A smart spectrum access technique for dynamic multi-hop routing in cognitive radio-based disaster response networks - Khaled F. I, Alaqad (2022)
Disasters frequently occur across both developed and developing countries, the existing communication systems are highly prone to malfunction and damage, these systems are necessary to coordinate disaster relief efforts, therefore, it is extremely important to autonomously deploy a network that can provide communication services for both victims and first responders in the first 48 critical hours, these consequences along with other distinct routing requirements imposed by disaster occurrence necessitate the availability of a Cognitive Radio based Disaster Response Network CR-DRN operated with a routing protocol that is designed considering the emerging routing requirements and variations imposed by disaster, In Cognitive Radio Network, the allocation of spectrum is a crucial process which affects the communication. The allocation of the spectrum during emergencies is a challenging task, which is not yet solved. In this research, a novel Smart Agent Aided Scalable Spectrum Access (SASSA) technique for Cognitive Radio networks CRN-based disaster networks is proposed in which the overall network is considered as hexagonal cells to achieve better coverage. The disaster-based cluster formation is carried out to locate the disaster region in the cell. The efficient spectrum sensing is performed by deploying Smart Spectrum Agents (SSAs) and the sensing is carried out using Enhanced Bayesian Compressive Sensing (EBayesCS). The cognitive base station implements the Combined Quality of Service Score (CoQS) to rank the available channels. A novel Dual– Environment Deep Deterministic Policy Gradient (DE-D2PG) is proposed to decide the QoS switching based on spectrum availability and data emergency. The multi-hop route selection is executed using the Hybrid Spiral Penguin Optimization (HSPO) algorithm based on the decision made by the DE-D2PG. The allocation of spectrum is carried out by performing one-to-K matching which enables multiple channels to the Secondary Users (SUs) for effective transmission. Further, the deployment of Mobile Cognitive Base Stations (McBS) using the Dynamic Rule-Based Movement (DRUM) algorithm facilitates the effective transmission of data with low latency. The proposed SASSA model was evaluated using NS- 3.26 through a comparative analysis with existing most recent approaches, the results of this analysis proved that SASSA empowered CR-DRNs with higher data ratios as network size and sensing time increase with minimum standard deviation and Min-Max variations of 1.02-1.2 Mbps. The proposed technique was also proved to be scalable, reliable and spectrum-efficient by achieving minimum delay, maximum probability of detection, maximum spectrum utilization and maximum throughput compared to other approaches concerning both network size and sensing time.