A Genetic AlgorithmBased Soft Decision Fusion Scheme in Cognitive IoT Networks with Malicious Users Internet of Things (IoT) is a new challenging paradigm for connecting a variety of heterogeneous networks. Since its introduction, many researchers have been studying how to efficiently exploit and manage spectrum resource for IoT applications. An explosive increase in the number of IoT devices accelerates towards the futureconnected society but yields a high system complexity. Cognitive radio (CR) technology is also a promising candidate for future wireless communications. CR via dynamic spectrum access provides opportunities to secondary users (SUs) to access licensed spectrum bands without interfering primary users by performing spectrum sensing before accessing available spectrum bands. However, multipath effects can degrade the sensing capability of an individual SU. Therefore, for more precise sensing, it is helpful to exploit multiple collaborative sensing users. The main problem in cooperative spectrum sensing is the presence of inaccurate sensing information received from the multipathaffected SUs and malicious users at a fusion center (FC). In this paper, we propose a genetic algorithmbased soft decision fusion scheme to determine the optimum weighting coefficient vector against SUsxe2x80x99 sensing information. The weighting coefficient vector is further utilized in a soft decision rule at FC in order to make a global decision. Through extensive simulations, the effectiveness of the proposed scheme is evaluated compared with other conventional schemes.

Optimization for Hard Combining Cooperative Spectrum Sensing with Heterogeneous Devices In this paper, we investigate the hard combining scheme of cooperative spectrum sensing in a practical scenario where secondary users (SUs) may use different local detectors and have different signaltonoise ratios (SNRs). For simplicity, energy detector and pilotbased detector are considered for local quantization. The suboptimal AND and OR rules are implemented in the fusion center (FC). We point out that the necessary condition of the optimal local threshold in distributed detection can be extended to the heterogeneous cooperative sensing network by some modification. Subsequently, two iterative threshold optimization algorithms are extended from methods in conventional cooperative sensing scenarios: the ascending order of Bayesian risk optimization (AOBO) and the personbyperson optimization (PBPO). Simulation results show that both the AOBO and PBPO outperform the uniformthreshold approach, when the sensing abilities at SUs are much different. When all SUs have an identical SNR and only use energy detectors, the PBPO method has a same near optimal performance compared to the uniformthreshold approach.