Availability-Reliability-Stability Trade-Offs in Ultra-Reliable Energy-Harvesting Cognitive Radio IoT Networks

Abstract

With the rise of the Internet-of-Things (IoT), new requirements have been brought into communication networks to make them more efficient, sustainable, and self-sufficient. Requirements, such as availability and ultra-reliability combined with the solutions of energy-harvesting and dynamic spectrum access, make the analyses of such networks more complex, while imposing different performance trade-offs. This paper analyzes the performance of ultra-reliable energy-harvesting cognitive radio Internet-of-Things (UR-EH-CR-IoT) networks, and provides analytical derivations for different IoT network metrics, such as GoodPut, reliability, collision probability, availability, and stability, so as to investigate their trade-offs. A new metric for network availability is defined based on energy availability and spectrum accessibility for UR-EH-CR-IoT networks, while incorporating transmission diversity. The effect of IoT network parameters, such as sensing time, diversity transmission, and number of packets in a data frame, is examined on the IoT network performance metrics. Lastly, the derived expressions are utilized to optimize the GoodPut, subject to various practical constraints. INDEX TERMS Availability, cognitive radio, collision probability, diversity transmission, energyharvesting, Internet-of-Things, stability, ultra-reliability. 1 This can be seen by noting that diversity transmission is only based on transmitting a fixed number of packet replicas. To the best of our knowledge, there are no existing results on the implementation complexity of diversity transmission.

Spectrum Sensing, Clustering Algorithms, and Energy-Harvesting Technology for Cognitive-Radio-Based Internet-of-Things Networks

“…Availability and ultrareliability demands, together with energy-harvesting technology and dynamic spectrum access, impose specific performance compromises [ 79 ], typifying sustainable and self-sufficient IoT networks, integrating sensing time, energy availability, transmission diversity, volume of data frame packets, and spectrum accessibility. Energy and spectrum resource scarcity, energy harvesting and cognitive radio technologies, and wireless devices and system expansion [ 80 ] shape deep-learning-based IoT network performance.…”

Design and Development of Hybrid Spectrum Access Technique for CR-IoT Network

“…In addition, a trained artificial neural network (ANN) was used to make decisions regarding PU channel occupancy [2]. Alternatively, [3] used a cluster-based cooperative spectrum sensing method to find the channel's status [16][17][18][19][20]. Recent work by the authors of [4] has proposed an iterative algorithm for interweave CR-IoT devices that incorporate a dynamic sensing threshold and sensing time.…”

Enhancing Hybrid Spectrum Access in CR-IoT Networks: Reducing Sensing Time in Low SNR Environments

“…[17]'s researchers discovered that in low SNR environments, the energy detector with a dynamic sensing threshold provides better results than one with a fixed threshold. Another study by [18][19][20][21][22][23][24] used an adaptive sensing threshold for CR-IoT devices using interweave spectrum access. Their results showed that the dynamic sensing threshold improves the ability of the energy detector to combat noise uncertainties and, therefore, spends less time sensing.…”