智能无线声传感器网络多层耗能模型附matlab代码

最新推荐文章于 2025-11-30 12:17:44 发布

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本文探讨了智能传感器在智能城市中的应用，重点介绍了无线声传感器网络的能量消耗问题。通过一个多层能量消耗模型，研究如何提高这些传感器的能源效率，以推动其在实际场景中的商业化。模型提供了一个定制设计和比较不同协议能耗的基础。

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智能优化算法神经网络预测雷达通信无线传感器电力系统

信号处理图像处理路径规划元胞自动机无人机

🔥 内容介绍

智能传感技术预计将成为智能城市和近期环境中的普遍技术。由于集成设备尺寸缩小,同时又保持其计算能力,这些服务正在提高其能力,这些设备可以运行各种机器学习算法,并在各种数据处理任务中实现高性能。一种可用于智能传感的有吸引力的传感器方式是声传感器,它可以在保持中等能耗的情况下传递高信息量的数据。不幸的是,目前无线传感器网络的能量预算通常不足以满足标准麦克风的要求。因此,需要提高各层次----传感、信号处理和通信----的能源效率,以便将无线智能声学传感器推向市场。为了实现这一目标,本文介绍了一种用于无线声传感器网络的能量消耗模型,其目的是帮助开发新技术,以提高智能无线声传感器的能源效率。该模型为智能无线声传感器的定制设计提供了第一步探索,也可用于比较不同协议的能耗。也可用于比较不同协议的能耗.也可用于比较不同协议的能耗.

📣 部分代码

% ----------------------------------------------------------------------%  Script which estimates the energy consumption, battery lifetime and%  needed computational resource of a smart audio sensor chain. %  The chain includes sensing, processing and wireless communication. %%  The model is fully described in the following paper:%   G. Dekkers, F. Rosas, S. Lauwereins, S. Rajendran, S. Pollin, B. Vanrumste, T. van Waterschoot, M. Verhelst and Peter Karsmakers, %   揂 multi-layered energy consumption model for smart wireless acoustic sensor networks,?KU Leuven, Tech. Rep., December 2018.%  Please refer to this paper when using this model for your research.%%  Authors: %   Gert Dekkers, KU Leuven%   Fernando Rosas, Imperial College London% ----------------------------------------------------------------------clc; clear;addpath(genpath(pwd))%% Code for acquiring energy consumption for a single estimate (network node)verbose = 1; %0: show final output, 1: show all% -- Init system -- %% define setupgen.method = 'default'; % name of the general config file sens.method = 'default'; % name of sensing config file% proc{1}.method = 'ADC';proc{1}.method = 'FE/framelogMel'; % name of the first processing chain (typically FE). if no FE => 'FE/ADC'proc{2}.method = 'NN/CNN_1D_example'; % name of second processing chain (typically classifier) or remove linecomm.method = 'default'; % name of communication config file% get general paramgen = general_loadparam(gen.method,[]); % get general parameters (fs, 礐, ..)% -- Sensing layer -- %if verbose, disp('%%%% Sensing %%%%'); end;sens.conf = sensing_loadparam(sens.method, [], gen); % get sensing parameters[sens.cons, sens.os] = sensing_consumption(sens.conf, gen); % [mJ]% -- Processing layer -- %input_shape = sens.os; prev_chain = []; E_proc = []; name_proc = cell(0); %init as empty at first runfor p=1:length(proc) % for each processing chain    if verbose, disp(['%%%% Processing layer: ' proc{p}.method ' %%%%']); end;    % get values    proc{p}.conf = proc_loadparam(proc{p}.method, [], input_shape, gen); % get params    [proc{p}.os, proc{p}.ops, proc{p}.par] = proc_info(proc{p}.conf, gen, input_shape,verbose); % get output shape, ops and nr. parameters    [proc{p}.ma, proc{p}.ms_o, proc{p}.ms_p] = memo_acc_stor(proc{p}.os, proc{p}.par, proc{p}.ops, proc{p}.conf, prev_chain, gen,verbose); % get memory used in storing ops/params    [proc{p}.cons.all,proc{p}.cons.op,proc{p}.cons.ms_o,proc{p}.cons.ms_p,proc{p}.cons.ma] = bits_to_energy(proc{p}.ma, proc{p}.ms_o, proc{p}.ms_p, proc{p}.ops, gen); % energy consumption    % keep from previous chain    input_shape = proc{p}.os(end,:);    prev_chain = proc{p}.conf;end% -- Communication layer -- %if verbose, disp('%%%% Communication %%%%'); end;comm.conf = comm_loadparam(comm.method,[],gen); % get comm paramscomm.conf.N_T = prod(input_shape)*gen.S; % informative bits to communicatecomm.conf.N_R = 0; % informative bits to receivecomm.cons = sum(comm_consumption(comm.conf,sens.conf,gen)); % -- Final output -- %fc = prep_chain_info(sens,proc,comm,gen); % get everything in proper format to plot/printprint_chain_info(fc) % Print energy distribution of the full sensor networkplot_energy_proc(proc) % Plot energy distribution of particular layers in the processing chainprint_mem_cycle(proc,gen) % Print needed cycli/s and mem storage