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Special Issue "Multiple Access Edge Computing in Non-Terrestial and Terrestrial Internet of Things Networks"

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Satellite Missions for Earth and Planetary Exploration".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 7771

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Special Issue Editors

Dr. Alberto Gotta

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Institute of Information Science and Technologies (ISTI), National Research Council of Italy (CNR), 56124 Pisa, Italy
Interests: wireless networks; satellite communication; wireless communications; Internet of Things; artificial intelligence; neural networks
Special Issues, Collections and Topics in MDPI journals

Dr. Tomaso de Cola

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Guest Editor

Institute of Communications and Navigation, German Aerospace Center (DLR), 82234 Wessling, Germany
Interests: satellite networks; telecommunication network reliability; mobile satellite communication; array signal processing; transport protocols
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The continuous growth and demand for connectivity from pervasively distributed devices requires the adoption of heterogeneous and capillary networks with ubiquitous coverage. In addition, modern Machine Learning and Artificial Intelligence techniques applied to the Internet of Things lead to not only communication requirements but also pervasive computational capacity.

Non-terrestrial networks include several radio segments, i.e., GEO, MEO, LEO, and vLEO satellites, airships, high altitude platforms, and unmanned aerial vehicles, and can scale in terms of communication and computing power. The complementary relationship of terrestrial and non-terrestrial sensing, computing, and communication systems offers an invaluable resource for IoT applications and services but requires a careful analysis of challenges and problems arising from the combination of such heterogeneous systems.

This Special Issue welcomes papers in the field of multiple-access edge computing, Internet of Things, AI-as-a-Service, and ubiquitous networks dealing with new developments in theory, analysis, simulation and modeling, experimentation, demonstration, case studies, field operational tests, and deployments.

Dr. Alberto Gotta
Dr. Tomaso de Cola
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (6 papers)

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Open AccessArticle

Dual Threshold Cooperative Sensing Based Dynamic Spectrum Sharing Algorithm for Integrated Satellite and Terrestrial System

and

Remote Sens. 2022, 14(23), 6061; https://doi.org/10.3390/rs14236061 - 29 Nov 2022

Viewed by 452

Abstract

In this paper, cognitive technology is introduced into the integrated satellite terrestrial system to realize the dynamic spectrum sharing of the system and improve the utilization rate of spectrum resources. To overcome the effects of low signal-to-noise ratio (SNR) and noise uncertainty in the channel, a dual-threshold cooperative sensing strategy based on energy detection is introduced. Spectrum sensing is considered as a binary hypothesis problem, but the uncertainty of noise interference in the integrated satellite terrestrial cognitive system will cause the perception to appear ambiguous. Moreover, the noise power varies with time and relative position within a certain range. In the fuzzy state, the perception technology adopts the equal-gain merging algorithm, and derives the voting optimization algorithm to improve the accuracy of decision-making. In addition, taking the minimum error probability as the optimization goal, the optimal adjustment of the adaptive double threshold is realized based on the equal-gain combining algorithm. The simulation results show that the spectrum detection accuracy under low SNR is improved, and the opportunity for terrestrial networks to share spectrum resources is increased. Full article

(This article belongs to the Special Issue Multiple Access Edge Computing in Non-Terrestial and Terrestrial Internet of Things Networks)

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Open AccessArticle

Energy-Efficient Controller Placement in Software-Defined Satellite-Terrestrial Integrated Network

and

Remote Sens. 2022, 14(21), 5561; https://doi.org/10.3390/rs14215561 - 04 Nov 2022

Viewed by 645

Abstract

The satellite-terrestrial integrated network (STIN), as an integration of the satellite network and terrestrial, has become a promising architecture to support global coverage and ubiquitous connection. The architecture of software-defined networking (SDN) is utilized to intelligently coordinate the global STIN, in which the placement schemes of SDN controllers, including the locations, number, and roles, would produce various performances. However, the uneven distribution of global users leads to the unbalanced energy consumption of satellite resources, which brings a heavy burden for satellites to maintain the control flows for network management. To provide green communication for international economic trade in the countries along the Belt and Road, in this paper, we focus on the energy-efficient controller placement (EECP) problem in the software-defined STIN. The satellite gateways are located in the countries along the Belt and Road, which accounts for a large number of traffic demands and a dense population. The controllers are deployed on the LEO satellites, where each LEO satellite is a candidate controller. The energy consumption for the control paths and the user data links is modeled and then formulated as the flow processing-oriented optimization problem. A modified simulated annealing placement (MSAP) algorithm is developed to solve the EECP problem, in which we use the greedy way to obtain the initial set of controllers, and then the final optimal controller placement result is obtained by the simulated annealing algorithm. Extensive simulations are conducted on the simulated Iridium satellite network topology and statistics data. Compared with other algorithms, the results show that MSAP reduces network energy consumption by 20% and average latency by 25%. Full article

(This article belongs to the Special Issue Multiple Access Edge Computing in Non-Terrestial and Terrestrial Internet of Things Networks)

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Open AccessArticle

Pico-Sat to Ground Control: Optimizing Download Link via Laser Communication

Revital Marbel

Boaz Ben-Moshe

and

Tal Grinshpoun

Remote Sens. 2022, 14(15), 3514; https://doi.org/10.3390/rs14153514 - 22 Jul 2022

