IoT applications with low-budget connected nodes are emerging for a variety of domains, such as smart cities, geomonitoring, parking sensors, surveillance etc. These low-cost nodes contain System-on-Chips (SoCs) with networking capabil- ities. In this paper, we propose to exploit this feature for their dependability management. In particular, we propose collective- awareness, which is a run-time system that emerges when cloud resources are provided to the SoCs for IoT applications for storing information related to their in-the-field status, such as preferable operating modes and performance degradation. Periodically, a dynamic dependability model is constructed by the collected data and SoCs software is updated to meet user-defined lifetime, reliability and performance requirements. To evaluate the operations of the proposed system, we emulate the in-the- field performance degradation of a fleet of a 10K IoT nodes using Monte Carlo on temperature and workload conditions using the largest IWLS'05 benchmarks. During the first two years of system operation, the dynamically constructed model performs lifetime estimation with up to 57% higher accuracy, compared to a static model that considers data only from the design phase of the circuits, while after three years the dynamic model is always accurate for all the devices.
Collective-Aware System-on-Chips for Dependable IoT Applications
Rossi D.;
2018-01-01
Abstract
IoT applications with low-budget connected nodes are emerging for a variety of domains, such as smart cities, geomonitoring, parking sensors, surveillance etc. These low-cost nodes contain System-on-Chips (SoCs) with networking capabil- ities. In this paper, we propose to exploit this feature for their dependability management. In particular, we propose collective- awareness, which is a run-time system that emerges when cloud resources are provided to the SoCs for IoT applications for storing information related to their in-the-field status, such as preferable operating modes and performance degradation. Periodically, a dynamic dependability model is constructed by the collected data and SoCs software is updated to meet user-defined lifetime, reliability and performance requirements. To evaluate the operations of the proposed system, we emulate the in-the- field performance degradation of a fleet of a 10K IoT nodes using Monte Carlo on temperature and workload conditions using the largest IWLS'05 benchmarks. During the first two years of system operation, the dynamically constructed model performs lifetime estimation with up to 57% higher accuracy, compared to a static model that considers data only from the design phase of the circuits, while after three years the dynamic model is always accurate for all the devices.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
