# An Enhanced Energy Balanced Data Transmission Protocol for Underwater Acoustic Sensor Networks
## 2 Literature Review
In [5], the authors improve DBR by choosing depth and residual energy based forwarder nodes (Energy-Efficient DBR (EEDBR) protocol). [PERSON] _et al._[17] extended DBR and EEDBR [5] to further improve the network lifetime. Their proposed technique limits the number of forwarder nodes using depth threshold parameter. Thus energy consumption is minimized. **Counterpart Schemes in Brief:** In simulations (_i.e._, Section 5), we have compared our proposed schemes with BTM [9] and UWDAR [19]. From the studied extensive state-of-the-art works, we choose BTM and UWDAR because their architecture and protocol operation is the closest to that of our work. Thus, we explain both these benchmark techniques in detail. BTM is a hybrid data transmission routing protocol for UASNs. This protocol presents two algorithms; Efficient Routing Algorithm (ERA) and Data Balance Transmission (DBT) algorithm. Initially, ERA establishes tree between nodes which determines path for routing data packets from source to destination. Then, data transmissions occur as per DBT algorithm. Average number of nodes in a tree slice \(i\) are defined as \(2i-1\), where \((2\leq i\leq N)\). BTM balances data load among sensor nodes by dividing the initial energy of each sensor node into energy chunks known as energy levels. A node transmits data to its one hop neighbor node till it depletes one energy level. The node which consumes one energy level earlier, starts direct data transmission towards sink till it again depletes one energy level. In this way, transmission mode is continuously shifted to achieve successive transmissions with balanced energy consumption costs. The proposed algorithms are implemented in both small and large scale UASNs, where radius considered for small scale UASNs is 0.2-1 km, while it is 1-5 km for large scale networks. However, BTM's performance degrades as the network radius is increased. Direct data transmissions are not favorable in network areas with very long network radii. They consume an abundance of energy to transmit data directly towards the sink, hence, network lifetime decreases. As a result, performance of this technique is very much degraded in large scale networks. BTM only balances energy consumption of sensor nodes within successive ring sectors, whereas, the difference of energy levels between near-to-the-sink nodes and the farthest nodes is notable. This difference increases with increase in transmission radius, thus, network lifetime decreases. Therefore, it is concluded here that this technique is impractical for large scale UASNs. The other technique in comparison is the Data Aggregating Ring (DAR) technique [19]. Though this technique is proposed for terrestrial WSNs, we have implemented DAR using the distinctive underwater energy consumption model (in Section 3) for UASNs, hence named it UWDAR. This technique calculates hop-grades based on the number of hops a node is away from the sink. Single hop-grade nodes always directly transmit data to the sink during their in-charge as well as not in-charge time. All other network nodes (_i.e._, nodes not in-charge hop grades), transmit data using multi-hop mode to in-charge hop-grade nodes, which further transmit it directly towards sink. Another contribution of this paper is the TDMA-like transmission scheduling mechanism defined to minimize collisions. Also, it helps sensor nodes to decide transmission delay with respect to their positions. A method is defined for determining in-charge time for nodes in different hop-grades. The major drawback of this technique is its backward data transmissions. Nodes far away also transmit data to in-charge hop-grade nodes even in-charge hop-grade nodes are at long distance from sink. This process consumes high energy which leads to short network lifetime. Sensor nodes switch between sleep and awake modes to save energy. This technique works for very small network radius, where direct data transmissions do not affect network performance. Figure 2 illustrates the data transmission mechanism in BTM and UWDAR.