Serverless computing platforms rely on fast container initialization to provide low latency and high throughput for requests. While hardware enforced trusted execution environments (TEEs) have gained popularity, confidential computing has yet to be widely adopted by latency-sensitive platforms due to its additional initialization overhead. We investigate the application of AMD's Secure Encrypted Virtualization (SEV) to microVMs and find that current startup times for confidential VMs are prohibitively slow due to the high cost of establishing a root of trust for each new VM.

Address space layout randomization (ASLR) is a widely used component of computer security aimed at preventing code reuse and/or data-only attacks. Modern kernels utilize kernel ASLR (KASLR) and finer-grained forms, such as functional granular KASLR (FGKASLR), but do so as part of an inefficient bootstrapping process we call bootstrap self-randomization. Meanwhile, under increasing pressure to optimize their boot times, microVM architectures such as AWS Firecracker have resorted to eliminating bootstrapping steps, particularly decompression and relocation from the guest kernel boot process, leaving them without KASLR. In this paper, we present in-monitor KASLR, in which the virtual machine monitor efficiently implements KASLR for the guest kernel by skipping the expensive kernel self-relocation steps. We prototype in-monitor KASLR and FGKASLR in the open-source Firecracker virtual machine monitor demonstrating, on a microVM configured kernel, boot times 22% and 16% faster than bootstrapped KASLR and FGKASLR methods, respectively. We also show the low overhead of in-monitor KASLR, with only 4% (2 ms) increase in boot times on average compared to a kernel without KASLR. We also discuss the implications and future opportunities for in-monitor approaches.

Micro-aerial vehicle (MAV) swarms are an emerging class of mobile sensing systems. Simulation and staged deployment to prototype testbeds are useful in the early stages of large-scale system design, when hardware is unavailable or deployment at scale is impractical. To faithfully represent the problem domain, a MAV swarm simulator must be able to model the key aspects of the system: actuation, sensing, and communication. We present Simbeeotic, a simulation framework geared toward modeling swarms of MAVs. Simbeeotic enables algorithm development and rapid MAV prototyping through pure simulation and hardware-in-the-loop experimentation. We demonstrate that Simbeeotic provides the appropriate level of fidelity to evaluate prototype systems while maintaining the ability to test at scale.

Micro-aerial vehicle (MAV) swarms are an emerging class of mobile sensing systems. Designing the next generation of such swarms requires the ability to rapidly test algorithms, sensors, and support infrastructure at scale. Simulation is useful in the early stages of such large-scale system design, when hardware is unavailable or deployment at scale is impractical. To faithfully represent the problem domain, an MAV swarm simulator must be able to model all key aspects of the system: actuation, sensing, and communication. Further, it is important to be able to quickly test swarm behavior using different control algorithms in a varied set of environments, and with a variety of sensors.

Research in micro-aerial vehicle (MAV) construction, control, and high-density power sources is enabling swarms of MAVs as a new class of mobile sensing systems. For efficient operation, such systems must adapt to dynamic environments, cope with uncertainty in sensing and control, and operate with limited resources. We propose a novel system architecture based on a hive-drone model that simplifies the functionality of an individual MAV to a sequence of sensing and actuation commands with no in-field communication. This decision simplifies the hardware and software complexity of individual MAVs and moves the complexity of coordination entirely to a central hive computer. We present Karma, a system for programming and managing MAV swarms. Through simulation and testbed experiments we demonstrate how applications in Karma can run on limited resources, are robust to individual MAV failure, and adapt to changes in the environment.

Energy in sensor networks is a distributed, non-transferable resource. Over time, differences in energy availability are likely to arise. Protocols like routing trees may concentrate energy usage at certain nodes. Differences in energy harvesting arising from environmental variations, such as if one node is in the sun and another is in the shade, can produce variations in charging rates and battery levels. Because many sensor network applications require nodes to collaborate -- to ensure complete sensor coverage or route data to the network's edge -- a small set of nodes whose continued operation is threatened by low batteries can have a disproportionate impact on the fidelity provided by the network as a whole. In the most extreme case, the loss of a single sink node may render the remainder of the network unreachable.While previous research has addressed reducing the energy usage of individual nodes, the challenge of collaborative energy management has been largely ignored. We present Integrated Distributed Energy Awareness (IDEA), a sensor network service enabling effective network-wide energy decision making. IDEA integrates into the sensor network application by providing an API allowing components to evaluate their impact on other nodes. IDEA distributes information about each node's load rate, charging rate, and battery level to other nodes whose decisions affect it. Finally, IDEA enables awareness of the connection between the behavior of each node and the application's energy goals, guiding the network toward states that improve performance.This paper describes the IDEA architecture and demonstrates its use through three case studies. Using both simulation and testbed experiments, we evaluate each IDEA application by comparing it to simpler approaches that do not integrate distributed energy awareness. We show that using IDEA can significantly improve performance compared with solutions operating with purely local information.

Energy in sensor networks is distributed and non-transferable. Over time, differences in energy availability across the network are likely to arise. Protocols such as routing engines can concentrate energy load at certain nodes. Variations in incident sunlight can produce different solar charging rates at different nodes. Because many sensor network applications require nodes collaborate --- to ensure complete sensor coverage or route data to the network's edge --- a small set of nodes threatened by low energy availability can have a disproportionate impact on the entire network. For example, the loss of a single sink node may render the application unable to communicate with all the other nodes.

This paper presents Pixie, a new sensor node operating system designed to support the needs of data-intensive applications. These applications, which include high-resolution monitoring of acoustic, seismic, acceleration, and other signals, involve high data rates and extensive in-network processing. Given the fundamentally resource-limited nature of sensor networks, a pressing concern for such applications is their ability to receive feedback on, and adapt their behavior to, fluctuations in both resource availability and load.

Monitoring vital signs and locations of certain classes of ambulatory patients can be useful in overcrowded emergency departments and at disaster scenes, both on-site and during transportation. To be useful, such monitoring needs to be portable and low cost, and have minimal adverse impact on emergency personnel, e.g., by not raising an excessive number of alarms. The SMART (Scalable Medical Alert Response Technology) system integrates wireless patient monitoring (ECG, SpO(2)), geo-positioning, signal processing, targeted alerting, and a wireless interface for caregivers. A prototype implementation of SMART was piloted in the waiting area of an emergency department and evaluated with 145 post-triage patients. System deployment aspects were also evaluated during a small-scale disaster-drill exercise.