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Miranda, R.F.; Barriquello, C.H.; Reguera, V.A.; Denardin, G.W.; Thomas, D.H.; Loose, F.; Amaral, L.S. Cognitive Hybrid RF/VLC Systems for Sensor Networks. Encyclopedia. Available online: https://encyclopedia.pub/entry/49539 (accessed on 09 July 2024).
Miranda RF, Barriquello CH, Reguera VA, Denardin GW, Thomas DH, Loose F, et al. Cognitive Hybrid RF/VLC Systems for Sensor Networks. Encyclopedia. Available at: https://encyclopedia.pub/entry/49539. Accessed July 09, 2024.
Miranda, Rodrigo Fuchs, Carlos Henrique Barriquello, Vitalio Alfonso Reguera, Gustavo Weber Denardin, Djeisson Hoffmann Thomas, Felipe Loose, Leonardo Saldanha Amaral. "Cognitive Hybrid RF/VLC Systems for Sensor Networks" Encyclopedia, https://encyclopedia.pub/entry/49539 (accessed July 09, 2024).
Miranda, R.F., Barriquello, C.H., Reguera, V.A., Denardin, G.W., Thomas, D.H., Loose, F., & Amaral, L.S. (2023, September 22). Cognitive Hybrid RF/VLC Systems for Sensor Networks. In Encyclopedia. https://encyclopedia.pub/entry/49539
Miranda, Rodrigo Fuchs, et al. "Cognitive Hybrid RF/VLC Systems for Sensor Networks." Encyclopedia. Web. 22 September, 2023.
Cognitive Hybrid RF/VLC Systems for Sensor Networks
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This entry is adapted from the peer-reviewed paper 10.3390/s23187815

Building on the foundations of Cognitive Radio (CR), Visible Light Communication (VLC), and Radio Frequency (RF), this integration of Cognitive Radio Sensor Networks (CRSNs) fuses CR, VLC, and RF technologies with sensor networks. This integration navigates the complexities of dynamic and heterogeneous wireless environments, offering a novel framework to harness the combined potential of these domains. CRSNs leverage the adaptability and intelligence of CR, the high data rates and energy efficiency of VLC, and the ubiquity and range of RF. Sensor nodes in CRSNs, equipped with CR, VLC, and RF capabilities, autonomously sense, analyze, and adapt to the wireless environment. This optimizes spectrum usage, enhances network performance, and facilitates efficient data transmission.

cognitive radio radio frequency sensor networks visible light communication

1. Introduction

CR augments wireless communication by adeptly identifying available communication channels and promptly transitioning to vacant ones, thus averting interference with Primary Users (PUs) who possess licensed spectrum rights [1]. This strategy, termed Dynamic Spectrum Access (DSA), permits Secondary Users (SUs) to opportunistically utilize underexploited frequency bands without inducing harmful interference, thereby optimizing the utilization of the RF spectrum. A complementary concept, Dynamic Spectrum Sharing (DSS), entails real-time spectrum sharing among multiple users, encompassing both PUs and SUs. Contrary to DSA, which centers on opportunistic access to underutilized spectrum bands, DSS dynamically allocates the spectrum to various users based on their immediate needs and the spectrum's availability. This mechanism necessitates coordination and collaboration between PUs and SUs to safeguard efficient spectrum utilization while preserving the Quality of Service (QoS) for PUs [2][3].

