## 1 Introduction  
Smart coasts (SCs) are rapidly gaining importance as a major contributing factor to sustainable communities [1]. Key features of SCs are the monitoring of water quality, water pollution, seismic activity, eco-system, and the overall management of the coastal zones. To effectively implement these features, uninterrupted collection, monitoring, detection, and management of various aquatic parameters are required. Such continuous monitoring of oceanographic parameters is only made possible by employing Underwater Wireless Sensor Networks (UWSNs) [2], which have become the technological underpinning of SCs. Several sensor nodes, operated by battery, are deployed in water-based environments to form UWSNs. Such nodes are equipped with sensing, communicating, and storing capabilities of various physical aquatic parameters. A sink floating at the surface of the sea receives the sensed data and further forwards it to the onshore monitoring station. Each communication approach is marred by unique challenges when employed in the underwater environment [3]. The communication among sensor nodes may be carried out through optical waves [4; 5], electromagnetic waves [6; 7], or acoustic waves [8]. Unlike underwater communication, the preferred choice for terrestrial communication has been electromagnetic signals at radio frequencies because of their wider bandwidth, lower energy consumption per bit, and smaller propagation delays. Therefore, communication from the floating sinks to the onshore monitoring stations employ this mode. On the other hand, high conductivity of seawater leads to severe attenuation and significant absorption losses, making it a bad choice for underwater communications. Similarly, sightline is a requirement in optical communications between parties and is not always possible. Moreover, the distance over which optical communication can successfully take place is drastically curtailed by water turbidity, making it unviable for underwater communications. The modality of choice, and hence the most widely used one for underwater networks is the acoustic communication, where the network is also termed as underwater acoustic sensor network (UASN). UASNs also face various challenges such as fading, resulting in high error rates and bandwidth limitations due to multipath [9]. Moreover, acoustic signals in an aquatic environment travel much slower compared to radio signals in a terrestrial environment. These challenges result in limitations in synchronization, data routing, and information regarding the network state. This explains why traditional communication approaches, otherwise successful for terrestrial sensor networks, cannot be effectively used in the underwater environment. Most of the challenges faced by UASNs listed above are interdependent, making it even more complex to design optimal solutions. In vector-based routing protocols, each node calculates the holding time based on the node/network parameters, e.g., the distance to sink, middle of the virtual pipeline, and receiving node distance from the previous hop's sender. Holding time is only estimated after a node first checks and ensures that it is located within the virtual pipeline. This procedure takes place upon receiving the first duplicate of a packet from downstream nodes. The timer is set up based on the (estimated) holding time. When the timer expires, the node forwards the packet, if no other copy from its neighbors is received. At the same time, the remaining nodes will suppress their packet forwarding protocols in favor of the node, which has the minimum holding time in all the surrounding nodes. For faster propagation, such protocols (vector-based) calculate the holding time based on the nodes' proximity. In addition, the proximity of the middle of the pipeline and the distance to the sink are also considered for estimation. End-to-end delay reductions occur while packets are forwarded through these nodes; however, it causes energy depletion, resulting in a void energy hole problem.