Trajectory Planing for Cooperating Unmanned Aerial Vehicles in the IoT

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.

Efficient Sensing Data Collection with Diverse Age of Information in UAV-Assisted System

“…Our objective is to maximize the system utility given in (18) while satisfying different data freshness requirements during the whole sensing period. There exists a trade-off between collecting more valuable data and consuming less energy.…”

“…Che et al in [21] proposed UAV-assisted wireless energy transmission and wireless information transmission modes, where the UAV transmitted wireless information and energy to a low-power terminal. The authors in [18] proposed a continuous and real-time path planning model and a task allocation model, where heterogeneous teams of UAVs from different BSs are used to collect readings from ground sensors and pass these readings to ground sensors.…”

A Dependable Digital System Model for Malaria Monitoring

“…Based on communication constraints, data may be locally captured and analysed to assist patients. Available means, such as electronic [41,42], aerial vehicles (UAVs [28,[43][44][45][46][47] or terrestrial vehicles, may be used to deliver the data to the health data centres for further processes.…”

Energy-Efficient Resource Allocation in Aerial Base Stations

“…UAVs are mobile and dynamic access points that are deployed when ground base stations are overloaded or if there are temporary power outages caused by disasters or excessive user flow at certain times. The authors in [2][3][4][5] address the way air base stations can provide access to users on the ground as an alternative to traditional networks. UAVs can help ground-based radio stations provide signal coverage and achieve high data rates [6], especially in situations where this excessive demand occurs in a way that is quite difficult to predict, as can be seen in Figure 1.…”