  Information from surveillance video is essential for situational awareness
(SAW). Nowadays, a prohibitively large amount of surveillance data is being
generated continuously by ubiquitously distributed video sensors. It is very
challenging to immediately identify the objects of interest or zoom in
suspicious actions from thousands of video frames. Making the big data
indexable is critical to tackle this problem. It is ideal to generate pattern
indexes in a real-time, on-site manner on the video streaming instead of
depending on the batch processing at the cloud centers. The modern
edge-fog-cloud computing paradigm allows implementation of time sensitive tasks
at the edge of the network. The on-site edge devices collect the information
sensed in format of frames and extracts useful features. The near-site fog
nodes conduct the contextualization and classification of the features. The
remote cloud center is in charge of more data intensive and computing intensive
tasks. However, exchanging the index information among devices in different
layers raises security concerns where an adversary can capture or tamper with
features to mislead the surveillance system. In this paper, a blockchain
enabled scheme is proposed to protect the index data through an encrypted
secure channel between the edge and fog nodes. It reduces the chance of attacks
on the small edge and fog devices. The feasibility of the proposal is validated
through intensive experimental analysis.
