike a balance between network connectivity and coverage, ultimately enhancing the overall performance of WSNs. We evaluate the performance of CFL-PSO by benchmarking it against nine established algorithms, including the conventional Fick’s law algorithm (FLA), Sine Cosine Algorithm (SCA), Multi-Verse Optimizer (MVO), Salp Swarm Optimization (SSO), War Strategy Optimization (WSO), Harris Hawk Optimization (HHO), African Vultures Optimization Algorithm (AVOA), Capuchin Search Algorithm (CapSA), Tunicate Swarm Algorithm (TSA), and PSO. The algorithm’s performance is extensively evaluated using 23 benchmark functions to assess its effectiveness in handling various optimization scenarios. Additionally, its performance on WSN router node placement is compared against the other methods, demonstrating its competitiveness in achieving optimal solutions. These analyses reveal that CFL-PSO outperforms the other algorithms in terms of network connectivity, client coverage, and convergence speed. To further validate CFL-PSO’s effectiveness, experimental studies were conducted using different numbers of clients, routers, deployment areas, and transmission ranges. The findings affirm the effectiveness of CFL-PSO as it consistently delivers favorable optimization results when compared to existing methods, highlighting its potential for enhancing WMN performance. Specifically, CFL-PSO achieves up to a 66.5% improvement in network connectivity, a 16.56% improvement in coverage, and a 21.4% improvement in the objective function value when compared to the standard FLA.

Enhancing Wireless Network Efficiency with the Techniques of Dynamic Distributed Load Balancing: A Distance-Based Approach.

The unique combination of the high data rates, ultra-low latency, and massive machine communication capability of 5G networks has facilitated the development of a diverse range of applications distinguished by varying connectivity needs. This has led to a surge in data traffic, driven by the ever-increasing number of connected devices, which poses challenges to the load distribution among the network cells and minimizes the wireless network performance. In this context, maintaining network balance during congestion periods necessitates effective interaction between various network components. This study emphasizes the crucial role that mobility management plays in mitigating the uneven load distribution across cells. This distribution is a significant factor impacting network performance, and effectively managing it is essential for ensuring optimal network performance in 5G and future networks. The study investigated the complexities associated with congested cells in wireless networks to address this challenge. It proposes a Dynamic Distance-based Load-Balancing (DDLB) algorithm designed to facilitate efficient traffic distribution among contiguous cells and utilize available resources more efficiently. The algorithm reacts with congested cells and redistributes traffic to its neighboring cells based on specific network conditions. As a result, it alleviates congestion and enhances overall network performance. The results demonstrate that the DDLB algorithm significantly improves key metrics, including load distribution and rates of handover and radio link failure, handover ping-pong, and failed attached requests.

Performance Evaluation of Predicting IoT Malicious Nodes Using Machine Learning Classification Algorithms

The prediction of malicious nodes in Internet of Things (IoT) networks is crucial for enhancing network security. Malicious nodes can significantly impact network performance across various scenarios. Machine learning (ML) classification algorithms provide binary outcomes ("yes" or "no") to accurately identify these nodes. This study implements various classifier algorithms to address the problem of malicious node classification, using the “SensorNetGuard” dataset. The dataset, comprising 10,000 records with 21 features, was preprocessed and used to train multiple ML models, including Logistic Regression, Decision Tree, Naive Bayes, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). Performance evaluation of these models followed the ML workflow, utilizing Python libraries such as scikit-learn, Seaborn, Matplotlib, and Pandas. The results indicated that the Naive Bayes classifier outperformed others with an accuracy of 98.1%. This paper demonstrates the effectiveness of ML classifiers in detecting malicious nodes in IoT networks, providing a robust predictive model for real-time application. The “SensorNetGuard” dataset is available on the IEEE data port and Kaggle platform.

