A discrete stochastic process for coverage analysis of autonomous UAV networks

Yanmaz, Evşen; Costanzo, Carmelo; Bettstetter, Christian; Elmenreich, Wilfried

doi:10.1109/glocomw.2010.5700247

Cited by 38 publications

(18 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Yanmaz et al [9] study single-hop and multi-hop scenarios. Results show that if the network density is high enough, the coverage optimization has less impact on the good performance.…”

Section: Related Workmentioning

confidence: 99%

Coverage Optimization with Connectivity Preservation for UAV Swarms Applying Chaotic Dynamics

Rosalie

Brust

Danoy

et al. 2017

2017 IEEE International Conference on Autonomic Computing (ICAC)

View full text Add to dashboard Cite

Abstract-Cooperative usage of multiple UAVs as a swarm can deliver high-quality surveillance performance. However, the communication capabilities within the UAV swarm must be maintained for local data propagation to swarm members in favor of achieving an efficient global behavior. In this paper, we address the problem of optimizing two adversary criteria for such a UAV swarm: (a) maximizing the area coverage, while (b) preserving network connectivity. Our approach, called CACOC 2 , solves the problem with a novel chaotic ant colony optimization approach, which combines an Ant Colony Optimization approach (ACO) with a chaotic dynamical system. CACOC 2 employs swarming behavior to obtain UAV clustering that result in maximized area coverage and preserved network connectivity. We show by extensive simulations how the size of the UAV swarm influences the coverage and connectivity. A metrics comparison chart shows the correlation of coverage and connectivity metrics.

show abstract

“…Yanmaz et al [9] study single-hop and multi-hop scenarios. Results show that if the network density is high enough, the coverage optimization has less impact on the good performance.…”

Section: Related Workmentioning

confidence: 99%

Coverage Optimization with Connectivity Preservation for UAV Swarms Applying Chaotic Dynamics

Rosalie

Brust

Danoy

et al. 2017

2017 IEEE International Conference on Autonomic Computing (ICAC)

View full text Add to dashboard Cite

show abstract

“…Nevertheless, they can only provide probabilistic guarantees for the goal achievement. Since the goal in this category is to improve coverage, the authors in [29] and in [30] craved to reduce the overlap between the covered areas by different mobile nodes, thus using a limited number of mobile nodes efficiently. More precisely, they assumed that there is a force between mobile nodes that causes them to repel each other.…”

Section: Coverage Oriented Mobility Modelsmentioning

confidence: 99%

Implementing an emerging mobility model for a fleet of UAVs based on a fuzzy logic inference system

Messous¹,

Sedjelmaci²,

Senouci³

2017

Pervasive and Mobile Computing

View full text Add to dashboard Cite

To cite this version:Mohamed-Ayoub Messous, Hichem Sedjelmaci, Sidi-Mohammed Senouci. Implementing an emerging mobility model for a fleet of UAVs based on a fuzzy logic inference system. Pervasive and Mobile Computing, Elsevier, 2017, <10.1016/j.pmcj.2017 Implementing an emerging mobility model for a fleet of UAVs based on a fuzzy logic inference system In this paper, we design and implement a novel generic mobility model, named Alphabased, for a fleet of small interconnected UAVs (Unmanned Aerial Vehicles) that collaborate to explore a geographic area (battle field, research and rescue missions, surveillance applications, etc.). In fact, due to the significant impact of mobility models on the networking performance, the mobility models must realistically capture the UAV's attributes. Hence, we propose to use a combination of energy level, coverage-area and network connectivity for mobility decision-making, in contrast to the literature where only network connectivity and area coverage are investigated. On the one hand, these two metrics are very important, especially for applications where achieving the best area-coverage and maintaining network connectivity represent an essential requirement. On the other hand, energy is another equally noteworthy constraint that should be taken into account. In fact, being a crucial resource for all mobile devices and especially for UAVs, the energy becomes vital to ensure the network lifetime and mission success. As far as we know, Alphabased mobility model is the first to ever consider a combination of these three metrics within the same decision-making criterion. A distributed scheme is adopted, where each UAV determines locally its future movement based on the information it receives from its neighbors. Moreover, a novel fuzzy inference system is implemented in order to compute the values of a followship weighting parameter, named Alpha. This latter is used to choose the most suitable neighboring UAV to follow. To validate the proposed mobility model, rigorous testing has been accomplished, through simulation work. Compared to Randombased and Forces-based mobility models, the Alpha-based mobility model achieves good coverage rate while maintaining connectivity.

show abstract

“…So it has continuity. It has been shown that the missions can be completed faster with a higher number of UAVs [6]. Instead of one large radar cross-section, multi-UAV systems produce very small radar cross-sections which are more accurate and crucial for military applications [7].…”

Section: Flying Ad-hoc Network (Fanet)mentioning

confidence: 99%

Study of Ad hoc Networks with Reference to MANET, VANET, FANET

.S¹

2017

IJARCSSE

View full text Add to dashboard Cite

A wireless ad hoc network is a decentralized type of wireless network. It is a type of temporary computer-tocomputer connection. It is a spontaneous network which includes mobile ad-hoc networks (MANET), vehicular adhoc networks (VANET) and Flying ad-hoc networks (FANET). A MANET is a network that has many free or autonomous nodes often composed of mobile devices that can operate without strict top-down network administration [1]. A VANET is a sub form of MANET. It is a technology that uses vehicles as nodes in a network to create a mobile network. FANET is an ad-hoc network of flying nodes. They can fly independently or can be operated distantly. This paper discusses the characteristics of these three ad-hoc networks.

show abstract

A discrete stochastic process for coverage analysis of autonomous UAV networks

Cited by 38 publications

References 13 publications

Coverage Optimization with Connectivity Preservation for UAV Swarms Applying Chaotic Dynamics

Coverage Optimization with Connectivity Preservation for UAV Swarms Applying Chaotic Dynamics

Implementing an emerging mobility model for a fleet of UAVs based on a fuzzy logic inference system

Study of Ad hoc Networks with Reference to MANET, VANET, FANET

Contact Info

Product

Resources

About