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The Internet of Things (IoT) in Remote Sensing: Opportunities and Challenges

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (16 June 2019) | Viewed by 14609

Special Issue Editor


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Guest Editor
School of Information Technology and Engineering, Melbourne Institute of Technology, 288 Latrobe Street, Melbourne, VIC 3000, Australia
Interests: remote sensing; sensors, smart environments; deep learning; IoT; radar (lidar); wireless power transer
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advances in home automation, smart environments, smart cities and sensor networks for civilian and military applications have combined to create the Internet of Things (IoT). With it has also emerged a transformation of the Internet and mobile communication networks into a single infrastructure. The goal is to be able to access, secure, manage, control and scale the single IoT infrastructure from anywhere and with any device. This transformation creates new industrial opportunities in smart environments, sensor software, IoT platforms, cyber security, cryptographic schemes for sensor networks, digital forensics and sales.

New frontiers for IoT include sensor networks on land, underwater and in space to monitor environmental conditions, the remote supervision of large-scale farms, precision agriculture, remote surgery, remote laboratories, the retail industry and autonomous devices including drones and unmanned aerial vehicles (UAV).

This Special Issue on “IoT in Remote Sensing: Opportunities and Challenges” provides a unique opportunity for researchers in these areas to publish current state of the art research and deployment findings. The Special Issue will accept research and tutorial papers on remote sensing applications and data analysis visualization and virtualization, environmental condition sensing applications, precision agriculture sensor networks, drone sensors, UAV sensing networks, and IoT platforms including military applications.

The objectives include deployment scenarios, management of ‘greenfield’ sensor networks and leveraging of ‘brownfield’ IoT infrastructure which could lead to new business models. Papers reporting on the state of the art, impact of IoT on human–machine interactions and machine intelligence, data collection and transfer, data processing to support food production, energy management, transformational health and transportation services in relation to the reduction of human carbon emissions will be considered.

Papers which target challenges including underwater sensor networks, swarm sensor techniques, embedded biological sensing, secure cryptographic schemes for sensors, and IoT security analysis will receive strong attention. Reports on industrial applications will be considered.

Prof. Johnson Agbinya
Guest Editor

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

  • Smart environments (cities, transport, homes, farms and health facilities) 
  • IoT deployment and management
  • IoT security analysis 
  • Autonomous sensor networks
  • Environmental monitoring and sensing 
  • Embedded biological sensing 
  • IoT applications and GIS
  • Match box cryptographic schemes for IoT 
  • IoT business models 
  • Applied IoT data analytics

Published Papers (3 papers)

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Research

17 pages, 785 KiB  
Article
Calibration of Wi-Fi-Based Indoor Tracking Systems for Android-Based Smartphones
by Miguel Martínez del Horno, Ismael García-Varea and Luis Orozco Barbosa
Remote Sens. 2019, 11(9), 1072; https://doi.org/10.3390/rs11091072 - 6 May 2019
Cited by 11 | Viewed by 4021
Abstract
With the growing development of smartphones equipped with Wi-Fi technology and the need of inexpensive indoor location systems, many researchers are focusing their efforts on the development of Wi-Fi-based indoor localization methods. However, due to the difficulties in characterizing the Wi-Fi radio signal [...] Read more.
With the growing development of smartphones equipped with Wi-Fi technology and the need of inexpensive indoor location systems, many researchers are focusing their efforts on the development of Wi-Fi-based indoor localization methods. However, due to the difficulties in characterizing the Wi-Fi radio signal propagation in such environments, the development of universal indoor localization mechanisms is still an open issue. In this paper, we focus on the calibration of Wi-Fi-based indoor tracking systems to be used by smartphones. The primary goal is to build an accurate and robust Wi-Fi signal propagation representation in indoor scenarios.We analyze the suitability of our approach in a smartphone-based indoor tracking system by introducing a novel in-motion calibration methodology using three different signal propagation characterizations supplemented with a particle filter. We compare the results obtained with each one of the three characterization in-motion calibration methodologies and those obtained using a static calibration approach, in a real-world scenario. Based on our experimental results, we show that the use of an in-motion calibration mechanism considerably improves the tracking accuracy. Full article
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16 pages, 1069 KiB  
Article
Energy Efficient and Delay Aware 5G Multi-Tier Network
by Nahina Islam, Ammar Alazab and Johnson Agbinya
Remote Sens. 2019, 11(9), 1019; https://doi.org/10.3390/rs11091019 - 29 Apr 2019
Cited by 4 | Viewed by 3042
Abstract
Multi-tier heterogeneous Networks (HetNets) with dense deployment of small cells in 5G networks are expected to effectively meet the ever increasing data traffic demands and offer improved coverage in indoor environments. However, HetNets are raising major concerns to mobile network operators such as [...] Read more.
Multi-tier heterogeneous Networks (HetNets) with dense deployment of small cells in 5G networks are expected to effectively meet the ever increasing data traffic demands and offer improved coverage in indoor environments. However, HetNets are raising major concerns to mobile network operators such as complex distributed control plane management, handover management issue, increases latency and increased energy expenditures. Sleep mode implementation in multi-tier 5G networks has proven to be a very good approach for reducing energy expenditures. In this paper, a Markov Decision Process (MDP)-based algorithm is proposed to switch between three different power consumption modes of a base station (BS) for improving the energy efficiency and reducing latency in 5G networks. The MDP-based approach intelligently switches between the states of the BS based on the offered traffic while maintaining a prescribed minimum channel rate per user. Simulation results show that the proposed MDP algorithm together with the three-state BSs results in a significant gain in terms of energy efficiency and latency. Full article
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21 pages, 5503 KiB  
Article
Research on Resource Allocation Method of Space Information Networks Based on Deep Reinforcement Learning
by Xiangli Meng, Lingda Wu and Shaobo Yu
Remote Sens. 2019, 11(4), 448; https://doi.org/10.3390/rs11040448 - 21 Feb 2019
Cited by 11 | Viewed by 4300
Abstract
The space information networks (SIN) have a series of characteristics, such as strong heterogeneity, multiple types of resources, and difficulty in management. Aiming at the problem of resource allocation in SIN, this paper firstly establishes a hierarchical and domain-controlled SIN architecture based on [...] Read more.
The space information networks (SIN) have a series of characteristics, such as strong heterogeneity, multiple types of resources, and difficulty in management. Aiming at the problem of resource allocation in SIN, this paper firstly establishes a hierarchical and domain-controlled SIN architecture based on software-defined networking (SDN). On this basis, the transmission, caching, and computing resources of the whole network are managed uniformly. The Asynchronous Advantage Actor-Critic (A3C) algorithm in deep reinforcement learning is introduced to model the process of resource allocation. The simulation results show that the proposed scheme can effectively improve the expected benefits of unit resources and improve the resource utilization efficiency of the SIN. Full article
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