Mobile Ad Hoc Network Routing Research Articles

Performance Analysis of Non-Realistic Routing Protocol using Random Waypoint Model in MANET

A mobile ad hoc network (MANET) is a collection of wireless nodes connected via wireless networks and has no set structure. MANETs feature a self-organized routing topology in which mobile nodes are free to move, making it difficult and crucial to construct a stable and reliable network. Thus, failure of the route is also regarded as a prime factor affecting the efficiency of any MANET routing protocol. The breaking of the connection between two routes or more nodes will cause the failure of the route specifically in the non-realistic routing protocol. In a network of mobile nodes, the link break is mainly based on the mobility of individual nodes. Therefore, the objective of this research is to investigate the performance of proactive DSDV and reactive AODV routing protocol using the Random Waypoint(RWP) mobility model in MANET. NS-2 network simulator is used to simulate the MANET environment and BonnMotion is to create a movement of mobile nodes that integrate with the routing protocol. The network performance metrics used are throughput, packet delivery ratio, and average end-to-end delay. In addition, three simulation scenarios have been conducted to compare AODV and DSDV routing protocols with varying numbers of nodes, a comparison of AODV and DSDV routing protocols with varying pause time, and a comparison of AODV and DSDV routing protocols with varying mobility speed. The result from the three scenarios analysed and concluded that the RWP mobility model with AODV gives a better performance of throughput with 869.69 kbps and Packet Delivery Ratio (PDR) with 83.00% meanwhile, RWP with DSDV is better for the average end-to-end delay(EED) with 212.970 bps.

Improved Routing Protocols: A First Step Towards Quality of Service in Mobile Ad-hoc Networks

The recent developments in wireless technology have piqued academics' curiosity. Mobile Adhoc Networks (MANET) is a fast-growing networking architecture used in emergency, disaster, and tactical operations applications. It covers a wide range of applications with some degradation due to its flexible and expandable nature. Because routing is the primary function of wireless networks, routing protocol design is critical in Adhoc networks. Various methods have been used to improve the performance of current routing protocols in different categories. Technical advances in routing protocols have recently been achieved, combined with optimization approaches to improve the overall performance of MANETs routing protocols. This research initially looked at several sorts of routing protocols before going through their variations for energy economy and latency reduction. The previous protocols, which were created utilizing optimization methods, will then illustrate the current requirement. Finally, the review analysis delves into many aspects such as commonly used protocols, optimization techniques, QoS parameters, performance metrics, and future paths. Keywords: MANETs, Energy, Delay, Optimized Protocols, QoS.

A Deep Learning based Reliable Link Prediction Model for Achieving Traffic Aware Routing in Mobile Adhoc Networks

A Deep Learning based Reliable Link Prediction Model for Achieving Traffic Aware Routing in Mobile Adhoc Networks

Analyze the Impact of Mobility and Pause Time on ACO based Manet Routing Protocols - Review

Abstract: Recently, many researchers have studied its performance within Mobile Ad-hoc Networks (MANETs). Every ad hoc network protocol focuses on a different set of measurements and traits. The majority of the currently utilized mobility models are employed to provide realistic movement patterns for MANET scenarios. A crucial element in the creation of MANET protocols is simulation. The future topology of a network can be predicted, which enables Quality of service- aware routing to choose a dependable link for data transmission. Ant algorithms, also known as swarm intelligence, are a wider field of study that deals with algorithmic approaches that are motivated by the behaviour of ant colonies and other insects. Ant Colony Optimization (ACO) is a subset of this larger topic. Based on the pheromone value deposited over the link, we identified two categories for the link quality in the proposed approach. We have demonstrated how the network's performance is enhanced by the suggested technique. The nodes are moving in the specified terrain dimensions through the Enhanced Manhattan Mobility Model (EMMM). The five QoS performance metrics are analyzed over three MANET routing protocols. The comparison is made over two important factors-mobility and pause time with the proposed ACO technique of reliable links. In the first experiment of different speeds, DSR is 25.5% better than AODV and 5.3% better than DSDVin metric packet dropping, 1.15% better than AODV and 1.01% better than DSDV in Packet Delivery Ratio, 1.5% better than AODV and 1.04% better than DSDV in packet overhead, and 62.7% better than AODV and 73.1% better than DSDV in average end-to-end delay. In the second experiment of pause times, DSR is 25.05% better than AODV and 4.5% better than DSDV in packet dropping, 1.18% better than AODV and 1.01% better than DSDV in packet delivery ratio, 1.01% better than DSDV in throughput, 0 to 0.002% in packet overhead, and 0.02% to 0.005% shorter delay.

