Maurizio Bocca
Papers
2
Total Citations
26
H-Index
2
About
Maurizio Bocca’s research focuses on the intersection of wireless sensor networks, robotics, and localization, with a particular emphasis on improving the accuracy of received signal strength (RSS)-based positioning in complex, real-world environments. His major contributions include pioneering methods to overcome the notorious unreliability of RSS measurements caused by multipath propagation and hardware variability. In his highly cited 2013 empirical study, Bocca systematically demonstrated how the log-normal path loss model’s sensitivity to environmental factors degrades localization performance—a foundational insight for the field. Building on this, his 2012 work introduced a recursive expectation-maximization algorithm that simultaneously localizes network nodes and calibrates per-node signal propagation models using a mobile robot. This innovative approach accounts for local environmental idiosyncrasies, significantly boosting positioning accuracy. With his key papers accumulating over 26 citations, Bocca’s work is recognized for bridging theoretical models with practical deployment challenges. His achievements highlight a career dedicated to making wireless localization robust enough for real-world applications, from search-and-rescue to smart environments.
Research Focus
Key Achievements
Top Papers
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- 2