QOS Improvement in MANET Routing by Route Optimization through Convergence of Mobile Agent

Verma, Vikas; Sharma, Divya; Lamba, C. S.

doi:10.1109/upcon.2018.8596852

Cited by 4 publications

(5 citation statements)

References 18 publications

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“…It promises vital contributions to enhance life quality of humanity. Some typical applications of mobile agent technology are described as follows [22][23][24][25][26][27][28].…”

Section: Related Workmentioning

confidence: 99%

“…The guarantee of QoS is one of the most important requirements of future routing algorithms [25]. However, the principle of MANET operates without any preinfrastructure.…”

Section: Related Workmentioning

confidence: 99%

“…The lack of central devices such as base stations led to the QoS support of MANET face challenges. Verma et al designed a new routing model based on mobile agents [25]. In this model, mobile agents obtain the vital routing metrics for route quality evaluation.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, many studies have been done to improve availability and reliability in MANET [2,5]. Through the related studies [6][7][8][9][10][11][12][13][14][15][16][17][18][19][21][22][23][24][25] showed that how to use mobile agent technology with the intention of improving the performance of MANET becomes an increasingly exciting research topic. The specific solution is to integrate mobile agents into the control of routing protocols in MANET to improve the overall performance of the system.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An Improved Agent-Based AODV Routing Protocol for MANET

Nguyen¹,

Nam²,

Linh³

et al. 2021

EAI Endorsed Transactions on Industrial Networks and Intelligen

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The mobile ad-hoc network (MANET) is a special mobile network that has self-configuration and self-establish abilities to communicate conveniently. MANET is forming mobile devices such as mobile phones, personal digital assistants (PDAs), laptops, etc. As a result of the rapid, flexibility, and facilitate in communication, nowadays, MANET has revolutionized many real-life applications, ranging from smart agriculture and smart cities to forest fire detection systems, and expected to have vital contributions into the future of the Internet. However, due to the mobile nature of network nodes combined with the network architecture that without relying on central devices pre-installed such as base stations, the high-performance routing problem is one of the most significant challenges in the MANET environment. In this study, we propose a highperformance routing protocol, namely agent-based ad-hoc on-demand distance vector (A-AODV), to select the optimal route with high throughput and low latency. Through the developed routing algorithm, we explain how the suitable route with the lowest cost based on agents. To demonstrate the effectiveness of the proposed protocol, we compared A-AODV's performance with two well-known traditional protocols on NS2. Simulation results show that A-AODV improves superior performance over the traditional protocols.

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“…It promises vital contributions to enhance life quality of humanity. Some typical applications of mobile agent technology are described as follows [22][23][24][25][26][27][28].…”

Section: Related Workmentioning

confidence: 99%

“…The guarantee of QoS is one of the most important requirements of future routing algorithms [25]. However, the principle of MANET operates without any preinfrastructure.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Improved Agent-Based AODV Routing Protocol for MANET

Nguyen¹,

Nam²,

Linh³

et al. 2021

EAI Endorsed Transactions on Industrial Networks and Intelligen

View full text Add to dashboard Cite

show abstract

“…One additional parameter req-BW or required bandwidth is appended to every RREQ so that the bandwidth of the next link (between the current node and its chosen successor) is always more significant than the bare minimum requirement. Based on the AODV routing algorithm, a QoS version is proposed by Verma et al 24 Their main intention was to encounter link breakage by applying mobile software agent technology with unique features like mobility, intelligence, and adaptability. The optimal path is selected by considering multiple QoS parameters like stability, delay, residual energy, congestion, etc.…”

Section: Related Workmentioning

confidence: 99%

Autoregressive moving average based anycast with support vector machine clustering in mobile ad‐hoc networks

Ghosh¹,

Banerjee

Sufian

et al. 2021

Trans Emerging Tel Tech

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Nodes in mobile ad-hoc networks (MANETs) are battery-powered, therefore, need energy-efficient techniques to cut down the cost of energy consumption. Energy consumption is directly related to route discovery, which could be reduced through anycasting. Anycast is an operation in MANETs, where the source sends messages to one particular destination node of anycast region.In general, the message transmitted is divided into certain packets, and those are transmitted continuously one after the other through selected routes. If the link from anycast source to destination breaks in between or an anycast destination goes out of the anycast region, in that case, a new anycast destination will have to be selected on a new route to the destination. This will incur huge message costs and energy. In this work, we select desirable anycast destinations based on their eligibility by using autoregressive moving average and support vector machine. Eligibility is modeled as a function of residual energy, location, the relative velocity concerning neighbors, and the number of packets successfully forwarded earlier by those neighbors' locations. Reducing the number of anycast receivers reduces the message cost of protocols significantly. We have compared our protocol's performance with the same of anycast-AODV, anycast-DSR, anycast-FAIR, and MQAR (mobility and QoS-aware anycast routing protocol) results show significant improvement. Collective improvement amounts to 89% more energy preservation and 39% delay on average.

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