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In another subsequent work [10], data aggregation techniques are designed on the basis of statistical information extraction.
The main objective of this paper is to analyse the existing work on artificial intelligence (AI) based data aggregation techniques in WSNs.
However, existing data aggregation techniques cannot be applied directly to WirelessHART networks due to the multi-channel Time Synchronized Mesh Protocol (TSMP) and the superframe-based communication slot scheduling in WirelessHART.
When the application scenario allows their use, data aggregation techniques can significantly reduce the amount of data exchanged over the wireless link at the price of an increased computational complexity and the potential exposition to data integrity risks in the presence of malicious nodes.
Moreover, hierarchical data aggregation techniques can be combined with our solution.
The aims of this resource are: (i) to provide easy access to cellular phenotype and molecular localization data for the broader research community; (ii) to facilitate integration of independent phenotypic studies by means of data aggregation techniques, including use of an ontology and (iii) to facilitate development of analytical methods in this field.
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It is also clarified that our proposed data aggregation technique performs in energy efficient manner.
In modern era WSN, data aggregation technique is the challenging area for researchers from long time.
In this paper, we enhance this scheme by a hierarchical data aggregation technique (HDA).
By using the data aggregation technique, our ASAS scheme can save bandwidth between the fog node and PCS.
The solution is based on LEACH by Heinzelman et al.; a well known data aggregation technique for wireless sensor networks.
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