Trading-Based Dynamic Spectrum Access and Allocation in Cognitive Internet of Things

Abstract

Next-generation mobile communication networks promise the support for vast number of IoT devices with strong demand for spectrum access. The cater for the continuous growth of IoT applications, one of the challenges to mobile communication network providers is to allow dynamic spectrum access to a gigantic number of densely distributed and low-power IoT networks. In this paper, a novel dynamic spectrum access method is proposed based on spectrum trading for distributed IoT devices. The notion of access benefit is introduced which is based on the purchased band number and modulation mode provided to the mobile user. In the proposed solution, IoT users aim achieve optimization of their spectrum access by ensuring that the spectrum price will not exceed the access benefits from the spectrum trading by solving the spectrum optimization function. Due to the complexity of optimization objective function, pattern search algorithm is utilized to complete the final spectrum allocation optimization. Numerical results are also provided to testify the performances of the proposed spectrum optimization method.

BlockCRN-IoCV: Secure Spectrum Access and Beamforming for Defense Against Attacks in mmWave Massive MIMO CRN in 6G Internet of Connected Vehicles

“…Authors proposed dynamic spectrum access and allocation method based on trading for cognitive Internet of Things network in [36]. The proposed work architecture includes four layers: the information sensing layer, network connection layer, cognitive layer and service layer.…”

“…• The trust value of the vehicles in the network was computed by the fusion center by incorporating the TOPSIS Beamforming in ultra-dense network Reinforcement learning algorithm Insecure beamforming [31] Secure Beamforming Power allocation policy Insufficient beamforming security [32] Interference less Beamforming Bi-LSTM Inaccurate CSI [33] Multiuser Beamforming Deep Neural Network High Attack Threats Spectrum Sensing and Access [34] Dynamic spectrum access Proactive and reactive techniques Poor spectrum access [35] Spectrum allocation Deep reinforcement algorithm High spectrum allocation latency [36] Dynamic spectrum access and allocation QAM modulation and Lagrange method Low performance of spectrum allocation [37] QoS aware spectrum access ANN algorithm Poor spectrum access efficiency [38] Detection of false sensing Dempster-Shafer theory Partially overcome the malicious SUs method; however, it faced difficulties in maintaining the consistency of the decision.…”

Machine Learning Approaches for Combating Distributed Denial of Service Attacks in Modern Networking Environments

“…The IoT has been the subject of significant research interest in recent years [94]. It connects a tremendous number of physical devices (or "things") around the world that are capable of collecting and sharing data over the Internet [95].…”

Fair Resource Allocation in Cooperative Cognitive Radio Iot Networks

“…Proportional fairness and max-min fairness schemes are implemented and compared in terms of total utility, average SINR, and fairness index. A trading-based spectrum optimization strategy is introduced in [27], in which IoT users try to optimize their access to the spectrum by ensuring that then spectrum price will not exceed the access benefits. A multi-objective optimization algorithm is presented in [28] for spectrum allocation cognitive radio-based IoT, aiming to maximize both end-to-end throughput and spectrum utilization.…”