Ant Colony Optimization Based Modified AODV for Secure Routing in Mobile Ad Hoc Networks

doi:10.22266/ijies2021.1231.11

Cited by 3 publications

(4 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this manuscript, the proposed ANN based OLSR protocol's effectiveness is validated by means of EC, ROR, PDR, and EED. In addition, the ANN based OLSR protocol performance is analysed by comparing its results with the existing protocols like AODV, Dynamic Source Routing (DSR), Zone Routing Protocol (ZRP), Destination Sequenced Distance Vector (DSDV), OLSR, Fuzzy Logic based OLSR [13], OLSR with multi-objective ALO algorithm [14], OLSR with bat algorithm [16], MAODV-ACO [19], and PSNHF based AODV protocol [20]. The performance is analysed by varying the node size such as 40, 50, 60, 70, 80, 90, and 100.…”

Section: Resultsmentioning

confidence: 99%

“…Anantapur and Patil, [19] implemented an ACO based MAODV for attaining secure transmission over MANET. In this literature, the ACO algorithm considers node degree, trust, distance and residual energy as fitness functions for detecting the optimum path under the constraints of black-hole attacks.…”

Section: Related Workmentioning

confidence: 99%

“…15 Table 5 represents the PDR performance of different protocols. Related to the comparative protocols such as AODV, DSR, DSDV, OLSR, ZRP, MAODV-ACO [19] and PSNHF based AODV protocols [20], the developed ANN based OLSR protocol obtained high PDR of 99.92%, 99.90%, and 99.89% on the number of nodes of 80, 90 and 100. Especially, this experiment is performed on a parameter specification with initial energy of 2 Joule, packet size of 512 bytes, propagation model of two ray ground, network interface type of phy/wireless phy, antenna model of omnidirectional, area of 300 × 300𝑚 2 and 500 × 500𝑚 2 and maximum mobility speed of 10 𝑚/𝑠.…”

Section: Quantitative Analysismentioning

confidence: 99%

See 2 more Smart Citations

Artificial Neural Network based Optimized Link State Routing Protocol in MANET

2022

IJIES

View full text Add to dashboard Cite

Mobile Ad hoc Network (MANET) includes many wireless mobile nodes, and higher energy consumption is the major concern in MANET. Compared to other protocols, the Optimized Link State Routing Protocol (OLSR) is a superior pro-active protocol, which works on limited configuration attribute variables in the MANET. In a dynamic environment, default variables of the attribute are not suitable as per the IETF draft. Therefore, the Artificial Neural Network (ANN) is integrated with the OLSR protocol in this manuscript. In addition, the OPNET modeler is used to evaluate the performance of ANN based OLSR protocol, which is simulated by utilizing the MATLAB software environment. The performance measures such as Routing Overhead Ratio (ROR), End to End Delay (EED), Energy Consumption (EC), and Packet Delivery Ratio (PDR) are employed for analysing the protocol's performance. The simulation result revealed that the ANN based OLSR protocol effectively increases the network lifespan and reduces EC of the MANET network. Compared to the Ant Colony Optimization (ACO) based Modified Ad-hoc On Demand Distance Vector (MAODV) and Prevention of Selfish Node using Hash Function (PSNHF) based AODV protocols, the proposed ANN based OLSR protocol achieved higher PDR of 99.92%, 99.90%, and 99.89% on the nodes of 80, 90 and 100.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Quantitative Analysismentioning

confidence: 99%

See 1 more Smart Citation

Artificial Neural Network based Optimized Link State Routing Protocol in MANET

2022

IJIES

View full text Add to dashboard Cite

show abstract

“…MAODV-ACO [22] is an ant colony optimization-based method that enhances the packet delivery ratio (PDR) and minimizes delay, ensuring secure data transmission over a mobile Ad hoc network. Compared to other routing protocols, the PDR of MAODV-ACO is significantly high at 99.66% for 100 nodes.…”

Section: Related Workmentioning

confidence: 99%

Learning Automata-Based AODV to Improve V2V Communication in A Dynamic Traffic Simulation

2024

IJIES

View full text Add to dashboard Cite

The study introduces the learning automata-based AODV (LA-AODV) protocol to enhance vehicle-tovehicle (V2V) communication in dynamic vehicular Ad-hoc networks (VANETs). Existing routing protocols, such as Ad-hoc on-demand distance vector (AODV) protocols, face significant challenges, including low data transfer rates, higher delay times, lower throughput, and data congestion resulting from rapidly changing network topologies. LA-AODV addresses these issues by optimizing the quality of service (QoS) through the real-time selection of relay nodes based on vehicle speed, distance, and actual position parameters. Simulations were conducted at the Gadjah Mada university (UGM) roundabout in Yogyakarta, Indonesia, using SUMO and NS3 simulators. LA-AODV outperforms AODV with Packet Delivery Ratios ranging from 95% to 99% and Average Throughputs between 36.90 Kbps and 56.50 Kbps. Although LA-AODV exhibits slightly higher End-to-End Delays, it effectively mitigates Packet Loss Ratios ranging from 1% to 4%. These enhancements optimize routing decisions, reduce communication overhead, and enhance network resource utilization.

show abstract

The Defending Mobile Ad Hoc Networks from Anonymity Threats with an Identity-less, OnDemand Routing Scheme

Ahluwalia

2023

2023 2nd International Conference on Futuristic Technologies (INCOFT)

View full text Add to dashboard Cite

Ant Colony Optimization Based Modified AODV for Secure Routing in Mobile Ad Hoc Networks

Cited by 3 publications

References 18 publications

Artificial Neural Network based Optimized Link State Routing Protocol in MANET

Artificial Neural Network based Optimized Link State Routing Protocol in MANET

Learning Automata-Based AODV to Improve V2V Communication in A Dynamic Traffic Simulation

The Defending Mobile Ad Hoc Networks from Anonymity Threats with an Identity-less, OnDemand Routing Scheme

Contact Info

Product

Resources

About