Energy harvesting systems have emerged as an alternative to battery-powered IoT devices. The systems utilize a just-in-time checkpoint protocol that stores volatile states when a power outage occurs, ensuring crash consistency. However, this paper uncovers a new security vulnerability in the checkpoint protocol, revealing its susceptibility to electromagnetic interference (EMI). If exploited, adversaries could cause denial of service or data corruption in victim devices. To defeat EMI attacks, this paper introduces GECKO, a compiler-directed countermeasure that operates on commodity platforms used in energy harvesting systems without requiring hardware support. Our experiments on real boards demonstrate that GECKO defeats the EMI attack with a trivial performance overhead by 6% on average.

An energy harvesting system (EHS) has emerged as an alternative to traditional battery-operated Internet of Things (IoT) devices. An EHS harnesses ambient energy and stores it in a small capacitor, enabling batteryless operation when sufficient energy is available. However, capacitors are susceptible to malicious charging/discharging and over-voltages, which can lead to a loss of capacitance. With the capacitor vulnerability in mind, this article introduces a capacitor hammering attack, simply Caphammer, that can undermine the security of every EHS. The idea is that Caphammer can degrade the capacitance by using frequent power outages. Once Caphammer degrades the capacitor of the victim EHS, it can suffer from denial of service, data corruption, data encryption failure, and abnormal termination. To defeat Caphammer, this article presents FanCap, a capacitor bank scheduling scheme that can dynamically transform energy storage organization, taking into account the capacitor vulnerability. The experimental results demonstrate that FanCap can successfully thwart Caphammer with a negligible run-time overhead.

Energy harvesting systems have emerged as an alternative to battery-operated Internet of Things (IoT) devices. To deal with frequent power outages in the absence of battery, energy harvesting systems rely on a capacitor-backed checkpoint mechanism also known as just-in-time (JIT) checkpointing. It checkpoints volatile data in nonvolatile memory (NVM) just before a power outage occurs—using the energy buffered in the capacitor—and restores the checkpointed data from NVM in the wake of the outage. While the JIT checkpointing gives an illusion that volatile data survive a power outage as if they were nonvolatile, it turns out that due to capacitor degradation, energy harvesting systems can unexpectedly fail the JIT checkpointing, losing or corrupting data across the outage. To address the problem, this article presents an operating system-driven solution called CapOS. At a high level, CapOS diagnoses the capacitor in a reactive yet safe manner. When the JIT checkpoint failure occurs, CapOS detects the capacitor degradation without causing the data corruption. To recover from such a capacitor error, CapOS electrically isolates the degraded capacitor—so that it restores its original capacitance by itself with the help of capacitor’s resilient nature—and disables the JIT checkpointing. In case, power outages occur during the capacitor isolation, CapOS leverages undo logging with interval-based checkpointing for their recovery. Once the capacitor is fully recovered, CapOS gets back to the capacitor-based JIT checkpointing. The experimental results demonstrate that CapOS can effectively address the capacitor error of energy harvesting systems at a low run-time cost, without compromising the recovery of power outages.

This paper presents ELASTIN, a stagnation-free intermittent computing system for energy-harvesting devices that ensures forward progress in the presence of frequent power outages without partitioning program into recoverable regions or tasks. ELASTIN leverages both timer-based checkpointing of volatile registers and copy-on-write mappings of nonvolatile memory pages to restore them in the wake of power failure. During each checkpoint interval, ELASTIN tracks memory writes on a per-page basis and backs up the original page using custom software-controlled memory protection without MMU or TLB. When a new interval starts at each timer expiration, ELASTIN clears the write permission of all the pages written during the previous interval and checkpoints all registers including a program counter as a recovery point. In particular, ELASTIN dynamically reconfigures both the checkpoint interval and the page size to achieve stagnation-free intermittent computation and maximize forward progress across power outages. The experiments on TI's MSP430 board with energy harvesting traces show that ELASTIN outperforms the state-of-the-art scheme by 3.5X on average (up to orders of magnitude speedup) and guarantees forward progress.

In this paper, we introduce a remote graphics library framework based on inter-virtual-machines communication in an embedded hypervisor. With this framework, there are no causality conflicts when multiple guest operating systems share one GPU. We adopted API remoting for GPU virtualization because it has relatively small overhead when connected with OpenGL ES standard library on embedded hypervisors. Interferences from the hypervisor during synchronization between front-end and back-end can be reduced by inter-VM commutation. To make improvement on size, weight and power for embedded systems, we opted for displaying both guest operating systems on a single display panel. The presented framework is applied to a real-world embedded hypervisor used for safety-critical systems. Our implementation runs an automotive digital instrument cluster on a real-time guest operating system and an in-vehicle infotainment application on a general purpose guest operating system within the hypervisor. We found it feasible for an embedded hypervisor to provide GPU service to heterogeneous industrial guest operating systems on a single hardware platform.

Mission critical embedded software for autonomous operation requires high development cost due to its long development cycle. One of the potential solutions for reducing the cost is to reuse the software developed at previous missions. Virtual machine platform such as JVM is a good example to provide code portability across various missions. Flight software in aerospace field is adopting this concept to improve reusability and eventually to reduce development cost. In this paper, we propose a Lua-based virtualization environment for spacecraft flight software. Flight software for spacecraft control consists of a few tasks that are highly autonomous. Lua is chosen as the script language for programming the control tasks. Though Lua was designed with simplicity and portability, it only supports multithreading with collaborative coroutines. To support preemptive multitasking, we implement time slicing coroutines as spacecraft control processes. New coroutine scheduler is devised and time slicing functionality is added into the scheduler. Scheduler locking and message passing with external flight software are also implemented. Instead of modifying the Lua interpreter, we have exploited the debug support APIs for our implementation. For evaluation, we have implemented the flight software virtualization environment on the flight computer. Accuracy of the time slicing scheduler is also analyzed.

Development of the space industry has led to a diversity of specialized electronics-units implemented on space system. However, this provoked new problems such as increased system's complexity and the size. As a solution to the problem, the Integrated Modular Avionics (IMA) architecture and ARINC 653 standard have been suggested. This paper introduces a Linux-based memory efficient ARINC 653 partition scheduler, which employs fixed-priority preemptive scheduling algorithm for partitions scheduling. The implemented partition scheduler has small memory footprint for each partition and produces low partition switching overhead. The prototype was executed on a LEON4 processor, which is the European Space Agency (ESA) Next Generation Multicore Processor (NGMP) in the space sector. In evaluation, we analyzed execution trace, memory footprint and scheduling overhead.