Vehicular Ad Hoc Networks Research Articles

AN EFFECTIVE FRAMEWORK TO IMPROVE PERFORMANCE AND SECURITY IN INTELLECTUAL VANET

Vehicular Ad Hoc Networks, also known as VANETs, are an essential component in the development of intelligent transportation systems. They hold the promise of improving commuters' safety, efficiency, and convenience. The dynamic and decentralised character of VANETs, on the other hand, presents a number of issues, notably with regard to performance and security. a complete framework that is aimed to enhance the operation of VANETs and address the difficulties that have been identified. Intelligent routing algorithms, collaborationbased collision avoidance methods, and robust security protocols are the three primary components that make up the system. In the first place, intelligent routing algorithms make use of machine learning techniques in order to dynamically adapt to shifting traffic circumstances. This increases the efficiency of data transmission while simultaneously reducing delays. Whendetermining how to properly route data packets over the network, these algorithms take into consideration a variety of parameters, including the density of vehicles, traffic patterns, and congestion on the network.

IPCAS: An improved conditional privacy-preserving certificateless aggregate signature scheme without bilinear pairing for VANETs

Recently, Gong et al. proposed a Certificateless Aggregate Signature (CLAS) scheme (shorted by PCAS) with conditional privacy-preserving without bilinear pairing for Vehicular Ad-hoc Networks (VANETs). Unfortunately, after analyzing security, the PCAS scheme fails to satisfy the necessary unlinkability in VANETs and is vulnerable to the public key replacement forgery attack of the Type-I adversary. Subsequently, this article analyzes the causes of these two security issues in detail and proposes an improved CLAS scheme for VANETs (called IPCAS). Through security analysis, this article proves that the IPCAS is existentially unforgeable under the adaptive chosen message attacks against Type-I and Type-II adversaries in the random oracle model, and satisfies the necessary security and privacy requirements in VANETs, including unlinkability. Finally, the performance analysis results show that compared to PCAS for single message and n aggregate messages (n=1000), the computational overhead of IPCAS is reduced by 20.01% and 49.08% respectively, and the communication overhead is reduced by 18.75% and 28.14% respectively. Therefore, IPCAS not only makes up for the security vulnerabilities of PCAS but also has better performance.

Machine Learning-assisted Distance Based Residual Energy Aware Clustering Algorithm for Energy Efficient Information Dissemination in Urban VANETs

A Vehicular Ad-hoc Network (VANET) is an essential component of intelligent transportation systems in the building of smart cities. A VANET is a self-configure high mobile and dynamic potential wireless ad-hoc network that joins all vehicle nodes in a smart city to provide in-vehicle infotainment services to city administrators and residents. In the smart city, the On-board Unit (OBU) of each vehicle has multiple onboard sensors that are used for data collection from the surrounding environment. One of the main issues in VANET is energy efficiency and balance because the small onboard sensors can’t be quickly recharged once installed on On-board Units (OBUs). Moreover, conserving energy stands out as a crucial challenge in VANET which is primarily contingent on the selection of Cluster Heads (CH) and the adopted packet routing strategy. To address this issue, this paper proposes distance and energy-aware clustering algorithms named SOMNNDP, which use a Self-Organizing Map Neural Network (SOMNN) machine learning technique to perform faster multi-hop data dissemination. Individual Euclidean distances and residual node energy are considered as mobility parameters throughout the cluster routing process to improve and balance the energy consumption among the participating vehicle nodes. This maximizes the lifetime of VANET by ensuring that all intermediate vehicle nodes use energy at approximately the same rate. Simulation findings demonstrate that SOMNNDP improves Quality of Service (QoS) better and consumes 17% and 14% less energy during cluster routing than distance and energy-aware variation of K-Means (KM) and Fuzzy C-Means (FCM) called KMDP and FCMDP respectively.

Hyper-Flophet: A neural Prophet-based model for traffic flow forecasting in transportation systems

Nowadays, an accurate and reliable traffic forecast is meaningful in making the right decisions for traffic management systems in vehicular environments. Nevertheless, traffic flow prediction is a significant challenge in Vehicular Ad Hoc Networks (VANETs) that has taken much attention. Therefore, in this paper, we propose a hybrid traffic prediction model based on Prophet model and Long Short-Term Memory neural network (LSTM), called Hyper-Flophet, to predict next traffic flow. Hyper-Flophet model adopts the traditional neural prophet model but with major parameter tuning. First, we propose an efficient algorithm for predicting the traffic flow trend then, we develop an interactive LSTM (I-LSTM) model for auto-regression components. After that, we implement a new future regressor component called network mobility and finally, we enhance the event and holiday component by introducing exponential growth term. Through simulations with real VANET data, we show that the proposed hybrid approach can achieve superior forecasting performance over other models.