A Reinforcement Learning-Based Multi-Objective Bat Algorithm Applied to Edge Computing Task-Offloading Decision Making

Amid the escalating complexity of networks, wireless intelligent devices, constrained by energy and resources, bear the increasing burden of managing various tasks. The decision of whether to allocate tasks to edge servers or handle them locally on devices now significantly impacts network performance. This study focuses on optimizing task-offloading decisions to balance network latency and energy consumption. An advanced learning-based multi-objective bat algorithm, MOBA-CV-SARSA, tailored to the constraints of wireless devices, presents a promising solution for edge computing task offloading. Developed in C++, MOBA-CV-SARSA demonstrates significant improvements over NSGA-RL-CV and QLPSO-CV, enhancing hypervolume and diversity-metric indicators by 0.9%, 15.07%, 4.72%, and 0.1%, respectively. Remarkably, MOBA-CV-SARSA effectively reduces network energy consumption within acceptable latency thresholds. Moreover, integrating an automatic switching mechanism enables MOBA-CV-SARSA to accelerate convergence speed while conserving 150.825 W of energy, resulting in a substantial 20.24% reduction in overall network energy consumption.

Auto-metric Graph Neural Network for Attack Detection on IoT-based Smart Environment and Secure Data Transmission using Advanced Wild Horse Standard Encryption Method

Smart cities (SCs) are being constructed with the huge placement of the Internet of Things (IoT). Real-time enhancements to life quality based on comfort and efficiency. The key concerns in most SCs that immediately impact network performance are security and privacy. Numerous approaches are proposed for secure data transmission, but the current methods do not provide high accuracy and it provide high computational time. To resolve these problems, an Auto-metric Graph Neural Network for Attack Detection and Secure Data Transmission using Optimized Enhanced Identity-Based Encryption in IoT (AGNN-AWHSE-ST-IoT) is proposed. Primarily, the input data is taken from the NSL-KDD dataset. The input data is gathered with the aid of NSL-KDD is pre-processed using three steps, crisp data conversion, splitting, and normalization. Then the Pre-processed input is fed into the Colour Harmony Algorithm (CHA) based feature selection to select the important features. After feature selection, the preferred features are given to the AGNN classifier. After classifying, the data is given to Enhanced Identity-Based Encryption (EIBE), and it is optimized using Wild Horse Optimizer (WHO) for transmitting the data more safely. The outcomes of the normal data are displayed using the LCD monitor. The AGNN-AWHSE-ST-IoT method is implemented in PYTHON. The AGNN-AWHSE-ST-IoT method attains 8.888%, 13.953%, 19.512% higher accuracy, 2.105%, 6.593%, 8.988% higher cumulative accuracy, 54.285%, 54.285%, 52.941% lower encryption time, 8.2%, 3.3%, 6.9% lower decryption time, 11.627%, 10.344%, 6.666% higher security level and 60.869%, 70% and 64% lower computational time than the existing approaches such as SBAS-ST-IoT, BDN-GWMNN-ST-IoT and DNN-LSTM-ST-IoT respectively.

MADRLOM: A Computation offloading mechanism for software-defined cloud-edge computing power network

Cloud-edge computing power network often exhibits complex and heterogeneous structures, posing several challenges to computation offloading that significantly impact network performance and the efficient utilization of computation resources. In this paper, we propose a cloud-edge computing power network architecture that efficiently integrates cloud and edge computing resources into a single network system using software-defined networking technology to support upper-layer business applications. In this context, existing computation offloading methods struggle with issues like users' personalized requirements for latency and energy consumption, as well as the inability to adapt to dynamically complex environments. To overcome these challenges, we introduce a computation offloading mechanism, MADRLOM, focusing on the network's heterogeneity and modeling the computation offloading problem at the cloud-edge end. We formalize the offloading problem as a Markov process and employ a multi-agent deep reinforcement learning algorithm based on priority experience replay sampling to address the planning problem of computation offloading, allowing user equipment to continuously optimize offloading strategies in response to environmental changes and achieve rational resource allocation. Through dynamic offloading strategies, computation tasks can be intelligently allocated to appropriate computing nodes, thereby achieving optimal resource utilization. We utilized the Mininet simulation platform to construct the experimental environment for the software-defined cloud-edge computing power network and compared it with several representative computation offloading strategies. The experimental results demonstrate that the MADRLOM significantly reduces the total system overhead in the software-defined cloud-edge computing network and shows excellent adaptability to environmental changes.

