NS-2 Simulator Research Articles

A Security-Enhanced Energy Conservation with Enhanced Random Forest Classifier for Low Execution Time Framework (S-2EC-ERF) for Wireless Sensor Networks

Wireless sensor networks (WSNs) play a pivotal role in diverse applications such as environmental monitoring, industrial automation, healthcare, and smart cities. The motivation behind the development of WSNs stems from their impact in providing real-time data on various environmental parameters. The challenge for WSNs is to achieve strong security and efficient energy saving together. Traditional methods sought to find solutions either through security or energy. In response, this study proposed a secure and energy-efficient framework for enhancing security measures in WSNs while minimizing the impact on energy resources by using the Enhanced Consumed Energy Leach (ECP-LEACH) protocol and the Enhanced Random Forest Classifier for Low Execution Time (ERF-LET) algorithm for attack detection named Security-Enhanced Energy Conservation with ERF-LET (S-2EC-ERF). The integration of the detection algorithm at the node level played a pivotal role in fortifying the security posture of individual nodes by detecting and mitigating potential security threats. Leveraging a comprehensive dataset obtained from NS3 simulations, the ERF-LET algorithm demonstrated its proficiency in differentiating between normal and attack packets, thereby laying a strong foundation for subsequent evaluations, where it achieved an accuracy of 98.193%. The proposed methodology was further validated through real-time simulations conducted on the NS3. The results demonstrated the superiority of the proposed S-2EC-ERF in terms of the packet delivery ratio (PDR), average throughput, end-to-end delay, and mean energy consumption compared to the Security-Enhanced Energy Conservation with Logistic Regression (S-2EC-LR), Security-Enhanced Energy Conservation with Decision Tree (S-2EC-DT), and Security-Enhanced Energy Conservation with AdaBoost (S-2EC-Ada) algorithms.

Artificial Neural Network-Based Mechanism to Detect Security Threats in Wireless Sensor Networks.

Wireless sensor networks (WSNs) are essential in many areas, from healthcare to environmental monitoring. However, WSNs are vulnerable to routing attacks that might jeopardize network performance and data integrity due to their inherent vulnerabilities. This work suggests a unique method for enhancing WSN security through the detection of routing threats using feed-forward artificial neural networks (ANNs). The proposed solution makes use of ANNs' learning capabilities to model the network's dynamic behavior and recognize routing attacks like black-hole, gray-hole, and wormhole attacks. CICIDS2017 is a heterogeneous dataset that was used to train and test the proposed system in order to guarantee its robustness and adaptability. The system's ability to recognize both known and novel attack patterns enhances its efficacy in real-world deployment. Experimental assessments using an NS2 simulator show how well the proposed method works to improve routing protocol security. The proposed system's performance was assessed using a confusion matrix. The simulation and analysis demonstrated how much better the proposed system performs compared to the existing methods for routing attack detection. With an average detection rate of 99.21% and a high accuracy of 99.49%, the proposed system minimizes the rate of false positives. The study advances secure communication in WSNs and provides a reliable means of protecting sensitive data in resource-constrained settings.

An integrated network pharmacology approach to discover therapeutic mechanisms of Commiphora wightii for the treatment of Bell’s palsy

Bell’s palsy (BP) can result in facial paralysis. Inflammation or injury to the cranial nerves that regulate the facial muscles is primarily responsible for that disease. Commiphora wightii remains recognized as a cure for a few human ailments. This study focused on therapeutic phenomena of C. wightii for the treatment of Bell’s palsy, utilizing the network drug discovery and molecular docking techniques. Active biological constituents of C. wightii were retrieved from literature and independent databases. Potential therapeutic targets (431) of 13 bioactive phytochemicals were fetched via SwissTargetPrediction tool. Putative intersecting targets (855) of Bell’s palsy were computed through the DisGeNET and GeneCards datasets. Subsequently, by the analysis of potential shared targets (87) of C. wightii and Bell’s palsy, a Venn diagram was drawn. DAVID database was used to evaluate gene functional annotations and enriched pathways that are involved in Bell’s palsy. STRING database was used for generating the protein–protein relationship complex. Visual presentations of the interactions of potential targets to active chemical constituents were done by the Cytoscape. Whereas, the conformational research sorted out 10 key targets through the protein–protein interactions network. Moreover, the capacity of therapeutic ingredients to interact with a target inhibiting Bell’s palsy was confirmed by molecular docking, which might ratify the findings of network pharmacology. In the molecular complex of AKT1–cholesterol, a 100-ns simulation unveiled a graceful stability, with a minimal 0.167 Å ligand shift and resilient hydrogen bonds (ASN54 and SER205). The final 20 ns showcased a P1 motif pirouette, gracefully forming aromatic bonds with H165 and W186, underscoring the complex’s dynamic finesse. This study evaluated compound–target interactions and their impact on disease-related genes. It revealed that five genes (AKT1, TNF, MAPK3, EGFR and SRC) of C. wightii might be useful therapeutic targets for the treatment of Bell’s palsy, as well as helping in lowering down the blood pressure. Communicated by Ramaswamy H. Sarma

