Drones path planning for WSN data gathering: A column generation heuristic approach

Garraffa, Michele; Bekhti, Mustapha; Létocart, Lucas; Achir, Nadjib; Boussetta, Khaled

doi:10.1109/wcnc.2018.8377391

Cited by 14 publications

(13 citation statements)

References 7 publications

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“…Research on sensor data collection with UAVs has developed various tour-planning techniques. Some works suggest the straight use of conventional techniques based on the TSP or VRP variations [5,32,33]. Other works focus on optimization to solve the TSP by applying genetic algorithm (GA) optimization, as in [34,35], and [26].…”

Section: Uav Tour Planningmentioning

confidence: 99%

“…The set M T i represents all delays of n messages sent by a CH that reach the GS during the period T, as illustrated in (4). In (5) we calculate the average delay as the sum of all delays divided by the number of messages. This metric is presented in seconds.…”

Section: Metrics Formalizationmentioning

confidence: 99%

“…The coordination of UAVs in a distributed manner is a complex task [5] because there is no central node that can have a consistent view of the state of all the UAVs (i.e., the instantaneous position, velocity, residual battery level); furthermore, the ad-hoc communication topology limits consensus on deciding how to perform a task sharing of a global mission. This work applies the term ad-hoc communication in the meaning of non-constant communication based on a certain level of proximity.…”

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: Uav Tour Planningmentioning

confidence: 99%

Section: Metrics Formalizationmentioning

confidence: 99%

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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“…That means that it can get optimal or bad results. Therefore, this propsedapproache used the genetic algorithm andthe hill climbing algorithm to work together using the principle of column generation (CG) [6] [7].In this pproache, the genetic algorithm will find the appropriate distribution of sensors in the uncovered area, while the other apply the principle of its work in finding the greatest value of the generated solutions, which excludes the principle of probability that the genetic algorithm based on.…”

Section: The Proposed Approachmentioning

confidence: 99%

Coverage Optimization for WNSs using AI Technique

Abdul-Aziz¹

2020

IJCA

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Coverage in wireless sensors networks (WSNs) is one of the most active research topics in wireless networks.ThisPaper presented a new way to ensure getting the best coverage of the targets in the region of interest (RoI) in WSNs.The proposed approache used the artificial intelligence technique by considering the principle of columns generation (CG) to select the appropriate coordinates for the sensors to cover targets distributed in a specifc region.Simulationresults proved the effectiveness of the proposed approachefor obtaining the optimal distribution of the sensor nodes to cover all the targets in the RoI. General TermsRegion of interest (RoI)

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“…Other solutions are based on Delay Tolerant Networks communication techniques. In particular, the optimization of the data collection by a mobile node, usually through the optimization of the mobile node's trajectory based on variants of the traveling salesman problem [4].…”

Section: When Crowdsensing Meets the Infrastructured Iot Networkmentioning

confidence: 99%

When Service-Oriented Computing Meets the IoT: A Use Case in the Context of Urban Mobile Crowdsensing

Issarny

Bouloukakis

Georgantas

et al. 2018

Service-Oriented and Cloud Computing

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The possibilities of new mobile distributed systems have reached unprecedented levels. Such systems are dynamically composed of networked resources in the environment, which may span from the immediate neighborhood of the users-as advocated by pervasive computingup to the entire globe-as envisioned by the Future Internet and one of its major constituents, the Internet of Things. This paper more specifically concentrates on urban participatory mobile distributed systems where people get involved in producing new knowledge about the urban environment. Service-oriented and cloud computing are evident baseline technologies for the target mobile distributed systems. Service orientation provides the abstraction to deal with the assembly of the relevant heterogeneous component systems. The cloud provides the infrastructure to deal with the gathering and analyses of the observations coming from the sensing infrastructure, including from people. However, cloud-based centralized solutions come at a price, regarding both resource consumption and privacy risk. Further, the high heterogeneity of the participating nodes results in diverse levels of sensing accuracy. This paper provides an overview of our past and ongoing research to overcome the challenges facing urban participatory mobile distributed systems, which leverages mobile collaborative sensing, networking and computing. The experience with the Ambiciti platform and associated mobile app for monitoring the individual and collective exposure to environmental pollution serves as an illustrative use case.

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Drones path planning for WSN data gathering: A column generation heuristic approach

Cited by 14 publications

References 7 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

Coverage Optimization for WNSs using AI Technique

When Service-Oriented Computing Meets the IoT: A Use Case in the Context of Urban Mobile Crowdsensing

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