LOC algorithm. Issue 4 (28th October 2014)

• Record Type: Journal Article
• Authors: Kashif Ashraf Khan, Sardar; Loo, Jonathan; Lasebae, Aboubaker; Awais Azam, Muhammad; Adeel, Muhammad; Kausar, Rehana; Sardar, Humaira
• Is Part Of: International journal of pervasive computing and communications. Volume 10:Issue 4(2014)
• Page Start: 481
• Page End: 496
• Publication Date: 2014-10-28
• Subjects: Ubiquitous computing -- Periodicals; Mobile computing -- Periodicals; Computer network protocols -- Periodicals; Computer network architectures -- Periodicals; Application software -- Development -- Periodicals; 004.6
• DOI: 10.1108/IJPCC-02-2014-0017
• Languages: English
• ISSNs: 1742-7371

Abstract

<abstract> <title> <x content-type="archive" xml:space="preserve">Abstract</x> </title> <sec> <title content-type="abstract-heading">Purpose</title> <p> – 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 <italic>ad hoc</italic> networks. </p> </sec> <sec> <title content-type="abstract-heading">Design/methodology/approach</title> <p> – 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. </p> </sec> <sec> <title content-type="abstract-heading">Findings</title> <p> – 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. </p> </sec> <sec> <title content-type="abstract-heading">Originality/value</title> <p> – The new contribution increases the overall energy and storage efficiency of nodes by targeting relevant forwarding nodes in the network.</p> </sec> </abstract>