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Open AccessArticle

Internet-Of-Things in Motion: A UAV Coalition Model for Remote Sensing in Smart Cities

Department of Computer Science, University of the Western Cape, Private Bag X17, Bellville 7535, South Africa
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Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2018, 18(7), 2184; https://doi.org/10.3390/s18072184
Received: 18 June 2018 / Revised: 2 July 2018 / Accepted: 2 July 2018 / Published: 6 July 2018
(This article belongs to the Special Issue Unmanned Aerial Vehicle Networks, Systems and Applications)
Unmanned aerial vehicles (UAVs) or drones are increasingly used in cities to provide service tasks that are too dangerous, expensive or difficult for human beings. Drones are also used in cases where a task can be performed more economically and or more efficiently than if done by humans. These include remote sensing tasks where drones can be required to form coalitions by pooling their resources to meet the service requirements at different locations of interest in a city. During such coalition formation, finding the shortest path from a source to a location of interest is key to efficient service delivery. For fixed-wing UAVs, Dubins curves can be applied to find the shortest flight path. When a UAV flies to a location of interest, the angle or orientation of the UAV upon its arrival is often not important. In such a case, a simplified version of the Dubins curve consisting of two instead of three parts can be used. This paper proposes a novel model for UAV coalition and an algorithm derived from basic geometry that generates a path derived from the original Dubins curve for application in remote sensing missions of fixed-wing UAVs. The algorithm is tested by incorporating it into three cooperative coalition formation algorithms. The performance of the model is evaluated by varying the number of types of resources and the sensor ranges of the UAVs to reveal the relevance and practicality of the proposed model. View Full-Text
Keywords: smart cities; Internet-of-Things; multi-drone task allocation; unmanned aerial vehicles; path planning; Dubins curves; particle swarm optimization smart cities; Internet-of-Things; multi-drone task allocation; unmanned aerial vehicles; path planning; Dubins curves; particle swarm optimization
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MDPI and ACS Style

Ismail, A.; Bagula, B.A.; Tuyishimire, E. Internet-Of-Things in Motion: A UAV Coalition Model for Remote Sensing in Smart Cities. Sensors 2018, 18, 2184. https://doi.org/10.3390/s18072184

AMA Style

Ismail A, Bagula BA, Tuyishimire E. Internet-Of-Things in Motion: A UAV Coalition Model for Remote Sensing in Smart Cities. Sensors. 2018; 18(7):2184. https://doi.org/10.3390/s18072184

Chicago/Turabian Style

Ismail, Adiel; Bagula, Bigomokero A.; Tuyishimire, Emmanuel. 2018. "Internet-Of-Things in Motion: A UAV Coalition Model for Remote Sensing in Smart Cities" Sensors 18, no. 7: 2184. https://doi.org/10.3390/s18072184

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