Primal Recovery from Consensus-Based Dual Decomposition for Distributed Convex Optimization

Dual decomposition has been successfully employed in a variety of distributed convex optimization problems solved by a network of computing and communicating nodes. Often, when the cost function is separable but the constraints are coupled, the dual decomposition scheme involves local parallel subgradient calculations and a global subgradient update performed by a master node. In this paper, we propose a consensus-based dual decomposition to remove the need for such a master node and still enable the computing nodes to generate an approximate dual solution for the underlying convex optimization problem. In addition, we provide a primal recovery mechanism to allow the nodes to have access to approximate near-optimal primal solutions. Our scheme is based on a constant stepsize choice and the dual and primal objective convergence are achieved up to a bounded error floor dependent on the stepsize and on the number of consensus steps among the nodes

Sparsity-aware TDOA localization of multiple sources

The problem of source localization from time-difference-of-arrival (TDOA) measurements is in general a non-convex and complex problem due to its hyperbolic nature. This problem becomes even more complicated for the case of multi-source localization where TDOAs should be assigned to their respective sources. We simplify this problem to an ℓ1-norm minimization by introducing a novel TDOA fingerprinting model for a multi-source scenario. Moreover, we propose an innovative trick to enhance the performance of our proposed fingerprinting model in terms of the number of identifiable sources. An interesting by-product of this enhanced model is that under some conditions we can convert the given underdetermined problem to an overdetermined one and efficiently solve it using clas-sical least squares (LS) approaches. Our simulation results illustrate a good performance for the introduced TDOA fingerprinting. Index Terms — Multi-source localization, TDOA fingerprint-ing, sparse reconstruction. 1

Lifetime Optimization via Network Sectoring in Cooperative Wireless Sensor Networks

Employing cooperative communication in multihop wireless sensor networks provides the network with sig-nificant energy efficiency. However, the lifetime of such a network is directly dependant upon the lifetime of each of its individual sections (or clusters). Ignoring the fact that those sections close to sink have to forward more data (their own data plus the data received from the previous sections) and hence die sooner with con-sidering equal section sizes, leads to a sub-optimal lifetime. In this paper, we optimize the section sizes of a multihop cooperative WSN so that it maximizes the network lifetime. Simulation results demonstrate a sig-nificant lifetime enhancement for the proposed optimal sectoring

Cooperative mobile network localization via subspace tracking

Two novel cooperative localization algorithms for mobile wireless networks are proposed. To continuously localize the mobile net-work, given the pairwise distance measurements between different wireless sensor nodes, we propose to use subspace tracking to track the variations in signal eigenvectors and corresponding eigenvalues of the double-centered distance matrix. We compare the compu-tational complexity of the new algorithms with a recently devel-oped algorithm exploiting the extended Kalman filter (EKF) and show that our proposed algorithms are computationally efficient, and hence, appropriate for practical implementations compared to the EKF. Simulation results further illustrate that the proposed al-gorithms are more accurate when the distance errors are small (low noise scenarios) in comparison with the EKF, while being more ro-bust to the sampling period in high noise scenarios. Index Terms — Wireless sensor networks, cooperative mobile localization, multidimensional scaling, subspace tracking. 1