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Challenges in Energy Perspective on Mobile Sensor Networks

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 41902

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Special Issue Editors

Prof. Dr. Moonseong Kim

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Guest Editor

Department of IT Convergence Software, Seoul Theological University, Bucheon 14754, Republic of Korea
Interests: IoT protocols; sensory networking; mobile computing; SDN/NFV; machine learning; numerical analysis; simulations
Special Issues, Collections and Topics in MDPI journals

Dr. Woochan Lee

E-Mail Website
Guest Editor

Department of Electrical Engineering, Incheon National University, Incheon 22012, Republic of Korea
Interests: electromagnetic numerical analysis; antennas and radar (development of time domain high speed electromagnetic numerical algorithm based on finite element method); network (wireless/wireless protocol optimization based on numerical analysis)

Special Issue Information

Dear Colleagues,

Big data, machine learning, and artificial intelligence change our way of life significantly. New data collection and utilization are crucial to enhance these technologies, and IoT technology for this improvement is continually evolving. Much research has been conducted on data collection/transmission technology and edge computing for data merging/security, but most research and utilization were performed only on fixed sensors. Therefore, mobile sensor technology has become necessary to remedy the defect of fixed sensors and to place sensors properly.

This Special Issue seeks a variety of technical activities for the mobile sensor network that will emerge, most importantly, in the data age. In this issue, we shall introduce technologies in various fields for mobile sensor networks such as mobile sensors, communication protocols, SDN (software-defined networking), NFV (network function virtualization), edge computing, wireless applications, and security, and provide opportunities to share new ideas with researchers around the world. Moreover, various new issues around computer science and electrical engineering, not limited to the above areas, will also be good material.

Prof. Dr. Moonseong Kim
Dr. Woochan Lee
Guest Editors

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Keywords

Wired/wireless mobile sensors
Sensor deployment/relocation protocols
Sensor communication/security protocols
Sensor fault-tolerant/energy-efficient protocols
VNF, NFV based on SDN for WSNs
Edge computing for WSNs
Simulation/numerical techniques
Machine learning applications
Applied electromagnetics for wireless applications

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Published Papers (12 papers)

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Research

18 pages, 5266 KiB

Open AccessArticle

The Secret Lives of Miniature Batteries

by Sivan Toledo and Shai Mendel

Sensors 2024, 24(3), 748; https://doi.org/10.3390/s24030748 - 24 Jan 2024

Viewed by 1104

Abstract

This article describes the design, implementation, and use of a new system to investigate the behavior of small batteries that power sensor and wireless systems that consume relatively high power during infrequent short activity periods. The system enables simple, low-cost, long-term (days to weeks) monitoring of batteries under such loads. Data collected by this system revealed a major cause of failures in wildlife tracking tags, an effect called concentration polarization, which causes a transient increase in the internal resistance of the battery. The article describes the goals and the design of the system, failures that it revealed, mechanisms to mitigate the limitations of miniature batteries, as well as a methodology to optimize and validate the design of tags powered by miniature batteries. Full article

(This article belongs to the Special Issue Challenges in Energy Perspective on Mobile Sensor Networks)

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18 pages, 2639 KiB

Open AccessArticle

Estimation of the Acoustic Transducer Beam Aperture by Using the Geometric Backscattering Model for Side-Scan Sonar Systems

by Van Duc Nguyen, Ngoc Minh Luu, Quoc Khuong Nguyen and Tien-Dung Nguyen

Sensors 2023, 23(4), 2190; https://doi.org/10.3390/s23042190 - 15 Feb 2023

Cited by 2 | Viewed by 2026

Abstract

In this paper, we propose an algorithm for estimating the beam aperture of the acoustic transducers by using the geometric backscattering model for side-scan sonar systems. The geometric backscattering model is developed to describe the propagation paths of the signal transmitted from the transducers towards the seabed and backscatters to the hydrophones. To evaluate our proposed algorithm, we have developed a side-scan sonar system. The side-scan sonar system uses two transducers, operating on two different frequencies and focusing on two different wave beams, to scan the images of the seabed. The proposed algorithm provides the estimated beam apertures of each transducer. Our obtained results agree quite well with the parameters provided by the manufacturers. Moreover, these results are used to calibrate the scanned images. We provide the scanned sonar 3D images of the Dong Do lakebed, Vietnam, to justify our proposal. Full article

