Review on Data Aggregation Techniques in Internet of Things
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Abstract
The "Internet of Things" is a new paradigm that consists of several connected and related instruments with embedded sensing components that communicate with one other and with central nodes across a cordless network and the internet. IoT-enabled health care systems have recently attracted a lot of attention due to the importance of human health. However, because IoT networks are large-scale and battery-powered, it is necessary to set up appropriate energy and resource management systems for them. How to effectively provide information to the right users is a challenging problem for data management. To aid in machine-to-machine communication with linked data, cheap solutions for semantic IoT include dependable circulation distribution systems. In response to specific system queries provided by users, the system compiles integrated information streams produced by multiple collectors and delivers pertinent data to relevant users. To meet the demands of high efficiency data flow propagation in two conditions, such as point-to-point systems and flow breeding in wireless transmission systems, two novel information structures must be developed. Analysis of techniques using real-world datasets reveals that they are much more effective at sending linked information streams than the current technology. In order to maximize the utilization of the network lifetime, this study proposes SUNFLOWER-ALGORITHM based information gathering systems for IoT-enabled in various applications.
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