Monitoring Ancient Buildings: Real Deployment of an IoT System Enhanced by UAVs and Virtual Reality

Bacco, Manlio; Barsocchi, Paolo; Cassarà, Pietro; Germanese, Danila; Gotta, Alberto; Leone, Giuseppe Riccardo; Moroni, Davide; Pascali, Maria Antonietta; Tampucci, Marco

doi:10.1109/access.2020.2980359

Cited by 58 publications

(27 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, they did not consider UAV position optimization, which is the main contribution in our proposed EFDC. In [44], Bacco et al used WSN and UAV to establish a monitoring system for the ancient buildings. However, the focus is given on 3D construction of the structure, and no optimization is done from the networking or data communication perspective.…”

Section: Related Workmentioning

confidence: 99%

Energy-Efficient and Fast Data Collection in UAV-Aided Wireless Sensor Networks for Hilly Terrains

Nazib

Moh

2021

IEEE Access

View full text Add to dashboard Cite

Energy-constrained sensor nodes are often deployed in remote, hilly, and hard-to-reach areas for civilian and military purposes. In such wireless sensor networks (WSNs), an unmanned aerial vehicle (UAV) can be used to collect data from the sensor nodes. Low-altitude UAVs can be utilized to reduce the energy consumption of WSNs by optimizing the data collection position. In this study, we designed an energy-efficient and fast data collection (EFDC) scheme in UAV-aided WSNs for hilly areas with the help of a UAV as a data mule. First, we proposed a central bias hybrid energy-efficient distributed clustering algorithm for grouping the sensors. Then, we applied a modified tabu search algorithm to optimize the UAV position for collecting data from a cluster. To achieve fast data collection, we developed the traveling salesman problem with the derived data collection positions and solved it by applying a modified genetic algorithm. Based on our simulation results, the proposed EFDC scheme outperforms the conventional ones in terms of energy consumption, scalability, control overhead, delay, and load balancing.

show abstract

Section: Related Workmentioning

confidence: 99%

Energy-Efficient and Fast Data Collection in UAV-Aided Wireless Sensor Networks for Hilly Terrains

Nazib

Moh

2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…IoT devices collect real-time health statuses and other patient information and then share these data with healthcare personnel. Patient health monitoring systems have the capability of monitoring patients by using the latest technologies [93][94][95][96][97][98][99][100]. In addition, it allows the sharing of patient records with the healthcare teams for data analysis.…”

Section: Iot-based Healthcare Applicationsmentioning

confidence: 99%

Comprehensive Survey of IoT, Machine Learning, and Blockchain for Health Care Applications: A Topical Assessment for Pandemic Preparedness, Challenges, and Solutions

Imran

Zaman²,

Imran

et al. 2021

Electronics

View full text Add to dashboard Cite

Internet of Things (IoT) communication technologies have brought immense revolutions in various domains, especially in health monitoring systems. Machine learning techniques coupled with advanced artificial intelligence techniques detect patterns associated with diseases and health conditions. Presently, the scientific community is focused on enhancing IoT-enabled applications by integrating blockchain technology with machine learning models to benefit medical report management, drug traceability, tracking infectious diseases, etc. To date, contemporary state-of-the-art techniques have presented various efforts on the adaptability of blockchain and machine learning in IoT applications; however, there exist various essential aspects that must also be incorporated to achieve more robust performance. This study presents a comprehensive survey of emerging IoT technologies, machine learning, and blockchain for healthcare applications. The reviewed articles comprise a plethora of research articles published in the web of science. The analysis is focused on research articles related to keywords such as `machine learning’, blockchain, `Internet of Things or IoT’, and keywords conjoined with `healthcare’ and `health application’ in six famous publisher databases, namely IEEEXplore, Nature, ScienceDirect, MDPI, SpringerLink, and Google Scholar. We selected and reviewed 263 articles in total. The topical survey of the contemporary IoT-based models is presented in healthcare domains in three steps. Firstly, a detailed analysis of healthcare applications of IoT, blockchain, and machine learning demonstrates the importance of the discussed fields. Secondly, the adaptation mechanism of machine learning and blockchain in IoT for healthcare applications are discussed to delineate the scope of the mentioned techniques in IoT domains. Finally, the challenges and issues of healthcare applications based on machine learning, blockchain, and IoT are discussed. The presented future directions in this domain can significantly help the scholarly community determine research gaps to address.

show abstract

“…Although there has been a growing interest on UAV-assisted WSNs [ 3 ], which has yielded to a vast literature on several aspects of the topic, scientific works directly detailing practical deployments and more specifically, giving details on the antennas used for the sensor nodes, which constitute a key component for the communication link, are quite scarce. Experimental works on WSNs are often based on commercial motes that use either built-in chip antennas or external dipole antennas, thus providing low gain omnidirectional patterns (e.g., [ 4 ]). However, directional antennas with higher gains [ 5 ] and polarization match [ 6 ] provide significant improvements in terms of energy consumption, enlarged communication range, and even security.…”

Section: Introductionmentioning

confidence: 99%

Circularly Polarized Dual-Band LoRa/GPS Antenna for a UAV-Assisted Hazardous Gas and Aerosol Sensor

et al. 2021

View full text Add to dashboard Cite

UAV assisted wireless sensor networks play a key role in the detection of toxic gases and aerosols. UAVs can be used to remotely deploy sensor nodes and then collect gas concentration readings and GPS positioning from them to delimit an affected area. For such purpose, a dual-band communication system is required, supporting GPS reception, and sensor reading data transfer, which is chosen to be at 2.4 GHz using LoRa physical layer. In this work we propose a switched-beam antenna subsystem for the sensor nodes capable not only of satisfying the dual band requirements but also of maximizing communication range or energy consumption through a good antenna polarization match and improved antenna gain. This antenna subsystem is built using dual-port, dual-band, circularly polarized antenna elements, whose design and experimental validation is carefully detailed. A low profile microstrip stacked structure has been used to obtain return loss in both bands better than 15 dB, axial ratios below 1.5 dB, and wide −3 dB beamwidths around 90° and 75° for GPS and 2.4 GHz bands, respectively, thus limiting the gain reduction of the switched-beam system in critical sensor orientations. Special attention has been paid to reduce the coupling between both ports through the optimization of the relative placement of both patches and their feeding points. The measured coupling is below −30 dB.

show abstract

Monitoring Ancient Buildings: Real Deployment of an IoT System Enhanced by UAVs and Virtual Reality

Cited by 58 publications

References 44 publications

Energy-Efficient and Fast Data Collection in UAV-Aided Wireless Sensor Networks for Hilly Terrains

Energy-Efficient and Fast Data Collection in UAV-Aided Wireless Sensor Networks for Hilly Terrains

Comprehensive Survey of IoT, Machine Learning, and Blockchain for Health Care Applications: A Topical Assessment for Pandemic Preparedness, Challenges, and Solutions

Circularly Polarized Dual-Band LoRa/GPS Antenna for a UAV-Assisted Hazardous Gas and Aerosol Sensor

Contact Info

Product

Resources

About