Using phase information from medium bandwidth Doppler Radars, it is possible to obtain motion information corresponding to changes in target range on the order of a wavelength. Comparable range resolution from ranging radars would require much wider bandwidth. For long-range radars, the power consumption depends mostly on transmitted power levels and only weakly on bandwidth. But for short range radars of the type that are most useful in Wireless Sensor Networks (WSNs), transmitted power is usually negligible compared to processing power (i.e., within the radar), and the power budget depends strongly on the bandwidth. Thus, medium-bandwidth Doppler Radars offer the opportunity to achieve finer resolution of relative range information at much lower power at the expense of absolute range information.

Wireless sensor network (WSN) testbeds are useful because they provide a way to test applications in an environment that makes it easy to deploy experiments, configure them statically or dynamically, and gather performance information. However, WSNs are typically composed of low-cost devices and tend to be unreliable, with failures a common phenomenon. Accurate knowledge of network health status, including nodes and links of each type, is critical for correctly configuring applications on WSN testbeds and for interpreting the data collected from them. In this article we present a stabilizing protocol, Chowkidar, that provides accurate and efficient network health monitoring in WSNs. Our approach adapts the well-known problem of message-passing rooted spanning tree construction and its use in propagation of information with feedback (PIF) for the case of a WSN. The Chowkidar protocol is initiated upon demand; that is, it does not involve ongoing maintenance, and it terminates with accurate results, including detection of failure and restart during the monitoring process. Chowkidar is distinguished from others in two important ways. Given the resource constraints of WSNs, it is message-efficient in that it uses only a few messages per node. Also, it tolerates ongoing node and link failure and node restart, in contrast to requiring that faults stop during convergence. We have implemented the Chowkidar protocol as part of enabling a network health status service that is tightly integrated with a remotely accessible wireless sensor network testbed, Kansei, at The Ohio State University. We present experimental results from this testbed that validate the correctness and performance of Chowkidar. We also report on initial experiences and lessons learnt from the integration of Chowkidar with Kansei, including feedback from both testbed users and administrators who have found Chowkidar to be a useful tool for improving the accuracy and efficiency of testbed experimentation and maintenance, and the need for well-defined policies to address issues such as minimizing interference with concurrently running experiments. Finally, we discuss extensions that enhance the functionality and usability of Chowkidar.

The Kansei testbed at the Ohio State University is designed to facilitate research on networked sensing applications at scale. Kansei embodies a unique combination of characteristics as a result of its design focus on sensing and scaling: (i) Heterogeneous hardware infrastructure with dedicated node resources for local computation, storage, data exfiltration and back-channel communication, to support complex experimentation, (ii) Time accurate hybrid simulation engine for simulating substantially larger arrays using testbed hardware resources, (iii) High fidelity sensor data generation and real-time data and event injection, (iv) Software components and associated job control language to support complex multi-tier experiments utilizing real hardware resources and data generation and simulation engines. In this paper, we present the elements of Kansei testbed architecture, including its hardware and software platforms as well as its hybrid simulation and sensor data generation engines.

Recent experiments have taken steps towards realizing the vision of extremely large wireless sensor networks, the largest of these being ExScal, in which we deployed about 1200 nodes over a 1.3 km by 300 m open area. Such experiments remain especially challenging because of: (a) prior observations of failure of sensor network protocols to scale, due to network faults and their spatial and temporal variability, (b) complexity of protocol interaction, (c) lack of sufficient data about faults and variability, even at smaller scales, and (d) current inadequacy of simulation and analytical tools to predict sensor network protocol behavior. In this paper, we present detailed data about faults, both anticipated and unanticipated, in ExScal. We also evaluate the impact of these faults on ExScal as well as the design principles that enabled it to satisfy its application requirements despite these faults. We describe the important lessons learnt from the ExScal experiment and suggest services and tools as a further aid to future large scale network deployments.

Project ExScal (for extreme scale) fielded a 1000+ node wireless sensor network and a 200+ node peer-to-peer ad hoc network of 802.11 devices in a 13km by 300m remote area in Florida, USA during December 2004. In comparison with previous deployments, the ExScal application is relatively complex and its networks are the largest ones of either type fielded to date. In this paper, we overview the key requirements of ExScal, the corresponding design of the hardware/software platform and application, and some results of our experiments.

The influence field of an object, a commonly exploited feature in science and engineering applications, is the region where the object is detectable by a given sensing modality. Being spatially distributed, this feature allows us to tradeoff nodal computation with network communication. By the same token, not only is its calculation subject to nodal failures and false detections, but also to channel fading and channel contention. In this paper, we study how to accurately and efficiently estimate the influence fields of objects in such an unreliable setting and how this reliable estimation of influence fields can be used to classify and track different types of objects. We derive, for node and network fault models, the necessary nodal density for reliably estimating the influence fields so that objects can be classified and tracked. We present four algorithmic techniques: temporal aggregation, probabilistic reporting, temporal segregation and spatial reconstruction, to deal with cases where the effective network density differs from this minimum. We provide corroboration of our analysis through field experiments with Mica2 sensor nodes wherever appropriate. Finally, we demonstrate how these results and techniques were applied to achieve reliable and efficient classification and tracking in a fielded system of 90 Mica2 sensor nodes that we called "A Line In The Sand'.