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Internet of Things (IoT) Remote Sensing

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Engineering Remote Sensing".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 27435

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Special Issue Editors

Prof. Dr. Prem Prakash Jayaraman

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Faculty of Science, Engineering & Technology, Swinburne University of Technology, 1 Alfred Street, Hawthorn, VIC 3122, Australia
Interests: internet of things; distributed computing; mobile and cloud computing
Special Issues, Collections and Topics in MDPI journals

Dr. Federico Montori

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Dipartimento di Informatica - Scienza e Ingegneria, Università di Bologna, Mura Anteo Zamboni 7, 40126 Bologna, Italy
Interests: mobile crowdsensing; data analytics; Pervasive and Ubiquitous computing
Special Issues, Collections and Topics in MDPI journals

Dr. Charith Perera

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Guest Editor

School of Computer Science and Informatics, Cardiff University, Cardiff CF24 3AA, UK
Interests: Internet of Things; sensing as a service; privacy; infrastructure and architectures; fog/edge computing
Special Issues, Collections and Topics in MDPI journals

Dr. Felip Marti

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Guest Editor

Swinburne's Digital Innovation Lab., Swinburne University of Technology, 1 Alfred Street, Hawthorn, VIC 3122, Australia
Interests: internet of things; computer vision; machine learning; robotics

Special Issue Information

Dear Colleagues,

Remote sensing has evolved significantly with the advent of the Internet of Things that has enabled fast and cheap acquisition of data from millions and billions of interconnected devices deployed across the globe. The IoT and the continuous data stemming from IoT has changed the traditional interpretation of remote sensing (including the ways we capture and extract knowledge from this data). IoT Remote Sensing provides the foundation to support the development of Digital Earth Twins that will create massive opportunities to address important environmental and societal challenges in areas such as agriculture, smart cities, geology, among others.

Grand challenges imposed by greenfield and brownfield IoT remote sensing deployments including big data acquisition, connectivity, scalability, security and privacy, unmanned aerial vehicles (UAVs), spatial data analytics, mobility, and federated learning. This special issue solicits high quality research papers, work in progress papers, surveys, real-world application/deployment studies that address these challenges. Potential topics of interest for this special issue (but not limited to) are:

Opportunities and challenges in IoT Remote Sensing
IoT Remote Sensing Applications, Use Cases, Real World Deployments
Geospatial standards and data standards for IoT Remote Sensing
Spatial data analytics
Federated Learning for IoT Remote Sensing (across space, cloud, edge and devices)
Mobile IoT Remote Sensing including UAVs
Privacy and Security in IoT Remote Sensing
Privacy-preserved data analytics for IoT Remote Sensing

Prof. Dr. Prem Prakash Jayaraman
Dr. Federico Montori
Dr. Charith Perera
Dr. Felip Marti
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 2700 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.

Keywords

IoT Geospatial applications
Mobile IoT Remote Sensing
SAT-IoT
Mobile Crowdsensing
Satellite and Airborne sensing
Geospatial data analysis
Privacy and Security in IoT Remote Sensing

Published Papers (9 papers)

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Research

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16 pages, 5186 KiB

Open AccessArticle

A Lightweight Privacy-Preserving System for the Security of Remote Sensing Images on IoT

by Denghui Zhang, Lijing Ren, Muhammad Shafiq and Zhaoquan Gu

Remote Sens. 2022, 14(24), 6371; https://doi.org/10.3390/rs14246371 - 16 Dec 2022

