Embedding Principal Component Analysis for Data Reductionin Structural Health Monitoring on Low-Cost IoT Gateways

Principal component analysis (PCA) is a powerful data reductionmethod for Structural Health Monitoring. However, its computa-tional cost and data memory footprint pose a significant challengewhen PCA has to run on limited capability embedded platformsin low-cost IoT gateways. This paper presents a memory-efficientparallel implementation of the streaming History PCA algorithm.On our dataset, it achieves 10x compression factor and 59x memoryreduction with less than 0.15 dB degradation in the reconstructedsignal-to-noise ratio (RSNR) compared to standard PCA. More-over, the algorithm benefits from parallelization on multiple cores,achieving a maximum speedup of 4.8x on Samsung ARTIK 710

Cooperative Hyper-Scheduling based improving Energy Aware Life Time Maximization in Wireless Body Sensor Network Using Topology Driven Clustering Approach

The Wireless Body Sensor Network (WBSN) is an incredible developing data transmission network for modern day communication especially in Biosensor device networks. Due to energy consumption in biomedical data transfer have impacts of sink nodes get loss information on each duty cycle because of Traffic interruptions. The reason behind the popularity of WBSN characteristics contains number of sensor nodes to transmit data in various dense regions. Due to increasing more traffic, delay, bandwidth consumption, the energy losses be occurred to reduce the lifetime of the WBSN transmission. So, the sensor nodes are having limited energy or power, by listening to the incoming signals, it loses certain amount of energy to make data losses because of improper route selection. To improve the energy aware lifetime maximization through Traffic Aware Routing (TAR) based on scheduling. Because the performance of scheduling is greatly depending on the energy of nodes and lifetime of the network. To resolve this problem, we propose a Cooperative Hyper-scheduling (CHS) based improving energy aware life time maximization (EALTM) in Wireless Body sensor network using Topology Driven Clustering Approach (TDCA).Initially the method maintains the traces of transmission performed by different Bio-sensor nodes in different duty cycle. The method considers the energy of different nodes and history of earlier transmission from the Route Table (RT) whether the transmission behind the Sink node. Based on the RT information route discovery was performed using Traffic Aware Neighbors Discovery (TAND) to estimate Data Transmission Support Measure (DTSM) on each Bio-sensor node which its covers sink node. These nodes are grouped into topology driven clustering approach for route optimization. Then the priority is allocated based on The Max-Min DTSM, the Cooperative Hyper-scheduling was implemented to schedule the transmission with support of DTSM to reduce the energy losses in WBSN. This improves the energy level to maximization the life time of data transmission in WBSN than other methods to produce best performance in throughput energy level

Energy-Aware Bio-signal Compressed Sensing Reconstruction on the WBSN-gateway

Technology scaling enables today the design of ultra-low power wearable bio-sensors for continuous vital signs monitoring or wellness applications. Such bio-sensing nodes are typically integrated in Wireless Body Sensor Network (WBSN) to acquire and process biomedical signals, e.g., Electrocardiogram (ECG), and transmit them to the WBSN gateway, e.g., smartphone, for online reconstruction or features extraction. Both bio-sensing node and gateway are battery powered devices, although they show very different autonomy requirements (weeks versus days). The rakeness-based Compressed Sensing (CS) proved to outperform standard CS, achieving a higher compression for the same quality level, therefore reducing the transmission costs in the node. However, most of the research focus has been on the efficiency of the node, neglecting the energy cost of the CS decoder. In this work, we evaluate the energy cost and real-time reconstruction feasibility on the gateway, considering different signal reconstruction algorithms running on a heterogeneous mobile SoC based on the ARM big.LITTLETM architecture. The experimental results show that it is not always possible to obtain the theoretical QoS under real-time constraints. Moreover, the standard CS does not satisfy real-time constraints, while the rakeness enables different QoS-energy trade-offs. Finally, we show that in the optimal setup (OMP, n=128) heterogeneous architectures make the CS decoding task suitable for wearable devices oriented to long-term ECG monitoring