Wireless sensor network is the significant element in pervasive computing for various distributed applications which divides the resources or data among the peers between the sensor nodes to managing the bandwidth of network, node participants of the network, processing powers of the nodes. During the process of the distribution of the data, the one to one environment of the sensor networks, computation complexity and decentralization is the important issues because it completely reduces the entire system performance. So, this paper implementing the system to distributing the datum among the nodes in one to one environment using the distributed data clustered through the help about the honey-bee optimization with genetic algorithm approach. The analyzes of approach of cluster and relationship of the neighboring managing on the nodes of peer of the sensors in order to assist to decentralization process on the dynamic environment. Further a clustering search space is identified and each node is evaluated with the help of position, velocity, distance values which is examined in terms of using fitness value. Then a performance at a system is analyzed using the results of experimental while discussions such as packet delivery ratio, energy consumption and total number of alive nodes.

As a result of the expansions that have taken place in the field of networking and the increase in the number of users of networks, there have recently been breakthroughs made in the techniques and methods used for network security. In this paper, a virtual private network (VPN) is proposed as a means of providing the necessary level of security for particular connections that span across vast networks. After the network performance metrics such as time delay and throughput have been accomplished, the suggested VPN is recommended for the purpose of assuring network security. In addition, artificial intelligence attack predictors and virtual private networks have been implemented with the purpose of preventing harmful activity within such connections. Using a wide variety of machine learning methods like Random Forests and Nave Bays, malicious assaults of any kind can be identified and thwarted in their tracks. Another technique for anticipating attacks is the use of an artificial neural network, which is a type of system that engages in deep learning and learns the behaviors of attacks while it is being trained so that it can then predict attacks. The results of this study demonstrate that the use of machine learning and artificial intelligence techniques can significantly improve the security and performance of virtual private networks and can effectively identify and prevent malicious attacks on networks.