Although appearance based trackers have been greatly improved in the last decade, they are still struggling with some challenges like occlusion, blur, fast motion, deformation, etc. As known, occlusion is still one of the soundness challenges for visual tracking. Other challenges are also not fully resolved for the existed trackers. In this work, we focus on tackling the latter problem in both color and depth domains. Neutrosophic set (NS) is as a new branch of philosophy for dealing with incomplete, indeterminate and inconsistent information. In this paper, we utilize the single valued neutrosophic set (SVNS), which is a subclass of NS, to build a robust tracker. First, the color and depth histogram are employed as the appearance features, and both features are represented in the SVNS domain via three membership functions T , I, and F. Second, the single valued neutrosophic cross-entropy measure is utilized for fusing the color and depth information. Finally, a novel SVNS based MeanShift tracker is proposed. Applied to the video sequences without serious occlusion in the Princeton RGBD Tracking dataset, the performance of our method was compared with those by the state-of-the-art trackers. The results revealed that our method outperforms these trackers when dealing with challenging factors like blur, fast motion, deformation, illumination variation, and camera jitter.

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A sensor node (SN) in Wireless Sensor Networks (WSNs) can decide whether to collaborate with others based on a trust management system (TMS) by making a trust decision. In this paper, we study the trust decision and its dynamics that play a key role to stabilize the whole network using evolutionary game theory. When SNs are making their decisions to select actionTrustorMistrust, a WSNs trust game is created to reflect their utilities. An incentive mechanism bound with one SN’s trust degree is incorporated into this trust game and effectively promotes SNs to select actionTrust. The replicator dynamics of SNs’ trust evolution, illustrating the evolutionary process of SNs selecting their actions, are given. We then propose and prove the theorems indicating that evolutionarily stable strategies can be attained under different parameter values, which supply theoretical foundations to devise a TMS for WSNs. Moreover, we can find out the conditions that will lead SNs to choose actionTrustas their final behavior. In this manner, we can assure WSNs’ security and stability by introducing a trust mechanism to satisfy these conditions. Experimental results have confirmed the proposed theorems and the effects of the incentive mechanism.

We consider a clustered wireless sensor network (WSN) under epidemic-malware propagation conditions and solve the problem of how to evaluate its reliability so as to ensure efficient, continuous, and dependable transmission of sensed data from sensor nodes to the sink. Facing the contradiction between malware intention and continuous-time Markov chain (CTMC) randomness, we introduce a strategic game that can predict malware infection in order to model a successful infection as a CTMC state transition. Next, we devise a novel measure to compute the Mean Time to Failure (MTTF) of a sensor node, which represents the reliability of a sensor node continuously performing tasks such as sensing, transmitting, and fusing data. Since clustered WSNs can be regarded as parallel-serial-parallel systems, the reliability of a clustered WSN can be evaluated via classical reliability theory. Numerical results show the influence of parameters such as the true positive rate and the false positive rate on a sensor node’s MTTF. Furthermore, we validate the method of reliability evaluation for a clustered WSN according to the number of sensor nodes in a cluster, the number of clusters in a route, and the number of routes in the WSN.