On-demand Distance Vector Routing Protocol Research Articles

Delay-sensitive Quality of Service Routing with Integrated Admission Control for Wireless Mesh Network

Wireless mesh networks (WMNs) extend and improve broadband Internet connectivity for the end-users roaming around the edges of the wired network. Amid the explosive escalation of users sharing multimedia content over the Internet, the WMNs need to support the effective implementation of various multimedia applications. The multimedia applications require assured quality of service (QoS) to fulfill the user requirements. The QoS routing in WMNs needs to guarantee the QoS requirements of multimedia applications. Admission control (AC) is the primary traffic control mechanism used to provide QoS provisioning. AC admits a new flow only if the QoS requirements of already admitted flows are not violated, even after the admission of a new flow. We propose a new QoS routing protocol integrated with AC called Delay-Sensitive QoS Routing with integrated Admission Control (DSQRAC) to control the admission of delay-sensitive flows. A delay-aware cross-layer routing metric is used to find the feasible path. DSQRAC is implemented using ad-hoc on-demand distance vector (AODV) routing protocol, where a delay-sensitive controlled flooding mechanism is used to forward the route request packets. In the proposed work, we adjust/reassign the channels to aid the QoS routing to increase the likelihood of accepting a new flow. The simulation results show that the performance of the proposed QoS routing protocol is better than the existing schemes.

Smart AODV Routing Protocol Strategies Based on Learning Automata to Improve V2V Communication Quality of Services in VANET

The Adhoc On-Demand Distance Vector (AODV) protocol faces challenges in selecting the best relay nodes, which requires optimization to improve performance in Vehicular ad-hoc networks (VANETs). This study aims to enhance Vehicle-to-Vehicle (V2V) communication in VANETs by implementing the Learning Automata-Driven Ad-hoc On-Demand Distance Vector (LA-AODV) routing protocol. LA-AODV is designed to achieve higher packet delivery ratios and optimize data transfer rates, even under congested network conditions, by dynamically adjusting to changing network scenarios. The performance evaluation includes six key metrics analyzed under varying node densities and time intervals, comparing LA-AODV against the standard AODV protocol. Results indicate that LA-AODV consistently outperforms AODV, demonstrating improved efficiency in flood identifier management, reduced data loss, higher packet delivery ratios, better throughput, and reduced end-to-end delay and jitter. Specifically, under a 20-node scenario, LA-AODV exhibits lower flood ID scores (54 vs. 88), reduced packet loss (11% vs. 12%), higher PDR (88.0% vs. 87.0%), and superior throughput (85.34 Kbps vs. 47.26 Kbps). Additionally, LA-AODV achieves lower end-to-end delay (6.84E+09 ns vs. 3.76E+10 ns) and jitter (2.52E+09 ns vs. 2.15E+10 ns). These findings suggest that LA-AODV significantly enhances Quality of Service (QoS) in vehicular ad-hoc networks, positioning it as a promising solution for optimizing V2V communication performance.

Detection of sequence number attacks using enhanced AODV protocol in MANETs

A Mobile Adhoc NETwork (MANET) operates without dedicated routing hardware, relying instead on dynamic wireless connections among nodes that can join or leave the network at any time. This dynamic nature demands a flexible routing protocol to manage variable number of nodes effectively. However, the absence of centralized infrastructure in MANETs poses security challenges, leaving nodes vulnerable to attacks. While several security solutions have been proposed, many face practical limitations. This paper introduces a novel approach to detect attacks on the Ad Hoc On-Demand Distance Vector (AODV) routing protocol within MANETs, specifically those involving malicious nodes masquerading as best routing paths by manipulating AODV's sequence numbers. An extension to the AODV protocol is introduced to ensure secure communication between different nodes in MANET. The proposed security mechanism includes a signature verification mechanism based on the Edwards digital signature curve (Ed25519), which is demonstrated on the Objective Modular Network Testbed in C++ (OMNet++) simulator. Our solution employs an extension to AODV protocol where asymmetric cryptography is introduced to generate and authenticate digital signatures, ensuring a high level of security. Additionally, a novel detection mechanism is introduced to identify malicious nodes within the network as a supplementary security layer. Simulation results demonstrate a high detection rate, accurately identifying between 83 % and 100 % of malicious nodes based on their presence in the network.

