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Cognitive Radio for Wireless Sensor Networks
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Cognitive Radio for Wireless Sensor Networks

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

Deadline for manuscript submissions: 25 October 2024 | Viewed by 3896

Special Issue Editors

Dr. Tao Peng

E-Mail Website
Guest Editor
School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: wireless communication
Dr. Yang Yang

E-Mail Website
Guest Editor
School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: mm wave communications; AI-based resource management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The integration of 5G networks and wireless sensor networks (WSNs) plays a very important role in the new era of the Internet of Things (IoT). It is used in many applications with a different quality of service (QoS) requirements, for example, military surveillance, vehicle tracking, health monitoring, industry monitoring, etc. In particular, it is a key tool for promoting the development of industry 4.0. Usually, resource-constrained sensor nodes have a limited processing and communication power, which makes designing WSNs challenging.

Conventional WSNs use a fixed spectrum allocation policy, and their performance is limited. For the efficient utilization of the spectrum, cognitive radio sensor networks (CRSNs) are proposed, which exploit the synergy between WSNs and cognitive radio (CR) technology. CR eliminates the interference and increases the communication quality with adaptability to the channel conditions. It can also overcome the problems caused by the dense deployment and bursty communication nature of WSNs. At the same time, some new challenges appear, for example, the tradeoff between the QoS and energy conservation.

In addition to CR, beyond 5G or 6G, it provides other key enabling technologies to integrate with WSNs, such as massive multiple-input multiple-output (M-MIMO), device-to-device (D2D) communications, a centralized radio access network (CRAN), software-defined networking (SDN), network function virtualization (NFV), etc.

This Special Issue seeks innovative works on a wide range of research topics, spanning both theoretical and systems research, including a wide range of areas such as industry, medicine, transportation and so on, related but not restricted to the following topics:

  • Energy-efficient resource allocation for CRSNs.
  • Communication in industrial scenarios for CRSNs.
  • D2D/V2V/E2E communications for CRSNs.
  • Relay selection and routing optimization for CRSNs.
  • Interference cancellation for CRSNs.
  • URLLC, eMBB, and mMTC applications for CRSNs.
  • Using M-MIMO for CRSNs.
  • Using RIS (reconfigurable intelligent surface) for CRSNs.
  • mmWave for CRSNs.
  • SWIPT (simultaneous wireless information and power transfer) for CRSNs.
  • Topology management for CRSNs.
  • Load balancing for CRSNs.
  • Improving coverage for CRSNs.
  • Artificial intelligence (AI) based for CRSNs.
  • Energy harvesting and management for CRSNs.
  • Software-defined networking (SDN) and NFV for CRSNs.
  • Security and privacy for CRSNs.
  • Data reliability and retransmission for CRSNs.
  • Tradeoff between connectivity and energy conservation for CRSNs.
  • Resource allocation in non-orthogonal multiple access (NOMA) for CRSNs.

Dr. Tao Peng
Dr. Yang Yang
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. Sensors 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 2600 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

  • cognitive radio
  • wireless sensor networks
  • resources allocation
  • Internet of Things
  • beyond 5G
  • quality of service
  • energy conservation

Published Papers (4 papers)

