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Optimization and Communication in UAV Networks

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (31 March 2020) | Viewed by 42595

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Dr. Nathalie Mitton

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Inria Lille-Nord Europe, 59650 Villeneuve-d'Ascq, France
Interests: Internet of Things; wireless sensor networks; RFID; wireless robots networks; services (localization, neighbor discovery, etc)
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Dr. Christelle Caillouet

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Université Côte d’Azur, CNRS, Inria, I3S, France

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Dear Colleagues,

With new technological advances, UAVs are becoming a reality and attract more and more attention. UAVs or drones are flying devices that can be remotely controlled or, more recently, completely autonomous. They can be used alone or as a fleet, and in a large set of applications: From rescue operations to event coverage going through servicing other networks such as sensor networks for replacing, recharging, or data offloading. They are hardware-constrained since they cannot be too heavy and rely on batteries. Depending on their use (alone or in a swarm) and the targeted applications, they must evolve differently and meet different requirements (energy preservation, delay of covering an area, coverage, limited number of devices, etc.) with limited resources (energy, speed, etc.).
Yet, their use still raises a large set of new exciting challenges, in terms of trajectory optimization, positioning, when they are used alone or in cooperation, coordination, and communication when they evolve in swarm, just to name a few.
This Special Issue calls for any new original submissions that deal with UAV or UAV swarm optimization or communication aspects.

Dr. Nathalie Mitton
Dr. Christelle Caillouet
Guest Editors

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Keywords

UAV
Drones
Wireless
Self-organization
Optimization
Swarm
Communication
Algorithms

Published Papers (13 papers)

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Editorial

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4 pages, 160 KiB

Open AccessEditorial

Optimization and Communication in UAV Networks

by Christelle Caillouet and Nathalie Mitton

Sensors 2020, 20(18), 5036; https://0-doi-org.brum.beds.ac.uk/10.3390/s20185036 - 04 Sep 2020

Cited by 4 | Viewed by 2324

Abstract

Nowadays, Unmanned Aerial Vehicles (UAVs) have received growing popularity in the Internet-of-Things (IoT) which often deploys many sensors in a relatively wide region. Current trends focus on deployment of a single UAV or a swarm of it to generally map an area, perform surveillance, monitoring or rescue operations, collect data from ground sensors or various communicating devices, provide additional computing services close to data producers, etc. Applications are very diverse and call for different features or requirements. But UAV remain low-power battery powered devices that in addition to their mission, must fly and communicate. Thanks to wireless communications, they participate to mobile dynamic networks composed of UAV and ground sensors and thus many challenges have to be addressed to make UAV very efficient. And behind any UAV application, hides an optimization problem. There is still a criterion or multiple ones to optimize such as flying time, energy consumption, number of UAV, quantity of data to send/receive, etc. Full article

(This article belongs to the Special Issue Optimization and Communication in UAV Networks)

Research

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17 pages, 1657 KiB

Open AccessArticle

A Decentralized Low-Chattering Sliding Mode Formation Flight Controller for a Swarm of UAVs

by Thiago F. K. Cordeiro, João Y. Ishihara and Henrique C. Ferreira

Sensors 2020, 20(11), 3094; https://0-doi-org.brum.beds.ac.uk/10.3390/s20113094 - 30 May 2020

Cited by 6 | Viewed by 2330

Abstract

In this paper, a nonlinear robust formation flight controller for a swarm of unmanned aerial vehicles (UAVs) is presented. It is based on the virtual leader approach and is capable of achieving and maintaining a formation with time-varying shape. By using a decentralized architecture, the local controller in each UAV uses information only from the UAV itself, its neighbors, and from the virtual leader. Also, a synchronization control objective provides a mechanism to weight between the fleet achieving the desired formation shape, that is, achieving the desired relative position between the UAVs, and each UAV achieving its desired absolute position. The use of a combination of a sliding mode controller and a low pass filter reduces the usual chattering effect, providing a smooth control signal while maintaining robustness. Simulation results show the effectiveness of the proposed decentralized controller. Full article

(This article belongs to the Special Issue Optimization and Communication in UAV Networks)

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23 pages, 4556 KiB

Open AccessArticle

Priority-Based Data Collection for UAV-Aided Mobile Sensor Network

by Xiaoyan Ma, Tianyi Liu, Song Liu, Rahim Kacimi and Riadh Dhaou

Sensors 2020, 20(11), 3034; https://0-doi-org.brum.beds.ac.uk/10.3390/s20113034 - 27 May 2020

