FlyZone is a testbed architecture to experiment with aerial drone applications. Unlike most existing drone testbeds that focus on low-level mechanical control, FlyZone offers a high-level API and features geared towards experimenting with application-level functionality. These include the emulation of environment influences, such as wind, and the automatic monitoring of developer-provided safety constraints, for example, to mimic obstacles. We conceive novel solutions to achieve this functionality, including a hardware/software architecture that maximizes decoupling from the main application and a custom visual localization technique expressly designed for testbed operation. We deploy two instances of FlyZone and study performance and effectiveness. We demonstrate that we realistically emulate the environment influence with a positioning error bound by the size of the smallest drone we test, that our localization technique provides a root mean square error of 9.2cm, and that detection of violations to safety constraints happens with a 50ms worst-case latency. We also report on how FlyZone supported developing three real-world drone applications, and discuss a user study demonstrating the benefits of FlyZone compared to drone simulators. 

We present the design and evaluation of a 3.5-year embedded sensing deployment at the Mithræum of Circus Maximus, a UNESCO-protected underground archaeological site in Rome (Italy). Unique to our work is the use of energy harvesting through thermal and kinetic energy sources. The extreme scarcity and erratic availability of energy, however, pose great challenges in system software, embedded hardware, and energy management. We tackle them by testing, for the first time in a multi-year deployment, existing solutions in intermittent computing, low-power hardware, and energy harvesting. Through three major design iterations, we find that these solutions operate as isolated silos and lack integration into a complete system, performing suboptimally. In contrast, we demonstrate the efficient performance of a hardware/software co-design featuring accurate energy management and capturing the coupling between energy sources and sensed quantities. Installing a battery-operated system alongside also allows us to perform a comparative study of energy harvesting in a demanding setting. Albeit the latter reduces energy availability and thus lowers the data yield to about 22% of that provided by batteries, our system provides a comparable level of insight into environmental conditions and structural health of the site. Further, unlike existing energy-harvesting deployments that are limited to a few months of operation in the best cases, our system runs with zero maintenance since almost 2 years, including 3 months of site inaccessibility due to a COVID19 lockdown

Cyberphysical systems (CPS) integrate embedded sensors,actuators, and computing elements for controlling physicalprocesses. Due to the intimate interactions with thesurrounding environment, CPS software must continuouslyadapt to changing conditions. Enacting adaptation decisionsis often subject to strict time requirements to ensure controlstability, while CPS software must operate within the tightresource constraints that characterize CPS platforms. Developersare typically left without dedicated programmingsupport to cope with these aspects. This results in either toneglect functional or timing issues that may potentially ariseor to invest significant efforts to implement hand-crafted solutions.We provide programming constructs that allow developersto simplify the specification of adaptive processingand to rely on well-defined time semantics. Our evaluationshows that using these constructs simplifies implementationswhile reducing developers’ effort, at the price of a modestmemory and processing overhead.

We present design concepts, programming constructs, and automatic verification techniques to support thedevelopment of adaptive Wireless Sensor Network (WSN) software. WSNs operate at the interface betweenthe physical world and the computing machine, and are hence exposed to unpredictable environment dynamics.WSN software must adapt to these dynamics to maintain dependable and efficient operation. Whilesignificant literature exists on the necessary adaptation logic, developers are left without proper support inmaterializing such a logic in a running system. Our work fills this gap with three key contributions: i) designconcepts help developers organize the necessary adaptive functionality and understand their relations,ii) dedicated programming constructs simplify the implementations, iii) custom verification techniques allowdevelopers to check the correctness of their design before deployment. We implement dedicated toolsupport to tie the three contributions, facilitating their practical application. Our evaluation considers representativeWSN applications to analyze code metrics, synthetic simulations, and cycle-accurate emulationof popular WSN platforms. The results indicate that our work is effective in simplifying the developmentof adaptive WSN software; for example, implementations are provably easier to test and to maintain, therun-time overhead of our dedicated programming construct is negligible, and our verification techniquesreturn results in a matter of seconds.

