# An Enhanced Energy Balanced Data Transmission Protocol for Underwater Acoustic Sensor Networks
## 2 Literature Review  
As the underwater sensor nodes are powered by onboard batteries with limited capacities, energy efficient or energy balancing strategies are always demanded at all layers of the protocol stack. Since this work is limited in scope to the routing/network layer only, we discuss some of the recent work done subject to energy balancing in UASNs as follows. Energy Balanced Hybrid data propagation (EBH) algorithm and Differential Initial Battery assignment (DIB) algorithm [3] consider linear UASN with hybrid transmission modes. In these algorithms, the sensor nodes switch between transmission modes according to their energy grades. The authors use differential battery assignment technique for different battery power levels. Similarly, authors in [18] proposed a multi-hop technique for UASNs subject to balanced energy consumption of the sensor nodes. In this technique, the downstream neighbors forward data packets as implicit ACKnowledgements (ACKs) for data packets that are sent a-priory. In [9], [PERSON] _et al._ investigated Balanced Transmission Mechanism (BTM) in UASNs. In BTM, every sensor node selects transmission mode according to its energy level. Hence, every node utilizes energy in a balanced way and energy consumption in the network becomes somehow balanced. A dynamic addressing based routing technique for UASNs has been introduced by authors in [8]. This technique provides time-efficient and scalable routing without requiring any dimensional information. \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Techniques** & **Features** & **Domain** & **Plasm/Dickness** & **Adbiewments** \\ \hline BTM [9] & Balanced energy consumption, Energy efficient & UASNs, 2D underwater terrains & High energy consumption within transmitting at positions, Transactions of transmission loops & Network & Istitoms, Energy consumption \\ EBH and Den [9] & Energy balancing, Airplicable for both shallower and deep water & 2D UASNs, Advanced mountains & Inefficient for other than linear networks, Oceanographic data collection & Inefficient for other than linear networks, Unsupervised only for sparse network & Network & Istitoms, Energy consumption \\ \hline ID-DAB [8] & Energy efficient, Differential location information is not required & & UASNs, Critical underwater monitoring missions & Inbalanced energy consumption, Numer to sink models despite energy entire, High end-to-end & High data delivery metric, Network lifetime \\ \hline RAD [  
The authors in [15] presented delay-sensitive and delay-insensitive distributed routing protocols for minimizing energy consumption of the sensor nodes. They have considered various application requirements along with the varying conditions of underwater channel. Based on the calling behavior of ultrasonic frog, a calling algorithm is proposed in [7]. The relay node selection process is based on the calling behavior of ultrasonic frog. In order to save energy, low residual energy and far away nodes choose sleep mode. In another work [16], the authors presented an energy efficient routing algorithm for UASNs based upon the residual energy of and distance between sensor nodes. In [11], a Relative Distance-Based Forwarding routing protocol (RDBF) for UASNs has been proposed. RDBF calculates relative distance between sensor nodes and sink as a routing metric. Based on this routing metric, the forwarder nodes are selected. Moreover, the authors introduce a fitness factor to calculate holding time based on which the packets are forwarded. The authors claim that fitness function based packet forwarding mechanism results decreased probability of packet collision. In [6], Depth-Based Routing protocol (DBR) is proposed for UASNs. In DBR, depth and holding time are used to forward the packets from source to destination, where the source lies at the bottom of the network field (high depth) and the destination resides at the surface of the network field (lowest depth).