Mobile Ad-Hoc Network (MANET) for disaster management

Jadhav, Sushant; Kulkarni, A. V.; Menon, Radhika

doi:10.1109/wocn.2014.6923074

Cited by 19 publications

(9 citation statements)

References 6 publications

(4 reference statements)

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“…The complexity of this estimator (THEO) is ( ) 3 O N . This high computational cost precludes the use of this optimal estimator in real-time applications.…”

Section: Discussionmentioning

confidence: 99%

Link Quality Prediction for 802.11 MANETs in Urban Microcells

Gaertner¹,

Nuallain²

2016

JCC

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In this paper we derive the optimal link quality predictor (LQPR) whose parameters are estimated from signal power and node speed samples. We propose a fast estimator for these parameters whose computational complexity is three orders lower than that of the optimal estimator with only a slight loss in accuracy thus enabling realtime execution. We show that using the most recent local mean of the signal as a predictor of future signal strength is also a very close approximation to the optimal predictor. This is the central result of this paper. It obviates the need for complex and/or computationally intensive link quality predictors for 802.11 in urban microcells and has the advantage of not requiring node speed information. The LQPRs are evaluated against the lower error bound. We show that the LQPR based on the most recent local mean of the signal predicts the packet reception probability for pedestrians in urban microcells on average with a mean absolute error of 13.47%, 16.54%, 18.21% and 19.38% for 1 s, 2 s, 3 s and 4 s into the future respectively. This LQP accuracy resembles closely the lower error bound with, for example, a difference of only 2.47% at 2 s into the future.

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“…The complexity of this estimator (THEO) is ( ) 3 O N . This high computational cost precludes the use of this optimal estimator in real-time applications.…”

Section: Discussionmentioning

confidence: 99%

Link Quality Prediction for 802.11 MANETs in Urban Microcells

Gaertner¹,

Nuallain²

2016

JCC

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“…Mobile ad hoc network [1] is a group of wireless mobile nodes in which nodes collaborate by forwarding packets for each other to allow them to communicate outside range of direct wireless transmission. The network topology is unpredictable and can vary over time.…”

Section: A Mobile Ad-hoc Network (Manet)mentioning

confidence: 99%

Evaluation of Ad-Hoc Routing Protocols in a Vehicular Traffic Environment using SUMO and NS2

Birari¹

2019

IJRASET

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Considering the transition from a peripatetic world to a world full of vehicles all over, the need for a well-defined communication between vehicles is something to be thought of. There may be circumstances where we cannot rely on traditional methods of communication for message or data transfer. At times, speed is prioritized over reliability and vice-versa. This paper focuses on evaluating the performances of different Ad Hoc routing protocols which are Ad Hoc On-Demand Distance Vector (AODV), Dynamic Source Routing (DSR), Destination Sequenced Distance Vector (DSDV) in terms of their average throughput and average end-to-end delay. All the three protocols have been implemented in the same traffic environment. Six cases have been considered where these routing protocols have been implemented using User Datagram Protocol (UDP) and Transmission Control Protocol (TCP) as the transport layer protocols. The scope of this paper is to find out the best routing protocol, both on the basis of average throughput and even end-to-end delay and optimize it to improve its performance. Finally, the paper is summarized.

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“…It is observed that the performance of the network after application of prevention condition is nearly same as the normal performance. The performance is evaluated in terms of Network Throughput, Packet Delivery Ratio, and Average end to end delay [12]. Dixit et al proposed a novel intrusion detection system for detecting black hole attack and gray hole attack which is based on table driven approach using Ad hoc on demand routing protocol and on the basis of behavior of node the computation of the vote for node and negative voting trunk the node from path nodes create a secure path using highest vote number, in this work simulation done on NS-2.35, result shows that network performance increased by the proposed principle.…”

Section: Related Workmentioning

confidence: 99%

Intrusion detection and avoidance of black and grey hole attacks using AODV Protocol based MANET

Shaikh¹,

Rajan²

2017

IJET

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Mobile Ad-hoc Network (MANET) is a mobile network which has a large scale of self-directed nodes which is powerful to form a shortterm means of communication network, without any use of prior communications. Due to its uniqueness like partial resources, varying loops and shortfall of controlling the networks, these networks are exposed to diverse network layer issues. The "Ad hoc on demand distance vector" is a self-starting directing procedure whose security is compromised with the distinct form of attack named as "BlackHole" and "Grey Hole" attacks. This "malicious node" publicize as such, it contains the supreme track to the target during the route discovery process and thus interrupt the real communication and corrupt network performance. This paper introduces a new method in which a base node is introduced in the network that increases the probability of detecting multiple malicious nodes in the network and further separate them from taking part in any communication. It detects the corrupted nodes and prevent it by causing an effect for the communication. The proposed method has been experimented using NS2 and the results found to be efficient.

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Mobile Ad-Hoc Network (MANET) for disaster management

Cited by 19 publications

References 6 publications

Link Quality Prediction for 802.11 MANETs in Urban Microcells

Link Quality Prediction for 802.11 MANETs in Urban Microcells

Evaluation of Ad-Hoc Routing Protocols in a Vehicular Traffic Environment using SUMO and NS2

Intrusion detection and avoidance of black and grey hole attacks using AODV Protocol based MANET

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