Emerging resource-constrained cellular Internet of Things (IoT) applications such as drone swarms, autonomous vehicles, and remote surgery via mixed reality demand millisecond-level time synchronization. Narrow-Band IoT (NB-IoT), the leading low-power wide-area technology, struggles to meet these requirements. The root cause lies in the non-deterministic delays inherent in the 5G protocol design. Uplink reliability and scheduling mechanisms introduce asymmetric latencies that disrupt conventional time synchronization algorithms such as the Network Time Protocol (NTP). Time-critical packets are further affected by deep-sleep wake-up latency, base station scheduling delays, uplink/downlink asymmetry, and unpredictable drift from inexpensive oscillators. Together, these factors can accumulate into timing errors on the order of hundreds of milliseconds. In this paper, we first quantify timing errors across five dimensions on a commercial NB-IoT network. We then present SynchroNB, an on-device framework that combines lightweight machine learning with a cross-layer control loop. SynchroNB forecasts 5G network volatility and crystal drift to adaptively wake the cellular modem, reserves uplink resources just in time, switches into resilience mode when the wireless link degrades, and prioritizes time synchronization packets in the MAC-layer queue. We deploy SynchroNB on commercial NB-IoT hardware and evaluate it over a live 5G network. Our experiments show that SynchroNB achieves single-millisecond-level synchronization accuracy under NB-IoT uplink/downlink asymmetry and, diverse wireless conditions, while requiring only \(36\%\) of the radio-on time and \(25\%\) of the bandwidth of the NTP baseline, transforming NB-IoT time synchronization from a reactive protocol into an intelligent, self-tuning control loop.

Accurate and secure timekeeping is a fundamental requirement for the dependable operation of cyber-physical systems and distributed applications deployed in virtualized environments. However, existing timing mechanisms are susceptible to compromise by privileged adversaries, such as the hypervisor, thereby undermining the integrity of local timing services. This paper introduces Seti,a system that employs an out-of-band inspection strategy to establish trust in time sources. Rather than modifying timing access mechanisms to preclude attacks, Setimonitors the integrity of existing timing paths, thus reducing system complexity and cost. It utilizes Remote Direct Memory Access (RDMA) in conjunction with System Management Mode (SMM)-based introspection to enable guest virtual machines (VMs) to access trusted time sources without interruption, while simultaneously detecting and mitigating malicious interference with timer interrupts. Setiis designed to impose minimal overhead on both host and guest systems, preserving high time accuracy and enabling prompt detection of adversarial activities. Experimental evaluation demonstrates that Setieffectively pro-tects critical timing functions achieving millisecond-level attack detection latency with less than 1% CPU overhead.

Batteryless IoT systems face energy constraints exacerbated by checkpointing overhead. Approximate computing offers solutions but demands manual expertise, limiting scalability. This paper presents CheckMate, an automated framework leveraging LLMs for context-aware code approximations. CheckMate integrates validation of LLM-generated approximations to ensure correct execution and employs Bayesian optimization to fine-tune approximation parameters autonomously, eliminating the need for developer input. Tested across six IoT applications, it reduces power cycles by up to 60% with an accuracy loss of just 8%, outperforming semi-automated tools like ACCEPT in speedup and accuracy. CheckMate's results establish it as a robust, user-friendly tool and a foundational step toward automated approximation frameworks for intermittent computing.

Timekeeping is a fundamental component of modern computing; however, the security of system time remains an overlooked attack surface, leaving critical systems vulnerable to manipulation. This paper examines time manipulation attacks that exploit kernel vulnerabilities to distort an application's perception of time. We categorize these attacks into constant, incremental, and randomized delay strategies and analyze their impact on system performance. Through experimental evaluation, we demonstrate how adversaries can manipulate system time via dynamic library injection and syscall modification, disrupting time-sensitive applications. While Trusted Execution Environments (TEEs) offer partial isolation, they fail to address time security concerns fully. Our results highlight the challenges of securing system time and underscore the need for robust mitigation strategies.

Batteryless IoT devices, powered by energy harvesting, face significant challenges in maintaining operational efficiency and reliability due to intermittent power availability. Traditional checkpointing mechanisms, while essential for preserving computational state, introduce considerable energy and time overheads. This paper introduces Approxify, an automated framework that significantly enhances the sustainability and performance of batteryless IoT networks by reducing energy consumption by approximately 40% through intelligent approximation techniques. Approxify balances energy efficiency with computational accuracy, ensuring reliable operation without compromising essential functionalities. Our evaluation of applications, SUSAN and Link Quality Indicator (LQI), demonstrates significant reductions in checkpoint frequency and energy usage while maintaining acceptable error bounds.