This paper describes Mercury, a wearable, wireless sensor platform for motion analysis of patients being treated for neuromotor disorders, such as Parkinson’s Disease, epilepsy, and stroke. In contrast to previous systems intended for short-term use in a laboratory, Mercury is designed to support long-term, longitudinal data collection on patients in hospital and home settings. Patients wear up to 8 wireless nodes equipped with sensors for monitoring movement and physiological conditions. Individual nodes compute high-level features from the raw signals, and a base station performs data collection and tunes sensor node parameters based on energy availability, radio link quality, and application specific policies. Mercury is designed to overcome the core challenges of long battery lifetime and high data fidelity for long-term studies where patients wear sensors continuously 12 to 18 hours a day. This requires tuning sensor operation and data transfers based on energy consumption of each node and processing data under severe computational constraints. Mercury provides a high-level programming interface that allows a clinical researcher to rapidly build up different policies for driving data collection and tuning sensor lifetime. We present the Mercury architecture and a detailed evaluation of two applications of the system for monitoring patients with Parkinson’s Disease and epilepsy.

Abstract. Remote management is essential for wireless sensor networks (WSNs) designed to run perpetually using harvested energy. A natural division of function for managing WSNs is to employ both an in-band data plane to sense, store, process, and forward data, and an out-of-band management plane to remotely control each node and its sensors. This paper presents SRCP, a Simple Remote Control Protocol that forms the core of an out-of-band management plane for WSNs. SRCP is motivated by our target environment: a perpetual deployment of high-power, aggressively duty-cycled nodes capable of handling high-bandwidth sensor data from multiple sensors. The protocol runs on low-power always-on control processors using harvested energy, distills an essential set of primitives, and uses them to control a suite of existing management functions on more powerful main nodes. We demonstrate SRCP’s utility by presenting a case study that (i) uses it to control a broad spectrum of management functions and (ii) quantifies its efficacy and performance. 1

This paper makes the case that operating system designs for sensor networks should focus on the coordination of resource management decisions across the network, rather than merely on individual nodes. We motivate this view by describing the challenges inherent to achieving a globally efficient use of sensor network resources, especially when the network is subject to unexpected variations in both load and resource availability. We present Peloton, a new distributed OS for sensor networks that provides mechanisms for representing distributed resource allocations, efficient state sharing across nodes, and decentralized management of network resources. We outline the Peloton OS architecture and present three sample use cases to illustrate its design. 1

The conception and development of pervasive systems, i.e, the systems which will be used in pervasive computing environments, involve interdisciplinary team work. Apparently, the team consists of people with a diverse research background and expertise. While such a composition is an essential prerequisite to solve real world problems, it brings with it also challenges that should be dealt with. To begin with, team members should establish a shared understanding of what should be done. This understanding includes the terminologies that are used as well as the expected project goals. Secondly, there has to be a division of task and a clear plan as to how different components or building blocks should come together to make up a unified, consistent, sideeffect free and wholesome system. In this paper we discuss the development of the Senceive System within a graduate

Many innovative mobile health applications can be enabled by augmenting wireless body sensors to mobile phones, e.g. monitoring personal fitness with on-body accelerometer and EKG sensors. However, it is difficult for the majority of smartphone developers to program wireless body sensors directly; current sensor nodes require developers to master node-level programming, implement the communication between the smartphone and sensors, and even learn new languages. The large gap between existing programming styles for smartphones and sensors prevents body sensors from being widely adopted by smartphone applications, despite the burgeoning Apple App Store and Android Market. To bridge this programming gap, we present Dandelion 1, a novel framework for developing wireless body sensor applications on smartphones. Dandelion provides three major benefits: 1) platform-agnostic programming abstraction for in-sensor data processing, called senselet, 2) transparent integration of senselets and the smartphone code, and 3) platform-independent development and distribution of senselets. We provide an implementation of Dandelion on the Maemo Linux smartphone platform and the Rice Orbit body sensor platform. We evaluate Dandelion by implementing realworld applications, and show that Dandelion effectively eliminates the programming gap and significantly reduces the development efforts. We further show that Dandelion incurs a very small overhead; in total less than 5 % of the memory capacity and less than 3 % of the processor time of a typical ultra low power sensor.

Sensor-actuator networks require sharing of actuators across multiple applications. Here, simple device arbitration is not enough because actuators have lasting (or irreversible) effects on their environment. For example, actuating a heater not only affects the internal program state of the controlling application, but the nearby temperature as well. This change in external environment often has impact on application-level decision making. We present CAhoot, a preliminary software infrastructure to enable cooperative sensor actuator applications through communication of actuator ranges, and show how it can be applied to automate building-level energy control. 1.

Energy in sensor networks is a distributed, non-transferable resource. Over time, differences in energy availability are likely to arise. Protocols like routing trees may concentrate energy usage at certain nodes. Differences in energy harvesting arising from environmental variations, such as if one node is in the sun and another is in the shade, can produce variations in charging rates and battery levels. Because many sensor network applications require nodes to collaborate — to ensure complete sensor coverage or route data to the network’s edge — a small set of nodes whose continued operation is threatened by low batteries can have a disproportionate impact on the fidelity provided by the network as a whole. In the most extreme case, the loss of a single sink node may render the remainder of the network unreachable. While previous

Dynamic networks—spontaneous, self-organizing groups of devices—are a promising new computing platform. Writing applications for such networks is a daunting task, however, due to their extreme variability and unpredictability, with many devices having significant resource limitations. Intelligent, automated distribution of work across network nodes is needed to get the most out of limited resource budgets. We propose a novel framework for distributing computations across a dynamic network, in which applications specify their spatiotemporal properties at a very high level. The underlying system makes node selection decisions to exploit these properties, producing high quality results within a fixed resource budget. A distributed computation is expressed as a semantically parallel loop over a geographic area and time