شبکه های حسگر الکتروانسفالوگرافی بی سیم
Abstract
۱٫ Introduction
۲٫ Data model and notation
۳٫ Proposed method
۴٫ Results and discussions
۵٫ Conclusions
CRediT authorship contribution statement
Declaration of Competing Interest
References
Abstract
Software Defined Networking (SDN) has focused enormous attractiveness in changing conventional network by means of offering flexible and dynamic network management. It has drawn important concentration of the researchers from together academia and industries. Mainly, integrating SDN in Wireless Body Area Network (WBAN) applications specifies capable results in terms of handling with the issues like traffic management, security, energy efficiency etc. Recent improvements in miniaturization and energy efficient physiological sensor designs in SDN based Wireless Body Area Networks (WBANs) paved the way for health monitoring systems for collection and processing the real-time physiological data. The collection of signals from different sensor allows reliable diagnosis in heterogeneous than in homogeneous WBANs. Inspired by the evolutions of heterogeneous WBANs, a study on Wireless Electroencephalography Sensor Networks (WESNs) is carried out under distributed signal processing. The distributed WESNs are designed under two different hierarchy i.e. Hierarchical Fully-Connected Topology (HFCT) and Ad-Hoc Nearest-Neighbor Topology (ANNT) to improve the energy-efficiency using distributed Multi-channel Weighted Weiner Filter design (MW2F). Here, each module transmits linear combination of local channels with other modules. The power efficiency is improved Journal Pre-proof in MW2F signal processing algorithm by avoiding centralization of EEG data. A case study is carried out to test the reduced energy consumption after the removal of eye blink artifacts and it is tested with centralized counterparts. The MW2F is evaluated in both topologies against centralized environments and significant reduction of eye blink artifacts improves the energy efficiency in HFCT than other topologies.
Introduction
The energy efficient and miniaturization is the most hot research area in health monitoring systems, where the body is placed with several sensor nodes. These nodes can collect the EEG data and process it in real time and measures are considered to improve the energy efficiency. This system is called as Wireless Body Area Networks (WBANs) [1], and it offers higher demand in the area of physiological monitoring in hospital environment. The WBAN system is composed of several nodes with physiological sensors, wireless communication devices and a signal processing unit. These nodes can communicate with other sensor nodes placed over the body or it can be connected to a fusion center or a storage device. The signals collected from various resources are analyzed jointly in heterogeneous WBANs in real time environment for medical diagnosis.