<p><span>Over some few decades, the communication distance and connectivity at wireless sensor network (WSN) has got noticeable attention from the researchers. In general, the communication process in WSN ad-hoc occurs without some wired infrastructure and, the communication process done through a ‘single-hop’ transmission, where the intermediate nodes called as relay nodes are used in long distance communication and the nodes are capable to transmit and receive the data packets. In this paper, we presented a computational analysis of communication distance in WSN in the presence of fading effects such as nakagami-m fading and lognormal fading. An extensive investigation at both nakagami-m fading and lognormal fading is carried out to provide optimize communication in ad-hoc WSN. In addition, individual wireless nodes have the similar range of communication, is assumed to get the precise communication distance and coverage area in a WSN, where it is required to get mean communication distance. In result analysis section, several validation parameters such as transmitted power, attenuation constant and different number of fading factor is considered to provide proper analytical view. </span></p>

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Localization is one of the most important technologies for many applications in wireless sensor networks (WSNs). Node localization is the process of discovering the exact location of the node. If the number of nodes and network size increase, it becomes very arduous to localize the nodes whose result leads to complexity and path loss. In this paper, we proposed an approach called probabilistic based optimal node localization to obtain the location of node in the WSNs. This approach provides an enhanced channel pathloss model by capturing the features of the additive noise in WSN. In addition, the complexity has been minimized by discovering a lower bound of the non-convex function. The problem of non-convex optimization and subsequent nonlinear is solved with the help of relaxation to achieve a sub-optimal solution. Simulation results show that our proposed localization approach has got better performance for considered scenario settings.