We present Yottos, an event driven operating system for wireless embedded devices that reduces energy consumption by coalescing tasks into workloads with similar resource requirements thereby reducing time and energy consumed from power cycling peripherals. With Yottos we target a different group of programmers than the ones well-versed in embedded C, TinyOS and Contiki, namely web and app developers who on one hand are familiar with event driven programming in the form of user interaction events, but on the other have no experience with low-level hardware access. We trade off memory footprint (both data and code) in support of a programming environment more akin to iOS and Android development by supporting Objective-C and C/C++.

Recent work has shown that network protocols which rely on precisely-timed concurrent transmissions can achieve reliable, energy-efficient, and conceptually simple network flooding. While these multi-transmitter schemes work well, they require all nodes in the network to forward every data packet, which has inherent inefficiencies for non-flooding traffic patterns (where not all nodes need to receive the data). In this work, we formalize the concept of the “useful” forwarder set for point-to-point transmissions in low power multi-transmitter networks, those nodes which help forward data to the destination. We present a mechanism for approximating membership in this set based on simple heuristics. Incorporating forwarder selection on our 66-node testbed reduced radio duty cycle by 30% and increased throughput by 49% relative to concurrent flooding while preserving a 99.4% end-to-end packet reception ratio under the collection traffic pattern. We envision forwarder selection as a fundamental task in an efficient multi-transmitter networking stack. This work demonstrates that adding forwarder selection can improve energy efficiency and throughput while providing similar end-to-end packet delivery rates to flooding.

Biking is one of the most efficient and environmentally friendly ways to control weight and commute. To precisely estimate caloric expenditure, bikers have to install a bike computer or use a smartphone connected to additional sensors such as heart rate monitors worn on their chest, or cadence sensors mounted on their bikes. However, these peripherals are still expensive and inconvenient for daily use. This work poses the following question: is it possible to use just a smartphone to reliably estimate cycling activity? We answer this question positively through a pocket sensing approach that can reliably measure cadence using the phone's on-board accelerometer with less than 2% error. Our method estimates caloric expenditure through a model that takes as inputs GPS traces, the USGS elevation service, and the detailed road database from OpenStreetMap. The overall caloric estimation error is 60% smaller than other smartphone-based approaches. Finally, the smartphone can aggressively duty-cycle its GPS receiver, reducing energy consumption by 57%, without any degradation in the accuracy of caloric expenditure estimates. This is possible because we can recover the bike's route, even with fewer GPS location samples, using map information from the USGS and OpenStreetMap databases.

Ecologists instrument ecosystems to collect time series representing the evolution in time and space of relevant abiotic and biotic factors. Sensor networks promise to improve on existing data acquisition systems by interconnecting stand-alone measurement systems into virtual instruments. Such ecological sensor networks, however, will only fulfill their potential if they meet the scientists requirements. In an ideal world, an ecologist expresses requirements in terms of a target dataset, which the sensor network then actually collects and stores. In fact, failures occur and interesting events happen, making uniform systematic ecosystem sampling neither possible nor desirable. Today, these anomalous situations are handled as exceptions treated by technicians that receive an alert at deployment time. In this paper, we detail how ecological sensor networks can maximize the utility of the collected datasets in a changing environment. More specifically, we present the design of a controller that continuously maintains its state based on the data obtained from the sensor network (as well as external systems), and configures motes with parameters that satisfy a constraint optimization problem derived from the current state, the system requirements, and the scientist requirements. We describe our implementation, discuss its scalability, and discuss its performance in the context of two case studies.