Energy harvesting enables long-running sensing applications on tiny Internet of Things (IoT) devices without a battery installed. To overcome the intermittency of ambient energy sources, system software creates intermittent computation using checkpoints. While the scope of intermittent computation is quickly expanding, there is a strong demand for data storage and local data processing in such IoT devices. When considering data storage options, flash memory is more compelling than other types of nonvolatile memory due to its affordability and availability. We introduce iFKVS, a flash-based key-value store for multisensor IoT devices. In this study, we aim at supporting efficient key-value operations while guaranteeing the correctness of program execution across power interruptions. For indexing of multidimensional sensor data, we propose a quadtree-based structure for the minimization of extra writes from splitting and rebalancing; for checkpointing in flash storage, we propose a rollback-based algorithm that exploits the capabilities of byte-level writing and one-way bit flipping of flash memory. Experimental results based on a real energy-driven testbed demonstrate that with the same index structure design, our rollback-based approach obtains a significant reduction of 45% and 84% in the total execution time compared with checkpointing using write-ahead logging (WAL) and copying on write (COW), respectively.

The purpose of this study is to apply the technology acceptance model to examine the employees' attitudes and acceptance of electronic learning (e-learning) systems in organisations. This study examines four factors (organisational support, computer self-efficacy, prior experience and task equivocality) that are believed to influence employees' perceived usefulness, perceived ease of use, attitudes and intention to use e-learning systems. Participants were selected from Taiwanese companies that have already implemented e-learning systems. Three hundred and thirty-two valid questionnaires were collected, and structure equation modelling was conducted to test the research hypotheses. The findings provided practical implications for organisational trainers, educators and e-learning system developers.