The cognitive wireless sensor network (CWSN) is an important development direction of wireless sensor networks (WSNs), and spectrum sensing technology is an essential prerequisite for CWSN to achieve spectrum sharing. However, the existing non-cooperative narrowband spectrum sensing technology has difficulty meeting the application requirements of CWSN at present. In this paper, we present a non-cooperative spectrum sensing algorithm for CWSN, which combines the multi-resolution technique, phase space reconstruction method, and singular spectrum entropy method to sense the spectrum of narrowband wireless signals. Simulation results validate that this algorithm can greatly improve the detection probability at a low signal-to-noise ratio (SNR) (from −19dB to −12dB), and the detector can quickly achieve the best detection performance as the SNR increases. This algorithm could promote the development of CWSN and the application of WSNs.

With the development of wireless communication technology, cognitive radio needs to solve the spectrum sensing problem of wideband wireless signals. Due to performance limitation of electronic components, it is difficult to complete spectrum sensing of wideband wireless signals at once. Therefore, it is required that the wideband wireless signal has to be split into a set of sub-bands before the further signal processing. However, the sequence of sub-band perception has become one of the important factors, which deeply-impact wideband spectrum sensing performance. In this paper, we develop a novel approach for sub-band selection through the non-stationary multi-arm bandit (NS-MAB) model. This approach is based on a well-known order optimal policy for NS-MAB mode called discounted upper confidence bound (D-UCB) policy. In this paper, according to different application requirements, various discount functions and exploration bonuses of D-UCB are designed, which are taken as the parameters of the policy proposed in this paper. Our simulation result demonstrates that the proposed policy can provide lower cumulative regret than other existing state-of-the-art policies for sub-band selection of wideband spectrum sensing.

Active signal-source seeking by swarms of unmanned aerial vehicles (UAVs) requires reliable waypoint decisions under measurement corruption, misleading response peaks, and geometric constraints. We propose MI-PSO-Adaptive, a high-level waypoint planning framework based on particle swarm optimization (PSO). The framework combines repeated sampling with robust aggregation, adaptive exploration with archive-based social target selection, candidate waypoint guidance, and geometric constraint repair. We evaluated the framework in a three-dimensional simulation using a five-sample measurement budget, waypoint limits, obstacles, and reproducible random seed protocols. In the default unimodal Gaussian field, MI-PSO-Adaptive achieved a mean localization error of 35.36 m and a success rate of 94%; model-matched Gaussian estimation baselines remained more accurate. In the fixed Gaussian mixture field, it achieved a mean localization error of 54.03 m and a success rate of 66%. In contrast, the other evaluated optimization methods and unimodal-model estimation baselines failed to reach the target region. Under 5% outlier contamination, robust aggregation achieved a mean localization error of 157.80 m and a success rate of 52%, outperforming arithmetic mean MI-PSO, although the error distribution remained heavy tailed. Functional ablation showed that adaptive exploration with social target selection helped the swarm escape misleading interference peaks, whereas candidate waypoint guidance further reduced localization errors after effective exploration. The method is therefore intended for discrete high-level waypoint planning under the tested model-mismatch conditions rather than serving as a general replacement for model-based estimation or a guarantee of low-level flight feasibility.