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Empowering the IoT: Scalable, Sustainable, and Ultra-Low Power Solutions

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

Deadline for manuscript submissions: closed (20 December 2025) | Viewed by 8439

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Special Issue Editors

Dr. Shimin Gong

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Guest Editor

School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen 518107, China
Interests: Internet of Things; backscatter communications; wireless powered transfer; wireless resource optimization, machine learning in wireless systems
Special Issues, Collections and Topics in MDPI journals

Dr. Lanhua Li

E-Mail Website
Guest Editor

School of Intelligent Systems Engineering, Sun Yat-Sen University, Guangzhou 510275, China
Interests: AIoT; backscatter communications; passive sensing systems; UAV communication networks; IRS-assisted wireless communication; and wireless resource optimization

Dr. Yueling Che

E-Mail Website
Guest Editor

College of Computer and Software Engineering, Shenzhen University, Shenzhen 518060, China
Interests: energy-efficient wireless communications and systems; UAV-enabled communication and sensing; AI-enabled wireless communications; wireless powered communications; stochastic modeling and optimization methods

Dr. Kongyang Chen

E-Mail Website
Guest Editor

Institutes of Artificial Intelligence, Guangzhou University, Guangzhou 510006, China
Interests: machine learning security; Internet of Things; edge computing
Special Issues, Collections and Topics in MDPI journals

Dr. Nguyen Cong Luong

E-Mail Website
Guest Editor

Faculty of Computer Science, Phenikaa University, Hanoi 100803, Vietnam
Interests: Internet of Things (IoT); wireless power transfer; intelligent reflecting surface; rate splitting multiple access; digital twin; semantic communications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As the Internet of Things (IoT) technology continues to evolve, it brings forth a multitude of emerging applications, ranging from smart homes and intelligent unmanned systems to the development of smart cities. These applications hold the promise of transforming our daily lives, but they also bring forth new challenges that must be addressed to ensure the long lifetime of IoT devices and systems. In light of these challenges, it is imperative that we redirect our focus towards the development of IoT solutions capable of supporting widely deployed devices while upholding principles of sustainability, scalability, and energy efficiency.

We are delighted to announce a Special Issue dedicated to exploring scalable, sustainable, and ultra-low-power communication, networking, and sensing technologies in IoT systems. This Special Issue aims to bring together researchers, academics, and industry professionals to address the pressing challenges facing the IoT landscape and to explore innovative solutions that can pave the way for a more sustainable and efficient future.

Potential topics for contributions include, but are not limited to:

Strategies for scalable deployment of IoT networks
Integration of artificial intelligence to empower IoT systems
Efficient resource optimization strategies for IoT networks
Wireless power transfer for sustainable IoT implementations
Ultra-lower power techniques for integrated sensing and communications
Ultra-low power enabled IoT computing and security
Ultra-low power enabled IoT systems and platforms
Unmanned aerial vehicle assisted IoT networks
Reconfigurable intelligent surfaces aided wireless communications
Semantic communications for IoT networks

Dr. Shimin Gong
Dr. Lanhua Li
Dr. Yueling Che
Dr. Kongyang Chen
Dr. Nguyen Cong Luong
Guest Editors

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Keywords

ultra-low-power IoT
edge AI
IoT security
wireless powered communications
integrated sensing, communication and computing
UAV
RIS
semantic communications

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Published Papers (5 papers)

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Research

19 pages, 6089 KB

Open AccessArticle

Energy-Efficient Automated Detection of OPGW Features for Sustainable UAV-Based Inspection

by Xiaoling Yan, Wuxing Mao, Xiao Li, Ruiming Huang, Chi Ye, Faguang Li and Zheyu Fan