Transiently-powered computers (TPCs) lay the basis for a battery-less Internet of Things, using energy harvesting and small capacitors to power their operation. This power supply is characterized by extreme variations in supply voltage, as capacitors charge when harvesting energy and discharge when computing. We experimentally find that these variations cause marked fluctuations in clock speed and power consumption, which determine energy efficiency. We demonstrate that it is possible to accurately model and concretely capitalize on these fluctuations. We derive an energy model as a function of supply voltage and develop EPIC, a compile-time energy analysis tool. We use EPIC to substitute for the constant power assumption in existing analysis techniques, giving programmers accurate information on worst-case energy consumption of programs. When using EPIC with existing TPC system support, run-time energy efficiency drastically improves, eventually leading up to a 350% speedup in the time to complete a fixed workload. Further, when using EPIC with existing debugging tools, programmers avoid unnecessary program changes that hurt energy efficiency.

Intermittently powered embedded devices ensure forward progress of programs through state checkpointing in non-volatile memory. Checkpointing is, however, expensive in energy and adds to the execution times. To minimize this overhead, we present DICE, a system that renders differential checkpointing profitable on these devices. DICE is unique because it is a software-only technique and efficient because it only operates in volatile main memory to evaluate the differential. DICE may be integrated with reactive (Hibernus) or proactive (MementOS, HarvOS) checkpointing systems, and arbitrary code can be enabled with DICE using automatic code-instrumentation requiring no additional programmer effort. By reducing the cost of checkpoints, DICE cuts the peak energy demand of these devices, allowing operation with energy buffers that are one-eighth of the size originally required, thus leading to benefits such as smaller device footprints and faster recharging to operational voltage level. The impact on final performance is striking: with DICE, Hibernus requires one order of magnitude fewer checkpoints and one order of magnitude shorter time to complete a workload in real-world settings.

Embedded devices running on ambient energy perform computations intermittently, depending upon energy availability. System support ensures forward progress of programs through state checkpointing in non-volatile memory. Checkpointing is, however, expensive in energy and adds to execution times. To reduce this overhead, we present DICE, a system design that efficiently achieves differential checkpointing in intermittent computing. Distinctive traits of DICE are its software-only nature and its ability to only operate in volatile main memory to determine differentials. DICE works with arbitrary programs using automatic code instrumentation, thus requiring no programmer intervention, and can be integrated with both reactive (Hibernus) or proactive (MementOS, HarvOS) checkpointing systems. By reducing the cost of checkpoints, performance markedly improves. For example, using DICE, Hibernus requires one order of magnitude shorter time to complete a fixed workload in real-world settings.

Transiently powered computers (TPCs) form the foundation of the battery-less Internet of Things, using energy harvesting and small capacitors to power their operation. This kind of power supply is characterized by extreme variations in supply voltage, as capacitors charge when harvesting energy and discharge when computing. We experimentally find that these variations cause marked fluctuations in clock speed and power consumption. Such a deceptively minor observation is overlooked in existing literature. Systems are thus designed and parameterized in overly conservative ways, missing on a number of optimizations.We rather demonstrate that it is possible to accurately model and concretely capitalize on these fluctuations. We derive an energy model as a function of supply voltage and prove its use in two settings. First, we develop EPIC, a compile-time energy analysis tool. We use it to substitute for the constant power assumption in existing analysis techniques, giving programmers accurate information on worst-case energy consumption of programs. When using EPIC with existing TPC system support, run-time energy efficiency drastically improves, eventually leading up to a 350% speedup in the time to complete a fixed workload. Further, when using EPIC with existing debugging tools, it avoids unnecessary program changes that hurt energy efficiency. Next, we extend the MSPsim emulator and explore its use in parameterizing a different TPC system support. The improvements in energy efficiency yield up to more than 1000% time speedup to complete a fixed workload.

We present the experimental evaluation of different security mechanisms applied to persistent state in intermittent computing. Whenever executions become intermittent because of energy scarcity, systems employ persistent state on non-volatile memories (NVMs) to ensure forward progress of applications. Persistent state spans operating system and network stack, as well as applications. While a device is off recharging energy buffers, persistent state on NVMs may be subject to security threats such as stealing sensitive information or tampering with configuration data, which may ultimately corrupt the device state and render the system unusable. Based on modern platforms of the Cortex M*series, we experimentally investigate the impact on typical intermittent computing workloads of different means to protect persistent state, including software and hardware implementations of staple encryption algorithms and the use of ARM TrustZone protection mechanisms. Our results indicate that i) software implementations bear a significant overhead in energy and time, sometimes harming forward progress, but also retaining the advantage of modularity and easier updates; ii) hardware implementations offer much lower overhead compared to their software counterparts, but require a deeper understanding of their internals to gauge their applicability in given application scenarios; and iii) TrustZone shows almost negligible overhead, yet it requires a different memory management and is only effective as long as attackers cannot directly access the NVMs

