The next generation internet will be the internet of things (and not just of computing devices like PCs, PDAs); this is presumed to be enabled by integrating simple computing plus communications capabilities into common objects of everyday use. Radio-frequency identification (RFID) is a compelling technology for creation of such pervasive sensor networks due to its potential for ubiquitous, low-cost/low-maintenance use. However, the current drivers for RFID deployment emphasize supply chain management using passive tags, implying that RFID sensor nets require advances beyond the components and system designs aimed at supply chain applications. This work provides a glimpse of how this may be achieved.

We explore a dense sensing approach that uses RFID sensor network technology to recognize human activities. In our setting, everyday objects are instrumented with UHF RFID tags called WISPs that are equipped with accelerometers. RFID readers detect when the objects are used by examining this sensor data, and daily activities are then inferred from the traces of object use via a Hidden Markov Model. In a study of 10 participants performing 14 activities in a model apartment, our approach yielded recognition rates with precision and recall both in the 90% range. This compares well to recognition with a more intrusive short-range RFID bracelet that detects objects in the proximity of the user; this approach saw roughly 95% precision and 60% recall in the same study. We conclude that RFID sensor networks are a promising approach for indoor activity monitoring.

This paper examines the performance of EPC Class-1 Generation-2 UHF RFID reader systems in a realistic setting. Specifically, we identify factors that degrade overall performance and reliability with a focus on the physical layer, and we explore the degree to which reader configuration options can mitigate these factors. We use a custom software-radio based RFID monitoring system and configurable RFID readers to gather fine-grained data and assess the effects of the different factors. We find that physical layer considerations have a significant impact on reader performance, and that this is exacerbated by a lack of integration between the physical and MAC layers. We show that tuning physical layer operating parameters can increase the read rate for a set of tags by more than a third. Additionally, we show that tighter integration of the physical and MAC layers has the potential for even greater improvements.

We demonstrate a simple RFID sensor network comprised of an Intel WISP and a commodity UHF RFID reader. WISPs are devices that gather their operating energy from RFID reader transmissions, in the manner of passive RFID tags, and further include sensors, e.g., accelerometers, and provide a very small-scale computing platform. We believe that the small form factor and lack of battery makes the WISP an attractive alternative to motes for many of the original smart dust applications that require very small or long-lived sensors. The Intel WISP that we demonstrate has an ultra-low-power microcontroller, 32K of program space, 8K of flash, and accelerometer and temperature sensors. It harvests power from and communicates sensor data to standard (EPC Class 1 Gen 2) UHF RFID readers with a range of roughly 10 feet. This combination of RFID technology and sensor networks raises many research challenges, such as how to function with intermittent power and how to modify RFID protocols to support sensor queries.

In this paper we present X-MAC, a low power MAC protocol for wireless sensor networks (WSNs). Standard MAC protocols developed for duty-cycled WSNs such as BMAC, which is the default MAC protocol for TinyOS, employ an extended preamble and preamble sampling. While this "low power listening" approach is simple, asynchronous, and energy-efficient, the long preamble introduces excess latency at each hop, is suboptimal in terms of energy consumption, and suffers from excess energy consumption at nontarget receivers. X-MAC proposes solutions to each of these problems by employing a shortened preamble approach that retains the advantages of low power listening, namely low power communication, simplicity and a decoupling of transmitter and receiver sleep schedules. We demonstrate through implementation and evaluation in a wireless sensor testbed that X-MAC's shortened preamble approach significantly reduces energy usage at both the transmitter and receiver, reduces per-hop latency, and offers additional advantages such as flexible adaptation to both bursty and periodic sensor data sources.