(This article belongs to the Special Issue Challenges in Energy Perspective on Mobile Sensor Networks)

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18 pages, 5176 KiB

Open AccessEditor’s ChoiceArticle

Energy Balance of Wireless Sensor Nodes Based on Bluetooth Low Energy and Thermoelectric Energy Harvesting

by Yuming Liu, Jordi-Roger Riba and Manuel Moreno-Eguilaz

Sensors 2023, 23(3), 1480; https://doi.org/10.3390/s23031480 - 28 Jan 2023

Cited by 11 | Viewed by 2481

Abstract

The internet of things (IoT) makes it possible to measure physical variables and acquire data in places that were impossible a few years ago, such as transmission lines and electrical substations. Monitoring and fault diagnosis strategies can then be applied. A battery or an energy harvesting system charging a rechargeable battery typically powers IoT devices. The energy harvesting unit and rechargeable battery supply the sensors and wireless communications modules. Therefore, the energy harvesting unit must be correctly sized to optimize the availability and reliability of IoT devices. This paper applies a power balance of the entire IoT device, including the energy harvesting module that includes two thermoelectric generators and a DC–DC converter, the battery, and the sensors and communication modules. Due to the small currents typical of the different communication phases and their fast-switching nature, it is not trivial to measure the energy in each phase, requiring very specific instrumentation. This work shows that using conventional instrumentation it is possible to measure the energy involved in the different modes of communication. A detailed energy balance of the battery is also carried out during charge and discharge cycles, as well as communication modes, from which the maximum allowable data transfer rate is determined. The approach presented here can be generalized to many other smart grid IoT devices. Full article

(This article belongs to the Special Issue Challenges in Energy Perspective on Mobile Sensor Networks)

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16 pages, 2469 KiB

Open AccessArticle

Exploiting an Intermediate Latent Space between Photo and Sketch for Face Photo-Sketch Recognition

by Seho Bae, Nizam Ud Din, Hyunkyu Park and Juneho Yi

Sensors 2022, 22(19), 7299; https://doi.org/10.3390/s22197299 - 26 Sep 2022

Cited by 1 | Viewed by 1889

Abstract

The photo-sketch matching problem is challenging because the modality gap between a photo and a sketch is very large. This work features a novel approach to the use of an intermediate latent space between the two modalities that circumvents the problem of modality gap for face photo-sketch recognition. To set up a stable homogenous latent space between a photo and a sketch that is effective for matching, we utilize a bidirectional (photo → sketch and sketch → photo) collaborative synthesis network and equip the latent space with rich representation power. To provide rich representation power, we employ StyleGAN architectures, such as StyleGAN and StyleGAN2. The proposed latent space equipped with rich representation power enables us to conduct accurate matching because we can effectively align the distributions of the two modalities in the latent space. In addition, to resolve the problem of insufficient paired photo/sketch samples for training, we introduce a three-step training scheme. Extensive evaluation on a public composite face sketch database confirms superior performance of the proposed approach compared to existing state-of-the-art methods. The proposed methodology can be employed in matching other modality pairs. Full article

(This article belongs to the Special Issue Challenges in Energy Perspective on Mobile Sensor Networks)

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17 pages, 5891 KiB

Open AccessArticle

Multi-User Joint Detection Using Bi-Directional Deep Neural Network Framework in NOMA-OFDM System

by Md Habibur Rahman, Mohammad Abrar Shakil Sejan, Seung-Geun Yoo, Min-A Kim, Young-Hwan You and Hyoung-Kyu Song

