Energy efficient key agreement scheme for ubiquitous and continuous remote healthcare systems using data mining technique

Abstract

Wireless body area networks (WBANs) based ubiquitous and fully automated healthcare systems provide a platform to share medical information. Energy efficiency and communication security will increase the confidence of the users in adopting such remote healthcare systems. Key agreement and authentication schemes play an important role in the security of remote healthcare systems. The nodes in a WBAN exchange information in order to complete the key agreement and authentication process. In the literature, numerous schemes have used heavy mathematical calculations or overloaded with excessive information exchange. This paper presents a bloom filter-based key agreement scheme using k-mean clustering for WBANs. The key agreement and authentication is performed in clustered environment using k-mean clustering. This makes the scheme more robust and energy efficient. The keys are generated from the EKG values of the human body. The proposed mechanism is energy efficient and secure, due to its more efficient key generation and less memory utilizations for remote healthcare systems. Moreover, the proposed scheme is analyzed and compared with a state-of-the-art scheme in terms of energy consumption, memory utilizations, processing complexity, and false positive rate (FPR). The results show that the proposed scheme outperform significantly the other scheme by consuming less energy and efficient memory utilization, while achieving a very low FPR and linear running complexity.