Deployment of efficient MANET testbed using aggregation based archive population optimization

In today’s 6G era, Mobile ad hoc networks (MANETs) have been enhanced by rapid technological advancement in the wireless communication industry to better support a wide range of domains, including military operations, emergency operations, urban settings, and natural crisesmanagement. In MANET, self-organizing mobile nodes collaborate to build a flexible network architecture. With a MANET, data must transit through numerous intermediate nodes even before reaches its destination node. Local link connectivity is essential for the establishment and preservation of routes. To decrease energy usage, these findings suggest the Aggregation-based Archive Population Optimization(AAPO) along with Ad-hoc On-demand Distance Vector (AODV) Routing Protocol. For both local and global optimal routes, the proposed optimized modelgives top priority. There existtwo different types of rules used by AAPO to evaluate the quality of a route before using it for data transmission. Instead of relying on just one path for transmitting data, AAPO leverages many paths. Also, it minimizes the issue of the network nodes being suddenly inaccessible, which eliminates interruptions in transmissions. However, the suggested optimal routing system considers mobility and energy availability when responding to queries, resulting in significant performance improvements, utilizing 42.69 % of the scenario’s power and functioning at 208.6 kbps with an average PDR of 87.1 %. Also, detailed simulations are conducted using the Matlab (R2021b)software to assess the suggested approach. Furthermore, the suggested protocol outperforms the current methods in terms of effectiveness.

Trust-Based Routing Selection Policy on Mobile Ad-Hoc Network Using Aodv Routing Protocol

This study presents an enhanced Ad-hoc On-demand Distance Vector (AODV) routing protocol, termed Proposed_TAODV, designed to improve security in Mobile Ad-Hoc Networks (MANETs) of 150 nodes by incorporating trust-based mechanisms. Through a comprehensive simulation, the Proposed_TAODV is evaluated against existing AODV and Dynamic Source Routing (DSR) protocols under conditions of increasing malicious node presence. The results reveal that Proposed_TAODV maintains a higher Packet Delivery Ratio, experiences lower Average End-to-End Delay, and achieves greater Throughput compared to the benchmarks, indicating its superior performance and robustness. The integration of Direct Trust Evaluation, Indirect Trust Evaluation, and Trust Aggregation methods into the AODV protocol clearly enhances the MANET's resilience to security threats, establishing the Proposed_TAODV as a promising approach for securing MANETs against various forms of attacks and network disruptions.

Irregularity Behaviour Detection - Ad-hoc On-Demand Distance Vector Routing Protocol (IBD - AODV): A Novel Method for Determining Unusual Behaviour in Mobile Ad-hoc Networks (MANET)

All the communication in the mobile ad-hoc network (MANET) will depend on the intermediate neighbour nodes or router nodes. Routing protocol is very important in MANET, because all the communication will be done in the MANET depending on the neighbour or intermediate node. If the intermediate node is a malicious node, all the data will be lost or changed by the intermediate or malicious node. Ad-hoc On-demand Distance Vector routing protocol is one of the moderate routing protocol in MANET. The Ad-hoc On-demand Distance Vector Routing protocol does not have any security mechanism. This work is going to find the Irregularity Behaviour Detection (IBD) over the Ad-hoc On-Demand Distance Vector Routing (AODV) protocol. IBD is finding a trusted node by using the trust value (TV) of the node. This TV includes network performance, node energy level, and node position value. IBD-AODV is implemented and tested in the OmNetpp 6.0 simulator.

