NS-2 Simulator Research Articles

Antibiotic resistance challenge: evaluating anthraquinones as rifampicin monooxygenase inhibitors through integrated bioinformatics analysis

ObjectiveAntibiotic resistance poses a pressing and crucial global public health challenge, leading to significant clinical and health-related consequences. Substantial evidence highlights the pivotal involvement of rifampicin monooxygenase (RIFMO) in the context of antibiotic resistance. Hence, inhibiting RIFMO could offer potential in the treatment of various infections. Anthraquinones, a group of organic compounds, have shown promise in addressing tuberculosis. This study employed integrated bioinformatics approaches to evaluate the potential inhibitory effects of a selection of anthraquinones on RIFMO. The findings were subsequently compared with those of rifampicin (RIF), serving as a positive control inhibitor.MethodsThe AutoDock 4.0 tool assessed the binding free energy between 21 anthraquinones and the RIFMO catalytic cleft. The ligands were ranked based on the most favorable scores derived from ΔGbinding. The docking analyses for the highest-ranked anthraquinone and RIF underwent a cross-validation process. This validation procedure utilized the SwissDock server and the Schrödinger Maestro docking software. Molecular dynamics simulations were conducted to scrutinize the stability of the backbone atoms in free RIFMO, RIFMO-RIF, and RIFMO complexed with the top-ranked anthraquinone throughout a 100-ns computer simulation. The Discovery Studio Visualizer tool visualized interactions between RIFMO residues and ligands. An evaluation of the pharmacokinetics and toxicity profiles of the tested compounds was also conducted.ResultsFive anthraquinones were indicated with ΔGbinding scores less than − 10 kcal/mol. Hypericin emerged as the most potent RIFMO inhibitor, boasting a ΔGbinding score and inhibition constant value of − 12.11 kcal/mol and 798.99 pM, respectively. The agreement across AutoDock 4.0, SwissDock, and Schrödinger Maestro results highlighted hypericin’s notable binding affinity to the RIFMO catalytic cleft. The RIFMO-hypericin complex achieved stability after a 70-ns computer simulation, exhibiting a root-mean-square deviation of 0.55 nm. Oral bioavailability analysis revealed that all anthraquinones except hypericin, sennidin A, and sennidin B may be suitable for oral administration. Furthermore, the carcinogenicity prediction analysis indicated a favorable safety profile for all examined anthraquinones.ConclusionInhibiting RIFMO, particularly with anthraquinones such as hypericin, holds promise as a potential therapeutic strategy for infectious diseases.

Design and computational analysis of a novel Leptulipin-p28 fusion protein as a multitarget anticancer therapy in breast cancer.

The search for novel therapeutic agents to treat breast cancer has compelled the development of fusion proteins that synergize the functional benefits of different bioactive peptides. Leptulipin, derived from scorpion venom, exhibits antitumor properties. On the other hand, p28, a peptide from the bacterial protein azurin, enhances cell penetration. The current study investigated the design and computational evaluation of a Leptulipin-p28 fusion protein for breast cancer treatment. The amino acid sequences of Leptulipin and p28 were joined via a rigid linker to maintain structural and functional integrity. Secondary and tertiary structure predictions were performed using online servers of GOR-IV and I-TASSER. Physicochemical properties and solubility were analyzed using ProtParam and Protein-Sol. Validation and quality assessment of the fusion protein were confirmed through Rampage and ERRAT2. Finally, the fusion protein was docked with 2 receptors (VEGFR and Cadherin) and docked complexes were simulated on GROMACS. The Leptulipin-p28 fusion protein exhibited a stable structure exhibiting a high quality score of 92 on ERRAT and Ramachandran plot analysis highlighting 76.3% of residues in the favorable region. Docking studies with VEGFR and Cadherin receptors followed by 100ns simulations on GROMACS showed stable complex formation. Molecular dynamics simulations confirmed the stability and robust interaction of the fusion protein-receptor complexes over time. The computational analysis indicates that the Leptulipin-p28 fusion protein holds promise as a multitarget therapeutic agent in breast cancer. The current findings warrant further investigation through in vitro and in vivo studies to validate the current outcomes.

