J. Penia Queralta
Papers
1
Total Citations
15
H-Index
1
About
J. Penia Queralta’s research lies at the intersection of edge computing, the Internet of Vehicles, and autonomous robotics, with a focus on enabling real-time, low-latency intelligence for distributed systems. Their most-cited work, “Visual Odometry Offloading in Internet of Vehicles with Compression at the Edge of the Network” (2019, 15 citations), addresses a critical challenge in autonomous navigation: how to offload computationally heavy visual odometry tasks from resource-constrained vehicles to edge servers without sacrificing accuracy. By introducing novel compression techniques, Queralta demonstrated that edge offloading can significantly reduce latency and bandwidth usage while maintaining reliable pose estimation—a key contribution for scalable, cooperative driving systems. This work exemplifies their broader impact in advancing fog and edge computing paradigms beyond traditional cloud-centric IoT models. Queralta’s research has been recognized for bridging theory and practical deployment, offering pathways for energy-efficient, real-time decision-making in autonomous fleets. Their contributions are particularly relevant for students and researchers exploring edge intelligence, vehicular networks, and the future of decentralized autonomy.
Research Focus
Key Achievements
Top Papers
- 1