# An Efficient Probe-Based Routing for Content-Centric Networking
## 5 Conclusions  
This paper presents an efficient _probe_-based routing algorithm for Content-Centric Networking that real-time explores in-network caching to ensure the routing table storing the optimal paths to the nearest content provider up to date. The _probe_-based approach modifies data structures of basic CCN, including packet formats and routing tables. Compared to other content exploration methods that relied on extra designated packets that increase the network burden, our approach minimizes the cost during the exploration process. The _probe_-based routing table updating algorithm designed in this paper improves the efficiency of FIB. Several _probe_ selections are simulated to demonstrate how to choose the probe and compared with basic CCN on different performance measurements. In terms of qualitative analysis, compared to the basic CCN, the additional computational overhead of our approach is O(\(N_{CS}+N_{tt}+N_{ FIB}*N_{SPT}\)) and O(\(N_{ FIB}\)) on processing interest packets and data packets, respectively. However, the quantitative experimental results show that our approach reduces the number of timeout interest by 6% and the average response time by 0.6 s. In addition, the QoS category of basic CCN and our approach are analyzed based on measurement results of throughput, packet loss, delay, and jitter. Although basic CCN and our approach belong to the same QoS category, our approach outperforms others in terms of real values. Furthermore, the impact of router failures and traffic variations on the performance of our approach, RECIF+PIF and EEGPR are simulated and compared on more complicated network topology. The results demonstrate that our probe-based approach performs better than RECIF + PIF and EEGPR. Owing to speedup FIB updating by probes, our approach provides more reliable interest packet routing when the router failures.  
In summary, the results reveal that the _probe_-based approach raises the accuracy of the routing table, and reduces the network congestion of basic CCN and response time of interests to ensure more efficient routing.  
**Author Contributions:** Conceptualization, P.-H.T., M.-H.T. and J.-B.Z.; methodology, P.-H.T., M.-H.T. and J.-[PERSON]; software, J.-B.Z.; validation, [PERSON]-[PERSON]; formal analysis, [PERSON]-[PERSON] and [PERSON]-[PERSON]; investigation, [PERSON]-[PERSON]; resources, J.-B.Z.; data curation, J.-B.Z.; writing--original draft preparation, [PERSON]-[PERSON] and [PERSON]-[PERSON]; writing--review and editing, [PERSON]-[PERSON] and [PERSON]-[PERSON]; supervision, [PERSON]-[PERSON] and [PERSON]-[PERSON]. All authors have read and agreed to the published version of the manuscript.  
Figure 9: Impact of traffic variations. (**a**) Delay; (**b**) packet loss.  
**Funding:** This work was supported in part by the grant from The Ministry of Science and Technology of Taiwan and the Industrial Technology Research Institute of Taiwan, under grant Nos. MOST 108-2221-E-006-095-MY2 and MOST 110-2221-E-006-008-.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.