This paper reports on recent development of a wearable wireless sensor platform for interactive dance performances. At a fraction of a cubic-centimeter in volume, this platform is truly wearable and scalable informing wireless networks. Integrated with a wide variety of different sensing devices, it is a real-time monitoring system for activities and physical conditions of the human body. The effectiveness of this platform is demonstrated with an interactive dance performance.

Quantitative evaluation of wireless sensor platforms is difficult. Unlike general purpose computers that can run SPEC benchmarks from a file, it is difficult to reproduce the environmental input needed to stimulate the sensor nodes. Even if possible, open-loop playback would be unable to correctly account for adaptivity built into the behavior of these nodes. As a result, researchers resort to simulations, which do not consider all relevant factors without significant speed penalty. To address this problem, we propose EmPro, an environment/energy emulation and profiling system for WSNs. It accurately outputs electrical signals to emulate not only digital and analog inputs to the sensors but also the power sources as well as RF attenuation according to pre-programmed sequences. This emulation approach enables researchers to run the networked sensors in real-time in a realistic manner with full controllability and reproducibility. EmPro in profiling mode can also capture the observable behavior of WSNs for detailed analysis. Experimental results on the Eco and MICA2 WSN platforms show that EmPro can drive these hardware systems in real-time with high accuracy. We expect EmPro will expedite testing and serve as a sorely needed standard benchmarking tool for WSN platforms

AmbiMax is an energy harvesting circuit and a supercapacitor based energy storage system for wireless sensor nodes (WSN). Previous WSNs attempt to harvest energy from various sources, and some also use supercapacitors instead of batteries to address the battery aging problem. However, they either waste much available energy due to impedance mismatch, or they require active digital control that incurs overhead, or they work with only one specific type of source. AmbiMax addresses these problems by first performing maximum power point tracking (MPPT) autonomously, and then charges supercapacitors at maximum efficiency. Furthermore, AmbiMax is modular and enables composition of multiple energy harvesting sources including solar, wind, thermal, and vibration, each with a different optimal size. Experimental results on a real WSN platform, Eco, show that AmbiMax successfully manages multiple power sources simultaneously and autonomously at several times the efficiency of the current state-of-the-art for WSNs

eCAM is an ultra-compact, high data-rate wireless sensor node(WSN) with a miniature camera. It is constructed by interfacing a VGA quality digital video camera with the Eco node. The purpose of this demo is to show that Eco is not only one of the world's smallest self-contained WSNs, but its expansion capability facilitates the construction of new sensing systems over its expansion port. The results show that Eco is not only ultra-compact but also much higher performance than sensor nodes in its class.

Real-world wireless sensing applications demand system platforms with a wide range of size, cost, power consumption, connectivity, performance, and flexibility requirements. These goals cannot be achieved without understanding the nature of the sensing functions in the first place, which can be classified into passive vs. active sensing, event detection vs. data acquisition, and real-time monitoring vs. data logging. This paper discusses platform design techniques for supporting these design goals through the trade-offs of sensing devices, wireless interfaces, and computation and control units. We also cover power subsystem design for supply-aware optimizations, including load/supply matching, power defragmentation in multi-supply systems, and use of supercapacitors. To evaluate these system platforms, we describe an emulation-based benchmarking methodology to quantify fitness metrics.

Eco is an ultra-compact wireless sensor node. Only 648 mm/sup 3/ in volume and weighing under 1.6 grams, Eco was initially designed to monitor the spontaneous motion of preterm infants over 2.4 GHz radio links at the maximum data rate of 1 Mbps. The compact form factor and low power consumption also make Eco nodes highly suitable for many other applications, including medicine, environmental monitoring, new computer-human interface, and ambient intelligence. This paper describes the hardware and software designs of the second generation Eco nodes and the host interface. We also present an evaluation and comparison against other popular sensor nodes in the similar class.

Abstract — Battery life extension is the principal driver for energy-efficient wireless sensor network (WSN) design. However, there is growing awareness that in order to truly maximize the operating life of battery-powered systems such as sensor nodes, it is important to discharge the battery in a manner that maximizes the amount of charge extracted from it. In spite of this, there is little published data that quantitatively analyzes the effectiveness with which modern wireless sensor nodes discharge their batteries, under different operating conditions. In this paper, we report on systematic experiments that we conducted to quantify the impact of key wireless sensor network design and environmental parameters on battery performance. Our testbed consists of MICA2DOT Motes, a commercial lithiumcoin battery, and a suite of techniques for measuring battery performance. We evaluate the extent to which known electrochemical phenomena, such as rate-capacity characteristics, charge recovery and thermal effects, can play a role in governing the selection of key WSN design parameters such as power levels, packet sizes, etc. We demonstrate that battery characteristics significantly alter and complicate otherwise well-understood trade-offs in WSN design. In particular, we analyze the non-trivial implications of battery characteristics on WSN power control strategies, and find that a battery-aware approach to power level selection leads to a 52 % increase in battery efficiency. We expect our results to serve as a quantitative basis for future research in designing battery-efficient sensing applications and protocols. I.

For embedded systems that rely on multiple power sources (MPS), power management must distribute the power by matching the supply and demand in conjunction with the traditional power management tasks. Proper load matching is especially critical for renewable power sources such as solar panels and wind generators, because it directly affects the utility of the available power. This paper proposes a power distribution switch and a source-consumption matching algorithm that maximizes the total utility of the available power from these ambient power sources. Our method yields over 30% more usable power than conventional MPS designs.

This paper describes B# (B-sharp), a programmable power supply that emulates the behavior of a battery. It measures the current load, calls a battery simulation program to compute the voltage in real time, and controls a linear regulator to mimic the voltage output of a battery. This instrument enables validation of battery-aware power-optimization techniques with accurate, controllable, reproducible results. This instrument also supports training mode with actual batteries, and it can even be used for recording and playback of a solar power source. This design has been prototyped and tested on hand-held devices with high accuracy and fast response time.