MECOR: Minimal Energy Consumption with Optimized Routing in MANET

Havinal, Ramanna; Attimarad, Girish V.; Prasad, M. N. Giri

doi:10.1007/s11277-016-3223-y

Cited by 17 publications

(20 citation statements)

References 12 publications

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“…However, bandwidth and delay time was not addressed. A Route Energy Comprehensive Index (RECI) technique was presented in [12] chooses the path with minimum hops for broadcasting data in MANETs. But, energy taken for data packet transmission was higher.…”

Section: Related Workmentioning

confidence: 99%

“…In NLDRO-QAR Technique, Data Loss Rate ( ) is precise as the ratio of group number of data dropped to the total number of data packets pass from one place to another in MANETs. The data loss rate is precise in terms of percentages (%) and precisely represented as, = N * 100 (12) From equation 12, the data loss rate is calculated with respect to different group number of data ( ). When the data loss rate is lower, the method is said to be more effectual.…”

Section: Impact Of Data Loss Ratementioning

confidence: 99%

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Non Linear Differential Optimization for Quality Aware Resource Efficient Routing in Mobile Ad Hoc Networks

Ilango,

Kumar

2019

IJEAT

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MANETs is the basic structure less network in which mobile nodes connecting point at which several line comes together to form a network structure. Network resources in MANETs are limited due to their forcefulness study of a given place. A mobile node in MANETs utilizes an interconnected system of things such as data transmission rate and battery power during data packet transmission. Thus the conserving network resource is important to maintain the quality of network services without delay. Therefore, Non-linear Differential Resource Optimization based Quality Aware Routing (NLDRO-QAR) Technique is proposed. The NLDRO-QAR Technique selects the resource optimized route path among multi paths for efficient data packet transmission in MANETs. The resource optimized route path is determined in proposed NLDRO-QAR Technique by choosing mobile node with higher resource availability as optimal nodes. The NLDRO-QAR Technique considers residual energy, residual bandwidth, and delay time as significant factor for computing resource availability of mobile nodes in MANETs. In order to effectively find out the optimal nodes (i.e. mobile node with higher resource availability) in dynamic environment. The NLDRO-QAR Technique chooses the resource optimized nodes in MANETs in order to transmit the packets between source and destination. As a result, NLDRO-QAR Technique efficiently reduces the energy and bandwidth utilization of data delivery in MANETs. The NLDRO-QAR method manage the simulation works on metrics such as energy utilization, average end to end delay, data loss rate and bandwidth utilization rate. The simulations results show that the NLDRO-QAR Technique is able to reduce the energy and bandwidth utilization for routing the data packet in MANETs as compared to state-of-the-artworks.

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Section: Related Workmentioning

confidence: 99%

Section: Impact Of Data Loss Ratementioning

confidence: 99%

Non Linear Differential Optimization for Quality Aware Resource Efficient Routing in Mobile Ad Hoc Networks

Ilango,

Kumar

2019

IJEAT

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“…However, performance of energy efficiency is not sufficient. Minimal energy consumption with optimized routing [17] uses minimal energy during packet transmission. However, it increases the execution time.…”

Section: Introductionmentioning

confidence: 99%

Levenberg–Marquart logistic deep neural learning based energy efficient and load balanced routing in MANET

Sangeetha

Rajendran

2021

IJEECS

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As the advent of new technologies grows, the deployment of mobile ad hoc networks (MANET) becomes increasingly popular in many application areas. In addition, all the nodes in MANET are battery operated and the node mobility affects the path stability and creates excessive traffic leads to higher utilization of energy, data loss which degrades the performance of routing. So, in this paper we propose Levenberg–Marquardt logistic deep neural learning based energy efficient and load balanced routing (LLDNL-EELBR) which is a machine learning method to deeply analyze the mobile nodes to calculate residual load and energy and it also uses logistic activation function to select the mobile node having higher residual energy and residual load to route the data packet. Experimental evaluations of three methods (LLDNL-EELBR, multipath battery and mobility-aware routing scheme (MBMA-OLSR) and opportunistic routing with gradient forwarding for MANETs (ORGMA)) were done and the result reveals that LLDNL-EELBR method is able to increase the through put and minimizes the delay and energy consumption in MANET when compared to works under consideration.<br /><div> </div>

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“…The LSP of MPLS forwards the packets over the network based on certain traffic rules [11]. The conventional routing protocols used in the MANET are mentioned as follows: binary particle swarm optimization based temporally ordered routing algorithm [12], efficient and stable multipath routing [13], minimal energy consumption with optimized routing [14] and MAC routing protocol in the clustered network [15], etc. The important contributions of this AODV-BBO methodology are stated as follows:…”

Section: Introductionmentioning

confidence: 99%

Delay Aware Routing Protocol Using Optimized AODV with BBO for MPLSMANET

Jayaramu¹,

Banga²

2020

IJIES

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Mobile Ad Hoc Network (MANET) is a wireless network that does not have any fixed structure. MANET is a collection of independent mobile nodes that can communicate with each other through radio waves. In conventional Internet Protocol (IP) forwarding, each router creates forwarding decision on the basis of IP header information. The router has to analyse the packet and routing table at each hop to take the decision for data transmission. Since, inappropriate decisions in data transmission causes delay in packet delivery. This can be overcome with the help of a special feature of Multiprotocol Label Switching (MPLS) called Label switching. MPLS is a switching mechanism that plays a good role in routing, switching and forwarding using small labels. The MPLS is integrated into MANET to improve network performance. In this paper, an effective routing strategy is introduced to reduce the transmission time between routers during the data transmission. Here the Ad Hoc On-Demand Distance Vector (AODV) with Biogeography Based Optimization (BBO) based routing method is used to identify the optimal route among the routers of the MPLS based MANET. The proposed methodology is named as AODV-BBO. Three different objective functions are considered in this routing strategy viz., residual energy, distance and number of hops. Moreover, the rerouting is performed when the node or link failure occurs in the network. This rerouting is used to overcome the packet drop through the MPLS based MANET. The performance of AODV-BBO methodology is analysed in terms of percentage of alive nodes, dead nodes, energy consumption, end to end delay and bandwidth. The performance of the AODV-BBO methodology is compared with two existing methods P2R2 and PS-ROGR. Result showed that, the energy consumption of the AODV-BBO methodology with 400 nodes is 536.52 J, which is less compared to the PS-ROGR method.

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MECOR: Minimal Energy Consumption with Optimized Routing in MANET

Cited by 17 publications

References 12 publications

Non Linear Differential Optimization for Quality Aware Resource Efficient Routing in Mobile Ad Hoc Networks

Non Linear Differential Optimization for Quality Aware Resource Efficient Routing in Mobile Ad Hoc Networks

Levenberg–Marquart logistic deep neural learning based energy efficient and load balanced routing in MANET

Delay Aware Routing Protocol Using Optimized AODV with BBO for MPLSMANET

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