Cited by 5 | Viewed by 2233

Abstract

The acquisition of massive remote sensing data makes it possible to deeply fuse remote sensing and artificial intelligence (AI). The mobility and cost advantages of new sensing platforms in the Internet of Things (IoT) make them ideal for continuous deployment rather than traditional airborne platforms. However, remote sensing devices are vulnerable to malicious attacks and privacy leaks when sharing data due to the complex architecture and heterogeneity of IoT and the lack of a unified security protection mechanism. Traditional protection methods based on public-key encryption require not only complex operations but also energy consumption, which poses new challenges for resources-limited IoT. The objective of this paper was to propose a lightweight privacy-preserving system for the security of remote-sensing images based on visual cryptography. This stacking-to-see feature of visual cryptography enables the efficient encryption of big data such as high-resolution and multi-scale remote sensing images in resource-constrained IoT. To alleviate image quality degradation in visual cryptography, we combined denoising neural networks to extract high-quality images from encrypted datasets, thus improving the recognition accuracy of loss datasets. We conducted extensive experiments, and the results verify the effectiveness of the proposed method in terms of privacy protection and classification accuracy. Full article

(This article belongs to the Special Issue Internet of Things (IoT) Remote Sensing)

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18 pages, 4851 KiB

Open AccessArticle

Monitoring Air Quality in Urban Areas Using a Vehicle Sensor Network (VSN) Crowdsensing Paradigm

by Paolo Diviacco, Massimiliano Iurcev, Rodrigo José Carbajales, Nikolas Potleca, Alberto Viola, Mihai Burca and Alessandro Busato

Remote Sens. 2022, 14(21), 5576; https://doi.org/10.3390/rs14215576 - 04 Nov 2022

Cited by 14 | Viewed by 2415

Abstract

We present new advances in monitoring particulate matter (PM) in urban areas within a participatory vehicle sensor network (VSN) that exploits the use of multiple mobile low-cost IoT devices. These devices send geolocated PM measurements to an IT infrastructure and enabled us to reconstruct, in real time, the spatial and temporal distribution of pollutants in the study area in a web-based environment. The newly acquired data were integrated with independent reference measurements available from governmental environmental agencies. We deployed the infrastructure in the city of Trieste (Italy), since the beginning of 2021, with the help of several volunteers and the local transportation authority (Trieste Trasporti). By analysing the data, we delineate areas with lower air quality and identify the possible causes of these anomalies. We were able to define a belt outside the urban center where an enhanced concentration of pollutants occurs due to a higher flux of vehicular traffic that tends to jam there. Overall, our results demonstrate that this approach can be helpful in supporting urban planning and can also stimulate the community to reflect on how they can improve air quality in the area they live by reducing the use of private cars in favour of more widespread public transportation usage. Full article

(This article belongs to the Special Issue Internet of Things (IoT) Remote Sensing)

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22 pages, 1203 KiB

Open AccessArticle

Granular Content Distribution for IoT Remote Sensing Data Supporting Privacy Preservation

by Xiaoshuai Zhang, Guangyuan Zhang, Xingru Huang and Stefan Poslad

Remote Sens. 2022, 14(21), 5574; https://doi.org/10.3390/rs14215574 - 04 Nov 2022

Cited by 2 | Viewed by 1515

Abstract

Facilitated by the Internet of Things (IoT) and diverse IoT devices, remote sensing data are evolving into the multimedia era with an expanding data scale. Massive remote sensing data are collected by IoT devices to monitor environments and human activities. Because IoT devices are involved in the data collection, there are probably private data contained in the collected remote sensing data, such as the device owner information and the precise location. Therefore, when data analysts, researchers, and other stakeholders require remote sensing data from numerous IoT devices for different analyses and investigations, how to distribute massive remote sensing data efficiently and regulate different people to view different parts of the distributed remote sensing data is a challenge to be addressed. Many general solutions rely on granular access control for content distribution but do not consider the low computational efficiency caused by the huge file size of the remote sensing data or certain IoT devices only have a constrained computational performance. Therefore, we propose a new granular content distribution scheme, which is more lightweight and practical for the distribution of multimedia remote sensing data with the consideration of the large data size to avoid complicated operations to the data. Furthermore, a dual data integrity check (hash summary and watermark) designed in our scheme can detect tampering or forgery from encrypted remote sensing data before decrypting it and validate it again after decryption. The security analyses and experimental results manifest that our new scheme can maintain high computational efficiency and block tampering and forgery during the granular content distribution for IoT remote sensing data. Full article

