Energy-harvesting (EH) Internet of Things (IoT) devices operate under intermittent energy availability, which disrupts task execution and makes energy-intensive over-the-air (OTA) updates particularly challenging. Conventional OTA update mechanisms rely on reboots and incur significant overhead, rendering them unsuitable for intermittently powered systems. Recent live OTA update techniques reduce reboot overhead but still lack mechanisms to ensure consistency when updates interact with runtime execution. This paper presents AERO, an Adaptive and Efficient Runtime-Aware OTA update mechanism that integrates update tasks into the device’s Directed Acyclic Graph (DAG) and schedules them alongside routine tasks under energy and timing constraints. By identifying update-affected execution regions and dynamically adjusting dependencies, AERO ensures consistent update integration while adapting to intermittent energy availability. Experiments on representative workloads demonstrate improved update reliability and efficiency compared to existing live update approaches.

The widespread deployment of various tiny energy harvesting devices has facilitated the expansion of Internet of Things (IoT) applications, notably in remote and hard-to-reach areas. Once deployed, a critical limitation of these devices is their inability to adapt code to evolving environmental conditions or user requirements. This challenge primarily arises from frequent power interruptions during code updates in energy harvesting devices, unlike their battery-powered counterparts, which can lead to significant errors or system failures. In response, we have designed an innovative framework for facilitating intermittent over-the-air (OTA) code updates in tiny energy harvesting devices. Our approach incorporates Intermittent-aware Update Operations, including insert, modify, delete, and copy, tailored for a variety of update scenarios while accommodating intermittent power and resource constraints. Furthermore, We have designed a Fault-tolerant bootloader that enables the intermittent update capability. This advanced bootloader enables code updates without system reboots and ensures correct task resumption of both routine and update tasks. This not only conserves energy by reducing the need for repetitive reboots but also ensures consistent code updates despite frequent power failures. Additionally, our framework integrates an update-aware checkpointing mechanism to provide reliable backups for both routine tasks and update tasks. This proposed framework presents a general solution for enabling intermittent code updates in tiny energy harvesting devices. Our experimental results demonstrate that the proposed approach outperforms existing approaches under conditions of insufficient harvested energy.

Deep Neural Networks (DNNs) are increasingly applied in various intelligent applications for enhanced accuracy for in-situ decision-making. Considering the cost and longevity, those intelligent applications usually employ energy harvesting (EH) for power supply. Nevertheless, due to inherent intermittency, EH power can frequently disrupt the runtime operation, resulting in subsequent forward progress loss when executing long computations of DNN inference. To address this issue, DNN tiling has been employed where the input data is partitioned into multiple smaller tiles for efficient runtime operation. However, under the energy harvesting scenarios, the size of the tiles can influence runtime energy efficiency significantly under different EH conditions. Therefore, we proposed environmentally adaptive dynamic DNN tiling methods to optimize energy efficiency and runtime reliability. The experimental results on a real testbed show that the proposed technique can outperform the state-of-the-art methods by 19.24% on average.

Deep learning neural networks are of critical importance to enable next-generation IoT devices. However, due to the limited computation power, memory space, and energy, it remains a grand challenge to deploy those algorithms on IoT devices efficiently as they demand high computation, energy, and memory footprint. Numerous pruning methods of deep learning algorithms have been proposed to minimize the latency, energy, and weights. However, few consider the running time memory footprint and the overhead caused by data movement between the volatile memory and non-volatile memory. This paper proposes four novel memory-ware mechanisms for implementing CNN models on self-restrained embedded systems. The proposed techniques maximize the use of high-speed volatile memory and provide three implementation choices to achieve the minimum energy cost, SRAM space usage, and inference latency, as well as a hybrid trade-off choice of the three features. The experimental evaluation compares their energy cost, time latency, and required run-time memory footprint and demonstrates high implementation efficiency.