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J. Sens. Actuator Netw., Volume 13, Issue 3 (June 2024) – 2 articles

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16 pages, 7898 KiB  
Article
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
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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 [...] Read more.
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)
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18 pages, 834 KiB  
Article
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
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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 [...] Read more.
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
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