Trusted environment provides safety measures for the sensor network. There are many problems that occur during the management of resources. Memory management and computation overhead or CPU usage are the major issues. Security issues is another problem in Wireless sensor network.. The three types of issues like issues of memory, computation overhead and intrusion detection is considered in this proposed research. The proposed model has provided a mechanism for resource management in a wireless sensor network. This model would also resolve one diff type of issue like computation overhead. The proposed work is capable to classify IDS attacks using a deep learning model. For improving accuracy a Network model is proposed during intrusion detection using a recurring neural network. The network model is used for testing by the help of a confusion matrix to calculate The accuracy, precision, recall and fscore. For clustered wireless sensor networks the advanced adaptive and dual data communication trust scheme (adct) have been used. Research is able to handle untrustworthy nodes inefficient manner. A function that is used in this research to assess direct trust among nodes is called Adaptive trust function. In intracluster as well as intercluster this Trust mechanism ADCT is used. With the help of compression mechanism Packets have been compressed for reducing the computation overhead. For security in Intrusion, this proposed model would also be capable. Moreover, research work would also deal with selfish nodes and malicious nodes to provide better of service for network lifetime for different network sizes.
