# An Efficient Void Aware Framework for Enabling Internet of Underwater Things
## 3 The Proposed Efficient Void Aware Routing Protocol for Enabling Internet of Underwater Things  
### Underwater Network Architecture  
As mentioned in the literature review, the communication void become one of the major issues in opportunistic efficient routing protocols in IoUT, especially in the sparse-based topology network [16; 35]. In such topologies, the communication void problem comes during the forwarding process if the forwarder node did not find any shallower next forwarding node. Consequently, it is necessary to design and develop an efficient routing scheme that identifies the void area and avoids forwarding the data packet to the sensors placed in these areas that resulting in minimizing packet loss. Figure 1 illustrates an underwater dynamic network topology that is comprised of dense and void areas and void nodes and ordinary nodes.  
The mechanism of the void detection and handling techniques in WSNs are not efficient to be employed in underwater environments. The void handling techniques in UWSNs are also not efficient in both location-based and beacon-based algorithms. In contrast, existing routing algorithms in the depth-based routing protocols in UWSNs such as DBR [36], EEDBR [37], RE-PBR [11] did not employ void detection algorithms as it taking into account selecting the next forwarding sensor based on either residual energy, depth, or link quality. Moreover, the void handling techniques provided by HydroCast, AMCTD [38], and VAPR have some drawbacks. The utilization of GPS information in HydroCast causes high energy consumption. AMCTD employs courier nodes that move towards the network area to collect the data packet that consumes high energy. The use of beacon messages in the VAPR causes high energy consumption and network overhead.  
Figure 1: Underwater dynamic network topology.  
As a result, it is necessary to investigate the void area in depth-based routing protocols without using any external information.  
As previously stated in our published algorithms, the RE-PBR and SPRE-PBR algorithms did not deal with void areas. As a result, the chance of packet loss is dramatically increased. Data packet could be lost once it hits the void sensors because the design approach of RE-PBR and SPRE-PBR did not provide any void detection mechanisms. Figure 2 shows an excellent scenario in SPRE-PBR of how the data packet dropped in the sensors in the void area. It is assumed that, as shown in Figure 2a, node \(n_{1}\) shall send a data packet. It thus calls optimal shortest path algorithm to identify the optimal candidate among neighbors \(n_{2}\), \(n_{3}\), and \(n_{4}\). Node \(n_{1}\) then receives the ID of the candidate nodes and picks \(n_{4}\) because it has the best route cost and it adds the ID of the \(n_{4}\) along with the data packet and then broadcasts it to its shallower neighbors. It assumes \(n_{4}\) to have successfully received the data packet and \(n_{1}\) to overhear the received data packet. As \(n_{5}\) and \(n_{6}\) are its candidate set, \(n_{5}\) has the lowest route cost between them and is considered the shortest path in the present hop. The ID of \(n_{5}\) is then embedded with the data packet and forwarded to its shallower neighbors [11; 17].  
Next, \(n_{5}\) has just one neighbor, as is shown. Thus, \(n_{6}\) as the next transmission node has been picked and the data packet is forwarded to the selected nodes. The following stage is shown in Figure 2b. \(n_{6}\) did not have any candidate set in its routing table. The communication void situation therefore occurs, and in this node, a data packet going to be dropped [11; 17].