Minimizing End-To-End Time Delay in Mobile Ad-Hoc Network using Improved Grey Wolf Optimization Based Ad-Hoc On-demand Distance Vector Protocol (IGWO-AODV)

Mobile Ad-Hoc network is one of the instant network with unknown mobile devices. Each and every node in the network may act differently. Because of this time taken to travel packet from source to destination will take more time. This work proposes Improved Grey Wolf Optimization Based Ad-Hoc On-Demand Distance Vector routing protocol for reduce the End-to-End time delay in MANET.

Design of a Self-Organizing Routing Protocol for Underwater Wireless Sensor Networks Based on Location and Energy Information

Underwater wireless sensor networks (UWSNs) are significantly different from terrestrial sensor networks in the following aspects: low bandwidth, high latency, variable topology, limited battery, low processing power and so on. These new features pose many challenges to the design of self-organizing routing protocol for UWSNs. This paper focuses on the application of Ad Hoc On-demand Distance Vector (AODV) routing protocol in UWSNs. In order to solve the problems of packet collision and excessive energy consumption associated with the flooding-based routing discovery method and the periodic hello packet routing maintenance mechanism of AODV, a routing discovery and maintenance method based on location and energy information is proposed, and it is referred to as the route-focusing AODV (RFAODV) routing protocol. In the RFAODV protocol, the routing discovery process is focused on a few nodes through forwarding area control and dynamic delay adjustment. In addition, feedback from a media access control layer and residual energy control are used for routing maintenance. We implement the RFAODV and evaluate its performance according to the sea trial data as parameters in the NS-2. The simulation results show that compared with the other protocols, RFAODV improves the routing discovery success ratio by at least 18%, increases the packet transmission ratio by at least 4%, reduces the protocol overhead by at least 15% and reduces the energy consumption by at least 5% under various simulation scenarios. RFAODV is suitable for large-scale, high-load and dynamic networks underwater wireless sensor networks.

SDODV: A smart and adaptive on-demand distance vector routing protocol for MANETs

SDODV: A smart and adaptive on-demand distance vector routing protocol for MANETs

Clustering based secure error report fault tolerant routing in mobile adhoc networks

A Mobile Ad-hoc Network (MANET) is a self-constructed network consisting of spatially distributed nodes that cooperatively arrange themselves without any centralized manager or fixed based stations. In MANET, the nodes are deployed in a dynamic scenario, so, the nodes may fail as a result of energy depletion, hardware failure, communication link errors, and malicious attacks. Therefore, it is necessary to design energy-efficient fault-tolerant algorithms and protocols for MANETs. Since the development of Mobile Ad-hoc networks was originally motivated by military applications, such as battlefield surveillance and healthcare applications it is required to have a fast recovery mechanism to overcome the fault condition. In this research work, a fault-tolerant Routing mechanism is designed and implemented to address the fault conditions such as node failure, link failure, and critical battery issues called Fault tolerant cluster based AODV with Error Reporting Routing (CAODVERR) protocol, and to improve the stability of the MANET. Also, an Error reporting mechanism has been incorporated with the Ad-hoc On-Demand Distance Vector (AODV) routing protocol.

ENHANCED ON-DEMAND DISTANCE VECTOR ROUTING PROTOCOL TO PREVENT BLACKHOLE ATTACK IN MANET

Wireless networks are becoming increasingly popular. Mobile ad hoc networks are one category among the different types of wireless networks that transmit packets from the sender node to the receiver node without the use of a base station or infrastructure, as the nodes serve as both hosts and routers. These networks are referred to as mobile because they are movable. MANET is an ad-hoc network that can change positions at any time, and nodes can join or leave at any moment, making it vulnerable to attacks such as Blackhole. Existing solutions, in some ways, led to more memory space consumption, while others led to an overhead. This research proposes an Enhanced On-demand Distance Vector (AODV) routing protocol to prevent Blackhole attacks on MANETs using Diffie Hellman and Message Digest 5 (DHMD), implemented using Network Simulator 2 (NS2). The performance of the proposed protocol was evaluated using the following parameters: Packet Delivery Ratio, throughput, End to End (E2E) Delay, and routing overhead. It was concluded that DHMD has reduced network overhead as it resulted to 23% while AODV resulted at 38% and memory consumption for DHMD gave 0.52ms compared to AODV that gave 0.81ms due to Blackhole prevention. This research will help to mitigate the effect of blackhole attacks in a network and increase network performance by reducing overhead and memory consumption.

