417 research outputs found
Sensor Management for Tracking in Sensor Networks
We study the problem of tracking an object moving through a network of
wireless sensors. In order to conserve energy, the sensors may be put into a
sleep mode with a timer that determines their sleep duration. It is assumed
that an asleep sensor cannot be communicated with or woken up, and hence the
sleep duration needs to be determined at the time the sensor goes to sleep
based on all the information available to the sensor. Having sleeping sensors
in the network could result in degraded tracking performance, therefore, there
is a tradeoff between energy usage and tracking performance. We design sleeping
policies that attempt to optimize this tradeoff and characterize their
performance. As an extension to our previous work in this area [1], we consider
generalized models for object movement, object sensing, and tracking cost. For
discrete state spaces and continuous Gaussian observations, we derive a lower
bound on the optimal energy-tracking tradeoff. It is shown that in the low
tracking error regime, the generated policies approach the derived lower bound
Performance of a Distributed Stochastic Approximation Algorithm
In this paper, a distributed stochastic approximation algorithm is studied.
Applications of such algorithms include decentralized estimation, optimization,
control or computing. The algorithm consists in two steps: a local step, where
each node in a network updates a local estimate using a stochastic
approximation algorithm with decreasing step size, and a gossip step, where a
node computes a local weighted average between its estimates and those of its
neighbors. Convergence of the estimates toward a consensus is established under
weak assumptions. The approach relies on two main ingredients: the existence of
a Lyapunov function for the mean field in the agreement subspace, and a
contraction property of the random matrices of weights in the subspace
orthogonal to the agreement subspace. A second order analysis of the algorithm
is also performed under the form of a Central Limit Theorem. The
Polyak-averaged version of the algorithm is also considered.Comment: IEEE Transactions on Information Theory 201
- …