Sundeep Pattem

Distributed online localization in sensor networks using a moving target

We describe a novel method for node localization in a sensor network where there are a fraction of reference nodes with known locations. For application-specific sensor networks, we argue that it makes sense to treat localization through online distributed learning and integrate it with an application task such as target tracking. We propose distributed online algorithm in which sensor nodes use geometric constraints induced by both radio connectivity and sensing to decrease the uncertainty of their position. The sensing constraints, which are caused by a commonly sensed moving target, are usually tighter than connectivity based constraints and lead to a decrease in average localization error over time. Different sensing models, such as radial binary detection and distance-bound estimation, are considered. First, we demonstrate our approach by studying a simple scenario in which a moving beacon broadcasts its own coordinates to the nodes in its vicinity. We then generalize this to the case when instead of a beacon, there is a moving target with a-priori unknown coordinates. The algorithms presented are fully distributed and assume only local information exchange between neighboring nodes. Our results indicate that the proposed method can be used to significantly enhance the accuracy in position estimation, even when the fraction of reference nodes is small. We compare the efficiency of the distributed algorithms to the case when node positions are estimated using centralized (convex) programming. Finally, simulations using the TinyOS-Nido platform are used to study the performance in more realistic scenarios.

Energy-quality tradeoffs for target tracking in wireless sensor networks

We study the tradeoffs involved in the energy-efficient localization and tracking of mobile targets by a wireless sensor network. Our work focuses on building a framework for evaluating the fundamental performance of tracking strategies in which only a small portion of the network is activated at any point in time. We first compare naive network operation with random activation and selective activation. In these strategies the gains in energy-savings come at the expense of increased uncertainty in the location of the target, resulting in reduced quality of tracking. We show that selective activation with a good prediction algorithm is a dominating strategy that can yield orders-of-magnitude energy savings with negligible difference in tracking quality. We then consider duty-cycled activation and show that it offers a flexible and dynamic tradeoff between energy expenditure and tracking error when used in conjunction with selective activation.

Energy-efficient data representation and routing for wireless sensor networks based on a distributed wavelet compression algorithm

We address the problem of energy consumption reduction for wireless sensor networks, where each of the sensors has limited power and acquires data that should be transmitted to a central node. The final goal is to have a reconstructed version of the data measurements at the central node, with the sensors spending as little energy as possible, for a given data reconstruction accuracy. In our scenario, sensors in the network have a choice of different coding schemes to achieve varying levels of compression. The compression algorithms considered are based on the lifting factorization of the wavelet transform, and exploit the natural data flow in the network to aggregate data by computing partial wavelet coefficients that are refined as data flows towards the central node. The proposed algorithm operates by first selecting a routing strategy through the network. Then, for each route, an optimal combination of data representation algorithms i.e. assignment at each node, is selected. A simple heuristic is used to determine the data representation technique to use once path merges are taken into consideration. We demonstrate that by optimizing the coding algorithm selection the overall energy consumption can be significantly reduced when compared to the case when data is just quantized and forwarded to the central node. Moreover, the proposed algorithm provides a tool to compare different routing techniques and identify those that are most efficient overall, for given node locations. We evaluate the algorithm using both a second-order autoregressive (AR) model and empirical data from a real wireless sensor network deployment

Spatially-Localized Compressed Sensing and Routing in Multi-hop Sensor Networks

We propose energy-efficient compressed sensing for wireless sensor networks using spatially-localized sparse projections. To keep the transmission cost for each measurement low, we obtain measurements from clusters of adjacent sensors. With localized projection, we show that joint reconstruction provides significantly better reconstruction than independent reconstruction. We also propose a metric of energy overlap between clusters and basis functions that allows us to characterize the gains of joint reconstruction for different basis functions. Compared with state of the art compressed sensing techniques for sensor network, our simulation results demonstrate significant gains in reconstruction accuracy and transmission cost.

Using Local Geometry for Tunable Topology Control in Sensor Networks

Neighbor-Every-Theta (NET) graphs are such that each node has at least one neighbor in every theta angle sector of its communication range. We show that for thetas < pi, NET graphs are guaranteed to have an edge-connectivity of at least floor (2pi)/thetas, even with an irregular communication range. Our main contribution is to show how this family of graphs can achieve tunable topology control based on a single parameter thetas. Since the required condition is purely local and geometric, it allows for distributed topology control. For a static network scenario, a power control algorithm based on the NET condition is developed for obtaining k-connected topologies and shown to be significantly efficient compared to existing schemes. In controlled deployment of a mobile network, control over positions of nodes can be leveraged for constructing NET graphs with desired levels of network connectivity and sensing coverage. To establish this, we develop a potential fields based distributed controller and present simulation results for a large network of robots. Lastly, we extend NET graphs to 3D and provide an efficient algorithm to check for the NET condition at each node. This algorithm can be used for implementing generic topology control algorithms in 3D.

Empirical evaluation of querying mechanisms for unstructured wireless sensor networks

In the last few years, several studies have analyzed the performance of flooding and random walks as querying mechanisms for unstructured wireless sensor networks. However, most of the work is theoretical in nature and while providing insights into the asymptotic behavior of these querying mechanisms, does not account for the non-idealities faced by the network in real deployments. In this paper, we propose a 3-way handshake protocol as a reliable implementation of a random walk and compare its performance with flooding in real environments. The metrics considered are delay, reliability and transmission cost. Our initial results suggest that flooding is better suited for low-interference environments, while random walks might be a better option in networks with high interference. We also present possible research directions to improve the performance oflooding and random walks.

Energy-efficient graph-based wavelets for distributed coding in Wireless Sensor Networks

This work presents a class of unidirectional lifting-based wavelet transforms for an arbitrary communication graph in a wireless sensor network. These transforms are unidirectional in the sense that they are computed as data is forwarded towards the sink on a routing tree. We derive a set of conditions under which a lifting transform is unidirectional, then find the full set of those transforms. Among this set, we construct a unidirectional transform that allows nodes to transform their own data using data forwarded to them from their descendants in the tree and data broadcasted to them from their neighbors not in the tree. This provides a higher quality data representation than existing methods for a fixed communication cost.