Coverage Research In 3D Wireless Multimedia Sensor Network Based On Improved Teaching And Learning Optimization

Wireless multimedia sensor network with advantage of catching rich and perfect monitoring information in the target scene has been widely used in many fields. The network coverage control mechanism which is basic perception protection, directly affects the quality of the service. However, due to the random character of the initial deployment, there are a large number of blind areas and the perceptual overlap region in the network, which leads to low network coverage rate to obtain little target information. At present, some scholars have introduced the swarm intelligence optimization algorithm to improve the network coverage.However, because of the existing swarm intelligence optimization algorithm with drawback of easy getting local optimum, the network coverage rate still has a large space for improvement.In order to improve the network coverage rate, this paper demonstrates teaching and learning optimization algorithm which owns stronger stability and convergence than other optimization algorithms. But like other optimization algorithms it is also easy to fall into local optimum. So in order to solve this issue, this paper first improves the teaching and learning algorithm,and then applies the improved algorithm to the process of cover enhancement. In term of redundant nodes in the network after cover enhancement, this paper proposes the redundant nodes sleep strategy, so as to ensure the network coverage rate and reduce the number of redundant nodes in the network. Specific research contents include the following two aspects.Firstly, on account of shortcoming of easy falling into local optimum in the teaching and learning optimization algorithm, a hybrid strategy based adaptive teaching and learning algorithm (MSTLBO) is proposed. The algorithm proposes the adaptive comprehensive cross learning strategy and the adaptive perturbation strategy, which have increased the search space and reduced the possibility of falling to local optimum. Simulation experiments on standard test functions show that the proposed algorithm has strong global search ability and a great improvement in convergence accuracy and convergence speed.Secondly, in view of existing algorithms with failure in sensor network coverage enhancement effectively and existing a large number of redundant monitoring nodes after coverage enhancement, the improved MSTLBO algorithm is applied to cover enhancement process and presented a redundant node sleep strategy after coverage enhancement. Relied on that, 3D wireless multimedia sensor networks coverage enhancement algorithm based on improved teaching and learning optimization is proposed. The algorithm will not only effectively enhance the network coverage rate, but also greatly reduce the number of redundant nodes in the network. Experimental results show that the proposed algorithm can achieve the maximum network coverage through using fewest sensor nodes. Compared with the contrast method, the proposed algorithm can effectively improve the network coverage rate and reduce the number of nodes in the network, so as to save the network energy.