We present Virtual Resources: a software architecture to resolve the tension between effective development and efficient operation of Internet of Things (IoT) applications. Emerging IoT architectures exhibit recurring traits: resource-limited sensors and actuators with RESTful interfaces at one end; full-fledged Cloud-hosted applications at the opposite end. The application logic resides entirely at the latter, creating performance issues such as excessive energy consumption and high latencies. To ameliorate these, Virtual Resources allows developers to push a slice of the application logic to intermediate IoT devices, creating a continuum between physical resources and Cloud-hosted applications. With Virtual Resources, for example, developers can push processing of sensed data to IoT devices close to the physical sensors, reducing the data to transmit and thus saving energy. We describe the key concepts of Virtual Resources and their realization in a CoAP prototype atop resource-constrained devices. Experimental results from cycle-accurate emulation indicate that Virtual Resources enable better performance than Cloud-centric architectures, while retaining the RESTful interaction pattern. For example, energy consumption in representative scenarios improves up to 40% and control loop latencies reduce up to 60%.

Radio link quality estimation inWireless SensorNetworks (WSNs) has a fundamental impact on the network performance and also affects the design of higher-layer protocols. Therefore, for about a decade, it has been attracting a vast array of research works. Reported works on link quality estimation are typically based on different assumptions, consider different scenarios, and provide radically different (and sometimes contradictory) results. This article provides a comprehensive survey on related literature, covering the characteristics of low-power links, the fundamental concepts of link quality estimation inWSNs, a taxonomy of existing link quality estimators, and their performance analysis. To the best of our knowledge, this is the first survey tackling in detail link quality estimation in WSNs. We believe our efforts will serve as a reference to orient researchers and system designers in this area.

The guest editors present a special issue on building software for the Internet of Things (IoT).

Radio interference may lead to packet losses, thus negatively affecting the performance of sensornet applications. In this paper, we experimentally assess the impact of external interference on state-of-the-art sensornet MAC protocols. Our experiments illustrate that specific features of existing protocols, e.g., hand-shaking schemes preceding the actual data transmission, play a critical role in this setting. We leverage these results by identifying mechanisms to improve the robustness of existing MAC protocols under interference. These mechanisms include the use of multiple hand-shaking attempts coupled with packet trains and suitable congestion backoff schemes to better tolerate interference. We embed these mechanisms within an existing X-MAC implementation and show that they considerably improve the packet delivery rate while keeping the power consumption at a moderate level.

This chapter addresses tuple spaces, a programming abstraction for data sharing and coordination among distributed processes. The shared memory is called a tuple space, a multiset of elementary data structures. Each tuple is a sequence of typed fields and coordination among processes occurs through the writing and reading of tuples. Originally introduced in the context of parallel computing, it has recently enjoyed wide popularity to develop wireless applications. This chapter concisely describes some of the most representative systems implementing this abstraction and analyzes them along some fundamental dimensions of comparison.

Wireless sensor networks (WSNs) enable decentralized architectures to monitor the behavior of physical processes and to detect deviations from a specified "safe" behavior, for example, to check the operation of control loops. Such correct behavior is typically expressed by global invariants over the state of different sensors or actuators. Nevertheless, to leverage the computing capabilities of WSN nodes, the application intelligence needs to reside inside the network. The task of ensuring that the monitored processes behave safely thus becomes inherently distributed, and hence more complex. In this article we present DICE, a system enabling WSN-based distributed monitoring of global invariants. A DICE invariant is expressed by predicates defined over the state of multiple WSN nodes, such as the expected state of actuators based on given sensed environmental conditions. Our modular design allows two alternative protocols for detecting invariant violations: both perform in-network aggregation but with different degrees of decentralization, therefore supporting scenarios with different network and data dynamics. We characterize and compare the two protocols using large-scale simulations and a real-world testbed. Our results indicate that invariant violations are detected in a timely and energy-efficient manner. For instance, in a 225-node 15-hop network, invariant violations are detected in less than a second and with only a few packets sent by each node.

