## 3 Design of SROA
### SROA Protocol Description
The HHVBF consumes more energy, because neighbors of the source build their own routing pipes and more nodes participating in data forwarding, which results in significant energy consumption. #### 4.2.6 Average Hop Count

Figure 13 depicts the average hop count of packets delivered from the source to the sink node. In some extreme cases, sensor nodes may be unable to entirely cover the shortest routing path; consequently, the average hop count and packet delivery ratio must be balanced. Figure 13 illustrates that, as the network size rises, so does the average hop count, and the results are consistent with all four protocols. The reason for this is that as node density increases, packets will be routed along optimum routing paths, thus fewer nodes participating in routing. In particular, when the network scale is 400, the average hop count is 4.23, whereas it is 4.95, 5.16, and 5.81 for QELAR, GEDAR, and HHVBF, respectively. Among these protocols, the SROA and QELAR, which are machine-learning-based protocols, takes less hop counts than others because they use the intelligent

Figure 11: Comparison of the packet delivery ratio for the four protocols. Figure 12: Comparison of energy consumption for the four protocols. algorithms to choose the best forwarders. In addition, a global view of the network architecture is enabled by the Q-Learning technique. This not only reduces the average hop-count but also adapts to various network sizes. Furthermore, the SROA outperforms the QELAR because the sink node is deployed close to the source node with the on-site architecture, thus shortening the forwarding path. HHVBF is restricted to the sensor nodes within the pipe radius, making it inflexible in terms of finding the routes with less hops to the destination. For the GEDAR, it applies the greedy forwarding method for advancing packets and does not take hop count into consideration, causing GEDAR to choose routing pathways with larger hop counts than SROA. In UASNs, it is expensive to increase the number of nodes and this makes a new deployment. Therefore, the scalability of network is very important. Considering the above experiments, the performance metric of network scalability can also be observed. Figures 8-13 demonstrate the comparison between SROA, QELAR, GEDAR and HHVBF in terms of average end-to-end delay, PDR, energy consumption, and average hop count with different scales of nodes varying from 100 to 500. The simulation results show that the performance metrics of the proposed SROA are excellent among the four protocols and remain stable irrespective of the increasing network size. By adding new nodes in the network, the four evaluation indexes are improved. ## 5 Conclusions

In this paper, a scalable and energy-efficient routing protocol based on the on-site architecture for UASN is proposed. The SROA groups the sensor nodes into clusters, which enables better resource allocation and easily adapts to the changes in the network scale. By deploying the data center close to the data source, the on-site architecture can shorten the routing path and greatly reduce transmission delay and energy consumption. The SROA follows a decentralized mechanism where the failure of a single node does not interrupt the connectivity in the network. Moreover, a reward function for Q-Learning is applied for routing decisions, which trades off multiple factors of the network. Considering both the instant rewards and the discounted long-term rewards, SROA is more likely to select the optimal candidate forwarders globally. Furthermore, in order to coordinate the forwarding among the candidate nodes, the SROA designs the waiting mechanism, which is developed from opportunistic routing. Different from the traditional OR, this mechanism picks a group of qualified forwarders and sets a waiting time based on the computed Q-values for each candidate node. The simulation results show that the on-site architecture enables

Figure 7.0 Comparison of the average hop count for the four protocols.