A page-granularity wear-leveling (PGWL) strategy for NAND flash memory-based sink nodes in wireless sensor networks

Abstract

Sink nodes are the data centers of wireless sensor networks (WSNs), and the storage management scheme for such nodes is vital, particularly in applications such as wireless multimedia sensor networks that involve the collection of massive amounts of data. NAND flash memory is often employed in sink nodes because of its excellent characteristics. Because the lifetime of NAND flash memory is highly restricted by the bit error rate (BER), we present a novel page-granularity wear-leveling (PGWL) strategy to extend the lifetime of NAND flash memory. The concept of PGWL is motivated by two main experimental observations obtained from our own experimental platform for NAND flash memory: first, the raw bit error rate (RBER) distribution exhibits a distinct variance in endurance among different pages, and this variance is more significant than that among different blocks; second, programming relief operations (consisting of only erasing, not programming) can clearly reduce both program-disturb and retention errors. In this study, we first present a practical average RBER prediction model to evaluate the reliability of flash pages using the system clock of the sink node. Thus, the PGWL strategy enables self-adaptive leveling of the RBER growths of different pages in real time by introducing page-granularity wear leveling instead of block-granularity wear leveling to exploit the lifetime potency of each page in a block. Experimental results show that PGWL can extend the lifetime of 2×-nm NAND flash memory by 88.3% compared with traditional bad block management (BBM), while experiencing at most a 0.85% degradation in data throughput speed compared with the conventional sector mapping scheme.