Shortest Paths Routing Problem in MANETs

El-Sayed, Hamdy H.

doi:10.18576/amis/100530

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…In this way the random selction of nodes are stopped and proper nodes are selected, which are in the right direction [15]. The communiction speed will be increased and the usuage of the ad hoc networks get minimized.…”

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confidence: 99%

Fault Tolerance & Load Balanced Ad-hoc Networks using LRDV Routing Protocol

2019

IJITEE

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In mobile devices, location management is a major issue due to the change in location while connected to the network. In this paper we proposed a new routing protocol for sensor embedded mobile devices to localize the resection of exposure nodes over highly dynamic vector. It highly supports fault tolerance to avoid route breaks and it will reestablish new route before a route break. And also recover the fault based on direction and location. It Improves the quality of service(QoS) by minimizing the complexity of routing and it also highly supports fault analysis, speed of service, increased intelligence of tracking devices and speed recovery of fault over communication failures like breakage of links.

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Section: Fig 5 Broadcasting Multiple Messagesmentioning

confidence: 99%

Fault Tolerance & Load Balanced Ad-hoc Networks using LRDV Routing Protocol

2019

IJITEE

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“…By exchanging the information with neighbours, these tables are updated. Bellman-Ford algorithm is an example of DVM (El-Sayed, 2016). But the main issue with this method is that it does not prevent routing loops from happening and severely suffers from count to infinity problem.…”

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confidence: 99%

Shortest Path Establishment Approaches for Static and Dynamic Mobile Nodes with Quality of Service

Sundar

Basu

Kar

et al. 2018

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An ideal routing algorithm is one which finds the best/shortest path(s) for transmitting the data between source and destination in a short time. In this study, we have proposed two approaches; one is based on minimum spanning tree (MST) and the other one is based on distance vector method (DVM). For MST, a genetic algorithm is used. The chromosomes used are of variable length and their genes are used for encoding. The crossover and mutation operations are used for genetic diversity and improving the convergence rate. The second method is based on DVM to cover the limitations of the first approach as well the traditional DVM. The developed algorithm finds the shortest path for a static mesh connected mobile nodes. It is then tested for dynamicity by varying the positions of a few nodes. Extensive simulations are carried out on both the approaches and the results are compared with other popular algorithms.

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