Distributed Algorithm for Real Time Cooperative Synthesis of Wireless Cell Coverage Patterns

2008 ◽  
Vol 12 (9) ◽  
pp. 702-704 ◽  
Author(s):  
Peng Jiang ◽  
J. Bigham ◽  
M. Anas Khan
Keyword(s):  
Real Time ◽  
Distributed Algorithm ◽  
Cell Coverage

The Design and Implementation of Parallel Distributed Algorithm FIR Filter Based on FPGA

2012 ◽  
Vol 571 ◽  
pp. 534-537
Author(s):  
Bao Feng Zhang ◽  
De Hu Man ◽  
Jun Chao Zhu
Keyword(s):  
Signal Processing ◽  
Real Time ◽  
Field Programmable Gate Array ◽  
Distributed Algorithm ◽  
Design Method ◽  
Fir Filter ◽  
Experimental Results ◽  
Linear Phase ◽  
Design And Implementation ◽  
Field Programmable

The article proposed a new method for implementing linear phase FIR filter based on FPGA. For the key to implementing the FIR filter on FPGA—multiply-add operation, a parallel distributed algorithm was presented, which is based on LUT. The designed file was described with VHDL and realized on Altera’s field programmable gate array (FPGA), giving the design method. The experimental results indicated that the system can run stably at 120MHz or more, which can meet the requirements of signal processing for real-time.

scholarly journals Optimal Computing Resource Management Based on Utility Maximization in Mobile Crowdsourcing

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Haoyu Meng ◽  
Ying Zhu ◽  
Ruilong Deng
Keyword(s):  
Resource Management ◽  
Real Time ◽  
Distributed Algorithm ◽  
Pricing Strategy ◽  
Mobile Crowdsourcing ◽  
Computing Resource ◽  
Time Interaction ◽  
Best Response ◽  
Real Time Pricing ◽  
Service Paradigm

Mobile crowdsourcing, as an emerging service paradigm, enables the computing resource requestor (CRR) to outsource computation tasks to each computing resource provider (CRP). Considering the importance of pricing as an essential incentive to coordinate the real-time interaction among the CRR and CRPs, in this paper, we propose an optimal real-time pricing strategy for computing resource management in mobile crowdsourcing. Firstly, we analytically model the CRR and CRPs behaviors in form of carefully selected utility and cost functions, based on concepts from microeconomics. Secondly, we propose a distributed algorithm through the exchange of control messages, which contain the information of computing resource demand/supply and real-time prices. We show that there exist real-time prices that can align individual optimality with systematic optimality. Finally, we also take account of the interaction among CRPs and formulate the computing resource management as a game with Nash equilibrium achievable via best response. Simulation results demonstrate that the proposed distributed algorithm can potentially benefit both the CRR and CRPs. The coordinator in mobile crowdsourcing can thus use the optimal real-time pricing strategy to manage computing resources towards the benefit of the overall system.

scholarly journals Distributed Algorithm for Real-Time Energy Optimal Routing Based on Dual Decomposition of Linear Programming

2011 ◽  
Vol 8 (1) ◽  
pp. 346163
Author(s):  
Jiří Trdlička ◽  
Zdeněk Hanzálek
Keyword(s):  
Linear Programming ◽  
Real Time ◽  
Network Flow ◽  
Distributed Algorithm ◽  
Cost Functions ◽  
Optimal Routing ◽  
Communication Delays ◽  
Dual Decomposition ◽  
Routing Problem ◽  
Linear Cost

This work proposes a novel in-network distributed algorithm for real-time energy optimal routing in ad hoc and sensor networks for systems with linear cost functions and constant communication delays. The routing problem is described as a minimum-cost multicommodity network flow problem by linear programming and modified by network replication to a real-time aware form. Based on the convex programming theory we use dual decomposition to derive the distributed algorithm. Thanks to the exact mathematical derivation, the algorithm computes the energy optimal real-time routing. It uses only peer-to-peer communication between neighboring nodes and does not need any central node or knowledge about the whole network structure. Each node knows only the produced and collected data flow and the costs of its outgoing communication links. According to our knowledge, this work is the first, which solves the real-time routing problem with linear cost functions and constant communication delays, using the dual decomposition.

scholarly journals Distributed Real-Time Pricing Method Incorporating Load Uncertainty Based on Nonsmooth Equations for Smart Grid

2019 ◽  
Vol 2019 ◽  
pp. 1-18
Author(s):  
Hongjie Wang ◽  
Yan Gao
Keyword(s):  
Smart Grid ◽  
Real Time ◽  
Distributed Algorithm ◽  
Energy Supply ◽  
Supply And Demand ◽  
Nonsmooth Equations ◽  
Lagrangian Multipliers ◽  
Load Uncertainty ◽  
The Real ◽  
Real Time Pricing

The real-time pricing mechanism of smart grid based on demand response is an effective means to adjust the balance between energy supply and demand, whose implementation will impact the user's electricity consumption behaviour, the operation, and management in the future power systems. In this paper, we propose a complementarity algorithm to solve the real-time pricing of smart grid. The Karush–Kuhn–Tucker condition is considered in the social welfare maximisation model incorporating load uncertainty to transforming the model into a system of nonsmooth equations with Lagrangian multipliers, i.e., the shadow prices. The shadow price is used to determine the basic price of electricity. The system of nonsmooth equations is a complementarity problem, which enables us to study the existence and uniqueness of the equilibrium price and to design an online distributed algorithm to achieve the equilibrium between energy supply and demand. The proposed method is implemented in a simulation system composed of an energy provider and 100 users. Simulations results show that the proposed algorithm can motivate the users’ enthusiasm to participate in the demand side management and shift the peak loading. Furthermore, the proposed algorithm can improve the supply shortage. When compared with an online distributed algorithm based on the dual optimisation method, the proposed algorithm has a significantly lower running time and more accurate Lagrangian multipliers.

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