In this work, we present a formal study on optimizing the energy consumption of energy harvesting embedded systems. To deal with the uncertainty inherent in solar energy harvesting systems, we propose the Stochastic Power Management (SPM) scheme, which builds statistical models of harvested energy based on historical data. The proposed stochastic scheme maximizes the lowest energy consumption across all time intervals while giving strict probabilistic guarantees on not encountering battery depletion. For situations where historical data is not available, we propose the use of (i) a Finite Horizon Control (FHC) scheme and (ii) a non-uniformly scaled energy estimator based on an astronomical model, which is used by FHC. Under certain realistic assumptions, the FHC scheme can provide guarantees on minimum energy usage that can be supported over all times. We further propose and evaluate a piece-wise linear approximation of FHC for efficient implementation in resource-constrained embedded systems. With extensive experimental evaluation for eight publicly available datasets and two datasets collected with our own deployments, we quantitatively establish that the proposed solutions are highly effective at providing a guaranteed minimum service level and significantly outperform existing solutions.

In this work we present the first formal study on optimizing the energy utilization of energy harvesting embedded systems while giving bounds on the minimum energy usage. Furthermore, to deal with the uncertainty inherent to solar energy harvesting we propose to use (i) a finite horizon scheme, and (ii) anon-uniformly scaled energy estimation based on an astronomical model. Under certain realistic assumptions, the finite horizon scheme can provide guarantees on minimum energy utilization, and therefore minimum utility. We show that a single non-uniform scaling function is applicable to solar energy traces from diverse locations. We further propose and evaluate a piece-wise linear approximation for efficient implementation as a small look-up table for resource constrained embedded systems. With extensive experimental evaluation for eight publicly available datasets and two datasets collected with our own deployments, we quantitatively establish that the proposed solution is highly effective at providing a guaranteed minimum utilization, and significantly out-performs four previously proposed solutions.

In this paper, we tackle the challenge of rapidly disseminating rare events through a multi-hop network, while achieving unprecedented energy-efficiency. Contrary to state-of-the-art approaches, we circumvent the undesirable trade-offs associated with low-power duty-cycled protocols and backscatter technologies, and demonstrate a paradigm shift in low-power protocol design. We present Zippy, an on-demand flooding technique that provides robust asynchronous network wake-up, fine-grained per-hop synchronization and efficient data dissemination by leveraging low-complexity transmitter and receiver hardware. We are the first to demonstrate the on-demand flooding of rare events through a multi-hop network with end-to-end latencies of tens of milliseconds, while dissipating less than 10 microwatts during periods of inactivity. We present a prototype implementation of our proposed approach using a wireless sensor platform constructed from commercially available components. We extensively evaluate Zippy's performance in a laboratory setting and in an indoor testbed.

In this work we consider a real-world environmental monitoring scenario that requires uninterrupted system operation over time periods on the order of multiple years. To achieve this goal, we propose a novel approach to dynamically adjust the system's performance level such that energy neutral operation, and thus long-term uninterrupted operation can be achieved. We first consider the annual dynamics of the energy source to design an appropriate power subsystem (i.e., solar panel size and energy store capacity), and then dynamically compute the long-term sustainable performance level at runtime. We show through trace-driven simulations using eleven years of real-world data that our approach outperforms existing predictive, e.g., EWMA, WCMA, and reactive, e.g., ENO-MAX, approaches in terms of average performance level by up to 177%, while reducing duty-cycle variance by up to three orders of magnitude. We further demonstrate the benefits of the dynamic power management scheme using a wireless sensor system deployed for environmental monitoring in a remote, high-alpine environment as a case study. A performance evaluation over two years reveals that the dynamic power management scheme achieves a two-fold improvement in system utility when compared to only applying appropriate capacity planning.

The field of Wireless Sensor Networks (WSNs) is now in a stage where serious applications of societal and economical importance are in reach. For example, it is well known that the global climate change dramatically influences the visual appearance of mountain areas like the European Alps. Very destructive geological processes may be triggered or intensified, impacting the stability of slopes, possibly inducing landslides. Unfortunately, the interactions between these complex processes is poorly understood. Therefore, one needs to develop wireless sensing technology as a new scientific instrument for environmental sensing under extreme conditions. Large variations in temperature, humidity, mechanical forces, snow coverage, and unattended operation play a crucial role in long-term deployments. We argue that, in order to significantly advance the application domain, it is inevitable that sensor networks be created as a quality scientific instrument with known and predictable properties, and not as a research toy delivering average observations at best. In this paper, key techniques for achieving highly reliable, yet resource efficient wireless sensor networks are discussed on the basis of productive wireless sensor networks measuring permafrost processes in the Swiss Alps.