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Proximal device discovery is an essential initial phase in the installment of a device-to-device communication system in cellular networks. Therefore, an efficient device discovery scheme must be proposed with characteristics of minimum latency, discover maximum devices, and energy-efficient discovery in dense areas. In this paper, a bat echolocation-based algorithm derived from the bat algorithm is proposed and analyzed to fulfill the requirement of a proximal device discovery procedure for the cellular networks. The algorithm is applied to multiple hops and cluster devices when they are in a poor coverage zone. In this proposed algorithm, devices are not required to have prior knowledge of proximal devices, nor device synchronization is needed. It allows devices to start discovering instantly at any time and terminate the proximal device discovery session on completion of the discovery of the required proximal devices. Finally, device feedback is utilized to discover the hop devices in the clusters and analyze proximal discovery in a multi-hop setting. Along with this, a random device mobility pattern is defined based on human movement, and the device discovery algorithm is applied. The device discovery probability is calculated based on the contact duration and meeting time of the devices. We set up an upper bound less than 10 ms in long-term evolution of running time of the bat echolocation-based algorithm; this upper bound signifies the maximum degree of device discovery (more than 75% of the system) and the total number of devices. The outcomes thus imply that the proposed bat echolocation-based algorithm upper bound is better than 10 ms.

At-site flood frequency analysis is a direct method of estimation of flood frequency at a particular site. The appropriate selection of probability distribution and a parameter estimation method are important for at-site flood frequency analysis. Generalized extreme value, three-parameter log-normal, generalized logistic, Pearson type-III and Gumbel distributions have been considered to describe the annual maximum steam flow at five gauging sites of Torne River in Sweden. To estimate the parameters of distributions, maximum likelihood estimation and L-moments methods are used. The performance of these distributions is assessed based on goodness-of-fit tests and accuracy measures. At most sites, the best-fitted distributions are with LM estimation method. Finally, the most suitable distribution at each site is used to predict the maximum flood magnitude for different return periods.