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Electronics
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IoT Sensor Network Application
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IoT Sensor Network Application

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 13337

Special Issue Editors

Asst. Prof. Dr. Younggoo Kwon

E-Mail Website
Guest Editor
Department of Electronic Engineering, Konkuk University, Seoul 143-701, Korea
Interests: ubiquitous networking; wireless networks and mobile computing; wireless medium access control (MAC); mobile ad hoc networks and wireless sensor networks; powerline communications and networking
Prof. Dr. ChangJoo Moon

E-Mail Website
Guest Editor
Department of Smart Vehicle Engineering, Konkuk University, Seoul 143-701, Korea
Interests: Internet of Things; big data; autonomous car; intelligent transportation system; unmanned aerial vehicle; urban air mobility

Special Issue Information

Dear Colleagues,

Ubiquitous sensor networks (USNs) with various resource-limited devices have the capability of sensing, collecting, and disseminating data in many different real-life applications. Theory, methodology, and applications of ubiquitous sensor networks (USNs) have a considerable and continuous research interest in networking applications. Current improvements in USN platforms, distributed efficient information handling for smart objects, enhanced real-time networking protocols, and several other ubiquitous networking solutions and technologies have enabled various Internet of Things (IoT) application developments in real life.

Diverse network-connected devices are being researched in the IoT field. In recent years, autonomous cars and UAVs are attracting great interest as IoT devices due to their marketability and industrial impact. The importance of sensors and networks is increasing in the unmanned transfer vehicle IoT system that provides control and services data. These data are generated during collecting and processing large amounts of sensor data of autonomous cars and UAVs in real-time.

IoT sensor network applications contribute to the significant research advances in the following areas such as ubiquitous and context-aware computing, USN location awareness services, protocols and algorithms of USN, sensor data processing, management and control of USN, IoT architectures, IoT network applications, etc.

Prof. Younggoo Kwon
Prof. Dr. ChangJoo Moon
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. Electronics 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 2400 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

  • ubiquitous sensor networks (USNs)
  • Internet of Things
  • Intelligent Transportation System (ITS)
  • Architecture of IoT
  • network protocols

Published Papers (6 papers)

