Energy-Optimal Data Collection for Unmanned Aerial Vehicle-Aided Industrial Wireless Sensor Network-Based Agricultural Monitoring System: A Clustering Compressed Sampling Approach

Lin, Chuan; Han, Guangjie; Qi, Xingyue; Du, Jian Hua; Xu, Tiantian; Martínez-García, Miguel

doi:10.1109/tii.2020.3027840

Cited by 67 publications

(28 citation statements)

References 32 publications

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“…The joint problem of optimizing node clustering, forwarding tree construction, CH selection per cluster, and UAV trajectory planning was studied. Lin et al also adopted a UAV to collect CS measurements among CHs in [40]. They used hybrid CDG intraclusters.…”

Section: Cdg Combined With Other Technologiesmentioning

confidence: 99%

A Survey of Compressive Data Gathering in WSNs for IoTs

Wang

Chen

2022

Wireless Communications and Mobile Computing

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Internet of Things (IoTs) are increasingly widespread in the field of health care, smart city and smart home application, industrial and agricultural monitoring, automation, etc. With its growing scale of networks, there are a large amount of data in IoTs needing to be sensed, transmitted, and processed. Resource-limited Wireless Sensor Networks (WSNs) as a perceptual layer of IoTs are hard to handle massive uncompressed sensing data. Compressive data gathering (CDG), which applies compressive sensing theory to data gathering, is a perfectly matching method for data compressing and gathering in WSNs. This promising method has attracted lots of researchers’ attention. In this paper, we attempt to survey substantial references about CDG in WSNs. According to their technology schemes, we classify the published references into three categories, i.e., routing protocol of CDG, clustering scheme of CDG, and CDG combined with other technologies. The merits and defects of each method are highlighted. Our work aims to provide an insight into CDG and promote improvements of this technology.

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Section: Cdg Combined With Other Technologiesmentioning

confidence: 99%

A Survey of Compressive Data Gathering in WSNs for IoTs

Wang

Chen

2022

Wireless Communications and Mobile Computing

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“…In the traditional systems, sensor nodes transmit the data to BS by multi-hop communication. In [38] and [39], UAV collect the data from the sensor nodes in an energyefficient way, which reduce the energy consumption and prolong network lifetime.…”

Section: Review Of Related Studiesmentioning

confidence: 99%

Bio-Inspired Approaches for Energy-Efficient Localization and Clustering in UAV Networks for Monitoring Wildfires in Remote Areas

Arafat

Moh

2021

IEEE Access

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In dynamic unmanned aerial vehicle (UAV) networks, localization and clustering are fundamental functions for cooperative control. In this article, we propose bio-inspired localization (BIL) and clustering (BIC) schemes in UAV networks for wildfire detection and monitoring. First, we develop a hybrid gray wolf optimization (HGWO) method and propose an energy-efficient three-dimensional BIL algorithm based on the HGWO, which reduces localization errors, avoids flip ambiguity in bounded distance measurement errors, and achieves high localization accuracy. In BIL, the bounding cube method is applied to reduce the initial search space. Second, we propose an energy-efficient BIC algorithm based on the HGWO. The BIC algorithm utilizes the gray wolf leadership hierarchy to improve clustering efficiency. We also develop an analytical model for determining the optimal number of clusters that provide the minimum number of transmissions. Finally, we propose a GWO-based compressive sensing (CS-GWO) algorithm to transmit data from cluster heads (CHs) to the base station (BS). The proposed CS-GWO constructs an efficient routing tree from CHs to the BS, thereby reducing the routing delay and the number of transmissions. Our performance evaluation shows that the proposed BIL and BIC significantly outperform conventional schemes in terms of various performance metrics under different scenarios.INDEX TERMS Bio-inspired algorithm, cluster head, clustering, energy efficiency, gray wolf optimization, localization, network lifetime, routing protocol, unmanned aerial vehicle network.

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“…In IoT applications, UAV-aided wireless sensing can deal with the high demands of flexibility, scalability, and reliability required of data gathering techniques. Hence, there are many UAV use cases such as in environmental remediation, agricultural monitoring systems [4], automated inspection [5], or performance of dangerous tasks in disaster relief [6]- [8]. In these instances, UAVs can be deployed to track and communicate with ground IoT devices, including wireless sensor nodes or actuators.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Optimized Path Planning and Energy Consumption for Data Collection in Unmanned Ariel Vehicles-Aided Intelligent Wireless Sensing

Huynh-Van

Duy

Nguyen

et al. 2022

IEEE Trans. Ind. Inf.

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In this paper, we consider a new UAV-aided intelligent wireless sensing scheme where the UAVs are deployed for smart sensing and collecting data from Internet-of-Things (IoT) devices. In particular, we propose optimal UAVs' path planing approaches for minimising the completion time and total energy consumption of the UAVs' deployment for data collection. Two optimal schemes, namely optimal energy consumption by peer-topeer UAV-IoT sensing networks and optimal energy consumption by clustering UAV-IoT sensing networks, are considered. The lowcomplexity procedures of our advanced optimisation techniques are suitably applied to disaster relief networks when the solving time must be strictly adhered to. Our real-time optimisation algorithms result in low computational complexity with fast deployment and low processing time for solving the problem of tracking and gathering sensor data, i.e., in very short time (milliseconds). Through simulations results we demonstrate that our proposed approaches in UAV-aided intelligent IoT wireless sensing are suitable for time-critical mission applications such as emergency communications, public safety, and disaster relief networks.

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Energy-Optimal Data Collection for Unmanned Aerial Vehicle-Aided Industrial Wireless Sensor Network-Based Agricultural Monitoring System: A Clustering Compressed Sampling Approach

Cited by 67 publications

References 32 publications

A Survey of Compressive Data Gathering in WSNs for IoTs

A Survey of Compressive Data Gathering in WSNs for IoTs

Bio-Inspired Approaches for Energy-Efficient Localization and Clustering in UAV Networks for Monitoring Wildfires in Remote Areas

Real-Time Optimized Path Planning and Energy Consumption for Data Collection in Unmanned Ariel Vehicles-Aided Intelligent Wireless Sensing

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