Proposed Coordinating Multiple Sampling Tasks in Sensor Field Using Geometric Progression Algorithm for Efficient Data Collection in Wireless Sensor Networks

Rajasekar, R.; Periasamy, P.

doi:10.1007/s11277-015-2315-4

Cited by 3 publications

(2 citation statements)

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“…Comparing the existing EAACK model, MAACK is efficient but it does not provide good efficiency in common. 4 Audit Based Misbehaviour Detection (AMD) [20] Advantage: It successfully and systematically differentiates continuous as well as selective packet droppers. It undergoes packet evaluation without packet overhead.…”

Section: Disadvantagementioning

confidence: 99%

“…MANETs usually have a networking environment that performs routing on the top of the link layer of the Ad-hoc network. Communication in MANET is carried out via multiple techniques [4] - [7]. Various challenges are faced by MANET that contains multiple resource network topology changes unpredictably and connection breaks are pretty frequent.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Mobile Ad-Hoc Delay Tolerant Network for Optimum Routing in Wireless Sensor Networks

Rathish¹,

P*²

2019

IJITEE

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Due to some misbehaving nodes in Mobile Ad-hoc networks (MANETs), data is lost or false data is delivered. In order to detect and re-configure the path from the source to terminal, Ad-Hoc On- Demand Distance Vector (AODV) routing protocol is used. In existing method the black holes are generated due to the Ad-Hoc On-Demand Distance Vector (AODV) routing protocol. Hence a Delay Tolerant Network (DTN) is proposed to cope up with the black holes. In this paper, it is proposed to operate by forming hybrid network with MANET and DTN as MADT Network is to deliver the correct data with increased bit rate and low delay. The proposed Hybrid MADT network has been verified and the results show that the throughput of the proposed network is increased by 0.7%, the packet transmission rate is enhanced by 7% and the end-to-end delay is reduced by 4%

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Section: Disadvantagementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%