(This article belongs to the Special Issue Internet of Things (IoT) Remote Sensing)

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31 pages, 3349 KiB

Open AccessArticle

Robustness of Deep Learning-Based Specific Emitter Identification under Adversarial Attacks

by Liting Sun, Da Ke, Xiang Wang, Zhitao Huang and Kaizhu Huang

Remote Sens. 2022, 14(19), 4996; https://doi.org/10.3390/rs14194996 - 07 Oct 2022

Cited by 7 | Viewed by 1990

Abstract

Deep learning (DL)-based specific emitter identification (SEI) technique can automatically extract radio frequency (RF) fingerprint features in RF signals to distinguish between legal and illegal devices and enhance the security of wireless network. However, deep neural network (DNN) can easily be fooled by adversarial examples or perturbations of the input data. If a malicious device emits signals containing a specially designed adversarial samples, will the DL-based SEI still work stably to correctly identify the malicious device? To the best of our knowledge, this research is still blank, let alone the corresponding defense methods. Therefore, this paper designs two scenarios of attack and defense and proposes the corresponding implementation methods to specializes in the robustness of DL-based SEI under adversarial attacks. On this basis, detailed experiments are carried out based on the real-world data and simulation data. The attack scenario is that the malicious device adds an adversarial perturbation signal specially designed to the original signal, misleading the original system to make a misjudgment. Experiments based on three different attack generation methods show that DL-based SEI is very vulnerability. Even if the intensity is very low, without affecting the probability density distribution of the original signal, the performance can be reduced to about 50%, and at −22 dB it is completely invalid. In the defense scenario, the adversarial training (AT) of DL-based SEI is added, which can significantly improve the system’s performance under adversarial attacks, with ≥60% improvement in the recognition rate compared to the network without AT. Further, AT has a more robust effect on white noise. This study fills the relevant gaps and provides guidance for future research. In the future research, the impact of adversarial attacks must be considered, and it is necessary to add adversarial training in the training process. Full article

(This article belongs to the Special Issue Internet of Things (IoT) Remote Sensing)

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13 pages, 1227 KiB

Open AccessCommunication

An HAP and UAVs Collaboration Framework for Uplink Secure Rate Maximization in NOMA-Enabled IoT Networks

by Dawei Wang, Menghan Wu, Yixin He, Linna Pang, Qian Xu and Ruonan Zhang

Remote Sens. 2022, 14(18), 4501; https://doi.org/10.3390/rs14184501 - 09 Sep 2022

Cited by 23 | Viewed by 1931

Abstract

In this paper, we propose a high-altitude platform (HAP) and unmanned aerial vehicles (UAVs) collaboration framework in non-orthogonal multiple access (NOMA)-enabled Internet of Things (IoT) networks with the presence of an eavesdropping UAV. For the considered framework, we investigate the uplink secure transmission by optimizing channel allocation from UAVs to HAP, users’ power, and UAVs’ three-dimensional (3D) position. To solve this non-convex problem, we adopt the K-means cluster pair algorithm to divide paired users into different groups and each cluster can be served by a corresponding UAV. Then, the formulated optimization problem is decoupled into three subproblems and tackled iteratively based on the block coordinate descent (BCD) algorithm. Finally, simulation results verify that the proposed network architecture can achieve a higher secure rate, faster convergence evolution, and lower complexity in comparison with the current works. Full article

(This article belongs to the Special Issue Internet of Things (IoT) Remote Sensing)

