Manish Kushwaha

OASiS: A Programming Framework for Service-Oriented Sensor Networks

Wireless sensor networks consist of small, inexpensive devices which interact with the environment, communicate with each other, and perform distributed computations in order to monitor spatio-temporal phenomena. These devices are ideally suited for a variety of applications including object tracking, environmental monitoring, and homeland security. At present, sensor network technologies do not provide off-the-shelf solutions to users who lack low-level network programming experience. Because of limited resources, ad hoc deployments, and volatile wireless communication links, the development of distributed applications require the combination of both application and system-level logic. Programming frameworks and middleware for traditional distributed computing are not suitable for many of these problems due to the resource constraints and interactions with the physical world. To address these challenges we have developed OASiS, a programming framework that provides abstractions for object-centric, ambient-aware, service-oriented sensor network applications. OASiS uses a well-defined model of computation based on globally asynchronous locally synchronous dataflow, and is complemented by a user-friendly modeling environment. Applications are realized as graphs of modular services and executed in response to the detection of physical phenomena. We have also implemented a suite of middleware services that support OASiS to provide a layer of abstraction shielding the low-level system complexities. A tracking application is used to illustrate the features of OASiS. Our results demonstrate the feasibility and the benefits of a service-oriented programming framework for composing and deploying applications in resource-constrained sensor networks.

Sensor node localization using mobile acoustic beacons

We present a mobile acoustic beacon based sensor node localization method. Our technique is passive in that the sensor nodes themselves do not need to generate an acoustic signal for ranging. This saves cost, power and provides stealthy operation. Furthermore, the beacon can generate much more acoustic energy than a severely resource constrained sensor node, thereby significantly increasing the range. The acoustic ranging method uses a linear frequency modulated signal that can be accurately detected by matched filtering. This provides longer range and higher accuracy than the current state-of-the-art. The localization algorithm was especially designed to work in such acoustically reverberant environment, as urban terrain. The algorithm presented handles non-Gaussian ranging errors caused by echoes. Node locations are computed centrally by solving a global non-linear optimization problem in an iterative and incremental fashion

Target tracking in heterogeneous sensor networks using audio and video sensor fusion

Heterogeneous sensor networks (HSNs) with multiple sensing modalities are gaining popularity in diverse fields. Tracking is an application that can benefit from multiple sensing modalities. If a moving target emits sound then both audio and video sensors can be utilized. These modalities can complement each other in the presence of high background noise that impairs the audio or visual clutter affecting the video. Audio-video tracking can also provide cues for the other modality for actuation. In this paper, we describe an approach for target tracking in urban environments utilizing an HSN of mote class devices equipped with acoustic sensor boards and embedded PCs equipped with web cameras. Our system employs a Markov Chain Monte Carlo Data Association algorithm for tracking vehicles emitting engine noise. Experimental results from a deployment in an urban environment are used to demonstrate our approach.

Efficient Integration of Web Services in Ambient-aware Sensor Network Applications

Sensor webs are heterogeneous collections of sensor devices that collect information and interact with the environment. They consist of wireless sensor networks that are ensembles of small, smart, and cheap sensing and computing devices that permeate the environment as well as high-bandwidth rich sensors such as satellite imaging systems, meteorological stations, air quality stations, and security cameras. Emergency response, homeland security, and many other applications have a very real need to interconnect such diverse networks and access information in real-time. While Internet protocols and Web standards provide well-developed mechanisms for accessing this information, linking such mechanisms with resource-constrained sensor networks is very challenging because of the volatility of the communication links. This paper presents a service-oriented programming model for sensor networks which permits discovery and access of Web services. Sensor network applications are realized as graphs of modular and autonomous services with well-defined interfaces that allow them to be described, published, discovered, and invoked over the network providing a convenient way for integrating services from heterogeneous sensor systems. Our approach provides dynamic discovery, composition, and binding of services based on an efficient localized constraint satisfaction algorithm that can be used for developing ambient-aware applications that adapt to changes in the environment. A tracking application that employs many inexpensive sensor nodes, as well as a Web service, is used to illustrate the approach. Our results demonstrate the feasibility of ambient-aware applications that interconnect wireless sensor networks and Web services.

Multi-Modal Target Tracking Using Heterogeneous Sensor Networks

The paper describes a target tracking system running on a heterogeneous sensor network (HSN) and presents results gathered from a realistic deployment. The system fuses audio direction of arrival data from mote class devices and object detection measurements from embedded PCs equipped with cameras. The acoustic sensor nodes perform beamforming and measure the energy as a function of the angle. The camera nodes detect moving objects and estimate their angle. The sensor detections are sent to a centralized sensor fusion node via a combination of two wireless networks. The novelty of our system is the unique combination of target tracking methods customized for the application at hand and their implementation on an actual HSN platform.

