School of Computing, SASTRA Deemed University, Thanjavur, India.
School of Computer Science and Engineering, VIT University, Chennai Campus, Tamil Nadu, India.
Big Data. 2022 Jun;10(3):186-203. doi: 10.1089/big.2020.0203. Epub 2021 Nov 5.
In recent years, the growth of internet of things (IoT) is immense, and the observations of their evolution need to be carried out effectively. The development of the IoT has been broadly adopted in the construction of intelligent environments. There are various challenging IoT issues such as routing messages, addressing, Localizing nodes, data blending, etc. Formerly learning eloquent information from big data systems to construct a data-gathering setup in an IoT environment is challenging. Among many viable data sources, the IoT is a rich big data source: Various IoT nodes produce a massive quantity of data. Localization is one of the crucial problems that make a significant impact inside the IoT system. It needs to be engaged with proper and effective procedures to collect all sorts of data without noise. Numerous localization procedures and schemes have been initiated by deploying the IoT sensor with wireless sensor networks for both interior and outside environments. To accomplish higher localization accuracy, with less cost for the large volume of data, it is considered a hectic task in the IoT sensor environment. By viewing the nature of the IoT, the merging of different technologies such as the internet, WiFi, etc., can aid diverse ways to acquire information about various objects' locations. Location-based service is an exceptional service of the IoT, whereas localization accuracy is a significant issue. The data generated from the sensor are available in both static and dynamic forms. In this article, a sophisticated accuracy localization scheme for big data is proposed with an optimization approach that can effectively produce proper and effective outcomes for IoT environments. The theme of the article is to develop an enriched Swarm Intelligence algorithm based on Artificial Bee Colony by using the EKF (Extended Kalman Filter) data blend technique for improving Localization in IoT for the unsuspecting environment. The performance of the proposed algorithm is evaluated by using communication consumption and Localization accuracy and its comparative advantage.
近年来，物联网（IoT）的发展势头迅猛，因此需要有效地对其发展进行观察。物联网的发展已广泛应用于智能环境的构建中。物联网存在各种具有挑战性的问题，如路由消息、寻址、定位节点、数据融合等。以前，从大数据系统中学习雄辩的信息以在物联网环境中构建数据采集设置是具有挑战性的。在许多可行的数据来源中，物联网是一个丰富的大数据源：各种物联网节点产生大量数据。定位是物联网系统中一个非常重要的问题，需要通过适当和有效的程序来收集各种没有噪声的数据。已经通过在内部和外部环境中部署具有无线传感器网络的物联网传感器启动了许多定位程序和方案。为了在物联网传感器环境中以较低的成本实现更高的定位精度和大量数据，这被认为是一项艰巨的任务。通过观察物联网的性质，可以融合互联网、WiFi 等不同技术，从而为获取各种物体位置的信息提供多种途径。基于位置的服务是物联网的一项特殊服务，而定位精度是一个重要问题。传感器生成的数据有静态和动态两种形式。在本文中，提出了一种复杂的大数据高精度定位方案，该方案采用优化方法，可以为物联网环境提供适当有效的结果。本文的主题是通过使用扩展卡尔曼滤波（EKF）数据融合技术开发一种基于人工蜂群的丰富群智能算法，以提高物联网中的定位精度。通过比较通信消耗和定位精度来评估所提出算法的性能及其优势。