Survey of the Influence of Routing Protocols to Network Performance Enhancement

Routing protocols play a crucial role in the operation of computer networks by determining the optimal paths for data transmission. The selection of an appropriate routing protocol can significantly impact network performance, including factors such as latency, throughput, reliability, and scalability. This survey aims to provide a comprehensive analysis of the influence of various routing protocols on network performance enhancement. The survey begins by presenting an overview of common routing protocols with their key characteristics. Subsequently, it explores the impact of these protocols on network performance metrics, focusing on their ability to adapt to changing network conditions, mitigate congestion, and ensure efficient resource utilization. Through a systematic review of literature, empirical studies, and real-world implementations, this survey aims to provide network administrators, researchers, and practitioners with valuable insights into selecting and optimizing routing protocols for specific network environments. Additionally, it identifies areas for further research and development to continue advancing the field of routing protocols and network performance enhancement. Keywords: Optimizing Routing Protocols, Performance Metrics, Reliability and Scalability, Resource Utilization, Wireless Local Area Networks, Accurate Routing Tables Juliana I. Consul & Bunakiye R. Japheth (2023): Survey of the Influence of Routing Protocols to Network Performance Enhancement. Journal of Advances in Mathematical & Computational Science. Vol. 11, No. 4. Pp 13-28 Available online at www.isteams.net/mathematics-computationaljournal. dx.doi.org/10.22624/AIMS/MATHS/V11N4P2

Load Balancing in the Combined Technology of Li-fi and Wi-Fi based on Collaborative Game

Wireless networks have become increasingly important in today's digital age. The combination of VLC and RF technologies, such as local Wi-Fi and Li-Fi networks, has emerged as a significant technology in this field. This combination enhances the coverage of weak points and strengthens the strong points of local wireless networks, resulting in higher data rates and increased coverage. However, load balancing is a crucial issue that can enhance network efficiency, especially when there are multiple access points from both networks. The selection of the appropriate access point at any given time by different nodes can significantly impact network performance. This research proposes a game theory-based method for selecting an appropriate access point, which uses multiple stages of computation and policy changes to achieve a Nash equilibrium in the game and subsequently in the network. The results show that this method can improve the quality of service in the local network by over 6% compared to previous methods such as the fuzzy method and by over 20% compared to the higher signal power selection policy.

Considering Performance Issues in Ad Hoc Networking : A Comprehensive Study

Ad hoc networks have emerged as a versatile and dynamic communication paradigm with applications spanning from mobile devices and sensor networks to emergency response and military operations. However, their inherent decentralized and self-organizing nature gives rise to a myriad of performance challenges that must be comprehensively addressed to ensure reliable and efficient communication. This paper presents a comprehensive study of performance issues in ad hoc networking, aiming to provide a holistic understanding of the various factors impacting network performance. The study begins by examining the fundamental characteristics of ad hoc networks, highlighting their unique advantages and challenges. We delve into critical performance metrics such as throughput, latency, scalability, and energy efficiency, dissecting the factors that influence each of these metrics in ad hoc environments. The role of routing protocols, MAC layer design, and network topologies in shaping network performance is thoroughly analyzed. Furthermore, this study explores the impact of mobility patterns, traffic models, and interference on ad hoc network performance. It delves into the challenges posed by dynamic network topologies, node failures, and the need for adaptive mechanisms to maintain robust connectivity. Quality of service (QoS) considerations and their implications on performance are also discussed in detail. To provide a comprehensive view, we survey state-of-the-art solutions and techniques proposed in the literature to mitigate performance issues in ad hoc networks. These include adaptive routing algorithms, cross-layer optimization approaches, and novel communication paradigms like cognitive radio networks. Finally, we present a roadmap for future research directions in the field of ad hoc networking performance optimization. The goal is to inspire further investigation and innovation in addressing the evolving challenges faced by ad hoc networks, making them more reliable, efficient, and adaptable for a wide range of applications.

Review of SDN-based load-balancing methods, issues, challenges, and roadmap

The development of the Internet and smart end systems, such as smartphones and portable laptops, along with the emergence of cloud computing, social networks, and the Internet of Things, has brought about new network requirements. To meet these requirements, a new architecture called software-defined network (SDN) has been introduced. However, traffic distribution in SDN has raised challenges, especially in terms of uneven load distribution impacting network performance. To address this issue, several SDN load balancing (LB) techniques have been developed to improve efficiency. This article provides an overview of SDN and its effect on load balancing, highlighting key elements and discussing various load-balancing schemes based on existing solutions and research challenges. Additionally, the article outlines performance metrics used to evaluate these algorithms and suggests possible future research directions.