Complexation process and binding parameters of curcumin and short amylose with V7-type helix structure

Pre-formed V7-type short amylose (SA) could interact with curcumin to form inclusion complex (IC) thereby to improve the stability of curcumin. However, the complexation mechanism of V7-type SA and curcumin is not clear, which limit the improvement of inclusion efficiency. To obtain a starch nanocarrier with high loading capacity, the encapsulation process and interaction parameters of V7-type SA-curcumin IC was studied. The analysis results demonstrated that stoichiometric ratio value of the SA-curcumin complex was around 1. V7-type SA performed excellently in the delivery of curcumin attributing to their high loading capacity (over 20%). It was found that curcumin could enter into the pre-formed helical cavity of SA to form an IC. The conformation change of SA caused the reduction in the interaction ratio in the last 20 ns of simulation. However, SA and curcumin always remained complexation status during the simulation. Hydrogen bonds (H-bonds) and hydrophobic interaction were the most critical acting forces involved in the formation and stability of V7-type SA-curcumin complex. Molecular docking presented that H-bonds interaction between curcumin ligand and V7-type SA chain (O3 at the 25th glucose unit, and O6 at the 17th and 20th glucose units) were found. Furthermore, the hydrophobic interactions were discovered between curcumin ligand and SA chain (18th, 19th, 21st, 22nd and 23rd glucose units).

Exploiting Conventional MANET Routing in UAV’s Based Environment

Nowadays, Unmanned Aerial Vehicles (UAVs), have indeed revolutionized various industries and opened up new possibilities. Additionally, a Flying Ad hoc Network (FANET), a group of UAVs can perform specific functions like delivering goods, assisting people after a disaster, monitoring the weather and environment, among others. FANET is another kind of ad-hoc mobile network. It is capable of connecting with its environment and other nodes to obtain critical information. It is particularly difficult to route across FANETs because of how mobility they are and how fast they may change layouts. Therefore, routing algorithms are crucial for improving the performance of ad-hoc networks. This article will analyse the performance of the AODV, DSDV, and TORA routing protocols in the FANET environment. The NS-2 simulator has been used for testing parameters such as jitter, throughput, End-to-End delay, and packet delivery ratio (PDR). The simulation results indicate that TORA outperforms AODV and DSDV routing protocols across all performance metrics except End-to-End Delay.

Reversal of P-glycoprotein Mediated Multidrug Resistance in MCF-7/R Cancer Cells by Esculetin Derivatives: Experimental and MD Simulation Studies

Coumarins of natural origin have been explored as potential inhibitors of P-glycoprotein (P-gp). Esculetin which belongs to the class of coumarin has been derivatized with known hydrazine pharmacophores viz; benzoyl hydrazine (BH), isonicotinyl hydrazine (INH), and hydrazino benzoic acid. The homology modeling approach was used to predict the three-dimensional structure of human P-gp. An <i>in-silico</i> study has been performed for the structural insight into the molecular mechanism of P-gp inhibition of the esculetin derivatives by molecular docking (MD) and simulation studies. The cell cytotoxic activities of the synthesized compounds were evaluated using in-vitro studies. The sublines resistant doxorubicin (MCF-7/R) were generated and the activities of P-gp proteins were estimated using fluorescent dye accumulation assays. The E-BH showed promising P-gp inhibitory activity and cell cytotoxicity against MCF7 and MCF7/R (resistant) breast cancer cell lines. In line with experimental observations, the E-BH (Esculetin benzoyl hydrazine) has yielded the lowest energy stable complex with P-gp and is stabilized by intermolecular hydrogen bonding and more hydrophobic interactions during 100 ns of simulation. This suggested that the activity of P-gp is probably controlled by hydrophobic interactions. Performed experimental and computational studies has helped to elucidate the mechanism of P-gp inhibition by E-BH. Thus, amongst the three derivatives; E-BH exhibits greater efficacy in blocking the efflux mechanism.

