Cognitive Radio for Smart Grid: Theory, Algorithms, and Security

Abstract

Recently, cognitive radio and smart grid are two areas which have received considerable research impetus. Cognitive radios are intelligent software defined radios (SDRs) that efficiently utilize the unused regions of the spectrum, to achieve higher data rates. The smart grid is an automated electric power system that monitors and controls grid activities. In this paper, the novel concept of incorporating a cognitive radio network as the communications infrastructure for the smart grid is presented. A brief overview of the cognitive radio, IEEE 802.22 standard and smart grid, is provided. Experimental results obtained by using dimensionality reduction techniques such as principal component analysis (PCA), kernel PCA, and landmark maximum variance unfolding (LMVU) on Wi-Fi signal measurements are presented in a spectrum sensing context. Furthermore, compressed sensing algorithms such as Bayesian compressed sensing and the compressed sensing Kalman filter is employed for recovering the sparse smart meter transmissions. From the power system point of view, a supervised learning method called support vector machine (SVM) is used for the automated classification of power system disturbances. The impending problem of securing the smart grid is also addressed, in addition to the possibility of applying FPGA-based fuzzy logic intrusion detection for the smart grid.

Cognitive Radio for Smart Grids: Survey of Architectures, Spectrum Sensing Mechanisms, and Networking Protocols

“…VII-A [208], [209] c Matched Filter Detection Sec. VII-B [210] c Feature Detection Sec. VII-C [197], [211], [212] r r r r r r r j Alternative Approaches Sec.…”

“…Dimensionality reduction techniques, such as principal component analysis (PCA), kernel PCA, and landmark maximum variance unfolding (LMVU), on Wi-Fi signal measurements are examined in a spectrum sensing context in [210]. Compressed sensing algorithms, such as Bayesian compressed sensing and compressed sensing Kalman filters, are also proposed.…”

“…The Kalman filtering approach is used for the recovery of sparse signals. The compressed sensing allows the smart meters to transmit simultaneously, in contrast to the CSMA protocol, which uses a random backoff to avoid collisions [210].…”

Parallelizing Under-Determined Inverse Problems for Network Applications

“…Demand estimation in smart grids, urban traffic estimation, traffic intensity estimation and detection in vehicular networks, and network anomaly detection are the examples of large-scale applications [24] [25] [26] [27] where the MDFE framework can be applied to speed up the process of applying highly complex computational intelligence algorithms, and provide a timely estimate of unknowns without compromising the accuracy of solution. In this regard, MDFE is complementary to a variety of CI methods by reducing the associated computational cost, a commonly perceived bottleneck for large-scale CI applications.…”

Cognitive Radio for Smart Grid with Security Considerations

“…While using the unlicensed spectrum can be efficient in terms of cost and installation, it suffers from crowdedness interference. Moreover, the fact that smart grid is a large-scale network makes it difficult to build a sensor network that can monitor the grid reliably due to electromagnetic interference, maintenance, shadowing and fading issues [20]. Therefore, using cognitive radio functionalities, such as spectrum sensing and opportunistic dynamic spectrum access, helps achieve a reliable monitoring operation with high efficiency and minimal cost.…”