A Localization Method Avoiding Flip Ambiguities for Micro-UAVs with Bounded Distance Measurement Errors

Guo, Qingbei; Zhang, Yuwei; Lloret, Jaime; Kantarcı, Burak; Seah, Winston K.G.

doi:10.1109/tmc.2018.2865462

Cited by 34 publications

(9 citation statements)

References 39 publications

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“…The researchers proposed several localization schemes to resolve the issue of target spots localization accurately but do not provide precise target spots without localization error. 5–7 Most of them consider the distance, localization time and signal strength to measure the localization error. The UAVs routing protocols used to locate the optimal TAs by considering communication range, topology, link expiration time, residual energy and mobility.…”

Section: Introductionmentioning

confidence: 99%

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Bio-inspired cluster–based optimal target identification using multiple unmanned aerial vehicles in smart precision agriculture

Salam

Javaid

Ahmad

2021

International Journal of Distributed Sensor Networks

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Farming is the major profession in several republics for centuries. However, due to the immigration of individuals from rural to urban, there is prevention in farming. The use of modern technology in the precision agriculture field increases productivity and also improves the exports of a country. The productivity may suffer due to different environmental factors, diseases and insects attacks on the crops, especially tomatoes. The target area (i.e. the affected crops area due to environmental factors) identification and delivery of timely information about diseases in the crops to the ground station are mandatory to make the precautionary measurements. In flying sensor networks, the localization and clustering of multiple unmanned aerial vehicles for target areas identification is a challenging task due to energy constraints, communication range, frequent change in topology, link expiration and high mobility. In this article, we proposed the localization and clustering of multiple unmanned aerial vehicles for the identification of affected target areas in the tomato crop field. The localization of unmanned aerial vehicles depends on the weights of environmental factors, that is, relative humidity, soil moisture, temperature, light intensity, NPK (nitrogen (n), phosphorus (p) and potassium (k)) and power of hydrogen (pH). A honey bee optimization approach is used for the localization and formation of multiple unmanned aerial vehicles’ cluster to accurately identify the target areas. The performance of our bio-inspired approach is compared in terms of communication overhead, packet delivery ratio, mean end-to-end delay and energy consumption with the existing swarm intelligence–based schemes and validated via a simulation. The simulation result shows that the bio-inspired approach performs better among the selected approaches.

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Section: Introductionmentioning

confidence: 99%

“…The range-based TAs identification scheme provides a more accurate and precise location as compared to range free-based localization, but the schemes may also vary based on error rate, accuracy and computation. 5…”

Section: Introductionmentioning

confidence: 99%

Bio-inspired cluster–based optimal target identification using multiple unmanned aerial vehicles in smart precision agriculture

Salam

Javaid

Ahmad

2021

International Journal of Distributed Sensor Networks

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“…So, the effective techniques are specifically designed for UAV swarms. Cauchy particle swarm optimization (CPSO) has an effective result bearing key factors enhancing effectiveness that are topology structure, parameter selection, and swarm initialization [11,12]. But in PSO, the disadvantage is that it easily falls into local optimum.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Routing and Coordination of Cluster for Unmanned Aerial Vehicle (UAV) Swarms

Abbas

Chowdhry

Saqib

et al. 2021

Mathematical Problems in Engineering

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The flying networks provide an efficient solution for a wide range of military and commercial purposes. The demand for portable and flexible communication is directed towards a quick growth in interaction among unmanned aerial vehicles (UAVs). Due to the frequent change in topology and high mobility of vehicles, routing and coordination becomes a challenging task. To maximize the throughput of the network, this study addresses the UAV swarm’s problems related to the coordination and routing and defines the proposed solution to solve these issues. For this, a network is assumed which contains an equal number of dynamic vehicles. It also presents the communication graph concept of UAVs and designs a fixed-wing UAV model to improve the efficiency of the network in terms of throughput. Furthermore, the proposed algorithm based on Cauchy particle swarm optimization (CPSO) aims towards the better performance of UAV swarms and aims to solve the combinatorial problem. The simulation results show and confirm the performance of the proposed algorithm.

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“…Unmanned aerial vehicle (UAV)-assisted Internet of Things (IoT) is a growing research theme in smart city-oriented applications [1,2]. The smart service-centric domain includes aerial delivery [3], smart healthcare, smart homes, Internet of vehicles [4], UAV simulation [5], pollution monitoring, smart agriculture, disaster management [6], industrial Internet of Things, and green mobility [7]. It has emerged as a prominent development area to revolutionize UAV-assisted IoT in daily day life [8].…”

Section: Introductionmentioning

confidence: 99%

Internet of Unmanned Aerial Vehicles: QoS Provisioning in Aerial Ad-Hoc Networks

Kumar

Kaiwartya

et al. 2020

Sensors

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Aerial ad-hoc networks have the potential to enable smart services while maintaining communication between the ground system and unmanned aerial vehicles (UAV). Previous research has focused on enabling aerial data-centric smart services while integrating the benefits of aerial objects such as UAVs in hostile and non-hostile environments. Quality of service (QoS) provisioning in UAV-assisted communication is a challenging research theme in aerial ad-hoc networks environments. Literature on aerial ad hoc networks lacks cooperative service-oriented modeling for distributed network environments, relying on costly static base station-oriented centralized network environments. Towards this end, this paper proposes a quality of service provisioning framework for a UAV-assisted aerial ad hoc network environment (QSPU) focusing on reliable aerial communication. The UAV’s aerial mobility and service parameters are modelled considering highly dynamic aerial ad-hoc environments. UAV-centric mobility models are utilized to develop a complete aerial routing framework. A comparative performance evaluation demonstrates the benefits of the proposed aerial communication framework. It is evident that QSPU outperforms the state-of-the-art techniques in terms of a number of service-oriented performance metrics in a UAV-assisted aerial ad-hoc network environment.

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A Localization Method Avoiding Flip Ambiguities for Micro-UAVs with Bounded Distance Measurement Errors

Cited by 34 publications

References 39 publications

Bio-inspired cluster–based optimal target identification using multiple unmanned aerial vehicles in smart precision agriculture

Bio-inspired cluster–based optimal target identification using multiple unmanned aerial vehicles in smart precision agriculture

Dynamic Routing and Coordination of Cluster for Unmanned Aerial Vehicle (UAV) Swarms

Internet of Unmanned Aerial Vehicles: QoS Provisioning in Aerial Ad-Hoc Networks

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