Sensors 2022, 22(18), 6994; https://doi.org/10.3390/s22186994 - 15 Sep 2022

Cited by 13 | Viewed by 2358

Abstract

Non-orthogonal multiple access (NOMA) has great potential to implement the fifth-generation (5G) requirements of wireless communication. For a NOMA traditional detection method, successive interference cancellation (SIC) plays a vital role at the receiver side for both uplink and downlink transmission. Due to the complex multipath channel environment and prorogation of error problems, the traditional SIC method has a limited performance. To overcome the limitation of traditional detection methods, the deep-learning method has an advantage for the highly efficient tool. In this paper, a deep neural network which has bi-directional long short-term memory (Bi-LSTM) for multiuser uplink channel estimation (CE) and signal detection of the originally transmitted signal is proposed. Unlike the traditional CE schemes, the proposed Bi-LSTM model can directly recover multiuser transmission signals suffering from channel distortion. In the offline training stage, the Bi-LTSM model is trained using simulation data based on channel statistics. Then, the trained model is used to recover the transmitted symbols in the online deployment stage. In the simulation results, the performance of the proposed model is compared with the convolutional neural network model and traditional CE schemes such as MMSE and LS. It is shown that the proposed method provides feasible improvements in performance in terms of symbol-error rate and signal-to-noise ratio, making it suitable for 5G wireless communication and beyond. Full article

(This article belongs to the Special Issue Challenges in Energy Perspective on Mobile Sensor Networks)

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17 pages, 1772 KiB

Open AccessArticle

Proactive Handover Decision for UAVs with Deep Reinforcement Learning

by Younghoon Jang, Syed M. Raza, Moonseong Kim and Hyunseung Choo

Sensors 2022, 22(3), 1200; https://doi.org/10.3390/s22031200 - 5 Feb 2022

Cited by 13 | Viewed by 3167

Abstract

The applications of Unmanned Aerial Vehicles (UAVs) are rapidly growing in domains such as surveillance, logistics, and entertainment and require continuous connectivity with cellular networks to ensure their seamless operations. However, handover policies in current cellular networks are primarily designed for ground users, and thus are not appropriate for UAVs due to frequent fluctuations of signal strength in the air. This paper presents a novel handover decision scheme deploying Deep Reinforcement Learning (DRL) to prevent unnecessary handovers while maintaining stable connectivity. The proposed DRL framework takes the UAV state as an input for a proximal policy optimization algorithm and develops a Received Signal Strength Indicator (RSSI) based on a reward function for the online learning of UAV handover decisions. The proposed scheme is evaluated in a 3D-emulated UAV mobility environment where it reduces up to 76 and 73% of unnecessary handovers compared to greedy and Q-learning-based UAV handover decision schemes, respectively. Furthermore, this scheme ensures reliable communication with the UAV by maintaining the RSSI above −75 dBm more than 80% of the time. Full article

(This article belongs to the Special Issue Challenges in Energy Perspective on Mobile Sensor Networks)

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14 pages, 5336 KiB

Open AccessArticle

A Real-Time Wearable Physiological Monitoring System for Home-Based Healthcare Applications

by Jin-Woo Jeong, Woochan Lee and Young-Joon Kim

Sensors 2022, 22(1), 104; https://doi.org/10.3390/s22010104 - 24 Dec 2021

Cited by 22 | Viewed by 6154

Abstract

The acquisition of physiological data are essential to efficiently predict and treat cardiac patients before a heart attack occurs and effectively expedite motor recovery after a stroke. This goal can be achieved by using wearable wireless sensor network platforms for real-time healthcare monitoring. In this paper, we present a wireless physiological signal acquisition device and a smartphone-based software platform for real-time data processing and monitor and cloud server access for everyday ECG/EMG signal monitoring. The device is implemented in a compact size (diameter: 30 mm, thickness: 4.5 mm) where the biopotential is measured and wirelessly transmitted to a smartphone or a laptop for real-time monitoring, data recording and analysis. Adaptive digital filtering is applied to eliminate any interference noise that can occur during a regular at-home environment, while minimizing the data process time. The accuracy of ECG and EMG signal coverage is assessed using Bland–Altman analysis by comparing with a reference physiological signal acquisition instrument (RHS2116 Stim/Recording System, Intan). Signal coverage of R-R peak intervals showed almost identical outcome between this proposed work and the RHS2116, showing a mean difference in heart rate of 0.15 ± 4.65 bpm and a Wilcoxon’s p value of 0.133. A 24 h continuous recording session of ECG and EMG is conducted to demonstrate the robustness and stability of the device based on extended time wearability on a daily routine. Full article