Network Automation

Purpose: The article "Network Automation in the Contemporary Economy" explores the concepts and methods of effective network management. The application stack, Jinja template engine, automation architecture, Nornir inventory management, application flow, logging, debugging, and live code testing are just a few of the subjects it covers. Network administrators are more important as digital technologies evolve quickly in order to maintain a safe and dependable network connection. The purpose of this article is to give network administrators the information and abilities they need to successfully traverse the intricacies of network management. It starts by going through the application stack and explaining the roles and relationships between each layer. The Ninja template engine is then described, along with an explanation of how its potent grammar makes network configuration management simple.
 Methodology: The research design employed in this study is a combination of qualitative and quantitative approaches. It involved an extensive literature review to gather existing knowledge on network automation and management practices. Additionally, empirical data was collected through surveys and interviews with network administrators to understand their experiences, challenges, and perspectives on network automation.
 Findings: The study found that network automation offers numerous benefits, including increased efficiency, reduced human errors, and enhanced network security. The application stack was identified as a critical component of network architecture, and its proper management can significantly impact network performance. The Jinja template engine proved to be an effective tool for simplifying network configuration tasks and promoting standardization across the network infrastructure.
 Recommendations: To policymakers, we recommend investing in training programs and resources to equip network administrators with the necessary skills to implement and manage network automation effectively. Developing clear guidelines and standards for network automation can also help organizations adopt automation practices seamlessly.
 Theory: The study was informed by the "Network Automation Theory," which posits that automating network management tasks can streamline operations, enhance reliability, and free up human resources for more strategic initiatives. The theory suggests that proper implementation of automation tools and frameworks can lead to a more agile and resilient network infrastructure. The validation of the theory was achieved through empirical data collected from network administrators and their experiences with network automation. The findings aligned with the propositions of the theory, confirming that network automation indeed brings significant benefits to organizations.
 Policy: For policymakers, we propose the formulation of a comprehensive policy framework that encourages the adoption of network automation technologies. The policy should focus on providing financial incentives for businesses to invest in automation tools, fostering partnerships between government and private sectors to promote knowledge exchange, and establishing regulatory guidelines to ensure network security and data privacy in automated environments.
 Practice: To network administrators and practitioners, we recommend staying updated with the latest advancements in network automation technologies and tools. Investing time in training and upskilling can help practitioners gain expertise in using automation frameworks like Nornir and Jinja template engine. Additionally, fostering a culture of continuous learning and experimentation within organizations can lead to successful implementations of network automation practices.

FedBully: A Cross-Device Federated Approach for Privacy Enabled Cyber Bullying Detection using Sentence Encoders

Cyberbullying has become one of the most pressing concerns for online platforms, putting individuals at risk and raising severe public concerns. Recent studies have shown a significant correlation between declining mental health and cyberbullying. Automated detection offers a great solution to this problem; however, the sensitivity of client-data becomes a concern during data collection, and as such, access may be restricted. This paper demonstrates FedBully, a federated approach for cyberbullying detection using sentence encoders for feature extraction. This paper introduces concepts of secure aggregation to ensure client privacy in a cross-device learning system. Optimal hyper-parameters were studied through comprehensive experiments, and a computationally and communicationally inexpensive network is proposed. Experiments reveal promising results with up to 93% classification AUC (Area Under the Curve) using only dense networks to fine-tune sentence embeddings on IID datasets and 91% AUC on non-IID datasets, where IID refers to Independent and Identically Distributed data. The analysis also shows that data independence profoundly impacts network performance, with AUC decreasing by a mean of 5.1% between Non-IID and IID. A rich and extensive study has also been performed on client network size and secure aggregation protocols, which prove the robustness and practicality of the proposed model. The novel approach presented offers an efficient and practical solution to training a cross-device cyberbullying detector while ensuring client-privacy.

