Energy-efficient sensing in robotic networks

•An integration of mobile sensing for distributed robots and compressed sensing.•Random mobility models for distributed robots are considered to be energy efficient.•Math models for data transmission in the robotic networks are proposed and analyzed.•The trade-off among main parameters are simulated...

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Bibliographic Details
Published in:Measurement : journal of the International Measurement Confederation Vol. 158; p. 107708
Main Authors: Nguyen, Minh T., Boveiri, Hamid R.
Format: Journal Article
Language:English
Published: London Elsevier Ltd 01.07.2020
Elsevier Science Ltd
Subjects:
Algorithms
Compressed sensing
Data collection
Data transmission
Distributed robotic networks
Energy consumption
Networks
Power consumption
Real time
Robot collaboration
Robotics
Robots
Sampling
Sensors
Data collection
Robot collaboration
Distributed robotic networks
Compressed sensing
ISSN:0263-2241, 1873-412X
Online Access:Get full text
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Summary:•An integration of mobile sensing for distributed robots and compressed sensing.•Random mobility models for distributed robots are considered to be energy efficient.•Math models for data transmission in the robotic networks are proposed and analyzed.•The trade-off among main parameters are simulated to optimize the power consumption.•Suggestions for such networks to be improved are provided for different scenarios. In this paper, we propose a distributed data collection algorithm for robotic networks, which exploits the integration between compressed sensing (CS) and collaboration of mobile robots. Based on the fact that the mobile robots can move into random positions in a sensing area that need to be observed, at a time instant, data collected from a certain number of connected robots can create a sparse random projection so-called CS measurement. At a sampling time, the robots collaborate and share their sensory readings to each other within their transmission range. This linear combination is called a CS measurement to be stored at each distributed robot. The greater the sampling times, the greater the number of CS measurements generated at each mobile robot. Each distributed robot can reconstruct sensory readings from all positions in the area based on the number of CS measurements that is much smaller than the number of positions in the sensing field. We analyze and formulate power consumption for data transmission in such networks. We also analyze the number of mobile robots, the trade-off between the convergence time and robot transmission range and suggest an optimal range for the mobile robots to consume the least power.
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ISSN:0263-2241
1873-412X
DOI:10.1016/j.measurement.2020.107708