With the widespread availability of cost effective wireless devices, the usage of such devices for the monitoring a patient's vital parameters have become ubiquitous. The use of such devices has given rise to Wireless Body Area Sensor Networks (WBASNs) that can enable a healthcare professional to remotely monitor an individual, thereby eliminating costly trips to the hospital. However, since WBASNs carry sensitive patient information, maintaining security is paramount. Public Key Cryptography (PKC) provides robust security than other cryptographic systems. However, PKC requires a substantial computational overhead to be effective which renders them unsuitable for low powered devices used in WBASNs. Elliptic Curve Cryptography (ECC) provides a computationally low overhead for achieving robust security. In this paper we define and propose a Modified Elliptic Curve Cryptography (MECC) technique for use in WBASNs. We also propose to use this method for the secure transmission of data to be used for the wireless monitoring of patients suffering from Parkinson's disease in an indoor environment such as the patient's residence or a hospice. Our system would continuously monitor a patient in real time and detect events that generally precede a fall or a Freezing of Gait (FoG).
Wireless Body area Sensor Network (WBSN) is a recent concept that can dramatically benefit healthcare applications through advances in wireless technology. Physiological and biokinetic parameters that require continuous monitoring are sensed by small and lightweight body sensors that transmit the values of these parameters over wireless links for monitoring at the other end. The sensors employed in WBSNs are limited in resources, with battery power being at the premium. Conservation of energy used by the network has a direct bearing on the longevity of the network. Therefore, there is no need to send data periodically and need to transmit selectively when needed. This paper presents a dual framework for predicting when to transfer physiological parameters in such a network that could save energy consumption while maintaining error to minimum level. The framework utilizes an artificial neural network (ANN) for prediction that not only saves energy, but also does it with lesser error than popular prediction algorithms. A comparison of performance of five data prediction algorithms in predicting physiological data is presented. The amount of network energy saved as a result of prediction is also considered in detail.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.