Abstract
In most of existing Internet of Things (IoT) applications, data compression, data encryption and error/erasure correction are implemented separately. To achieve reliable communication, in particular, in harsh wireless environment with strong interference, error/erasure correction codes with higher correction capability or Automatic repeat request (ARQ) scheme are desirable but at the cost of increasing complexity and energy consumption. Due to resource-constrained IoT device, it is often challenging to implement all of them. In this paper, we propose a novel lightweight efficient secure error-robust scheme, ENCRUST, which is able to achieve these three functions using simple matrix multiplication. ENCRUST is built on the new theoretical foundation of projection-based encoding presented in this paper, by leveraging the sparsity inherent in the signal. We perform theoretical analysis and experimental study of the proposed scheme in comparison with the conventional schemes. It shows that the proposed scheme can work in low SINR range and the reconstructed signal quality shows graceful degradation. Furthermore, we apply the proposed scheme on real-life electrocardiogram (ECG) dataset and images. The results demonstrate that ENCRUST achieves decent compression, information secrecy as well as strong error recovery in one go.
Highlights
T HE Internet of Things (IoT) has been developing at an accelerating pace in the recent years
The study results show that ENCRUST achieves much better reconstructed signal quality and higher transmission efficiency under various inference channel conditions
In this paper, we prove that the projection-based encoding can be used for error recovery exploiting the sparsity present in the signal without expanding the signal dimension
Summary
T HE Internet of Things (IoT) has been developing at an accelerating pace in the recent years. A variety of IoT services are solving business problems or create added values across different verticals ranging from industrial automation, smart city all the way to E-health. To facilitate the sustainable development of IoT, we need to tackle multiple critical challenges in IoT. The massive number of installed IoT device devices generates a huge amount of data. Data compression methods can handle exponential IoT data growth to alleviate the stress on the communication network infrastructure and data storage. Data compression introduces additional processing complexity in IoT devices. Conventional compression schemes typically make the compressed data sensitive to channel errors as well; forward error correction (FEC) is needed to ensure good quality of decompressed data
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