Towards clean transportation systems: Infrastructure planning for EVs charging while driving

Charging while driving, or wireless charging lane (WCL), has been considered a promising solution to promote the widespread adoption of electric vehicles (EVs) for clean transportation systems. However, the WCL location problem is a strategic decision that costs a significant amount of money and greatly impacts network performance due to their interactions with traffic flow patterns. The inappropriate deployment of charging infrastructure may potentially cause more congestion, leading to more pollutants emitted from internal combustion vehicles. This paper proposes a decision-making framework to assist the system planner in finding the preferred optimal WCL location based on incorporating different stakeholders’ preferences, i.e. capital cost, congestion and environmental considerations. To capture the traffic evolution of different classes of vehicles (i.e. EVs and internal combustion vehicles), the travellers’ optimal time-dependent route choice is modelled using the multi-class dynamic traffic assignment. The proposed framework is transformed into a single-objective problem, i.e. minimizing total system cost, and solved efficiently by a meta-heuristic based on the cross-entropy method. The framework has been intensively tested to demonstrate the capability of tracking congestion propagation and energy consumption. Finally, the trade-offs between social welfare and the capital cost of WCL infrastructure are also investigated.

An Efficient Model-Based Clustering via Joint Multiple Sink Placement for WSNs

Wireless sensor networks consist of many restrictive sensor nodes with limited abilities, including limited power, low bandwidth and battery, small storage space, and limited computational capacity. Sensor nodes produce massive amounts of data that are then collected and transferred to the sink via single or multihop pathways. Since the nodes’ abilities are limited, ineffective data transmission across the nodes makes the network unstable due to the rising data transmission delay and the high consumption of energy. Furthermore, sink location and sensor-to-sink routing significantly impact network performance. Although there are suggested solutions for this challenge, they suffer from low-lifetime networks, high energy consumption, and data transmission delay. Based on these constrained capacities, clustering is a promising technique for reducing the energy use of wireless sensor networks, thus improving their performance. This paper models the problem of multiple sink deployment and sensor-to-sink routing using the clustering technique to extend the lifetime of wireless sensor networks. The proposed model determines the sink placements and the most effective way to transmit data from sensor nodes to the sink. First, we propose an improved ant clustering algorithm to group nodes, and we select the cluster head based on the chance of picking factor. Second, we assign nodes to sinks that are designated as data collectors. Third, we provide optimal paths for nodes to relay the data to the sink by maximizing the network’s lifetime and improving data flow. The results of simulation on a real network dataset demonstrate that our proposal outperforms the existing state-of-the-art approaches in terms of energy consumption, network lifetime, data transmission delay, and scalability.

Temporal progression along discrete coding states during decision-making in the mouse gustatory cortex.

The mouse gustatory cortex (GC) is involved in taste-guided decision-making in addition to sensory processing. Rodent GC exhibits metastable neural dynamics during ongoing and stimulus-evoked activity, but how these dynamics evolve in the context of a taste-based decision-making task remains unclear. Here we employ analytical and modeling approaches to i) extract metastable dynamics in ensemble spiking activity recorded from the GC of mice performing a perceptual decision-making task; ii) investigate the computational mechanisms underlying GC metastability in this task; and iii) establish a relationship between GC dynamics and behavioral performance. Our results show that activity in GC during perceptual decision-making is metastable and that this metastability may serve as a substrate for sequentially encoding sensory, abstract cue, and decision information over time. Perturbations of the model's metastable dynamics indicate that boosting inhibition in different coding epochs differentially impacts network performance, explaining a counterintuitive effect of GC optogenetic silencing on mouse behavior.

A Lightweight Encryption Algorithm to Enhance Wireless Sensor Network Security on the Internet of Things

The Internet of Things (IoT) is an anticipated future technology that promises to connect a massive number of devices over the internet. Wireless Sensor Networks (WSNs) are regarded as one of the most essential subnetworks of the IoT. Sensor networks are being utilized by IoT to gather, monitor, and send sensitive data across wireless networks. Because the information transferred through WSNs is easily exposed to cyber-attacks, data security is critical. In WSNs, the attacker's adversary aims are to deteriorate and halt the network's effective use, as well as to interrupt network services, rendering them unreachable to the users or providing a user with false feedback. Since the users don’t have control over their data transmitted on the wireless medium or stored in the middleware, anyone with internet access can access it. This puts data confidentiality, authenticity, and integrity at risk since users with unauthorized access can easily access, alter, and manipulate data in transit. The proposed Lightweight Security Algorithm (LSA) is a hybrid algorithm created by combining the Security Protocol for Sensor Networks (SPINS) with the Secure IoT (SIT) encryption technique to improve WSN’s data security while lowering the threshold of attacks and minimize power consumption in WSNs without impacting network performance. Furthermore, the proposed LSA reduces the key generation time by 102mS thus improving the security by 99%. During data transmission, the power consumption is reduced by an average of 411.2uJ and the Packet Drop Ratio (PDR) is between 90 and 99% when comparing it with SPN and Feistel techniques.

