Eco: A Hardware-Software Co-Design for In Situ Power Measurement on Low-end IoT Systems

Energy-constrained sensor nodes can adaptively optimize their energy consumption if a continuous measurement exists. This is of particular importance in scenarios of high dynamics such as energy harvesting or adaptive task scheduling. However, self-measuring of power consumption at reasonable cost and complexity is unavailable as a generic system service. In this paper, we present Eco, a hardware-software co-design enabling generic energy management on IoT nodes. Eco is tailored to devices with limited resources and thus targets most of the upcoming IoT scenarios. The proposed measurement module combines commodity components with a common system interfaces to achieve easy, flexible integration with various hardware platforms and the RIOT IoT operating system. We thoroughly evaluate and compare accuracy and overhead. Our findings indicate that our commodity design competes well with highly optimized solutions, while being significantly more versatile. We employ Eco for energy management on RIOT and validate its readiness for deployment in a five-week field trial integrated with energy harvesting

Energy harvesting and wireless transfer in sensor network applications: Concepts and experiences

Advances in micro-electronics and miniaturized mechanical systems are redefining the scope and extent of the energy constraints found in battery-operated wireless sensor networks (WSNs). On one hand, ambient energy harvesting may prolong the systems lifetime or possibly enable perpetual operation. On the other hand, wireless energy transfer allows systems to decouple the energy sources from the sensing locations, enabling deployments previously unfeasible. As a result of applying these technologies to WSNs, the assumption of a finite energy budget is replaced with that of potentially infinite, yet intermittent, energy supply, profoundly impacting the design, implementation, and operation of WSNs. This article discusses these aspects by surveying paradigmatic examples of existing solutions in both fields and by reporting on real-world experiences found in the literature. The discussion is instrumental in providing a foundation for selecting the most appropriate energy harvesting or wireless transfer technology based on the application at hand. We conclude by outlining research directions originating from the fundamental change of perspective that energy harvesting and wireless transfer bring about

Entraining a Robot to its Environment with an Artificial Circadian System

As robots become persistent agents in a complex and dynamic world, they must deal with changing environments. This challenge has grown research in long-term autonomy, with particular focus on localization and mapping in dynamic environments. Less attention has been paid to learning these dynamics to adapt or entrain an agent’s behavior to them. Inspired by circadian systems in nature, this dissertation seeks to answer how a robotic agent can both learn the regular cycles and patterns that exist in many environments, and how it can exploit that knowledge. In this work, relevant environmental states are modeled as time series and forecasted into the future. These forecasts are used to estimate the utility of executing some behavior at any point in time during the dominant environmental cycle. The relative utility of executing a behavior at the current time, compared to the potential utility for waiting until later, is passed as one component in an activation-based action selection system. While other components still impact what behaviors execute when, the ‘circadian’ component derived from forecasts biases the behavior to execute at the better times in the environmental cycle. This approach was dubbed the artificial circadian system. As forecasting the future is inherently unreliable, methods to make the approach robust to degrading forecast accuracy are presented. A unitless error measure is used to adapt the weight of the forecasting component in action selection, allowing an autonomous agent to leverage forecasts when they are good, and fall back onto reactive strategies when forecasts fail. As time series models rely on the history for predictions, a temporary disruption to the environment can potentially degrade forecast accuracy for many cycles. Methods to create modified forecasts which exclude potential outlier data are presented, and these forecasts are leveraged only if they improve accuracy. The ideas in this work were validated using an experimental test bed designed to approximate a precision agricultural task, where a solar powered robot monitored individual plants for pests and weeds. The artificial circadian system was shown to effectively entrain the agent’s behavior to the dynamics of its environment in some cases, improving performance. It was also noted that dynamics not on the same time-scale as the robot’s actions could not be exploited, even with forecasted knowledge. The artificial circadian system was reliably able to detect deviations in the environment and remove the influence of forecasts when their accuracy degraded. Successfully removing outlier data to generate better forecasts was less consistent, but achieved in a significant portion of trials.Ph.D