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22 pages, 8348 KiB

Open AccessArticle

Multi-Classifier Fusion for Open-Set Specific Emitter Identification

by Yurui Zhao, Xiang Wang, Ziyu Lin and Zhitao Huang

Remote Sens. 2022, 14(9), 2226; https://doi.org/10.3390/rs14092226 - 06 May 2022

Cited by 11 | Viewed by 1905

Abstract

To safeguard the privacy and security of IoT systems, specific emitter identification is utilized to recognize device identity with hardware characteristics. In view of the growing demand for identifying unknown devices, this paper aims to discuss open-set specific emitter identification. We firstly build up a problem formulation for open-set SEI by discussing the working mechanisms of radio signals and open-set recognition. And then it is pointed out that feature coincidence is an intractable challenge in open-set SEI. The reason, accounting for this phenome, is that pretrained fingerprint feature extractors are incapable of clustering unknown device features and differentiating them from known ones. Considering that feature coincidence leads to error recognition of unknown devices, we propose to fuse multi-classifiers in the decision layer to improve accuracy and recall. Three distinct inputs and four different fusion methods are adopted in this paper to implement multi-classifier fusion. The datasets collected at Huanghua Airport demonstrate that the proposed method can avoid the coincidence of feature space and achieve higher accuracy and recall. Full article

(This article belongs to the Special Issue Internet of Things (IoT) Remote Sensing)

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31 pages, 3127 KiB

Open AccessArticle

Managing Time-Sensitive IoT Applications via Dynamic Application Task Distribution and Adaptation

by Harindu Korala, Dimitrios Georgakopoulos, Prem Prakash Jayaraman and Ali Yavari

Remote Sens. 2021, 13(20), 4148; https://doi.org/10.3390/rs13204148 - 16 Oct 2021

Cited by 9 | Viewed by 2694

Abstract

The recent proliferation of the Internet of Things has led to the pervasion of networked IoT devices such as sensors, video cameras, mobile phones, and industrial machines. This has fueled the growth of Time-Sensitive IoT (TS-IoT) applications that must complete the tasks of (1) collecting sensor observations they need from appropriate IoT devices and (2) analyzing the data within application-specific time-bounds. If this is not achieved, the value of these applications and the results they produce depreciates. At present, TS-IoT applications are executed in a distributed IoT environment that consists of heterogeneous computing and networking resources. Due to the heterogeneous and volatile nature (e.g., unpredictable data rates and sudden disconnections) of the IoT environment, it has become a major challenge to ensure the time-bounds of TS-IoT applications. Many existing task management techniques (i.e., techniques that are used to manage the execution of IoT applications in distributed computing resources) that have been proposed to support TS-IoT applications to meet their time-bounds do not provide a sophisticated and complete solution to manage the TS-IoT applications in a manner in which their time-bounds are guaranteed. This paper proposes TIDA, a comprehensive platform for managing TS-IoT applications that includes a task management technique, called DTDA, which incorporates novel task sizing, distribution, and dynamic adaptation techniques. DTDA’s task sizing technique measures the computing resources required to complete each task of the TS-IoT application at hand in each available IoT device, edge computer (e.g., network gateways), and cloud virtual machine. DTDA’s task distribution technique distributes and executes the tasks of each TS-IoT application in a manner that their time-bound requirements are met. Finally, DTDA includes a task adaptation technique that dynamically adapts the distribution of tasks (i.e., redistributes TS-IoT application tasks) when it detects a potential application time-bound violation. The paper describes a proof-of-concept implementation of TIDA that uses Microsoft’s Orleans Actor Framework. Finally, the paper demonstrates that the DTDA task management technique of TIDA meets the time-bound requirements of TS-IoT applications by presenting an experimental evaluation involving real time-sensitive IoT applications from the smart city domain. Full article

(This article belongs to the Special Issue Internet of Things (IoT) Remote Sensing)

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15 pages, 2762 KiB

Open AccessArticle

A Comprehensive Study of Geochemical Data Storage Performance Based on Different Management Methods

by Yinyi Cheng, Kefa Zhou, Jinlin Wang, Philippe De Maeyer, Tim Van de Voorde, Jining Yan and Shichao Cui

