Open AccessAge‐optimal path planning for finite‐battery UAV‐assisted data dissemination in IoT networks
Author(s) -
Changizi Abolfazl,
Emadi Mohammad Javad
Publication year - 2021
Publication title -
iet communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 62
eISSN - 1751-8636
pISSN - 1751-8628
DOI - 10.1049/cmu2.12105
Subject(s) - dissemination , computer science , network packet , wireless sensor network , travelling salesman problem , knapsack problem , real time computing , metric (unit) , benchmark (surveying) , motion planning , computer network , trajectory , the internet , artificial intelligence , robot , telecommunications , algorithm , engineering , operations management , physics , geodesy , astronomy , world wide web , geography
Unmanned aerial vehicles have been widely used to assist wireless sensor networks due to ever‐increasing demands for Internet‐of‐things applications. To support timely delivery of information characterised by a recently introduced metric, termed as the age of information, this paper explores freshness of data in an unmanned aerial vehicle assisted wireless sensor network. Specifically, the authors consider a limited‐energy unmanned aerial vehicle moving towards the Internet‐of‐things devices to disseminate data packets provided by a data centre. Since the unmanned aerial vehicle cannot visit all the nodes in each flight turn due to its finite‐sized battery, the best sequence of nodes, from an age of information perspective, should be selected at the beginning of each flight turn. Thus, an unmanned aerial vehicle trajectory planning for data dissemination is proposed taking into account both maximal use of energy and freshness of data. To minimise the weighted sum age of information metric, by utilising the well‐known knapsack and travelling salesman problems, the authors propose an algorithm to efficiently select devices and the corresponding visiting order in each flight turn. Finally, to highlight performance of the proposed algorithm, and to investigate the effect of limited‐energy unmanned aerial vehicles, the number of nodes and flight turns, and simulation results are also provided and compared with other benchmark algorithms.