Genetic Algorithm-Based Hybrid Spectrum Handoff Strategy in Cognitive Radio-Based Internet of Things

In Cognitive radio-based Internet of Things (CR-IoT) systems, the return of the primary user (PU) causes the secondary user (SU) that is communicating to face the spectrum handoff problem. In the process of spectrum handoff, the user terminal can’t get the idle channels in time because of the unknown channel usage state. To solve this problem, a hybrid spectrum handoff algorithm based on the channel idle probability is proposed. The algorithm considers the regularity of PU activities in space and time, defines the idle probability of channels from the perspective of week attributes and time periods, obtains the optimal time period length using genetic algorithm, generates a channel idle probability table, and provides the target channel sequence for SUs in combination with the proposed channel ordering scheme. Simulation results show that the hybrid spectrum switching algorithm based on the channel idle probability can reduce the energy consumption of SUs during spectrum switching, reduce the communication interruption rate, enable SUs to find idle channels faster and reduce the number of sensing times.

Analyzing Cognitive Radio Network Operation With the Mechanism of Deciding Handoff and Process of Handoff Employing Varied Distribution Models (5G)

Cognitive radio networks (CRNs) deal with sensing, decision making, sharing, and mobility. Among these aspects, spectrum mobility assumes a vital role for spectrum handoff to occur. When the spectrum handoff process is analyzed, there are call drops, spectrum handoff, and interferences with the adjacent channels. For minimizing the handoff of the spectrum's probability as well as the call drops, which involves three processes, which are followed in sequential order, results demonstrate a better QoS. In the following three processes, the first process starts with the Markov model that identifies a channel's states and selects the channel which is apt. The second process, multiple attributes decision making (MADM), methods choose one of the best possible channels among all the channels based on various metrics in the proposed hybrid method. In the third process, the spectrum handoff is analyzed through different distribution models to indicate which model is the desirable one for a handoff process such as the one with stationary users or the other with non-stationary users.

Spectrum Handoff Aware AODV Routing Protocol for Cognitive Radio Vehicular Ad Hoc Networks

End-to-end application performance and throughput of vehicular cognitive transport control protocol depend on how efficiently the segments (TCP header) are being transmitted from source to destination. One way to enhance the performance of vehicular cognitive TCP protocol is by reducing the packet drops in between the source and destination. In general, packet drops occur in between the source and destination of Cognitive Radio Vehicular Ad Hoc Network (CR-VANET) because of spectrum handoff or cognitive node handoff, or network congestion. In this paper, we focus on enhancing the performance of cognitive TCP protocol through “cognitive AODV routing protocol with the spectrum handoff mechanism.” In the proposed work, channel-route control messages of cognitive AODV routing protocol are updated with the support of spectrum handoff which helps to provide the backup opportunistic channel during PU active and helps to reduce the end-to-end spectrum handoff packet drops. Simulation results reveal that the overall performance of the vehicular cognitive TCP protocol with the proposed spectrum handoff aware cognitive AODV routing protocol is enhanced as compared to the existing cognitive TCP protocol.