A Novel Cross-Layer Adaptive Fuzzy-Based Ad Hoc On-Demand Distance Vector Routing Protocol for MANETs

One of the essential processes in Mobile Ad hoc Networks (MANETs) is blind flooding to discover routes between source and destination mobile nodes. As the density of nodes in the network increases, the number of broadcast packets increases exponentially. This can lead to broadcast storms, a drain on the device’s battery, and reduced network efficiency. We propose a Cross-layer Adaptive Fuzzy-based Ad hoc On-Demand Distance Vector routing protocol (CLAF-AODV) to minimize the routing broadcast traffic by considering the quality of service (QoS) (e.g. delay, throughput, packet loss), stability, and adaptability of the network. The suggested method employs two-level fuzzy logic and a cross-layer design approach to select the appropriate nodes with a higher probability of participating in broadcasting by considering parameters from the three first layers of the Open Systems Interconnection (OSI) model to achieve a quality of service, stability, and adaptability. It not only investigates the quality of the node and the network density around the node to make a decision but also investigates the path that the broadcast packet traveled to reach this node. Simulation results reveal that our proposed protocol reduces the number of broadcast packets and significantly improves network performance with respect to throughput, packet loss, normalized routing load, collision rate, and average energy consumption compared to the standard AODV and the Fixed Probability AODV (FP-AODV) algorithms.

A Deep Learning-Based Routing Approach for Wireless Mesh Backbone Networks

Optimal routing decisions are key in communication network environments to minimize bottlenecks such as traffic congestion and limited bandwidth. Routing in wireless mesh backbone networks is the focus of this study given that they are popular particularly for providing broadband connectivity to a huge number of users accessing and transmitting multimedia data hence are susceptible to communication-oriented bottlenecks. Existing routing solutions are mainly optimistic approaches. These mainly depend on link states, distance and hop counts which present a routing generalization bottleneck especially in huge wireless mesh networks because it is difficult to get the entire footprint of the network. Simply put, it is very difficult to determine an optimal route in a huge wireless mesh network (WMN) with dynamic network conditions. Since deep learning has a strong generalization ability. In this paper, a deep learning-based routing approach is proposed with the goal of ensuring a defined optimal quality of service (QoS) in a WMN. In order to achieve the study purpose, a wireless mesh network simulation environment is built and a network data feature set is orchestrated to generate a data set used to train a Long short Term Memory (LSTM)-based deep learning model that estimates the route with the optimal QoS. The generated data set is validated by training other learning models including the Multilayer Perceptron (MLP), Logistic Regression (LR) and Random Forest (RF). Our results show that the routes selected by the LSTM-based model provides the best packet delivery ratio (PDR) and throughput. Our results further show that the learning models (MLP, LR, RF) also provide better PDR and throughput compared to the traditional Ad-hoc On-demand Distance Vector (AODV) routing protocol.

Avoid Suspicious Route of Blackhole Nodes in MANET’s: Using A Cooperative Trapping

Mobile Ad hoc Network (MANET) is decentralized wireless network and can communicate without existing infrastructure in many areas. MANET is vulnerable to various attacks that affect its performance such as blackhole attack. Blackhole attacker, inject fault routing information to persuade the source node to select the path with malicious node as the shortest path. To eliminate malicious nodes from launching any collaborative attack. A cooperative Trapping Approach (CTA) was proposed based on modifying Ad-hoc On-demand Distance Vector (AODV) routing protocol and trapping the malicious nodes by responding to the trap request message. The approach aims to eliminate and rule out both single and collaborative malicious blackhole nodes from any attack. The approach realizes a backward tracking mechanism to perform the elimination process. The proposed algorithm (CTA) was executed using NS-2 network simulator. The performance metrics that has been considered to evaluate the performance of the proposed algorithm such as throughput, end to end delay, packet delivery ratio, and consuming energy. The experimental results have shown the performance metrics of the proposed approach outperformed other state of at algorithms.

