In the world of networked system, average consensus is an important dimension of co-ordinate control and cooperation. Since the communication medium is digital, real value cannot be transmitted and we need to perform quantization before data transmission. But for the quantization, error is introduced in exact value and initial average is lost. Based on this limitation, my 16 bit quantization method (sending MSB in 1-4 cycle and MSB+LSB in 5th cycle) reduces error significantly and preserves initial average. Besides, it works on all types of graphs (star, complete, ring, random geometric graph). My other algorithm, distributing averaging algorithm (PQDA) with …
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In the world of networked system, average consensus is an important dimension of co-ordinate control and cooperation. Since the communication medium is digital, real value cannot be transmitted and we need to perform quantization before data transmission. But for the quantization, error is introduced in exact value and initial average is lost. Based on this limitation, my 16 bit quantization method (sending MSB in 1-4 cycle and MSB+LSB in 5th cycle) reduces error significantly and preserves initial average. Besides, it works on all types of graphs (star, complete, ring, random geometric graph). My other algorithm, distributing averaging algorithm (PQDA) with probabilistic quantization also works on random geometric graph, star, ring and slow co-herency graph. It shows significant reduced error and attain strict consensus.
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