Cited by 15 | Viewed by 3953

Abstract

In this work, we study data collection in multiple unmanned aerial vehicle (UAV)-aided mobile wireless sensor networks (WSNs). The network topology is changing due to the mobility of the UAVs and the sensor nodes, so the design of efficient data collection protocols is a major concern. We address such high dynamic network and propose two mechanisms: prioritized-based contact-duration frame selection mechanism (PCdFS), and prioritized-based multiple contact-duration frame selection mechanisms (PMCdFS) to build collision-free scheduling and balance the nodes between the multi-UAV respectively. Based on the two mechanisms, we proposed a Balance algorithm to conduct the collision-free communication between the mobile nodes and the multi-UAVs. Two key design ideas for a Balance algorithm are: (a) no need of higher priority for those nodes that have lower transmission rate between them and the UAV and (b) improve the communication opportunity for those nodes that have shorter contact duration with the UAVs. We demonstrate the performance of proposed algorithms through extensive simulations, and real experiments. These experiments using 15 mobile nodes at a path with 10 intersections and 1 island, present that network fairness is efficiently enhanced. We also confirm the applicability of proposed algorithms in a challenging and realistic scenario through numerous experiments on a path at Tongji campus in Shanghai, China. Full article

(This article belongs to the Special Issue Optimization and Communication in UAV Networks)

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23 pages, 1211 KiB

Open AccessArticle

Energy-Aware Management in Multi-UAV Deployments: Modelling and Strategies

by Victor Sanchez-Aguero, Francisco Valera, Ivan Vidal, Christian Tipantuña and Xavier Hesselbach

Sensors 2020, 20(10), 2791; https://0-doi-org.brum.beds.ac.uk/10.3390/s20102791 - 14 May 2020

Cited by 13 | Viewed by 3246

Abstract

Nowadays, Unmanned Aerial Vehicles (UAV) are frequently present in the civilian environment. However, proper implementations of different solutions based on these aircraft still face important challenges. This article deals with multi-UAV systems, forming aerial networks, mainly employed to provide Internet connectivity and different network services to ground users. However, the mission duration (hours) is longer than the limited UAVs’ battery life-time (minutes). This paper introduces the UAV replacement procedure as a way to guarantee ground users’ connectivity over time. This article also formulates the practical UAV replacements problem in moderately large multi-UAV swarms and proves it to be an NP-hard problem in which an optimal solution has exponential complexity. In this regard, the main objective of this article is to evaluate the suitability of heuristic approaches for different scenarios. This paper proposes betweenness centrality heuristic algorithm (BETA), a graph theory-based heuristic algorithm. BETA not only generates solutions close to the optimal (even with 99% similarity to the exact result) but also improves two ground-truth solutions, especially in low-resource scenarios. Full article

(This article belongs to the Special Issue Optimization and Communication in UAV Networks)

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21 pages, 2359 KiB

Open AccessArticle

Data Offloading in UAV-Assisted Multi-Access Edge Computing Systems: A Resource-Based Pricing and User Risk-Awareness Approach

by Giorgos Mitsis, Eirini Eleni Tsiropoulou and Symeon Papavassiliou

Sensors 2020, 20(8), 2434; https://0-doi-org.brum.beds.ac.uk/10.3390/s20082434 - 24 Apr 2020

Cited by 34 | Viewed by 4398

Abstract

Unmanned Aerial Vehicle (UAV)-assisted Multi-access Edge Computing (MEC) systems have emerged recently as a flexible and dynamic computing environment, providing task offloading service to the users. In order for such a paradigm to be viable, the operator of a UAV-mounted MEC server should enjoy some form of profit by offering its computing capabilities to the end users. To deal with this issue in this paper, we apply a usage-based pricing policy for allowing the exploitation of the servers’ computing resources. The proposed pricing mechanism implicitly introduces a more social behavior to the users with respect to competing for the UAV-mounted MEC servers’ computation resources. In order to properly model the users’ risk-aware behavior within the overall data offloading decision-making process the principles of Prospect Theory are adopted, while the exploitation of the available computation resources is considered based on the theory of the Tragedy of the Commons. Initially, the user’s prospect-theoretic utility function is formulated by quantifying the user’s risk seeking and loss aversion behavior, while taking into account the pricing mechanism. Accordingly, the users’ pricing and risk-aware data offloading problem is formulated as a distributed maximization problem of each user’s expected prospect-theoretic utility function and addressed as a non-cooperative game among the users. The existence of a Pure Nash Equilibrium (PNE) for the formulated non-cooperative game is shown based on the theory of submodular games. An iterative and distributed algorithm is introduced which converges to the PNE, following the learning rule of the best response dynamics. The performance evaluation of the proposed approach is achieved via modeling and simulation, and detailed numerical results are presented highlighting its key operation features and benefits. Full article

