Open AccessSpatiotemporal compression‐transmission strategies for energy‐harvesting wireless sensor networks
Author(s) -
Li Chengtie,
Wang Jinkuan,
Li Mingwei
Publication year - 2019
Publication title -
iet communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 62
eISSN - 1751-8636
pISSN - 1751-8628
DOI - 10.1049/iet-com.2018.5353
Subject(s) - computer science , transmission (telecommunications) , wireless sensor network , energy consumption , energy harvesting , energy (signal processing) , data compression , data transmission , real time computing , wireless , markov process , compressed sensing , algorithm , telecommunications , computer network , engineering , electrical engineering , mathematics , statistics
In energy‐harvesting wireless sensor networks (EHWSNs), sensors perform many functions such as sensing, compression, and transmission. It is known that the sensing and transmission processes consume most of the energy by the sensors. The maximisation of the lifetime by balancing energy acquisition and consumption has been the focus of many research activities. This study focuses on the data compression‐transmission optimisation problem of the EHWSNs in the presence of energy input and output processes, wherein compressive sensing is employed as the compression scheme for data transmission. Both transmission and energy dynamics are modelled, and the Markov process of data transmission is assumed. The jointly optimised compression and transmission strategies are formulated using the spatial‐temporal feature via a Lagrangian relaxation approach, and the theoretical results of the optimal modelling structure are derived. Finally, extensive simulations are presented to validate the effectiveness of the proposed algorithm in comparison with existing algorithms.