# Receiver-Initiated Handshaking MAC Based on Traffic Estimation for Underwater Sensor Networks +
## 1 Introduction  
Underwater sensor networks (UWSNs) have demonstrated a wide range of important applications in oceanographic data collection, pollution detection, and marine surveillance [1,2]. Recently, UWSNs have spurred a great wave of efforts from the research community. However, the unique characteristics of UWSNs, such as their limited available bandwidth, long propagation delays, and high energy consumption, introduce great challenges to almost every layer of the network design [3,4]. In a network with shared media, medium access control (MAC) serves a fundamental role in network protocol stack coordinating multi-user communications. It is especially critical for acoustic networks with limited channel bandwidth, long propagation delays, and a constrained power supply. To date, numerous MAC protocols, for example, [5, 6, 7], have been proposed and designed for UWSNs. Random access-based MAC protocols, such as those described in [8], are designed for simple implementation as well as robustness to adapt to dynamic networks. Nevertheless, their high collision probability limits their application in energy-constrained underwater networks. Alternatively, handshaking-based methods, e.g., [9, 10], are a viable option for resolving collisions in the network system. The majority of existing handshaking MAC protocols for UWSNs have focused on how to handle the long propagation delays [11]. However, long preambles and the resulting high overhead on control packets have not been investigated thoroughly in the literature. The handshaking MAC protocols can be generally classified into two categories. One is sender-initiated MAC, where the sender starts one round of handshakes by sending out a Request-to-Send (\(RTS\)) message to the intended receiver(s). Most of the existing handshake-based MAC protocols [9, 10, 11] are in this category. The other one is receiver-initiated MAC [12, 13, 14, 15] where the receiver initiates the handshake process by sending a Request-to-Receive (\(RTR\)) message to poll data from intended senders. Despite improving the energy efficiency and enabling data aggregations, existing solutions to receiver-initiated-based handshake MAC protocols face great challenges in terms of efficient data polling. On the one hand, without knowing the status of neighboring senders, it is difficult for the receiver to decide when to poll data and how much data to request. On the other hand, the frequent information exchange between the sender and receiver leads to low efficiency in data communication. Following this concern, we propose a receiver-initiated handshaking MAC with adaptive data polling. The contribution of our work is three-fold. First, we propose a traffic estimation-based receiver-initiated MAC (TERI-MAC). The receiver collecting data from neighboring nodes enables the data aggregation at the link layer, which benefits the data fusion and sensing process in underwater sensor networks. Second, we propose an adaptive data polling mechanism to solve the _when-to-poll_ and _how-much-to-poll_ problems in the receiver-initiated MAC protocols. With the assistance of online traffic estimation, TERI-MAC can be applied to networks with arbitrary traffic patterns while maintaining high energy efficiency and channel utilization for each user request. Third, extensive simulations have been conducted to evaluate the performance of TERI-MAC. It has been verified that TERI-MAC can achieve good energy efficiency and channel utilization by delaying the data requests at the receivers and adjusting the channel resource assignment among different senders. The rest of this paper is organized as follows. We first discuss the applications and motivations of receiver-initiated MACs and enumerate challenges in Section 2. In Section 3, we survey the related research on the receiver-initiated MAC protocols and network traffic predictions. The network architecture is presented in Section 4. The details of TERI-MAC are elaborated in Section 5, with the adaptive data polling presented in Section 6.