# Design Considerations and Performance Evaluation of Gossi Routing in LoRa-Based Linear Networks
## 5 Result Evaluation and Discussion
### Gossip Routing  
Lastly, in the fourth trial, we used the random probabilistic distribution (\(T_{prob}\)) in the presence of two and three active relay nodes using a one-hop model. The evaluation of gossip-based probabilistic transmission involved the following steps:  
* Set up the network, and configure a transmitting node to continuously transmit at least 1000 packets, with a rate of 1 packet every four seconds.
* Relay nodes that relay packets to the destination node using various probabilities (1.00, 0.95, 0.90, 0.80, 0.70, 0.60, 0.50, 0.40).
* Re-run the experiments by configuring the relay nodes using various \(T_{prob}\) at the relay nodes.
* Compare the performance using two and three active relay nodes using a one-hop model.  
The concept of employing a gossip-based probabilistic method is quite straightforward. In this approach, a relay node is configured with a designated probability threshold, denoted as \(T_{prob}\). Once the relay node receives a packet from the source, it calculates the probability of forwarding the packet. If this calculated probability is equal to or less than \(T_{prob}\), the packet is forwarded. Alternatively, if the probability is greater than \(T_{prob}\), the packet is discarded. When \(T_{prob}\) is set to 1, the Packet Reception Rate (PRR) attains its peak performance. Every incoming packet is relayed to the destination node without loss. At \(T_{prob}\)= 0.9, around 10% of packets are dropped. Due to the utilisation of a random function for packet dropping, it is highly improbable that both relay nodes will discard the exact same packets. To verify the arbitrary dropping at each relay node, we logged the instances of dropped packets. To ensure the robustness of the gossip-based probabilistic method, we conducted two sets of experimental scenarios with two and three active relay nodes. The results of these experiments are presented in Tables 2 and 3. We sought to determine the optimal threshold values for the probability values in the presence of both two and three relay nodes, using the one-hop model, as illustrated in Figure 13.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(T_{prob}\) & **1.00** & **0.95** & **0.90** & **0.80** & **0.70** & **0.60** & **0.50** & **0.40** \\ \hline Ideal & 100.00\% & 99.75\% & 99.00\% & 96.00\% & 91.00\% & 84.00\% & 75.00\% & 64.00\% \\ Experiment 1 & 96.21\% & 93.43\% & 98.00\% & 94.72\% & 90.54\% & 80.99\% & 73.73\% & 61.99\% \\ Experiment 2 & 99.40\% & 98.40\% & 98.01\% & 93.83\% & 89.86\% & 79.82\% & 74.82\% & 63.61\% \\ Average & 97.80\% & 95.91\% & 98.00\% & 94.27\% & 90.20\% & 80.40\% & 74.27\% & 62.81\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Impact on performance using various \(T_{prob}\) with two active relay nodes with a single-hop count.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \(T_{prob}\) & **1.00** & **0.95** & **0.90** & **0.80** & **0.70** & **0.60** & **0.50** & **0.40** \\ \hline Ideal & 100.00\% & 99.98\% & 99.90\% & 99.20\% & 97.30\% & 93.60\% & 87.50\% & 78.40\% \\ Experiment 1 & 98.90\% & 99.00\% & 97.71\% & 97.61\% & 91.24\% & 91.14\% & 81.29\% & 74.02\% \\ Experiment 2 &