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2014 (Vol. 7, Issue: 7)
Article Information:

Scalable Resource Discovery Architecture for Large Scale MANETs

Saad Al-Ahmadi and Abdullah Al-Dhelaan
Corresponding Author:  Saad Al-Ahmadi 

Key words:  Distributed hash table, gray cube, mobile ad hoc networks, multi-hop clustering, resource discovery, ,
Vol. 7 , (7): 1351-1363
Submitted Accepted Published
April 24, 2013 June 19, 2013 February 20, 2014
Abstract:

The study conducted a primary investigation into using the Gray cube structure, clustering and Distributed Hash Tables (DHTs) to build an efficient virtual network backbone for Resource Discovery (RD) tasks in large scale Mobile Ad hoc NET works (MANETs). MANET is an autonomous system of mobile nodes characterized by wireless links. One of the major challenges in MANET is RD protocols responsible for advertising and searching network services. We propose an efficient and scalable RD architecture to meet the challenging requirements of reliable, scalable and power-efficient RD protocol suitable for MANETs with potentially thousands of wireless mobile devices. Our RD is based on virtual network backbone created by dividing the network into several non overlapping localities using multi-hop clustering. In every locality we build a Gray cube with locally adapted dimension. All the Gray cubes are connected through gateways and access points to form virtual backbone used as substrate for DHT operations to distribute, register and locate network resources efficiently. The Gray cube is characterized by low network diameter, low average distance and strong connectivity. We evaluated the proposed RD performance and compared it to some of the well known RD schemes in the literature based on modeling and simulation. The results show the superiority of the proposed RD in terms of delay, load balancing, overloading avoidance, scalability and fault-tolerance.
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  Cite this Reference:
Saad Al-Ahmadi and Abdullah Al-Dhelaan, 2014. Scalable Resource Discovery Architecture for Large Scale MANETs.  Research Journal of Applied Sciences, Engineering and Technology, 7(7): 1351-1363.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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