针对物联网感知层节点计算资源、电源资源受限与数据传输可靠性之间的矛盾，本项目将具有强大纠错能力的Turbo码应用于感知层，以降低状态度量缓存(State Metrics Cache: SMC)容量的译码结构设计为基础，实现低功耗的高效多跳转发技术。通过分解译码网格图，构造非对称max*成对近似与反向计算的译码算法，使得误码率、译码复杂度在低功耗译码结构设计策略下得到了统一；采用标志寄存器位代替状态度量值存储策略和嵌入反向计算模块的方式，将SMC容量降低了95%；将超低存储容量的Turbo码译码结构，用于构造基于FPGA的感知层传感节点，提出了两种降低数据帧重发功耗的多跳转发技术；项目预期将揭示在非对称max*成对近似的译码算法和超低SMC容量的结构设计方式下，能获得接近最优译码算法的性能，提高感知层节点生命周期，为物联网感知层的低功耗可靠通信提供科学依据和硬件验证平台。

To deal with the contradiction between computing, energy resource constraints and reliable data transmission in the perception layer of IoT, the powerful error correction code, i.e. the Turbo code, is applied to the perception layer in the research of this project. Based on the design of state metrics cache (SMC) reduced low-power decoding architecture, this project aims to achieve energy-efficient multi-hop relay technologies. By decomposing the trellis diagram of Turbo code, the approximated asymmetrical max* operator pair and the corresponding reverse calculation are modeled for a new decoding algorithm, in which the bit error rate and the decoding complexity are unified under the low-power decoding architecture design strategy. Furthermore, by replacing the state metrics with register flag bit and inserting reverse calculation module, the SMC size is reduced by 95%. Based on FPGA, This research applies the ultra-low SMC storage decoding architecture to construct the nodes of perception layer, and two kinds of multi-hop relay schemes are specified for energy-efficient data retransmission. The research of this project is expected to reveal that, by adopting the approximated asymmetrical max* operator pair decoding algorithm and the ultra-low SMC storage decoding architecture, near optimal decoding performance and improved life cycle for the nodes of perception layer can be achieved. As a whole, this research intends to provide scientific basis and hardware verification platform for low-power dissipation and reliable communication in the perception layer of IoT.