Sequential detection with feedback information for two‐way cooperative spectrum sensing in cognitive internet of things

• Published in: Transactions on emerging telecommunications technologies Vol. 35; no. 8
• Main Authors: Wu, Jun, Su, Mingkun, Bao, Jianrong, Qiao, Lei, Xu, Xiaorong, Wang, Hao, Zhu, Gefei, Cao, Weiwei
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 01.08.2024
• ISSN: 2161-3915; 2161-3915
• DOI: 10.1002/ett.5023

With the rapid growth of internet of thing (IoT) devices, cooperative spectrum sensing (CSS) has emerged as a promising solution to leverage the spatial diversity of multiple secondary IoT sensing nodes (SNs) for spectrum availability. However, the cooperative paradigm also incurs increased cooperative costs between each SN and the fusion center (FC), leading to decreased cooperative efficiency and achievable throughput, especially in large‐scale cognitive IoT (CIoT). To address these challenges, we present a sequential detection with feedback information (SD‐FI) approach in this paper. To achieve this objective, we propose a two‐way CSS model that formulates an optimization problem of Bayes cost in a quickest detection framework with feedback. To solve this optimization problem, we derive the structure of the optimal local decision rule from the local decision function and determine the optimal detection threshold in conjunction with the cost function. Following the optimal threshold pair, we implement the optimal SD‐FI and theoretically demonstrate the uniqueness of the optimal threshold and optimal sensing time. Simulation results demonstrate superiority of SD‐FI in terms of cooperative performance (i.e., detection performance and Bayes cost) and sample size. Notably, even with limited sensing time, our proposed SD‐FI exhibits high throughput, highlighting its effectiveness in enhancing spectrum availability and utilization in CIoT. A two‐way cooperative spectrum sensing model among two internet of things and the fusion center is formulated. With the help of sequential idea and the feedback information from the fusion center at each sensing, we derive Bayes cost as the objective function and optimize the proposed sequential detection with feedback information by the optimal thresholds.