Modified Adhoc On-Demand Distance Vector for Trust Evaluation And Attack Detection

Recently, Wireless Sensor Network (WSN) becomes most potential technologies for providing improved services to several data gathering and tracking applications. Because of the wireless medium, multi-hop communication, absence of physical protectivity, and accumulated traffic, WSN is highly vulnerable to security concerns. Therefore, this study explores a specific type of DoS attack identified as a selective forwarding attack where the misbehaving node in the network drops packet on a selective basis. It is challenging to determine if packet loss is caused by a collision in the medium access path, poor channel quality, or a selective forwarding assault. Identifying misbehaving nodes at the earliest opportunity is an acceptable solution for performing secure routing in such networks. As a result, in this study effort, we present a unique Modified Ad Hoc On-Demand Distance Vector (AODV) Routing protocol depending upon the One time password (OTP) method that employs the RSA algorithm. Finally, a trust evaluation process determines which approach is the most optimal. According to the simulation findings of the suggested routing protocol and comparison with existing routing protocols provided in this article, the proposed work is both efficient and cost-effective.

An improved algorithm for delay constrained relay node deployment in wireless sensor networks

Given that Delay Constrained Relay Node Placement (DCRNP) problem is getting more and more attention in time-critical domains, this paper proposes an Improved Greedy-based Selection Algorithm (IGSA) to tackle such problem under delay and budget constraints. IGSA first employs communication cost matrix and Dijkstra’s algorithm to detect whether the problem is solvable, then selects the amount and locations of deployed relay nodes (RNs) layer by layer by continuously calling the greedy coverage algorithm until all sensors have a feasible path to the sink under a preset delay constraint, and finally removes redundant RNs to reach the approximate optimal solution. Extensive simulations show that our approach can save 15%–30% of deployed RNs than the Two-phase Set-Covering-based Algorithm (TSCA), which is the state-of-the-art algorithm for the DCRNP problem, only with a slight sacrifice of solving time. Meanwhile, Ad Hoc On-Demand Distance Vector (AODV) routing protocol is applied to further examine the performance of the deployment results solved by TSCA and IGSA, respectively. Results show that every RN deployed by IGSA is involved in data forwarding, while IGSA deploys leisure RNs which do not participate in the data forwarding. Besides, compared with TSCA, the network formed by IGSA has lower network delay in the data transmission process, and the energy consumption of the central node and throughput of the two topologies are almost at the same level.

An Optimized and Energy-Efficient Ad-Hoc On-Demand Distance Vector Routing Protocol Based on Dynamic Forwarding Probability (AODVI)

MANET (mobile ad-hoc network) is a wireless ad-hoc network made up of mobile devices that use peer-to-peer routing to provide network access instead of using a preexisting network infrastructure. Despite the network infrastructure’s simplicity, it faces issues such as changeable connection capacity, dynamic topology, node battery power exhaustion, and inadequate physical security. Broadcasting is a standard MANET approach for sending messages from a source node to all other nodes in the network. Flooding is a frequent method for broadcasting route request (RREQ) packets, which is susceptible to broadcast storms. The high retransmission rate is caused by the standard flooding technique, which causes media congestion and packet collisions, which can drastically reduce throughput and network performance. In a mobile ad-hoc network, efficient broadcasting focuses on selecting a compact forward node set while assuring broadcast coverage. The goal is to find a limited number of forward nodes that will provide complete coverage. In this paper, we propose an optimized and energy-efficient routing protocol for MANET (mobile ad-hoc network) based on dynamic forwarding probability in general and AODV (ad hoc on-demand distance vector) in particular, in which the route request packets are randomly controlled to increase the network lifetime and reduce packet loss in the flooding algorithm. We tested and assessed the results of our proposed solution using various network performance factors after implementing and integrating it into NS-2. According to simulation findings, our proposed technique effectively reduced route request propagation messages (RREQ). The suggested technique is more efficient, has a longer network lifetime, and uniformly utilizes node residual energy, enhancing network throughput and minimizing routing overhead when compared to regular and modified AODV protocols.

