Submit to JSAN Review for JSAN

Journal Menu

Journal Browser

J. Sens. Actuator Netw., Volume 13, Issue 3 (June 2024) – 5 articles

Cover Story (view full-size image): Overcome challenges of the in situ monitoring of essential variables of soil or other porous media (concrete, silos, compost, etc.), such as water content, salinity, and temperature. In situ metrology with the measurement of real soil variables, representativity, limited invasiveness, vertical profiles, a spatial scale from 50 m to several km, a temporal scale from 5 min to years, autonomy, real-time and remote data retrieval, ease of installation, low maintenance, robust, and cost-effective for duplication. The sensor contains all that is necessary in a compact form, reducing its footprint and any perturbation of water flows. Networks of a series of sensors provide spatial mapping and meet multiple applications. View this paper

Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
You may sign up for e-mail alerts to receive table of contents of newly released issues.
PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.

Order results

Result details

Section

Show export options Show export options

Select all

Export citation of selected articles as:

21 pages, 14370 KiB

Open AccessArticle

AI-Based Pedestrian Detection and Avoidance at Night Using Multiple Sensors

by Hovannes Kulhandjian, Jeremiah Barron, Megan Tamiyasu, Mateo Thompson and Michel Kulhandjian

J. Sens. Actuator Netw. 2024, 13(3), 34; https://doi.org/10.3390/jsan13030034 - 14 Jun 2024

Cited by 2 | Viewed by 1715

Abstract

In this paper, we present a pedestrian detection and avoidance scheme utilizing multi-sensor data collection and machine learning for intelligent transportation systems (ITSs). The system integrates a video camera, an infrared (IR) camera, and a micro-Doppler radar for data acquisition and training. A deep convolutional neural network (DCNN) is employed to process RGB and IR images. The RGB dataset comprises 1200 images (600 with pedestrians and 600 without), while the IR dataset includes 1000 images (500 with pedestrians and 500 without), 85% of which were captured at night. Two distinct DCNNs were trained using these datasets, achieving a validation accuracy of 99.6% with the RGB camera and 97.3% with the IR camera. The radar sensor determines the pedestrian’s range and direction of travel. Experimental evaluations conducted in a vehicle demonstrated that the multi-sensor detection scheme effectively triggers a warning signal to a vibrating motor on the steering wheel and displays a warning message on the passenger’s touchscreen computer when a pedestrian is detected in potential danger. This system operates efficiently both during the day and at night. Full article

(This article belongs to the Special Issue Advances in Intelligent Transportation Systems (ITS))

► Show Figures

Figure 1

24 pages, 1104 KiB

Open AccessArticle

A Learning-Based Energy-Efficient Device Grouping Mechanism for Massive Machine-Type Communication in the Context of Beyond 5G Networks

by Rubbens Boisguene, Ibrahim Althamary and Chih-Wei Huang

J. Sens. Actuator Netw. 2024, 13(3), 33; https://doi.org/10.3390/jsan13030033 - 28 May 2024

Viewed by 1170

Abstract

With the increasing demand for high data rates, low delay, and extended battery life, managing massive machine-type communication (mMTC) in the beyond 5G (B5G) context is challenging. MMTC devices, which play a role in developing the Internet of Things (IoT) and smart cities, need to transmit short amounts of data periodically within a specific time frame. Although blockchain technology is utilized for secure data storage and transfer while digital twin technology provides real-time monitoring and management of the devices, issues such as constrained time delays and network congestion persist. Without a proper data transmission strategy, most devices would fail to transmit in time, thus defying their relevance and purpose. This work investigates the problem of massive random access channel (RACH) attempts while emphasizing the energy efficiency and access latency for mMTC devices with critical missions in B5G networks. Using machine learning techniques, we propose an attention-based reinforcement learning model that orchestrates the device grouping strategy to optimize device placement. Thus, the model guarantees a higher probability of success for the devices during data transmission access, eventually leading to more efficient energy consumption. Through thorough quantitative simulations, we demonstrate that the proposed learning-based approach significantly outperforms the other baseline grouping methods. Full article

