Energy Optimal Wireless Data Transmission for Wearable Devices: A Compression Approach

• Published in: IEEE transactions on vehicular technology Vol. 67; no. 10; pp. 9605 - 9618
• Main Authors: Zhang, Wei, Fan, Rui, Wen, Yonggang, Liu, Fang
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Approximation algorithms; Compression; Data communication; Data compression; Data transmission; Electronic devices; energy; Energy conservation; Energy consumption; Energy transmission; Markov processes; Sensors; Speed control; wearable devices; Wearable technology; Wireless communication; Wireless sensor networks; wireless transmission
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2018.2859433

Wearable devices are designed to have a small size and be lightweight. Consequently, the battery life is constrained and becomes a crucial limitation. In this paper, we use both data compression and wireless transmission speed control to minimize the energy consumption of wearable devices for data transmission, subject to a deadline constraint. We consider both an off-line setting where future channel gains are known ahead of time and a stochastic setting where channel gains change stochastically according to a Markov process. For the first case, we present an efficient <inline-formula><tex-math notation="LaTeX">(1+\epsilon)</tex-math></inline-formula> approximation algorithm for an arbitrarily small <inline-formula><tex-math notation="LaTeX">\epsilon</tex-math></inline-formula>, while in the latter case we give a stochastic algorithm to minimize the total expected energy use. We also conduct experimental studies on the proposed algorithms and show that the stochastic algorithm, despite not knowing future channel gains, closely approximates the performance of the nearly optimal off-line solution with less than 0.1% difference in energy consumption on an average. We also compared the stochastic algorithm with several other practical algorithms and showed that our algorithm achieves significant improvements in the overall energy use.