Probabilistic broadcast for dense AUV networks

Koseoglu, Mehmet; Bereketli, Alper; Yazgi, Ilkay; Yeni, Burcu

doi:10.1109/oceans.2016.7761118

Cited by 10 publications

(6 citation statements)

References 8 publications

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“…An adaptive probabilistic mechanism based on the wave propagation is proposed in [50], while an energy efficient routing algorithm using probabilistic flooding is proposed in [51]. Probabilistic flooding has also been proposed for the area of nanonetworks by [52,53], and underwater environments by [54], whereas vehicular networks have also been the focus by [55,56]. An adaptive version for multi-path routing purposes is proposed by [57].…”

Section: Past Related Workmentioning

confidence: 99%

Adapting Probabilistic Flooding in Energy Harvesting Wireless Sensor Networks

Koufoudakis

Οικονόμου

Tsoumanis

2018

JSAN

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Technological advantages in energy harvesting have been successfully applied in wireless sensor network environments, prolonging network’s lifetime, and, therefore, classical networking approaches like information dissemination need to be readdressed. More specifically, Probabilistic Flooding information dissemination is revisited in this work and it is observed that certain limitations arise due to the idiosyncrasies of nodes’ operation in energy harvesting network environments, resulting in reduced network coverage. In order to address this challenge, a modified version of Probabilistic Flooding is proposed, called Robust Probabilistic Flooding, which is capable of dealing with nodes of about to be exhausted batteries that resume their operation after ambient energy collection. In order to capture the behavior of the nodes’ operational states, a Markov chain model is also introduced and—based on certain observations and assumptions presented here—is subsequently simplified. Simulation results based on the proposed Markov chain model and a solar radiation dataset demonstrate the inefficiencies of Probabilistic Flooding and show that its enhanced version (i.e., Robust Probabilistic Flooding) is capable of fully covering the network on the expense of increased termination time in energy harvesting environments. Another advantage is that no extra overhead is introduced regarding the number of disseminated messages, thus not introducing any extra transmissions and therefore the consumed energy does not increase.

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Section: Past Related Workmentioning

confidence: 99%

Adapting Probabilistic Flooding in Energy Harvesting Wireless Sensor Networks

Koufoudakis

Οικονόμου

Tsoumanis

2018

JSAN

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“…Although, it is a simple mechanism suitable for the wired, small scale network paradigm of the early '80s and capable of reaching all network nodes in a deterministic manner (i.e., global outreach), it requires a large amount of usually redundant messages in the network, also known as the broadcast storm problem [80], thus, consuming valuable network resources (e.g., energy, bandwidth etc). An increasing volume of research attention has been observed recently regarding probabilistic flooding (e.g., vehicular networks [91], energy balancing [92], underwater broadcasting [93], advanced probabilistic flooding [94], nanonetworks [95], [96]). Probabilistic flooding can be seen as a suitable alternative for pruning transmissions by employing some fixed probability for message forwarding among neighbor nodes [90].…”

Section: A3 Past Related Workmentioning

confidence: 99%

“…The simulations on two and three dimensional grid structures verify the existence of phase transition phenomena and it is shown that the minimum forwarding probability that allows for global outreach depends on the number of nodes within the network, the node density and the transmission model. Probabilistic flooding is also studied for underwater environments by Koseoglu et al [93]. In particular, they investigate the performance of probabilistic broadcast for autonomous underwater vehicles (AUV) networks where the AUVs are not aware of locations of their neighbors.…”

Section: A31 Probabilistic Floodingmentioning

confidence: 99%

“…Simulations are performed on Erdős-Rényi [100] (ER) and Geometric Random Graph [135] (GRG) topologies. For the simulation purposes, the simplified model of circular radio communication is considered as it is a common practice in the literature (e.g., [91,92,93,118,128]). The particular model does not take into account certain aspects like radio irregularities, e.g., [136], etc.…”

Section: A42 Proposed Approaches and Solutionsmentioning

confidence: 99%

“…log n πn , where ǫ → 0 + . The particular graph model is widely used in the literature to model ad-hoc and wireless sensor networks (e.g., [91,92,93,118,128]).…”

Section: Bipartite Graphmentioning

confidence: 99%

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Analysis of probabilistic information dissemination in wireless networks employing algebraic graph theory elements

Koufoudakis¹,

Κουφουδάκης²

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Η διάχυση πληροφοριών στα δίκτυα, αποτελούσε και αποτελεί ένα από τα μεγαλύτερα πεδία μελέτης της επιστημονικής κοινότητας. Κατά το παρελθόν, τα δίκτυα απαρτίζονταν κυρίως από ένα σχετικά μικρό αριθμό κόμβων με προκαθορισμένη δομή και ιεραρχία. Με το πέρασμα των χρόνων, τόσο η μορφή τους όσο και το μέγεθός τους άλλαξαν ριζικά. Έτσι σήμερα έχουμε δίκτυα πολύ μεγάλου μεγέθους τα οποία χαρακτηρίζονται από υψηλή κινητικότητα των κόμβων τους, από την ανυπαρξία προκαθορισμένης δομής αλλά και από το γεγονός ότι στις περισσότερες των περιπτώσεων υπάρχει ισοτιμία μεταξύ των κόμβων. Η διάχυση επομένως μιας πληροφορίας σε ένα σύγχρονο δίκτυο, αποτελεί μεγαλύτερη πρόκληση συγκριτικά με το παρελθόν, αφού υπεισέρχονται νέοι παράγοντες που αυξάνουν την πολυπλοκότητα της διαδικασίας. Μέθοδοι όπως το κλασσικό flooding δεν μπορούν να εφαρμοστούν στα σημερινά δίκτυα, αφού οι άσκοπες μεταδόσεις μηνυμάτων σπαταλούν πολύτιμους πόρους. Είναι επομένως αναγκαίο, η διάχυση της πληροφορίας να γίνεται με τέτοιο τρόπο ώστε να ελαχιστοποιούνται οι άσκοπες μεταδόσεις και ταυτόχρονα να μεγιστοποιείται ο αριθμός των κόμβων που τελικά την έχουν λάβει. Σκοπός της συγκεκριμένης διατριβής θα είναι η εύρεση βελτιστοποιημένων αλγόριθμων για την διάχυση των πληροφοριών στα σύγχρονα δίκτυα, με τρόπους οι οποίοι θα εξοικονομούν πολύτιμους πόρους, αυξάνοντας έτσι την διαθεσιμότητα των κόμβων και την διάρκεια ζωής του δικτύου.

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Accurate deep and direction classification model based on the antiprism graph pattern feature generator using underwater acoustic for defense system

Yaman

Tuncer

2022

Multimed Tools Appl

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Probabilistic broadcast for dense AUV networks

Cited by 10 publications

References 8 publications

Adapting Probabilistic Flooding in Energy Harvesting Wireless Sensor Networks

Adapting Probabilistic Flooding in Energy Harvesting Wireless Sensor Networks

Analysis of probabilistic information dissemination in wireless networks employing algebraic graph theory elements

Accurate deep and direction classification model based on the antiprism graph pattern feature generator using underwater acoustic for defense system

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