► Show Figures

Figure 1

25 pages, 2165 KiB

Open AccessArticle

A Sensor to Monitor Soil Moisture, Salinity, and Temperature Profiles for Wireless Networks

by Xavier Chavanne and Jean-Pierre Frangi

J. Sens. Actuator Netw. 2024, 13(3), 32; https://doi.org/10.3390/jsan13030032 - 27 May 2024

Cited by 1 | Viewed by 1124

Abstract

This article presents a wireless in situ sensor designed to continuously monitor profiles of parameters in porous media, such as soil moisture, salinity, and temperature. A review of existing in situ soil sensors reveals that it is the only device capable of measuring the complex permittivity of the medium, allowing for conversions into moisture and salinity that are independent of the instrument. Flow perturbation and invasiveness have also been minimized to maintain good representativeness. Plans include autonomous networks of such sensors, facilitated by the use of the recent radio mode LoRaWAN and cost optimizations for series production. Costs were reduced through electronic simplification and integration, and the use of low-cost modular sensing parts in soil, while still maintaining high measurement quality. A complete set of sensor data recorded during a three-month trial is also presented and interpreted. Full article

► Show Figures

Figure 1

16 pages, 7898 KiB

Open AccessArticle

Time Delay Characterization in Wireless Sensor Networks for Distributed Measurement Applications

by Šarūnas Kilius, Darius Gailius, Mindaugas Knyva, Gintautas Balčiūnas, Asta Meškuotienė, Justina Dobilienė, Simas Joneliūnas and Pranas Kuzas

J. Sens. Actuator Netw. 2024, 13(3), 31; https://doi.org/10.3390/jsan13030031 - 16 May 2024

Cited by 1 | Viewed by 1051

Abstract

This paper investigates the critical aspect of synchronization in wireless sensor networks (WSNs) across diverse industrial applications. The low-cost sensor network topologies are analyzed. The communication delay measurements and quantitative jitter analysis are performed under different conditions, and dependencies of the propagation time delay on the data bitrate and modulation type for different hardware implementations of the WSNs are presented. The time delay distribution influence on the time synchronization error propagation over WSN layers was assessed from the experimental probability density functions. The network synchronization based on the controlled propagation delay jitter approach has been proposed. This research contributes quantitative insights into the complexities of synchronization in WSNs, offering a foundation for optimizing network configurations and parameters to extend the operational life of low-power sensor nodes. Full article

(This article belongs to the Section Actuators, Sensors and Devices)

► Show Figures

Figure 1

18 pages, 834 KiB

Open AccessArticle

A Multi-Agent Reinforcement Learning-Based Grant-Free Random Access Protocol for mMTC Massive MIMO Networks

by Felipe Augusto Dutra Bueno, Alessandro Goedtel, Taufik Abrão and José Carlos Marinello

J. Sens. Actuator Netw. 2024, 13(3), 30; https://doi.org/10.3390/jsan13030030 - 30 Apr 2024

Viewed by 1459

Abstract

The expected huge number of connected devices in Internet of Things (IoT) applications characterizes the massive machine-type communication (mMTC) scenario, one prominent use case of beyond fifth-generation (B5G) systems. To meet mMTC connectivity requirements, grant-free (GF) random access (RA) protocols are seen as a promising solution due to the small amount of data that MTC devices usually transmit. In this paper, we propose a GF RA protocol based on a multi-agent reinforcement learning approach, applied to aid IoT devices in selecting the least congested RA pilots. The rewards obtained by the devices in collision cases resemble the congestion level of the chosen pilot. To enable the operation of the proposed method in a realistic B5G network scenario and aiming to reduce signaling overheads and centralized processing, the rewards in our proposed method are computed by the devices taking advantage of a large number of base station antennas. Numerical results demonstrate the superior performance of the proposed method in terms of latency, network throughput, and per-device throughput compared with other protocols. Full article

(This article belongs to the Special Issue Security and Smart Applications in IoT and Wireless Sensor and Actuator Networks)

► Show Figures