Exploiting stream scheduling in QUIC: Performance assessment over wireless connectivity scenarios

The advent of wireless technologies has led to the development of novel services for end-users, with stringent needs and requirements. High availability, very high throughput, low latency, and reliability are all of them crucial performance parameters. To address these demands, emerging technologies, such as non-terrestrial networks or millimeter wave (mmWave), are being included in 5G and Beyond 5G (B5G) specifications. mmWave enables massive data transmissions, at the expense of a more hostile propagation, typical for high frequency bands. Consequently, the inherent instability of the physical channel significantly affects the upper layers of the protocol stack, resulting in congestion and data losses, which might strongly hinder the overall communication performance. These challenges can be addressed not only at the link layer, but at any affected layer. QUIC is a new transport protocol designed to reduce communications latency in many ways. Among other features, it enables the use of multiple streams to effectively manage data flows sent through its underlying UDP socket. This paper introduces an implementation of priority-based stream schedulers along with the design of a flexible interface. Exploiting the proposed approach, applications are able to set the required scheduling scheme, as well as the stream priorities. The feasibility of the proposed approach is validated through an extensive experiment campaign, which combines Docker containers, the ns-3 simulator and the Mahimahi framework, which is exploited to introduce realistic mmWave channel traces. The results evince that an appropriate stream scheduler can indeed yield lower delays for time-sensitive applications by up to 36% under unreliable conditions.

New combined Inverse-QSAR and molecular docking method for scaffold-based drug discovery

Computer-aided drug discovery plays a vital role in developing novel medications for various diseases. The COVID-19 pandemic has heightened the need for innovative approaches to design lead compounds with the potential to become effective drugs. Specifically, designing promising inhibitors of the SARS-CoV-2 main protease (Mpro) is crucial, as it plays a key role in viral replication. Phytochemicals, primarily flavonoids and flavonols from medicinal plants, were screened. Fifty small molecules were selected for molecular docking analysis against SARS-CoV-2 Mpro (PDB ID: 6LU7). Binding energies and interactions were analyzed and compared to those of the anti-SARS-CoV-2 inhibitor Nirmatrelvir. Using these 50 structures as a training set, a QSAR model was built employing simple, reversible topological descriptors. An inverse-QSAR analysis was then performed on 2⁹ = 512 hydroxyl combinations at nine possible positions on the flavone and flavonol scaffold. The model predicted three novel, promising compounds exhibiting the most favorable binding energies (−8.5 kcal/mol) among the 512 possible hydroxyl combinations: 3,6,7,2′,4′-pentahydroxyflavone (PF9), 6,7,2′,4′-tetrahydroxyflavone (PF11), and 3,6,7,4′-tetrahydroxyflavone (PF15). Molecular dynamics (MD) simulations demonstrated the stability of the PF9/Mpro complex over 300 ns of simulation. These predicted structures, reported here for the first time, warrant synthesis and further evaluation of their biological activity through in vitro and in vivo studies.

DRTP: A generic Differentiated Reliable Transport Protocol

With rapid development of network communications, the performance requirements of applications are getting more differentiated. Many applications like high-definition video transfer require high throughput but do tolerate occasional packet losses. Traditional generic transport protocols however, only provide inflexible data transfer guarantees (TCP (Transmission Control Protocol) offers full reliability guarantees and UDP (User Datagram Protocol) offers no guarantees). Moreover, TCP pays a significant price to ensure a full reliability guarantee over lossy wireless communications environment like 5G millimeter wave (mmWave) communications. While existing, “partially” reliable transport protocols are either specifically designed for certain applications or need router’s support. In this paper, we design a new generic Differentiated Reliable Transport Protocol (DRTP), aiming to provide a differentiated and deterministic reliability guarantee for upper layer applications while maximizing the throughput under the constraint of guaranteeing a required reliability of data transfer. DRTP is a generic and pure end-to-end partially reliable transport protocol, and as such is easy to deploy regardless of the application in use and with no need for router’s support. The performance of DRTP is evaluated under various network conditions using extensive NS-3 (Network Simulator) simulations and practical experiments over the mmWave communications environment. The results show much higher throughput compared to typical transport protocols while guaranteeing the required transfer reliability.

