Critical data-based incremental cooperative communication for wireless body area network

Wireless Body Area Networks (WBANs) are single-hop network systems, where sensors gather the body’s vital signs and send them directly to master nodes (MNs). The sensors are distributed in or on the body. Therefore, body posture, clothing, muscle movement, body temperature, and climatic conditions generally influence the quality of the wireless link between sensors and the destination. Hence, in some cases, single hop transmission (‘direct transmission’) is not sufficient to deliver the signals to the destination. Therefore, we propose an emergency-based cooperative communication protocol for WBAN, named Critical Data-based Incremental Cooperative Communication (CD-ICC), based on the IEEE 802.15.6 CSMA standard but assuming a lognormal shadowing channel model. In this paper, a complete study of a system model is inspected in the terms of the channel path loss, the successful transmission probability, and the outage probability. Then a mathematical model is derived for the proposed protocol, end-to-end delay, duty cycle, and average power consumption. A new back-off time is proposed within CD-ICC, which ensures the best relays cooperate in a distributed manner. The design objective of the CD-ICC is to reduce the end-to-end delay, the duty cycle, and the average power transmission. The simulation and numerical results presented here show that, under general conditions, CD-ICC can enhance network performance compared to direct transmission mode (DTM) IEEE 802.15.6 CSMA and benchmarking. To this end, we have shown that the power saving when using CD-ICC is 37.5% with respect to DTM IEEE 802.15.6 CSMA and 10% with respect to MI-ICC

Cooperative Compressed Sensing Schemes for Telemonitoring of Vital Signals in WBANs

International audienceWireless Body Area Networks (WBANs) are composed of various sensors that either monitor and transmit real time vital signals or act as relays that forward the received data packets to a nearby Body Node Coordinator (BNC). The design of an accurate and energy efficient wireless telemonitoring system can be achieved by: i) minimizing the amount of data that should be transmitted for an accurate reconstruction at the BNC, and ii) increasing the robustness of the telemonitoring system to link failures due to the nature of wireless medium. To this end, we present a novel Compressed Sensing (CS) based telemonitoring scheme, called Cooperative Compressed Sensing (CCS), that exploits the benefits of Random Linear Network Coding (RLNC) along with key characteristics of the transmitted biosignals in order to achieve an energy efficient signal reconstruction at the BNC. Simulation studies, carried out with real electrocardiographic (ECG) data, show the benefits of: i) employing RLNC, compared to the case where relays simply store and forward the original data packets, and ii) applying the proposed CCS scheme, compared to traditional CS recovery approaches

Cooperative compressed sensing schemes for telemonitoring of vital signals in WBANs

Wireless Body Area Networks (WBANs) are composed of various sensors that either monitor and transmit real time vital signals or act as relays that forward the received data packets to a nearby Body Node Coordinator (BNC). The design of an accurate and energy efficient wireless telemonitoring system can be achieved by: i) minimizing the amount of data that should be transmitted for an accurate reconstruction at the BNC, and ii) increasing the robustness of the telemonitoring system to link failures due to the nature of wireless medium. To this end, we present a novel Compressed Sensing (CS) based telemonitoring scheme, called Cooperative Compressed Sensing (CCS), that exploits the benefits of Random Linear Network Coding (RLNC) along with key characteristics of the transmitted biosignals in order to achieve an energy efficient signal reconstruction at the BNC. Simulation studies, carried out with real electrocardiographic (ECG) data, show the benefits of: i) employing RLNC, compared to the case where relays simply store and forward the original data packets, and ii) applying the proposed CCS scheme, compared to traditional CS recovery approaches.Peer ReviewedPostprint (published version

Cooperative compressed sensing schemes for telemonitoring of vital signals in WBANs

Wireless Body Area Networks (WBANs) are composed of various sensors that either monitor and transmit real time vital signals or act as relays that forward the received data packets to a nearby Body Node Coordinator (BNC). The design of an accurate and energy efficient wireless telemonitoring system can be achieved by: i) minimizing the amount of data that should be transmitted for an accurate reconstruction at the BNC, and ii) increasing the robustness of the telemonitoring system to link failures due to the nature of wireless medium. To this end, we present a novel Compressed Sensing (CS) based telemonitoring scheme, called Cooperative Compressed Sensing (CCS), that exploits the benefits of Random Linear Network Coding (RLNC) along with key characteristics of the transmitted biosignals in order to achieve an energy efficient signal reconstruction at the BNC. Simulation studies, carried out with real electrocardiographic (ECG) data, show the benefits of: i) employing RLNC, compared to the case where relays simply store and forward the original data packets, and ii) applying the proposed CCS scheme, compared to traditional CS recovery approaches.Peer Reviewe

Compressive Sensing with Low-Power Transfer and Accurate Reconstruction of EEG Signals

Tele-monitoring of EEG in WBAN is essential as EEG is the most powerful physiological parameters to diagnose any neurological disorder. Generally, EEG signal needs to record for longer periods which results in a large volume of data leading to huge storage and communication bandwidth requirements in WBAN. Moreover, WBAN sensor nodes are battery operated which consumes lots of energy. The aim of this research is, therefore, low power transmission of EEG signal over WBAN and its accurate reconstruction at the receiver to enable continuous online-monitoring of EEG and real time feedback to the patients from the medical experts. To reduce data rate and consequently reduce power consumption, compressive sensing (CS) may be employed prior to transmission. Nonetheless, for EEG signals, the accuracy of reconstruction of the signal with CS depends on a suitable dictionary in which the signal is sparse. As the EEG signal is not sparse in either time or frequency domain, identifying an appropriate dictionary is paramount. There are a plethora of choices for the dictionary to be used. Wavelet bases are of interest due to the availability of associated systems and methods. However, the attributes of wavelet bases that can lead to good quality of reconstruction are not well understood. For the first time in this study, it is demonstrated that in selecting wavelet dictionaries, the incoherence with the sensing matrix and the number of vanishing moments of the dictionary should be considered at the same time. In this research, a framework is proposed for the selection of an appropriate wavelet dictionary for EEG signal which is used in tandem with sparse binary matrix (SBM) as the sensing matrix and ST-SBL method as the reconstruction algorithm. Beylkin (highly incoherent with SBM and relatively high number of vanishing moments) is identified as the best dictionary to be used amongst the dictionaries are evaluated in this thesis. The power requirements for the proposed framework are also quantified using a power model. The outcomes will assist to realize the computational complexity and online implementation requirements of CS for transmitting EEG in WBAN. The proposed approach facilitates the energy savings budget well into the microwatts range, ensuring a significant savings of battery life and overall system’s power. The study is intended to create a strong base for the use of EEG in the high-accuracy and low-power based biomedical applications in WBAN