Workshops

Session AoI-S1: Session 1: Information Updates for Estimation, Computing and Control Session AoI-S2: Session 2: Age of Information and Energy Efficiency Session AoI-S3: Session 3: Age of Information and Emerging Applications Session AoI-S4: Session 4: Age of Information in Wireless Networks
Session BigSecurity-S1: Big Security 1 Session BigSecurity-S2: Big Security 2 Session BigSecurity-S3: Big Security 3 Session BigSecurity-S4: Big Privacy 1
Session BlockSecSDN-KT1: Keynote Talk 1 Session BlockSecSDN-1: Software-Defined Networks Session BlockSecSDN-2: Internet of Things Session BlockSecSDN-KT2: Keynote Talk 2 Session BlockSecSDN-3: Vehicular Networks Session BlockSecSDN-4: Smart Grid and Heterogeneous Networks
Session Coffee-Break-1-AM: Virtual Coffee Break Session Lunch-Break-1: Virtual Lunch Break Session Coffee-Break-1-PM: Virtual Coffee Break
Session CNERT-Opening: Opening Session Session CNERT-Keynote: Keynote Session CNERT-Session-I: Session I: Wireless Networks Session CNERT-Panel-on-Reproducibility: Panel on Reproducibility Session CNERT-Session-II: Session II: Wired Networks Session CNERT-Demo-Session: Demo Session Session CNERT-Closing: Reproducibility Award and Closing
Session DDINS-OS: Opening Session — Message from the Chairs Session DDINS-S1: Data Driven Smart Cities Session DDINS-K1: Keynote Session 1 Session DDINS-S2: Data Driven Intelligent Computing and Application Session DDINS-S3: Data Driven Distributed Computing Session DDINS-K2: Keynote Session 2 Session DDINS-S4: Data Driven Brain Computing Session DDINS-S5: Data Driven Networking 1 Session DDINS-S6: Data Driven Networking 2
Session DroneCom-Opening: Opening Session Session DroneCom-KS1: Keynote Session 1 Session DroneCom-TS1: Technical Session I: Industrial Internet of Things Session DroneCom-IT1: Industrial Talk 1 Session DroneCom-IT2: Industrial Talk 2 Session DroneCom-TS2: Technical Session II: Heterogeneous Networks Session DroneCom-KS2: Keynote Session 2 Session DroneCom-TS3: Technical Session III: Security and Energy Efficiency Session DroneCom-IT3: Industrial Talk 3 Session DroneCom-TS4: Technical Session IV: Other Drone Applications Session DroneCom-Closing: Concluding Remarks
Session EdgeBlock-Open: Opening Session — Message From Chairs Session EdgeBlock-S1: Blockchain and Fog/Edge Session EdgeBlock-S2: Blockchain Application 1 Session EdgeBlock-S3: Smart Contract Session EdgeBlock-S4: Blockchain and RNG Session EdgeBlock-S5: Decentralization Session EdgeBlock-S6: Blockchain Application 2
Session Opening-HotPOST: Opening Remarks Session Keynote-HotPOST: Keynote Talk Session S1-HotPOST: Session 1: Pervasive Applications Session S2-HotPOST: Session 2: Social Systems Session Closing-HotPOST: Closing Remarks
Session ICCN-Opening: Opening Session Session ICCN-S1: Session 1: Cloud Applications 1 Session ICCN-KS1: Keynote Session 1 Session ICCN-S2: Session 2: Cloud Network Session ICCN-S3: Session 3: Cloud System Session ICCN-S4: Session 4: Cloud Security Session ICCN-KS2: Keynote Session 2 Session ICCN-S5: Session 5: Cloud Applications 2 Session ICCN-S6: Session 6: Edge Cloud
Session IWECN-S1: Session 1: Network Planning Session IWECN-S2: Session 2: Network Reliability
Session MobiSec-S1: Mobile Security Session MobiSec-Keynote: Keynote Session MobiSec-S2: Network & Communication Security Session MobiSec-S3: Privacy, Trust & Threats Session MobiSec-S4: General Topics
Session NI-Opening: Opening Session NI-Session-1: Session 1: Networking Aspects Session NI-Keynote: Keynote Session NI-Session-2: Session 2: Resource Management Aspects Session NI-Closing: Closing
Session New-IP-Opening: Opening Session Session New-IP-Keynote: Keynote Speech Session New-IP-S1: Session 1: The X-D-N for New Protocol Session New-IP-S2: Session 2: The New IP Capabilities and Protocol Design
Session PERSIST-IoT-Opening: Opening Session Session PERSIST-IoT-S1: Physical Layer Session PERSIST-IoT-kS1: Keynote Session Session PERSIST-IoT-S2: NOMA Session PERSIST-IoT-S3: Automated Connected Vehicles Session PERSIST-IoT-S4: Edge Computing & IoT
Session WCNEE-Opening: Opening Session Session WCNEE-Keynote: Keynote Session Session WCNEE-S1: Session 1: Secure and Reliable Communications in Smart Grid, Deep Space, Underwater and Wireless UAV Networks Session WCNEE-S2: Session 2: Wireless Communication and Localization Protocols for Underground Sensing, Autonomous Vehicles and In-Vivo NanoSensor Networks Session WCNEE-Closing: Closing Session
Session WNA-Opening: Opening Session Session WNA-Session-I: Session 1: Traffic Optimization Algorithms Session WNA-Demo-Session: Demo and Banner Show Session WNA-Session-II: Session 2: Traffic Engineering and Machine Learning
Session WISARN-O: Opening Session Session WISARN-K: Keynote Session Session WISARN-S1: Session I: Communication Technologies for UAV Tracking and Networking Session WISARN-S2: Session II: Integrating 5G and UAV Networks Session WISARN-S3: Session III: Frameworks for UAV-Based Networking and Computation