Cognitive Radio Sensor Networks (CRSNs) operate within intricate physical environments, where the adept management of various physical domains is critical to their performance. The vital physical domains in CRSNs encompass spectrum resources, time resources, power resources, and space resources.
  • Spectrum resources: The range of electromagnetic frequencies employed for wireless communication is of paramount importance in CRSNs. As the spectrum hosts multiple critical applications, it is tightly regulated. CR is distinctive in its ability to dynamically access and utilize underutilized spectrum segments, known as “white spaces” or “spectrum holes”. The ability to intelligently detect unoccupied communication channels and adapt spectrum usage enables CR to coexist efficiently with other wireless systems.
  • Time resources: Time synchronization is integral to CRSNs, as it facilitates coordination among sensor nodes and optimizes resource allocation. Through precise time alignment, CRSNs mitigate collisions and ensure efficient communication.
  • Power resources: Power management is vital in CRSNs due to the limited energy resources of sensor nodes. By dynamically adjusting transmission power and routing paths, CR optimizes energy consumption, prolongs network lifespan, and enhances energy efficiency.
  • Space resources: CRSNs leverage the spatial characteristics of wireless environments to optimize resource allocation and interference management. The strategic deployment of sensor nodes based on their physical locations aids in maximizing spectrum usage and enhancing network performance.
An example of the utilization three of these resources in a CRSNs is illustrated in Figure 1.
Figure 1. Example of the utilization of resources in a CRSNs.
In this figure, each color block represents the utilized resources of different PUs, and the hashed line represents the spectrum holes where the DSA can allocate the SUs.
Different strategies for managing these resources can be combined in CRSNs to optimize their operation in dynamic wireless environments. At the heart of CR lies spectrum management, a strategy comprising techniques that enable efficient spectrum allocation and utilization. Some key elements are listed below:
  • Spectrum sensing: CR devices detect unused or underutilized frequency bands;
  • Spectrum decision: based on sensed spectra, CR devices select the most suitable frequency bands for transmission;
  • Spectrum sharing: spectrum sharing strategies, including underlay, overlay, and hybrid, manage the allocation and sharing of the spectrum between PUs and SUs;
  • Interference mitigation: techniques such as power control mechanisms and adaptive modulation and coding schemes are employed to mitigate interference;
  • Spectrum mobility: CR devices utilize spectrum mobility techniques to efficiently use the available spectrum resources in dynamic spectrum environments;
  • Spectrum management database: a centralized repository of spectrum utilization data which informs about the availability of spectrum resources;
  • Dynamic spectrum Access: allows CR devices to dynamically adapt frequency usage based on real-time conditions and spectrum availability;
  • Spectrum monitoring and enforcement: involves continuous spectrum monitoring to maintain its integrity and ensure fair and efficient use.
Figure 2 illustrates an example of the interaction of some main CR techniques. This is one of many possible examples of CR structure, also known as CR cycle, which helps to set the adaptive operation of cognitive capability [4][5][6].
Figure 2. The CR cycle.
These techniques aim at optimizing spectrum utilization, sharing resources among different users, maximizing spectrum efficiency, and ensuring fair and interference-free coexistence. They represent many aspects of managing the radio spectrum and enabling efficient access and utilization. Analogous strategies are applicable to the visible light spectrum, making them vital for hybrid communication systems that use both RF and VLC.
In the realm of hybrid RF/VLC systems, understanding and evaluating performance is paramount. The combination of RF and VLC technologies offers unique advantages, but also presents challenges that require careful analysis. Performance metrics provide essential insights into the system’s efficiency, reliability, and quality, guiding the design and optimization of these complex networks.

2. Performance Metrics

CRSNs, as intricate systems combining the adaptive intelligence of CR with the multi-node potential of sensor networks, necessitate the assessment of diverse performance metrics.
Performance metrics are essential in CRSNs, as they provide a quantitative means to evaluate multiple aspects of the network. They guide decision-making for network optimization, resource allocation, proactive fault detection, and QoS monitoring. The selection of specific metrics for in-depth analysis is based on their relevance to hybrid RF/VLC systems [7][8][9].

2.1. Fairness

This gauges the equitability of resource allocation among users or nodes. Fairness in resource allocation typically means that each user or device in the network should receive a fair share of the available resources. Fairness can be defined in various ways, depending on the specific goals and requirements of the network. Common fairness metrics include proportionally fair allocation, max-min fairness, and Jain’s fairness index, among others [10]. This metric is often evaluated using mathematical models [11].
Achieving fairness in resource allocation often involves a trade-off with network efficiency. Ensuring that every user receives a fair share may result in suboptimal resource utilization. Therefore, network operators and designers must strike a balance between fairness and efficiency based on the specific network goals and user requirements.
Fairness metrics become especially important in dynamic and heterogeneous wireless environments where users have varying channel conditions, QoS requirements, and traffic patterns. Adaptive resource allocation algorithms are often used to address these challenges [12][13].

2.2. Outage

This serves as a metric for network reliability, indicating the chance of a wireless link’s quality falling below an acceptable level [14]. This implies the disruption or interruption in the normal functioning of a communication system or network, resulting in a temporary loss of connectivity or service. Outages can be caused by various factors, including hardware failures, software glitches, environmental conditions, interference, or deliberate attacks. Managing and mitigating outages are essential aspects of ensuring the reliability and performance of communication systems and networks.
Outage management in CRSNs is essential for maintaining reliable communication, data integrity, and network performance, particularly in dynamic and challenging wireless environments. Effective outage management strategies encompass adaptive routing, spectrum management, energy-efficient operation, and robust security measures to ensure that CRSNs continue to function even in the face of disruptions [15][16][17].