We present MicNest: an acoustic localization system enabling precise drone landing. In MicNest, multiple microphones are deployed on a landing platform in carefully devised configurations. The drone carries a speaker transmitting purposefully-designed acoustic pulses. The drone may be localized as long as the pulses are correctly detected. Doing so is challenging: i) because of limited transmission power, propagation attenuation, background noise, and propeller interference, the Signal-to-Noise Ratio (SNR) of received pulses is intrinsically low; ii) the pulses experience non-linear Doppler distortion due to the physical drone dynamics; iii) as location information is used during landing, the processing latency must be reduced to effectively feed the flight control loop. To tackle these issues, we design a novel pulse detector, Matched Filter Tree (MFT), whose idea is to convert pulse detection to a tree search problem. We further present three practical methods to accelerate tree search jointly. Our experiments show that MicNest can localize a drone 120 m away with 0.53% relative localization error at 20 Hz location update frequency. For navigating drone landing, MicNest can achieve a success rate of 94 %. The average landing error (distance between landing point and target point) is only 4.3 cm.

We present 64Key, a hardware/software platform that enables impromptu sensing, data sharing, collaborative working, and social networking among physically co-located users independently of their own hardware platform, operating system, network stack, and of the availability of Internet access. 64Key caters to those scenarios such as computer labs, large conferences, and emergency situations where the network infrastructure is limited in operation or simply not available, and peer-to-peer interactions are prevented or not possible. By plugging a 64Key device in one’s mobile device USB port, an independent network is created on the fly, which users access from their own device though a web-based interface. In addition to default apps such as chat, file sharing, and collaborative text editing, 64Key’s functionality may be extended through the run-time installation of third-party apps, available at a public app store. We demonstrate our proof-of-concept implementation of 64Key with multiple apps in a set of key scenarios.

Wireless sensor networks (WSNs) are characterized by localized interactions, that is, protocols are often based on message exchanges within a node's direct radio range. We recognize that for these protocols to work effectively, nodes must have consistent information about their shared neighborhoods. Different types of faults, however, can affect this information, severely impacting a protocol's performance. We factor this problem out of existing WSN protocols and argue that a notion of neighborhood view consistency (NVC) can be embedded within existing designs to improve their performance. To this end, we study the problem from both a theoretical and a system perspective. We prove that the problem cannot be solved in an asynchronous system using any of Chandra and Toueg's failure detectors. Because of this, we introduce a new software device called pseudocrash failure detector (PCD), study its properties, and identify necessary and sufficient conditions for solving NVC with PCDs. We prove that, in the presence of transient faults, NVC is impossible to solve with any PCDs, thus define two weaker specifications of the problem. We develop a global algorithm that satisfies both specifications in the presence of unidirectional links, and a localized algorithm that solves the weakest specification in networks of bidirectional links. We implement the latter atop two different WSN operating systems, integrate our implementations with four different WSN protocols, and run extensive micro-benchmarks and full-stack experiments on a real 90-node WSN testbed. Our results show that the performance significantly improves for NVC-equipped protocols; for example, the Collection Tree Protocol (CTP) halves energy consumption with higher data delivery.

Mosquitoes spread disases such as Dengue and Zika that affect a significant portion of the world population. One approach to hamper the spread of the disases is to identify the mosquitoes’ breeding places. Recent studies use drones to detect breeding sites, due to their low cost and flexibility. In this paper, we investigate the applicability of drone-based multi-spectral imagery and mmWave radios to discover breeding habitats. Our approach is based on the detection of water bodies. We introduce our Faster R-CNN-MSWD, an extended version of the Faster R-CNN object detection network, which can be used to identify water retention areas in both urban and rural settings using multi-spectral images. We also show promising results for estimating extreme shallow water depth using drone-based multi-spectral images. Further, we present an approach to detect water with mmWave radios from drones. Finally, we emphasize the importance of fusing the data of the two sensors and outline future research directions. 

The resource-constrained devices of the battery-less Internet of Things are powered off energy harvesting and compute intermittently, as energy is available. Forward progress of programs is ensured by creating persistent state. Mixed-volatile platforms are thus an asset, as they map slices of the address space onto non-volatile memory. However, these platforms also possibly introduce intermittence bugs, where intermittent and continuous executions differ. Our ongoing work on intermittence bugs includes (i) an analysis that demonstrates their presence in settings that current literature overlooks; (ii) the design of efficient testing techniques to check their presence in arbitrary code, which would be otherwise prohibitive given the sheer number of different executions to check; (iii) the implementation of an offline tool called ScEpTIC that implements these techniques. ScEpTIC finds the same bugs as a brute-force approach, but is six orders of magnitude faster. © 2019 Copyright held by the owner/author(s).