A local filtering-based energy-aware routing scheme in flying ad hoc networks

Flying ad hoc network (FANET) is a new technology, which creates a self-organized wireless network containing unmanned aerial vehicles (UAVs). In FANET, routing protocols deal with important challenges due to limited energy, frequent link failures, high mobility of UAVs, and limited communication range of UAVs. Thus, a suitable path is always essential to transmit data between UAVs. In this paper, a local filtering-based energy-aware routing scheme (LFEAR) is proposed for FANETs. LFEAR improves the template of the route request (RREQ) packet by adding three fields, namely the energy, reliable distance, and movement similarity of the relevant route to create stable and energy-efficient paths. In the routing process, LFEAR presents a local filtering construction technique to avoid the broadcasting storm issue. This filter limits the broadcasting range of RREQs in the network. Accordingly, only UAVs inside this local filtered area can rebroadcast RREQs and other UAVs must eliminate these packets. After ending the route discovery process, the destination begins the route selection phase and extracts information about each discovered route, including the number of hops, route energy, reliable distance, and movement similarity. Then, the destination node calculates a score for each path based on the extracted information, selects the route with the highest score, and sends a route reply (RREP) packet to the source node through this route. Finally, the simulation process of LFEAR is performed using the NS2 simulator, and two simulation scenarios, namely change in network density and change in the speed of UAVs, are defined to evaluate network performance. In the first scenario, LFEAR improves energy consumption, packet delivery rate, network lifespan, and delay by 1.33%, 1.77%, 6.74%, and 1.71%, while its routing overhead is about 16.51% more than EARVRT. In the second scenario, LFEAR optimizes energy consumption and network lifetime by 5.55% and 5.67%, respectively. However, its performance in terms of routing overhead, packet delivery rate, and delay is 23%, 2.29%, and 6.67% weaker than EARVRT, respectively.

Enhancing routing and quality of service using AI-driven technique for internet of vehicles contexts

This paper introduces an Enhancing Routing and Quality of Service (QoS) Using AI-driven Technique for Internet of Vehicles (IoV) Contexts. The proposed approach aims to enhance QoS and overall network performance in connected vehicle networks. Vehicles utilize Road-Side Units and the Internet to aggregate data from neighboring vehicles, optimizing routing and data management. The proposed approach utilizes a graph traversal algorithm, a widely adopted technique in Artificial Intelligence for graph search, which facilitates traversal in routing. The proposed approach integrates a mobility score based on metrics such as velocity, acceleration, and neighboring information, ensuring optimal routing for dynamic vehicular networks. Objectives include improving QoS and network performance by reducing overheads, optimizing load balancing, and extending network lifetime. Implemented in NS-3 and MOVE simulators, results demonstrate significant performance enhancements compared to existing approaches. This approach addresses the challenges of extensive vehicle mobility and frequent topology changes in connected environments. Overall, the paper presents a comprehensive solution for IoV networks, combining AI-driven routing with mobility-aware strategies to advance network efficiency and QoS.

Modulation of the intermolecular interactions of cucurbit[7]uril with phenylalanine derivatives by the functional groups

Abstract Intermolecular interactions in 1:1 inclusion complexes of cucurbit[7]uril (CB[7]) with phenylalanine derivatives were investigated by density functional theory (B97-D) calculations. For each complex, 2 optimized geometries were found: conformer C in which a guest molecule resided in the center of CB[7], and conformer E where a guest molecule was present at the edge of CB[7]. The order of energy differences between these conformers agreed well with previously reported differences in the affinity of guest molecules for CB[7]. Molecular dynamics simulations of the complexes showed that the guest molecules in CB[7] had different stabilities, and the calculated binding free energies between them also qualitatively agreed with the experimental results. Pair-interaction energy decomposition analyses of the complexes at FMO-MP2/6-31G(d) and aug-cc-pVDZ level of theory were performed using snapshot structures at 500 ns of simulations. The dispersion interaction characterized the interaction, and the order of total interaction energies between the guest molecules and CB[7] was also associated with the experimental results. A significant difference in electrostatic interaction energies was observed in conformer E, which was correlated with the stability of the guest molecules at the edge of CB[7]. The balance between the stabilities of these conformers was correlated with the affinity of guest molecules for CB[7].

A new priority aware routing protocol for efficient emergency data transmissions in MANETs

