Measurement Errors in Range-Based Localization Algorithms for UAVs: Analysis and Experimentation

Sorbelli, Francesco Betti; Pinotti, Cristina M.; Silvestri, Simone; Das, Sajal K.

doi:10.1109/tmc.2020.3020584

Cited by 22 publications

(10 citation statements)

References 31 publications

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“…3) The execution times for VRC and JSC match their time complexity: We use the execution times for JSC to schedule the 300+ workload instances to fit a cubic function in n, the number of activities, to match its time complexity of O(n 3 •l 2 ); since in our runs, l ∈ [1,5] and l ≤ n, we omit that term in the fit. Similarly, we fit a degree-4 polynomial for VRC in n. The correlation coefficient for these two fits are high at 0.86 and 0.99, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…Unmanned Aerial Vehicles (UAVs), also called drones, are enabling a wide range of applications in smart cities [1], such as traffic monitoring [2], construction surveys [3], package delivery [4], localization [5], and disaster (including COVID-19) management [6], assisted by 5G wireless roll-out [7]. The mobility, agility, and hovering capabilities of drones allow them to rapidly fly to points of interest (i.e., waypoints) in the city to accomplish specific activities.…”

Section: Introductionmentioning

confidence: 99%

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Heuristic Algorithms for Co-scheduling of Edge Analytics and Routes for UAV Fleet Missions

Khochare

Simmhan

Sorbelli

et al. 2021

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications

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Unmanned Aerial Vehicles (UAVs) or drones are increasingly used for urban applications like traffic monitoring and construction surveys. Autonomous navigation allows drones to visit waypoints and accomplish activities as part of their mission. A common activity is to hover and observe a location using on-board cameras. Advances in Deep Neural Networks (DNNs) allow such videos to be analyzed for automated decision making. UAVs also host edge computing capability for on-board inferencing by such DNNs. To this end, for a fleet of drones, we propose a novel Mission Scheduling Problem (MSP) that coschedules the flight routes to visit and record video at waypoints, and their subsequent on-board edge analytics. The proposed schedule maximizes the utility from the activities while meeting activity deadlines as well as energy and computing constraints. We first prove that MSP is NP-hard and then optimally solve it by formulating a mixed integer linear programming (MILP) problem. Next, we design two efficient heuristic algorithms, JSC and VRC, that provide fast sub-optimal solutions. Evaluation of these three schedulers using real drone traces demonstrate utility-runtime trade-offs under diverse workloads.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Heuristic Algorithms for Co-scheduling of Edge Analytics and Routes for UAV Fleet Missions

Khochare

Simmhan

Sorbelli

et al. 2021

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications

Self Cite

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“…In addition, if some users are not in Line-of-Sight (LoS) of UAVs and thus cannot be seen by the UAVs, the users can broadcast a probe request with their wireless communication devices. The UAVs then can estimate the locations of the users by the received radio signal strength index (RSSI) measurements [9], [31].…”

Section: System Modelmentioning

confidence: 99%

Throughput Maximization of UAV Networks

Sun

Zou

et al. 2021

Preprint

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In this paper we study the deployment of multiple unmanned aerial vehicles (UAVs) to form a temporal UAV network for the provisioning of emergent communications to affected people in a disaster zone, where each UAV is equipped with a lightweight base station device and thus can act as an aerial base station for users. Unlike most existing studies that assumed that a UAV can serve all users in its communication range, we observe that both computation and communication capabilities of a single lightweight UAV are very limited, due to various constraints on its size, weight, and power supply. Thus, a single UAV can only provide communication services to a limited number of users. We study a novel problem of deploying K UAVs in the top of a disaster area such that the sum of the data rates of users served by the UAVs is maximized, subject to that (i) the number of users served by each UAV is no greater than its service capacity; and (ii) the communication network induced by the K UAVs is connected. We then propose a-approximation algorithm for the problem, improving the current best result of the problem by five times (the best approximation ratio so far is)), where e is the base of the natural logarithm. We finally evaluate the algorithm performance via simulation experiments. Experimental results show that the proposed algorithm is very promising. Especially, the solution delivered by the proposed algorithm is up to 12% better than those by existing algorithms.

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“…Energy limitations affect the efficiency and performance of FANETs. Therefore, to extend the lifespan of the UAVs, it is desirable to distribute energy accurately and evenly through the network 26–31 . In FANET networks, data packets generated by drones reach the base station (BS) via a multihop link.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, to extend the lifespan of the UAVs, it is desirable to distribute energy accurately and evenly through the network. [26][27][28][29][30][31] In FANET networks, data packets generated by drones reach the base station (BS) via a multihop link. In this regard, the UAV must interact to send the packet to the receiving node (SN).…”

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confidence: 99%

ARMS‐EGR—Adaptive ranking and mobile sink‐enabled energy‐efficient geographic routing protocol in flying ad hoc networks

Agrawal

Kapoor

2022

Int J Communication

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Unmanned aerial vehicles (UAVs) have become widespread because of their involvement in a variety of applications. The task of designing the energyefficient routing between UAVs has been considered a matter of great interest due to the inherent challenges of controlling the dynamics exhibited by UAVs.Energy limitations are considered the main limitations of UAVs. This research paper proposes a novel routing protocol, adaptive ranking and mobile sink (MS)-enabled energy-efficient geographic routing (ARMS-EGR) for flying ad hoc networks. In ARMS-EGR, the whole network is partitioned into cells. The cell contains cell members (CM) and cell heads (CHs). The CH works as a cluster head. Additionally, two MSs have been used to collect data captured by CM. Multihop communication on the network leads to an increase in traffic and consumes the energy of the UAVs located near the base station (BS). MSs are used for power distribution and load balancing across the network. Adaptive ranking of forwarder UAVs and CHs is performed during intracell and intercell multihop communication, respectively, using adaptive ranking. A cell with one-hop communication can directly send packets to the MS, but the ARMS-EGR routing protocol has been proposed for multihop communication. The proposed approach is simulated in NS-2.35 software. The results show that end-to-end latency and power consumption during packet transmission are greatly minimized. ARMS-EGR also demonstrates improvements in message success rates, number of alive nodes, and packet delivery ratio, making ARMS-EGR particularly suitable for flying ad hoc networks (FANETs).adaptive ranking, energy-efficient routing, flying ad hoc network, mobility management model, unmanned aerial vehicle | INTRODUCTIONThe flying ad hoc network (FANET) includes a set of unmanned aerial vehicles (UAVs). UAVs coordinate and communicate with each other to complete all tasks. UAVs, also known as drones, come in a variety of sizes and specifications. UAVs can be deployed quickly when needed. 1,2 FANETs have grown significantly from single large UAVs in the past to

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Measurement Errors in Range-Based Localization Algorithms for UAVs: Analysis and Experimentation

Cited by 22 publications

References 31 publications

Heuristic Algorithms for Co-scheduling of Edge Analytics and Routes for UAV Fleet Missions

Heuristic Algorithms for Co-scheduling of Edge Analytics and Routes for UAV Fleet Missions

Throughput Maximization of UAV Networks

ARMS‐EGR—Adaptive ranking and mobile sink‐enabled energy‐efficient geographic routing protocol in flying ad hoc networks

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