We present ALFRED: a virtual memory abstraction that resolves the dichotomy between volatile and non-volatile memory in intermittent computing. Mixed-volatile microcontrollers allow programmers to allocate part of the application state onto non-volatile memory. Programmers are therefore to manually explore the tradeoff between simpler management of persistent state against energy overhead and possibility of intermittence anomalies due to nonvolatile memory operations. This approach is laborious and yields sub-optimal performance. We take a different stand with ALFRED: we provide programmers with a virtual memory abstraction detached from the specific volatile nature of memory and automatically determine an efficient mapping from virtual to volatile or non-volatile memory. Unlike existing works, ALFRED does not require programmers to learn a newlanguage syntax and the mapping is entirely resolved at compile-time, reducing the run-time energy overhead.We implement ALFRED through a series of machine-level code transformations. Compared to existing systems, we demonstrate that ALFRED reduces energy consumption by up to two orders of magnitude given a fixed workload. This enables workloads to finish sooner, as the use of available energy shifts from ensuring forward progress to useful application processing.

We consider a new perspective on intermittence anomalies arising in intermittently-computing mixed-volatile systems. Existing forward progress techniques avoid such anomalies by enforcing a computation that corresponds to a continuous one, introducing a significant overhead. We take a different stand: By allowing the presence of specific anomalies, we make the program aware of intermittence, unlocking new design patterns. We argue about the various possibilities emerging from this and we make the concept concrete by applying it to loops. We show how intermittence anomalies allow to preserve the results of loop iterations across power failures, without requiring to save the device's volatile state after each iteration. Compared to existing checkpoint mechanisms, our technique shows on average a 35.2x lower energy consumption and a 48.4x lower execution time across several staple benchmarks. 

Energy harvesting battery-less embedded devices compute intermittently, as energy is available. Intermittent executions may differ from continuous ones due to repeated executions of non-idempotent code. This anomaly is normally recognized as a “bug” and solutions exist to retain equivalence between intermittent and continuous executions. We argue that our current understanding of these “bugs” is limited. We address this issue by devising techniques to comprehensively identify where and how intermittent and continuous executions possibly differ and by implementing them in SCEPTIC: a code analysis tool for intermittent programs. Thereby, we find execution anomalies and their manifested impact on program behavior in ways previously not considered. This analysis is enabled by SCEPTIC design, implementation, and performance. SCEPTIC runs up to ten orders of magnitude faster than the baselines we consider, enabling many types of analyses that would be otherwise impractical.

Predictable performance is key for many WSN applications. Recent efforts use models of the environment, the employed hardware, and protocols to predict network performance. Towards this end, we present an intentionally simple model of ContikiMAC, Contiki’s default MAC layer, targeting worst-case bounds for packet delivery rate and latency. Our experiments reveal problems in the performance of ContikiMAC, which make the protocol perform much worse than predicted, and hence prohibit predictable performance with the current ContikiMAC implementation. We show that the reason for this performance degradation is that ContikiMAC loses phase-lock. To solve this problem, we add fine-grained timing information into the acknowledgment packets. We show that this mechanism solves these problems and enables predictable performance with ContikiMAC even under high external interference.

Sensing, communication, computation and control technologies are the essential building blocks of a cyber-physical system (CPS). Wireless sensor networks (WSNs) are a way to support CPS as they provide fine-grained spatial-temporal sensing, communication and computation at a low premium of cost and power. In this article, we explore the fundamental concepts guiding the design and implementation of WSNs. We report the latest developments in WSN software and services for meeting existing requirements and newer demands; particularly in the areas of: operating system, simulator and emulator, programming abstraction, virtualization, IP-based communication and security, time and location, and network monitoring and management. We also reflect on the ongoing efforts in providing dependable assurances for WSN-driven CPS. Finally, we report on its applicability with a case-study on smart buildings.

