# 3D Void Handling Geographic P2P-RPL for Indoor Multi-Hop IR-UWB Networks
## 2 Related Works  
For the routing support of LLNs, IETF standardized the IPv6 routing protocol for low-power and lossy networks (RPL) [9]. RPL constructs a tree-like topology called destination-oriented directed acyclic graph (DODAG) to ensure communication capability for all three kinds of traffic: multipoint-to-point (MP2P), point-to-multipoint (P2 MP) and point-to-point (P2P). For P2P traffic, a discovered path must contain the common ancestor of the source and destination, and hence leads to a long and inefficient route discovery. In the non-storing mode of RPL, the common ancestor becomes the root node of the constructed DODAG. Thus, the root node suffers from bottleneck and severe congestion problems [10; 11]. To support optimal route discovery following a P2P traffic request, IETF has also standardized P2P-RPL [1], which creates a temporary DODAG rooted at the source node. This temporary DODAG is utilized solely for the purpose of discovering an optimal route between the source and destination. P2P-RPL has to flood the P2P mode DODAG information object (P2P-DIO) messages. The standard RPL control messages for P2 Proute discovery are flooded throughout the network, which leads to high communication overheads. In 3D environments, far more nodes need to be randomly or uniformly placed to ensure full network connectivity [4]. As a result, P2P-RPL is required to flood a higher number of P2P-DIO messages and is energy inefficient for 3D network environments. LA-P2P-RPL [2] restricts the flooding zone of P2P-RPL by using the position information of the source and destination nodes to reduce routing overheads. Nevertheless, LA-P2P-RPL still incurs quite an amount of routing overheads due to its flooding strategy and can fail in route discovery if there exists a route only through the nodes outside the flooding zone. In [3], energy-efficient region-based routing protocol for low-power and lossy networks (ER-RPL), inheriting the DODAG mechanism of RPL, is proposed, which reduces flooding overheads by dividing the network area into non-overlapping regions and allowing nodes only in a few selected regions to participate in the P2P route discovery. Only static nodes are considered, and each node estimates its own region with a distributed self-regioning algorithm. Regions for P2P route discovery packet forwarding are selected based on the regions in which the source and destination nodes reside. Although ER-RPL has better performance than P2P-RPL in terms of overhead, it generates more overhead than LA-P2P-RPL because of the larger forwarding area and less accurate node location information, compared to LA-P2P-RPL. As a solution to the high overhead problem found in the existing RPL-based P2P routing algorithms in 3D environments, 3D geographic routing is an attractive option because its greedy next-hop selection requires only a few routing packets in its route discovery operation. Geographic routing algorithms usually comprise next-hop selection and void (local minimum) handling steps. The next-hop selection step greedily selects a locally optimal one-hop neighbor with positive advance as its next-hop forwarder. The greedy routing (forwarding) next-hop selection strategy selects a one-hop neighbor that is closest to the destination as its next forwarder. In the void handling step, void situations are recovered for guaranteeing high route discovery reliability of the routing algorithm. In 3D networks, void situations appear more often [7] and hence, a reliable void handling technique suitable for 3D networks must be accompanied. To provide energy-efficient and reliable 3D P2P routing functionality, we propose the GVA-P2P-RPL routing algorithm based on the IETF's P2P-RPL standard. GVA-P2P-RPL uses the greedy forwarding next-hop selection strategy when it is possible. When a local minimum is encountered, P2P-DIO messages are broadcast temporarily and locally. To shorten the route discovery time, the ATT algorithm uses a smaller minimum trickle timer interval value than the standard trickle algorithm.