{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T00:56:08Z","timestamp":1770512168655,"version":"3.49.0"},"reference-count":13,"publisher":"Emerald","issue":"4","license":[{"start":{"date-parts":[[2014,10,28]],"date-time":"2014-10-28T00:00:00Z","timestamp":1414454400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2014,10,28]]},"abstract":"<jats:sec>\n               <jats:title content-type=\"abstract-heading\">Purpose<\/jats:title>\n               <jats:p> \u2013 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 <jats:italic>ad hoc<\/jats:italic> networks. <\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title content-type=\"abstract-heading\">Design\/methodology\/approach<\/jats:title>\n               <jats:p> \u2013 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. <\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title content-type=\"abstract-heading\">Findings<\/jats:title>\n               <jats:p> \u2013 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. <\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title content-type=\"abstract-heading\">Originality\/value<\/jats:title>\n               <jats:p> \u2013 The new contribution increases the overall energy and storage efficiency of nodes by targeting relevant forwarding nodes in the network.<\/jats:p>\n            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algorithms\u201d, IEEE Transactions on Mobile Computing, Vol. 6 No. 6, pp. 606-620.","DOI":"10.1109\/TMC.2007.1060"},{"key":"key2020122803080201200_b9","doi-asserted-by":"crossref","unstructured":"Eagle, N.\n                and \n                  Pentland, A.\n                (2006), \u201cReality mining: sensing complex social systems\u201d, Personal and Ubiquitous Computing, Vol. 10 No. 4, pp. 255-268.","DOI":"10.1007\/s00779-005-0046-3"},{"key":"key2020122803080201200_b6","doi-asserted-by":"crossref","unstructured":"Ekman, F.\n               , \n                  Keranen, A.\n               , \n                  Karvo, J.\n                and \n                  Ott, J.\n                (2008), \u201cWorking day movement model\u201d, Proceeding ACM Conference 1st ACM SIGMOBILE workshop on Mobility models (MobilityModels \u201908), ACM, New York, NY.","DOI":"10.1145\/1374688.1374695"},{"key":"key2020122803080201200_b10","doi-asserted-by":"crossref","unstructured":"Holme, P.\n                and \n                  Ghoshal, P.\n                (2009), \u201cThe diplomat\u2019s dilemma: maximal power for minimal effort in social networks\u201d, Adaptive Networks: Theory, Models and Applications, Springer, Heidelberg, pp. 269-288.","DOI":"10.1007\/978-3-642-01284-6_13"},{"key":"key2020122803080201200_b11","doi-asserted-by":"crossref","unstructured":"Hui, P.\n               , \n                  Crowcroft, J.\n                and \n                  Yoneki, E.\n                (2008), \u201cBubble rap: social-based forwarding in delay tolerant networks\u201d, Proceeding ACM Symposium Mobile ad hoc networking and computing (MobiHoc \u201908), IEEE, New York, NY, pp. 241-250.","DOI":"10.1145\/1374618.1374652"},{"key":"key2020122803080201200_b3","doi-asserted-by":"crossref","unstructured":"Keranen, A.\n               , \n                  Ott, J.\n                and \n                  Karkkainen, T.\n                (2009), \u201cThe ONE simulator for DTN protocol 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