The 13th International Workshop on Wireless Sensor, Robot and UAV Networks (WiSARN 2020)

Session WISARN-O

Opening Session

Conference
9:00 AM — 9:05 AM EDT
Local
Jul 6 Mon, 6:00 AM — 6:05 AM PDT

Opening Session

To Be Determined

0
This talk does not have an abstract.
Session Chair

To Be Determined

Session WISARN-K

Keynote Session

Conference
9:05 AM — 10:05 AM EDT
Local
Jul 6 Mon, 6:05 AM — 7:05 AM PDT

Boosting the cellular systems with UAVs: how to learn from a quality and energy perspective

Francesca Cuomo (Sapienza University of Rome), Stefania Colonnese (SAPIENZA Università di Roma)

0
The adoption of Unmanned Aerial Vehicles (UAVs) as mobile Base Stations is a promising solution to boost the capacity in hotspot areas and to provide energy aware services in different smart environments.

The adoption of UAV-BSs involves the planning of their missions over time with the aim to provide specific services to ground users, which includes the scheduling of recharging actions of each UAV-BS at ground sites. This is of particular interest in two rising future technologies: Heterogeneous Cellular Networks using 5G and beyond techniques, and Internet of Things. The former leverages the utilization of mmWave technology on UAVs that recently gained attention due to high available bandwidth. The bandwidth budget can be provided to support demanding services, such as uplink live video streaming, or for edge computing purposes, such as those required seamless user interaction with real and virtual objects throughout extended reality multimedia services. As for the IoT, transmitting the huge amount of sensing data through UAVs may on a side alleviate the cellular network from a massive data collection and on the other side entail less power at the IoT end devices.

In the literature, Artificial Intelligence and Machine Learning based UAV flight planning algorithms aimed at improving energy efficiency as well as service quality related metrics are provided. Specifically, several algorithms leverage Q-learning and Reinforcement learning algorithms by introducing rewards related to key Quality of Service (QoS) and users’ Quality of Experience (QoE) metrics.

We will explore the potential for using UAVs BS in 5G/6G HetNet as well in cyber-physical systems. Emphasis will be placed on recent results using the Q-learning approaches, considering also several exciting emergent research directions paving the way towards novel applications such as eXtended Realty Multimedia Communications, Smart Health, Internet of Everything.
Session Chair

To Be Determined

Session WISARN-S1

Session I: Communication Technologies for UAV Tracking and Networking

Conference
10:30 AM — 11:30 AM EDT
Local
Jul 6 Mon, 7:30 AM — 8:30 AM PDT

Combining LoRaWAN and a New 3D Motion Model for Remote UAV Tracking

Federico Mason (University of Padova, Italy); Federico Chiariotti (Aalborg University, Italy); Martina Capuzzo, Davide Magrin, Andrea Zanella and Michele Zorzi (University of Padova, Italy)

1
Over the last few years, the many uses of Unmanned Aerial Vehicles (UAVs) have captured the interest of both the scientific and the industrial communities. A typical scenario consists in the use of UAVs for surveillance or target-search missions over a wide geographical area. In this case, it is fundamental for the command center to accurately estimate and track the trajectories of the UAVs by exploiting their periodic state reports. In this work, we design an ad hoc tracking system that exploits the Long Range Wide Area Network (LoRaWAN) standard for communication and an extended version of the Constant Turn Rate and Acceleration (CTRA) motion model to predict drone movements in a three-dimensional environment. Simulation results on a publicly available dataset show that our system can reliably estimate the position and trajectory of a UAV, significantly outperforming baseline tracking approaches.