FogSec: A secure and effective mutual authentication scheme for fog computing

SummaryAs opposed to cloud servers, fog servers, and fog users may be malicious, so developing a mutual identity‐preserving authentication mechanism between them is a crucial and difficult problem in fog computing. Such a technique must conceal the user's true identity from the adversary; otherwise, the adversary will be able to determine which fog user and fog server are in communication. This article suggests a secure and reliable anonymous mutual authentication system for use at the network's edge between fog users and fog servers. With the aid of the registration authority (RA) in our system, they can verify one another and decide on a new session key that will be used to encrypt messages throughout the session. Fog users don't need to re‐register with RA to wander freely over the network and authenticate to any fog server that is within their range. The proposed technique only needs a small number of symmetric encryption/decryption and one‐way hash functions, making it easy to implement for fog‐user devices with limited resources. The new scheme's performance is evaluated in comparison to the existing one, showing that it is more resilient to various types of assaults (such as known plaintext attacks, man‐in‐the‐middle attacks, session hijacking, etc.). The widely used Automated Validation of Internet Security Protocols and Applications tool is used to verify the proposed system. The outcomes demonstrate that our approach can safely withstand different attacks and accomplish the desired outcomes. Additionally, the proposed method is tested in real‐world scenarios with the NS3 simulator.

A secure and privacy preserved data aggregation scheme in IoMT

The Internet of Things (IoT) is a network of intelligent devices especially in healthcare-based systems. Internet of Medical Things (IoMT) uses wearable sensors to collect data and transmit to central repositories. The security and privacy of healthcare data is a challenging task. The aim of the study is to provide a secure data sharing mechanism. The existing studies provide secure data sharing schemes but still have limitations in terms of hiding the patient identify in the messages exchanged to upload the data on central repositories. This paper presents a Secure Aggregated Data Collection and Transmission (SADCT) that provides anonymity for the identities of patient's mobile device and the intermediate fog nodes. Our system involves an authenticated server for node registration and authentication by saving security credentials. The proposed scheme presents the novel data aggregation algorithm at the mobile device, and the data extraction algorithm at the fog node. The work is validated through extensive simulations in NS along with a security analysis. Results prove the supremacy of SADCT in terms of energy consumption, storage, communication, and computational costs.

Improve quality of service for the Internet of Things using Blockchain & machine learning algorithms

The quality of service (QoS) parameters in IoT applications plays a prominent role in determining the performance of an application. Considering the significance and popularity of IoT systems, it can be predicted that the number of users and IoT devices are going to increase exponentially shortly. Therefore, it is extremely important to improve the QoS provided by IoT applications to increase their adaptability. Majority of the IoT systems are characterized by their heterogeneous and diverse nature. It is challenging for these systems to provide high-quality access to all the connecting devices with uninterrupted connectivity. Considering their heterogeneity, it is equally difficult to achieve better QoS parameters. Artificial intelligence-based machine learning (ML) tools are considered a potential tool for improving the QoS parameters in IoT applications. This research proposes a novel approach for enhancing QoS parameters in IoT using ML and Blockchain techniques. The IoT network with Blockchain technology is simulated using an NS2 simulator. Different QoS parameters such as delay, throughput, packet delivery ratio, and packet drop are analyzed. The obtained QoS values are classified using different ML models such as Naive Bayes (NB), Decision Tree (DT), and Ensemble, learning techniques. Results show that the Ensemble classifier achieves the highest classification accuracy of 83.74 % compared to NB and DT classifiers.

