A new scheme for evaluating energy efficiency of data compression in wireless sensor networks

Data communication incurs the highest energy cost in wireless sensor networks, and restricts the application of wireless sensor networks. Data compression is a promising technique that can reduce the amount of data exchanged between nodes and results in energy saving. However, there is a lack of effective methods to evaluate the efficiency of data compression algorithms and to increase nodes’ energy efficiency. The energy saving of nodes is related to both hardware and software, this article proposes a new scheme for evaluating energy efficiency of data compression in wireless sensor networks according to the node’s hardware and software. The relationship between the energy efficiency and the hardware and software factors is expressed by a formula. In this formula, energy efficiency can be improved by increasing the compression ratio and decreasing the ratio of s/k, in which k represents the node’s hardware factor related to energy consumption of processor, wireless module, and so on and s represents the ...

[1]  Xiong Luo,et al.  A Human-Centered Activity Tracking System: Toward a Healthier Workplace , 2017, IEEE Transactions on Human-Machine Systems.

[2]  Kah Phooi Seng,et al.  A Simple Data Compression Algorithm for Wireless Sensor Networks , 2012, SOCO.

[3]  Zhining Liao,et al.  A Visual Analytics Approach for Detecting and Understanding Anomalous Resident Behaviors in Smart Healthcare , 2017 .

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

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

[6]  Nikolaos S. Voros,et al.  A Data Compression Hardware Accelerator Enabling Long-Term Biosignal Monitoring Based on Ultra-Low Power IoT Platforms , 2017 .

[7]  Francesco Marcelloni,et al.  Exploiting Multi-Objective Evolutionary Algorithms for Designing Energy-Efficient Solutions to Data Compression and Node Localization in Wireless Sensor Networks , 2013, Evolutionary Based Solutions for Green Computing.

[8]  Andreas Koch,et al.  Hardware-Accelerated Data Compression in Low-Power Wireless Sensor Networks , 2014, ARC.

[9]  Song Guo,et al.  Big Data Meet Green Challenges: Greening Big Data , 2016, IEEE Systems Journal.

[10]  Francesco Marcelloni,et al.  An Efficient Lossless Compression Algorithm for Tiny Nodes of Monitoring Wireless Sensor Networks , 2009, Comput. J..

[11]  Xiong Luo,et al.  A kernel machine-based secure data sensing and fusion scheme in wireless sensor networks for the cyber-physical systems , 2016, Future Gener. Comput. Syst..

[12]  Yunfeng Zhang,et al.  Efficient seismic response data storage and transmission using ARX model‐based sensor data compression algorithm , 2006 .

[13]  Beihua Ying,et al.  Evaluation of Tunable Data Compression in Energy-Aware Wireless Sensor Networks , 2010, Sensors.

[14]  Xiong Luo,et al.  Towards enhancing stacked extreme learning machine with sparse autoencoder by correntropy , 2017, J. Frankl. Inst..

[15]  Kah Phooi Seng,et al.  Performance comparison of data compression algorithms for environmental monitoring wireless sensor networks , 2013, Int. J. Comput. Appl. Technol..

[16]  Xiong Luo,et al.  Efficient DV-HOP Localization for Wireless Cyber-Physical Social Sensing System: A Correntropy-Based Neural Network Learning Scheme , 2017, Sensors.

[17]  Fang-Min Li Power Control for Wireless Sensor Networks: Power Control for Wireless Sensor Networks , 2008 .