We present the design and evaluation of a 3.5-year embedded sensing deployment at the Mithraeum of Circus Maximus, a UNESCOprotected 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 batteryoperated 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. CCS CONCEPTS• Computer systems organization → Sensor networks; Embedded software.

Abstract-We present a context-oriented approach to design and implement self-adaptive component-based software in resource-constrained Cyberphysical Systems (CPSs). Because of unpredictable environment dynamics, developers must design and implement CPS software to dynamically adapt to widely different situations. Our approach provides design concepts and language support to meet this requirement against severe resource constraints. To this end, we bring a notion of context-oriented design and programming down to platforms that-because of extreme resource constraints-currently leverage fairly undisciplined design techniques and rather rudimentary componentbased frameworks. Early results demonstrate that our approach improves the quality of the resulting implementations facilitating testing, maintenance, and evolution at the price of a negligible system overhead.

Aerial drones represent a new breed of mobile computing. Compared to mobile phones and connected cars that only opportunistically sense or communicate, aerial drones offer direct control over their movements. They can thus implement functionality that were previously beyond reach, such as collecting high-resolution imagery, exploring near-inaccessible areas, or inspecting remote areas to gather fine-grain environmental data.

We present design concepts, programming constructs, and automatic verification techniques to support the development of adaptive Wireless Sensor Network (WSN) software. WSNs operate at the interface between the 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. However, developers are left without proper support to develop adaptive functionality in WSN software. Our work fills this gap with three key contributions: i) design concepts help developers organize the necessary adaptive functionality and understand their relations, ii) dedicated programming constructs simplify the implementations, iii) custom verification techniques allow developers to check the correctness of their design before deployment. We implement dedicated tool support to tie the three contributions, facilitating their practical application. Our evaluation considers representative WSN applications to analyze code metrics, synthetic simulations, and cycle-accurate emulation of popular WSN platforms. The results indicate that our work is effective in simplifying the development of adaptive WSN software; for example, implementations are provably easier to test and to maintain, the run-time overhead of our dedicated programming constructs is negligible, and our verification techniques return results in a matter of seconds.

Abstract-We present programming abstractions for implementing adaptive Wireless Sensor Network (WSN) software. The need for adaptability arises in WSNs because of unpredictable environment dynamics, changing requirements, and resource scarcity. However, after about a decade of research in WSN programming, developers are still left with no dedicated support. To address this issue, we bring concepts from Context-Oriented Programming (COP) down to WSN devices. Contexts model the situations that WSN software needs to adapt to. Using COP, programmers use a notion of layered function to implement context-dependent behavioral variations of WSN code. To this end, we provide language-independent design concepts to organize the context-dependent WSN operating modes, decoupling the abstractions from their concrete implementation in a programming language. Our own implementation, called CONESC, extends nesC with COP constructs. Based on three representative applications, we show that CONESC greatly simplifies the resulting code and yields increasingly decoupled implementations compared to nesC. For example, by model-checking every function in either implementations, we show a ≈50% reduction in the number of program states that programmers need to deal with, indicating easier debugging. In our tests, this comes at the price of a maximum 2.5% (4.5%) overhead in program (data) memory.