IoT energy efficiency routing protocol using FHO‐based clustering and improved CSO model‐based routing in MANET

SummaryMany protocols, services, and electrical devices with built‐in sensors have been developed in response to the rapid expansion of the Internet of Things. Mobile ad hoc networks (MANETs) consist of a collection of autonomous mobile nodes that can form an ad hoc network in the absence of any pre‐existing infrastructure. System performance may suffer due to the changeable topology of MANETs. Since most mobile hosts operate on limited battery power, energy consumption poses the biggest challenge for MANETs. Both network lifetime and throughput improve when energy usage is reduced. However, existing approaches perform poorly in terms of energy efficiency. Scalability becomes a significant issue in large‐scale networks as they grow, leading to overhead associated with routing updates and maintenance that can become unmanageable. This article employs a MANET routing protocol combined with an energy conservation strategy. The clustering hierarchy is used in MANETs to maximize the network's lifespan, considering its limited energy resources. In the MANET communication process, the cluster head (CH) is selected using Fire Hawk Optimization (FHO). When choosing nodes to act as a cluster for an extended period, CH election factors in connectivity, mobility, and remaining energy. This process is achieved using an optimized version of the Ad hoc On‐Demand Distance Vector (AODV) routing protocol, utilizing Improved Chicken Swarm Optimization (ICSO). In comparison to existing protocols and optimization techniques, the proposed method offers an extended network lifespan ranging from 90 to 160 h and reduced energy consumption of 80 to 110 J, as indicated by the implementation results.

Data aggregation and routing in Mobile Ad hoc network: Introduction to Self-Adaptive Tasmanian Devil Optimization

Mobile Ad-Hoc Network (MANETs) is referred to as the mobile wireless nodes that make up ad hoc networks. The network topology may fluctuate on a regular basis due to node mobility. Each node serves as a router, passing traffic throughout the network, and they construct the network’s infrastructure on their own. MANET routing protocols need to be able to store routing information and adjust to changes in the network topology in order to forward packets to their destinations. While mobile networks are the main application for MANET routing techniques, networks with stationary nodes and no network infrastructure can also benefit from using them. In this paper, we proposed a Self Adaptive Tasmanian Devil Optimization (SATDO) based Routing and Data Aggregation in MANET. The first step in the process is clustering, where the best cluster heads are chosen according to a number of limitations, such as energy, distance, delay, and enhanced risk factor assessment on security conditions. In this study, the SATDO algorithm is proposed for this optimal selection. Subsequent to the clustering process, routing will optimally take place via the same SATDO algorithm introduced in this work. Finally, an improved kernel least mean square-based data aggregation method is carried out to avoid data redundancy. The efficiency of the suggested routing model is contrasted with the conventional algorithms via different performance measures.

Selecting Optimal Path in Multiple-Path Routing for MANETs Using Fuzzy Cost

Background/ Objectives: Mobile Ad Hoc Networks (MANETs) consists of a set of nodes, where the nodes tend to change their positions time to time because of their infrastructure less characteristic. In MANETs, the protocol that is considered to be the appropriate for constructing an efficient route is AODV. Broadcast storm is another approach that can be used for establishing a path between the sender and receiver. Due to the concerns related to contention and collision, broadcast is not considered as an efficient mechanism for path selection. Though the aim of broadcasting is to minimize the flooding other issues like bandwidth consumption, collision and contention cannot be controlled. Since MANETs are associated with inadequate bandwidth, the approach of broadcasting is not efficient. Multipath routing approach permits in constructing multiple paths between the communicating nodes (sender and receiver). This approach is complicated due to the problem of node mobility in MANETs. The other paths available are not reliable because of the ad hoc nature of MANETs. The intention is to design a fuzzy controller that assists in reducing the reconstruction of paths using fuzzy cost assigned to each node in the network. Methods: The concept of this paper is to establish an optimal path from sender node to the receiver node from the available multiple paths in MANETs. The path established in this approach uses fuzzy cost. Fuzzy measure is the criteria used for establishing a path. The concept ImRMR used in this paper has been included with few improvements. The basic functionality of ImRMR is that paths are classified based on rank fitness. The proposal is to classify the paths based on its fuzzy cost. This fuzzy cost classifies the paths fitness. Findings: The proposed fuzzy cost approach discussed in this study is compared with On-Demand Multicast Routing Protocol (ODMRP). The result exhibits that the proposed method works better under differing conditions like traffic load, transmission range between nodes and also considers node mobility. The proposed mechanism with inclusion of fuzzy cost shows 5% energy efficiency, reduces the collisions by about 35% and throughput is enhanced by 30%. There has been a considerable decrease in the number of queries by 23%. The simulation results indicate that ODMRP with fuzzy cost is more useful and significant. Hence, consideration of fuzzy cost as a means for selecting an effective route is assumed to be the best. Novelty: This method contributes in reducing the drawbacks of routing approaches whether it is unipath and multi-path approach. The proposed approach changes the value of the attributes for a given route with fuzzy cost. A threshold value will be considered initially and the route having a cost more than the initialized threshold would be advised as the best route. This route would be considered as the efficient route for transmission between the sender and the receiver. Keywords: Uni­path, multiple paths, Fuzzy cost, Fuzzy fitness, Rank fitness

