In this paper, we address the problem of extending a relational database system to facilitate efficient real-time application of dynamic probabilistic models to streaming data. We use the recently proposed abstraction of model-based views for this purpose, by allowing users to declaratively specify the model to be applied, and by presenting the output of the models to the user as a probabilistic database view. We support declarative querying over such views using an extended version of SQL that allows for querying probabilistic data. Underneath we use particle filters, a class of sequential Monte Carlo algorithms commonly used to implement dynamic probabilistic models, to represent the present and historical states of the model as sets of weighted samples (particles) that are kept up-to-date as new readings arrive. We develop novel techniques to convert the queries on the model-based view directly into queries over particle tables, enabling highly efficient query processing. Finally, we present experimental evaluation of our prototype implementation over sensor data from the Intel Lab dataset that demonstrates the feasibility of online modeling of streaming data using our system and establishes the advantages of such tight integration between dynamic probabilistic models and database systems. 1

Abstract — Camera sensors constitute an information rich sensing modality with many potential applications in sensor networks. Their effectiveness in a sensor network setting however greatly relies on their ability to calibrate with respect to each other, and other sensors in the field. This paper examines node localization and camera calibration using the shared field of view of camera pairs. Using a new distributed camera sensor network we compare two approaches from computer vision and propose an algorithm that combines a sparse set of distance measurements with image information to accurately localize nodes in 3D. Our algorithms are evaluated using a network of iMote2 nodes equipped with COTS camera modules. The sensor nodes identify themselves to cameras using modulated LED emissions. Our indoor experiments yielded a 2-7cm error in a 6x6m room. Our outdoor experiments in a 30x30m field resulted in errors 20-80cm, depending on the method used. I.

Although imaging is an information-rich sensing modality, the use of cameras in sensor networks is very often prohibited by factors such as power, computation cost, storage, communication bandwidth and privacy. In this paper we consider information selective and privacy-preserving address-event imagers for sensor networks. Instead of providing full images with a high degree of redundancy, our efforts in the design of these imagers specialize on selecting a handful of features from a scene and outputting these features in address-event representation. In this paper we present our initial results in modeling and evaluating address-event sensors in the context of sensor networks. Using three different platforms that we have developed, we illustrate how to model address-event cameras and how to build an emulator using these models. We also present a lightweight classification scheme to illustrate the computational advantages of address-event sensors. The paper concludes with an evaluation of the classification algorithm and a feasibility study of using COTS components to emulate address-event inside a sensor network.

This paper presents a lightweight method for localizing and counting people in indoor spaces using motion and size criteria. A histogram designed to filter moving objects within a specified size range, can operate directly on frame difference output to localize human-sized moving entities in the field of view of each camera node. Our method targets a custom, ultra-low power imager architecture operating on address-event representation, aiming to implement the proposed algorithm on silicon. In this paper we describe the details of our design and experimentally determine suitable parameters for the proposed histogram. The resulting histogram and counting algorithm are implemented and tested on a set of iMote2 camera sensor nodes deployed in our lab. 1.

Abstract — This paper provides an overview of the research aspects of our DSC06 demonstration. We present a new camera sensor network for behavior recognition. Two new technologies are explored, biologically inspired address-event image sensors and sensory grammars. This paper explains how these two technologies are used together and reports of the current status of our prototyping effort. The application of the resulting system in assisted living is also described. I.

This paper presents BScope, a new system for interpreting human activity patterns using a sensor network. BScope provides a run-time, user-programmable framework that processes streams of timestamped sensor data along with prior context information to infer activities and generate appropriate notifications. The users of the system are able to describe human activities with high level scripts that are directly mapped to hierarchical probabilistic grammars used to parse low level sensor measurements into high level distinguishable activities. Our approach is presented, though not limited, in the context of an assisted living application in which a small, privacy preserving camera sensor network of five nodes is used to monitor activity in the entire house over a period of 25 days. Privacy is preserved by the fact that camera sensors only provide discrete high-level features, such as motion information in the form of image locations, and not actual images. In this deployment, our primary sensing modality is a distributed array of image sensors with wide-angle lens that observe people’s locations in the house during the course of the day. We demonstrate that our system can successfully generate summaries of everyday activities and trigger notifications at run-time by using more than 1.3 million location measurements acquired through our real home deployment.

Abstract. In-network data processing and event detection on resourceconstrained devices are widely regarded as distinctive and novel features of wireless sensor networks. The vision is that through cooperation of many sensor nodes the accuracy of event detection can be greatly improved. On the practical side however, little real-world experience exists in how far these goals can be achieved. In this paper, we present the results of a small deployment of sensor nodes attached to a fence with the goal of collaboratively detecting and reporting security relevant incidents, such as a person climbing over the fence. Based on experimental data we discuss in detail the process of innetwork event detection both from the conceptual side and by evaluating the results obtained. Reusing the same traces in a simulated network, we also look into the impact of multi-hop event reporting.

In this paper we demonstrate the application of a probabilistic grammar-based formulation to detect complex activities from simple sensor measurements. In particular, we present a grammar hierarchy for identifying “cooking activity” from low-level location measurements in an assisted living application. Using real data from a pilot network deployment, we show that our system can recognize complex behaviors in a manner that is invariant across multiple different instances of the same activity. Our experiments also demonstrate that substantial data interpretation can take place at the node level, allowing the network to operate on compact symbolic representations. 1

Recent advances in sensor networks permit the use of a large number of relatively inexpensive distributed computational nodes with camera sensors linked in a network and possibly linked to one or more central servers. We argue that the full potential of such a distributed system can be realized if it is designed as a distributed search engine where images from different sensors can be captured, stored, searched and queried. However, unlike traditional image search engines that are focused on resource-rich situations, the resource limitations of camera sensor networks in terms of energy, bandwidth, computational power, and memory capacity present significant challenges. In this paper, we describe the design and implementation of a distributed search system over a camera sensor network where each node is a search engine that senses, stores and searches information. Our work involves innovation at many levels including local storage, local search, and distributed search, all of which are designed to be efficient under the resource constraints of sensor networks. We present an implementation of the search engine on a network of iMote2 sensor nodes equipped with low-power cameras and extended flash storage. We evaluate our system for a dataset comprising book images, and demonstrate more than two orders of magnitude reduction in the amount of data communicated and up to 5x reduction in overall energy consumption over alternate techniques.

A major issue of activity recognition in sensor networks is automatically recognizing a user’s highlevel goals accurately from low-level sensor data. Traditionally, solutions to this problem involve the use of a location-based sensor model that predicts the physical locations of a user from the sensor data. This sensor model is often trained offline, incurring a large amount of calibration effort. In this article, we address the problem using a goal-based segmentation approach, in which we automatically segment the low-level user traces that are obtained cheaply by collecting the signal sequences as a user moves in wireless environments. From the traces we discover primitive signal segments that can be used for building a probabilistic activity model to recognize goals directly. A major advantage of our algorithm is that it can reduce a significant amount of human effort in calibrating the sensor data while still achieving comparable recognition accuracy. We present our theoretical framework for activity recognition, and demonstrate the effectiveness of our new approach using the data collected in an indoor wireless environment.