Trajectory Planing for Cooperating Unmanned Aerial Vehicles in the IoT

Emmanuel Tuyishimire,Noureddine Boudriga,Antoine Bagula,Slim Rekhis

doi:10.3390/iot3010010

Emmanuel Tuyishimire, Noureddine Boudriga + Show 2 more

Open Access

https://doi.org/10.3390/iot3010010

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Journal: IoT	Publication Date: Feb 24, 2022
Citations: 6	License type: CC BY 4.0

Affiliation: University of Cape Town, University of the Western Cape

Abstract

The use of Unmanned Aerial Vehicles (UAVs) in data transport has attracted a lot of attention and applications, as a modern traffic engineering technique used in data sensing, transport, and delivery to where infrastructure is available for its interpretation. Due to UAVs’ constraints such as limited power lifetime, it has been necessary to assist them with ground sensors to gather local data, which has to be transferred to UAVs upon visiting the sensors. The management of such ground sensor communication together with a team of flying UAVs constitutes an interesting data muling problem, which still deserves to be addressed and investigated. This paper revisits the issue of traffic engineering in Internet-of-Things (IoT) settings, to assess the relevance of using UAVs for the persistent collection of sensor readings from the sensor nodes located in an environment and their delivery to base stations where further processing is performed. We propose a persistent path planning and UAV allocation model, where a team of heterogeneous UAVs coming from various base stations are used to collect data from ground sensors and deliver the collected information to their closest base stations. This problem is mathematically formalised as a real-time constrained optimisation model, and proven to be NP-hard. The paper proposes a heuristic solution to the problem and evaluates its relative efficiency through performing experiments on both artificial and real sensors networks, using various scenarios of UAVs settings.

Highlights

The use of Unmanned Aerial Vehicles (UAVs) has emerged as a flexible and cost-efficient alternative to traditional traffic engineering techniques that have been used in IoT settings, in order to transport sensor readings from their points of collection to their processing places
We propose a persistent and real-time path planning model together with a task allocation model, where a team of heterogeneous UAVs coming from various base stations are used to collect sensor readings from ground sensors and deliver the collected information to their closest base stations
Note here that the sink’s network is a complete graph where nodes are randomly deployed on a 1 km2 area

Summary

Introduction

The use of UAVs has emerged as a flexible and cost-efficient alternative to traditional traffic engineering techniques that have been used in IoT settings, in order to transport sensor readings from their points of collection to their processing places. Accurate solutions to data muling problems are still scarce, especially when considering the limited flying capacity of the battery-powered UAVs. The issues related to the efficient task allocation to a team of UAVs under stringent data collection requirements, such as real time data collection, still need to be addressed. The issues related to the efficient task allocation to a team of UAVs under stringent data collection requirements, such as real time data collection, still need to be addressed This would benefit many task assignment models. Especially when UAVs have different specifications (speeds, battery, lifetime, memory, functionalities, etc.) and only fresh and complete information need to be collected. Persistent collection requirement needs to be addressed and this requires the data muling system to deal with outdated or premature sensor readings This would benefit many task assignment models. especially when UAVs have different specifications (speeds, battery, lifetime, memory, functionalities, etc.) and only fresh and complete information need to be collected.

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