Multi-TA model-based conditional privacy-preserving authentication protocol for fog-enabled VANET

The rapid growth of Vehicular Ad-hoc Networks (VANET), fueled by advancements in the Internet-of-Things, cloud computing, Intelligent Transportation Systems, and fog computing, has led to the introduction of fog node-based VANET to serve resource-constrained devices. In the traditional security models of VANET, due to the use of a centralized trusted authority, there is a chance of single-point-of-failure and service unavailable with the increased service access requests. Also, there was one-to-one communication between each roadside unit and trusted authority. This may increase the system complexity and increase the traffic load. To address these issues, a novel authentication protocol for fog-enabled VANET based on multiple trusted authority model is discussed which reduces the chance of service unavailability and single-point-of-failure as the entire traffic load is distributed among multiple sub-trusted authority. Due to the incorporation of fog node, a group of roadside units can be controlled centrally, where trusted authority does not need to perform individual authentication for each roadside unit. The proposed protocol's security is rigorously examined through both informal and formal security analysis. Additionally, the protocol exhibits enhanced security features, as demonstrated in a performance comparison section, showcasing its ability to meet the security and privacy requirements while incurring relatively low communication and computation and storage costs. Thus, the proposed protocol offers a secure and efficient authentication protocol for fog-enabled VANET.

Knowledge-based system and expectation-maximization to discovering causes of imperfect labels in vehicular networks clustering

This paper focuses on the development of a knowledge-based system for automatically diagnosing issues in Vehicular ad hoc networks (VANETs). VANETs enable communication between vehicles and infrastructure, enhancing road safety and efficiency through timely information exchange. The proposed system aims to efficiently maintain and ensure the continuity of network service by leveraging innovative pattern recognition methods tailored to VANETs. The automatic diagnosis problem in VANETs involves estimating the operating class of network components based on sensor observations. This entails associating sensor measurements with specific operating modes. By implementing condition-based preventive maintenance procedures, potential component failures can be detected early, mitigating network disruptions. Various approaches, such as expert systems, fault trees, network state models, and statistical learning through pattern recognition, can be employed to address this problem. This paper primarily focuses on the statistical learning approach, where a classification or regression function is learned from a set of examples to assign operation modes to new measurements. It discusses relevant metrics and preprocessing techniques to simplify the decision-making process. The diagnostic system's results are determined based on the formulation of the classification or regression problem. The learning base is constructed, and an appropriate classification method is selected to develop and validate the automatic diagnosis system. While non-parametric models like support vector machines are commonly used, this article emphasizes the significance of considering assumptions and leveraging additional information to enhance performance. It proposes a more specific formalization of the problem, integrating the unique characteristics of VANETs. The contributions of this article revolve around the theory of belief functions, a generative approach, and the utilization of parametric models defined using graphical models. Experimental studies conducted on artificial datasets have demonstrated the benefits of the semi-supervised approach within the context of VANET networks.

Fuzzy-AHP based optimal RSU deployment (Fuzzy-AHP-ORD) approach using road and traffic analysis in VANET

Vehicular Ad Hoc Network (VANET) is a proven technology in Intelligent Transportation Systems (ITS) that provides various safety and non-safety applications. The Roadside Unit (RSU) is one of the essential components of the VANET, which allows vehicles to disseminate and exchange safety and infotainment information in a broader range. RSUs are also valuable for offloading the internet data traffic of vehicular users from cellular networks. However, due to high deployment and maintenance costs and security overhead, it is essential to optimally deploy these RSUs and place them in prominent places to increase the overall network coverage and efficiency. The existing deployment strategies either analyzed the road network on one or a few topological parameters or vehicular traffic parameters and considered equal or random weights for each parameter. None of them deal with the uncertainty of parameter weightage. Thus, this paper proposes a Fuzzy-Analytic Hierarchy Process-based optimal RSU deployment (Fuzzy-AHP-ORD) approach to finding the highly influential intersection nodes for RSU deployment. The Fuzzy-AHP-ORD considers different static and dynamic parameters concerning road topology and the vehicle’s traffic behavior. The weights of the parameter are calculated through Fuzzy-AHP, then the various intersection nodes of the road networks are ranked using the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) based MADM (Multiple Attribute Decision Making) method. The simulation results reveal the effectiveness of the proposed method in comparison to other benchmark methods in terms of coverage ratio, connection time, packet delivery ratio, and delay.

