Budhaditya Deb

Wireless Propagation and Coexistence of Medical Body Sensor Networks for Ambulatory Patient Monitoring

In this paper we present the technical requirements and system issues for wireless Medical Body Sensor Networks (BSNs). Design guidelines are driven by the need to improve ambulatory patient monitoring and care while reducing logistic constraints for patients as well as healthcare professionals. We present our study on three key components of Medical BSN: On-body wireless link (to characterize the RF channel for body worn wireless devices), Coupling between bodies (to characterize the RF interaction between bodies) and Coexistence of Medical BSNs in the RF spectrum. Results and conclusions are presented through simulation and measurement studies. We also discuss our FCC petition for spectrum allocation.

Iterative Estimation of Location and Trajectory of Radioactive Sources With a Networked System of Detectors

We consider the problem of estimating the parameters (location and intensity) of multiple radioactive sources using a system of radiation detectors. The problem formulated as maximum likelihood estimation (MLE) requires the optimization of a high-dimensional objective function and presents significant computational challenges. We propose Fishers scoring iterations approach (a special case of Newtons iterative method) for finding the MLE. While being computationally scalable, an inherent problem with this approach is finding good initial estimates specifically when multiple sources are present. We propose an expectation maximization (EM) based approach which finds the approximate distribution of the source intensity in space. Peaks in this distribution are used as initial estimates of the parameters to bootstrap the iterative MLE procedure. Next, we consider the problem of estimating the trajectory of a moving and maneuvering source. Since a priori motion model cannot be assumed, the trajectory is approximated as a set of points which again presents a high dimensional estimation problem. The trajectory estimation is posed as a constrained weighted least squares problem which is iteratively solved using the Interior Point Method (IPM). Simulation results are presented which illustrate the behavior and performance of our proposed approaches.

Radioactive Source Estimation Using a System of Directional and Non-Directional Detectors

We derive source parameter estimation algorithms based on maximum likelihood estimation (MLE) for a system of non-directional (scintillator) and directional (CZT based Compton) radiation detectors. For multiple non-directional detectors, the joint likelihood of registered counts is maximized to estimate the source parameters. For directional detectors, the well-known List Mode Maximum Likelihood Expectation Maximization (MLEM) algorithm is extended to fuse information from multiple detectors and locate the source in Cartesian coordinates. We then develop multi-sensor fusion algorithms for a system of non-directional and directional detectors by combining MLE and MLEM algorithms. Results are presented which illustrate the behavior and performance of our proposed approaches.

Distributed optimization of Contention Windows in 802.11e MAC to provide QoS differentiation and maximize channel utilization

We propose a distributed algorithm for optimizing the Contention Windows in IEEE 802.11e based WLANs with the dual intention of providing fine-grained QoS and maximizing the channel utilization. The underlying concept behind the algorithm is modeling the network state as a function of MAC parameters and solving this analytical model constrained by the QoS requirements of multiple nodes. The main contribution of this paper is the completely distributed realization of this concept. The problem appears as a system of non-linear equations which is solved by an iterative gradient-based method. Distributed solution is achieved by first decoupling the equations and second by implicit message passing through local measurements. This allows local computation of partial differentials and residuals of the iterative process. Local measurements serve as inputs for the next iterative step and as natural feedback mechanism to handle dynamic channel conditions. Convergence of iterations is ensured through progressive target setting of QoS requirements. Extensive simulation results and a proof of concept with a test bed show that the algorithm achieves fine-grained QoS differentiation while minimizing delays, collisions and packet losses. As a result, when the network scales, the algorithm is shown to maximize the channel utilization and maintain a near optimal total throughput of the system. Finally, sub-minute convergence time makes the algorithm suitable for real-time flows.

Towards systems level prognostics in the Cloud

Many application systems are transforming from device centric architectures to cloud based systems that leverage shared compute resources to reduce cost and maximize reach. These systems require new paradigms to assure availability and quality of service. In this paper, we discuss the challenges in assuring Availability and Quality of Service in a Cloud Based Application System. We propose machine learning techniques for monitoring systems logs to assess the health of the system. A web services data set is employed to show that variety of services can be clustered to different service classes using a k-means clustering scheme. Reliability, Availability, and Serviceability (RAS) logs and Job logs dataset from high performance computing system is employed to show that impending fatal errors in the system can be predicted from the logs using an SVM classifier. These approaches illustrate the feasibility of methods to monitor the systems health and performance of compute resources and hence can be used to manage these systems for high availability and quality of service for critical tasks such as health care monitoring in the cloud.

Radioactive source estimation using a system of directional and non-directional detectors

We derive Maximum Likelihood based source estimation algorithms for a system of Non-Directional (CsI based scintillation detectors) and Directional (CZT based Compton detectors) radiation detectors. For multiple non-directional detectors, source is estimated by grid search on the joint log-likelihood of registered counts to find the MLE. For directional detectors, the well-known List Mode Maximum Likelihood Expectation Maximization Algorithm (MLEM) is extended to fuse information from multiple detectors and estimate the source in cartesian coordinates. Here, the angular event likelihoods from Compton scattering are projected into a common Cartesian grid followed by List Mode MLEM computations. We also derive multi-sensor fusion algorithms for a mix of both non-directional and directional detectors using combinations of MLE and MLEM methods. Results are presented which illustrate the behavior and performance of our proposed approaches.

Quality of Service In Wireless Sensor Networks through the Connectionless Scheduling Protocol

The connectionless scheduling protocol is a cross layer media access protocol that uses a pseudorandom message scheduling approach to achieve near optimal channel utilization with near optimal energy utilization, providing benefit for resource constrained multi-hop applications such as wing stress monitoring. In this paper we describe how quality of service differentiation can be easily applied to this protocol by providing simple message length or schedule availability constraints. Theoretical analysis is conducted through a derived probabilistic model.