Bibliographic Review on Distributed Kalman Filtering

In recent years, a compelling need has arisen to understand the effects of distributed information structures on estimation and filtering. In this paper, a bibliographical review on distributed Kalman filtering (DKF) is provided.\ud The paper contains a classification of different approaches and methods involved to DKF. The applications of DKF are also discussed and explained separately. A comparison of different approaches is briefly carried out. Focuses on the contemporary research are also addressed with emphasis on the practical applications of the techniques. An exhaustive list of publications, linked directly or indirectly to DKF in the open literature, is compiled to provide an overall picture of different developing aspects of this area

No-Sense: Sense with Dormant Sensors

Wireless sensor networks (WSNs) have enabled continuous monitoring of an area of interest (body, room, region, etc.) while eliminating expensive wired infrastructure. Typically in such applications, wireless sensor nodes report the sensed values to a sink node, where the information is required for the end-user. WSNs also provide the flexibility to the end-user for choosing several parameters for the monitoring application. For example, placement of sensors, frequency of sensing and transmission of those sensed data. Over the years, the advancement in embedded technology has led to increased processing power and memory capacity of these battery powered devices. However, batteries can only supply limited energy, thus limiting the lifetime of the network. In order to prolong the lifetime of the deployment, various efforts have been made to improve the battery technologies and also reduce the energy consumption of the sensor node at various layers in the networking stack. Of all the operations in the network stack, wireless data transmission and reception have found to consume most of the energy. Hence many proposals found in the literature target reducing them through intelligent schemes like power control, reducing retransmissions, etc. In this article we propose a new framework called Virtual Sensing Framework (VSF), which aims to sufficiently satisfy application requirements while conserving energy at the sensor nodes.Comment: Accepted for publication in IEEE Twentieth National Conference on Communications (NCC-2014

Information reuse in dynamic spectrum access

Dynamic spectrum access (DSA), where the permission to use slices of radio spectrum is dynamically shifted (in time an in different geographical areas) across various communications services and applications, has been an area of interest from technical and public policy perspectives over the last decade. The underlying belief is that this will increase spectrum utilization, especially since many spectrum bands are relatively unused, ultimately leading to the creation of new and innovative services that exploit the increase in spectrum availability. Determining whether a slice of spectrum, allocated or licensed to a primary user, is available for use by a secondary user at a certain time and in a certain geographic area is a challenging task. This requires 'context information' which is critical to the operation of DSA. Such context information can be obtained in several ways, with different costs, and different quality/usefulness of the information. In this paper, we describe the challenges in obtaining this context information, the potential for the integration of various sources of context information, and the potential for reuse of such information for related and unrelated purposes such as localization and enforcement of spectrum sharing. Since some of the infrastructure for obtaining finegrained context information is likely to be expensive, the reuse of this infrastructure/information and integration of information from less expensive sources are likely to be essential for the economical and technological viability of DSA. © 2013 IEEE

Cram\'er-Rao Bounds for Polynomial Signal Estimation using Sensors with AR(1) Drift

We seek to characterize the estimation performance of a sensor network where the individual sensors exhibit the phenomenon of drift, i.e., a gradual change of the bias. Though estimation in the presence of random errors has been extensively studied in the literature, the loss of estimation performance due to systematic errors like drift have rarely been looked into. In this paper, we derive closed-form Fisher Information matrix and subsequently Cram\'er-Rao bounds (upto reasonable approximation) for the estimation accuracy of drift-corrupted signals. We assume a polynomial time-series as the representative signal and an autoregressive process model for the drift. When the Markov parameter for drift \rho<1, we show that the first-order effect of drift is asymptotically equivalent to scaling the measurement noise by an appropriate factor. For \rho=1, i.e., when the drift is non-stationary, we show that the constant part of a signal can only be estimated inconsistently (non-zero asymptotic variance). Practical usage of the results are demonstrated through the analysis of 1) networks with multiple sensors and 2) bandwidth limited networks communicating only quantized observations.Comment: 14 pages, 6 figures, This paper will appear in the Oct/Nov 2012 issue of IEEE Transactions on Signal Processin

On Mitigation of Side-Channel Attacks in 3D ICs: Decorrelating Thermal Patterns from Power and Activity

Various side-channel attacks (SCAs) on ICs have been successfully demonstrated and also mitigated to some degree. In the context of 3D ICs, however, prior art has mainly focused on efficient implementations of classical SCA countermeasures. That is, SCAs tailored for up-and-coming 3D ICs have been overlooked so far. In this paper, we conduct such a novel study and focus on one of the most accessible and critical side channels: thermal leakage of activity and power patterns. We address the thermal leakage in 3D ICs early on during floorplanning, along with tailored extensions for power and thermal management. Our key idea is to carefully exploit the specifics of material and structural properties in 3D ICs, thereby decorrelating the thermal behaviour from underlying power and activity patterns. Most importantly, we discuss powerful SCAs and demonstrate how our open-source tool helps to mitigate them.Comment: Published in Proc. Design Automation Conference, 201

Photon counting compressive depth mapping

We demonstrate a compressed sensing, photon counting lidar system based on the single-pixel camera. Our technique recovers both depth and intensity maps from a single under-sampled set of incoherent, linear projections of a scene of interest at ultra-low light levels around 0.5 picowatts. Only two-dimensional reconstructions are required to image a three-dimensional scene. We demonstrate intensity imaging and depth mapping at 256 x 256 pixel transverse resolution with acquisition times as short as 3 seconds. We also show novelty filtering, reconstructing only the difference between two instances of a scene. Finally, we acquire 32 x 32 pixel real-time video for three-dimensional object tracking at 14 frames-per-second.Comment: 16 pages, 8 figure