This paper presents TCFlash, a practical and immediately deployable technique for executing bulk bitwise operations directly within commercial off-the-shelf (COTS) 3D NAND flash chips, using only standard user-mode commands. TCFlash enables in-place bitwise computation by combining logical data encoding of triple level-cell (TLC) storage with dynamic read reference voltage shifting. We demonstrate TCFlash across multiple 3D TLC NAND devices spanning both floatinggate and charge-trap technologies from two major vendors. To our knowledge, this is the first on-chip demonstration of error-free bitwise operations in 3D NAND. Experimental evaluation across vertical layers in the 3D NAND stack shows that TCFlash achieves zero raw bit error rate (RBER) for two-operand OR, AND, and XNOR operations, and RBER is below 0.006% for NAND, NOR, and XOR. Additionally, for the first time, we also demonstrate simultaneous three-operand bitwise operations with RBER below 0.008%.

Abstract The growing ubiquity of Internet of Things (IoT) devices within smart homes demands the use of advanced strategies in IoT implementation, with an emphasis on energy efficiency and security. The incorporation of Artificial Intelligence (AI) within the IoT framework improves the overall efficiency of the network. An inefficient mechanism of parent selection at the network layer of IoT causes energy drain in the nodes, particularly near the sink node. As a result, nodes die earlier, causing network holes that further increase the control message overhead as well as the energy consumption of the network, compromising network security. This research introduces an AI‐based approach to parent selection of the Routing Protocol for Low Power and Lossy networks (RPL) at the network layer of IoT to enhance security and energy efficiency. A novel objective function, named Energy and Parent Load Objective Function (EA‐EPL), is also proposed that considers the composite metrics, including energy and parent load. Extensive experiments are conducted to assess EA‐EPL against OF0 and MRHOF algorithms. Experimental results show that EA‐EPL outperformed these algorithms in improving energy efficiency, network stability, and packet delivery ratio. The results also demonstrate a significant enhancement in the overall efficiency of IoT networks and increased security in smart home environments.