Sensors 2026, 26(2), 658; https://doi.org/10.3390/s26020658 - 19 Jan 2026

Viewed by 438

Abstract

Unmanned Aerial Vehicle (UAV)-based inspection is crucial for the maintenance and monitoring of high-voltage transmission lines, but detecting small objects in inspection images presents significant challenges, especially under complex backgrounds and varying lighting. These challenges are particularly evident when detecting the wire features of optical fiber composite overhead ground wire and conventional ground wires. Optical fiber composite overhead ground wire (OPGW) is a specialized cable designed to replace conventional shield wires on power utility towers. It contains one or more optical fibers housed in a protective tube, surrounded by layers of aluminum-clad steel and/or aluminum alloy wires, ensuring robust mechanical strength for grounding and high-bandwidth capabilities for remote sensing and control. Existing detection methods often struggle with low accuracy, insufficient performance, and high computational demands when dealing with small objects. To address these issues, this paper proposes an energy-efficient OPGW feature detection model for UAV-based inspection. The model incorporates a Feature Enhancement Module (FEM) to replace the C3K2 module in the sixth layer of the YOLO11 backbone, improving multi-scale feature extraction. A P2 shallow detection head is added to enhance the perception of small and edge features. Additionally, the traditional Intersection over Union (IoU) loss is replaced with Normalized Wasserstein Distance (NWD) loss function, which improves boundary regression accuracy for small objects. Experimental results show that the proposed method achieves a

{mAP}_{50}

of 78.3% and

{mAP}_{50 - 95}

of 52.0%, surpassing the baseline by 2.3% and 1.1%, respectively. The proposed model offers the advantages of high detection accuracy and low computational resource requirements, providing a practical solution for sustainable UAV-based inspections. Full article

(This article belongs to the Special Issue Empowering the IoT: Scalable, Sustainable, and Ultra-Low Power Solutions)

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24 pages, 783 KB

Open AccessArticle

Weighted Sum-Rate Maximization and Task Completion Time Minimization for Multi-Tag MIMO Symbiotic Radio Networks

by Long Suo, Dong Wang, Wenxin Zhou and Xuefei Peng

Sensors 2026, 26(2), 644; https://doi.org/10.3390/s26020644 - 18 Jan 2026

Viewed by 330

Abstract

Symbiotic radio (SR) has recently emerged as a promising paradigm for enabling spectrum- and energy-efficient massive connectivity in low-power Internet-of-Things (IoT) networks. By allowing passive backscatter devices (BDs) to coexist with active primary link transmissions, SR significantly improves spectrum utilization without requiring dedicated spectrum resources. However, most existing studies on multi-tag multiple-input multiple-output (MIMO) SR systems assume homogeneous traffic demands among BDs and primarily focus on rate-based performance metrics, while neglecting system-level task completion time (TCT) optimization under heterogeneous data requirements. In this paper, we investigate a joint performance optimization framework for a multi-tag MIMO symbiotic radio network. We first formulate a weighted sum-rate (WSR) maximization problem for the secondary backscatter links. The original non-convex WSR maximization problem is transformed into an equivalent weighted minimum mean square error (WMMSE) problem, and then solved by a block coordinate descent (BCD) approach, where the transmit precoding matrix, decoding filters, backscatter reflection coefficients are alternatively optimized. Second, to address the transmission delay imbalance caused by heterogeneous data sizes among BDs, we further propose a rate weight adaptive task TCT minimization scheme, which dynamically updates the rate weight of each BD to minimize the overall TCT. Simulation results demonstrate that the proposed framework significantly improves the WSR of the secondary system without degrading the primary link performance, and achieves substantial TCT reduction in multi-tag heterogeneous traffic scenarios, validating its effectiveness and robustness for MIMO symbiotic radio networks. Full article

(This article belongs to the Special Issue Empowering the IoT: Scalable, Sustainable, and Ultra-Low Power Solutions)

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37 pages, 9459 KB

Open AccessArticle

Diffusion-Based Frequency Hopping for Collision Mitigation in Dense Bluetooth Networks