Intelligent High-Awareness and Channel-Efficient Adaptive Beaconing Based on Density and Distribution for Vehicular Networks

In vehicle ad hoc networks (VANETs), a beacon is a periodic message sent to nearby vehicles containing essential details like the sender’s vehicle ID, location, speed, and direction. Maintaining the freshness of this information without causing network congestion requires adaptive beaconing to adjust to changes in mobility and network density. Our research, based on extensive simulation experiments, identifies specific parameter sets optimal for adapting beaconing rates to different scenarios. From this analysis, we introduce a novel scheme called high-awareness and channel-efficient adaptive beaconing (HACEAB), employing fuzzy logic to adapt to various environments and conditions. Initially, the protocol gauges network density using an adaptive threshold function, followed by estimating the node spatial distribution through the quadrat statistic method to discern uniform distribution or clustering. Utilizing these data, the protocol adjusts beaconing rates via appropriate input parameters for the fuzzy logic system. Remarkably, HACEAB represents the first beaconing scheme capable of simultaneously adjusting to changes in network density and spatial distribution. Furthermore, the protocol enhances performance by adapting transmission power to fluctuations in node density and distribution. NS-3 simulations validate the efficacy of these improvements.

Anti-Eavesdropping by Exploiting the Space–Time Coupling in UANs

Due to the space–time coupling access, we find that anti-eavesdropping opportunities exist in underwater acoustic networks (UANs), where packets can be successfully received only by the intended receiver, but collide at the unintended receivers. These opportunities are highly spatially dependent, and this paper studies the case that linearly deployed sensor nodes directly report data toward a single collector. We found an eavesdropping ring centered around these linearly deployed sensor nodes, where the eavesdropper could steal all the reported data. Since the typical receiving-alignment-based scheduling MAC (TRAS-MAC) will expose the relative spatial information among the sensor nodes with the collector, the eavesdropper can locate the eavesdropping ring. Although moving the collector into the one-dimensional sensor node chain can degrade the eavesdropping ring to a point that constrains the eavesdropping risk, the collector’s location will be subsequently exposed to the eavesdropper. To efficiently protect the reported data and prevent the exposure of the collector’s location, we designed a slotted- and receiving-alignment-based scheduling MAC (SRAS-MAC). The NS-3 simulation results showed the effectiveness of the SRAS-MAC and the TRAS-MAC in protecting data from eavesdropping, which protect 90% of the data from eavesdropping in the one-eavesdropper case and up to 80% of data from eavesdropping in ten-eavesdropper cases. Moreover, unlike TRAS-MAC, which will expose the collector’s location, SRAS-MAC provides multiple positions for the collector to hide, and the eavesdropper cannot distinguish where it is.

StegoBackoff: Creating a Covert Channel in Smart Grids Using the Backoff Procedure of IEEE 802.11 Networks

A smart grid constitutes an electrical infrastructure that integrates communication technologies to optimize electricity production, distribution, and consumption. Within the smart grid, IEEE 802.11 networks play a crucial role in facilitating communication between smart meters and data collectors, operating within a shared transmission medium. However, a notable challenge arises due to the lack of certainty regarding the genuine identity of data recipients. In response, we present a solution—a novel covert channel leveraging the IEEE 802.11 backoff procedure—to transmit data that requires special protection. Implemented using the ns-3 simulator, our covert channel achieved a throughput of 140,000 bps when single covert station realized transmission in the wireless channel, and 880 bps in a populated environment characterized by high traffic volumes. This performance metric shows that our mechanism is better than other covert channels, where the performance in saturated conditions usually does not exceed several hundred bps. This covert channel represents a new approach to fortifying data integrity and privacy within smart grid communication.