Enhancing Ad hoc Networks Performance by Using Location and Energy Information

Mobile ad hoc network (MANET) is considered to be a promising technology for the near future. This calls for more efficient mechanisms and methods to fulfil the expectation of end users and at the same time make the best use of resources. Although of its variety of applications and potentials, MANET has issues such as resource limitation, routing overhead and changing of wireless link. This paper proposes three algorithms that can diminish the overhead and energy consumption of MANET and at the same time improve the overall performance of the network. By Controlling the Route Request (RREQ) broadcasting in the Ad-hoc On-Demand Distance Vector (AODV) routing protocol using location and energy information, a better resource management and hence a better performance for the overall network is achieved.

Enhanced-AODV: A Robust Three Phase Priority-Based Traffic Load Balancing Scheme for Internet of Things

One of the operational challenges in the Internet of Things (IoT) is load balancing, which is the focus of interest of this article. We propose a three-phase enhanced <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ad hoc</i> on-demand distance vector (enhanced-AODV) routing protocol for multiwireless sensor networks (multi-WSNs). The three phases are categorized based on traffic priority, namely: 1) high priority; 2) low priority; and 3) ordinary network traffic. The network architecture is divided into chains, i.e., local and public chains, where the cluster heads (CHs) and base stations (BSs) are used, respectively, to manage the network traffic based on priority information with alternative route allocation. Moreover, our three-phase enhanced-AODV protocol provides traffic categorization with alternatives route allocation to minimize energy consumption and prolong the lifetime of participating devices in the network. The proposed model is implemented in the simulation environment to overview results statistics in terms of network lifetime, prioritize traffic, computation and communication costs, latency, and packet lost ratio (PLR). Findings from the simulation suggest that our scheme achieves 15% improvement in network lifetime, 17% latency, 22% PLR, and approximately 10% in the computation and communication costs of the network, in comparison to three other similar protocols.

Performance Enhancement of Cluster-Based Ad Hoc On-Demand Distance Vector Routing in Vehicular Ad Hoc Networks

Recently, extensive research has focused on the design of clustering algorithms for grouping vehicles in a set of clusters in Vehicular Ad Hoc Networks (VANETs). However, due to the dynamic nature of VANETS, frequently connected or detached nodes threaten the stability of the network. When these nodes are clustered heads (CHs), the impact of these disruptions on network performance is even worse. Therefore, the stability of clusters is an important issue that should be maintained to enhance the performance of the network with minimum data loss. In this article, a novel clustering approach is proposed, which is based on the average weight concept of three main attributes, namely, the geostationary coordinates (x, y) and drop density. The average of the above-mentioned attributes is calculated and compared with the values of other vehicles’ attributes. Those vehicles having minimum average values are dropped and not to be considered in the cluster formation. The cluster members having minimum Euclidean distance are considered as Cluster Head (CH) until a certain checkpoint is not attained. The checkpoints are created in order to generate the social score of the vehicles. Once the social score is attained, a multiobjective framework is developed, which considers minimizing the Euclidian distance and maximizing the social score. The social score is generated based on the Quality of Service (QoS) attained attributes, and the data transmission is performed using Ad Hoc On-Demand Distance Vector (ADOV) routing protocol. A comparison of the proposed work and existing work has also been undertaken to demonstrate the improvement in the proposed work. As a result, throughput, PDR, network longevity, and energy consumption have been improved by 28.27%, 20.9%, 23.12%, and 27.48 percent, respectively. Extensive simulations show that the proposed scheme, unlike other popular clustering algorithms, can significantly increase the stability of the network by extending the life of the cluster head.