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Research

19 pages, 3926 KiB  
Article
A Proposed Waiting Time Algorithm for a Prediction and Prevention System of Traffic Accidents Using Smart Sensors
by Seongsoo Cho, Bhanu Shrestha, Bashir Salah, Inam Ullah and Nermin M. Salem
Electronics 2022, 11(11), 1765; https://doi.org/10.3390/electronics11111765 - 2 Jun 2022
Cited by 3 | Viewed by 2274 | Correction
Abstract
One of the applications of neural networks is to predict the fault section results of traffic utilizing the combined model estimation of the fault section and self-learning models with smart sensors. The prediction of the fault section can autonomously develop the internal model [...] Read more.
One of the applications of neural networks is to predict the fault section results of traffic utilizing the combined model estimation of the fault section and self-learning models with smart sensors. The prediction of the fault section can autonomously develop the internal model of the network to fit the pre-entered “traffic accident” section data and predict the occurrence of traffic accident sections. In this paper, we propose the results of waiting time for traffic accidents in case of traffic accidents by using a neural network and fuzzy expert system, in comparison with existing algorithms and algorithms for determining traffic accidents. It is used to estimate or predict traffic accident reliability as well. Typically, the type of fault data collected is the number of faults (the number of faults recorded during a given time interval) or the time of fault (the time-of-fault data recorded when each fault occurred), and this can be utilized only for group data types, rather than the time-of-fault data type. Full article
(This article belongs to the Special Issue IoT Sensor Network Application)
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22 pages, 5150 KiB  
Article
3D Void Handling Geographic P2P-RPL for Indoor Multi-Hop IR-UWB Networks
by Dongwon Kim, Jiwon Jung and Younggoo Kwon
Electronics 2022, 11(4), 625; https://doi.org/10.3390/electronics11040625 - 17 Feb 2022
Viewed by 1051
Abstract
IETF has standardized the point-to-point RPL (P2P-RPL) to ensure reliable and optimal P2P route discovery for low-power and lossy networks (LLNs). P2P-RPL propagates route discovery packets to all nodes in the network, which results in high routing communication overheads. Recently, other RPL-based P2P [...] Read more.
IETF has standardized the point-to-point RPL (P2P-RPL) to ensure reliable and optimal P2P route discovery for low-power and lossy networks (LLNs). P2P-RPL propagates route discovery packets to all nodes in the network, which results in high routing communication overheads. Recently, other RPL-based P2P routing algorithms have been proposed to reduce such overheads, but still, quite an amount of overheads occur due to their flooding-based approach. In real life 3D environments, a larger number of nodes should be deployed to guarantee the full network connectivity, and thus the flooding strategy incurs higher overheads. In effort to alleviate high overheads, geographic routing is an attractive solution that exploits the nodes’ geographic locations in its next-hop routing selection. However, geographic routing inherently suffers from the local minimum (void) problem following greedy next-hop selection. Local minima occur more often in 3D space, and therefore, a reliable 3D void handling technique is required. In this paper, we propose greedy forwarding and void handling point-to-point RPL with adaptive trickle timer (GVA-P2P-RPL), which is a novel RPL-based P2P routing protocol that quickly discovers energy-efficient and reliable P2P routes in 3D networks. In GVA-P2P-RPL, P2P-RPL is modified to greedily forward routing packets when it is possible. IR-UWB-based 3D multi-hop self-positioning is conducted in advance to obtain the geographical location of each node. When local minima are encountered, routing packets are temporarily broadcast just like in the traditional P2P-RPL. A new trickle algorithm called adaptive trickle timer (ATT) is also presented to reduce route discovery time and provide better collision avoidance effects. The performance of GVA-P2P-RPL is compared with that of P2P-RPL, partial flooding-based P2P-RPL (PF-P2P-RPL) and ER-RPL. It shows significant improvements in route discovery overheads and route discovery time against these state-of-the-art RPL-based P2P routing methods in 3D environments. Performance evaluation in the special network case where a huge 3D void volume exists in the center is also presented to show the strong void recovery capability of the proposed GVA-P2P-RPL in 3D environments. Full article
(This article belongs to the Special Issue IoT Sensor Network Application)
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16 pages, 43324 KiB  
Article
Road Dynamic Object Mapping System Based on Edge-Fog-Cloud Computing
by Sooyeon Shin, Jungseok Kim and Changjoo Moon
Electronics 2021, 10(22), 2825; https://doi.org/10.3390/electronics10222825 - 17 Nov 2021
Cited by 7 | Viewed by 1939
Abstract
Dynamic objects appearing on the road without notice can cause serious accidents. However, the detection ranges of roadside unit and CCTV that collect current road information are very limited. Moreover, there are a lack of systems for managing the collected information. In this [...] Read more.
Dynamic objects appearing on the road without notice can cause serious accidents. However, the detection ranges of roadside unit and CCTV that collect current road information are very limited. Moreover, there are a lack of systems for managing the collected information. In this study, a dynamic mapping system was implemented using a connected car that collected road environments data continuously. Additionally, edge-fog-cloud computing was applied to efficiently process large amounts of road data. For accurate dynamic mapping, the following steps are proposed: first, the classification and 3D position of road objects are estimated through a stereo camera and GPS data processing, and the coordinates of objects are mapped to a preset grid cell. Second, object information is transmitted in real time to a constructed big data processing platform. Subsequently, the collected information is compared with the grid information of an existing map, and the map is updated. As a result, an accurate dynamic map is created and maintained. In addition, this study verifies that maps can be shared in real time with IoT devices in various network environments, and this can support a safe driving milieu. Full article
(This article belongs to the Special Issue IoT Sensor Network Application)
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23 pages, 1056 KiB  
Article
Collision Avoidance Geographic P2P-RPL in Multi-Hop Indoor Wireless Networks
by Yunyoung Choi, Jaehyung Park, Jiwon Jung and Younggoo Kwon
Electronics 2021, 10(12), 1484; https://doi.org/10.3390/electronics10121484 - 20 Jun 2021
Cited by 2 | Viewed by 1567
Abstract
