Real world complex networks are indirect representation of complex systems. they grow over time. these networks are fragmented and raucous in practice. An important concern about complex network is link prediction. Link prediction aims to determine the possibility of probable edges. the link prediction demand is often spotted in social networks for recommending new friends, and, in recommender systems for recommending new items (movies, gadgets etc) based on earlier shopping history. in this work, we propose a new link prediction algorithm namely "common neighbor and centrality based parameterized Algorithm" (ccpA) to suggest the formation of new links in complex networks. Using AUC (Area Under the receiver operating characteristic curve) as evaluation criterion, we perform an extensive experimental evaluation of our proposed algorithm on eight real world data sets, and against eight benchmark algorithms. the results validate the improved performance of our proposed algorithm.

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A successful development of UWSN in different areas is going on where a continuous and remote examining, detection and monitoring required under the water. Ample of research papers have been available that emphasis on different parameters such as energy consumption, throughput, stability period, end-to-end delay in UWSNs. In our research, we focus on the decrease of endto-delay, increase its stability period and throughput as well as load balancing of motes. In order to attain our objective we propose a scheme called DAE with major goal to implement the technique of anycasting. Finally we compared DAE with two existing protocols DBR and AMCTD. The performance of DAE is much efficient than these schemes in various parameters. Outcomes shows that the throughput and stability period of DAE are much better than DBR as well as AMCTD and these results are achieved with minimum delay.