Energy-Constrained UAV Trajectory Design for Ground Node Localization

Sallouha, Hazem; Azari, Mohammad Mahdi; Pollin, Sofie

doi:10.1109/glocom.2018.8647530

Cited by 61 publications

(56 citation statements)

References 13 publications

(32 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As all other services, localization services using ground base stations are exposed to damage caused by natural or man-made disasters. Therefore, rapid on-demand deployment of UAVs furnished with radio platforms for serving and localizing ground users has, recently, attracted considerable research focus [17,77,86,87,96]. In particular, because of the higher LOS probability at high altitudes which results in better localization accuracy compared with ground base stations [86,87].…”

Section: Localization Services System Requirementsmentioning

confidence: 99%

Tutorial on UAVs: A Blue Sky View onWireless Communication

Vinogradov

Sallouha

Bast

et al. 2018

JMM

Self Cite

View full text Add to dashboard Cite

The growing use of Unmanned Aerial Vehicles (UAVs) for various applications requires ubiquitous and reliable connectivity for safe control and data exchange between these devices and ground terminals. Depending on the application, UAV-mounted wireless equipment can either be an aerial user equipment (AUE) that co-exists with the terrestrial users, or it can be a part of wireless infrastructure providing a range of services to the ground users. For instance, AUE can be used for real-time search and rescue and/or video streaming (surveillance, broadcasting) and Aerial Base Station (ABS) can enhance coverage, capacity and energy efficiency of wireless networks. In both cases, UAV-based solutions are scalable, mobile, easy and fast to deploy. However, several technical challenges have to be addressed before such solutions will become widely used. In this work, we present a tutorial on wireless communication with UAVs, taking into account a wide range of potential applications. The main goal of this work is to provide a complete overview of the main scenarios (AUE and ABS), channel and performance models, compare them, and discuss open research points. This work is intended to serve as a tutorial for wireless communication with UAVs, which gives a comprehensive overview of the research done until now and depicts a comprehensive picture to foster new ideas and solutions while avoiding duplication of past work. We start by discussing the open challenges of

show abstract

Section: Localization Services System Requirementsmentioning

confidence: 99%

Tutorial on UAVs: A Blue Sky View onWireless Communication

Vinogradov

Sallouha

Bast

et al. 2018

JMM

Self Cite

View full text Add to dashboard Cite

show abstract

“…9 is in the same order of magnitude as the motion energy of UGV when µ = 1. This is different from UAV communications, where the propulsion energy of UAV is much larger than the communication energy to keep the UAV aloft [22], [23]. Finally, we analyze the impact of the number of vertices M on the energy consumption.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

Backscatter Data Collection With Unmanned Ground Vehicle: Mobility Management and Power Allocation

Wang

Xia

2019

IEEE Trans. Wireless Commun.

View full text Add to dashboard Cite

Collecting data from massive Internet of Things (IoT) devices is a challenging task, since communication circuits are power-demanding while energy supply at IoT devices is limited. To overcome this challenge, backscatter communication emerges as a promising solution as it eliminates radio frequency components in IoT devices. Unfortunately, the transmission range of backscatter communication is short. To facilitate backscatter communication, this work proposes to integrate unmanned ground vehicle (UGV) with backscatter data collection. With such a scheme, the UGV could improve the communication quality by approaching various IoT devices. However, moving also costs energy consumption and a fundamental question is: what is the right balance between spending energy on moving versus on communication? To answer this question, this paper studies energy minimization under a joint graph mobility and backscatter communication model. With the joint model, the mobility management and power allocation problem unfortunately involves nonlinear coupling between discrete variables brought by mobility and continuous variables brought by communication. Despite the optimization challenges, an algorithm that theoretically achieves the minimum energy consumption is derived, and it leads to automatic trade-off between spending energy on moving versus on communication in the UGV backscatter system. Simulation results show that if the noise power is small (e.g., ≤ −100 dBm), the UGV should collect the data with small movements. However, if the noise power is increased to a larger value (e.g., −60 dBm), the UGV should spend more motion energy to get closer to IoT users.Index Terms-Backscatter communication, Internet of Things (IoT), mixed integer optimization, quality-of-service (QoS), unmanned ground vehicle (UGV).

show abstract

“…However, the authors considered an optimistic scenario with a very dense gateway deployment. In [33] and [34], the localization of IoT nodes using mobile anchors is proposed. Although mobile anchors introduce swift and flexible localization solutions, they typically serve limited areas [34].…”

Section: A Related Workmentioning

confidence: 99%

“…In [33] and [34], the localization of IoT nodes using mobile anchors is proposed. Although mobile anchors introduce swift and flexible localization solutions, they typically serve limited areas [34]. A method that uses information about nearby Wi-Fi access points to localize Sigfox nodes is proposed in [35].…”

Section: A Related Workmentioning

confidence: 99%

Localization in Ultra Narrow Band IoT Networks: Design Guidelines and Tradeoffs

Sallouha

Chiumento

Rajendran

et al. 2019

IEEE Internet Things J.

Self Cite

View full text Add to dashboard Cite

Localization in long-range Internet of Things networks is a challenging task, mainly due to the long distances and low bandwidth used. Moreover, the cost, power, and size limitations restrict the integration of a GPS receiver in each device. In this work, we introduce a novel received signal strength indicator (RSSI) based localization solution for ultra narrow band (UNB) long-range IoT networks such as Sigfox. The essence of our approach is to leverage the existence of a few GPSenabled sensors (GSNs) in the network to split the wide coverage into classes, enabling RSSI based fingerprinting of other sensors (SNs). By using machine learning algorithms at the network backed-end, the proposed approach does not impose extra power, payload, or hardware requirements. To comprehensively validate the performance of the proposed method, a measurement-based dataset that has been collected in the city of Antwerp is used. We show that a location classification accuracy of 80% is achieved by virtually splitting a city with a radius of 2.5 km into seven classes. Moreover, separating classes, by increasing the spacing between them, brings the classification accuracy up-to 92% based on our measurements. Furthermore, when the density of GSN nodes is high enough to enable device-to-device communication, using multilateration, we improve the probability of localizing SNs with an error lower than 20 m by 40% in our measurement scenario.

show abstract

Energy-Constrained UAV Trajectory Design for Ground Node Localization

Cited by 61 publications

References 13 publications

Tutorial on UAVs: A Blue Sky View onWireless Communication

Tutorial on UAVs: A Blue Sky View onWireless Communication

Backscatter Data Collection With Unmanned Ground Vehicle: Mobility Management and Power Allocation

Localization in Ultra Narrow Band IoT Networks: Design Guidelines and Tradeoffs

Contact Info

Product

Resources

About