  Smart mobility becomes paramount for meeting net-zero targets. However,
autonomous, self-driving and electric vehicles require more than ever before an
efficient, resilient and trustworthy computational offloading backbone that
expands throughout the edge-to-cloud continuum. Utilizing on-demand
heterogeneous computational resources for smart mobility is challenging and
often cost-ineffective. This paper introduces SMOTEC, a novel open-source
testbed for adaptive smart mobility experimentation with edge computing. SMOTEC
provides for the first time a modular end-to-end instrumentation for
prototyping and optimizing placement of intelligence services on edge devices
such as augmented reality and real-time traffic monitoring. SMOTEC supports a
plug-and-play Docker container integration of the SUMO simulator for urban
mobility, Raspberry Pi edge devices communicating via ZeroMQ and EPOS for an
AI-based decentralized load balancing across edge-to-cloud. All components are
orchestrated by the K3s lightweight Kubernetes. A proof-of-concept of
self-optimized service placements for traffic monitoring from Munich
demonstrates in practice the applicability and cost-effectiveness of SMOTEC.
