FEBSRA: Fuzzy Trust Based Energy Aware Balanced Secure Routing Algorithm for Secured Communications in WSNs

Energy efficiency is playing major role in the design of a sensor network for improving the network lifetime and the trust is also important for providing the security to the data communication process. Delay is also major challenge today due to the enormous volume of network users. For overcoming all these issues, many researchers have been developed energy efficient security mechanisms for fulfilling the requirements. Even though, they are not able to satisfy the current requirements and users in terms of energy consumption, delay and security. For this purpose, this paper propose a new algorithm called Fuzzy Trust Based Energy Aware Balanced Secure Routing Algorithm (FEBSRA) which is able to provide the effective delay constrained secured routing algorithm which uses the fuzzy logic is a form of many-valued logic in which the truth values of variables may be any real number between 0 and 1 both inclusive for making final decision over sensor nodes with the consideration of number of hops between the source and destination nodes, energy level of the nodes and the trust scores. Moreover, a new trust model is also introduced new formulae for calculating the trust scores with the consideration of energy level of the communication delay which is calculated by using number of hops used for the specific communication. The experimental results of the proposed secured routing algorithm demonstrated that the performance in terms of energy consumption, less delay and high throughput with security is better when compared to the existing systems.

Fuzzy trust based collaborative filtering analysis for mobile user preferences

With the rapid development of mobile terminal devices, mobile user activities can be carried out anytime and anywhere through various mobile terminals. The current research on mobile communication network is mainly focused on extracting useful and interesting information for mobile user from massive and disordered information. However, the sparsity of scoring data matrix results in low quality of recommendation algorithm. In order to overcome this drawback, the traditional collaborative filtering algorithm is improved. First, the user-interest matrix and item-feature matrix were obtained by analyzing mobile user behavior and item attributes. Fuzzy trust based model is utilized for collaborative filtering analysis for mobile user preferences. Then, the similarity between different mobile users was calculated by weighted calculation. With this method, mobile user preference can be predicted effectively, making it possible to recommend rational resource and waste less time in extracting resources out of the massive information. Experimental results show that the proposed algorithm reduces the mean absolute error (MAE) and the impact of sparse scoring matrix data compared with the traditional collaborative filtering algorithm, and improves the recommendation effect to a certain extent.