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Smart grid (SG) communication requires multilevel communication among multiple layers. To handle this paramount spectrum requirement, cognitive radio network (CRN) is one of the promising solutions. However, the integration of CRN for SG communications introduces new challenges in spectrum allocation. Still, achieving high spectrum utilization is restricted by security vulnerability and poor algorithm design. This paper resolves all these issues to achieve the objective of spectrum utilization efficiency. A novel dynamic cluster switching approach is designed for secure channel allocation for cognitive radio-based smart grid (SCAN-CogRSG) communications. Strong authentication is enabled for secondary users (SUs) by Binate physically unclonable function (Bi-PUF) authentication mechanism. The key idea behind Bi-PUF mechanism is to prevent spectrum resources from unauthorized access. The formed neighborhood area network (NAN) clusters are dynamically switched to distribute uniform spectrum availability. For dynamic switching, a novel Topology aware fuzzy-reinforcement learning (TA-FuReil) algorithm is presented. For an effectual channel allocation, feature-based K-means (FK-means) algorithm and tri-objective cuttlefish optimization (TriO-CFO) algorithm are proposed. The formulated objectives are interference minimization, power minimization, and data rate maximization. In the case of priority situations, the one-to-many matching-based TriO-CFO (OMTriO-CFO) algorithm is utilized. The proposed SCAN-CogSG is modeled in NS-3 and validated for performance evaluation. The observed results show betterment in throughput, retransmission probability, latency, and authentication time.

SCAN-CogRSG: Secure Channel Allocation by Dynamic Cluster Switching for Cognitive Radio Enabled Smart Grid Communications