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Figure 10 Estimation for random deployment topology.
For random deployment scenarios, each problem is solved for 100 random topologies, and the results are averaged.
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Moreover, due to the nature of random deployment, there is not any deployment or neighbor information available for all the sensor nodes prior to network deployment.
For such 3D heterogeneous directional WSNs, we derive probabilistic expressions for k-coverage and m-connectivity that are useful to optimize the cost of random deployment.
Sensor density prediction method improves the efficiency of random deployment of sensor nodes.
Therefore, the probability mass function can be defined for the random deployment as (6).
Figures 10, 11, 12 and 13 illustrate two more samples for the random deployment results.
The unconditional MSE can then be calculated by averaging a large number of random deployments.
We classify node deployment methods as random deployment for anisotropic topology and grid deployment for isotropic topology.
When it is not possible to deploy the network manually, random deployment, for example, dropping sensors from an aircraft, is used.
Besides HDS and DDS, we include random deployment strategy (RDS) for better performance comparison.
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