Service based VEINS framework for vehicular Ad-hoc network (VANET): A systematic review of state-of-the-art

Service based VEINS framework for vehicular Ad-hoc network (VANET): A systematic review of state-of-the-art

Multiple-Junction-Based Traffic-Aware Routing Protocol Using ACO Algorithm in Urban Vehicular Networks.

The burgeoning interest in intelligent transportation systems (ITS) and the widespread adoption of in-vehicle amenities like infotainment have spurred a heightened fascination with vehicular ad-hoc networks (VANETs). Multi-hop routing protocols are pivotal in actualizing these in-vehicle services, such as infotainment, wirelessly. This study presents a novel protocol called multiple junction-based traffic-aware routing (MJTAR) for VANET vehicles operating in urban environments. MJTAR represents an advancement over the improved greedy traffic-aware routing (GyTAR) protocol. MJTAR introduces a distributed mechanism capable of recognizing vehicle traffic and computing curve metric distances based on two-hop junctions. Additionally, it employs a technique to dynamically select the most optimal multiple junctions between source and destination using the ant colony optimization (ACO) algorithm. We implemented the proposed protocol using the network simulator 3 (NS-3) and simulation of urban mobility (SUMO) simulators and conducted performance evaluations by comparing it with GSR and GyTAR. Our evaluation demonstrates that the proposed protocol surpasses GSR and GyTAR by over 20% in terms of packet delivery ratio, with the end-to-end delay reduced to less than 1.3 s on average.

Design of CSKAS-VANET model for stable clustering and authentication scheme using RBMA and signcryption

A public key infrastructure-enabled system authentication model is developed to provide essential security functions for Vehicular ad hoc networks (VANETs). An intelligent transportation system is provided by VANET, an emerging technology. Dedicated short-range communication is used to disseminate messages wirelessly. Communications may be hacked, and messages can be stolen or fabricated. Hence, authenticated communication is crucial in the VANET environment. Some parameters such as trust, authentication, privacy, and security are at high risk. This article suggests a VANET with secure authentication and trust-based clustering mechanisms to provide stable and secure communication. Initially, the Restricted Boltzmann Machine learning algorithm (RBMA) is used to select the cluster head, which depends upon trust, vehicle lifetime, and buffer level. Then, cluster members are formed, followed by grouping. Diffie–Hellman Hyperelliptic Curve Cryptography and cryptographic hash functions are used by signcryption for secure communication in VANET. Therefore, the essential component of the key agreement strategy that will give superior authentication is this signcryption mechanism. Over the medium access protocol layer, all of these security characteristics are updated. The proposed method of clustering signcryption key agreement scheme (CSKAS) approach reduces time complexity and increases packet delivery ratio which is vital in providing stable, secure communication.

A Decentralized Authenticated Key Agreement Scheme Based on Smart Contract for Securing Vehicular Ad-Hoc Networks

Since the communication channels in vehicular ad-hoc networks (VANETs) are wireless and open, malicious adversaries can monitor or fabricate messages transmitted across them. To secure vehicular communications, an authenticated key agreement (AKA) scheme needs to be designed for VNAETs. Traditional VANETs AKA schemes require the trusted authority (TA) to authenticate the legality of message and corresponding sender. However, the TA in these schemes is vulnerable to suffer from single-point-of-failure issues. Some blockchain-based VANETs AKA schemes have been proposed recently to address the deficiency. However, these schemes rely on the consortium or private blockchain in which TAs are still required for key generation, resulting that the practicality is limited. To solve the issue, we design a smart contract-based VANETs AKA scheme, where the AKA algorithm of our proposed scheme is implemented on smart contract deployed on a public blockchain system and the TA that is responsible for key generation will not be required. The security proof and analysis show that our proposed scheme satisfies the session-key semantic security and essential security and privacy requirements, respectively. The performance analysis demonstrates that our proposed scheme outperforms existing blockchain-based VANETs AKA schemes.

