Mobile wireless sensor networks (WSNs) are facing threats from malicious nodes that disturb packet transmissions, leading to poor mobile WSN performance. Existing studies have proposed a number of methods, such as decision tree-based classification methods and reputation based methods, to detect these malicious nodes. These methods assume that the malicious nodes follow only pre-defined attack models and have no learning ability. However, this underestimation of the capability of malicious node is inappropriate due to recent rapid progresses in machine learning technologies. In this study, we design reinforcement learning-based malicious nodes, and define a novel observation space and sparse reward function for the reinforcement learning. We also design an adaptive learning method to detect these smart malicious nodes. We construct a robust classifier, which is frequently updated, to detect these smart malicious nodes. Extensive experiments show that, in contrast to existing attack models, the developed malicious nodes can degrade network performance without being detected. We also investigate the performance of our detection method, and confirm that the method significantly outperforms the state-of-the-art methods in terms of detection accuracy and false detection rate.

We propose an adaptive plastic-landmine visualizing radar system employing a complex-valued self-organizing map (CSOM) dealing with a feature vector that focuses on variance of spatial- and frequency-domain inner products (V-CSOM) in combination with aperture synthesis. The dimension of the new feature vector is greatly reduced in comparison with that of our previous texture feature-vector CSOM (T-CSOM). In experiments, we first examine the effect of aperture synthesis on the complex-amplitude texture in space and frequency domains. We also compare the calculation cost and the visualization performance of V- and T-CSOMs. Then we discuss merits and drawbacks of the two types of CSOMs with/without the aperture synthesis in the adaptive plastic-landmine visualization task. The V-CSOM with aperture synthesis is found promising to realize a useful plastic-landmine detection system.

In this paper, we propose a communication-efficient top-k continuous query processing method on distributed local nodes where data are horizontally partitioned. A designated coordinator server takes the role of issuing queries from users to local nodes and delivering the results to users. The final results are requested via a top-k subscription which lets local nodes know which data and updates need to be returned to users. Our proposed method makes use of the active previously posed queries to identify a small set of needed top-k subscriptions. In addition, with the pre-indexed nodes' skylines, the number of local nodes to be subscribed can be significantly reduced. As a result, only a small number of subscriptions are informed to a small number of local nodes resulting in lower communication overhead. Furthermore, according to dynamic data updates, we also propose a method that prevents nodes from reporting needless updates and also maintenance procedures to preserve the consistency. The results of experiments that measure the volume of transferred data show that our proposed method significantly outperforms the previously proposed methods.