In home and building automation applications, wireless sensor devices need to be connected via unreliable wireless links within a few hundred milliseconds. Routing protocols in Low-power and Lossy Networks (LLNs) need to support reliable data transmission with an energy-efficient manner and short routing [...] Read more.
In home and building automation applications, wireless sensor devices need to be connected via unreliable wireless links within a few hundred milliseconds. Routing protocols in Low-power and Lossy Networks (LLNs) need to support reliable data transmission with an energy-efficient manner and short routing convergence time. IETF standardized the Point-to-Point RPL (P2P-RPL) routing protocol, in which P2P-RPL propagates the route discovery messages over the whole network. This leads to significant routing control packet overhead and a large amount of energy consumption. P2P-RPL uses the trickle algorithm to control the transmission rate of routing control packets. The non-deterministic message suppression nature of the trickle algorithm may generate a sub-optimal routing path. The listen-only period of the trickle algorithm may lead to a long network convergence time. In this paper, we propose Collision Avoidance Geographic P2P-RPL, which achieves energy-efficient P2P data delivery with a fast routing request procedure. The proposed algorithm uses the location information to limit the network search space for the desired route discovery to a smaller location-constrained forwarding zone. The Collision Avoidance Geographic P2P-RPL also dynamically selects the listen-only period of the trickle timer algorithm based on the transmission priority related to geographic position information. The location information of each node is obtained from the Impulse-Response Ultra-WideBand (IR-UWB)-based cooperative multi-hop self localization algorithm. We implement Collision Avoidance Geographic P2P-RPL on Contiki OS, an open-source operating system for LLNs and the Internet of Things. The performance results show that the Collision Avoidance Geographic P2P-RPL reduced the routing control packet overheads, energy consumption, and network convergence time significantly. The cooperative multi-hop self localization algorithm improved the practical implementation characteristics of the P2P-RPL protocol in real world environments. The collision avoidance algorithm using the dynamic trickle timer increased the operation efficiency of the P2P-RPL under various wireless channel conditions with a location-constrained routing space. Full article
(This article belongs to the Special Issue IoT Sensor Network Application)
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18 pages, 4673 KiB  
Article
Spatial Mapping of Distributed Sensors Biomimicking the Human Vision System
by Sandip Dutta and Martha Wilson
Electronics 2021, 10(12), 1443; https://doi.org/10.3390/electronics10121443 - 16 Jun 2021
Viewed by 2350
Abstract
Machine vision has been thoroughly studied in the past, but research thus far has lacked an engineering perspective on human vision. This paper addresses the observed and hypothetical neural behavior of the brain in relation to the visual system. In a human vision [...] Read more.
Machine vision has been thoroughly studied in the past, but research thus far has lacked an engineering perspective on human vision. This paper addresses the observed and hypothetical neural behavior of the brain in relation to the visual system. In a human vision system, visual data are collected by photoreceptors in the eye, and these data are then transmitted to the rear of the brain for processing. There are millions of retinal photoreceptors of various types, and their signals must be unscrambled by the brain after they are carried through the optic nerves. This work is a forward step toward explaining how the photoreceptor locations and proximities are resolved by the brain. It is illustrated here that unlike in digital image sensors, there is no one-to-one sensor-to-processor identifier in the human vision system. Instead, the brain must go through an iterative learning process to identify the spatial locations of the photosensors in the retina. This involves a process called synaptic pruning, which can be simulated by a memristor-like component in a learning circuit model. The simulations and proposed mathematical models in this study provide a technique that can be extrapolated to create spatial distributions of networked sensors without a central observer or location knowledge base. Through the mapping technique, the retinal space with known configuration generates signals as scrambled data-feed to the logical space in the brain. This scrambled response is then reverse-engineered to map the logical space’s connectivity with the retinal space locations. Full article
(This article belongs to the Special Issue IoT Sensor Network Application)
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15 pages, 7168 KiB  
Article
Design and Implementation of Edge-Fog-Cloud System through HD Map Generation from LiDAR Data of Autonomous Vehicles
by Junwon Lee, Kieun Lee, Aelee Yoo and Changjoo Moon
Electronics 2020, 9(12), 2084; https://doi.org/10.3390/electronics9122084 - 7 Dec 2020
Cited by 21 | Viewed by 3337
Abstract
Self-driving cars, autonomous vehicles (AVs), and connected cars combine the Internet of Things (IoT) and automobile technologies, thus contributing to the development of society. However, processing the big data generated by AVs is a challenge due to overloading issues. Additionally, near real-time/real-time IoT [...] Read more.
Self-driving cars, autonomous vehicles (AVs), and connected cars combine the Internet of Things (IoT) and automobile technologies, thus contributing to the development of society. However, processing the big data generated by AVs is a challenge due to overloading issues. Additionally, near real-time/real-time IoT services play a significant role in vehicle safety. Therefore, the architecture of an IoT system that collects and processes data, and provides services for vehicle driving, is an important consideration. In this study, we propose a fog computing server model that generates a high-definition (HD) map using light detection and ranging (LiDAR) data generated from an AV. The driving vehicle edge node transmits the LiDAR point cloud information to the fog server through a wireless network. The fog server generates an HD map by applying the Normal Distribution Transform-Simultaneous Localization and Mapping(NDT-SLAM) algorithm to the point clouds transmitted from the multiple edge nodes. Subsequently, the coordinate information of the HD map generated in the sensor frame is converted to the coordinate information of the global frame and transmitted to the cloud server. Then, the cloud server creates an HD map by integrating the collected point clouds using coordinate information. Full article
(This article belongs to the Special Issue IoT Sensor Network Application)
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