Resilient Hybrid SatCom and Terrestrial Networking for Unmanned Aerial Vehicles

Paresh Saxena (BITS Pilani, India); Thomas Dreibholz (Simula Metropolitan Centre for Digital Engineering, Norway); Ozgu Alay (University of Oslo & Simula Metropolitan, Norway); Harald Skinnemoen (AnsuR Technologies, Norway); Angeles Vazquez-Castro (Universidad Autónoma de Barcelona, Spain); Simone Ferlin (Ericsson AB, Sweden); Guray Acar (European Space Agency - ESTEC, The Netherlands)

1
Today, Unmanned Aerial Vehicles (UAVs) are widely used in many different scenarios including search, monitoring, inspection, and surveillance. To be able to transmit the sensor data from the UAVs to the destination reliably within tangible response times to the relevant content is crucial, especially for tactical use cases. In this paper, we propose network coded torrents (NECTOR) to leverage multiple network interfaces for resilient hybrid satellite communications (SatCom) and terrestrial networking for UAVs. NECTOR is significantly different from the state of the art multipath protocols such as multipath TCP (MPTCP). Unlike MPTCP, NECTOR is a UDP-based user-space application that is deployable without any change in the operating system. Furthermore, NECTOR does not require any additional packet scheduler, rate-adaptation or forward error correction. We present the design and implementation of NECTOR, and evaluate its performance compared to MPTCP. Our experimental results show that NECTOR provides goodput (up to 70%) higher than MPTCP with less signaling overhead of nearly 5.49 times.
Session Chair

Jianping He

Session WISARN-S2

Session II: Integrating 5G and UAV Networks

Conference
11:30 AM — 12:30 PM EDT
Local
Jul 6 Mon, 8:30 AM — 9:30 AM PDT

Connecting flying backhauls of drones to enhance vehicular networks with fixed 5G NR infrastructure

Philippe Jacquet (INRIA, France); Dalia Georgiana Popescu (Nokia Bell Labs, France); Bernard Mans (Macquarie University, Australia)

1
To extend connectivity and guarantee data rates, drones require a pertinent choice of hovering locations to cope with limitations such as flight time and coverage. To this end, we provide analytic bounds on the requirements of connectivity extension for vehicular networks served by fixed eMBB infrastructure, where both vehicular networks and fixed telecommunication infrastructure are modeled using stochastic and fractal geometry as a macro model for urban environment, providing a unique perspective into the smart city. Namely, we prove that assuming $n$ mobile nodes (distributed according to a hyperfractal distribution of dimension dF and an average of ρ gNBs (of dimension dr if ρ=nθwith θ>dr/4 then the average fraction of mobile nodes not covered by a gNB tends to zero like O(n−(dF−2)dr(2θ−dr2)). Interestingly, we then prove that the number of drones needed to connect the isolated mobile nodes is asymptotically equivalent to the number of isolated mobile nodes.

Energy Minimization for MEC-enabled Cellular-Connected UAV: Trajectory Optimization and Resource Scheduling

Zhaohua Lv, JianJun Hao and Yijun Guo (Beijing University of Posts and Telecommunications, China)

0
Recent years, under the cooperation of 5G techniques which enables high rate and low delay communication, cellular-connected unmanned aerial vehicles (UAVs) have attracted tremendous attentions due to its broad applications. In this paper, we focus on a mobile edge computing (MEC) enabled cellular-connected UAV system, where a UAV carried a fixed amount of computation tasks is deployed to fly from an initial location to a final location and served by ground base stations (GBSs) in the presence of a ground eavesdropper. During the flight, the UAV can offload part of the tasks to GBSs for remote execution. We aim at minimize the total energy consumption of UAV to complete the task, including computation energy, communication energy and flight-propulsion energy, by jointly optimizing UAV trajectory, computation task allocation, UAV-GBS association and transmit power allocation. To tackle the non-convexity of the original problem, we resort to the block coordinate decent and successive convex approximation techniques through decomposing it into three sub-problems, which are alternately solved until the algorithm converges. Numerical results show that the proposed design significantly saves UAV's energy consumption for complete the given tasks.

