Deployment of an aerial platform system for rapid restoration of communications links after a disaster: a machine learning approach

Soufiene

Journal of Healthcare Engineering

et al. 2021

Self Cite

Healthcare is one of the most promising domains for the application of Internet of Things- (IoT-) based technologies, where patients can use wearable or implanted medical sensors to measure medical parameters anywhere and anytime. The information collected by IoT devices can then be sent to the health care professionals, and physicians allow having a real-time access to patients’ data. However, besides limited batteries lifetime and computational power, there is spatio-temporal correlation, where unnecessary transmission of these redundant data has a significant impact on reducing energy consumption and reducing battery lifetime. Thus, this paper aims to propose a routing protocol to enhance energy-efficiency, which in turn prolongs the sensor lifetime. The proposed work is based on Energy Efficient Routing Protocol using Dual Prediction Model (EERP-DPM) for Healthcare using IoT, where Dual-Prediction Mechanism is used to reduce data transmission between sensor nodes and medical server if predictions match the readings or if the data are considered critical if it goes beyond the upper/lower limits of defined thresholds. The proposed system was developed and tested using MATLAB software and a hardware platform called “MySignals HW V2.” Both simulation and experimental results confirm that the proposed EERP-DPM protocol has been observed to be extremely successful compared to other existing routing protocols not only in terms of energy consumption and network lifetime but also in terms of guaranteeing reliability, throughput, and end-to-end delay.

Section: Evaluation Of Performance Indicatorsmentioning

confidence: 99%

EERP-DPM: Energy Efficient Routing Protocol Using Dual Prediction Model for Healthcare Using IoT

Soufiene

Journal of Healthcare Engineering

et al. 2021

Self Cite

“…Wireless connectivity can be attained via a propagation model which comes in two types for aerial platforms: Free space models such as Two-Rays and Air-to-Ground [22,[51][52][53] which relay on a closed-form formula that includes both Line-of-Sight (LoS) and Non-Line-of-Sight [40,[54][55][56][57] which relay on a pre-defined set of constants and constraints for different geomorphologies.…”

Section: Related Research Reviewmentioning

confidence: 99%

“…where d refers to transmitter to receiver separation in km,E r denotes the Earth's radius at 6378 km, G h t ð Þ refers to the transmitter antenna height gain, G h r ð Þ refers to the receiver antenna height gain, h t refers to the tethered aerostat's altitude, h r refers to the receiver antenna height, P t refers to the transmitter power, L refers to the connector and cable loss, N refers to the Noise figure, RSSI refers to the received signal strength indicator, and B refers to the bandwidth [51,62].…”

Section: A Model Of a 5g Wireless Fixed Aerial Access Station (Wifiaas)mentioning

confidence: 99%

An enhanced design of a 5G MIMO antenna for fixed wireless aerial access

Angelides

2021

Cluster Comput

Self Cite

A recent market prediction is that 5G Fixed Wireless Access (FWA) will more than double over the next five years and trials at the same period in London suggest promising results. However, the shift to 5G FWA has raised a new set of research challenges in relation to speed of deployment and re-deployment, coverage, power consumption, end user mobility and last mile connectivity, to name just a few, because of the much higher expectations. A recent review reveals that key 5G Physical Layer technologies that will enable wide mobile and FWA have not kept up pace. In response to some of those research challenges, this paper presents the design of a 5G Multiple Input Multiple Output (MIMO) Antenna that is mounted on a tethered aerostat, and the combination of which serves as a 5G FWA aerial station. The antenna design features several novelties and the aerial station can provide last mile connectivity to a wide coverage footprint, with moderate power consumption and operating at high speeds. Both the evaluation of the antenna performance using several key performance indicators and the validation of the aerial station as a 5G FWA in a wireless sensor network (WSN) proof-of-concept application reveal efficiency gains.

“…To further enhance the energy efficiency, multiple UAVs can be employed, with individual tasks assigned to each of them. The use of aerial platforms flying in varying altitudes and elevation angles for the rapid deployment of a communication network in emergency situations was proposed in [109]. More specifically, an A2G physical propagation model along with a ML-based method based on a radial basis function (RBF) ANN were presented, aiming at providing optimized link budget performance and energy efficiency as well as enhanced LoS connections with rescue teams randomly distributed in an urban area.…”

Section: Airborne-based Intelligent Iiotmentioning

confidence: 99%

AI-Inspired Non-Terrestrial Networks for IIoT: Review on Enabling Technologies and Applications

Michailidis

Potirakis

Kanatas

2020

IoT

During the last few years, various Industrial Internet of Things (IIoT) applications have emerged with numerous network elements interconnected using wired and wireless communication technologies and equipped with strategically placed sensors and actuators. This paper justifies why non-terrestrial networks (NTNs) will bring the IIoT vision closer to reality by providing improved data acquisition and massive connectivity to sensor fields in large and remote areas. NTNs are engineered to utilize satellites, airships, and aircrafts, which can be employed to extend the radio coverage and provide remote monitoring and sensing services. Additionally, this paper describes indicative delay-tolerant massive IIoT and delay-sensitive mission-critical IIoT applications spanning a large number of vertical markets with diverse and stringent requirements. As the heterogeneous nature of NTNs and the complex and dynamic communications scenarios lead to uncertainty and a high degree of variability, conventional wireless communication technologies cannot sufficiently support ultra-reliable and low-latency communications (URLLC) and offer ubiquitous and uninterrupted interconnectivity. In this regard, this paper sheds light on the potential role of artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL), in the provision of challenging NTN-based IIoT services and provides a thorough review of the relevant research works. By adding intelligence and facilitating the decision-making and prediction procedures, the NTNs can effectively adapt to their surrounding environment, thus enhancing the performance of various metrics with significantly lower complexity compared to typical optimization methods.