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This paper addresses the energy-based localization problem in wireless sensor networks. The maximum likelihood (ML) location estimation problem is a difficult optimization problem due to the non-convexity of the objective function, and finding an exact solution is difficult. In this work, an approximate solution to the ML localization is presented, by relaxing the minimization problem into semidefinite...
Consider a wireless sensor network where each node has K radios r1,r2, ⋯ , rK such that the one hop reachability distance (resp. energy expended) of (resp. by) radio ri is greater than that of rj 1 ≤ j <; i ≤ K. Given such a network, the problem of energy efficient radio activation is to minimize the total energy spent by the active radios across all nodes in order to maintain a connected network...
In a sensor-aided cognitive radio network, collaborating battery-powered sensors are deployed to aid the network in cooperative spectrum sensing. These sensors consume energy for spectrum sensing and therefore deplete their life-time, thus we study the key issue in minimizing the sensing energy consumed by such group of collaborating sensors. The IEEE P802.22 standard specifies spectrum sensing accuracy...
In this paper, following the Compressed Sensing (CS) paradigm, we study the problem of recovering sparse or compressible signals from uniformly quantized measurements. We present a new class of convex optimization programs, or decoders, coined Basis Pursuit DeQuantizer of moment p (BPDQp), that model the quantization distortion more faithfully than the commonly used Basis Pursuit DeNoise (BPDN) program...
A space sense based random access (SSRA) scheme is introduced for the uplink of time division duplex (TDD) WLANs with multiple receive antennas. In SSRA scheme, each user performs space sense process to predict the post-detection signal-to-interference-and-noise-ratio (SINR) of data packets. If the predicted SINR is larger than a given threshold, the user considers the channel idle and is permitted...
Compressive sensing (CS) is an alternative to Shannon/Nyquist sampling for acquiring sparse or compressible signals. Instead of taking N periodic samples, we measure M ?? N inner products with random vectors and then recover the signal via a sparsity-seeking optimization or greedy algorithm. A new framework for CS based on unions of subspaces can improve signal recovery by including dependencies between...
We apply the theory of partially observable Markov decision processes (POMDPs) to the design of guidance algorithms for controlling the motion of unmanned aerial vehicles (UAVs) with on-board sensors for tracking multiple ground targets. While POMDPs are intractable to optimize exactly, principled approximation methods can be devised based on Bellman's principle.We introduce a new approximation method...
It has been known for a while that lscr1-norm relaxation can in certain cases solve an under-determined system of linear equations. Recently, proved (in a large dimensional and statistical context) that if the number of equations (measurements in the compressed sensing terminology) in the system is proportional to the length of the unknown vector then there is a sparsity (number of non-zero elements...
We consider the placement of sensors to detect propagative sources where the sensing area of each sensor is anisotropic and arbitrarily-shaped due to the terrain and meteorological conditions. The propagation and detection times are non-negligible due to the propagation of source effects through space at a slow speed. We formulate the problem as placing the minimum number of sensors to ensure a detection...
Compressive sensing (CS) is an alternative to Shannon/Nyquist sampling for acquisition of sparse or compressible signals; instead of taking periodic samples, we measure inner products with M < N random vectors and then recover the signal via a sparsity-seeking optimization or greedy algorithm. Initial research has shown that by leveraging stronger signal models than standard sparsity, the number...
In this paper, we study the sensor subset selection problem with the determinant of the (Bayesian) Fisher information matrix (FIM) as the metric of estimation accuracy. As a combinatorial optimization problem, we analyze two well-known upper bounds for this problem: (i) the Lagrangian bound and (ii) the continuous bound. We show that the determinant of the FIM is a supermodular function from which...
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