Paging Group Size Distribution for Multicast Services in 5G Networks

Olga Vikhrova, Sara Pizzi, Antonella Molinaro and Giuseppe Araniti (University Mediterranea of Reggio Calabria, Italy)

0
Group-oriented/multicast services in cellular networks are actually gaining momentum as the number of connected Internet of Things (IoT) devices is constantly growing and the need can rise of delivering the same content at the same time. Different sensors, video cameras, robots, and general-purpose IoT devices can be organised into multicast groups to receive the multicast service over a Point-to-Multipoint (PTM) transmission, thus improving the system spectral efficiency. When the group-oriented content is available in the radio access network (RAN) segment, the network wakes up devices by means of paging to inform them about the upcoming multicast transmission. However, most of the battery-powered devices are not immediately available for paging. In fact, devices can be reached at specific paging opportunities which vary from device to device. In a highly dense deployment, many devices will inevitably share the same paging opportunity forming a paging group. In this work, we analyze the fundamental trade-off between length of paging interval and size of paging subgroup in affecting the time to wait for the group content delivery. Furthermore, the impact of different paging group size distributions on the system performance is investigated.
Session Chair

Marco Di Felice

Session WISARN-S3

Session III: Frameworks for UAV-Based Networking and Computation

Conference
1:30 PM — 2:30 PM EDT
Local
Jul 6 Mon, 10:30 AM — 11:30 AM PDT

Drone-assisted Edge Computing: a game-theoretical approach

Fabio Busacca (CNIT, Italy); Laura Galluccio (University of Catania, Italy); Sergio Palazzo (Unict, Italy)

0
Edge computing is an evolution of cloud computing, which brings application hosting from centralized data-centers down to the network edge, closer to mobile users and IoT devices. In this paper we focus on scenarios where edge computing is implemented by way of drones which behave as mobile edge servers and provide storage and processing services to offload data and maintain ubiquitous connectivity. In this scenario, Servers (i.e., drones) behave as sellers of network functions and Users (i.e. IoT devices) act as buyers, and interact with each other. To model these interactions, we hereby present a Stackelberg game which describes the system dynamics; we also show that an optimal equilibrium and convergence point for the game exists, which can be reached by way of a simple learning algorithm.

A Real-time Framework for Trust Monitoring in a Network of Unmanned Aerial Vehicles

Mahsa Keshavarz, Alireza Shamsoshoara and Fatemeh Afghah (Northern Arizona University, USA); Jonathan Ashdown(United States Air Force, USA)

0
Unmanned aerial vehicles (UAVs) have been increasingly utilized in various civilian and military applications such as remote sensing, border patrolling, disaster monitoring, and communication coverage extension. However, there are still prone to several cyber attacks such as GPS spoofing attacks, distributed denial-of-service (DDoS) attacks, and man-in-the-middle attacks to obtain their collected information or to enforce the UAVs to perform their requested actions which may damage the UAVs or their surrounding environment or even endanger the safety of human in the operation field. In this paper, we propose a trust monitoring mechanism in which a centralized unit (e.g. the ground station) regularly observe the behavior of the UAVs in terms of their motion path, their consumed energy, as well as the number of their completed tasks and measure a relative trust score for the UAVs to detect any abnormal behaviors in a real-time manner. Our simulation results show that the trust model can detect malicious UAVs, which can be under various cyber-security attacks such as flooding attacks, man-in-the-middle attacks, GPS spoofing attack in real-time.

Trajectory Optimization of Flying Energy Sources using Q-Learning to Recharge Hotspot UAVs

Sayed Amir Hoseini (University of New South Wales, Australia); Jahan Hassan and Ayub Bokani (Central Queensland University, Australia); Salil S Kanhere (UNSW Sydney, Australia)

0
Despite the increasing popularity of commercial usage of UAVs or drone-delivered services, their dependence on the limited-capacity on-board batteries hinders their flight-time and mission continuity. As such, developing in-situ power transfer solutions for topping-up UAV batteries have the potential to extend their mission duration. In this paper, we study a scenario where UAVs are deployed as base stations (UAV-BS) providing wireless Hotspot services to the ground nodes, while harvesting wireless energy from flying energy sources. These energy sources are specialized UAVs (Charger or transmitter UAVs, tUAVs), equipped with wireless power transmitting devices such as RF antennae. tUAVs have the flexibility to adjust their flight path to maximize energy transfer. With the increasing number of UAV-BSs and environmental complexity, it is necessary to develop an intelligent trajectory selection procedure for tUAVs so as to optimize the energy transfer gain. In this paper, we model the trajectory optimization of tUAVs as a Markov Decision Process (MDP) problem and solve it using Q-Learning algorithm. Simulation results confirm that the Q-Learning based optimized trajectory of the tUAVs outperforms two benchmark strategies, namely random path planning and static hovering of the tUAVs.
Session Chair

Kaushik Chowdhury

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