Shared group session key-based conditional privacy-preserving authentication protocol for VANETs

Vehicular Ad-Hoc Networks (VANETs) have significantly enhanced driving safety and comfort by leveraging vehicular wireless communication technology. Due to the open nature of VANETs, conditional privacy-preserving authentication protocol should be offered against potential attacks. Efficient and secure authentication among vehicles in VANETs are important requirements, but there are various limitations in the existing conditional privacy-preserving authentication protocols for securing VANETs. To cope with the inherent issues, we propose a conditional privacy-preserving authentication protocol based share group session key (SGSK) by integrating the self-healing key distribution technique, blockchain, and MTI/C0 protocol. In our protocol, we use SGSK instead of the time-consuming Certificate Revocation List (CRL) checking, and we revoke malicious vehicles by updating SGSK. It is shared among unrevoked vehicles within a domain and across-domain. As a result, when a malicious revoked vehicle enters a new domain, it is difficult for it to access the system and send false messages. Furthermore, our protocol can not only achieve computation efficiency by reducing the number of computing operations of bilinear pairing but also resist various attacks while keeping conditional privacy protection. We implement our protocol in the Hyperledger Fabric platform. The experimental results show that our protocol is available to revoke 180 malicious vehicles in across-domain scenarios within one second, and it can meet the requirement of verifying 600 messages per second easily. Moreover, our comprehensive performance evaluations demonstrates that our protocol outperforms other approaches in terms of vehicle revocation checking cost, computation overhead, and communication overhead. In addition, to show the feasibility and validity of our protocol, we use SUMO and NS2 to simulate the actual VANET scenario and validate the efficiency and performance of our protocol. Simulation results prove the practicability of our protocol for VANETs.

Enhancing Security and Sensors Emerging Internet of Things (IoT) Technology of Homophone-Based Encryption using MANET‐IoT Networks Technique

The emerging Internet of Things (IoT) technology brings several benefits to the real-time applications of the smart city project. The IoT-enabled technologies are driven by the networks like Mobile Ad hoc Network (MANET), Vehicular Ad Hoc Network (VANET), Wireless Sensor Network (WSN), Underwater Wireless Sensor Network (UWSN), etc. The core functionality of IoT-enabled technologies mainly depends on routing functionality. The focus of this work is on the IoT-enabled MANET, i.e., IoT-MANET routing methods. There are several types of research conducted so far on the efficiency of MANET in different areas such as routing protocol, MAC protocol, queue, etc. Routing protocols are the major domain on which the performance of IoT-MANET has been analyzed. There are different types of routing protocols have introduced for the MANETs. The use and selection routing methods are done as per the real-time application needs. An average Ad hoc On-Demand Vector (AODV) routing protocol had shown good results under any type of network. The increase in internet usage for communication has led to a significant growth in the volume of data being transmitted over the internet, including computer data, text, video, audio, sound, and images. However, due to limited network capacities, efficient compression methods are needed to reduce the amount of data while preserving its quality and ensuring timely transmission. Data confidentiality is a crucial aspect of network security, and the homophone-based encryption with compression (HEC) technique offers a viable solution in this regard.

A Method for the Rapid Propagation of Emergency Event Notifications in a Long Vehicle Convoy

Convoys composed of autonomous vehicles could improve the transportation and freight industries in several ways. One of the avenues of improvement is in fuel efficiency, where the vehicles maintain a close following distance to each other in order to reduce air resistance by way of the draft effect. While close following distances improve fuel efficiency, they also reduce both the margin of safety and the system’s tolerance to disturbances in relative position. The system’s tolerance to disturbances is known as string stability, where the error magnitude either grows or decays as it propagates rearward through the convoy. One of the major factors in a system’s string stability is its delay in sending state updates to other vehicles, the most pertinent being a hard braking maneuver. Both external sensors and vehicle-to-vehicle communication standards have relatively long delays between peer vehicle state changes and the information being actionable by the ego vehicle. The system presented here, called the Convoy Vehicular Ad Hoc Network (Convoy VANET), was designed to reliably propagate emergency event messages with low delay while maintaining reasonable channel efficiency. It accomplishes this using a combination of several techniques, notably relative position-based retransmission delays. Our results using Network Simulator 3 (ns3) show the system propagating messages down a 20-vehicle convoy in less than 100 ms even with more than a 35% message loss between vehicles that are not immediately adjacent. These simulation results show the potential for this kind of system in situations where emergency information must be disseminated quickly in low-reliability wireless environments.

