Focused on the limitation of monitoring distance, restriction of online transmission, large amount of information and that high-power data-link is disable to board small environment monitoring Unmanned Aerial Vehicle (UAV), a multiple-UAV environment monitoring task scheduling method considering 3G/4G network features was proposed. First, the time characteristic of 3G/4G network and the multiple-UAV environment monitoring task scheduling were combined, and this issue was modeled as Team Orienteering Problem with Time Window (TOPTW). Secondly, since the problem of huge computation and easily falling into local optimum, an Iterated Local Search (ILS) algorithm was proposed to get the optimization solution. Thirdly, a large amount of test data sets were applied into experiments to verify the feasibility and computing performance, and the comparative result between ILS and Ant Colony Algorithm (ACA) about the average profit and computing time were proposed. Last, the algorithm was applied in typical two UAV environment monitoring task scheduling under 3G/4G network. The results show that, the most profits received from ILS were worse than those from ACA. The average Gap of all test data sets was 1.09% and the largest was 10.8%. There were some results better than those in ACA. And the computing time of ILS was nearly reduced to a thousandth of the computing time of ACA. The experimental results show that ILS algorithm can fast solve the issue of multi-UAV environment monitoring task scheduling and effectively reduce the computing time with profit results in an acceptable scope.

To deal with the huge spatial and temporal consumption caused by large-scale self-set data, the authors designed a self-set matching mechanism based on artificial immune Network Intrusion Detection System (NIDS). To improve the detection efficiency of the intrusion detection system, an efficient probability matching optimization mechanism was proposed. The authors first proved the relative concentration of the network data, and analyzed the validity of the probability matching mechanism by calculating the Average Search Length (ASL), then verified the fast matching efficiency of the mechanism through simulation experiments. The mechanism has been used in a project application in a new artificial immune network intrusion detection system based on self-set scale simplified mechanism, which has achieved satisfactory matching results.