This paper introduces a new priority-aware routing protocol for mobile Ad-hoc networks to be utilized in emergencies, which is based on AODV. Mobile Ad-hoc networks find extensive use in various domains including military operations, environmental monitoring, healthcare, disaster response, smart transportation systems, unmanned aerial vehicles, and smart homes. During emergencies, communication can be severely restricted or even impossible due to the congestion of physical communication channels and unexpected technical failures in the infrastructure. Mobile Ad-hoc networks offer a solution to maintain continuous and reliable communication under such challenging conditions. In emergency scenarios, it is crucial for any node in the network to promptly deliver urgent messages to the intended destination, especially when certain nodes require ongoing active communication. The proposed routing protocol effectively addresses this requirement through its priority-aware mechanisms. The protocol ensures that nodes not involved in emergency tasks select the least congested route to prevent any delays or disruptions in the transmission of critical emergency data. This approach guarantees seamless communication for emergency nodes while allowing non-emergency nodes to communicate with each other as well. The study proposed in this paper introduces a new priority-aware routing protocol based on AODV for mobile Ad-hoc networks in emergencies. The packet transmission ratio of emergency nodes within the network is improved while maintaining the overall network performance unaffected. The adoption of proposed mechanisms to enhance performance does not necessitate an expansion in the size of data and control packets. These mechanisms do not inflict any supplementary latency or incur packet loss expenses on the network. The proposed protocol has been implemented and evaluated using ns-3 simulation software across various emergency scenarios. The results show that emergency nodes using the proposed protocol, achieve better packet delivery ratios compared to the original AODV, DSR, P-AODV, and AOMDV protocols, with improvements of 10.8%, 15.9%, 6.2%, and 5.9% respectively. This improvement in the packet delivery ratio for emergency data traffic is achieved without causing any disruptions in the overall network communication flow.

Energy Efficient AODV routing protocols for FANETs

All system units in flying UAV like transmitter, receiver equipment, control unit, information processing unit and payloads are powered by in build power sources. UAVs equipped with the limited on-board energy capability restrict the flying time, which will significantly affect the performance of a FANETs. Optimizing the energy consumption among nodes is one of the important research challenges. Optimization techniques for energy usage can be implemented at different OSI layer level. In our study we focused on OSI networks layers solution for performance improvements based on energy efficient routing techniques in flying ad hoc network environments. Routing algorithm that is used in MANETs applications can be optimized and used in FANETS as in both networks majority of the operational characteristics similar. But Few characteristics in FANETs like distance coverage, node mobility velocity, capacity and types of power supply used differentiate from other mobiles networks (VANETs,MANETs). During first phase of our work we evaluated the performance of classical routing protocols AODV, DSR, DSDV in FANETs using energy metrics. Result in first phase revel that AODV performance is superior to other protocols in FANETs. In second phase we designed EEAODV protocols which use energy metrics in addition to hop count for path optimization. Finally details performance comparison study between EEAODV and AODV performed, the result shows that EEAODV performance is much better that AODV. For our performance evaluation we used NS-3 simulator and random waypoint mobility models.

A novel Q-learning-based secure routing scheme with a robust defensive system against wormhole attacks in flying ad hoc networks

Nowadays, unmanned aerial vehicles (UAVs) organized in a flying ad hoc network (FANET) can successfully carry out complex missions. Due to the limitations of these networks, including the lack of infrastructure, wireless communication channels, dynamic topology, and unreliable communication between UAVs, cyberattacks, especially wormholes, weaken the performance of routing schemes. Therefore, maintaining communication security and guaranteeing the quality of service (QoS) are very challenging. In this paper, a novel Q-learning-based secure routing scheme (QSR) is presented for FANETs. QSR seeks to provide a robust defensive system against wormhole attacks, especially wormhole through encapsulation and wormhole through packet relay. QSR includes a secure neighbor discovery process and a Q-learning-based secure routing process. Firstly, each UAV gets information about its neighboring UAVs securely. To secure communication in this process, a local monitoring system is designed to counteract the wormhole attack through packet relay. This system checks data packets exchanged between neighboring UAVs and defines three rules according to the behavior of wormholes. In the second process, UAVs perform a distributed Q-learning-based routing process to counteract the wormhole attack through encapsulation. To reward the safest paths, a reward function is introduced based on five factors, the average one-hop delay, hop count, data loss ratio, packet transmission frequency (PTF), and packet reception frequency (PRF). Finally, the NS2 simulator is applied for implementing QSR and executing different scenarios. The evaluation results show that QSR works better than TOPCM, MNRiRIP, and MNDA in terms of accuracy, malicious node detection rate, data delivery ratio, and data loss ratio. However, it has more delay than TOPCM.