The Need of Trustworthy Announcements to Achieve Driving Comfort

An Intelligent Transport System (ITS) is more demanding nowadays and it can be achieved through deploying Vehicular Ad Hoc Networks (VANETs). Vehicles and Roadside Units (RSUs) exchange traffic events. Malicious drivers generate false events. Thus, they need to be identified to maintain trustworthy communication. When an authorised user acts maliciously, the security scheme typically fails. However, a trust model can isolate false messages. In this paper, the significance of trustworthy announcements for VANETs is analysed. To this end, a series of experiments is conducted in Veins to illustrate how the trustworthiness of announcements affects travel time. A traffic scenario is created where vehicles detour to an alternate route with an announcement from the leading vehicle. Both true and false announcements are considered. Results confirm that false announcements and refraining from announcements increase travel time. However, the travel time is reduced with trustworthy announcements. From this analysis, it can be concluded that trustworthy announcements facilitate driver comfort.

IoT-Driven solutions for VANET trustworthiness: Examining misconduct and position security challenges

Vehicular ad hoc networks (VANETs) improve the traffic management and road safety by exchanging real-time data between automobiles and roadside infrastructure. Reliable Intelligent Transport Systems (ITS) solutions tend to boost VANET data security, integrity and dependability. These systems communicate VANET data between infrastructure and automobiles using sensors, Internet of Things (IoT) devices and communication technologies. IoT-driven includes a Real-Time Traffic Observation optimizes VANET route design. Traffic management and safety improve with ITS-connected cars and infrastructure. Remote communication technologies enhance traffic management and decision-making by sending a real-time data between cars and infrastructure. Smart parking systems use real-time parking availability data from IoT sensors and devices to maximize space use and reduce congestion. Security and misbehavior are dependable problems in 40 IoT-driven VANET studies. Miscommunication and unstandardized are the main security issues which pose hazards. This review shows VANETs that requires authentication, encryption and anomaly detection to prevent attacks and IoT for transportation sector. This extensive review covers VANET reliability and IoT-driven real-time traffic monitoring, ITS, remote communication and intelligent parking systems. This review analyzes the metrics to assess the many detection techniques which includes, misconduct-Aware On-Demand Collaborative Intrusion Detection System (MACIDS) and Collision-Induced Dissociation Spectroscopy (CIDS), (i) Accuracy (ii) False Positive Rate, (iii) precision, (iv) recall (v) False Negative Rate and (vi) F1-Score in VANET security in Attack Detection Rates (ADR), misbehavior detection, evaluation metrics and detection algorithms were studied using CIC-IDS data. Due to misconduct and security issues, a strong IoT-driven solutions are required to improve VANET trustworthiness and offers a secure and reliable vehicular communication networks for safer and more efficient transportation systems.

Intelligent Resource Allocation Scheme Using Reinforcement Learning for Efficient Data Transmission in VANET.

Vehicular ad hoc networks (VANETs) use multiple channels to communicate using wireless access in vehicular environment (WAVE) standards to provide a variety of vehicle-related applications. The current IEEE 802.11p WAVE communication channel structure is composed of one control channel (CCH) and several service channels (SCHs). SCHs are used for non-safety data transmission, while the CCH is used for broadcasting beacons, control, and safety. WAVE devices transmit data that alternate between CCHs and SCHs, and each channel is active for a duration called the CCH interval (CCHI) and SCH interval (SCHI), respectively. Currently, both intervals are fixed at 50 ms. However, fixed-length intervals cannot effectively respond to dynamically changing traffic loads. Additionally, when many vehicles are simultaneously using the limited channel resources for data transmission, the network performance significantly degrades due to numerous packet collisions. Herein, we propose an adaptive resource allocation technique for efficient data transmission. The technique dynamically adjusts the SCHI and CCHI to improve network performance. Moreover, to reduce data collisions and optimize the network's backoff distribution, the proposed scheme applies reinforcement learning (RL) to provide an intelligent channel access algorithm. The simulation results demonstrate that the proposed scheme can ensure high throughputs and low transmission delays.