Remote Sens. 2021, 13(16), 3208; https://doi.org/10.3390/rs13163208 - 13 Aug 2021

Cited by 1 | Viewed by 2113

Abstract

The spatial calculation of vector data is crucial for geochemical analysis in geological big data. However, large volumes of geochemical data make for inefficient management. Therefore, this study proposed a shapefile storage method based on MongoDB in GeoJSON form (SSMG) and a shapefile storage method based on PostgreSQL with open location code (OLC) geocoding (SSPOG) to solve the problem of low efficiency of electronic form management. The SSMG method consists of a JSONification tier and a cloud storage tier, while the SSPOG method consists of a geocoding tier, an extension tier, and a storage tier. Using MongoDB and PostgreSQL as databases, this study achieved two different types of high-throughput and high-efficiency methods for geochemical data storage and retrieval. Xinjiang, the largest province in China, was selected as the study area in which to test the proposed methods. Using geochemical data from shapefile as a data source, several experiments were performed to improve geochemical data storage efficiency and achieve efficient retrieval. The SSMG and SSPOG methods can be applied to improve geochemical data storage using different architectures, so as to achieve management of geochemical data organization in an efficient way, through time consumed and data compression ratio (DCR), in order to better support geological big data. The purpose of this study was to find ways to build a storage method that can improve the speed of geochemical data insertion and retrieval by using excellent big data technology to help us efficiently solve problem of geochemical data preprocessing and provide support for geochemical analysis. Full article

(This article belongs to the Special Issue Internet of Things (IoT) Remote Sensing)

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Review

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31 pages, 1294 KiB

Open AccessReview

Boost Precision Agriculture with Unmanned Aerial Vehicle Remote Sensing and Edge Intelligence: A Survey

by Jia Liu, Jianjian Xiang, Yongjun Jin, Renhua Liu, Jining Yan and Lizhe Wang

Remote Sens. 2021, 13(21), 4387; https://doi.org/10.3390/rs13214387 - 30 Oct 2021

Cited by 70 | Viewed by 7788

Abstract

In recent years unmanned aerial vehicles (UAVs) have emerged as a popular and cost-effective technology to capture high spatial and temporal resolution remote sensing (RS) images for a wide range of precision agriculture applications, which can help reduce costs and environmental impacts by providing detailed agricultural information to optimize field practices. Furthermore, deep learning (DL) has been successfully applied in agricultural applications such as weed detection, crop pest and disease detection, etc. as an intelligent tool. However, most DL-based methods place high computation, memory and network demands on resources. Cloud computing can increase processing efficiency with high scalability and low cost, but results in high latency and great pressure on the network bandwidth. The emerging of edge intelligence, although still in the early stages, provides a promising solution for artificial intelligence (AI) applications on intelligent edge devices at the edge of the network close to data sources. These devices are with built-in processors enabling onboard analytics or AI (e.g., UAVs and Internet of Things gateways). Therefore, in this paper, a comprehensive survey on the latest developments of precision agriculture with UAV RS and edge intelligence is conducted for the first time. The major insights observed are as follows: (a) in terms of UAV systems, small or light, fixed-wing or industrial rotor-wing UAVs are widely used in precision agriculture; (b) sensors on UAVs can provide multi-source datasets, and there are only a few public UAV dataset for intelligent precision agriculture, mainly from RGB sensors and a few from multispectral and hyperspectral sensors; (c) DL-based UAV RS methods can be categorized into classification, object detection and segmentation tasks, and convolutional neural network and recurrent neural network are the mostly common used network architectures; (d) cloud computing is a common solution to UAV RS data processing, while edge computing brings the computing close to data sources; (e) edge intelligence is the convergence of artificial intelligence and edge computing, in which model compression especially parameter pruning and quantization is the most important and widely used technique at present, and typical edge resources include central processing units, graphics processing units and field programmable gate arrays. Full article

(This article belongs to the Special Issue Internet of Things (IoT) Remote Sensing)

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