Viewed by 1337

Abstract

Consider a constellation of over a hundred low Earth orbit satellites that aim to capture every point on Earth at least once a day. Clearly, there is a need to download from each satellite a large set of high-quality images on a daily basis. In this paper, we present a laser communication (lasercom) framework that stands as an alternative solution to existing radio-frequency means of satellite communication. By using lasercom, the suggested solution requires no frequency licensing and therefore allows such satellites to communicate with any optical ground station on Earth. Naturally, in order to allow laser communication from a low Earth orbit satellite to a ground station, accurate aiming and tracking are required. This paper presents a free-space optical communication system designed for a set of ground stations and nano-satellites. A related scheduling model is presented, for optimizing the communication between a ground station and a set of lasercom satellites. Finally, we report on SATLLA-2B, the first 300 g pico-satellite with basic free-space optics capabilities, that was launched on January 2022. We conjecture that the true potential of the presented network can be obtained by using a swarm of few hundreds of such lasercom pico-satellites, which can serve as a global communication infrastructure using existing telescope-based observatories as ground stations. Full article

(This article belongs to the Special Issue Multiple Access Edge Computing in Non-Terrestial and Terrestrial Internet of Things Networks)

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Open AccessArticle

Multi-Controller Deployment in SDN-Enabled 6G Space–Air–Ground Integrated Network

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Remote Sens. 2022, 14(5), 1076; https://doi.org/10.3390/rs14051076 - 22 Feb 2022

Cited by 8 | Viewed by 1588

Abstract

The space–air–ground Integrated Network (SAGIN) is considered to be a significant framework for realizing the vision of “6G intelligent connection of all things”. The birth of 6G SAGIN also brings many problems, such as ultra-dense dense networks, leading to a decrease in the efficiency of traditional flat network management, and traditional satellite networking solidified network functions, etc. Therefore, combining the 6G SAGIN network with the software-defined network (SDN) is an excellent solution. However, the satellite network topology changes dynamically and the ground user unbalanced distribution leads to the unbalanced load of the SDN controller, which further leads to the increased communication delay and throughput drop, etc. For these problems, a hierarchical multi-controller deployment strategy of an SDN-based 6G SAGIN is proposed. Firstly, the delay model of the network, the load model of the SDN controller, and a loss value as a measure of whether the network delay and controller load are optimal are defined. Then, using the distribution relationship between the SDN controller and the switch node as the solution space, and taking the loss value as the optimization goal, a multi-controller deployment strategy based on the simulated annealing algorithm is used to search for the optimal solution space. Lastly, considering the network topology changes dynamically and the SDN controller imbalance, a switch migration strategy oriented toward load balancing is proposed. We aimed to determine the controller deployment plan through the above two points, balance the controller load, and then improve the network performance. The simulation results show that the controller load is increased by about 7.71% compared to OCLDS, and the running time is increased by 17.7% compared to n-k-means. Full article

(This article belongs to the Special Issue Multiple Access Edge Computing in Non-Terrestial and Terrestrial Internet of Things Networks)

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Open AccessArticle

UAV-Assisted Privacy-Preserving Online Computation Offloading for Internet of Things

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Remote Sens. 2021, 13(23), 4853; https://doi.org/10.3390/rs13234853 - 29 Nov 2021

Cited by 6 | Viewed by 1226

Abstract

Unmanned aerial vehicle (UAV) plays a more and more important role in Internet of Things (IoT) for remote sensing and device interconnecting. Due to the limitation of computing capacity and energy, the UAV cannot handle complex tasks. Recently, computation offloading provides a promising way for the UAV to handle complex tasks by deep reinforcement learning (DRL)-based methods. However, existing DRL-based computation offloading methods merely protect usage pattern privacy and location privacy. In this paper, we consider a new privacy issue in UAV-assisted IoT, namely computation offloading preference leakage, which lacks through study. To cope with this issue, we propose a novel privacy-preserving online computation offloading method for UAV-assisted IoT. Our method integrates the differential privacy mechanism into deep reinforcement learning (DRL), which can protect UAV’s offloading preference. We provide the formal analysis on security and utility loss of our method. Extensive real-world experiments are conducted. Results demonstrate that, compared with baseline methods, our method can learn cost-efficient computation offloading policy without preference leakage and a priori knowledge of the wireless channel model. Full article

(This article belongs to the Special Issue Multiple Access Edge Computing in Non-Terrestial and Terrestrial Internet of Things Networks)

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Open AccessTechnical Note

Tropospheric Attenuation in GeoSurf Satellite Constellations

Emilio Matricciani

Carlo Riva

and

Lorenzo Luini

Remote Sens. 2021, 13(24), 5180; https://doi.org/10.3390/rs13245180 - 20 Dec 2021

Cited by 2 | Viewed by 1539

Abstract

In GeoSurf satellite constellations, any transmitter/receiver, wherever it is located, is linked to a satellite with zenith paths. We have studied the tropospheric attenuation predicted for some reference sites (Canberra, Holmdel, Pasadena, Robledo, and Spino d’Adda), which also set the meridian along which we have considered sites with latitudes ranging between 60° N and 60° S. At the annual probability of 1% of an average year, in the latitude between 30° N and 30° S, there are no significant differences between GEO slant paths and GeoSurf zenith paths. On the contrary, at 0.1% and 0.01% annual probabilities, large differences are found for latitudes greater than 30° N or 30° S. For comparing the tropospheric attenuation in GeoSurf paths with that expected in LEO highly variable slant paths, we have considered, as reference, a LEO satellite constellation orbiting in circular at 817 km. GeoSurf zenith paths “gain” several dBs compared to LEO slant paths. The more static total clear-sky attenuation (water vapor, oxygen, and clouds) in both GEO and LEO slant paths shows larger values than GeoSurf zenith paths. Both for rain and clear-sky attenuations, Northern and Southern Hemispheres show significant differences. Full article

(This article belongs to the Special Issue Multiple Access Edge Computing in Non-Terrestial and Terrestrial Internet of Things Networks)

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