Fog Computing to Enable Geospatial Video Analytics for Disaster‐incident Situational Awareness

Geospatial video analytics involves “data collection” of massive image/video data from the Internet of Things (IoT), and “data processing” through seamless computing at the infrastructure edge and core/public cloud platforms. Particularly in cases of (man‐made or natural) disaster incident response coordination scenarios, geospatial video analytics is valuable to provide situational awareness. The edge computing or fog computing needs to be designed with resilient performance while interfacing with the core cloud in order to cater to user's (i.e. incident commander's, first responder's) quality of experience (QoE) expectations. An additional barrier for geospatial video analytics is the fact that the distributed locations generating imagery or video content are rarely equipped with high‐performance computation capabilities to run computer vision algorithms. In this chapter, we explain the main architectural concepts of fog computing that can help in overcoming the resource scale/diversity limitations for real‐time geospatial video analytics, while also ensuring that reliability and management challenges are met.More specifically, we describe a novel cloud‐fog computing paradigm that integrates computer vision, edge routing, and computer/network virtualization areas in geospatial video analytics. We detail the state‐of‐the‐art techniques and illustrate our new/improved solution approaches based on “function‐centric” computing for the two problems of (1) high‐throughput data collection from IoT devices at the wireless edge, and (2) seamless data processing at the infrastructure edge and core cloud platforms. To assist the high‐throughput IoT data collection at the wireless edge, we present a novel deep learning‐augmented geographic edge routing that relies on physical area knowledge obtained from satellite imagery. To assist the seamless data processing, we describe a novel cloud‐fog computing framework that utilizes microservice decomposition and service chaining supported by a constrained‐shortest‐path algorithm. We conclude with a list of open challenges for adopting fog computing in geospatial video analytics for a variety of applications (e.g. face recognition in crowds, object tracking in aerial wide‐area motion imagery, reconnaissance, and video surveillance) relevant to delivering disaster‐incident situational awareness.