To defend against internal attacks in wireless sensor networks (WSNs), building a trust model between sensors nodes has been proved to be an effective way in this paper. The most current trust models only consider communication behavior when calculating direct trust, which is directly calculated based on the interactions between sensor nodes. However, this is not enough because of the various types of attacks. Furthermore, the adverse effect of poor-quality links on the trust value of normal nodes is not discussed in the current trust models. In this paper, we propose a beta and link quality indicator (LQI)-based trust model (BLTM) for the WSNs. First, communication trust, energy trust, and data trust are considered when calculating direct trust. Then, the weight of communication trust, energy trust, and data trust are discussed. Finally, an LQI analysis mechanism is proposed to maintain the accuracy and stability of the trust value of normal nodes in a network with poor-quality links. Compared with other models, e.g., beta-based trust and reputation evaluation system (BTRES), the simulation results show that the BLTM can defend against internal attacks, e.g., DoS attack and data tampering attack which the BTRES cannot resist and can reduce the adverse effect of poor-quality links on the trust value of normal nodes effectively. INDEX TERMS Wireless sensor networks, beta distribution, link quality indicator, trust model.

In conventional directional sensor networks, coverage control for each sensor is based on a 2D directional sensing model. However, 2D directional sensing model failed to accurately characterize the actual application scene of image/video sensor networks. To remedy this deficiency, we propose a 3D directional sensor coverage-control model with tunable orientations. Besides, a novel criterion for judgment is proposed in view of the irrationality that traditional virtual potential field algorithms brought about on the criterion for the generation of virtual force. Furthermore, cross-set test is used to determine whether the sensory region has any overlap and coverage impact factor is introduced to reduce profitless rotation from coverage optimization, thereby the energy cost of nodes was restrained and the performance of the algorithm was improved. The extensive simulations results demonstrate the effectiveness of our proposed 3D sensing model and IPA3D (improved virtual potential field based algorithm in three-dimensional directional sensor networks).

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