Global flooding and mobile sink location
updating frequently in wireless sensor network
are energy consuming. Thus, recently, extensive
researches have been dedicated to the study of
energy efficient data dissemination protocols [1,
3, 4, 6].
A scalable energy-efficient asynchronous
dissemination protocol (SEAD) [4], proposes a
recursive algorithm that searches for minimum
energy dissemination tree and saves energy in
managing mobile sinks. The sink does not
report its current location to the tree. The
entire path to the tree is not needed to be
readjusted when sinks move out of the tree range.
SEAD minimizes energy consumption in both
building the dissemination tree and
disseminating data to mobile sinks.
Visvanathan et al. proposed a hierarchical
data dissemination scheme (HDDS) [1] for
large-scale sensor network. HDDS routes data
towards sinks using a hierarchy of randomly
selected dissemination nodes. Because
dissemination nodes have limited resources,
whenever a dissemination node is overloaded, it
inserts another level of dissemination nodes to
reduce its load. HDDS reduces energy
consumption in sensor network that dynamically
adapt to demands for sensor data.
In [3], TTDD grid structures are created
according to the location of the source node to
prevent global flooding and frequent location
updates. The grid structure supports mobility
of the sink nodes. Query and data are
transmitted along the grids and flooding is
confined within the local grids only. However
grid construction per each source and local query
flooding also consume great energy.
One of the disadvantages of TTDD is short
of the support of the mobile source. The
method of Railroad was proposed in [6], it
defines data dissemination architecture for
large-scale wireless sensor networks. Railroad
system adopts a virtual infrastructure called rail,
which is placed in the middle area of the
networks so that every node can easily access it.
There is only one rail in the network and it acts
as a rendezvous area of the events and the
queries. Rail communicates among nodes,
sinks and sources, and mobility of source nodes
are also supported by the Railroad.
We have noticed that TTDD consumes a
considerable energy on grid construction and
local query flooding. In our scheme, we reduce
the energy consumption of grid construction by
using global grid structure (i.e. the infrastructure
of transfer posts) and avoid local query flooding
by using transfer posts. Also, we have
observed that the query forwarding in the
Railroad system is inefficient that a query
request might have to travel around the whole
rail until it reaches a relevant data node. We
have solved this problem by using the immediate
transfer post, which is the transfer post in the
same grid with sink node or source node.