@article{uninimx3487,
            year = {2022},
         journal = {Scientific Reports},
           month = {Julio},
          volume = {12},
          author = {Shalli Rani and Himanshi Babbar and Syed Hassan Ahmed Shah and Aman Singh},
          number = {1},
           title = {Improvement of energy conservation using blockchain-enabled cognitive wireless networks for smart cities},
             url = {http://repositorio.unini.edu.mx/id/eprint/3487/},
        abstract = {In Smart Cities? applications, Multi-node cooperative spectrum sensing (CSS) can boost spectrum sensing efficiency in cognitive wireless networks (CWN), although there is a non-linear interaction among number of nodes and sensing efficiency. Cooperative sensing by nodes with low computational cost is not favorable to improving sensing reliability and diminishes spectrum sensing energy efficiency, which poses obstacles to the regular operation of CWN. To enhance the evaluation and interpretation of nodes and resolves the difficulty of sensor selection in cognitive sensor networks for energy-efficient spectrum sensing. We examined reducing energy usage in smart cities while substantially boosting spectrum detecting accuracy. In optimizing energy effectiveness in spectrum sensing while minimizing complexity, we use the energy detection for spectrum sensing and describe the challenge of sensor selection. This article proposed the algorithm for choosing the sensing nodes while reducing the energy utilization and improving the sensing efficiency. All the information regarding nodes is saved in the fusion center (FC) through which blockchain encrypts the information of nodes ensuring that a node?s trust value conforms to its own without any ambiguity, CWN-FC pick high-performance nodes to engage in CSS. The performance evaluation and computation results shows the comparison between various algorithms with the proposed approach which achieves 10\% sensing efficiency in finding the solution for identification and triggering possibilities with the value of {\ensuremath{\alpha}}=1.5 and {\ensuremath{\gamma}}=2.5 with the varying number of nodes.}
}