The smart city is often presented as the combination of sensor networks (IoT) with big data processing, but this largely ignores the issues of bridging these two universes, as well as the need to integrate other envir...
详细信息
ISBN:
(纸本)9781538634585
The smart city is often presented as the combination of sensor networks (IoT) with big data processing, but this largely ignores the issues of bridging these two universes, as well as the need to integrate other environments, which is typically swept aside as back end concerns. We present in this paper the challenges of integrating networks of sensor and actuator devices into a framework suitable for smart city applications, which includes various forms of efficient sharing and processing of information. We propose architectural elements for distributed implementations of gateways in a data flow architecture. We finally discuss how implementing this framework can benefit from contemporary hybrid networking/computing technology, such as SDN/NFV.
Fast and accurate numerical algorithms for Eigen-Value Decomposition (EVD) are of great importance in solving many engineering problems. In this paper, we aim to develop algorithms for finding the leading eigen pairs ...
详细信息
ISBN:
(纸本)9781467385763
Fast and accurate numerical algorithms for Eigen-Value Decomposition (EVD) are of great importance in solving many engineering problems. In this paper, we aim to develop algorithms for finding the leading eigen pairs with improved convergence speed compared to existing methods. We introduce several accelerated methods based on the power iterations where the main modification is to introduce a memory term in the iteration, similar to Nesterov's acceleration. Results on convergence and the speed of convergence are presented on a proposed method termed Memory-based Accelerated Power with Scaling (MAPS). Nesterov's acceleration for the power iteration is also presented. We discuss possible application of the proposed algorithm to (distributed) clustering problems based on spectral clustering. Simulation results show that the proposed algorithms enjoy faster convergence rates than the power method for matrix eigen-decomposition problems.
In this paper, a distributed implementation of temperature data denoising operator using the steepest descent method is presented. First, the smoothness-based denoising method using normalized Laplacian matrix is desc...
详细信息
ISBN:
(纸本)9781665449588
In this paper, a distributed implementation of temperature data denoising operator using the steepest descent method is presented. First, the smoothness-based denoising method using normalized Laplacian matrix is described and the conventional Neumann series implementation is reviewed briefly. Then, the steepest descent method is applied to develop a distributed implementation of denoising operator and its convergence condition is studied. It can be also shown that the Neumann series method is a special case of steepest descent method. Next, the momentum term is added to the update term of the steepest descent method for speeding up the convergence of implementation method. Finally, the real temperature data collected from the sensor network at Taiwan is used to demonstrate the effectiveness of the proposed denoising method and some discussions are made.
Due to good elastic scalability, multi-head network is favored in incremental learning (IL). During IL process, the model size of multi-head network continually grows with the increasing number of branches, which make...
详细信息
Location-aware networks are of great importance for both civil lives and military applications. Methods based on line-of-sight (LOS) measurements suffer sever performance loss in harsh environments such as indoor scen...
详细信息
Location-aware networks are of great importance for both civil lives and military applications. Methods based on line-of-sight (LOS) measurements suffer sever performance loss in harsh environments such as indoor scenarios, where sensors can receive both LOS and non-line-of-sight (NLOS) measurements. In this paper, we propose a data association (DA) process based on the expectation maximization (EM) algorithm, which enables us to exploit multipath components (MPCs). By setting the mapping relationship between the measurements and scatters as a latent variable, coefficients of the Gaussian mixture model are estimated. Moreover, considering the misalignment of sensor position, we propose a space-alternating generalized expectation maximization (SAGE)-based algorithms to jointly update the target localization and sensor position information. A two dimensional (2-D) circularly symmetric Gaussian distribution is employed to approximate the probability density function of the sensor's position uncertainty via the minimization of the Kullback-Leibler divergence (KLD), which enables us to calculate the expectation step with low computational complexity. Moreover, a distributed implementation is derived based on the average consensus method to improve the scalability of the proposed algorithm. Simulation results demonstrate that the proposed centralized and distributed algorithms can perform close to the Monte Carlo-based method with much lower communication overhead and computational complexity.
This invited paper outlines some recent results on the max-min SIR balancing problem in wireless networks in which power control and beamforming are the only mechanisms for resource allocation and interference managem...
详细信息
ISBN:
(纸本)9781936968091
This invited paper outlines some recent results on the max-min SIR balancing problem in wireless networks in which power control and beamforming are the only mechanisms for resource allocation and interference management. In addition, we describe several potential extensions and improvements to existing algorithmic solutions, as well as prove the convergence of a distributed algorithm for joint power control and receive beamforming to a global optimum of the max-min SIR balancing problem. Finally we briefly discuss a possibility of how to incorporate the optimization of transmit beamformers.
We consider the joint power and admission control problem for a wireless network consisting of multiple interfering *** goal is to support a maximum number of links at their specified signal to interference plus noise...
详细信息
We consider the joint power and admission control problem for a wireless network consisting of multiple interfering *** goal is to support a maximum number of links at their specified signal to interference plus noise ratio(SINR) targets while using a minimum total transmission *** presented a centralized new linear programming deflation(NLPD) algorithm for the joint power and admission control problem,which was shown to outperform the *** this work,we develop a distributed implementation of the NLPD *** propose to use the projected alternate Barzilai- Borwein(PABB) algorithm with the continuation technique to carry out power *** power control strategy enables each transmitter to update its power efficiently and locally with limited information *** simulations are reported, illustrating the effectiveness of the developed approach.
暂无评论