By collectively leveraging advanced communications systems, sensing, drones, wearable technologies and large-scale data analysis, smart firefighting is envisioned as the next generation firefighting with the capacities of gathering massive real-time scene data, transferring them into useful information and insights for fire responders, and even providing them with more safe and accurate decisions. For smart firefighting, timeliness and accuracy are two foremost system requirements, yet they are unsatisfied in many applications. One reason for such dilemma is due to the underlying used computing architecture (i.e. cloud computing) that can produce extra latency in large-scale data transmission. To address this problem, we explore the firefighting field utilizing edge computing and discuss the overall system architecture, opportunities, challenges, as well as some early technical suggestions on building edge-enabled smart firefighting. To validate the feasibility of edge computing, we simulate the firefighting context and respectively deploy a video-based flame detection algorithm on a local Intel's edge computing platform and a remote Amazon EC2. The preliminary results show that edge computing can significantly increase system's reactive speed, with on average 50% reduction in system latency.