A wide gap exists between the state of the art in developing Wireless Sensor Network (WSN) software and current practices concerning the design, execution, and maintenance of business processes. WSN software is most often developed based on low-level OS abstractions, whereas business process development leverages high-level languages and tools. This state of affairs places WSNs at the fringe of industry. The makeSense system addresses this problem by simplifying the integration of WSNs into business processes. Developers use BPMN models extended with WSN-specific constructs to specify the application behavior across both traditional business process execution environments and the WSN itself, which is to be equipped with application-specific software. We compile these models into a high-level intermediate language-Also directly usable by WSN developers-And then into OS-specific deployment-ready binaries. Key to this process is the notion of meta-Abstraction, which we define to capture fundamental patterns of interaction with and within the WSN. The concrete realization of meta-Abstractions is application-specific; developers tailor the system configuration by selecting concrete abstractions out of the existing codebase or by providing their own. Our evaluation of makeSense shows that i) users perceive our approach as a significant advance over the state of the art, providing evidence of the increased developer productivity when using makeSense; ii) in large-scale simulations, our prototype exhibits an acceptable system overhead and good scaling properties, demonstrating the general applicability of makeSense; and, iii) our prototype-including the complete tool-chain and underlying system support-sustains a real-world deployment where estimates by domain specialists indicate the potential for drastic reductions in the total cost of ownership compared to wired and conventional WSN-based solutions.

In modern distributed computing, applications are rarely built directly atop operating system facilities, e.g., sockets. Higher-level middleware abstractions and systems are often employed to simplify the programmer’s chore or to achieve interoperability. In contrast, real-world wireless sensor network (WSN) applications are almost always developed by relying directly on the operating system. Why is this the case? Does it make sense to include a middleware layer in the design of WSNs? And, if so, is it the same kind of software system as in traditional distributed computing? What are the fundamental concepts, reasonable assumptions, and key criteria guiding its design? What are the main open research challenges, and the potential pitfalls? Most importantly, is it worth pursuing research in this field? This paper provides a (biased) answer to these and other research questions, preceded by a brief account on the state of the art in the field.

We study the benefits of electronically-switched directional antennas in low-power wireless networks. This antenna technology may improve energy efficiency by increasing the communication range and by alleviating contention in directions other than the destination, but in principle requires a dedicated network stack. Unlike most existing works, we start by characterizing a real-world antenna prototype, and apply this to an existing low-power wireless stack, which we adapt with minimal changes. Our results show that: i) the combination of a low-cost directional antenna and a conventional network stack already brings significant performance improvements, e.g., nearly halving the radio-on time per delivered packet; ii) the margin of improvement available to alternative clean-slate protocol designs is similarly large and concentrated in the control rather than the data plane; iii) by artificially modifying our antenna's link-layer model, we can point at further potential benefits opened by different antenna designs.

Autonomous drones represent a new breed of mobile computing system. Compared to smartphones and connected cars that only opportunistically sense or communicate, drones allow motion control to become part of the application logic. The efficiency of their movements is largely dictated by the low-level control enabling their autonomous operation based on high-level inputs. Existing implementations of such low-level control operate in a timetriggered fashion. In contrast, we conceive a notion of reactive control that allows drones to execute the low-level control logic only upon recognizing the need to, based on the influence of the environment onto the drone operation. As a result, reactive control can dynamically adapt the control rate. This brings fundamental benefits, including more accurate motion control, extended lifetime, and better quality of service in end-user applications. Based on 260+ hours of real-world experiments using three aerial drones, three different control logic, and three hardware platforms, we demonstrate, for example, up to 41% improvements in motion accuracy and up to 22% improvements in flight time.

We present our ongoing work on the design of macropro- gramming abstractions to program sensing and actuating applications using robot swarms. Robots can sample the environment and act on it where no other sensor can reach, e.g., to monitor the environment at altitude with aerial robots. Programming the individual behavior of multiple coordinat- ing robots is dificult. We design LiftOff, a macropro- gramming abstraction that allows to program robot swarms collectively, by creating the illusion of a single computing device that occupies the entire physical space of interest. We achieve this by giving variables and values in a pro- gramming language a spatial semantics. In LiftOff, values may be associated to a location, and programmers use the same variable to access diferent values at diferent locations, sparing the need to manually create a mapping from variables to spatial values. LiftOff applications execute synchronously or based on lazy evaluation. The former allows precise program analysis, e.g., usng model checking, whilst the latter potentially executes faster. In this paper, we report on LiftOff's initial design and prototypes.