(This article belongs to the Special Issue Optimization and Communication in UAV Networks)

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18 pages, 1337 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Edge Computing Resource Allocation for Dynamic Networks: The DRUID-NET Vision and Perspective

by Dimitrios Dechouniotis, Nikolaos Athanasopoulos, Aris Leivadeas, Nathalie Mitton, Raphael Jungers and Symeon Papavassiliou

Sensors 2020, 20(8), 2191; https://0-doi-org.brum.beds.ac.uk/10.3390/s20082191 - 13 Apr 2020

Cited by 29 | Viewed by 4396

Abstract

The potential offered by the abundance of sensors, actuators, and communications in the Internet of Things (IoT) era is hindered by the limited computational capacity of local nodes. Several key challenges should be addressed to optimally and jointly exploit the network, computing, and storage resources, guaranteeing at the same time feasibility for time-critical and mission-critical tasks. We propose the DRUID-NET framework to take upon these challenges by dynamically distributing resources when the demand is rapidly varying. It includes analytic dynamical modeling of the resources, offered workload, and networking environment, incorporating phenomena typically met in wireless communications and mobile edge computing, together with new estimators of time-varying profiles. Building on this framework, we aim to develop novel resource allocation mechanisms that explicitly include service differentiation and context-awareness, being capable of guaranteeing well-defined Quality of Service (QoS) metrics. DRUID-NET goes beyond the state of the art in the design of control algorithms by incorporating resource allocation mechanisms to the decision strategy itself. To achieve these breakthroughs, we combine tools from Automata and Graph theory, Machine Learning, Modern Control Theory, and Network Theory. DRUID-NET constitutes the first truly holistic, multidisciplinary approach that extends recent, albeit fragmented results from all aforementioned fields, thus bridging the gap between efforts of different communities. Full article

(This article belongs to the Special Issue Optimization and Communication in UAV Networks)

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20 pages, 7833 KiB

Open AccessArticle

Complex Field Network Coding for Multi-Source Multi-Relay Single-Destination UAV Cooperative Surveillance Networks

by Rui Xue, Lu Han and Huisi Chai

Sensors 2020, 20(6), 1542; https://0-doi-org.brum.beds.ac.uk/10.3390/s20061542 - 11 Mar 2020

Cited by 10 | Viewed by 2744

Abstract

Relay-based cooperative communication for unmanned aerial vehicle (UAV) networks can obtain spatial diversity gains, expand coverage, and potentially increase the network capacity. A multi-source multi-relay single-destination structure is the main topology structure for UAV cooperative surveillance networks, which is similar to the structure of network coding (NC). Compared with conventional NC schemes, complex field network coding (CFNC) can achieve a higher throughput and is introduced to surveillance networks in this paper. According to whether there is a direct communication link between any source drone and the destination, the information transfer mechanism at the downlink is set to one of two modes, either mixed or relay transmission, and two corresponding irregular topology structures for CFNC-based networks are proposed. Theoretical analysis and simulation results with an additive white Gaussian noise (AWGN) channel show that the CFNC obtains a throughput as high as 1/2 symbol per source per channel use. Moreover, the CFNC applied to the proposed irregular structures under the two transmission modes can achieve better reliability due to full diversity gain as compared to that based on the regular structure. Moreover, the reliability of the CFNC scheme can continue to be improved by combining channel coding and modulation techniques at the expense of rate loss. Full article

(This article belongs to the Special Issue Optimization and Communication in UAV Networks)

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19 pages, 9074 KiB

Open AccessArticle

A Comparison of the Influence of Vegetation Cover on the Precision of an UAV 3D Model and Ground Measurement Data for Archaeological Investigations: A Case Study of the Lepelionys Mound, Middle Lithuania

by Algimantas Česnulevičius, Artūras Bautrėnas, Linas Bevainis and Donatas Ovodas

Sensors 2019, 19(23), 5303; https://0-doi-org.brum.beds.ac.uk/10.3390/s19235303 - 02 Dec 2019