Auto-metric Graph Neural Network based Blockchain Technology for Secured Dynamic Optimal Routing in MANET

Mobile ad hoc network (MANET) routing is a generous tactic used for allocating packets to the base station (BS). During the operations of routing, occurrence of malicious node embellishes the mobile ad hoc network operations. For that reason, a trusted distributed routing protocol is obliged that maintains the routing buttressing and the proficiency of mobile ad hoc network. To overcome these challenging issues, Auto-Metric Graph Neural Network based Blockchain technology is proposed in this manuscript for Secured Dynamic Optimal Routing in MANET (BC-SDOR-MANET-AGNN). The proposed approach is simulated in NS-2 tool. The proposed BC-SDOR-MANET-AGNN approach attains 76.26%, 65.57%, 42.9% minimal delay during 25% malicious routing environment, 73.06%, 63.82%, 38.84% less delay during 50% malicious routing environment when analyzed to the existing models, like enhanced hybrid secure multipath routing protocol for MANET (BC-SDOR-MANET-GAHC), an improved ad hoc on-demand distance vector routing security approach based on BC technology in MANET (BC-SDOR-MANET-AODV-MQS) and block chain-based better approach for the mobile ad-hoc networking protocol using ensemble algorithm (BC-SDOR-MANET-E-BATMAN) methods.

Hybrid heuristic driving training-rider optimization algorithm for QoS-aware multicast communication system in MANET

ABSTRACT Mobile Ad hoc Network (MANET) is broadly applicable in various sectors within a short amount of time, which is connected to mobile developments. However, the communication in the MANET faces several issues like synchronization, managing power, resources, and routing management. Often, finding the optimal path in the MANET becomes complicated in the existing approaches. In this work, the latest QoS-assisted multiple-cast communication system in MANET is presented using hybridized approaches for better communication over the MANET domain. Especially, an Exploitation-assisted Driving Training-Rider Optimization (EDT-RO) model is suggested for the multicast routing in MANET. The developed EDT-RO algorithm optimizes the paths for communication in MANET, where the network considers more than two receivers. The proposed EDT-RO-based multicast routing algorithm aims to attain the objective function considering the data packet delivery rate, the throughput, the end-to-end latency, and the Quality of Service (QoS). Throughout the result analysis, the offered model shows better performance in terms of 0.2%, 0.3%, 0.5%, and 1.5% than DHOA, Jaya, DTBO, and ROA regarding mean. Simulations are conducted by comparing with other optimization algorithms used for QoS-assisted multiple casts routing to showcase and attain better routing performance of the developed models.

Simulative Analysis of Column Mobility Model for Proactive and Reactive Routing Protocols in Highly Dense MANET

Abstract: One of the most promising fields of research in recent years is Mobile Ad Hoc Networks (MANET). The well-known advantages of the internet for specific types of applications lead to the fact that it is a wireless ad-hoc network. As a result, such networks can be utilized in circumstances where no other wireless communication infrastructure is present. A MANET is a network of wireless devices without any centralized control. A device can directly communicate with other devices using a wireless connection. For nodes that are located far from other nodes, multi-hop routing is employed. The functionality of route-finding is performed by routing protocols. The mobility model creates the movement pattern for nodes. This article discusses early research to address concerns about performance indicators for MANET routing protocols under the Column Mobility Model (CMM). Moreover, we discuss concerns regarding the designs of the related work, followed by the designed CMM model on the behavior of routing protocols.

