GROWN: Local Data Compression in Real-Time To Support Energy Efficiency in WBAN

The evolution of wireless technologies has enabled the creation of networks for several purposes as health care monitoring. The Wireless Body Area Networks (WBANs) enable continuous and real-time monitoring of physiological signals, but that monitoring leads to an excessive data transmission usage, and drastically affects the power consumption of the devices. Although there are approaches for reducing energy consumption, many of them do not consider information redundancy to reduce the power consumption. This paper proposes a hybrid approach of local data compression, called GROWN, to decrease information redundancy during data transmission and reduce the energy consumption. Our approach combines local data compression methods found in WSN. We have evaluated GROWN by experimentation, and the results show a decrease in energy consumption of the devices and an increase in network lifetime.

[1]  Jacques Demerjian,et al.  Using DWT Lifting Scheme for Lossless Data Compression in Wireless Body Sensor Networks , 2018, 2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC).

[2]  Deborah Estrin,et al.  Lightweight temporal compression of microclimate datasets [wireless sensor networks] , 2004, 29th Annual IEEE International Conference on Local Computer Networks.

[3]  Raphaël Couturier,et al.  On the performance of resource-aware compression techniques for vital signs data in wireless body sensor networks , 2018, 2018 IEEE Middle East and North Africa Communications Conference (MENACOMM).

[4]  Jukka Teuhola,et al.  A Compression Method for Clustered Bit-Vectors , 1978, Inf. Process. Lett..

[5]  Chun-Huat Heng,et al.  A Hybrid Data Compression Scheme for Power Reduction in Wireless Sensors for IoT , 2017, IEEE Transactions on Biomedical Circuits and Systems.

[6]  Wim Sweldens,et al.  The lifting scheme: a construction of second generation wavelets , 1998 .

[7]  Abbas Jamalipour,et al.  Wireless Body Area Networks: A Survey , 2014, IEEE Communications Surveys & Tutorials.

[8]  Dhananjay Patel,et al.  Simulation and Comparison of Various Lossless Data Compression Techniques based on Compression Ratio and Processing Delay , 2013 .

[9]  Francesco Marcelloni,et al.  A Simple Algorithm for Data Compression in Wireless Sensor Networks , 2008, IEEE Communications Letters.

[10]  Deborah Estrin,et al.  Lightweight Temporal Compression of Microclimate Datasets , 2004 .

[11]  Francesco Marcelloni,et al.  Enabling energy-efficient and lossy-aware data compression in wireless sensor networks by multi-objective evolutionary optimization , 2010, Inf. Sci..

[12]  Youakim Badr,et al.  Building IoT-Enabled Wearable Medical Devices: An Application to a Wearable, Multiparametric, Cardiorespiratory Sensor , 2018, BIODEVICES.

[13]  Eduardo Cerqueira,et al.  Assessing Data Traffic Classification to Priority Access for Wireless Healthcare Application , 2019, 2019 IEEE Latin-American Conference on Communications (LATINCOM).

[14]  Giada Giorgi.  A Combined Approach for Real-Time Data Compression in Wireless Body Sensor Networks , 2017, IEEE Sensors Journal.

[15]  Christos P. Antonopoulos,et al.  Resource efficient data compression algorithms for demanding, WSN based biomedical applications , 2016, J. Biomed. Informatics.

[16]  Tsung-Han Tsai,et al.  An Efficient ECG Lossless Compression System for Embedded Platforms With Telemedicine Applications , 2018, IEEE Access.

[17]  Michele Rossi,et al.  On the Performance of Lossy Compression Schemes for Energy Constrained Sensor Networking , 2014, TOSN.

[18]  Qingsong Ai,et al.  Mutual-Information-Based Incremental Relaying Communications for Wireless Biomedical Implant Systems , 2018, Sensors.

[19]  Eduardo Cerqueira,et al.  Traffic Model Based on Autoregression for PPG Signals in Wearable Networks , 2020, IEEE Networking Letters.