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Space-air-ground integrated Internet of things can improve the scope of Internet of things applications significantly by offering truly global coverage all over the world. While space-air-ground integrated Internet of things is promising to be very useful in many aspects, its deployment and application should overcome severe security threats, for example, interceptions, identity forgery, data tampering, and so on. Authentication is an essential step to protect the Internet of things security, and mutual authentication (i.e. two-way authentication) is especially important to ensure the security of both communication parties simultaneously. However, the intrinsical properties of network dynamics and wide coverage make the authentication concern in space-air-ground integrated Internet of things extremely challenging than traditional Internet of things networks. In this article, we propose MASIT, an identity-based efficient and lightweight mutual authentication scheme for space-air-ground integrated Internet of things. MASIT exploits the natural broadcast property of space-air-ground integrated Internet of things to speed up authentication process, and leverage the distinguished feature of IPv6 to support concurrent numerous nodes. Theoretically, we prove that MASIT is existential unforgeable secure under adaptively chosen message and identity Attacks. We also implement MASIT and other existing typical identity-based encryption schemes and evaluate their performance in real platforms. Experimental results showed that, MASIT outperforms the existing identity-based encryption schemes significantly, that is, the signature verification time can be reduced by 50% to 60%, and the user signature size can be reduced by 13% to 50%.

Lossless data compression is a crucial and computing-intensive application in data-centric scenarios. To reduce the CPU overhead, FPGA-based accelerators have been proposed to offload compression workloads. However, most existing schemes have the problem of an imbalanced resource utilization and a poor practicability. In this paper, we propose HybriDC, an adaptive resource-efficient CPU-FPGA heterogeneous acceleration system for lossless data compression. Leveraging complementary advantages of the heterogeneous architecture, HybriDC provides a universal end-to-end compression acceleration framework with application compatibility and performance scalability. To optimize the hardware compression kernel design, we build a performance–resource model of the compression algorithm taking into account the design goal, compression performance, available resources, etc. According to the deduced resource-balanced design principle, the compression algorithm parameters are fine-tuned, which reduces 32% of the block RAM usage of the LZ4 kernel. In the parallel compression kernel implementation, a memory-efficient parallel hash table with an extra checksum is proposed, which supports parallel processing and improves the compression ratio without extra memory. We develop an LZ4-based HybriDC system prototype and evaluate it in detail. Our LZ4 compression kernel achieves state-of-the-art memory efficiency, 2.5–4× better than existing designs with comparable compression ratios. The evaluation of total resource utilization and end-to-end throughput demonstrates the excellent scalability of HybriDC. In power efficiency, the four-kernel HybriDC prototype achieves a threefold advantage over the standard LZ4 algorithm.