Fuzzy C Means Clustering and Sea Lion Optimized Routing for Mobile Adhoc Networks

Fuzzy C Means Clustering and Sea Lion Optimized Routing for Mobile Adhoc Networks

Routing in Mobile ad Hoc Networks Using Machine Learning Techniques

The significance of Mobile Ad Hoc Networks (MANETs) has increased in the current period due to the proliferation of mobile devices and the emergence of Internet of Things (IoT) applications. The function of routing in decentralized wireless networks is of paramount importance in facilitating efficient data transfer. Conventional routing techniques need help in effectively adjusting to the dynamic changes in network topology and the constraints imposed by restricted network resources. To tackle these concerns, a Machine Learning-Based Optimized Routing Algorithm (ML-ORA) is proposed in this research. ML-ORA has been developed to offer adaptable and intelligent routing solutions, especially in MANET. This is achieved via the integration of parameter settings, the use of a Hybrid Particle Swarm Optimization (HPSO) algorithm for Cluster Head (CH) selection, the inclusion of a clustering stage, and the incorporation of a k-Nearest Neighbors (k-NN)-based intrusion detection system. The performance of ML-ORA is assessed by conducting simulations using Network Simulator 3 (NS-3), demonstrating its efficacy in dynamic network settings. The findings exhibit a latency of 15 milliseconds, a packet delivery ratio of 95%, a network throughput of 4Mbps, and an energy efficiency of 85%. ML-ORA presents a potentially advantageous resolution for tackling the obstacles encountered in MANET routing, thereby creating opportunities for enhanced efficacy and security in the transmission of data inside dynamic wireless networks.

An Energy Efficient and Trust-based Quality of Service Routing in Mobile Adhoc Networks

Mobile Ad-hoc Networks are widely used in all applications and are gaining attention towards reliable data delivery and efficient energy usage. Reliability of the data delivery is assured when the routing protocols are securely reliable and energy efficient. Limited resources being the main limitation of the wireless networks, there is a lack in providing security to the data and energy utilization. Trust aware routing can resolve the resource problems in MANETS. So, to overcome these limitations, an Energy Efficient and Trust-based Quality of Service Routing in Mobile Adhoc Networks is proposed. This protocol evaluates the trust score of the nodes based on the connectivity of the nodes and ACK sincerity, based on the comprehensive trust of the node's energy consumption required and communication cost for the communication between node is evaluated. This protocol efficiently identifies the malicious nodes based on the trust score it is the additional advantage of evaluating the trust score of nodes. The simulation results prove that the proposed protocol guarantees the reliable data delivery and aid to extend the life time of the network thereby improves the throughput of the network.

Enhancing Security in Mobile Ad Hoc Networks: Enhanced Particle Swarm Optimization-driven Intrusion Detection and Secure Routing Algorithm

Wireless technologies have grown in popularity and are used in many applications. Transient Mobile Ad hoc Networks (MANETs) serve specific goals without infrastructure. The dynamism of these networks makes them useful for ubiquitous computing. However, high mobility, the lack of a centralized authority, and open media make MANETs vulnerable to various security risks. Thus, an Intrusion Detection System (IDS) should be used to monitor and detect system security issues. To prevent and improve security against unauthorized access, intrusion screening is crucial. The depletion of a mobile node's power supply can impact its capacity to transmit packets, as this functionality is contingent upon the system's overall lifespan. Computational Optimization-driven solutions have been prevalent in the context of IDS and secure routing inside MANETs. This research employs the Enhanced Particle Swarm Optimization-driven Intrusion Detection and Secure Routing Algorithm (EPSO-IDSRA). Enhanced Particle Swarm Optimization technique (EPSO) has provided confidence-based, secure, and energy-efficient routing in MANETs. The EPSO technique is applied to identify optimal hops for enhancing the routing process. The initial step involves the activation of the fuzzy clustering algorithm, followed by selecting Cluster Heads (CHs) based on assessing their indirect, direct, and recent confidence values. Furthermore, the identification of value nodes was contingent upon assessing confidence levels. Also, the CHs are involved in multi-hop routing, and determining the optimal route depends on the anticipated protocol, which chooses the most favorable paths considering factors such as latency, throughput, and connectivity within the designated area. The EPSO method, presented for secure routing (at time 50ms), yielded an optimal energy consumption of 0.15 millijoules, a minimal delay of 0.008 milliseconds, a maximum throughput of 0.8 bits per second, and an 89% detection rate.

