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Erschienen in: The Journal of Supercomputing 6/2018

31.01.2018

Self-adaptive risk-aware routing in opportunistic network

verfasst von: Narges Arastouie, Masoud Sabaei

Erschienen in: The Journal of Supercomputing | Ausgabe 6/2018

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Abstract

An opportunistic network is a wireless ad hoc network that has frequently intermittent connectivity due to nodes’ mobility. These spare, unpredictable networks aim to solve the prolonged delay paths by a store-carry-forward scheme. It is difficult to determine the appropriate nodes to forward the messages since there are few opportunistic contacts. The nodes might decide based on the obtained data of the network as a guide to reach a destination. The aforementioned technique is not helpful in case the rate of change in the network topology is higher than the rate of data gathering due to usage restrictions and uncertain available information/knowledge of the future contacts. In this paper, to cope with the challenges imposed by the un-deterministic environment, a risk assessment strategy is considered to evaluate the short and long-term impact of each decision to find the optimal node/paths. This routing scheme can take advantage of the unanticipated connection to make the routing more flexible in short time and with less buffer usage. Our proposed risk assessment algorithms that are based on MALP are mixed with the knowledge about buffer management and network capacity. The prioritized messages to be disseminated as well as better decision to take a risk are the main contributions of this paper. Moreover, the self-adaptive threshold, distributed PID controller, is considered to tackle different threshold levels for each node individually. Numerical results prove that the proposed method drastically increases the delivery ratio and also minimizes the risk of exceeding resource consumption and packet loss.

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Metadaten
Titel
Self-adaptive risk-aware routing in opportunistic network
verfasst von
Narges Arastouie
Masoud Sabaei
Publikationsdatum
31.01.2018
Verlag
Springer US
Erschienen in
The Journal of Supercomputing / Ausgabe 6/2018
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI
https://doi.org/10.1007/s11227-018-2264-2

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