In the aftermath of a disaster, the impacted communication infrastructure is
unable to provide first responders with a reliable medium of communication. Delay
tolerant networks that leverage mobility in the area have been proposed as a scalable
solution that can be deployed quickly. Such disaster response networks (DRNs)
typically have limited capacity due to frequent disconnections in the network, and
under-perform when saturated with data. On the other hand, there is a large amount
of data being produced and consumed due to the recent popularity of smartphones
and the cloud computing paradigm.
Fog Computing brings the cloud computing paradigm to the complex environments
that DRNs operate in. The proposed architecture addresses the key challenges
of ensuring high situational awareness and energy efficiency when such DRNs are saturated
with large amounts of data. Situational awareness is increased by providing
data reliably, and at a high temporal and spatial resolution. A waypoint placement
algorithm places hardware in the disaster struck area such that the aggregate good-put
is maximized. The Raven routing framework allows for risk-averse data delivery
by allowing the user to control the variance of the packet delivery delay. The Pareto
frontier between performance and energy consumption is discovered, and the DRN
is made to operate at these Pareto optimal points. The FuzLoc distributed protocol
enables mobile self-localization in indoor environments. The architecture has
been evaluated in realistic scenarios involving deployments of multiple vehicles and
devices