Mobile wireless ad-hoc network routing protocols comparison for real-time military application

Mobile wireless Ad-hoc has become more popular because it forms quickly, has an easy setup, and has easy extensibility. The mobile ad-hoc wireless networks can be further classified according to their applications as follows: Regular user ad-hoc networks are commercial communication that applies to vehicles to help avoid collisions and accidents and live connections to transfer data from car to car. Another application is disaster rescue ad-hoc networking, usually used when a normal infrastructure network is destroyed by storms, earthquakes, tsunamis, etc. Nowadays, a lot of applications, particularly those related to the military and emergency situations, rely on mobile ad hoc wireless networks, where security needs are more challenging to provide than in regular networks. We present the tactical network needs for the military. This platform attempts to assess the possible advantages of mobile ad hoc networks in tactical military applications. This work proposes route discovery using reactive (on-demand) routing protocols where nodes need to just transfer data. This eliminates the requirement for each node to store and maintain any routing tables. This study presents and contrasts the benefits and drawbacks of two fundamental mobile ad hoc routing systems (AODV and DSR). Both protocols are On-Demand routing techniques, and when data needs to be sent, the discovery phase begins. The results of the simulation, the AODV routing approach outperforms the DSV routing method under identical simulated conditions.

AOR: Adaptive opportunistic routing based on reinforcement learning for planetary surface exploration

Planetary surface exploration mobile ad-hoc networks (PSEMANET) show the characteristics of routing void caused by craters and electromagnetic interference, relatively high dynamics caused by node heterogeneity, and small node capacity caused by load constraints, which is the reason for the increase of communication delay and packet loss. Adaptive opportunistic routing based on reinforcement learning (AOR) is an opportunistic routing protocol that determines the forwarding node based on the current coordinates, queue length, and the number of neighbors. The full forwarding areas are used to avoid routing void, and the dual competition mechanism is designed to avoid the node hidden problem. The Q-value obtained by reinforcement learning is used to design the dynamic delay cost (DDC) so that nodes in the forwarding areas can compete to achieve adaptive path switching to the environment. Compared with representative proactive routing OLSR, reactive routing AODV, geographic routing GPSR and opportunistic routing BLR, AOR and AOR with memory(AOR-M) have high packet delivery ratio (PDR) and low end-to-end delay in planetary surface exploration scenarios implemented by our simulation system. And the AOR-M protocol shows the lowest expected end-to-end delay and highest channel utilization in both high and low speed scenarios. The result shows that the AOR-M provides efficient and robust routing in planetary surface exploration mobile ad-hoc networks with a complex environment, highly dynamic nodes, and performance constraints.

A Novel Cryptography-Based Multipath Routing Protocol for Wireless Communications

Communication in a heterogeneous, dynamic, low-power, and lossy network is dependable and seamless thanks to Mobile Ad-hoc Networks (MANETs). Low power and Lossy Networks (LLN) Routing Protocol (RPL) has been designed to make MANET routing more efficient. For different types of traffic, RPL routing can experience problems with packet transmission rates and latency. RPL is an optimal routing protocol for low power lossy networks (LLN) having the capacity to establish a path between resource constraints nodes by using standard objective functions: OF0 and MRHOF. The standard objective functions lead to a decrease in the network lifetime due to increasing the computations for establishing routing between nodes in the heterogeneous network (LLN) due to poor decision problems. Currently, conventional Mobile Ad-hoc Network (MANET) is subjected to different security issues. Weathering those storms would help if you struck a good speed-memory-storage equilibrium. This article presents a security algorithm for MANET networks that employ the Rapid Packet Loss (RPL) routing protocol. The constructed network uses optimization-based deep learning reinforcement learning for MANET route creation. An improved network security algorithm is applied after a route has been set up using (ClonQlearn). The suggested method relies on a lightweight encryption scheme that can be used for both encryption and decryption. The suggested security method uses Elliptic-curve cryptography (ClonQlearn+ECC) for a random key generation based on reinforcement learning (ClonQlearn). The simulation study showed that the proposed ClonQlearn+ECC method improved network performance over the status quo. Secure data transmission is demonstrated by the proposed ClonQlearn + ECC, which also improves network speed. The proposed ClonQlearn + ECC increased network efficiency by 8-10% in terms of packet delivery ratio, 7-13% in terms of throughput, 5-10% in terms of end-to-end delay, and 3-7% in terms of power usage variation.

