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 Acoustic Inertial Measurement ($\textbackslashsf AIM$AIM), a one-of-a-kind technique for indoor drone localization and tracking. Indoor drone localization and tracking are arguably a crucial, yet unsolved challenge: in GPS-denied environments, existing approaches enjoy limited applicability, especially in Non-Line of Sight (NLoS), require extensive environment instrumentation, or demand considerable hardware/software changes on drones. In contrast, $\textbackslashsf AIM$AIM exploits the acoustic characteristics of the drones to estimate their location and derive their motion, even in NLoS settings. We tame location estimation errors using a dedicated Kalman filter and the Interquartile Range rule (IQR) and demonstrate that AIM can support indoor spaces with arbitrary ranges and layouts. We implement AIM using an off-the-shelf microphone array and evaluate its performance with a commercial drone under varied settings. Results indicate that the mean localization error of AIM is 46% lower than that of commercial UWB-based systems in a complex 10 x 10 m indoor scenario, where state-of-the-art infrared systems would not even work because of NLoS situations. When distributed microphone arrays are deployed, the mean error can be reduced to less than 0.5 m in a 20 m range, and even support spaces with arbitrary ranges and layouts.

In a long-term commitment to designing for the aesthetics of human–drone interactions, we have been troubled by the lack of tools for shaping and interactively feeling drone behaviours. By observing participants in a three-day drone challenge, we isolated components of drones that, if made transparent, could have helped participants better explore their aesthetic potential. Through a bricolage approach to analysing interviews, feld notes, video recordings, and inspection of each team’s code, we describe how teams 1) shifted their eforts from aiming for seamless human–drone interaction, to seeing drones as fragile, wilful, and prone to crashes; 2) engaged with intimate, bodily interactions to more precisely probe, understand and defne their drone’s capabilities; 3) adopted diferent workaround strategies, emphasising either training the drone or the pilot. We contribute an empirical account of constraints in shaping the potential aesthetics of drone behaviour, and discuss how programming environments could better support somaesthetic perception–action loops for design and programming purposes.

We present TaDA, a system architecture enabling efficient execution of Internet of Things (IoT) applications across multiple computing units, powered by ambient energy harvesting. Low-power microcontroller units (MCUs) are increasingly specialized; for example, custom designs feature hardware acceleration of neural network inference, next to designs providing energy-efficient input/output. As application requirements are growingly diverse, we argue that no single MCU can efficiently fulfill them. TaDA allows programmers to assign the execution of different slices of the application logic to the most efficient MCU for the job. We achieve this by decoupling task executions in time and space, using a special-purpose hardware interconnect we design, while providing persistent storage to cross periods of energy unavailability. We compare our prototype performance against the single most efficient computing unit for a given workload. We show that our prototype saves up to 96.7% energy per application round. Given the same energy budget, this yields up to a 68.7x throughput improvement. 

We study how to mitigate the effects of energy attacks in the battery-less Internet of Things∼(IoT). Battery-less IoT devices live and die with ambient energy, as they use energy harvesting to power their operation. They are employed in a multitude of applications, including safety-critical ones such as biomedical implants. Due to scarce energy intakes and limited energy buffers, their executions become intermittent, alternating periods of active operation with periods of recharging their energy buffers. Experimental evidence exists that shows how controlling ambient energy allows an attacker to steer a device execution in unintended ways: energy provisioning effectively becomes an attack vector. We design, implement, and evaluate a mitigation system for energy attacks. By taking into account the specific application requirements and the output of an attack detection module, we tune task execution rates and optimize energy management. This ensures continued application execution in the event of an energy attack. When a device is under attack, our solution ensures the execution of 23.3% additional application cycles compared to the baselines we consider and increases task schedulability by at least 21%, while enabling a 34% higher peripheral availability. 

We present a machine learning approach that uses a custom Convolutional Neural Network (CNN) for estimating the depth of water pools from multispectral drone imagery. Using drones to obtain this information offers a cheaper, timely, and more accurate solution compared to alternative methods, such as manual inspection. This information, in turn, represents an asset to identify potential breeding sites of mosquito larvae, which grow only in shallow water pools. As a significant part of the world’s population is affected by mosquito-borne viral infections, including Dengue and Zika, identifying mosquito breeding sites is key to control their spread. Experiments with 5-band drone imagery show that our CNN-based approach is able to measure shallow water depths accurately up to a root mean square error of less than 0.5 cm, outperforming state-of-the-art Random Forest methods and empirical approaches.

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. 

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.

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.

Low-power wireless communication is a central building block of cyber-physical systems and the Internet of Things. Conventional low-power wireless protocols make avoiding packet collisions a cornerstone design choice. The concept of synchronous transmissions challenges this view. As collisions are not necessarily destructive, under specific circumstances, commodity low-power wireless radios are often able to receive useful information even in the presence of superimposed signals from different transmitters. We survey the growing number of protocols that exploit synchronous transmissions for higher robustness and efficiency as well as unprecedented functionality and versatility compared to conventional designs. The illustration of protocols based on synchronous transmissions is cast in a conceptional framework we establish, with the goal of highlighting differences and similarities among the proposed solutions. We conclude this article with a discussion on open questions and challenges in this research field