Abstract
On the Internet of Things, massive sensor-based embedded devices are used to monitor specific processes in industries. It is crucial to prolong the network lifetime during data transmission. In this paper, two novel differential data processing-based data gathering algorithms are proposed to reduce the size of data. One is the Continue Differential Data Processing with Medians (CDDPM) algorithm, in which each node reverts the received differential data into the initial data, then clusters all the data to obtain multiple clusters with high cohesion by adopting a clustering algorithm. Then, select the median of each cluster as the reference value for differential processing. The other is Aggregation-based Differential Data Processing with Medians (ADDPM), which aggregates routes into a tree before data collection and converges more data during routing. Both theoretical analysis and experimental results demonstrate that the proposed schemes can reduce the amount of data to prolong the network lifetime, which have a wider range of adaptation and better than main existing schemes. For a WSN with 200 nodes, the CDDPM algorithm and ADDPM algorithm can reduce the data size by 27.51% and 29.44%, prolong the network lifetime by 35.79% and 44.21%, respectively. For larger networks, the effect is more remarkable.
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