A buffer-aware dynamic UAV trajectory design for data collection in resource-constrained IoT frameworks

The emergence of unmanned aerial vehicle (UAV)-enabled technology in the Internet of Things (IoT) era leads to a significant reduction in data collection delays when accumulating sensory data from ground IoT nodes (INs). As a flying data collector, the UAV hovers at a limited number of Access Points...

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Vydané v:Computers & electrical engineering Ročník 100; s. 107934
Hlavní autori: Mazumdar, Nabajyoti, Roy, Saugata, Nag, Amitava, Singh, Jyoti Prakash
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Amsterdam Elsevier Ltd 01.05.2022
Elsevier BV
Predmet:
Algorithms
Ant colony optimization
Ant colony optimization (ACO)
Buffer aware
Buffers
Data collection
Data collector
Data integrity
Data loss
Energy consumption
Hovering
Internet of Things
Internet of things (IoT)
IoT node (IN)
Machine learning
Network latency
Overflow
Trajectories
Unmanned aerial vehicle (UAV)
Unmanned aerial vehicles
Data collector
Unmanned aerial vehicle (UAV)
Ant colony optimization (ACO)
IoT node (IN)
Buffer aware
Internet of things (IoT)
ISSN:0045-7906, 1879-0755
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Shrnutí:The emergence of unmanned aerial vehicle (UAV)-enabled technology in the Internet of Things (IoT) era leads to a significant reduction in data collection delays when accumulating sensory data from ground IoT nodes (INs). As a flying data collector, the UAV hovers at a limited number of Access Points (APs) to collect data, outperforming ground data collectors in terms of transmission energy consumption, data delivery reliability, and timeliness. However, the INs have a finite amount of buffer capacity to store the data that must be collected before they overflow. As a result, the data gathering route for UAVs should be adaptable to INs’ buffer deadline in order to minimize data loss. In this paper, a buffer-aware dynamic UAV trajectory design protocol is proposed for data collection from resource-constrained INs. A distributed AP nomination strategy is proposed in order to reduce UAV hovering latency. Furthermore, using machine learning approaches, a modified ant colony optimization algorithm is constructed to minimize the data loss penalty due to buffer overflow. Finally, the performance of the proposed scheme is evaluated against several state-of-the-art protocols with regards to parameters such as data loss penalty, packet delivery ratio, and network lifetime. [Display omitted] •The UAV Access points selected ensure one-hop connectivity with IoT nodes.•To reduce the data collection latency, a minimal set of Access points are nominated.•A modified ACO algorithm is used to create a dynamic UAV trajectory.
Bibliografia:ObjectType-Article-1
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ISSN:0045-7906
1879-0755
DOI:10.1016/j.compeleceng.2022.107934