Experimental Design of Wireless Sensor Network Simulation Teaching Based on OBE Concept

In response to the problem that students cannot effectively master the theoretical knowledge of wireless sensor networks, this study proposes an outcome-based education (OBE) guided teaching method and designs a simulation experiment on wireless sensor networks using the NS3 simulation platform. In the experiment, access point (AP) and station (STA) nodes are set up to present different network environments, and the transmission layer protocols (TCP and UDP) are changed to analyze different network performance. Through the analysis of experimental results, students can gain a vivid understanding of the basic knowledge of wireless sensor networks and achieve the expected teaching goals. This teaching method helps improve students' understanding and application ability of wireless sensor network theory, and promotes their learning and research in related fields.

Enhanced Wireless Communication Optimization with Neural Networks, Proximal Policy Optimization and Edge Computing for Latency and Energy Efficiency

This research proposes a novel approach for efficient resource allocation in wireless communication systems. It combines dynamic neural networks, Proximal Policy Optimization (PPO), and Edge Computing Orchestrator (ECO) for latency-aware and energy-efficient resource allocation. The proposed system integrates multiple components, including a dynamic neural network, PPO, ECO, and a Mobile Edge Computing (MEC) server. The experimental methodology involves utilizing the NS-3 simulation platform to assess latency and energy efficiency in resource allocation within a wireless communication network, incorporating an ECO, MEC server, and dynamic task scheduling algorithms. It demonstrates a holistic and adaptable approach to resource allocation in dynamic environments, showcasing a notable reduction in latency for devices and tasks. Latency values range from 5 to 20 milliseconds, with corresponding resource utilization percentages varying between 80% and 95%. Additionally, energy-efficient resource allocation demonstrates a commendable reduction in energy consumption, with measured values ranging from 10 to 30 watts, coupled with efficient resource usage percentages ranging from 70% to 85%. These outcomes validate the efficacy of achieving both latency-aware and energy-efficient resource allocation for enhanced wireless communication systems. The proposed system has broad applications in healthcare, smart cities, IoT, real-time analytics, autonomous vehicles, and augmented reality, offering a valuable solution to optimize energy consumption, reduce latency, and enhance system efficiency in these industries.

EVALUATING ROUTING ALGORITHMS ACROSS DIFFERENT WIRELESS MESH NETWORK TOPOLOGIES USING NS-3 SIMULATOR

Wireless Mesh Networks are gaining traction as a solution for delivering reliable connectivity without centralized infrastructure. They operate through wireless node interconnections, forming self-configuring networks ideal for scenarios where wired networks are impractical. Routing is crucial in Wireless Mesh Networks to ensure efficient communication among nodes. However, the suitability of routing algorithms for Wireless Mesh Network’s topology requires further investigation. This paper proposes an investigation into the effectiveness of routing algorithms like AODV, DSDV, and OLSR across various Wireless Mesh Networks topologies using NS-3 simulation. It also aims to determine the optimal number of nodes and protocols to maximize throughput and minimize packet loss within a limited area. Through rigorous NS-3 simulations, the study demonstrates that AODV, DSDV, and OLSR exhibit differing effectiveness across random, mesh grid, and Fruchterman-Reingold topologies. These results emphasize the importance of considering topology-specific factors when selecting and optimizing routing protocols for Wireless Mesh Networks. In summary, Wireless Mesh Networks offer decentralized connectivity, but the effectiveness of routing algorithms in different topologies remains understudied. This investigation addresses this gap by evaluating routing algorithms across various topologies, shedding light on their suitability and performance in Wireless Mesh Networks.

Enhancing intelligent transport systems: A cutting-edge framework for context-aware service management with hybrid deep learning

This study presents a comprehensive framework for optimizing intelligent transport systems (ITS) by integrating advanced communication and information technologies into vehicles, roads, and infrastructure. The primary goal is to enhance transportation efficiency, safety, and environmental sustainability while improving overall mobility for people and goods. Leveraging contextual information, the framework offers personalized, proactive services such as real-time traffic updates, route recommendations, and parking availability. Additionally, it enhances safety and security by providing early hazard warnings and adapting to changing road conditions. Our proposed framework utilizes the enhanced coral reef optimization (ECRO) algorithm to efficiently group vehicles for energy-saving data collection, maximizing information gathering efficiency. Collected data is then transmitted to a central data gathering center via a sink node optimized through the modified pelican optimization (MPO) algorithm, considering various vehicle node design constraints. An incident detection module accurately classifies and detects road incidents, enabling timely emergency service requests and alternate route recommendations. To facilitate incident detection, we introduce the deep Rigdelet neural network (DRNN), a novel deep learning technique tailored for decision-making in incident classification. We validate our framework's performance through NS-2 simulations using the SUMO traffic generator, demonstrating its effectiveness in meeting quality of service (QoS) metrics. Through comparative analysis with existing frameworks, our proposed approach stands out for its superior performance and ability to optimize ITS operations.