A Comparative Study and Analysis of LoRaWAN Performance in NS3

Long Range Wide Area Network (LoRaWAN) is a widely adopted Internet of Things (IoT) protocol due to its high range and lower energy consumption. LoRaWAN utilizes Adaptive Data Rate (ADR) for efficient resource (e.g., spreading factor and transmission power) management. The ADR manages these two resource parameters on the network server side and end device side. This paper focuses on analyzing the ADR and Gaussian ADR performance of LoRaWAN. We have performed NS3 simulation under a static scenario by varying the antenna height. The simulation results showed that antenna height has a significant impact on the packet delivery ratio. Higher antenna height (e.g., 50 m) has shown an improved packet success ratio when compared with lower antenna height (e.g., 10 m) in static and mobility scenarios. Based on the results, it is suggested to use the antenna at higher allevation for successful packet delivery

The identification of c-Abl inhibitors as potential agents for Parkinson's disease: a preliminary in silico approach.

Parkinson's disease (PD) is the most common movement disorder worldwide. PD is primarily associated with the mutation, overexpression, and phosphorylation of α-synuclein. At the molecular level, the upstream protein c-Abl, a tyrosine kinase, has been shown to regulate α-synuclein activation and expression patterns. This study aimed to identify potential c-Abl inhibitors through in silico approaches. Molecular docking was performed using PyRx software, followed by Prime MM-GBSA studies. BBB permeability and toxicity were predicted using CBligand and ProTox-II, respectively. ADME was assessed using QikProp. Molecular dynamics were carried out using Desmond (Academic version). DFT calculations were performed using the Gaussian 16 suite program. The binding scores of the top hits, norimatinib, DB07326, and entinostat were - 11.8kcal/mol, - 11.8kcal/mol, and - 10.8kcal/mol, respectively. These hits displayed drug-likeness with acceptable ADME properties, except for the standard, nilotinib, which violated Lipinski's rule of five. Similarly, the molecular dynamics showed that the top hits remained stable during the 100ns simulation. DFT results indicate DB04739 as a potent reactive hit. While based on toxicity prediction, entinostat may be a potential candidate for preclinical and clinical testing in PD. Further studies are warranted to confirm the activity and efficacy of these ligands for PD.

Assessment of Cyber-Physical Inverter-Based Microgrid Control Performance under Communication Delay and Cyber-Attacks

The integration of communication infrastructures into traditional power systems, transforming them into cyber-physical power systems (CPPS), accentuates the significance of communication in influencing system performance and sustainability. This paper presents a versatile, innovative cyber-physical co-simulation framework that integrates the physical power system and communication networks, uniting OPAL-RT, a network simulator (ns3), and Docker containers into a sophisticated platform, facilitating intensive studies into CPPS dynamics. The proposed experimental study provides an innovative way to assess the frequency control response of a cyber-physical inverter-based microgrid (MG), addressing the MG sustainability challenges. We consider diverse real-world scenarios, focusing on communication delays and distributed denial of service (DDoS) attacks within the communication channels. We propose a precise ns3-based communication model that bridges the MG’s primary and secondary control layers, an aspect often overlooked in previous studies; this is a noteworthy contribution to elucidating the adverse impacts of communication latency on MG frequency performance. The experimental results demonstrate the effectiveness of the centralized secondary controller in eliminating the frequency deviations. Furthermore, the findings offer insights into stable and unstable regions, revealing how the communication delay value affects the frequency stability under different operating conditions. In addition, the developed real-time DDoS attacks model within the proposed communication surface unveils crucial insights into the MG’s resilience to cyber threats. This work’s revelations offer a foundational awareness of MG vulnerabilities, paving the way for designing robust and resilient communication networks and control strategies within the cyber-physical inverter-based microgrids.

Development of a provably secure and privacy-preserving lightweight authentication scheme for roaming services in global mobility network

The accelerated development of Internet of Things services surfaced the way for providing uninterrupted and adaptable smart mobility services for mobile clients to communicate without any zone or time constraints. The global mobility network is an appropriate contender to be adopted as a communication platform to offer roaming services for swift data exchange and fast movement of mobile users in smart mobility. Nevertheless, the confidentiality and secrecy of the exchanged messages have become major concerns due to the insecure nature of the public communication channel. Additionally, it is worth monitoring that enhanced security measures must not increase the burden on limited-powered, small computationally efficient mobile devices. To fulfill these purposes, multiple authentication and key agreement protocols in the context of roaming services have been proposed in recent days. This research demonstrates that most of the previous authentication schemes for the GLOMONET environment have failed to achieve the aforesaid secrecy goals. To satisfy the purposes, this article has proposed a mutual authentication and session key negotiation scheme for roaming services that resists all security susceptibilities and provides essential requirements like anonymity with minimized computation and communication overhead. To support our claim, the security of the proposed scheme is formally verified using the well-known random oracle model and aProVerif simulation tool. Moreover, the implementation of the protocol using the broadly acknowledged NS-2 simulation tool visualizes the practical efficiency of the protocol.

