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We propose a new model with flexible failure rate called complementary Poisson generalized half logistic (CPGHL). Various properties of the model are explored and examine numerically such as the explicit expressions of the moments, mean deviations, Bonferroni and Lorenz curves, Shannon and Renyi entropy. The distribution of mixture of two CPGHL and some related models based on the log-transform of CPGHL are discussed. The asymptotic of moments of residual life and asymptotic distribution of order statistics are obtained. The characterization of Poisson half logistic (PHL) by truncated moments of a certain function of a random variable is discussed. Estimation of the model parameters was approached by maximum likelihood, least square, and percentile methods. Further, the estimation by maximum likelihood for right censored data of the model were considered. The proposed estimation techniques were assessed by simulation studies. Three data applications are provided one of them is a censored data to demonstrate how the new model outperforms some other existing distribution in practice.

Underwater localization is an important and fundamental part of the Underwater Acoustic Networks (UANs). The problem we must face is that radio waves and optical waves are heavily attenuated underwater, so acoustic signals become the most common form of communication. However, the speed of sound wave is not constant and will be affected by the environmental factors. The inaccurate sound velocity will have a serious impact on the traditional positioning results. Therefore, the symmetry correction based on least square estimation (SC-LSE) is proposed in this paper. SC-LSE mitigates the influence of the imprecise velocity estimation on the localization by means of a special symmetrical relationship. We consider a realistic case, where the actual speed of sound is uncertain and the unknown nodes may be moving. The simulation results exhibit that our algorithm can achieve good performance and is not sensitive to the change of sound velocity and node movement.