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LOC algorithm.

Authors :
Khan, Sardar Kashif Ashraf
Loo, Jonathan
Lasebae, Aboubaker
Azam, Muhammad Awais
Adeel, Muhammad
Kausar, Rehana
Sardar, Humaira
Source :
International Journal of Pervasive Computing & Communications; 2014, Vol. 10 Issue 4, p481-496, 16p
Publication Year :
2014

Abstract

Purpose -- This paper aims to propose an algorithm, location-aware opportunistic content forwarding (LOC), to improve message directivity using direction vectors in opportunistic networks. The LOC is based on the assumption that if approximate location of the destination node is known, then overall message delivery and cost can be improved. Efficient message delivery with low communication cost is a major challenge in current opportunistic networks. In these networks, nodes do not have prior knowledge of their recipients, and message forwarding can be achieved by selecting suitable forwarder based on some forwarding criteria, as compared to its ancestor mobile ad hoc networks. Design/methodology/approach -- In this paper, the authors tested LOC in two sets of mobility models, synthetic movement model and real mobility data sets. In the first set, working day movement is used as synthetic movement model, where proposed algorithm is compared against Lobby Influence (LI) and Epidemic algorithms. In the second set of experiments, the new algorithm is tested in three mobility data sets, namely, Cambridge, Reality and Sassy, and results compared against LI algorithm. The reason of using various movement models is to establish strengths and weaknesses of the proposed algorithm in different scenarios. Findings -- The experimental results show that the new algorithm performed extremely well in different scenarios, not only in terms of overall message delivery but also successfully managed to reduce the communication cost. Originality/value -- The new contribution increases the overall energy and storage efficiency of nodes by targeting relevant forwarding nodes in the network. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
17427371
Volume :
10
Issue :
4
Database :
Complementary Index
Journal :
International Journal of Pervasive Computing & Communications
Publication Type :
Academic Journal
Accession number :
100780342
Full Text :
https://doi.org/10.1108/IJPCC-02-2014-0017