An intelligent clustering scheme based on whale optimization algorithm in flying ad hoc networks

Due to the progress of unmanned aerial vehicles (UAVs), this new technology is widely applied in military and civilian areas. Multi-UAV networks are often known as flying ad hoc networks (FANETs). Due to these applications, FANET must ensure communication stability and have high scalability. These goals are achieved by presenting clustering techniques in FANETs. However, the characteristics of these networks, like high-mobility nodes, limited energy, and dynamic topology, have created great challenges in two important processes of clustering protocols, namely cluster construction and the selection of cluster heads. In this paper, an intelligent clustering scheme based on the whale optimization algorithm called ICW is suggested in flying ad hoc networks. Firstly, each UAV specifies its hello interval based on the lifespan of adjacent links to guarantee the adaptability of ICW to FANET. Then, a centralized clustering process is done using a whale optimization algorithm (WOA) to find the best cluster centers on the network. To determine the membership of each UAV in a cluster, ICW employs a new criterion, i.e. closeness ratio, so that each UAV joins a cluster with the best closeness ratio. In addition, the evaluation of each whale is carried out based on a fitness function, consisting of three components, namely the number of isolated clusters, the ratio of inter-cluster distance to intra-cluster distance, and cluster size. Then, a cluster head is selected for each cluster based on a score value. This score is dependent on the weighted sum of four metrics, namely remaining energy, the average link lifespan between each UAV and its neighbors, neighbor degree, and the average distance between each UAV and its neighbors. In the last step, two routing processes, namely intra-cluster routing and inter-cluster routing, are introduced in FANET. Then, the evaluation and implementation of ICW is performed through the NS2 simulator. After completing the simulation process, ICW is compared to MWCRSF, DCM, and GWO, and the evaluation results are presented in two scenarios, namely network evaluation in the clustering process and network evaluation in the routing process. Accordingly, in the first scenario, ICW has low clustering time and a high cluster lifetime. In the second scenario, ICW optimizes energy consumption, network longevity, packet delivery rate, routing overhead, and delay compared to other approaches. However, throughput in ICW is about 3.9% lower than that in MWCRSF.

Distributed neighbor discovery with mobility adaptive probing for beyond 5G D2D communications

Representing a paradigm shift in cellular communication, Device-to-Device (D2D) Communication is an emerging technology that enables direct communication between two User Equipments(UEs) without going through the Base Station. It enables decentralization of cellular networks, enhances network capacity, increases spectral efficiency, and offloads network traffic. In order to proceed with direct D2D communication, the devices must have knowledge of the proximal devices and should work in all coverage scenarios such as in-coverage, partial-coverage and out-of-coverage. Most of the existing works on Device Discovery are based on 3GPP Model A and Model B. However, these schemes suffer from overhead of discovery message exchanges with the increase in the number of devices in a mobile environment. To address these issues, we propose a distributed and adaptive device discovery mechanism that can discover the proximal UEs in all possible scenarios. To achieve this, we propose a Hybrid Discovery Model ‘H’, which uses the concept of ‘Who are there?’. In Model ‘H’, the discoverer sends a single request message to discover all proximal devices. The responses of the proximal UEs are staggered based on the estimated number of neighbors to avoid collisions. In addition to that, we adapt the discovery mechanism to the relative mobility of the neighborhood using our proposed Adaptive Probing Algorithm named ‘ReMAP’. The relative mobility is determined based on the current and previous neighbor lists of each UE. The key advantage of our proposed mechanism is that we do not require the knowledge of position and mobility pattern of the UEs. We implement and evaluate our proposed work in D2D Module of the NS3 simulator. From the simulation results, we observe that our proposed model shows better performance in terms of the number of UEs discovered and reduced number of discovery message exchanges compared to the state-of-the-art. We also compare the performance of ReMAP with a constant probing mechanism and show the improvement in terms of adaptability to the mobility of the neighborhood.