Modeling the power consumption of a Wifibot and studying the role of communication cost in operation time

Mobile robots are becoming part of our every day living at home, work or entertainment. Due to their limited power capabilities, the development of new energy consumption models can lead to energy conservation and energy efficient designs. In this paper, we carry out a number of experiments and we focus on the motors power consumption of a specific robot called Wifibot. Based on the experimentation results, we build models for different speed and acceleration levels. We compare the motors power consumption to other robot running modes. We, also, create a simple robot network scenario and we investigate whether forwarding data through a closer node could lead to longer operation times. We assess the effect energy capacity, traveling distance and data rate on the operation time

Spread and shrink: Point of interest discovery and coverage with mobile wireless sensors

International audienceIn this paper we tackle the problem of deploying mobile wireless sensors while maintaining connectivity with a sink throughout the deployment process. These mobile sensors should discover some points of interest (PoI) in an autonomous way and continuously report information from the observed events to the sink. Unlike previous works, we design an algorithm that uses only local information and local interactions with surrounding sensors. Moreover, unlike other approaches, our algorithm implements both the discovery and the coverage phase. In the discovery phase, the mobile sensors spread to discover new events all over the field and in the second phase, they shrink to concentrate only on the discovered events, named points of interest. We prove that connectivity is preserved during both phases and the spreading phase is terminated in a reasonable amount of time. Real experiments are conducted for small-scale scenarios that are used as a “proof of concept”, while extensive simulations are performed for more complex scenarios to evaluate the algorithm performance. A comparison with an existing work which uses virtual forces has been made as well. The results show the capability of our algorithm to scale fast in both discovery, coverage and shrinking phases