A trusted security approach to detect and isolate routing attacks in mobile ad hoc networks

BackgroundSecure routing is of utmost importance in protecting mobile devices from external threats. However, the dynamic characteristics of Mobile Ad hoc Networks (MANETs) pose significant challenges in achieving a secure routing path. ObjectivesThis paper aims to propose a Trust and Anonymous Model (TAM) for efficient and secure data routing in MANETs. The objective is to address the limitations of existing routing approaches and enhance the security of the network. MethodsThe TAM protocol is designed with a Two-Tier Security Mechanism (TTSM). In the first level of security, trustable nodes are selected based on their ability to process control messages. The recommended trust value of a node is determined by estimating the speed at which it processes control messages, with higher energy nodes being considered more trustable. In the second level of security, the original node identity is concealed, and data is transmitted through selected trusted nodes with duplicate identities generated using a factorial recursive function. This ensures secure transmission, as malicious observers are unable to identify participating nodes in the routing operation. ResultsThe proposed TAM model is evaluated and compared with state-of-the-art schemes such as A Multi-attribute-based Trusted Routing for Embedded devices (EMBTR) and a cognitive energy-efficient-based trusted model (CEMT). Experimental evaluation demonstrates that the TAM model achieves better performance in terms of energy consumption, delay, packet delivery rate, and false node detection rate, thereby improving network optimality. ConclusionThe Trust and Anonymous Model (TAM) with its Two-Tier Security Mechanism (TTSM) presents a promising approach for secure data routing in MANETs. By selecting trustable nodes and utilizing duplicate identities, the TAM model enhances network security while achieving improved performance compared to existing schemes. The findings of this study contribute to the advancement of secure routing techniques in MANETs and provide insights for future research in this domain.

S-Octopus: A Novel Scalable Secure Position-Based Routing Protocol for MANETs

Mobile Ad-Hoc Networks (MANET) are self-organized wireless networks that are becoming progressively popular. Determining an efficient route leading from a source to a specific destination in these networks is an essential issue since nodes are continuously moving. Furthermore, finding a secure route is a difficult area to deal with since adversaries might insert themselves into these routes unless a strict secure routing procedure is implemented. In this paper, a novel scalable secure routing protocol called S-Octopus has been proposed. Via dividing the network area into sectors and utilizing restricted directional flooding, our protocol intents to achieve improved scalability. Moreover, S-Octopus seeks to enhance robustness against the single point of failure and compromise by introducing several Sector Certificate Authority servers. Together with S-Octopus a location service and a misbehavior detection system have been proposed. Using GloMoSim simulator, S-Octopus security and performance have been evaluated and compared with the basic Authenticated Routing for Ad-Hoc Networks (ARAN) as well as Zone-based Authenticated Routing for Ad-Hoc Networks (ARANz). Simulation results assure that S-Octopus is able to effectively initiate and maintain secure routes in MANETs. Results also confirm that S-Octopus has significantly mitigated the scalability problem by achieving the maximum packet delivery fraction and the minimum network and routing loads within fairly large networks with high-mobility nodes and large malicious node percentage. Thus, S-Octopus is a good choice for MANET established among students on a campus or peers at a conference, where keys and certificates might be previously deployed.

A Survey of Ant Colony Optimization Based Algorithms for Efficient Routing in Mobile Ad-Hoc Networks

Mobile ad hoc networks are self organizing mobile nodes. Given that these portable gadgets are free to join, leave and the network topology can alter regularly. Devices in such networks must interact with one another and function autonomously through wireless channels because to a lack of infrastructure. It is difficult to create a proper routing protocol for MANETS. Swarm Intelligence (SI), may provide possible optimum solutions that guarantee high resilience, adaptability, and affordability. Additionally, they are capable of handling complex large-scale issues without the need for a centralized control system. In addition to the fundamental requirements for routing in MANETs are expected to function independently organized and provide minimal packet lag, rate of increase in packet delivery, and effective assimilation to changes in the network configuration. In dynamic MANETs, ant colony optimization has been effectively employed to strike a balance between the different connectivity limitations. This paper offers a thorough analysis of different categories of MANET ACO-based routing methods