Comprehensive Mobility Prediction Based Clustering Algorithm for Ad Hoc UAV Networks

Yu, Yunlong; Ru, Le; Fang, Kun; Jia, Xufeng

doi:10.2991/icmmct-16.2016.121

Cited by 13 publications

(33 citation statements)

References 17 publications

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“…The author used a k-means algorithm to enhance the network lifetime by finding optimal cluster. Similarly, in [10] authors presented the Bio-Inspired Mobility Prediction Clustering (BIMPC) algorithm for the cluster formation and maintenance of large scale UAV networks. However, in both schemes, the authors did not consider the randomness and high mobility patterns of the UAVs.…”

Section: Literature Reviewmentioning

confidence: 99%

Mobility and Location-Aware Stable Clustering Scheme for UAV Networks

2020

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In Unmanned Aerial Vehicle (UAV) networks, mobility of the UAV and the corresponding network dynamics cause frequent network adaptation. One key challenge caused by this in Flying Ad-hoc Network (FANET) is how to maintain the link stability such that both the packet loss rate and network latency can be reduced. Clustering of UAVs could effectively improve the performance of large-scale UAV swarm. However, the use of conventional clustering schemes in dynamic and high mobility FANET will lead to more link outages. Besides, frequent updates of cluster structure would cause the instability of network topology and the increase of control overhead and latency. To solve this problem, we propose a locationbased k-means UAV clustering algorithms by incorporating the mobility and relative location of the UAVs to enhance the performance and reliability of the UAV network with limited resource. The objective of the proposed Mobility and Location-aware Stable Clustering (MLSC) mechanism is to enhance the stability and accuracy of the network by reducing unnecessary overheads and network latency through incorporating several design factors with minimum resource constraints. Furthermore, we derive the relationship between the maximum coverage probability of Cluster Head (CH) and cluster size to find the optimal cluster size to minimize the network overhead. Our simulation results show that the proposed MLSC scheme significantly reduces the network overheads, and also improves packet delivery ratio and network latency as compared to the conventional clustering methods. INDEX TERMS Unmanned aerial vehicles (UAVs), coverage probability, stable clustering, k-means clustering.

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Section: Literature Reviewmentioning

confidence: 99%

Mobility and Location-Aware Stable Clustering Scheme for UAV Networks

2020

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“…In Reference [102], AI is used to form clusters and improve energy efficiency. In Reference [103], a swarm-based approach is applied to routing in a multi-UAV swarm scenario.…”

Section: What Are the Roles Of Artificial Intelligence In Fanets?mentioning

confidence: 99%

“…Yu et al [103] propose a hybrid routing scheme that enables nodes in a swarm of UAVs (or multi-UAV swarm) to form clusters and to establish a route in which nodes are geographically nearer to each other in order to increase link expiration time. The main objective is to enhance route stability (O.1) in FANETs.…”

Section: Enhancing Routing Performance Using the Node Density Of Uavsmentioning

confidence: 99%

“…Since clustering can increase network lifetime, it can increase route stability and lifetime [139]. While clustering schemes have been proposed for FANETs [103], there is a lack of focus on scenarios under ultra-densification. Since the network densification changes with geographical locations and time, further investigation could be pursued to perform clustering using context-aware approaches, such as artificial intelligence approaches and bio-inspired algorithms [140].…”

Section: Performing Clustering For Supporting Routing In Multi-uavsmentioning

confidence: 99%

“…In addition to high dynamicity (X.1) of network topology, high cost (X.2), and low residual energy (X.3), there are two new challenges: (a) high mobility degree that causes the next geographical locations of nodes to become unpredictable and (b) movement in the three-dimensional space, which differs from the two-dimensional space in MANETs and VANETs [137]. Nevertheless, some routing schemes have been investigated under two-dimensional space in FANETs, including those in References [102,103,112]. To the best of our knowledge, an extensive mobility model that covers the two new challenges are yet to be investigated to support the investigation of routing in FANETs [141]; nevertheless, movement with high mobility degree in a three-dimensional space is the core characteristic of FANETs.…”

Section: Enhancing Mobility Models For the Investigation Of Routing Imentioning

confidence: 99%

See 2 more Smart Citations

Routing Schemes in FANETs: A Survey

Khan

Yau

Noor

et al. 2019

Sensors

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Flying ad hoc network (FANET) is a self-organizing wireless network that enables inexpensive, flexible, and easy-to-deploy flying nodes, such as unmanned aerial vehicles (UAVs), to communicate among themselves in the absence of fixed network infrastructure. FANET is one of the emerging networks that has an extensive range of next-generation applications. Hence, FANET plays a significant role in achieving application-based goals. Routing enables the flying nodes to collaborate and coordinate among themselves and to establish routes to radio access infrastructure, particularly FANET base station (BS). With a longer route lifetime, the effects of link disconnections and network partitions reduce. Routing must cater to two main characteristics of FANETs that reduce the route lifetime. Firstly, the collaboration nature requires the flying nodes to exchange messages and to coordinate among themselves, causing high energy consumption. Secondly, the mobility pattern of the flying nodes is highly dynamic in a three-dimensional space and they may be spaced far apart, causing link disconnection. In this paper, we present a comprehensive survey of the limited research work of routing schemes in FANETs. Different aspects, including objectives, challenges, routing metrics, characteristics, and performance measures, are covered. Furthermore, we present open issues.

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The Study of Cluster-Based Energy-Efficient Algorithms of Flying Ad-Hoc Networks

Sharma

Singh

2023

Lecture Notes in Networks and Systems

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Comprehensive Mobility Prediction Based Clustering Algorithm for Ad Hoc UAV Networks

Cited by 13 publications

References 17 publications

Mobility and Location-Aware Stable Clustering Scheme for UAV Networks

Mobility and Location-Aware Stable Clustering Scheme for UAV Networks

Routing Schemes in FANETs: A Survey

The Study of Cluster-Based Energy-Efficient Algorithms of Flying Ad-Hoc Networks

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