Enhancing the broadcast process in mobile ad hoc networks using community knowledge

Schleich, Julien; Herbiet, Guillaume-Jean; Ruiz, Patricia; Bouvry, Pascal; Wagener, Jérôme; Bicheler, Paul; Guinand, Frédéric; Chaumette, Serge

doi:10.1145/2069000.2069005

Cited by 2 publications

(3 citation statements)

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“…In Schleich et al [2011] community knowledge is used for improving the coverage achieved by message dissemination. Nodes that belong to two different communities always rebroadcast the message, although the forwarding policy prevents retransmission.…”

Section: Variable Transmission Rangementioning

confidence: 99%

“…However, nodes in RandomCast ] [Cartigny et al 2005] local 2-hop yes yes source-independent [Wang et al 2010] PABLO [Chen et al 2003] local 2-hop no no source-independent INOP [Chiganmi et al 2008] local 2-hop no no source-indep./dep. [Reumerman and Runi 2005] local 1-hop yes no source-independent EROB [Park and Yoo 2013] local 1-hop no yes source-independent RandomCast [Lim et al 2009] EDB [Ruiz and Bouvry 2010b] local 1-hop no no source-independent AEDB [Ruiz and Bouvry 2010a] [Schleich et al 2011] consistently operate in PS mode. The transmitter can specify the level of overhearing for unicast packets.…”

Section: Variable Transmission Rangementioning

confidence: 99%

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Survey on Broadcast Algorithms for Mobile Ad Hoc Networks

Ruiz

Bouvry

2015

ACM Comput. Surv.

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Networking at any time and any place paves the way for a large number of possible applications in ad hoc networks, from disaster relief in remote areas to network extension. Thus, for the past decades, many works have been proposed trying to make ad hoc networks a reality. The importance of broadcasting in networking and the broadcast nature of the wireless medium have encouraged researchers to join their efforts on designing efficient dissemination algorithms for Mobile Ad Hoc Networks (MANETs). The many different challenges that MANETs face, such as limited network resources, network partitions, or energy restrictions, gave rise to many different approaches to overcome one or more of those problems. Therefore, literature reveals a huge variety of techniques that have been proposed for efficient message dissemination. In this article, we make an in-depth review of the existing state-of-the-art techniques, as well as propose a new taxonomy that provides a global overview of the most relevant existing algorithms.

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Section: Variable Transmission Rangementioning

confidence: 99%

Section: Variable Transmission Rangementioning

confidence: 99%

Survey on Broadcast Algorithms for Mobile Ad Hoc Networks

Ruiz

Bouvry

2015

ACM Comput. Surv.

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“…It was studied in the single-level swarm context by Schleich et al in [19] proposed the CEDBBT broadcasting algorithm for FANETs that relies on a community detection algorithm.…”

Section: Related Workmentioning

confidence: 99%

Connectivity Stability in Autonomous Multi-level UAV Swarms for Wide Area Monitoring

Danoy

Brust

Bouvry

2015

Proceedings of the 5th ACM Symposium on Development and Analysis of Intelligent Vehicular Networks and Applications

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Many different types of unmanned aerial vehicles (UAVs) have been developed to address a variety of applications ranging from searching and mapping to surveillance. However, for complex wide-area surveillance scenarios, where fleets of autonomous UAVs must be deployed to work collectively on a common goal, multiple types of UAVs should be incorporated forming a heterogeneous UAV system. Indeed, the interconnection of two levels of UAVs-one with high altitude fixed-wing UAVs and one with low altitude rotarywing UAVs-can provide applicability for scenarios which cannot be addressed by either UAV type. This work considers a bi-level flying ad hoc networks (FANETs), in which each UAV is equipped with ad hoc communication capabilities, in which the higher level fixed-wing swarm serves mainly as a communication bridge for the lower level UAV fleets, which conduct precise information sensing. The interconnection of multiple UAV types poses a significant challenge, since each UAV level moves according to its own mobility pattern, which is constrained by the UAV physical properties. Another important challenge is to form network clusters at the lower level, whereby the intra-level links must provide a certain degree of stability to allow a reliable communication within the UAV system. This article proposes a novel mobility model for the low-level UAVs that combines a pheromone-based model with a multi-hop clustering algorithm. The pheromones permit to focus on the least explored areas with the goal to optimize the coverage while the multi-hop clustering algorithm aims at keeping a stable and connected network. The proposed model works online and is fully distributed. The connection stability is evaluated against different measurements such as stability coefficient and volatility. The performance of the proposed model is compared to other state-of-the-art contributions using simulations. Experimental results demonstrate the ability of the proposed mobility model to significantly improve the Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org. DIVANet'15, November 2-6, 2015, Cancun, Mexico.c 2015 ACM. ISBN 978-1-4503-3760-1/15/11 ...$15.00.

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Enhancing the broadcast process in mobile ad hoc networks using community knowledge

Cited by 2 publications

References 24 publications

Survey on Broadcast Algorithms for Mobile Ad Hoc Networks

Survey on Broadcast Algorithms for Mobile Ad Hoc Networks

Connectivity Stability in Autonomous Multi-level UAV Swarms for Wide Area Monitoring

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