# Lightweight Path Recovery in IPv6 Internet-of-Things Systems
## 2 Related Work  
For the path recovery problem, some of the existing works are highlighted as follows. PathZip [14] stores the routing path into a f geometry-assistant techniques by making each sensor node passively label every packet forwarded. As the network scales increase, PathZip is facing the problem of rapid search space growth. PAD [15] employs an intelligent packet-marking scheme for efficiently reconstructing and dynamically maintaining the network topology. Based on a probabilistic inference model that encodes internal dependencies among different nodes, PAD is more suitable for sensor network systems that are not very dynamic. MNT [16] exploits inter-packet correlations generated from spatially different nodes along the routing path to reconstruct the routing path, the per-hop arrival order, as well as the per-hop arrival time of individual packets. MNT shows excessive performance with relatively static topology and high packet delivery rates, but is more vulnerable in coping with low node activity rates. PAT [17] applies path compression, making each packet able to carry path information with a length limit. Then, long paths can be inferred from sub-paths by path correlation. For small-scale networks, PAT can demonstrate good performance. However, when the network size increases, it will go through a longer initialization phase to discover the network topology. In addition, the PAT algorithm has high overhead because it carries compressed path information. RTI [18] adopts packet tracing and local probing to reconstruct the route path. RTI determines whether the packet needs to be tagged according to specific rules for each forwarder on the packet path. The significant advantage of RTI is that it does not require the knowledge of the initial network topology or the prerequisites that restrict single-path routing, and there is no need to repeat recovery for unchanged paths. However, RTI requires resource-constrained relay nodes to dynamically maintain a cache table and learn complex marking rules, which will significantly impact recovery performance if a relay node fails. In addition, the storage overhead of the algorithm is significant. \(c\)Path\({}_{ST}\)[19] improves and integrates the two existing approaches. The initial network topology is first obtained using compression-aware techniques in the path recovery process, and then iterative recovery is performed using information of path correlation. \(c\)Path\({}_{ST}\) receives better path recovery performance in dynamic and complex networks. For this reason, \(c\)Path\({}_{ST}\) needs to be improved in terms of path exploration efficiency. Table 1 shows the key ideas, limitations, and packet overhead of the existing path reconstruction algorithms. In summary, these existing efforts described above have limitations in the context of dynamic changes in network topology, low packet delivery rates, and increasing network size. In contrast, our proposal advances in more complex network scenarios and lower node activity. There are several studies related to IPv6 and RPL routing protocols, indicating that IoT systems using IPv6 face network instability. [PERSON] et al. [20] review the history of research efforts in RPL and present a topic-oriented survey, pointing out that load balancing, which has the potential to cause the death of resource-constrained devices, is a crucial and practical issue of RPL. [PERSON] et al. [21] presented a measurement study of IPv6 stability and performance measurements compared with IPv4 from the control and data plane, indicating that the IPv6 routing system is less stable than IPv4. [PERSON] et al. [22] propose a distributed RPL-based wormhole detection mechanism, while the wormhole attack that threatens the network availability by disturbing routing paths is one of the most common attacks on sensor networks. [PERSON] et al.