Extended OLSR and AODV based on multi-criteria decision making method

Kim, Beom-Su; Roh, Byeong‐hee; Ham, Jae-Hyun; Kim, Ki-Il

doi:10.1007/s11235-019-00609-0

Cited by 15 publications

(18 citation statements)

References 16 publications

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“…We can observe that the packet delivery ratio of the proposed protocol is better than all other basic routing protocols due to congestion free route selection of ACOLBR in the network. On the other hand, AODV [4], DSDV [3] and DSR [36] don't follow any strategy to balance load and control congestion in the network. In ACOLBR, packet distribution phase and agent selection module assist the source node to transmit the packet in such a way that load in the network is balanced.…”

Section: Performance Analysis and Simulation Resultsmentioning

confidence: 99%

“…Initially the performance of ACOLBR is slightly degraded compared to AODV [4], DSDV [3] and DSR [36] due to agent selection and path set up. End to end delay of AODV [4] and DSR [36] are again highly increased after pause time 15 second while the performance of ACOLBR is stable compared to others. It shows that overall performance of ACOLBR is better throughout the simulation period compared to AODV [4], DSDV [3] and DSR [36] due to selection of optimised sequence of red and blue ant during packet transmission.…”

Section: Performance Analysis and Simulation Resultsmentioning

confidence: 99%

“…8 compares proposed protocol with DSDV, DSR, CA-ARTT and AODV with respect to throughput and simulation time. The throughput of AODV [4], DSDV [3] and DSR [36] is reduced after simulation time (200sec) due to congestion in the network. On the other hand, CA-ARTT [40] is a congestion avoidance algorithm which differentiates the cause of packet losses, which reduces the packet losses and increases the throughput.…”

Section: Performance Analysis and Simulation Resultsmentioning

confidence: 99%

“…10 performance metrics are throughput and node speed. The throughput of AODV [4], DSDV [3], MOAODV [41] and DSR [36] are reduced after node speed (20m/s) because of congestion in the network. The throughput of MOAODV [41] is constant throughout the simulation.…”

Section: Performance Analysis and Simulation Resultsmentioning

confidence: 99%

“…This way different colour agents select different routes and overall congestion will be minimised as well as resource utilisation will be optimised. 4. Route failure is also considered here.…”

Section: Contributionmentioning

confidence: 99%

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ACOLBR: ACO Based Load Balancing Routing In MANET

Dholey

Sinha

2021

Preprint

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Real-time data transmission is one of the objectives of MANET (mobile ad-hoc network) to handle emergencies like a forest fire, flood, and earthquake. In this scenario, quick delivery of data is itself a challenging task for MANET and there is a possibility of load imbalance due to packet transmission in between source-destination pairs via the shortest path due to congestion or control overhead. In this paper, a novel routing protocol, called ACOLBR (ACO based load balancing routing), is design to control the congestion and balancing the load among the multiple paths in between source to destination. The similarity between the environment of Ant Colony and the MANET inspires the authors to apply ACO (Ant colony optimization) technique during routing in MANET to control congestion and balance the load in the network. In our proposal, two colonies of ants (red/blue) carry their packets based on the network condition. A decision variable is designed to select red/blue ant for transferring packets based on different network parameters such as bandwidth, energy, mobility, and distance. The selection of red ant means the route where the concentration of red pheromone is maximum and reverse is true for blue ant. This protocol is also concerns about the link failure during packet transmission in a route. Simulation results using OMNET++ show that ACOLBR outperforms ARA, ANTHOCNET, FACO, AODV, DSDV, DSR, CA-ARTT, and MOAODV in terms of load balancing efficiently in the route for data transmission in between source to destination.

