We present BISen, a framework for efficient and reliable convolutional neural network (CNN) inference on battery-free, energy-harvesting IoT sensor nodes. Battery-powered deployments suffer from limited lifetimes, high replacement costs, and environmental impacts, problems that will intensify as IoT scales to billions of devices. Energy-harvesting nodes remove batteries but face intermittent power, resulting in frequent failures that corrupt the intermediate CNN state, require costly checkpointing and rollback, and amplify non-volatile memory (NVM) traffic under tight on-chip memory constraints, leaving little harvested energy for useful sensing and inference. BISen introduces a reactive intermittent execution model for CNN workloads on off-the-shelf ultra-low-power microcontrollers. An energy-aware state machine with a safe-stop mechanism halts execution before brownout, while selective checkpointing preserves only the minimal CNN state needed for forward progress. This enables seamless resumption across power cycles while sharply reducing NVM reads/writes and memory-access overheads. Across two commercial MCU+radio platforms, three real harvested power traces, and nine CNNs, BISen cuts NVM operations by up to 86.4%, reduces standby/load/store operations by up to 94.1%, 94.5%, and 90.7%, and improves sensing throughput by about 1.3−1.4× compared to a state-of-the-art reactive baseline under the same energy budget, enabling long-lived, battery-free, carbon-aware IoT deployments.

Mobile devices with dynamic refresh rate (DRR) switching displays have recently become increasingly common. For power optimization, these devices switch to lower refresh rates when idling, and switch to higher refresh rates when the content displayed requires smoother transitions. However, the security and privacy vulnerabilities of DRR switching have not been investigated properly. In this paper, we propose a novel attack vector called RefreshChannels that exploits DRR switching capabilities for mobile device attacks. Specifically, we first create a covert channel between two colluding apps that are able to stealthily share users' private information by modulating the data with the refresh rates, bypassing the OS sandboxing and isolation measures. Second, we further extend its applicability by creating a covert channel between a malicious app and either a phishing webpage or a malicious advertisement on a benign webpage. Our extensive evaluations on five popular mobile devices from four different vendors demonstrate the effectiveness and widespread impacts of these attacks. Finally, we investigate several countermeasures, such as restricting access to refresh rates, and find they are inadequate for thwarting RefreshChannels due to DDR's unique characteristics.

Side-channels have traditionally been exploited as a means of uncovering sensitive information such as cryptographic keys from a computing device. In particular, past work has shown that electromagnetic (EM) radiation from a device’s processor and memory during the execution of code and data can be used by attackers to extract private information. In contrast, instead of considering side-channels and electromagnetic radiation as vulnerabilities, we see them as opportunities for wireless communication on resource-limited IoT devices. We present SideComm, a side-channel-based communication system that leverages processors’ EM side-channels to enable resource-limited IoT devices to wirelessly send their data without having any radios. The main advantage of this approach is completely eliminating the need for a conventional radio and antenna, which offers energy savings, simplicity, and flexibility for IoT devices. Our evaluation demonstrates SideComm’s ability to achieve a communication range of more than 10m (enabling ≥ 3 dB SNR at 15m) and to work in non-line-of-sight scenarios, such as around corners and through walls. We believe SideComm can enable increased connectivity for many resource-constrained IoT devices in smart environments.

Traditionally, side channels have been exploited to uncover sensitive information such as cryptographic keys from computing devices. An attacker can extract private information from a device’s processor and memory by using electromagnetic (EM) radiation while code and data are being executed. Rather than seeing side-channels and electromagnetic radiation as vulnerabilities, we consider them as potential wireless communication channels for resource-constrained devices. The main advantage of this approach is completely eliminating the need for a conventional radio and antenna, which offers energy savings, simplicity, and flexibility for resource-constrained devices.