The Internet of Things (IoT) relies on resource-constrained devices for data acquisition, but the vast amount of data generated and security concerns present challenges for efficient data handling and confidentiality. Conventional techniques for data compression and secrecy often lack energy efficiency for these devices. Compressive sensing has the potential to compress data and maintain secrecy, but many solutions do not address the issue of packet loss or errors caused by unreliable wireless channels. To address these issues, we have developed the ENCRUST scheme, which combines compression, secrecy, and error recovery. In this paper, we present a prototype of ENCRUST that uses energy-efficient operations, as well as a lighter variant called L-ENCRUST. We also perform security analysis and compare the performance of ENCRUST and L-ENCRUST with a state-of-the-art solution in terms of memory, encryption time, and energy consumption on a resource-constrained TelosB mote. Our results show that both ENCRUST and L-ENCRUST outperform the state-of-the-art solution in these metrics.

翻译：物联网依赖资源受限的设备进行数据采集，但海量数据生成与安全问题对高效数据处理和保密性提出了挑战。传统的数据压缩与保密技术通常缺乏针对此类设备的能效性。压缩感知技术具有压缩数据并保持保密性的潜力，但许多解决方案未能解决不可靠无线信道导致的数据包丢失或错误问题。为应对这些问题，我们开发了结合压缩、保密与错误恢复的ENCRUST方案。本文提出了采用节能操作的ENCRUST原型及其轻量化变体L-ENCRUST，并进行了安全性分析。我们在资源受限的TelosB节点上，从内存占用、加密时间和能耗三个维度，将ENCRUST和L-ENCRUST与前沿解决方案进行了性能对比。实验结果表明，ENCRUST与L-ENCRUST在上述指标上均优于现有最优方案。