Discussion on tradeoffs among delay, energy and accuracy of data aggregation for Multi-Robot Sensor Networks

Abstract

Due to the recent development in micro mechanics, electronics and wireless communication technologies, Multi-Robot Sensor Network (MRSN) becomes a hot issue for many robotic applications. In MRSN, from a viewpoint of sensor network communication, each robot senses data and transmits it to the adjacent robot to collect all data at the sink in a multi-hop manner. According to applications, delay taken to collect data, energy consumed by each robot for communication and data accuracy of the collected data are critical concerns. However, these three are in trade-off one another. In this paper, we discuss the tradeoffs among communication delay, energy consumption, and data accuracy of data collection in multi-robots systems. We focus on the data aggregation technique, which suppresses number of data to be transmitted. First, we analyze with Markovian chain the three categories of data aggregations, i.e., non-aggregation (conventional), full aggregation, and partial aggregation. The partial aggregation proposed by us in the previous paper can control aggregation by a simple parameter set called random pushing rate vector. The analytical result shows that, conventional method suffers large energy consumption with the highest accuracy, while full aggregation suffers long transmission delay with the least accuracy. We also find that the partial aggregation can trade off energy, delay and accuracy. Then, we discuss the tradeoffs among data accuracy, transmission delay and energy consumption with different significance according to different applications by proposing tradeoff index (TOI). Based on the TOI we discuss the several applications of MRSN with different significance of delay, energy and accuracy. From the results, we find that non-aggregation has the best TOI for low data generation rate, that the partial aggregation does for moderate generation rate, and that the full aggregation does for large data generation rate. The obtained results can provide the metric of aggregation for different applications.