A Novel Machine Language-Driven Data Aggregation Approach to Predict Data Redundancy in IoT-Connected Wireless Sensor Networks

Table 1

Reference comparison table.

Author details	Journal details	Observation
Li et al. [5]	IEEE Transactions on Mobile Computing (2021)	Approximation of data collection from sensor nodes fully and partially and heuristic approach maximization of collected data with NP-hard problem are used. Proposed methods proved promising results.
Sanyal and Zhang [7]	IEEE Access (2018)	Data veracity problem is addressed. Generated true sensor data matrix and proved higher efficiency in the presence of noise and outliers.
Huo et al. [12]	Wireless Communications and Mobile Computing (2018)	A real-time stream data aggregation framework with adaptive-event differential privacy is proposed. Authors used privacy protection and smart grouping based on -means clustering. Results shown outperform the existing works.
Anbalagan et al. [8]	Future Generation Computer Systems (2020)	Alternate complementary network is utilized. Results proved that LWA-SA aggregates data with minimal latency and selects an optimal AP as prescribed in the GA-based EWS algorithm.