2013
DOI: 10.1016/j.sysarc.2013.09.002
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Intelligent Adjustment Forwarding: A compromise between end-to-end and hop-by-hop transmissions in VANET environments

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Cited by 5 publications
(4 citation statements)
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“…More detailed information will be described in next part. (1) initialization: determine the relationship between RSUs and vehicles periodically (2) if S is covered by a RSU (3) deliver packets to RSU s in V2R mode else fuzzy-rule-based wireless transmission method vehicle-based short-term vehicle speed prediction (4) (1) find potential paths (5) (2) evaluate potential paths (6) (3) determine wireless transmission path (optimum) (7) S sends packets to RSU s (8) if RSU fails to receive packets after threshold time (9) select another path (sub-optimum) (10) go to step (7) else (11) end (12) RSU s sends packets to RSU d (13) if D is covered by a RSU (14) deliver packets to RSU d in V2R mode else machine learning system (15) (1) ML1 predicts D's turning direction (16) (2) ML2 predicts RSU n (highest possibility) (17) (3) ML3 predicts travelling path (18) two-way mode transfer fuzzy-rule-based wireless transmission method (19) if D fails to receive packets after threshold time (20) select RSU n again (second highest possibility) (21) go to step (17) else (22) end Algorithm 1…”
Section: Proposed Modelmentioning
confidence: 99%
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“…More detailed information will be described in next part. (1) initialization: determine the relationship between RSUs and vehicles periodically (2) if S is covered by a RSU (3) deliver packets to RSU s in V2R mode else fuzzy-rule-based wireless transmission method vehicle-based short-term vehicle speed prediction (4) (1) find potential paths (5) (2) evaluate potential paths (6) (3) determine wireless transmission path (optimum) (7) S sends packets to RSU s (8) if RSU fails to receive packets after threshold time (9) select another path (sub-optimum) (10) go to step (7) else (11) end (12) RSU s sends packets to RSU d (13) if D is covered by a RSU (14) deliver packets to RSU d in V2R mode else machine learning system (15) (1) ML1 predicts D's turning direction (16) (2) ML2 predicts RSU n (highest possibility) (17) (3) ML3 predicts travelling path (18) two-way mode transfer fuzzy-rule-based wireless transmission method (19) if D fails to receive packets after threshold time (20) select RSU n again (second highest possibility) (21) go to step (17) else (22) end Algorithm 1…”
Section: Proposed Modelmentioning
confidence: 99%
“…The number of nearest neighbors is often chosen empirically by crossvalidation or domain experts in practice [33]. Therefore, a simple empirical or experimental test [3][4][5] is sufficient to find a suitable K-value. KNN considers the correlation between the testing vehicle and other vehicles by selecting the K-nearest neighbors according to the Euclidean distance metrics, but the K selected neighbors are treated equally without considering their differences according to (16).…”
Section: Relay Node Selectionmentioning
confidence: 99%
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“…If the Convergence speed is improved to its best, then the Routers can form an internetwork between cars easily [9,21]. If the inter-network can be formed without any delay, then the message can be easily transferred to the desired destination from the sender [20]. Hence, if these two issues can overcome the route discovery can no longer remain a tedious task in VANET.…”
Section: Visionmentioning
confidence: 99%