Cited by 4 | Viewed by 2226

Abstract

The aim of this research was to conduct a comparative analysis of the precision of ground geodetic data versus the three-dimensional (3D) measurements from unmanned aerial vehicles (UAV), while establishing the impact of herbaceous vegetation on the UAV 3D model. Low (up to 0.5 m high) herbaceous vegetation can impede the establishment of the anthropogenic roughness of the surface. The identification of minor surface alterations, which enables the determination of their anthropogenic origin, is of utmost importance in archaeological investigations. Vegetation cover is regarded as one of the factors influencing the identification of such minor forms of relief. The research was conducted on the Lepelionys Mound (Prienai District Municipality, Lithuania). Ground measurements were obtained using Trimble GPS, and UAV “Inspire 1” was used for taking aerial photographs. Following the data from the ground measurements and aerial photographs, large scale surface maps were drawn and the errors in the measurement of the position of the isolines were compared. The results showed that the largest errors in the positional measurements of fixed objects were conditioned by the height of grass. Grass with a height of up to 0.1 m resulted in discrepancies of up to 0.5 m, whereas grass that was up to 0.5 m high led to discrepancies up to 1.3 m high. Full article

(This article belongs to the Special Issue Optimization and Communication in UAV Networks)

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19 pages, 1251 KiB

Open AccessArticle

Trajectory Planning for Data Collection of Energy-Constrained Heterogeneous UAVs

by Zhen Qin, Chao Dong, Hai Wang, Aijing Li, Haipeng Dai, Weihao Sun and Zhengqin Xu

Sensors 2019, 19(22), 4884; https://0-doi-org.brum.beds.ac.uk/10.3390/s19224884 - 08 Nov 2019

Cited by 14 | Viewed by 2963

Abstract

Nowadays, Unmanned Aerial Vehicles (UAVs) have received growing popularity in the Internet-of-Things (IoT) which often deploys many sensors in a relatively wide region. Since the battery capacity is limited, sensors cannot transmit over a long distance. It is necessary for designing efficient sensor data collection mechanisms to prolong the lifetime of the IoT and enhance data collection efficiency. In this paper, we consider a UAV-enabled data collection scenario, where multiple heterogeneous UAVs with different energy constraints are employed to collect data from sensors. The value of data depends on the importance of the monitoring area of the sensor and the freshness of collected data. Our objective is to maximize the data collection utility by jointly optimizing the communication scheduling and trajectory of each UAV. The data collection utility is determined by the amount and value of the collected data. This problem is a variant of multiple knapsack problem, which is a classical NP-hard problem. First, we transform the initial problem into a submodular function maximization problem under energy constraints, and then we design a novel trajectory planning algorithm to maximize the data collection utility, while accounting for different values of data and different energy constraints of heterogeneous UAVs. Finally, under different network settings, the performance of the proposed trajectory planning algorithm is evaluated via extensive simulations. The results show that the proposed algorithm can obtain maximum data collection utility. Full article

(This article belongs to the Special Issue Optimization and Communication in UAV Networks)

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16 pages, 398 KiB

Open AccessArticle

Aerial Cooperative Jamming for Cellular-Enabled UAV Secure Communication Network: Joint Trajectory and Power Control Design

by Hanming Sun, Bin Duo, Zhengqiang Wang, Xiaochen Lin and Changchun Gao

Sensors 2019, 19(20), 4440; https://0-doi-org.brum.beds.ac.uk/10.3390/s19204440 - 14 Oct 2019

Cited by 8 | Viewed by 2724

Abstract

To improve the secrecy performance of cellular-enabled unmanned aerial vehicle (UAV) communication networks, this paper proposes an aerial cooperative jamming scheme and studies its optimal design to achieve the maximum average secrecy rate. Specifically, a base station (BS) transmits confidential messages to a UAV and meanwhile another UAV performs the role of an aerial jammer by cooperatively sending jamming signals to oppose multiple suspicious eavesdroppers on the ground. As the UAVs have the advantage of the controllable mobility, the objective is to maximize the worst-case average secrecy rate by the joint optimization of the two UAVs’ trajectories and the BS’s/UAV jammer’s transmit/jamming power over a given mission period. The objective function of the formulated problem is highly non-linear regarding the optimization variables and the problem has non-convex constraints, which is, in general, difficult to achieve a globally optimal solution. Thus, we divide the original problem into four subproblems and then solve them by applying the successive convex approximation (SCA) and block coordinate descent (BCD) methods. Numerical results demonstrate that the significantly better secrecy performance can be obtained by using the proposed algorithm in comparison with benchmark schemes. Full article

(This article belongs to the Special Issue Optimization and Communication in UAV Networks)

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22 pages, 1135 KiB

Open AccessArticle

Completion Time Minimization for Multi-UAV Information Collection via Trajectory Planning

by Zhen Qin, Aijing Li, Chao Dong, Haipeng Dai and Zhengqin Xu

Sensors 2019, 19(18), 4032; https://0-doi-org.brum.beds.ac.uk/10.3390/s19184032 - 18 Sep 2019