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Section: Performance Analysis and Simulation Resultsmentioning

confidence: 99%

Section: Performance Analysis and Simulation Resultsmentioning

confidence: 99%

Section: Performance Analysis and Simulation Resultsmentioning

confidence: 99%

Section: Performance Analysis and Simulation Resultsmentioning

confidence: 99%

Section: Contributionmentioning

confidence: 99%

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ACOLBR: ACO Based Load Balancing Routing In MANET

Dholey

Sinha

2021

Preprint

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Proficient link state routing in mobile ad hoc network‐based deep Q‐learning network optimized with chaotic bat swarm optimization algorithm

Rahul¹,

Kaarthick²

2022

Int J Communication

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Mobile ad-hoc network (MANET) is a category of ad-hoc network that can be reconfigurable its network. MANETS are self-organized networks, that can use the wireless links to connect various networks via mobile nodes: but it consumes more energy and it also has routing problems. This is the major drawback of being connected with the MANET technology. Therefore, this study proposes a new protocol as deep Q-learning network optimized with chaotic bat swarm optimization algorithm (CBS)-based optimized link state routing (OLSR) (CBS-OLSR) for MANET. This protocol reduces MANET energy usage and adopts OLSR multi-point relay (MPR) technology. MANET's OLSR and the CBS algorithm utilize a similar method to locate the best optimum path from source to destination node. By embedding the new improved deep Qlearning and OLSR algorithms, both are used for optimizing the MPR sets selection, it can efficiently diminish the energy consumption in the network topology, but automatically increase the lifespan of the network. It also enhances the package delivery ratio and decreases end-to-end delay. The experimental outcomes prove that the proposed protocol is reliable and proficient that is appropriate for numerous MANET applications. K E Y W O R D Schaotic bat swarm optimization, deep Q-learning algorithm, end-to-end delay, mobile adhoc network, MPR (multi-point relay), optimized link state routing, packet delivery rate | INTRODUCTIONMANET is an autonomous system that consists of individually connected set of self-configuring nodes that is implemented using a number of techniques, viz Bluetooth or WLAN. 1 The feature of MANET is autonomous, due to every node treating its host and router simultaneously. 2 To communicate among the distant nodes, they depend on direct communication with multi-hops. These advantages allow them to accommodate multiple nodes and decentralize management with its structure at any time. 3 Unlike other wireless systems, MANET has lack of central authority to monitor traffic transmission. 4 MANET systems have an infrastructure with a lesser configuration of corresponding nodes that are connected by wireless connections. The nodes are moving spontaneously or rather in a random section. 5 From node to network level, security in MANETs is important. 6 Owing to the dynamic nature of MANETs, there needs to be an acceptable level of trust. 7,8 Due to the dynamic nature and characteristics of MANETs, security on the MANET is more challenge

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A cross‐layer routing approach for MANET‐assisted IoT applications

Quy,

Van Anh,

Ban

et al. 2023

Trans Emerging Tel Tech

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The Internet of things (IoT) is a concept that allows all things such as sensors, actuators, humans, platforms, and software to interact with others relying on Internet infrastructure. The launch of the 5th generation communication networks allows providing network services with extremely high performance aiming to realize IoT concepts and forming a series of other smart IoT ecosystems. Nowadays, IoT is emerging as a dominant technology in almost domain. One of the most crucial technologies forming IoT networks is device‐to‐device (D2D) communication. D2D technology allows mobile nodes self‐setup, and self‐config to identify optimal paths for data transmission without relying on pre‐installed infrastructure, which is the principle of forming mobile ad‐hoc networks (MANETs). The fusing of MANETs and IoT technologies forming MANET‐assisted IoT networks architecture to provide abilities and novel unpredict services. However, mobility and network structure often change are the main limitations of this architecture, hence, identifying the optimal route is a significant challenge. In this study, we propose a cross‐layer routing approach for MANET‐assisted IoT networks. Our routing mechanism is based on considering three factors, including number of hops, link quality, and queue length. The simulation results show that our proposed solution improves performance compared to the traditional solutions. We hope that our simple and efficient solution will be applied popular for future MANET‐assisted IoT networks.

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Extended OLSR and AODV based on multi-criteria decision making method

Cited by 15 publications

References 16 publications

ACOLBR: ACO Based Load Balancing Routing In MANET

ACOLBR: ACO Based Load Balancing Routing In MANET

Proficient link state routing in mobile ad hoc network‐based deep Q‐learning network optimized with chaotic bat swarm optimization algorithm

A cross‐layer routing approach for MANET‐assisted IoT applications

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