Applying Message Forwarding and Replication to Multi-UAV Message Ferry Networks

Harounabadi, Mehdi; Mitschele‐Thiel, Andreas

doi:10.1007/s11036-018-1038-7

Cited by 13 publications

(8 citation statements)

References 11 publications

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“…Else, the MAV checks whether the destination is its own neighbor node. If the destination is the neighbor, the packet is forwarded to the destination, and the algorithm ends (as shown in steps [6][7][8][9][10][11][12][13][14]. Otherwise, the MAV that is the optimal neighbor i is found by FindBestNeighbour function (step 15).…”

Section: The Dtn Est Algorithm Based On Location Estimationmentioning

confidence: 99%

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Delay Tolerant Packet Forwarding Algorithm Based on Location Estimation for Micro Aerial Vehicle Networks

2020

KSII TIIS

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In search and rescue mission, micro aerial vehicles (MAVs) are typically used to capture image and video from an aerial perspective and transfer the data to the ground station. Because of the power limitation, a cluster of MAVs are required for a large search area, hence an ad-hoc wireless network must be maintained to transfer data more conveniently and fast. However, the unstable link and the intermittent connectivity between the MAVs caused by MAVs' movement may challenge the packet forwarding. This paper proposes a delay tolerant packet forwarding algorithm based on location estimation for MAV networks, called DTN est algorithm. In the algorithm, ferrying MAVs are used to transmit data between MAVs and the ground station, and the locations of both searching MAVs and ferrying MAVs are estimated to compute the distances between the MAVs and destination. The MAV that is closest to the destination is selected greedy to forward packet. If a MAV cannot find the next hop MAV using the greedy strategy, the packets will be stored and re-forwarded once again in the next time slot. The experiment results show that the proposed DTN est algorithm outperforms the typical DTN geo algorithm in terms of packet delivery ratio and average routing hops.Micro aerial vehicles (MAVs) which weigh only a few kilograms, have been widely used in many applications, such as aerial photography, agricultural monitoring, disaster relief, military and so on. MAVs are usually equipped with computing and wireless communication devices, sensors and cameras to collect image and video and transmit them to the ground station [1] [2]. With the characteristics of low cost, portability, and no casualty risk, MAVs have become a hotspot in both academic and industrial fields.A cluster of MAVs is usually required to complete large and complex tasks coordinately, since the energy, flight distance and communication range of each MAV are limited. The cluster of MAVs is typically organized as an ad-hoc multi-hop network to transmit data from MAVs to the ground station [3]. However, packet forwarding is a challenge in practical MAV networks because of unstable links and intermittent connectivity of the network caused by rapid movements of MAVs [4].Since global positioning system (GPS) and inertial measurement unit provide the location information of MAVs [5][6], Mozaffari et al. proposed a geographic routing based method to forward packets to the nodes that are spatially closer to the destination [7]. In practical applications, the motion and the unstable link of MAVs may change the topology of the network frequently, but these geographic routing methods are not adequate for MAV networks that face intermittent connectivity [8].To cope with intermittent connectivity, delay tolerant networking (DTN) is a feasible approach. Using the communication opportunity formed by nodes flying into each other's transmission range to transmit messages hop by hop, Raffelsberger et al. integrated the "store-carry-forward" with the adaptive routing method [9]. However, traditi...

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Section: The Dtn Est Algorithm Based On Location Estimationmentioning

confidence: 99%

“…A single packet forwarding strategy cannot fit well with MAV network. Fortunately, it is found that properly combining different strategies can improve the performance of packet forwarding [11]. Inspire by this hybrid strategy, this paper combines the geographic location based routing method and the "store-carry-forward" concept in DTN.…”

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confidence: 99%

Delay Tolerant Packet Forwarding Algorithm Based on Location Estimation for Micro Aerial Vehicle Networks

2020

KSII TIIS

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“…Recently, there have been some studies on UAV-assisted routing [7], [10]. These works aim to solve the reliable data delivery problem in the UAV-assisted ground networks.…”