(This article belongs to the Special Issue Challenges in Energy Perspective on Mobile Sensor Networks)

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19 pages, 3435 KiB

Open AccessArticle

Progressive Deep Learning Framework for Recognizing 3D Orientations and Object Class Based on Point Cloud Representation

by Sukhan Lee and Yongjun Yang

Sensors 2021, 21(18), 6108; https://doi.org/10.3390/s21186108 - 12 Sep 2021

Cited by 1 | Viewed by 2162

Abstract

Deep learning approaches to estimating full 3D orientations of objects, in addition to object classes, are limited in their accuracies, due to the difficulty in learning the continuous nature of three-axis orientation variations by regression or classification with sufficient generalization. This paper presents a novel progressive deep learning framework, herein referred to as 3D POCO Net, that offers high accuracy in estimating orientations about three rotational axes yet with efficiency in network complexity. The proposed 3D POCO Net is configured, using four PointNet-based networks for independently representing the object class and three individual axes of rotations. The four independent networks are linked by in-between association subnetworks that are trained to progressively map the global features learned by individual networks one after another for fine-tuning the independent networks. In 3D POCO Net, high accuracy is achieved by combining a high precision classification based on a large number of orientation classes with a regression based on a weighted sum of classification outputs, while high efficiency is maintained by a progressive framework by which a large number of orientation classes are grouped into independent networks linked by association subnetworks. We implemented 3D POCO Net for full three-axis orientation variations and trained it with about 146 million orientation variations augmented from the ModelNet10 dataset. The testing results show that we can achieve an orientation regression error of about 2.5° with about 90% accuracy in object classification for general three-axis orientation estimation and object classification. Furthermore, we demonstrate that a pre-trained 3D POCO Net can serve as an orientation representation platform based on which orientations as well as object classes of partial point clouds from occluded objects are learned in the form of transfer learning. Full article

(This article belongs to the Special Issue Challenges in Energy Perspective on Mobile Sensor Networks)

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23 pages, 1745 KiB

Open AccessArticle

Machine Learning-Based 5G-and-Beyond Channel Estimation for MIMO-OFDM Communication Systems

by Ha An Le, Trinh Van Chien, Tien Hoa Nguyen, Hyunseung Choo and Van Duc Nguyen

Sensors 2021, 21(14), 4861; https://doi.org/10.3390/s21144861 - 16 Jul 2021

Cited by 68 | Viewed by 9445

Abstract

Channel estimation plays a critical role in the system performance of wireless networks. In addition, deep learning has demonstrated significant improvements in enhancing the communication reliability and reducing the computational complexity of 5G-and-beyond networks. Even though least squares (LS) estimation is popularly used to obtain channel estimates due to its low cost without any prior statistical information regarding the channel, this method has relatively high estimation error. This paper proposes a new channel estimation architecture with the assistance of deep learning in order to improve the channel estimation obtained by the LS approach. Our goal is achieved by utilizing a MIMO (multiple-input multiple-output) system with a multi-path channel profile for simulations in 5G-and-beyond networks under the level of mobility expressed by the Doppler effects. The system model is constructed for an arbitrary number of transceiver antennas, while the machine learning module is generalized in the sense that an arbitrary neural network architecture can be exploited. Numerical results demonstrate the superiority of the proposed deep learning-based channel estimation framework over the other traditional channel estimation methods popularly used in previous works. In addition, bidirectional long short-term memory offers the best channel estimation quality and the lowest bit error ratio among the considered artificial neural network architectures. Full article

(This article belongs to the Special Issue Challenges in Energy Perspective on Mobile Sensor Networks)

