## 5 Simulations
### Network Parameters  
The proposed method is most suitable for circumstances in which losing any sensing data is not acceptable, e.g., WSN-based perimeter surveillance in battlefields or other high security areas [23]. According to this scenario, sensor nodes can be randomly deployed by helicopters or aircrafts [7]. They are then self-organized into a functional WSN for comprehensive and continuous perimeter surveillance. In addition, the proposed method is also suitable for the WSN-based smart healthcare that requires continuous and remote monitoring [24]. Here, we take the battlefield surveillance as an example; a network with 100 sensor nodes is randomly distributed over a battle area of 50 \(\times\) 50 m\({}^{2}\). The deployed nodes equipped with vibration sensors that can detect any object passing through the area within its sensing range. The sensing range \(r\) is set at 7.5 m. In addition, this battlefield consists of 2,500 POIs that are grid distributed. In each round live nodes need to report the sensed data to the \(BS\), and the \(BS\) is located at the coordinate \((25,-50)\). In order to prevent long distance transmissions and to reduce power dissipation in data transmission, a suitable communication range \(C_{r}\) is chosen to make the average hop-count of nodes equal to 7.5. The initial energy of all nodes is assumed to be 1 joule, and the nodes cannot be recharged. Furthermore, the parameters of the radio model are summarized in Table 1, which are the same as those adopted by [18] in order to perform fair comparisons between the proposed method and the previous studies. The path loss of the radio model is set the same as the previous studies [11,18], in which \(\gamma\) is 2 for data transmissions between nodes in a free-space. We specifically set \(\gamma\) to 2.5 for the long range data transmissions from the root node to the BS [25]. The compression coefficient \(\mu\) of Equation (12) is set at 0.05. In this study, each node should have some specific data which is different than other nodes, and the setting of compression coefficient \(\mu\) set at 0.05 is much suitable in the real-world works. Hence, the compression coefficient \(\mu\) is set at 0.05 in this work to evaluate the ECHR performance. Simulation results are obtained by averaging those obtained from 100 network topologies.  
In this section, the performance analysis of the proposed ECHR protocol is conducted via MATLAB simulation. The simulation consists of two parts. In the first part, the performance of the proposed ECHR is evaluated using different weighting coefficients. In the second part of the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Acronym** & **Setting*** \\ \hline Radio circuitry & \(E_{elec}\) & 50 nJ/bit \\ Transmit amplifier & \(\varepsilon_{amp}\) & 0.1 nJ/bit/m\({}^{\gamma}\) \\ Aggregation cost & \(E_{DA}\) & 5 nJ/bit \\ Data packet size & \(k\) & 2,000 bits \\ \hline \hline \end{tabular}  
* These parameter settings were adopted from [18].  
\end{table}
Table 1: Parameter settings used in the simulations.  
simulation, the performance of the ECHR protocol is compared with those of the LEACH and the LEACH-Coverage-U via numerical simulation.