by Giwon Yang, Hyungjoon Shin and Hyogon Kim

Sensors 2025, 25(18), 5893; https://doi.org/10.3390/s25185893 - 20 Sep 2025

Viewed by 1299

Abstract

This paper challenges the conventional wisdom of using uniform random resource selection for collision resolution in distributed scheduling, particularly in wireless protocols. Bluetooth, being one such technology, is analyzed through its frequency hopping mechanism to explore for a better alternative in random access MAC (medium access control). Using diffusion theory, we characterize Bluetooth’s original frequency hopping as exhibiting maximum diffusivity, which correlates with unnecessarily high collision rates and a short mean first encounter time (MFET) between nodes. MFET, defined as the expected time until two independent hopping sequences first collide on the same channel, serves as an intuitive metric for evaluating collision likelihood. This insight leads to the proposal of a new collision avoidance mechanism with reduced diffusivity, effectively increasing MFET while maintaining efficient spectrum utilization. Our analysis and simulation results demonstrate that it can significantly lower packet collisions, outperforming existing techniques such as adaptive frequency hopping. The results are further corroborated by a real-life prototype implementation that closely replicates the predicted performance. The proposed diffusion-based MAC, by explicitly targeting longer MFETs, is expected to better handle dense Bluetooth environments, which are becoming increasingly common. Full article

(This article belongs to the Special Issue Empowering the IoT: Scalable, Sustainable, and Ultra-Low Power Solutions)

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19 pages, 1868 KB

Open AccessArticle

Constrained Flooding Based on Time Series Prediction and Lightweight GBN in BLE Mesh

by Junxiang Li, Mingxia Li and Li Wang

Sensors 2024, 24(14), 4752; https://doi.org/10.3390/s24144752 - 22 Jul 2024

Cited by 2 | Viewed by 1589

Abstract

Bluetooth Low Energy Mesh (BLE Mesh) enables Bluetooth flexibility and coverage by introducing Low-Power Nodes (LPNs) and enhanced networking protocol. It is also a commonly used communication method in sensor networks. In BLE Mesh, LPNs are periodically woken to exchange messages in a stop-and-wait way, where the tradeoff between energy and efficiency is a hard problem. Related works have reduced the energy consumption of LPNs mainly in the direction of changing the bearer layer, improving time synchronization and broadcast channel utilization. These algorithms improve communication efficiency; however, they cause energy loss, especially for the LPNs. In this paper, we propose a constrained flooding algorithm based on time series prediction and lightweight GBN (Go-Back-N). On the one hand, the wake-up cycle of the LPNs is determined by the time series prediction of the surrounding load. On the other, LPNs exchange messages through lightweight GBN, which improves the window and ACK mechanisms. Simulation results validate the effectiveness of the Time series Prediction and LlightWeight GBN (TP-LW) algorithm in energy consumption and throughput. Compared with the original algorithm of BLE Mesh, when fewer packets are transmitted, the throughput is increased by 214.71%, and the energy consumption is reduced by 65.14%. Full article

(This article belongs to the Special Issue Empowering the IoT: Scalable, Sustainable, and Ultra-Low Power Solutions)

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26 pages, 3188 KB

Open AccessArticle

Deep Reinforcement Learning for Optimizing Restricted Access Window in IEEE 802.11ah MAC Layer

by Xiaojun Jiang, Shimin Gong, Chengyi Deng, Lanhua Li and Bo Gu

Sensors 2024, 24(10), 3031; https://doi.org/10.3390/s24103031 - 10 May 2024

Cited by 7 | Viewed by 3444

Abstract

The IEEE 802.11ah standard is introduced to address the growing scale of internet of things (IoT) applications. To reduce contention and enhance energy efficiency in the system, the restricted access window (RAW) mechanism is introduced in the medium access control (MAC) layer to manage the significant number of stations accessing the network. However, to achieve optimized network performance, it is necessary to appropriately determine the RAW parameters, including the number of RAW groups, the number of slots in each RAW, and the duration of each slot. In this paper, we optimize the configuration of RAW parameters in the uplink IEEE 802.11ah-based IoT network. To improve network throughput, we analyze and establish a RAW parameters optimization problem. To effectively cope with the complex and dynamic network conditions, we propose a deep reinforcement learning (DRL) approach to determine the preferable RAW parameters to optimize network throughput. To enhance learning efficiency and stability, we employ the proximal policy optimization (PPO) algorithm. We construct network environments with periodic and random traffic in an NS-3 simulator to validate the performance of the proposed PPO-based RAW parameters optimization algorithm. The simulation results reveal that using the PPO-based DRL algorithm, optimized RAW parameters can be obtained under different network conditions, and network throughput can be improved significantly. Full article

(This article belongs to the Special Issue Empowering the IoT: Scalable, Sustainable, and Ultra-Low Power Solutions)

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