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22 pages, 2195 KiB

Open AccessArticle

A Novel Approach to the Job Shop Scheduling Problem Based on the Deep Q-Network in a Cooperative Multi-Access Edge Computing Ecosystem

by Junhyung Moon, Minyeol Yang and Jongpil Jeong

Sensors 2021, 21(13), 4553; https://doi.org/10.3390/s21134553 - 2 Jul 2021

Cited by 8 | Viewed by 3718

Abstract

In this study, based on multi-access edge computing (MEC), we provided the possibility of cooperating manufacturing processes. We tried to solve the job shop scheduling problem by applying DQN (deep Q-network), a reinforcement learning model, to this method. Here, to alleviate the overload of computing resources, an efficient DQN was used for the experiments using transfer learning data. Additionally, we conducted scheduling studies in the edge computing ecosystem of our manufacturing processes without the help of cloud centers. Cloud computing, an environment in which scheduling processing is performed, has issues sensitive to the manufacturing process in general, such as security issues and communication delay time, and research is being conducted in various fields, such as the introduction of an edge computing system that can replace them. We proposed a method of independently performing scheduling at the edge of the network through cooperative scheduling between edge devices within a multi-access edge computing structure. The proposed framework was evaluated, analyzed, and compared with existing frameworks in terms of providing solutions and services. Full article

(This article belongs to the Special Issue Challenges in Energy Perspective on Mobile Sensor Networks)

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19 pages, 5600 KiB

Open AccessArticle

An Efficient Indoor Positioning Method Based on Wi-Fi RSS Fingerprint and Classification Algorithm

by Balaji Ezhumalai, Moonbae Song and Kwangjin Park

Sensors 2021, 21(10), 3418; https://doi.org/10.3390/s21103418 - 14 May 2021

Cited by 20 | Viewed by 3608

Abstract

Wi-Fi received signal strength (RSS) fingerprint-based indoor positioning has been widely used because of its low cost and universality advantages. However, the Wi-Fi RSS is greatly affected by multipath interference in indoor environments, which can cause significant errors in RSS observations. Many methods have been proposed to overcome this issue, including the average method and the error handling method, but these existing methods do not consider the ever-changing dynamics of RSS in indoor environments. In addition, traditional RSS-based clustering algorithms have been proposed in the literature, but they make clusters without considering the nonlinear similarity between reference points (RPs) and the signal distribution in ever-changing indoor environments. Therefore, to improve the positioning accuracy, this paper presents an improved RSS measurement technique (IRSSMT) to minimize the error of RSS observation by using the number of selected RSS and its median values, and the strongest access point (SAP) information-based clustering technique, which groups the RPs using their SAP similarity. The performance of this proposed method is tested by experiments conducted in two different experimental environments. The results reveal that our proposed method can greatly outperform the existing algorithms and improve the positioning accuracy by 89.06% and 67.48%, respectively. Full article

(This article belongs to the Special Issue Challenges in Energy Perspective on Mobile Sensor Networks)

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22 pages, 3360 KiB

Open AccessArticle

A Joint Energy Replenishment and Data Collection Strategy in Heterogeneous Wireless Rechargeable Sensor Networks

by Mengqiu Tian, Wanguo Jiao and Yaqian Chen

Sensors 2021, 21(9), 2930; https://doi.org/10.3390/s21092930 - 22 Apr 2021

Cited by 4 | Viewed by 2030

Abstract

In wireless rechargeable sensor networks, mobile vehicles (MVs) combining energy replenishment and data collection are studied extensively. To reduce data overflow, most recent work has utilized more vehicles to assist the MV to collect buffered data. However, the practical network environment and the limitations of the vehicle in the data collection are not considered. UAV-enabled data collection is immune to complex road environments in remote areas and has higher speed and less traveling cost, which can overcome the lack of the vehicle in data collection. In this paper, a novel framework joining the MV and UAV is proposed to prolong the network lifetime and reduce data overflow. The network lifetime is correlated with the charging order; therefore, we first propose a charging algorithm to find the optimal charging order. During the charging period of the MV, the charging time may be longer than the collecting time. An optimal selection strategy of neighboring clusters, which could send data to the MV, was found to reduce data overflow. Then, to further reduce data overflow, an algorithm is also proposed to schedule the UAV to assist the MV to collect buffered data. Finally, simulation results verified that the proposed algorithms can maximize network lifetime and minimize the data loss simultaneously. Full article

(This article belongs to the Special Issue Challenges in Energy Perspective on Mobile Sensor Networks)

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