An algorithm for aerial data collection from wireless sensors networks by groups of UAVs

Olivieri, Bruno; Endler, Markus

doi:10.1109/iros.2017.8202262

Cited by 12 publications

(17 citation statements)

References 14 publications

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“…We do not claim to get better results than optimization approaches, but we propose DADCA as a viable alternative to centralized approaches. An early version of this method and some essential details were introduced in [9], with improvements and further details presented in [10].…”

Section: Dadcamentioning

confidence: 99%

“…To address these issues, this work investigates whether it is possible to implement a distributed algorithm to coordinate several fully autonomous UAVs (i.e., non-humancontrolled) collecting data from a WSN without centralized control or knowledge of internal UAV states and relying on only ad-hoc communication. In response to these questions, we previously proposed the DADCA core idea in [9] and some better formulations in [10]. DADCA is a distributed algorithm that combines well-known algorithms of path planning with a cooperative and oscillatory behavior that relies on only the exchange of all payload data from pairs of UAVs' (whenever they approach) decision making.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating flight coordination approaches of UAV squads for WSN data collection enhancing the internet range on WSN data collection

Souza

Endler

2020

J Internet Serv Appl

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Wireless sensor networks (WSNs) are an important means of collecting data in a variety of situations, such as the monitoring of large or hazardous areas. The retrieval of WSN data can yield better results through the use of unmanned aerial vehicles (UAVs), for example, concerning the increase in the amount of data collected and the decrease in the time between the collection and use of the data. In particular, disaster areas may be left without communication resources and with high residual risk to humans, at which point a WSN can be quickly launched by air to collect relevant data until other measures can be established. The set of rules of each problem's component (e.g., number of UAVs, UAVs dislocation control, sensors, communication) is considered the approaches to solve the problem. In this meaning, some studies present approaches for the use of UAVs for the collection of WSN data, focusing primarily on optimizing the path to be covered by a single UAV and relying on long-range communication that is always available; these studies do not explore the possibility of using several UAVs or the limitations on the range of communication. This work describes DADCA, a distributed scalable approach capable of coordinating groups of UAVs in WSN data collection with restricted communication range and without the use of optimization techniques. The results reveal that the amount of data collected by DADCA is similar or superior to path optimization approaches by up to 1%. In our proposed approach, the delay in receiving sensor messages is up to 46% shorter than in other approaches, and the required processing onboard UAVs can reach less than 75% of those using optimization-based algorithms. The results indicate that the DADCA can match and even surpass other presented approaches, since the path optimization is not a focus, while also incorporating the advantages of a distributed approach.

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Section: Dadcamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluating flight coordination approaches of UAV squads for WSN data collection enhancing the internet range on WSN data collection

Souza

Endler

2020

J Internet Serv Appl

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“…Based on TSP, Goudarzi et al propose the BL-TSP algorithm that is able to smooth the flying path, control UAVs, and reduce redundant data, which increases the distance UAV could travel and the rate of packet delivery [12]. Olivieri and Endler made use of a dynamic set of UAVs for data collection, achieving 13% better efficiency over the TSP solution [13]. Particle swarm optimization to elect cluster heads is considered by Ho et al [14], reporting that the UAV traveling time is reduced without sacrificing Bit Error Rate (BER) and energy consumption.…”

Section: Related Workmentioning

confidence: 99%

“…The majority of these studies provide little by way of practical evaluation and rely heavily on simulation and analytical analyses [11], [13], [14], [15], [16], [17], [18]. It has been found that traditional communications protocols for WSNs tend to perform poorly in practice [19], and researchers have begun to design bespoke protocols that specifically cater to aerial mobility.…”

Section: Related Workmentioning

confidence: 99%

Efficient and Reliable Aerial Communication With Wireless Sensors

Qin

Boyle

Yeatman

2019

IEEE Internet Things J.

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This paper describes the design, implementation and evaluation of a first of its kind cross-layer protocol for wireless communication between flying agents and terrestrial wireless sensors. The protocol is comprised of 3 layers: a new application layer built upon a modified implementation of ContikiMAC over the IEEE 802.15.4 2.4 GHz physical layer. Experimental evaluation shows the protocol to have excellent energy efficiency, low latency and high reliability-approaching 100% for certain parameter settings and operational conditions. The effects of speed, altitude and direction of approach are also experimentally evaluated, demonstrating that it is of critical importance to take these into account when planning mobile aerial data collection campaigns.

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“…However, they focused their studies on the communication channel model with the UAV and an in-depth detailed analysis on transmission rates and signal strength at the interfaces of the elements involved. Olivieri et al [23] investigated the advantages of working with collaborative UAVs gathering sensory data using DTNs-Delayed and Disconnection Tolerant Networks, besides the advantages that this communication model adds to the system. The authors also reported several benefits in using UAVs when compared to land vehicles in agriculture, and proposed route optimization models for problems such as the traveling salesman problem (TSP) and the vehicle routing problem (VRP).…”

Section: Introductionmentioning

confidence: 99%

UAV Path Optimization for Precision Agriculture Wireless Sensor Networks

Just

Pellenz

Lima

et al. 2020

Sensors

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The use of monitoring sensors is increasingly present in the context of precision agriculture. Usually, these sensor nodes (SNs) alternate their states between periods of activation and hibernation to reduce battery usage. When employing unmanned aerial vehicles (UAVs) to collect data from SNs distributed over a large agricultural area, we must synchronize the UAV route with the activation period of each SN. In this article, we address the problem of optimizing the UAV path through all the SNs to reduce its flight time, while also maximizing the SNs’ lifetime. Using the concept of timeslots for time base management combined with the idea of flight prohibition list, we propose an efficient algorithm for discovering and reconfiguring the activation time of the SNs. Experimental results were obtained through the development of our own simulator—UAV Simulator. These results demonstrate a considerable reduction in the distance traveled by the UAV and also in its flight time. In addition, the model provides a reduction in transmission time by SNs after reconfiguration, thus ensuring a longer lifetime for the SNs in the monitoring environment, as well as improving the freshness and continuity of the gathered data, which support the decision-making process.

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An algorithm for aerial data collection from wireless sensors networks by groups of UAVs

Cited by 12 publications

References 14 publications

Evaluating flight coordination approaches of UAV squads for WSN data collection enhancing the internet range on WSN data collection

Evaluating flight coordination approaches of UAV squads for WSN data collection enhancing the internet range on WSN data collection

Efficient and Reliable Aerial Communication With Wireless Sensors

UAV Path Optimization for Precision Agriculture Wireless Sensor Networks

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