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Another technique for data propagation based on history is proposed in [ 45 ]. The
considered case study for habitat monitoring assumes that the sensor nodes (zebras
in this case) are mobile and the base station is also mobile (sinks mounted on
vehicles). Transmitting the data by flooding can provide high success gain, but it
requires high bandwidth and capacity, and it's energy consuming. A more efficient
protocol is further proposed, where each node is assigned with hierarchy level
depending on its success rate on delivering the messages to the base station.
A higher level is assigned to nodes that have higher probability of being in range of
the base station, based on the nodes' past behavior. When the node has a data which
needs to be transmitted, it sends the data to the neighbor with the highest hierarchy
level, after it has scanned all the neighbors to acquire the information about their
levels. There is a mechanism for decreasing or increasing the hierarchy level in
defined intervals, depending on whether the nodes are approaching or leaving the
base station. Although the algorithm outperforms flooding in a high-mobility case
it can decrease the success rate because of redirecting the traffic toward nodes that
are no longer near the base station.
The presented approach in [ 46 ] assumes diverse sensor mobility and uses adap-
tive data dissemination protocols that use mobility level estimation. The approach
exploits high mobility as the sensors will dynamically propagate less data in the
presence of high mobility, while nodes of high mobility will be favored for moving
data around. Furthermore, the message flooding scheme is mobility and progress
sensitive, which means that probabilistic forwarding will decrease when the hop
count increases and the probability of flooding will decrease when the level of
mobility is increased.
The presented protocol assumes that the sensors are mobile (following diverse
and time variant mobility) and that they are aware of their position. Novel metric,
called mobility level, is introduced - it assigns higher values to nodes that move fast
and tend to traverse new areas, while smaller values are assigned to nodes moving
slowly and traversing the same or neighboring areas frequently. To increase the
probability of data delivery and at the same time reduce delivery delay, data mes-
sages are disseminated to several neighbor nodes. If the nodes move slowly, they
should choose a greater number of nodes to whom they should transmit the mes-
sage, and vice versa.
In addition, proficient data dissemination can be obtained using cooperative
transmission. This new communication principle can overcome connectivity prob-
lems in sparse settings or heavily partitioned topologies. With cooperative trans-
mission, a group of nodes can combine its emission power and achieve a higher
cumulative emission power. Cooperatively transmitting nodes emit identical symbols
synchronously, and thus, by superimposing the emitted waves on the physical
medium the destination will receive the sum of waves, resulting in a higher total
power. That is how the nodes can reach destinations that are very far away [ 47 ].
In [ 47 ], a continuously changing environment is considered and four types of
communication principles are analyzed: (i) traditional multi-hop communication
(flooding); (ii) wave propagation cooperative transmission; (iii) accumulating
cooperative transmission; and (iv) ideal hybrid multi-hop cooperative transmission.
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