REVIEW ON CRYPTOGRAPHY TECHNIQUES IN WSN FOR ATTACK PREVENTION

Abstract

Scalability of routing protocols used in wireless sensor networks (WSNs) is a critical issue due to the extremely high node numbers and relatively high node density. The analysis of scalability of wireless sensor networks is a challenging performance issue. Complexity is caused by several issues. First, the large number of nodes heavily impacts simulation performance and scalability. Second, credible results demand an accurate characterization of the sensor radio channel. New aspects, inherent in WSN, must be included in simulators, e.g. a physical environment and an energy model, leading to different degrees of accuracy versus performance. A good routing protocol has to be scalable and adaptive to the changes in the network topology. Thus protocols must perform well as the network grows larger or as the workload increases. In this paper, we compare the algorithms in WSN to improve the scalability of the network. This computation showed the limitation and capability of the WSN scalability and specifically for Flooding. In this work, Supervised learning and Unsupervised learning algorithms like k-nn, Fuzzy Logic based has been compared successfully to analyze the efficiency of scalability in WSN.