Decentralized sequential change detection using physical layer fusion

The problem of decentralized sequential detection with conditionally independent observations is studied. The sensors form a star topology with a central node called fusion center as the hub. The sensors make noisy observations of a parameter that changes from an initial state to a final state at a random time where the random change time has a geometric distribution. The sensors amplify and forward the observations over a wireless Gaussian multiple access channel and operate under either a power constraint or an energy constraint. The optimal transmission strategy at each stage is shown to be the one that maximizes a certain Ali-Silvey distance between the distributions for the hypotheses before and after the change. Simulations demonstrate that the proposed analog technique has lower detection delays when compared with existing schemes. Simulations further demonstrate that the energy-constrained formulation enables better use of the total available energy than the power-constrained formulation in the change detection problem.Comment: 10 pages, two-column, 10 figures, revised based on feedback from reviewers, accepted for publication in IEEE Trans. on Wireless Communication

An adiabatic approach to analysis of time inhomogeneous Markov chains : a queueing policy application

In this thesis, convergence of time inhomogeneous Markov chains is studied using an adiabatic approach. The adiabatic framework considers slowly changing systems and the adiabatic time quantifies the time required for the change such that the final state of the system is close to some equilibrium state. This approach is used in Markov chains to measure the time to converge to a stationary distribution. Continuous time reversible Markov chains on a finite state space with generators changing at fixed time intervals are studied. This characterization is applied to a Markovian queueing model with unknown arrival rate. The time inhomogeneous Markov chain is induced by a queueing policy dependent on uncertainties in arrival rate estimation. It is shown that the above convergence happens with high probability after a sufficiently large time. The above evolution is studied via simulations as well and compared to the bounds suggested by the analysis. These results give the sufficient amount of time one must wait for the queue to reach a stationary, stable distribution under our queueing policy

Analysis of Adaptive Queueing Policies via Adiabatic Approach

Abstract-We introduce an adiabatic framework for studying adaptive queuing policies. The adiabatic framework provides analytical tools for stability analysis of slowly changing systems that can be modeled as time-inhomogeneous reversible Markov chains. In particular, we consider queuing policies whose service rate is adaptively changed based on the estimated arrival rates that tend to vary with time. As a result, the packet distribution in the queue over time behaves like a time-inhomogeneous reversible Markov chain. Our results provide an upper bound on the time for an initial distribution of packets in the queue to converge to a stationary distribution corresponding to some pre-specified queueing policy. These results are useful for designing adaptive queueing policies when arrival rates are unknown, and may or may not change with time. Furthermore, our analysis is readily extended for any system that can be modeled as timeinhomogeneous reversible Markov chain. We provide simulations that confirms our theoretical results

Decentralized Sequential Change Detection Using Physical Layer Fusion

Abstract — We study the problem of decentralized sequential change detection with conditionally independent observations. The sensors form a star topology with a central node called fusion center as the hub. The sensors transmit a simple function of their observations in an analog fashion over a wireless Gaussian multiple access channel and operate under either a power constraint or an energy constraint. Simulations demonstrate that the proposed techniques have lower detection delays when compared with existing schemes. Moreover we demonstrate that the energy-constrained formulation enables better use of the total available energy than a power-constrained formulation. I

An analog MVUE for a wireless sensor network

An analog minimum-variance unbiased estimator(MVUE) over an asymmetric wireless sensor network is studied.Minimisation of variance is cast into a constrained non-convex optimisation problem. An explicit algorithm that solves the problem is provided. The solution is obtained by decomposing the original problem into a finite number of convex optimisation problems with explicit solutions. These solutions are then juxtaposed together by exploiting further structure in the objective function

Decentralized sequential change detection using physical layer fusion

The problem of decentralized sequential detection with conditionally independent observations is studied. The sensors form a star topology with a central node called fusion center as the hub. The sensors transmit a simple function of their observations in an analog fashion over a wireless Gaussian multiple access channel and operate under either a power constraint or an energy constraint. The optimal transmission strategy at each stage is shown to be the one that maximizes a certain Ali-Silvey distance between the distributions for the hypotheses before and after the change. Simulations demonstrate that the proposed analog technique has lower detection delays when compared with existing schemes. Simulations further demonstrate that the energy-constrained formulation enables better use of the total available energy than the power-constrained formulation in the change detection problem