QoS-Based Secure Data Communication for Software-Defined Autonomous Vehicles Using Blockchain

Data security in autonomous vehicles is of primary importance due to the possibility of cyberattacks on these interconnected and complex systems. As these vehicles are equipped with a number of sensors, software, and communication infrastructure, they are more vulnerable to different types of security issues. In this article. blockchain-supported QoS-based secure data communication (QoS-SDC) is proposed for high-speed, software-defined autonomous vehicles. This proposed mechanism will provide a promising solution for delivering protected data among autonomous vehicles. This model is evaluated by using the NS3 simulator, and the simulation results exhibit satisfactory secure data transactions.

QoS optimization for full-duplex enabled relaying system with consecutive interference annulment

In this research work QoS optimization issue while power division in with full-duplex enable transfer capability in the presence of a direct connection. A novel system model for power division based relaying system with full-duplex capability and a strategy that employs techniques such as consecutive interference cancellation. They also use the existing system to define the channel capacity alleviation issue. Then, using the suggested repeated power division ratio method and the planned information architecture, the defined issue is solved. FDBPT created a Power dividing based relaying technology with FD Decode and forwarding capability in this research paper, taking into account the existence of a direct connection. Then, to solve the spectrum efficiency optimization model, information decoding strategy based on SIC and MRC algorithms was presented to decipher signals from the destination node. An IPDP methodology was presented in the proposed way to solve the stated enormous performance maximizing issue. The proposed information decoding technique takes into account the benefits of direct connection, FD, and SIC. Furthermore, a number of simulations demonstrate the higher performance of the proposed approach. NS2 simulations are used to attain higher performance than previous techniques.

Fine-grained load balancing with proactive prediction and adaptive rerouting in data center

Though the existing load balancing designs successfully make full use of available parallel paths and attain high bisection network bandwidth, they reroute flows regardless of their dissimilar performance requirements. But traffic in modern data center networks exhibits short bursts characteristic, which can easily lead to network congestion. The short flows suffer from the problems of large queuing delay and packet reordering, while the long flows fail to obtain high throughput due to low link utilization and packet reordering. In order to solve these inefficiency, we designed a fine-grained load balancing method (FLB), which uses an active monitoring mechanism to split traffic, and flexibly transfers flowlet to non-congested path, effectively reducing the negative impact of burst flow on network performance. Besides, to avoid packet reordering, FLB leverages the probe packets to estimate the end-to-end delay, thus excluding paths that potentially cause packet reordering. The test results of NS2 simulation show that FLB significantly reduces the average and tail flow completion time of flows by up to 59% and 56% compared to the state-of-the-art multi-path transmission scheme with less computational overhead, as well as increases the throughput of long flow.

AD-RED: A new variant of random early detection AQM algorithm

Intensive continuing research has been noticed among scholars in the literature with a particular appreciable interest in developing new enhanced variants for the long-standing Random Early Detection (RED) algorithm. Contemporary trends shows that researchers continue to follow a research line thereby exchanging the linear curve needed in RED with nonlinear curves. Several reports have shown that RED’s sole linear function is insufficiently powered for managing rising degrees of traffic congestion in the network. In this paper, Amended Dropping – Random Early Detection (AD-RED), a revised version of RED is presented. AD-RED algorithm consists in combining two nonlinear packet dropping functions: quadratic plus exponential. What’s more, results from ns-3 simulator shows that AD-RED reasonably stabilized and minified the (average) queue size; and obtained a whittled down end-to-end delay when compared with RED itself and another variant of RED. Hence, AD-RED is offered as a fully sufficient replacement for RED’s algorithm implementation in routers.

