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IEEE Transactions on Signal Processing. 01/2011; 59:4331-4340.
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Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011, May 22-27, 2011, Prague Congress Center, Prague, Czech Republic; 01/2011
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Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011, May 22-27, 2011, Prague Congress Center, Prague, Czech Republic; 01/2011
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ABSTRACT: The problem of minimizing the sum of a number of component functions is of great importance in the real world. In this paper, a new incremental optimization algorithm, named normalized incremental subgradient (NIS) algorithm, is proposed for a class of such problems where the component functions have common local minima. The NIS algorithm is performed incrementally just as the general incremental subgradient (IS) algorithm and thus can be implemented in a distributed way. In the NIS algorithm, the update of each subiteration is based on a search direction obtained by individually normalizing each component of subgradients of component functions, resulting in much better convergence performance as compared to the IS algorithm and other traditional optimization methods (e.g., Gauss-Newton method). The convergence of the NIS algorithm with both diminishing stepsizes and constant stepsizes is proved and analyzed theoretically. Two important applications are presented. One is to solve a class of convex feasibility problems in a distributed way and the other is distributed maximum likelihood estimation. Numerical examples, arising from two important topics in the area of wireless sensor networks-source localization and node localization-demonstrate the effectiveness and efficiency of the NIS algorithm.
IEEE Transactions on Signal Processing 11/2009; · 2.63 Impact Factor
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ABSTRACT: Knowing the positions of nodes is essential to many wireless sensor network applications. In contrast to range-based localization methods, range-free methods are more appealing since they do not need additional expensive hardware for ranging. A range-free localization problem is a generalization of unit disk graph coordinates realization problem and in essence a feasibility problem with quadratic inequalities, which is NP- hard. In this paper, we formulate the range-free localization problem as a nondifferentiable optimization problem solved by a polynomial-time algorithm, normalized incremental subgradient (NIS) algorithm. Extensive simulations have been conducted. The simulation results show that the NIS-based localization algorithm significantly outperforms the MDS-MAP method and the SDP method, whether the network is regular or not.
Global Telecommunications Conference, 2008. IEEE GLOBECOM 2008. IEEE; 01/2009
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IEEE Transactions on Signal Processing. 01/2009; 57:3759-3774.
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Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009, 19-24 April 2009, Taipei, Taiwan; 01/2009
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ABSTRACT: Location awareness is of great importance for many wireless sensor network applications. However, it is too expensive to equip all nodes with GPS receivers or configure the location for each node manually. Consequently, many localization methods have been proposed. While most of them are range-based, we propose a range-free localization method, which is based on semidefinite programming. The method can be not only used for relative localization but also for absolute localization. Extensive simulations have been conducted. The results show that 1) the method performs better with fewer anchors, especially for relatively uniform networks of high connectivity level, and 2) the method is more robust to anchor placement, as compared to the popular MDS-MAP method which is based on multidimensional scaling(MDS) technique. Moreover, the method can intrinsically keep the proximity of neighbor nodes, whether the network is regular or not.
Communications, 2008. ICC '08. IEEE International Conference on; 06/2008
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ABSTRACT: When addressing the energy-based source localization problem using wireless sensor networks, distributed localization method is necessary to reduce the energy and bandwidth consumption. In this paper, a novel distributed source localization method called projection onto the nearest local minimum (PONLM) is proposed, which can be carried out at each of active nodes with quite lightweight computation, in contrast to most existing centralized method. Simulation results show our method can yield much better performance than the previous methods.
Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on; 05/2008 · 4.63 Impact Factor
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ABSTRACT: A new incremental optimization algorithm called normalized incremental subgradient (NIS) algorithm is proposed in this letter, which can be used for distributed maximum likelihood estimation (MLE). Its convergence with a diminishing stepsize has been proved and analyzed theoretically. We then apply the NIS algorithm to the energy-based sensor network source localization problem where the decay factor of the energy decay model is unknown. Simulation results show it can achieve very high estimation performance, which is only somewhat lower than that of the centralized localization method based on global optimization techniques, but with hundreds of times lower computational complexity than the centralized method.
IEEE Signal Processing Letters 02/2008; · 1.39 Impact Factor
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Proceedings of the Global Communications Conference, 2008. GLOBECOM 2008, New Orleans, LA, USA, 30 November - 4 December 2008; 01/2008