Section: Related Workmentioning

confidence: 99%

Time-Dependent Ad-Hoc Routing Structure for Delivering Delay-Sensitive Data Using UAVs

et al. 2020

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In disaster scenarios where communication networks have broken down, it is important to ensure a reliable data delivery from an emergency operations center to the local target nodes within its effective time limit. We propose a hybrid data delivery mechanism that exploits the load-carry-and-delivery by UAVs with a mixture of localized ad-hoc routing over partially connected terrestrial networks. We aim to achieve reliable on-time data delivery to the target nodes, while preserving the low routing cost. Our proposed routing methodology consists of three steps: 1) localized network construction, 2) network probing by UAVs, and 3) localized ad-hoc routing based on a dynamic depth routing tree depending on the data urgency. Here, we present an innovative cost-effective local data sharing structure called localized minimal routing tree that balances with the direct data delivery by UAVs. After the initial network setup and probing procedure, each UAV makes a series of near-optimal decisions of which grid points to visit considering its localized network topology and data urgency. Our time-dependent routing mechanism dynamically decides data recipient nodes to serve more urgent data delivery with a higher priority at a time. The simulation experiments validated our combined path planning and routing approach, achieving 71% higher reliability than the best possible performance by using only the network nodes and consuming 20% lower energy than the UAV-only approach, while maintaining high reliability. Thus, our work makes a strong case for systematically combining the two approaches. INDEX TERMS Time-dependent routing, delay constraints, load-carry-and-delivery, vehicular ad-hoc networks, unmanned aerial vehicles, ad-hoc data delivery.

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“…The Delay Tolerant Network (DTN) is devised for handling extreme network platforms. However, the DTN routing technique produced high overhead due to multiple routing techniques 11,19 . Similarly, the optimization algorithms like Stochastic gradient descent–whale optimization algorithm, 20 Firefly, 21 and many other methods 22,23 are used for the performance enhancement.…”

Section: Introductionmentioning

confidence: 99%

Intelligent agent and optimization‐based deep residual network to secure communication in UAV network

Francelin

Daniel

Velliangiri³

2022

Int J of Intelligent Sys

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Unmanned Aerial Vehicle (UAV) is adapted as a novel unit in upcoming wireless infrastructures wherein UAVs can play various roles in different applications. The UAV is utilized for navigating commands to attain surveillance. However, routing and localization are the major issues that own higher mobility and unstable links. This paper devises a novel technique for initiating secured communication in a UAV network (UAVN). At first, the simulation of the UAVN is done. After that, the data transmission is carried out amongst nodes using a routing path; hence, an optimal routing path is formed using the newly devised Tunicate Swarm Political Optimization (TSPO) algorithm. The newly devised TSPO algorithm integrates the Tunicate Swarm Algorithm and Political Optimizer. In addition, the data communication is done using an evaluation and monitoring agent. In addition, the malicious detection is done by a decision‐making agent using the deep residual network (DRN). Here, the newly devised TSPO algorithm is used to train the deep residual. The input parameters considered in the DRN are round trip time, signal strength, packet delivery, packet size, and the number of incoming packets. Once a DRN classifies the attack and genuine users, the defensive agent is employed for attack mitigation. Thus, the attacks in data are mitigated by the defensive agents. The newly devised TSPO‐based DRN obtained enhanced performance with the smallest delay of 0.0014 s, the highest detection rate of 0.9325%, and the highest packet delivery ratio of 11491.2%.

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Applying Message Forwarding and Replication to Multi-UAV Message Ferry Networks

Cited by 13 publications

References 11 publications

Delay Tolerant Packet Forwarding Algorithm Based on Location Estimation for Micro Aerial Vehicle Networks

Delay Tolerant Packet Forwarding Algorithm Based on Location Estimation for Micro Aerial Vehicle Networks

Time-Dependent Ad-Hoc Routing Structure for Delivering Delay-Sensitive Data Using UAVs

Intelligent agent and optimization‐based deep residual network to secure communication in UAV network

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