Cognitive Radio, Internet of Things (CR-IoT) is two different technologies which are integrated in order to adapt to any king of circumstances for enhancing the performance of the system. You can learn more and gain better knowledge on this topic CR-IoT by completely reading this research paper.
- Define CR-IOT
CR-IoT is a system of wireless communication with combination of two concepts that is the IoT ecosystem operating based on algorithms of Cognitive Radio. The CR technology allows the devices that are connected wirelessly in its network to adapt their parameters required for transmission (frequency, modulation scheme and power) with the available resources of spectrum and with the environment in which they are working.
- What is CR-IOT?
This CR-IoT technology was developed to adapt any radio frequency environment in order to enhance spectrum utilization. When CR is integrated with IoT communication network, the performance of the system will be increased with increase in reliability and efficiency. It is designed specifically for IoT environment with limited spectrum resources.
- Where CR-IOT used?
In this section we are going to discuss about the uses of CR-IoT. It is used in several applications of IoT network to increase reliability and performance, specifically in the environment where it has challenging conditions of radio spectrum. This method will help IoT devices to perform efficiently and effectively to collect data, analyze it and making decisions.
- Why CR-IOT technology proposed? Previous technology issues
Moving on to the next section, here we are going to discuss about the challenges faced by this CR-IoT technology. This method is proposed because of the following reasons:
Spectrum efficiency: One of the main reasons behind developing CR-IoT is to increase the spectrum efficiency. The earlier technology faces problem of spectrum scarcity and ineffective usage of it. With the help of CR, IoT devices can reduce interference and enhance performance.
Spectrum sharing: When there are only limited resources available in radio frequency for more applications and devices, the CR-IoT will help with dynamic sharing by which the IoT devices can coexist with some other wireless systems.
Spectrum access flexibility: The CR-IoT can adapt to any kind of spectrum policies and changes without violating any regulations; which makes installation of IoT devices easier.
- Algorithms / protocols
After knowing about the technology, uses of it and the issues faced by them in the earlier stage, now we are going to learn about the algorithms used for this technology. The algorithms provided for CR-IoT should consider some factors like particular use case, regulatory constraints and network requirements. The algorithms used in this case to enhance performance are: Genetic algorithm, Hybrid matched filter detection, Multi-objective Ant Colony Optimization, Residual Neural Network and Support vector machine-based red deer algorithm.
- Comparative study / Analysis
Here in this section we are going to compare different algorithms related to this study in order to find the best one. They are: “Deep Reinforcement Learning” (DRL) method and “6G Cognitive Radio Network – Internet of Things” (6GCRN-IoT).
- Simulation results / Parameters
The approaches which were proposed to overcome the issues faced by CR-IoT in the above section are tested using different methodologies to analyze its performance. The comparison is done by using metrics like Average Utility, Delay, Handoff Probability, Revenue, Spectrum Utilization and Throughput.
- Dataset LINKS / Important URL
Here are some of the links provided for you below to gain more knowledge about CR-IoT which can be useful for you:
- https://ieeexplore.ieee.org/abstract/document/9205906
- https://ieeexplore.ieee.org/abstract/document/9905718
- https://ieeexplore.ieee.org/abstract/document/10170314
- CR-IOT Applications
In this next section we are going to discuss about the applications of CR-IoT technology for Anomaly detection. This technology has been employed in many industries, from which some of them are listed here: Autonomous operation, Dynamic power management, Dynamic Spectrum Access, Enhanced signal quality, Interference mitigation, Smart cities, Spectrum sharing and healthcare.
- Topology
Here you are going to learn about the different choices of topologies which can be used in the network of CR-IoT based on specific requirements. They are: Communication infrastructure, CR-enabled gateways, Dynamic spectrum access, Cognitive engine, IoT devices, Monitoring and management, Flexibility, Spectrum sensing nodes, Privacy measures and Security, Spectrum databases and Scalability.
- Environment
This CR-IoT technology can function better in any kind of environment because of integration of two techniques CR and IoT. This only need careful planning, addressing issues and standardization.
- Simulation Tools
Here we provide some simulation software for CR-IoT technology, which is established with the usage of NS 3 tool with version 3.36 or above for increasing its performance.
- Results
After going through this paper based on the research topic CR-IoT technology you might now clearly understand about this technique with the help of various sections provided in this paper like its definition, uses, applications, algorithms used in it, issues face by this method previously and many more.
