2 research outputs found
A Fog Computing Architecture for Disaster Response Networks
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
A Fuzzy Logic-Based Approach for Node Localization in Mobile Sensor Networks
In most range-based localization methods, inferring distance from radio signal
strength using mathematical modeling becomes increasingly unreliable and complicated
in indoor and extreme environments, due to effects such as multipath propagation
and signal interference. We propose FuzLoc, a range-based, anchor-based,
fuzzy logic enabled system system for localization. Quantities like RSS and distance
are transformed into linguistic variables such as Low, Medium, High etc. by binning.
The location of the node is then solved for using a nonlinear system in the fuzzy
domain itself, which outputs the location of the node as a pair of fuzzy numbers. An
included destination prediction system activates when only one anchor is heard; it
localizes the node to an area. It accomplishes this using the theoretical construct of
virtual anchors, which are calculated when a single anchor is in the node’s vicinity.
The fuzzy logic system is trained during deployment itself so that it learns to
associate an RSS with a distance, and a set of distances to a probability vector.
We implement the method in a simulator and compare it against other methods like
MCL, Centroid and Amorphous. Extensive evaluation is done based on a variety of
metrics like anchor density, node density etc