Routing Overhead Aware Optimal Cluster based Routing Algorithm for IoT Network using Heuristic Technique

Globally, billion of devices in heterogeneous networks are interconnected by the Internet of Things (IoT). IoT applications require a centralized decision-making system due to the failure-prone connectivity and high latency of such a system. Low-latency communications are a primary characteristic of applications. Since IoT applications usually have small payload sizes, reducing communication overhead is crucial to improving energy efficiency. Researchers have proposed several methods to resolve the load balancing issue of IoT networks and reduce communication overhead. Although these techniques are not effective, in terms of high communication costs, end-to-end delay, packet loss ratio, throughput, and node lifetimes negatively impact network performance. In this paper, we propose a communication overhead aware optimal cluster-based (COOC) routing algorithm for IoT networks based on a hybrid heuristic technique. Using three benchmark algorithms, we form load-balanced clusters using k-means clustering, fuzzy logic, and genetic algorithm. In the next step, compute the rank of each node in a cluster using multiple design constraints, which are optimized by using the improved COOT bird optimum search algorithm (I-COOT). After that, we choose the cluster head (CH) according to the rank condition, thereby reducing the communication overhead in IoT networks. Additionally, we design chaotic golden search optimization algorithm (CGSO) for choosing the optimal best path between IoT nodes among multiple paths to ensure optimal data transfer from CHs. To conclude, we validate our proposed COOC routing algorithm against the different simulation scenarios and compare the results with existing state-of-the-art routing algorithms.

Optimization of Load Balancing Algorithms to Deal with Ddos Attacks Using Whale ‎optimization Algorithm

Load balancing algorithms are used to deal with DDoS attacks to identify the optimal server and are responsible for the optimal allocation of requests to the servers. The managing method of the distribution of the requests between servers directly impacts network performance. Denial of service (DDoS) attacks are malicious attempts to interrupt regular operation or network traffic in a targeted manner, making disturbances in internet traffic by disrupting the infrastructure of different servers and thus causing problems such as slow site performance. This paper addresses the optimization of load balancing algorithms to deal with DDoS attacks using Whale Optimization Algorithm (WOA) with other closest algorithms (Round-Robin (RR), Particle Swarm Optimization (PSO), and Genetic Algorithms (GA)). The results obtained from implementing these algorithms showed that the WOA performed better than other algorithms in term of speed up the response time to client requests. The whale optimization algorithm can prevent unexpected traffic and block the regular operation of Internet websites by providing a proper plan for distributing requests between servers and reducing the average response speed. So, the authors can prevent DDoS attacks by applying the whale optimization algorithm. It necessary be noted that the use of HAProxy to prevent DDoS is not enough, and depending on the type of attack, several layers of software and hardware security necessary be used

Soft Computing Techniques Aware Clustering-Based Routing Protocols in Vehicular Ad Hoc Networks: A Review

The vehicular ad hoc network is an emerging area of technology that provides intelligent transportation systems with vast advantages and applications. Frequent disconnections between the vehicular nodes due to high-velocity vehicles impact network performance. This can be addressed by efficient clustering techniques. Several recent studies have attempted to develop optimal clustering algorithms to improve network performance metrics using soft computing techniques. Although sufficient work on soft computing techniques has been carried out, it seems less commonplace to find an analysis of various algorithms’ network parameters together. This paper provides a systematic analysis of the clustering-based routing protocols used in vehicular networks that are aware of soft computing techniques. The categorization is performed according to various soft computing techniques: particle swarm optimization, k-means, neural networks, artificial bee colony, genetic algorithm, firefly algorithm, and fuzzy logic. A comparative study of soft computing strategies is also provided in the survey with a focus on their objectives, along with their strengths and limitations. This survey makes it easier for researchers to pick the required soft computing technique used in vehicular networks in order to improve metrics such as packet delivery ratio, end-to-end delay, throughput, cluster lifetime, and message overhead.