Routing is a challenging task in Wireless
Sensor Networks (WSNs) due to the limitation in energy and hardware
capabilities in WSN nodes. This challenge prompted researchers to develop
routing protocols that satisfy WSNs needs. The main design objectives are
reliable delivery, low energy consumption, and prolonging network lifetime. In
WSNs, routing is based on local information among neighboring nodes. Routing
decisions are made locally; each node will select the next hop without any clue
about the other nodes on the path. Although a full knowledge about the network
yields better routing, that is not feasible in WSNs due to memory limitation
and to the high traffic needed to collect the needed data about all the nodes
in the network. As an effort to try to overcome this disadvantage, we are
proposing in this paper aware diffusion routing protocol. Aware diffusion
follows a semi-holistic approach by collecting data about the available paths
and uses these data to enforce healthier paths using machine learning. The data
gathering is done by adding a new stage called data collection stage. In this
stage, the protocol designer can determine which parameters to collect then use
these parameters in enforcing the best path according to certain criteria. In
our implementation of this paradigm, we are collecting total energy on the
path, lowest energy level on the path, and hop count. Again, the data collected
is designer and application specific. The collected data will be used to
compare available paths using non-incremental learning, and the outcome will be
preferring paths that meet the designer criteria. In our case, healthier and
shorter paths are preferred, which will result in less power consumption,
higher delivery rate, and longer network life since healthier and fewer nodes
will be doing the work.
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