Cited by 30 | Viewed by 3928

Abstract

Unmanned Aerial Vehicles (UAVs) are widely used as mobile information collectors for sensors to prolong the network time in Wireless Sensor Networks (WSNs) due to their flexible deployment, high mobility, and low cost. This paper focuses on the scenario where rotary-wing UAVs complete information collection mission cooperatively. For the first time, we study the problem of minimizing the mission completion time for a multi-UAV system in a monitoring scenario when considering the information collection quality. The mission completion time includes flying time and hovering time. By optimizing the trajectories of all UAVs, we minimize the mission completion time while ensuring that the information of each sensor is collected. This problem can be formulated as a mixed-integer non-convex one which has been proved to be NP-hard. To solve the formulated problem, we first propose a hovering point selection algorithm to select appropriate hovering points where the UAVs can sequentially collect the information from multiple sensors. We model this problem as a BS coverage problem with the information collection quality in consideration. Then, we use a min-max cycle cover algorithm to assign these hovering points and get the trajectory of each UAV. Finally, with the obtained UAVs trajectories, we further consider the UAVs can also collect information when flying and optimize the time allocations. The performance of our algorithm is verified by simulations, which show that the mission completion time is minimum compared with state-of-the-art algorithms. Full article

(This article belongs to the Special Issue Optimization and Communication in UAV Networks)

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24 pages, 3546 KiB

Open AccessArticle

Modeling and Flight Experiments for Swarms of High Dynamic UAVs: A Stochastic Configuration Control System with Multiplicative Noises

by Hongbo Zhao, Sentang Wu, Yongming Wen, Wenlei Liu and Xiongjun Wu

Sensors 2019, 19(15), 3278; https://0-doi-org.brum.beds.ac.uk/10.3390/s19153278 - 25 Jul 2019

Cited by 5 | Viewed by 2524

Abstract

UAV Swarm with high dynamic configuration at a large scale requires a high-precision mathematical model to fully exploit its boundary performance. In order to instruct the engineering application with high confidence, uncertainties induced from either systematic measurement or the environment cannot be ignored. [...] Read more.

I t \hat{o}

stochastic model of the UAV Swarm system with multiplicative noises. By combining the cooperative kinematic model with a simplified individual dynamic model of fixed-wing-aircraft for the first time, the configuration control model is derived. Considering the uncertainties in actual flight, multiplicative noises are introduced to complete the

I t \hat{o}

stochastic model. Following that, the estimator and controller are designed to control the formation. The mean-square uniform boundedness condition of the proposed stochastic system is presented for the closed-loop system. In the simulation, the stochastic robustness analysis and design (SRAD) method is used to optimize the properties of the formation. More importantly, the effectiveness of the proposed model is also verified using real data of five unmanned aircrafts collected in outfield formation flight experiments. Full article

(This article belongs to the Special Issue Optimization and Communication in UAV Networks)

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19 pages, 3013 KiB

Open AccessArticle

A Sensor-Driven Analysis of Distributed Direction Finding Systems Based on UAV Swarms

by Zhong Chen, Shihyuan Yeh, Jean-Francois Chamberland and Gregory H. Huff

Sensors 2019, 19(12), 2659; https://0-doi-org.brum.beds.ac.uk/10.3390/s19122659 - 12 Jun 2019

Cited by 6 | Viewed by 3373

Abstract

This paper reports on the research of factors that impact the accuracy and efficiency of an unmanned aerial vehicle (UAV) based radio frequency (RF) and microwave data collection system. The swarming UAVs (agents) can be utilized to create micro-UAV swarm-based (MUSB) aperiodic antenna arrays that reduce angle ambiguity and improve convergence in sub-space direction-of-arrival (DOA) techniques. A mathematical data model is addressed in this paper to demonstrate fundamental properties of MUSB antenna arrays and study the performance of the data collection system framework. The Cramer–Rao bound (CRB) associated with two-dimensional (2D) DOAs of sources in the presence of sensor gain and phase coefficient is derived. The single-source case is studied in detail. The vector-space of emitters is exploited and the iterative-MUSIC (multiple signal classification) algorithm is created to estimate 2D DOAs of emitters. Numerical examples and practical measurements are provided to demonstrate the feasibility of the proposed MUSB data collection system framework using iterative-MUSIC algorithm and benchmark theoretical expectations. Full article

(This article belongs to the Special Issue Optimization and Communication in UAV Networks)

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