OASiS: A Service-Oriented Architecture for Ambient-Aware Sensor Networks

Heterogeneous sensor networks are comprised of ensembles of small, smart, and cheap sensing and computing devices that permeate the environment, as well as resource intensive sensors such as satellite imaging systems, meteorological stations, and security cameras. Emergency response, homeland security, and many other applications have a very real need to interconnect these diverse networks and access information in real-time. Web service technologies provide well-developed mechanisms for exchanging data between heterogeneous computing devices, but they cannot be used in resource-constrained wireless sensor networks. This paper presents OASiS, a lightweight service-oriented architecture for sensor networks, which provides dynamic service discovery and can be used to develop ambient-aware applications that adapt to changes in the network and the environment. An important advantage of OASiS is that it allows seamless integration with Web services. We have developed a middleware implementation that supports OASiS, and a simple tracking application to illustrate the approach. Our results demonstrate the feasibility of a service-oriented architecture for wireless sensor networks.

Fusion-based localization for a Heterogeneous camera network

Heterogeneous sensor networks (HSNs) are becoming more commonly used for purposes such as monitoring and surveillance, as they offer richer sources of data for situational awareness. An important aspect of HSNs is localization. In this paper, we describe a novel method for localizing a network of cameras equipped with wireless radios. Our method fuses both the image data and radio interferometry data in order to determine the position of the sensors and the orientation of each camerapsilas field of view. While existing methods that rely solely on image data alone are often limited in that they can only recover position up to scale factors, by fusing the image data and radio interferometry data, we are able to recover the position and orientation with no scale factor ambiguity. In contrast, localization of sensor nodes using radio alone only recovers the position of the sensors and often relies on computationally expensive methods. The method discussed in this paper exploits both the image and radio data for a more computationally efficient process of localization. We discuss both a linear and nonlinear approach to fusing the data which depend on different constraints on the network. We demonstrate our approach on a real network of camera and radio nodes.

Target tracking in urban environments using audio-video signal processing in heterogeneous wireless sensor networks

Heterogeneous sensor networks (HSNs) with multiple sensing modalities are gaining popularity in diverse fields. In this paper, we describe an approach for target tracking in urban environments utilizing a wireless HSN of mote class devices equipped with acoustic sensor boards and embedded PCs equipped with web cameras. Our system uses acoustic beamforming and motion detection for audio and video sensors, respectively. We also employ MCMCDA algorithm for data association and tracking. Experimental results from a deployment in an urban environment are used to demonstrate our approach.

Collaborative 3D Target Tracking in Distributed Smart Camera Networks for Wide-Area Surveillance

With the evolution and fusion of wireless sensor network and embedded camera technologies, distributed smart camera networks have emerged as a new class of systems for wide-area surveillance applications. Wireless networks, however, introduce a number of constraints to the system that need to be considered, notably the communication bandwidth constraints. Existing approaches for target tracking using a camera network typically utilize target handover mechanisms between cameras, or combine results from 2D trackers in each camera into 3D target estimation. Such approaches suffer from scale selection, target rotation, and occlusion, drawbacks typically associated with 2D tracking. In this paper, we present an approach for tracking multiple targets directly in 3D space using a network of smart cameras. The approach employs multi-view histograms to characterize targets in 3D space using color and texture as the visual features. The visual features from each camera along with the target models are used in a probabilistic tracker to estimate the target state. We introduce four variations of our base tracker that incur different computational and communication costs on each node and result in different tracking accuracy. We demonstrate the effectiveness of our proposed trackers by comparing their performance to a 3D tracker that fuses the results of independent 2D trackers. We also present performance analysis of the base tracker along Quality-of-Service (QoS) and Quality-of-Information (QoI) metrics, and study QoS vs. QoI trade-offs between the proposed tracker variations. Finally, we demonstrate our tracker in a real-life scenario using a camera network deployed in a building.

On the feasibility of determining angular separation in mobile wireless sensor networks

Mobile sensors require periodic position measurements for navigation around the sensing region. Such information is often obtained using GPS or onboard sensors such as optical encoders. However, GPS is not reliable in all environments, and odometry accrues error over time. Although several localization techniques exist for wireless sensor networks, they are typically time consuming, resource intensive, and/or require expensive hardware, all of which are undesirable for lightweight mobile nodes. We propose a technique for obtaining angle-of-arrival information that uses the wheel encoder data from the mobile sensor, and the RF Doppler-shift observed by stationary nodes. These sensor data are used to determine the angular separation between stationary beacons, which can be used for navigation. Our experimental results demonstrate that using this technique, a robot is able to determine angular separation between four pairs of sensors in a 40 × 40 meter sensing region with an average error of 0.28 radian.