Abstract In recent times, spectrum sensing and spectrum management become a crucial design issue in cognitive radio networks (CRN). To improve the spectrum utilization in CRN, the secondary users (SUs) will try to utilize the spectrum resource when it is unoccupied by the authorized primary users (PUs). At the same time, blockchain principle has been introduced to efficiently identify the legitimate SUs and allocate the spectrum resource as per the demand specified by the SUs. In this view, this article presents a new machine learning (ML) with blockchain‐based spectrum management technique in CRN. The proposed model undergoes three processes, namely spectrum sensing, blockchain‐based spectrum access, and malicious user (MU) identification. Initially, ML‐based extreme learning machine (ELM) technique is applied for spectrum sensing. Then, the presented blockchain approach provides secured spectrum allocation for SUs. Finally, the MUs are identified and to be blocked from accessing the available spectrum resource. An extensive simulation analysis is carried out to ensure the goodness of the proposed model. The obtained results indicated that the proposed model has offered better performance compared with other methods. The experimental outcome stated that under the presence of −20 dB SNR, the proposed method has attained a maximum detection rate of 0.68, whereas the KNN and OR rule methods have demonstrated a minimum detection rate of 0.58 and 0.5, respectively.
Muhammad Adnan Khan, Sagheer Abbas, Abdur Rehman, Yousaf Saeed · 8 authors
Realizing secure and private communications on the Internet of Things (IoT) is challenging, primarily due to IoT's projected vast scale and extensive deployment. Recent efforts have explored the use of blockchain in decentralized protection and privacy supported. Such solutions, however, are highly demanding in terms of computation and time requirements, barring these solutions from the majority of IoT applications. Specifically, in this paper, we introduce a resource-efficient, blockchain-based solution for secure and private IoT. The solution is made possible through novel exploitation of computational resources in a typical IoT environment (e.g., smart homes), along with the use of an instance of Deep Extreme Learning Machine (DELM). In this proposed approach, the Smart Home Architecture based in Blockchain is protected by carefully evaluating its reliability in regard to the essential security aims of privacy, integrity, and accessibility. In addition, we present simulation results to emphasize that the overheads created by our method (in terms of distribution, processing time, and energy consumption) are marginal related to their protection and privacy benefits.
To security support large-scale intelligent applications, distributed machine learning based on blockchain is an intuitive solution scheme. However, the distributed machine learning is difficult to train due to that the corresponding optimization solver algorithms converge slowly, which highly demand on computing and memory resources. To overcome the challenges, we propose a distributed computing framework for L-BFGS optimization algorithm based on variance reduction method, which is a lightweight, few additional cost and parallelized scheme for the model training process. To validate the claims, we have conducted several experiments on multiple classical datasets. Results show that our proposed computing framework can steadily accelerate the training process of solver in either local mode or distributed mode.
Artificial neural networks are modern methods suitable for solving the problem of nonlinear dependency approximation, which is successfully applied in many fields. This paper compares the predictive capabilities of Back Propagation, Radial Basis Function, Extreme Learning Machine, and Long-Short Term Memory neural networks to determine which artificial intelligence algorithm is best for modeling the price of Bitcoin opening. The criterion for comparing network performance was the standard deviation, the mean absolute deviation, and the accuracy of predicting the direction of change of course. At the same time, in the study of time series, it is recommended to perform a comprehensive data analysis using appropriate networks, depending on the length of the series and the specificity of the database.
Ricardo Carreño, Verónica Aguilar-Esteva, Daniel Pacheco, Antonio Acevedo · 6 authors
Knowledge society blockchain is one of the most powerful and recent tools to make the internet environment safer and reliable. Manufacturing has traditionally been dominated by standard designs that are mass-produced, due to the fact, that custom production causes additional costs that make it less affordable than mass production. This paper proposes to develop a designer expert system for IoT installation layout designs, using blockchain distributed system based on a machine learning, with users entering data to the expert system by a smart bot software. This expert system will work using extreme learning machine as inference engine; therefore, this is a shell to develop any expert system with fast learning. The whole system is represented by a smart contract with a value linked to the value of the expert system, the more this expert system be quoted on the web, the more the shares of the smart contract will cost.