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Energy optimal on-line Self-Test of microprocessors in WSN nodes

机译:WSN节点中微处理器的能量最佳在线自检

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Wireless Sensor Network (WSN) applications often need to be deployed in harsh environments, where the possibility of faults due to environmental hazards is significantly increased, while silicon aging and wearout effects are also exacerbated. For such applications, periodic on-line testing of the WSN nodes is an important step towards correctness of operation. However, on-line testing of processors integrated in WSN nodes has to address the additional challenge of minimum energy consumption, because these devices operate on battery, which usually cannot be replaced and in the absence of catastrophic failures determines the lifetime of the system. In this paper initially we derive analytically the optimal way for executing on-line periodic test with adjustable period, taking into account the degrading behavior of the system due to silicon aging effects but also the limited energy budget of WSN applications. The test is applied in the form of Software-Based Self-Test (SBST) routines, thus we proceed to the power optimized development of SBST routines targeting the transition delay fault model that is well suited for detecting timing violations due to silicon aging. Simulation results show that energy savings for the final SBST routine at processor level are up to 35.4% and the impact of test in the battery life of the system is negligible.
机译:无线传感器网络(WSN)应用程序通常需要部署在恶劣的环境中,其中由于环境危害导致的故障的可能性显着增加,而硅老化和磨损效果也加剧。对于这种应用,WSN节点的周期性在线测试是朝向朝着操作的正确性的重要步骤。但是,在WSN节点中集成的处理器的在线测试必须解决最小能耗的额外挑战,因为这些设备在电池上操作,通常不能更换,并且在没有灾难性故障的情况下确定系统的寿命。本文首先,我们通过可调整时期进行了分析地推导了在线定期测试的最佳方式,考虑到系统由于硅老化效果而且是WSN应用的有限能量预算。该测试以基于软件的自检(SBST)例程的形式应用,因此我们进行了靶向过渡延迟故障模型的SBST例程的功率优化,非常适合于检测由于硅老化引起的定时违规。仿真结果表明,处理器水平最终SBST常规的节能高达35.4%,测试在系统电池寿命中的影响可忽略不计。

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