Cognitive Radio IOT Project Topics & Ideas
- RL-IoT: Reinforcement Learning-Based Routing Approach for Cognitive Radio-Enabled IoT Communications
- ProMETHEUS: A Secure Lightweight Spectrum Allocation Protocol against SSDF Attacks in Cognitive Radio IoT Networks
- Convergence of IoT and Cognitive Radio Networks: A Survey of Applications, Techniques, and Challenges
- Optimal Status Updates in Cognitive Radio-Enabled IoT Networks: An Age of Information Approach
- ASAA: Multihop and Multiuser Channel Hopping Protocols for Cognitive-Radio-Enabled Internet of Things
- Investigation on Throughput Maximization of CR-IoT Network through Hybrid Spectrum Access
- Embedded CR Assisted NOMA for IoT Resource Allocation: A Case Study of Vehicle Networks
- A Novel method of Improving Spectrum sensing Management system for CR-IoT Networks
- Energy Harvesting-enabled Cognitive Radio-Internet of Things Using Machine Learning
- Malicious Users Detection in OFDM-based Cognitive Radio-Internet of Things using Machine Learning: Simulation and Performance
- Age of Information for Short-Packet Relay Communications in Cognitive-Radio-Based Internet of Things with Outdated Channel State Information
- An Improved Two-Hop Rendezvous Model with Buffer-Aided Relays in Channel-Hopping Cognitive-Radio Wireless Networks for Internet of Things
- Multiple Features-Aided Malicious Users Detection in OFDM-based Cognitive Radio-Internet of Things
- Security-Reliability Analysis in CR-NOMA IoT Network under me/Q Imbalance
- Performance Analysis of CR-NOMA Based on Untrusted Relay in IoT
- Dual-Mode Path Selection for In-Band Full-Duplex Multihop CR-IoT Networks
- Attacking Modulation Recognition with Adversarial Federated Learning in Cognitive Radio-Enabled IoT
- Adversarial Attacking and Defensing Modulation Recognition with Deep Learning in Cognitive Radio-Enabled IoT
- MASSnet: Deep Learning-Based Multiple-Antenna Spectrum Sensing for Cognitive Radio-Enabled Internet of Things
- Reliability and Security of CR-STAR-RIS-NOMA Assisted IoT Networks
- Performance Analysis of STAR-RIS-CR-NOMA Based Consumer IoT Networks for Resilient Industry 5.0
- Attacking Spectrum Sensing With Adversarial Deep Learning in Cognitive Radio-Enabled Internet of Things
- CR-IOT based selfish attack detection via RSSI-LSTM
- Routing in cognitive radio networks using adaptive full-duplex communications over IoT environment
- Efficient Routing Protocol for Optimal Route Selection in Cognitive Radio Networks over IoT Environment
- Hybrid NN-based green cognitive radio sensor networks for next-generation IoT
- Security Energy Efficiency Analysis of CR-NOMA Enabled IoT Systems for Edge-cloud Environment
- A Study on the Implications of NLARP to Optimize Double Q-Learning for Energy Enhancement in Cognitive Radio Networks with IoT Scenario
- An Optimal Asymmetric Synchronous Blind Rendezvous Algorithm in Cognitive Radio Networks for Internet of Things
- Fuzzy ELM-based optimal spectrum sensing in CR-IoT network
- A Deep Learning-Based Discrete-Time Markov Chain Analysis of Cognitive Radio Network for Sustainable Internet of Things in 5G-Enabled Smart City
- A novel fast and fair asynchronous channel hopping rendezvous scheme in cognitive radio networks for internet of things
- Internet of Things and Cognitive Radio Networks: Applications, Challenges and Future
- Automatic Jammer Signal Classification Using Deep Learning in the Spectrum of AI-Enabled CR-IoT
- An Adaptive Matching Bridged Resource Allocation over Correlated Energy Efficiency and AoI in CR-IoT System
- Machine Learning-based Malicious Users Detection in Blockchain-Enabled CR-IoT Network for Secured Spectrum Access
- Queuing Analysis of QoS Aware Microwave Power Transfer Enabled CR-IoT Network
- Sustainability Analysis of Opportunistic CR-IoT Network Employing Microwave Power Transfer
- Highly Reliable Transmission and Channel Assignment for CR-IoT Networks
- GS-QRNN: A High-Efficiency Automatic Modulation Classifier for Cognitive Radio IoT
- Jamming resilient multi-channel transmission for cognitive radio IoT-based medical networks
- Real-Time Implementation and Analysis of Age of Information for Cognitive Radio Internet-of-Things
- Performance Analysis of a Multi-Channel Non-persistent CSMA Protocol in Delay-sensitive CR-based IoT Sensor Networks
- An Improved and Stable Routing Protocol for Cognitive Radio Based IoT Networks
- Design of Computationally Efficient FRM Based Reconfigurable Filter Structure for Spectrum Sensing in Cognitive Radio for IoT Networks
- A Deep Autoencoder Trust Model for Mitigating Jamming Attack in IoT Assisted by Cognitive Radio
- Reliability Analysis of Cognitive Radio Networks with Reserved Spectrum for 6G-IoT
- Real-Time Implementation and Analysis of Age of Information for Cognitive Radio Internet-of-Things
- Efficient Channel Management Scheme for Cognitive Radio based Internet of Things
- An Improved and Stable Routing Protocol for Cognitive Radio Based IoT Networks