2.3. Sum Rate

This represents the total maximum achievable data rates of all individual channels, providing a theoretical perspective of the network’s potential performance [18].
The sum rate metric in CRSNs quantifies the collective network capacity to transport information among sensor nodes while considering spectrum availability, interference management, and cognitive radio adaptation. Maximizing the sum rate in CRSNs is a pivotal objective, as it directly correlates with the network’s ability to efficiently utilize available resources, support various applications, and ensure reliable data exchange among sensor nodes in dynamic and spectrum-constrained environments [19][20].

2.4. Throughput

This is the real rate of successful data delivery over the channel, presenting a more realistic performance measure compared to the theoretical sum rate. It accounts for the effective data transfer rate, accounting for factors like packet loss, retransmissions, and any protocol overhead [9][21].
In CRSNs, throughput is a critical performance metric as it directly reflects the network’s capacity to deliver data efficiently from source to destination. Maximizing throughput is essential for ensuring timely and reliable data communication among sensor nodes, particularly in applications where real-time data collection and exchange are vital. Achieving high throughput in CRSNs often involves optimizing resource allocation, spectrum utilization, and routing strategies to overcome challenges such as interference, fading, and dynamic spectrum access, all of which are common in cognitive radio environments [16][17][22].

2.5. QoS

This aims to meet specific service requirements, which is particularly important in various applications, including voice and video communication, real-time monitoring, and critical data transmission. QoS ascertains the service level provided to users or nodes within a CRSNs, managing network resources and controlling performance characteristics [7][23].
QoS is typically measured using various metrics, including latency (delay), jitter (variation in latency), packet loss rate, and throughput (data transfer rate). These metrics help assess the overall quality and performance of a network or service.
In the context of CRSNs, QoS is vital to provide efficient and reliable data transmission, especially in scenarios with diverse traffic types, dynamic spectrum availability, and resource-constrained sensor nodes. By implementing QoS mechanisms, CRSNs can effectively adapt to changing conditions, optimize resource usage, and support a wide range of applications while meeting their specific QoS requirements [24][25].

3. Types of Hybrid RF/VLC Systems in CRSNs

Hybrid systems denote the harmonious fusion of two or more distinctive network types. These systems' configuration relies on unique network typologies and respective application use-cases. A hybrid system can function as an uplink, a downlink, or both, depending on the specific requirements [18].

To illustrate a HetNet, consider a hybrid RF/VLC system within an indoor scenario. In Figure 3, RF is employed for both uplink and downlink communication, while VLC is used exclusively for downlink communication.
Sensors 23 07815 g003
Figure 3. Illustration of a hybrid RF/VLC network in an indoor scenario.

3.1. Dual-Hop Hybrid RF/VLC System

In this topology, the RF and VLC networks are connected through a relay, facilitating dual-hop communication. This setup is particularly useful for scenarios requiring extended coverage.

3.2. Opportunistic Separate Networks (RF/VLC)

Here, the RF and VLC networks operate independently but can switch between each other opportunistically based on environmental conditions or network requirements.

3.3. Heterogeneous Networks (HetNets) with a Centralized Unit

In this configuration, multiple cellular typologies are merged to craft a seamless, high-efficiency network infrastructure. The centralized unit manages resource allocation and network coordination.

