# An Energy Efficient Routing Approach for IoT Enabled Underwater WSNs in Smart Cities
## 3 Motivation for U-(ACH)\({}^{2}\): Underwater Implementation of (ACH)\({}^{2}\)  
New challenges are observed to deploy a clustering based underwater acoustic sensor network in a very large area. Limitations of the underwater environment are: sensor nodes in underwater are having limited energy, low bandwidth, and small communication range [55]. To overcome these  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Protocol** & \begin{tabular}{l} **Forwarder Node** \\ **Dependency** \\ \end{tabular} & **Routing** & \begin{tabular}{l} **Energy** \\ **Dependency** \\ \end{tabular} & \
\begin{tabular}{l} **Operational** \\ **Mode** \\ \end{tabular} &
\begin{tabular}{l} **Sink** \\ **Mobility** \\ \end{tabular} \\ \hline DBR & Depth & Multi-hop & Homogeneous & Proactive & Static sink \\ \hline EEDBR & Depth \& residual energy & Multi-hop & Homogeneous & Proactive & Static sink \\ \hline CDBR /CEEDBR & Depth & Multi-hop & Homogeneous & Proactive & Static sink \\ \hline CoDBR & Depth & Cooperative & Homogeneous & Proactive & Static sink \\ \hline RE-PBR & Depth \& residual energy & Multi-hop & Homogeneous & Proactive & Static sink \\ \hline iAMCTD & Depth, residual energy \& SNR & Multi-hop & Homogeneous & Reactive & Static sink \\ \hline DEADS & Depth \& residual energy & Cooperative & Homogeneous & Reactive & Mobile sinks \\ \hline DNAR \& & Depth \& residual energy & Non-Coop & Heterogeneous & Proactive & Mobile sinks \\ Co-DNAR & & & Cooperative & & & \\ \hline EERD \& & Depth \& residual energy & Non-Coop & Heterogeneous & Proactive & Mobile sinks \\ Co-EERD & & & \& Cooperative & & \\ \hline LEACH & Residual energy & Clustering & Homogeneous & Proactive & Static sink \\ \hline TEEN & Residual energy & Clustering & Homogeneous & Reactive & Static sink \\ \hline SEP & Residual energy & Clustering & Heterogeneous & Proactive & Static sink \\ \hline DEEC & Residual energy & Clustering & Heterogeneous & Proactive & Static sink \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of Existing Protocols.  
deficiencies, reliable transmission of data packet and survivability of the network for a long duration is required for smart cities.  
Many energy efficient underwater routing protocols are proposed to date; nevertheless, the desired parameters are still yet to be achieved. Challenges that are faced by underwater routing protocols are low throughput, latency in data transmission, and loss of energy at higher levels of speed. The demands of smart cities are demanding in terms of parallel processing and the shortcomings listed above need to be addressed in the existing routing protocols. In order to overcome the shortcomings, to meet the modern research criteria of a smart city, a routing protocol, namely: U-(ACH)\({}^{2}\), is proposed (general architecture is shown in Figure 1). To examine the efficiency of the projected routing protocol, a terrestrial-based (ACH)\({}^{2}\) routing protocol is implemented in the IoT enabled underwater environment and the effect on throughput and energy consumption is examined by using an extra feature, i.e., \"depth\" in it.  
A terrestrial based routing algorithm (ACH)\({}^{2}\) is modified in this study, in which the challenges of the underwater environment are examined. To accomplish the objectives, such as: reduced energy consumption and maximization of the throughput of sensor nodes, a new scheme in underwater is proposed, named U-(ACH)\({}^{2}\). In the proposed scheme, both homogeneous and heterogeneous acoustic modems are used to get the optimum result.