However, obtaining classification labels is an expensive and infeasible task for unattended WMSN, which determines that the traditional SVM classifier learning algorithm cannot be achieved online in WMSN.This paper proposes a collaborative semi-supervised classifier learning algorithm for target classification with hybrid computing paradigm in hierarchical WMSN, which is enhanced by the ant optimization routing. The proposed algorithm progressively implements semi-supervised learning process in hybrid computing paradigm with the collaboration of a proper set of sensor nodes, which is so-called collaborative hybrid (CH) learning. The semi-supervised learning is based on the transductive support vector machine (TSVM) algorithm [7]. It can take as much advantage of unlabeled samples as possible, according to the guidance of labeled samples.

Thus, the decision function can be effectively constructed based on the labeled and unlabeled samples. Actually, the semi-supervised learning makes it possible to achieve online learning with unlabeled samples extracted from all sensor nodes. Certainly, the proposed learning algorithm also takes the advantages of incremental learning to decrease the energy consumption in data transmission. During the incremental learning process, two metrics, effectiveness metric and access probability are introduced to evaluate the effectiveness and necessity of the samples in specific sensor nodes. According to the evaluation of historical contribution of sensor nodes, the incremental semi-supervised learning is implemented with the collaboration of some purposefully selected sensor nodes.

By using the sensor nodes selection strategy, the imprecise sensor nodes will be ignored. Thus the impact of missing detection and false detection can be largely reduced. For further decreasing the energy consumption, the ant optimization routing is adopted to arrange the information transmission of collaborative hybrid learning paradigm in hierarchical WMSN.Usually, a WMSN is always built on a hierarchical architecture, which comprises several clusters. Each cluster consists of several sensor nodes and a cluster head. In the collaborative hybrid learning paradigm, the in-network signal processing in each cluster is not constructed by client/server paradigm as usual, because the traditional client/server paradigm will greatly deteriorate the quality of service (QoS) of the network.

Instead, the progressive distributed computing paradigm AV-951 [8] is used for the in-network signal processing in each cluster, and a peer-to-peer (P2P) computing paradigm is used for the further signal processing between all cluster heads. The CH learning paradigm has the advantages of collaboration and parallelism. Thus, the CH learning paradigm can reduce the energy consumption and network congestion of data transmission.