Fuzzy-Logic Based Model for VANET Performance Metrics Analysis

Vehicular Ad-hoc Networks (VANETs) allows a spontaneous communication between vehicle to vehicles and road-side units(RSU) using wireless interfaces and road-side infrastructure to form Intelligent Transportation System (ITS). VANET, prime objective is to provide information about accidents or unwanted situations and traffic conditions to the drivers. As VANETs are dynamic in nature, the vehicle nodes to discover the best possible route to broadcast an emergency message is a challenge. To study different highway and urban road scenarios, there is a need to simulate and evaluate the network performance analysis of routing protocols beforehand since real-time implementation is rather challenging or expensive. This paper focuses on the VANET routing protocols evaluation, to find an appropriate protocol which provide suitable Quality of Service (QoS) to end users in both safety and non-safety conditions. To simplify the QoS evaluation problem, Hierarchical Fuzzy Inference System (HFIS) model has been designed and simulated in MATLAB R2018b that analysis the network performance metrics of VANET routing protocols. From the simulations, it is observed that that OLSR routing protocol for both safety and non-safety applications, outperforms other routing protocols as its global QoS reaches to approximately 84% efficiency.

An approach for offloading with multi-hop considerations in an RSU signal overlay setting

In recent years, significant advancements in vehicle technology have spurred growing interest in Vehicular Ad hoc Networks (VANETs). This interest is driven by concerns for road safety and the need to alleviate network congestion, leading to the emergence of Intelligent Transport Systems (ITS). ITS focuses on improving road traffic management and safety through the utilization of wireless and mobile network communication technologies. VANETs play a pivotal role within the realm of ITS, facilitating tasks such as enhancing road safety, traffic monitoring, and ensuring passenger comfort by mitigating accidents and congestion. These objectives rely on the timely and accurate delivery of data to vehicle agents and relevant authorities, facilitated by reliable VANETs and Road Signal Units (RSUs). Achieving this necessitates identifying optimal routes with minimal distance, high radio access, and quality-awareness levels. To address these objectives, this study proposes the utilization of the Congestion Network with Predicted K-means multi-hop RSU algorithm (CN-MHMR) to enhance vehicular networking and communication. This algorithm facilitates efficient node transfer from base nodes to destination nodes via the shortest and energy-efficient paths, thereby enabling viable and reliable vehicular communications. The performance of the proposed model was evaluated based on various metrics, including energy consumption, throughput, delay, packet delivery ratio, accuracy, precision, and recall values.

A novel swarm intelligence‐based fuzzy logic in efficient connectivity of vehicles

SummaryA vehicular ad hoc network (VANET) is a wireless network composed of a collection of mobility devices that link via a wireless channel without the need for permanent infrastructure or centralized administration. Multicasting is a method of sending data from a single node to multiple recipients at the same time. Because of its energizing network topology and constricted resources, a mobile ad hoc network (MANET) presents a number of issues. Because network execution measurements such as latency, bandwidth, throughput, and energy fluctuate so rapidly owing to node movement, these network properties in a wireless mobile ad hoc network are ambiguous. Because of the influence of these uncertainty issues, determining the top‐grade path from the originating node to a set of recipient nodes is challenging. In order to preserve network resources, we used a Swarm Intelligence‐based fuzzy logic strategy in this study that includes channel caliber factor and connection termination time to try to alleviate these uncertainty issues. This strategy integrates all of the network indicators available for the routes into an individual quantity named as fuzzy cost or cost of communication. The routes with the lowest fuzzy cost will be regarded optimum, and data will be delivered along this route from the originating point to a set of receivers. The simulation was carried out using NS‐3.38 and MATLAB, and the results reveal that the suggested protocol, HACOFLM, outperforms ODMRP, OFAODV, MAODV, and DFMCRP by means of packet delivery ratio, latency, control packet overhead, and throughput. DFMCRP outperforms MAODV by 3% to 6%, OFAODV by 13% to 19%, and ODMRP by 16% to 23%. According to HACOSMO, based on the number of vehicles and velocities, routing overhead is 7% to 10% less than DFMCRP, 9% to 13% less than MAODV, 17% to 29% less than OFAODV, and 22% to 36% less than ODMRP. It improvise throughput by 4% to 6% when contrasted to DFMCRP, 5% to 9% when contrasted to MAODV, 10% to 14% when contrasted to OFAODV, and 14% to 23% when contrasted to ODMRP.