IoT Time-Series Traffic Data: Smart City, eHealth, and Smart Factory

This dataset provides realistic Internet of Things (IoT) traffic time-series data generated using the novel Tiered Markov-Modulated Stochastic Process (TMMSP) framework. The dataset captures the unique temporal dynamics and stochastic characteristics of three distinct IoT applications: smart city, eHealth, and smart factory systems. Each application's traffic pattern reflects real-world behaviors including human-machine correlation (HMC), sudden data bursts, and application-specific seasonality patterns.The traffic data is presented as time-series with 1-minute resolution over multiple days, incorporating:Daily traffic volume fluctuations reflecting human activity patternsApplication-specific coordinated transmission phases resulting in data burstsVarying traffic intensities based on application characteristicsTemporal correlation between IoT nodesRealistic traffic behavior validated against real IoT application tracesThis dataset is particularly valuable for:Evaluating resource allocation algorithms for edge/cloud computingTesting traffic prediction modelsAnalyzing application-specific IoT network behaviorsDeveloping and validating network slicing strategiesStudying autonomous resource scaling mechanismsThe dataset has been validated through comparison with real IoT traffic patterns and demonstrated utility in evaluating autonomous edge slicing (AES) mechanisms. The included traffic patterns exhibit different human-machine correlations and burst frequencies that match expected behaviors of real-world IoT deployments.

本数据集提供基于新颖分层马尔可夫调制随机过程（TMMSP）框架生成的真实物联网（IoT）流量时间序列数据。该数据集捕捉了三种不同物联网应用（智能城市、eHealth 和智能工厂系统）独特的时序动态和随机特性。每个应用的流量模式均反映了现实世界的动态，包括人机相关性（HMC）、突发数据以及特定应用的季节性模式。流量数据以具有1分钟分辨率的时序形式呈现，覆盖多日，包含以下内容：反映人类活动模式的日流量波动；由特定应用协调的传输阶段导致的数据爆发；基于应用特性的不同流量强度；物联网节点之间的时序相关性；与真实物联网应用轨迹验证的真实流量行为。该数据集对于评估边缘/云计算的资源分配算法、测试流量预测模型、分析特定应用的物联网网络行为、开发与验证网络切片策略、研究自主资源伸缩机制具有特别的价值。该数据集通过与真实物联网流量模式进行比较进行了验证，并在评估自主边缘切片（AES）机制方面展示了其实用性。包含的流量模式表现出不同的人机相关性和爆发频率，与真实物联网部署的预期行为相匹配。

搜集汇总

背景与挑战

背景概述

该数据集提供了基于分层马尔可夫调制随机过程（TMMSP）框架生成的物联网（IoT）时间序列流量数据，涵盖智能城市、电子健康(eHealth)和智能工厂三个应用场景，以1分钟分辨率模拟了5天内的真实流量动态，包括人机关联、突发数据流和季节性模式。数据集适用于资源分配算法评估、流量预测模型测试和网络行为分析，已通过真实IoT流量验证，并包含三个MATLAB文件，数据以每分钟数据包数为单位。