References

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  2. Kumar, A.; Kumar, K. Multiple Access Schemes for Cognitive Radio Networks: A Survey. Phys. Commun. 2020, 38, 100953.
  3. Naeem, A.; Rehmani, M.H.; Saleem, Y.; Rashid, I.; Crespi, N. Network Coding in Cognitive Radio Networks: A Comprehensive Survey. IEEE Commun. Surv. Tutor. 2017, 19, 1945–1973.
  4. Ding, J.; I, C.L.; Zhao, K.; Liu, S. Exploring the Effect of Optical Beams on Hybrid VLC/RF Transmission Characteristics. Photonics 2023, 10, 185.
  5. Liu, J.; Wang, J.; Wang, Q. Secrecy Performance for Hybrid RF/VLC DF Relaying Systems. In Proceedings of the International Conference on Frontiers of Electronics, Information and Computation Technologies, Changsha, China, 21–23 May 2021; pp. 1–5.
  6. Al-Khori, J.; Nauryzbayev, G.; Abdallah, M.M.; Hamdi, M. Secrecy Performance of Decode-and-Forward Based Hybrid RF/VLC Relaying Systems. IEEE Access 2019, 7, 10844–10856.
  7. Ahmad, A.; Ahmad, S.; Rehmani, M.H.; Hassan, N.U. A Survey on Radio Resource Allocation in Cognitive Radio Sensor Networks. IEEE Commun. Surv. Tutor. 2015, 17, 888–917.
  8. Hu, P.; Ibnkahla, M. A Consensus-Based Protocol for Spectrum Sharing Fairness in Cognitive Radio Ad Hoc and Sensor Networks. Int. J. Distrib. Sens. Netw. 2012, 8, 370251.
  9. Aroua, S.; El Korbi, I.; Ghamri-Doudane, Y.; Saidane, L.A. A Distributed Cooperative Spectrum Resource Allocation in Smart Home Cognitive Wireless Sensor Networks. In Proceedings of the 2017 IEEE Symposium on Computers and Communications (ISCC), Heraklion, Greece, 3–6 July 2017; pp. 754–759.
  10. Stephan, T.; Suresh Joseph, K. Particle Swarm Optimization-Based Energy Efficient Channel Assignment Technique for Clustered Cognitive Radio Sensor Networks. Comput. J. 2018, 61, 926–936.
  11. Obeed, M.; Chaaban, A.; Salhab, A.M.; Zummo, S.A.; Alouini, M.S. Physical Layer Security in Cooperative NOMA Hybrid VLC/RF Systems. arXiv 2020, arXiv:2008.10839.
  12. Han, B.; Zeng, M.; Guo, Q.; Jiang, H.; Zhang, Q.; Feng, L. Energy-Efficient Sensing and Transmission for Multi-Hop Relay Cognitive Radio Sensor Networks. China Commun. 2018, 15, 106–117.
  13. Kumar, D.; Singya, P.K.; Choi, K.; Bhatia, V. SWIPT Enabled Cooperative Cognitive Radio Sensor Network With Non-Linear Power Amplifier. IEEE Trans. Cogn. Commun. Netw. 2023, 9, 884–896.
  14. Sarthi, A.; Gurjar, D.S.; Sai, C.; Pattanayak, P.; Bhardwaj, A. Performance Impact of Hardware Impairments on Wireless Powered Cognitive Radio Sensor Networks. IEEE Sens. Lett. 2020, 4, 7500704.
  15. Abuella, H.; Elamassie, M.; Uysal, M.; Xu, Z.; Serpedin, E.; Qaraqe, K.A.; Ekin, S. Hybrid RF/VLC Systems: A Comprehensive Survey on Network Topologies, Performance Analyses, Applications, and Future Directions. IEEE Access 2021, 9, 160402–160436.
  16. Zhang, L.; Xin, Y.; Liang, Y.C.; Poor, H. Cognitive Multiple Access Channels: Optimal Power Allocation for Weighted Sum Rate Maximization. IEEE Trans. Commun. 2009, 57, 2754–2762.
  17. Zheng, L.; Tan, C.W. Maximizing Sum Rates in Cognitive Radio Networks: Convex Relaxation and Global Optimization Algorithms. IEEE J. Sel. Areas Commun. 2014, 32, 667–680.
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  19. Ren, J.; Zhang, H.; Hu, H.; Cheng, F.; Qin, Y. Achievable Throughput Analysis and Channel Access in Energy Harvesting Cognitive Radio Sensor Network. IEEE Access 2019, 7, 82277–82287.
  20. Liu, J.; Wang, J.; Zhang, B.; Wang, Q. Secrecy Performance Analysis of Hybrid RF/VLC Dual-Hop Relaying Systems. Front. Phys. 2021, 9, 683479.
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  22. Ogbodo, E.U.; Dorrell, D.; Abu-Mahfouz, A.M. Cognitive Radio Based Sensor Network in Smart Grid: Architectures, Applications and Communication Technologies. IEEE Access 2017, 5, 19084–19098.
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Subjects: Engineering, Electrical & Electronic; Telecommunications
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Rodrigo Fuchs Miranda
,
Carlos Henrique Barriquello
,
Vitalio Alfonso Reguera
,
Gustavo Weber Denardin
,
Djeisson Hoffmann Thomas
,
Felipe Loose
,
Leonardo Saldanha Amaral
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Revisions: 5 times (View History)
Update Date: 25 Sep 2023
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