Trust Aware Nero Fuzzy Based Agglomerative Hierarchical Clustering with Secure Whale Optimization Routing for Enhancing Energy Efficiency in WSN

Wireless sensor networks (WSNs) comprise a network of dispersed, carefully positioned sensor nodes in their deployment environment to monitor and collect data on natural phenomena. These sensor nodes collaborate to transmit data via multi-hop communication, ultimately reaching a central base station for processing. However, WSNs face significant challenges due to the resource-constrained nature of these devices and the harsh, open environments in which they operate. Addressing energy optimization and ensuring secure communication are primary concerns in the successful operation of WSNs. This paper introduces anovelTrust aware Neuro Fuzzy Clustering head selection (TNFCH) and agglomerative hierarchical clustering approach (AHC) with Secure Whale Optimization (SWO) Algorithm Routing to enhance energy-efficient transmission in WSNs. Our proposed protocol (TNFCH-AHWO) efficiently organizes nodes by utilizing neural network and Fuzzy logic then securely transfers the data into the communication network. We employ a Trust calculation algorithm in our system to ensure Trust and data integrity, facilitating efficient lightweight operations such as key generation, encryption, decryption, and verification. This ensures hop-to-hop authentication among the nodes in WSNs. To assess the performance of our proposed protocol, we conducted simulations using the NS3 simulator. The findings of the simulation show that the suggested protocol greatly enhances various performance metrics, including energy consumption analysis, throughput, network delay, network lifetime, and packet delivery ratio when compared to existing protocols.

Internet of Things Security Early Warning Model Based on Deep Learning in Edge Computing Environment

Aiming at the problems of poor real-time processing and low security performance caused by the massive information of the Internet of things, a security early warning model of the Internet of things based on deep learning in edge computing environment is proposed. Firstly, the system architecture of the Internet of things is designed by using the edge computing technology, in which the intelligent router is used to obtain the network stub and send it to the nearest edge computing node for anomaly detection. Then, the attention mechanism is used to improve the long short-term memory network (LSTM), and the multidimensional LSTM model is constructed. At the same time, it is used to analyze the combined network data. Finally, according to the set threshold value, judge whether there is abnormal behavior in the network, and give early warning in time to take security defense measures. The experimental analysis of the proposed model based on NS2 simulation platform shows that its early warning success rate and time are 95.2% and 20.6[Formula: see text]ms, respectively, and it can detect and defend various network attacks well, with high security performance.

Влияние конформации спирали H38 на стабильность предреакционного состояния пептидилтрансферазного центра рибосомы: молекулярно-динамическое исследование

Molecular dynamics simulations of the 70 E.coli ribosome complex in the canonical A/A, P/P-state, carrying ErmBL stalling peptide, enabled us to observe formation and destabilization of pre-reactional conformation of the peptidyl transferase center (PTC). In the absence of antibiotics in the nascent peptide exit tunnel (NPET), the peptidyl transferase reaction (PTR) substrates, specifically, the Lys-aa-tRNA aminogroup and the Asp-P-tRNA ester, were found to be steadily close to each other at a distance of no more than 4.5 Å during 200 ns of simulation. In the presence of erythromycin in NPET that distance increased to 6 Å and more during the same time of simulation; moreover, the binding site of the CCA-end of A-tRNA became distorted. An important role in the A-tRNA positioning was played by H38 of 23S rRNA: its binding patterns with A-tRNA and the H84 neighboring helix, which participates in the P-tRNA positioning, altered significantly. In the trajectory, where the PTR substrates are the most converged, the H38 and H84 helices are linked to each other by stacking interaction of the U890 and G2308 residues, and the A896 residue formed a stacking contact with G19 of A-tRNA. The presence of erythromycin in NPET destructs the stacking contact between H38 and H84, while the A-tRNA position alters to such an extent, that it interacts with H38 by its another residue, C56. Such a distortion of the A-tRNA binding results in destruction of the pre-reactional state of PTR, which could explain the antibiotic’s action. It is significant that the H38 position is worth to be additionally optimized for obtaining the pre-reactional ribosomal structures due to insufficiency of the structural data regarding this helix.