The main objective of Internet of Things (IoT) is connecting with different objects via Internet without human intervention. Wireless Sensor Networks (WSNs) which involves ubiquitous computing through which small sensors are connected to the Internet and are used for collecting data. Significant amount of information flowing in the internet is made up of sensory data. To resolve the storage issues of the huge data generated by IoT, the Hadoop Distributed File System are used that streams data to user applications as required. It is difficult to accomplish analysis of vast amount of data (big data) with existing data processing methods. To avoid redundant and irrelevant data, the data needs to be classified. This work presents the use of Support Vector Machine, and Adaboost classifiers, and modifying Adaboost classifier with Genetic Algorithm (GA), Stochastic Diffusion Search (SDS), and Particle Swarm Optimization (PSO). To avoid redundant classifiers, an ensemble algorithm is proposed in this work, PSO with Adaboost classifier and SDS-GA with Adaboost classifier, that can reinitialize attributes, thus avoiding reaching local optimum, and optimizing the coefficients of Adaboost weak classifiers. The proposed algorithms effectively classify the data gathered from WSN and IoT applications. The outcomes of the experiment showed that the proposed SDS-GA algorithm is efficient over other algorithms with respect to accuracy, precision, recall, f measure and false discovery rate.

物联网（IoT）的主要目标是在没有人工干预的情况下通过Internet与不同的对象连接。无线传感器网络（WSN）涉及无处不在的计算，通过该计算，小型传感器连接到Internet并用于收集数据。互联网中流动的大量信息由感觉数据组成。为了解决物联网生成的海量数据的存储问题，使用了Hadoop分布式文件系统，可根据需要将数据流式传输到用户应用程序。用现有的数据处理方法很难完成对大量数据（大数据）的分析。为了避免冗余和不相关的数据，需要对数据进行分类。这项工作介绍了支持向量机和Adaboost分类器的用法，并通过遗传算法（GA）修改了Adaboost分类器，随机扩散搜索（SDS）和粒子群优化（PSO）。为避免冗余分类器，本文提出了一种集成算法，即带Adaboost分类器的PSO和带Adaboost分类器的SDS-GA，它们可以重新初始化属性，从而避免达到局部最优，并优化Adaboost弱分类器的系数。提出的算法有效地分类了从WSN和IoT应用程序收集的数据。实验结果表明，与其他算法相比，所提SDS-GA算法在准确性，准确性，查全率，f测度和错误发现率方面均优于其他算法。可以重新初始化属性，从而避免达到局部最优，并优化Adaboost弱分类器的系数。提出的算法有效地分类了从WSN和IoT应用程序收集的数据。实验结果表明，与其他算法相比，所提SDS-GA算法在准确性，准确性，查全率，f测度和错误发现率方面均优于其他算法。可以重新初始化属性，从而避免达到局部最优，并优化Adaboost弱分类器的系数。提出的算法有效地分类了从WSN和IoT应用程序收集的数据。实验结果表明，所提SDS-GA算法在准确性，精确度，召回率，f度量和错误发现率方面优于其他算法。