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Research

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14 pages, 2669 KiB  
Article
A Feedback Active Control Approach to Road Noise Based on a Single Microphone Sensor to Improve Automotive Cabin Sound Comfort
by Hao Liu and Jaecheon Lee
Sensors 2024, 24(8), 2515; https://doi.org/10.3390/s24082515 - 14 Apr 2024
Viewed by 449
Abstract
Tire–road noise deteriorates the sound quality of a vehicle’s interior and affects the driving safety and comfort. Obtaining low interior noise is a challenge for passenger car manufacturers. Traditional passive noise control (PNC) is efficient for canceling high frequency noise but not useful [...] Read more.
Tire–road noise deteriorates the sound quality of a vehicle’s interior and affects the driving safety and comfort. Obtaining low interior noise is a challenge for passenger car manufacturers. Traditional passive noise control (PNC) is efficient for canceling high frequency noise but not useful for low frequency noise, while active noise control (ANC), according to the residual error signal, can generate an anti-noise signal to reduce the original noise. Most research has focused on improving the control effect for a feedforward ANC system. However, this paper emphasizes a feedback ANC system based on a signal microphone sensor. There are two main contributions in this study to improve automotive cabin sound comfort. One is that the algorithm of the feedback ANC system using a single microphone sensor without a reference noise signal is proposed based on the Filtered-x Least Mean Square method. The other is that the algorithm applies additive random noise online to estimate the secondary path model. A simulation was implemented based on measured real road noise data, and the simulation results indicate that the proposed feedback ANC system with the single microphone sensor can effectively attenuate road noise. This study shows the feasibility of applying a feedback ANC system in automobiles to increase the cabin sound quality. Full article
(This article belongs to the Special Issue Cognitive Radio for Wireless Sensor Networks)
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15 pages, 341 KiB  
Article
Deep Reinforcement Learning-Based Energy Consumption Optimization for Peer-to-Peer (P2P) Communication in Wireless Sensor Networks
by Jinyu Yuan, Jingyi Peng, Qing Yan, Gang He, Honglin Xiang and Zili Liu
Sensors 2024, 24(5), 1632; https://doi.org/10.3390/s24051632 - 01 Mar 2024
Viewed by 586
Abstract
The fast development of the sensors in the wireless sensor networks (WSN) brings a big challenge of low energy consumption requirements, and Peer-to-peer (P2P) communication becomes the important way to break this bottleneck. However, the interference caused by different sensors sharing the spectrum [...] Read more.
The fast development of the sensors in the wireless sensor networks (WSN) brings a big challenge of low energy consumption requirements, and Peer-to-peer (P2P) communication becomes the important way to break this bottleneck. However, the interference caused by different sensors sharing the spectrum and the power limitations seriously constrains the improvement of WSN. Therefore, in this paper, we proposed a deep reinforcement learning-based energy consumption optimization for P2P communication in WSN. Specifically, P2P sensors (PUs) are considered agents to share the spectrum of authorized sensors (AUs). An authorized sensor has permission to access specific data or systems, while a P2P sensor directly communicates with other sensors without needing a central server. One involves permission, the other is direct communication between sensors. Each agent can control the power and select the resources to avoid interference. Moreover, we use a double deep Q network (DDQN) algorithm to help the agent learn more detailed features of the interference. Simulation results show that the proposed algorithm can obtain a higher performance than the deep Q network scheme and the traditional algorithm, which can effectively lower the energy consumption for P2P communication in WSN. Full article
(This article belongs to the Special Issue Cognitive Radio for Wireless Sensor Networks)
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14 pages, 1962 KiB  
Article
Distributed Sequential Detection for Cooperative Spectrum Sensing in Cognitive Internet of Things
by Jun Wu, Zhaoyang Qiu, Mingyuan Dai, Jianrong Bao, Xiaorong Xu and Weiwei Cao
Sensors 2024, 24(2), 688; https://doi.org/10.3390/s24020688 - 22 Jan 2024
Viewed by 573
Abstract
The rapid development of wireless communication technology has led to an increasing number of internet of thing (IoT) devices, and the demand for spectrum for these devices and their related applications is also increasing. However, spectrum scarcity has become an increasingly serious problem. [...] Read more.
The rapid development of wireless communication technology has led to an increasing number of internet of thing (IoT) devices, and the demand for spectrum for these devices and their related applications is also increasing. However, spectrum scarcity has become an increasingly serious problem. Therefore, we introduce a collaborative spectrum sensing (CSS) framework in this paper to identify available spectrum resources so that IoT devices can access them and, meanwhile, avoid causing harmful interference to the normal communication of the primary user (PU). However, in the process of sensing the PUs signal in IoT devices, the issue of sensing time and decision cost (the cost of determining whether the signal state of the PU is correct or incorrect) arises. To this end, we propose a distributed cognitive IoT model, which includes two IoT devices independently using sequential decision rules to detect the PU. On this basis, we define the sensing time and cost functions for IoT devices and formulate an average cost optimization problem in CSS. To solve this problem, we further regard the optimal sensing time problem as a finite horizon problem and solve the threshold of the optimal decision rule by person-by-person optimization (PBPO) methodology and dynamic programming. At last, numerical simulation results demonstrate the correctness of our proposal in terms of the global false alarm and miss detection probability, and it always achieves minimal average cost under various costs of each observation taken and thresholds. Full article
(This article belongs to the Special Issue Cognitive Radio for Wireless Sensor Networks)
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Review

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28 pages, 1068 KiB  
Review
A Review of Cognitive Hybrid Radio Frequency/Visible Light Communication Systems for Wireless Sensor Networks
by Rodrigo Fuchs Miranda, Carlos Henrique Barriquello, Vitalio Alfonso Reguera, Gustavo Weber Denardin, Djeisson Hoffmann Thomas, Felipe Loose and Leonardo Saldanha Amaral
Sensors 2023, 23(18), 7815; https://doi.org/10.3390/s23187815 - 12 Sep 2023
Cited by 4 | Viewed by 1425
Abstract
The development and growth of Wireless Sensor Networks (WSNs) is significantly propelled by advances in Radio Frequency (RF) and Visible Light Communication (VLC) technologies. This paper endeavors to present a comprehensive review of the state-of-the-art in cognitive hybrid RF-VLC systems for WSNs, emphasizing [...] Read more.
The development and growth of Wireless Sensor Networks (WSNs) is significantly propelled by advances in Radio Frequency (RF) and Visible Light Communication (VLC) technologies. This paper endeavors to present a comprehensive review of the state-of-the-art in cognitive hybrid RF-VLC systems for WSNs, emphasizing the critical task of seamlessly integrating Cognitive Radio Sensor Networks (CRSNs) and VLC technologies. The central challenge addressed is the intricate landscape of this integration, characterized by notable trade-offs between performance and complexity, which escalate with the addition of more devices and increased data rates. This scenario necessitates the development of advanced cognitive radio strategies, potentially facilitated by Machine Learning (ML) and Deep Learning (DL) approaches, albeit introducing new complexities such as the necessity for pre-training with extensive datasets. The review scrutinizes the fundamental aspects of CRSNs and VLC, spotlighting key areas like Energy Efficient Resource Allocation, Industrial Scenarios, and Energy Harvesting, and explores the synergistic amalgamation of these technologies as a promising pathway for enhanced spectrum utilization and network performance. By delving into the integration of cognitive radio technology with visible light, this study furnishes valuable insights into the potential for innovative applications in wireless communication, presenting a balanced overview of the current advancements and prospective avenues in the field of cognitive hybrid RF/VLC systems. Full article
(This article belongs to the Special Issue Cognitive Radio for Wireless Sensor Networks)
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