Abstract
OppNets is a subset of MANET in which there is no point to point connection between devices. Despite challenges of intermittent connectivity and unpredictable mobility characteristics, mobile nodes need to communicate and share valuable information without infrastructure. The routing strategy in OppNet follows a store-carry-forward paradigm, in which a node store messages in its buffer, carry the messages as it moves about its daily activities and opportunistically relay the messages one after another upon meeting their target destinations. This paper presents a movement pattern extraction, a new approach for extracting regular movement pattern of nodes from existing traces to predict a node future location. Human movement is predictable with a high degree of certainty that people visit certain locations and have the opportunity to meet other people on a regular basis. The proposed method incorporate message lifetime and previous history of encounter to extract locations from the GPS trace with respect to message lifetime using Neural Network to predict future location visit of nodes. Prediction method can be used in geocast routing schemes to determine the node’s contribution in forwarding a message to a destination location. Experiment result indicates that the proposed method achieves significant performance over existing methods in terms of accuracy and stability in prediction.
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Abali, A.M., Ithnin, N.B., Dawood, M., Ebibio, T.A., Gadzama, W.A., Ghaleb, F.A. (2020). Movement Pattern Extraction Method in OppNet Geocast Routing. In: Saeed, F., Mohammed, F., Gazem, N. (eds) Emerging Trends in Intelligent Computing and Informatics. IRICT 2019. Advances in Intelligent Systems and Computing, vol 1073. Springer, Cham. https://doi.org/10.1007/978-3-030-33582-3_67
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