Automated Field Monitoring by a Group of Light Aircraft-Type UAVs

Pantelej, Ekaterina; Gusev, Nikolay; Voshchuk, G. Yu.; Zhelonkin, A. I.

doi:10.1007/978-3-030-01821-4_37

Cited by 4 publications

(1 citation statement)

References 6 publications

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“…Thus FANETs can not only improve coverage area but assist in rescue operations. Multiple networks of drones have the ability to monitor the crop in agriculture [5], automated surveillance in the field [6], and various public venues [7]. However, the reliable connections and power limitations of these small devices are major challenges to investigate.…”

Section: Introductionmentioning

confidence: 99%

Link and Loss Aware GW-COOP Routing Protocol for FANETs

Hameed

Alyahya²,

Minhas

et al. 2021

IEEE Access

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The dynamic network topology of Flying Ad hoc Networks (FANETs) leads to challenges during communications. This becomes complicated when dealing with multiple transmission paths and relays. Conventional routing protocols proposed fail to address the dynamic issues inherent in FANETs. This work addresses it by diversifying the selection of relay in order to establish the significance of cooperative diversity technique. Inspiration is taken from bio inspired computing which assists in finding solutions to several challenging tasks. The natural behavior of the different species leads to elevated design concepts of protocols. This paper proposes GW-COOP (Gray Wolf Algorithm using Cooperative Diversity Technique) routing protocol for FANETs. This protocol consists of Gray Wolf Optimizer (GWO) that drives gray wolves' social hierarchy and collaboration technique. First, we opt the design implementation of gray wolves natural posture GWO to handle flying node requirements. Second, we envision the idea of cooperative diversity using two relays to sustain either of the source-to-destination links. The design of protocol is novel in that previously such cooperation techniques are not employed in FANETs. Moreover, the concept of two relays with bio inspired algorithm is first time proposed here. In order to establish the performance of the GW-COOP protocol, this paper compares GW-COOP with two different protocols BAT-COOP (Bat Algorithm using Cooperative Diversity Technique) and BAT-FANETs protocols. The simulation results show that the GW-COOP protocol outperforms BAT-COOP protocol in terms of transmission loss, energy consumption, link delay and packet loss ratio. The results indicate improved GW-COOP performance over BAT-FANET and BAT-COOP. Approximately 67% and 52% reduction is observed in transmission losses compared to BAT-FANET and BAT-COOP respectively. Moreover, a decrease of 61% and 54% in terms of energy consumption, 24% and 9% in terms of link delay, and 58% and 48% reduction in terms of packet loss ratio, is also observed.INDEX TERMS FANETs, Cooperative diversity, Routing protocol, Gray wolf optimization, bio-inspired protocol I.

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Section: Introductionmentioning

confidence: 99%

Link and Loss Aware GW-COOP Routing Protocol for FANETs

Hameed

Alyahya²,

Minhas

et al. 2021

IEEE Access

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Comparative Analysis of Toroidal Propeller and APC Propeller Performance for UAV using Computational Fluid Dynamics

Jayarajan

2024

2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)

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A Review on UAV-Based Applications for Precision Agriculture

2019

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Emerging technologies such as Internet of Things (IoT) can provide significant potential in Smart Farming and Precision Agriculture applications, enabling the acquisition of real-time environmental data. IoT devices such as Unmanned Aerial Vehicles (UAVs) can be exploited in a variety of applications related to crops management, by capturing high spatial and temporal resolution images. These technologies are expected to revolutionize agriculture, enabling decision-making in days instead of weeks, promising significant reduction in cost and increase in the yield. Such decisions enable the effective application of farm inputs, supporting the four pillars of precision agriculture, i.e., apply the right practice, at the right place, at the right time and with the right quantity. However, the actual proliferation and exploitation of UAVs in Smart Farming has not been as robust as expected mainly due to the challenges confronted when selecting and deploying the relevant technologies, including the data acquisition and image processing methods. The main problem is that still there is no standardized workflow for the use of UAVs in such applications, as it is a relatively new area. In this article, we review the most recent applications of UAVs for Precision Agriculture. We discuss the most common applications, the types of UAVs exploited and then we focus on the data acquisition methods and technologies, appointing the benefits and drawbacks of each one. We also point out the most popular processing methods of aerial imagery and discuss the outcomes of each method and the potential applications of each one in the farming operations.

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Automated Field Monitoring by a Group of Light Aircraft-Type UAVs

Cited by 4 publications

References 6 publications

Link and Loss Aware GW-COOP Routing Protocol for FANETs

Link and Loss Aware GW-COOP Routing Protocol for FANETs

Comparative Analysis of Toroidal Propeller and APC Propeller Performance for UAV using Computational Fluid Dynamics

A Review on UAV-Based Applications for Precision Agriculture

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