Energy Efficient ERS for Multicast Routing in MANET

Adhvaryu, Kinjal

doi:10.1109/icraecc43874.2019.8995085

Cited by 4 publications

(5 citation statements)

References 7 publications

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“…Many routing protocols in MANETs make use of methods like expanding ring search (ERS), random early detection (RED) and many other techniques and their variants to exploit these routing parameters in various formats in order to ensure QoS and deal with issues like congestion due to flooding, energy drainage, overheads and link breakage [10][11][12][13][14][15][16][17][18][19][20]. In ERS the route search is managed in the form of rings of predefined steps instead of linear search methods.…”

Section: A Expanding Ring Search Techniquementioning

confidence: 99%

“…ERS mechanism resolves the issue of flooding but network may lead to counter overheads and loads that cause energy drainage in highly dense networks [23]. To tackle such issues many version of ERS enhancements [10][11][12][13] has been proposed that manages the route search with modified parametric setups and techniques to improve routing and QoS.…”

Section: A Expanding Ring Search Techniquementioning

confidence: 99%

“…Another technique proposed in [12] exploits the route request (RREQ) parameter and the TTL_INCREMENT values to reduce the energy consumption by the nodes and improving the QoS in terms of PDR, E2E delay and throughput. A packet priority mechanism based on network coding scheme is proposed in [13] that takes in to account a class based priority to the ERS rings and uses proportional fair scheduling to deal with congestion. The ML technique MLP was exploited for WRED method to automatically adapt the end users to the network and improving the QoS [14].…”

Section: Related Workmentioning

confidence: 99%

“…The data specifications are given in terms of minimum values (Dmin, Thmin, Pmin), maximum values (Dmax, Thmax, Pmax), average (Davg, Thavg, Pavg) and standard deviation (Dsdv, Thsdv, Psdv). The data has been normalized for experiments using (13), where X = {x1, x2, x3,… xn}is the set of input data and Norm (xi) is the i th normalized data item.…”

Section: A Setup For Machine Learning Algorithmsmentioning

confidence: 99%

See 3 more Smart Citations

Prediction of Scenarios for Routing in MANETs Based on Expanding Ring Search and Random Early Detection Parameters Using Machine Learning Techniques

2021

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Routing protocols in Mobile Ad Hoc Networks (MANETs) play a pivotal role in ensuring quality of service (QoS) and improving network performance. Selection of optimal routing protocol and suitable parameters for a given network scenario is a major task that ultimately affects the behavior of network. This work exploits machine learning (ML) techniques for the selection of adequate routing parameters and protocol by regression of parameters in given network scenario to ensure optimal performance. The network is trained based on parametric setup of expanding ring search mechanism (ERS) and random early detection (RED) technique to estimate network throughput, end to end (E2E) delay and packets delivery ratio (PDR) and is tested via wide-ranging simulations in varying network topologies. Both RED and ERS mechanisms are aimed to control link and node level congestion in the reactive routing protocols and our aim is to select the best suited parameters for given network topologies based on ERS and RED parametric setups and improve performance for ensuring QoS. ML algorithms are trained and tested for their performance in varying network topologies. We have exploited these models with best performance for ERS and RED based routing in given topological arrangements. The performance of the ML algorithms is evaluated on the basis of root mean squared error (RMSE) and mean absolute error (MAE) for regression settings. Prediction models with up to par RMSE and MAE out-turns are attained and exploited for selection of suitable ERS and RED parameters and routing protocols in order to ensure the QoS for given network scenario. Variants of standard routing protocols are devised based on their performance and the ML techniques are exploited for prediction of QoS parameters to decide on the optimal variant that attains significant improvement in performance. Results are shown to confirm that considerable improvement in QoS is attained. INDEX TERMS Ad hoc multi hop wireless networks, congestion control, expanding ring search, machine learning, mobile ad hoc networks, on demand routing protocols, random early detection, regression, quality of service.

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Section: A Expanding Ring Search Techniquementioning

confidence: 99%

Section: A Expanding Ring Search Techniquementioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: A Setup For Machine Learning Algorithmsmentioning

confidence: 99%

See 2 more Smart Citations

Prediction of Scenarios for Routing in MANETs Based on Expanding Ring Search and Random Early Detection Parameters Using Machine Learning Techniques

2021

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“…Adaptive values of waiting time are used to enhance the performance. An Energy Ef cient ERS (E2-ERS) is proposed in [18] for optimizing the routing protocol. E2-ERS controls the number of RREQ retransmissions during route discovery process.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Expanding Ring Search Based Per Hop Behavior Rendition of Routing in MANETs

Durr-e-Nayab¹,

Zafar²,

Basheri³

2021

Computers, Materials &Amp; Continua

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Routing protocols in Mobile Ad Hoc Networks (MANETs) operate with Expanding Ring Search (ERS) mechanism to avoid ooding in the network while tracing step. ERS mechanism searches the network with discerning Time to Live (TTL) values described by respective routing protocol that save both energy and time. This work exploits the relation between the TTL value of a packet, traf c on a node and ERS mechanism for routing in MANETs and achieves an Adaptive ERS based Per Hop Behavior (AERSPHB) rendition of requests handling. Each search request is classi ed based on ERS attributes and then processed for routing while monitoring the node traf c. Two algorithms are designed and examined for performance under exhaustive parametric setup and employed on adaptive premises to enhance the performance of the network. The network is tested under congestion scenario that is based on buffer utilization at node level and link utilization via back-off stage of Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA). Both the link and node level congestion is handled through retransmission and rerouting the packets based on ERS parameters. The aim is to drop the packets that are exhausting the network energy whereas forward the packets nearer to the destination with priority. Extensive simulations are carried out for network scalability, node speed and network terrain size. Our results show that the proposed models attain evident performance enhancement.

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