###### Abstract  
Because of the complicated underwater environment, the efficiency of data transmission from underwater sensor nodes to a sink node (SN) is faced with great challenges. Aiming at the problem of energy consumption in underwater wireless sensor networks (UWSNs), this paper proposes an energy-efficient clustering routing algorithm based on an improved ant colony optimization (ACO) algorithm. In clustering routing algorithms, the network is divided into many clusters, and each cluster consists of one cluster head node (CHN) and several cluster member nodes (CMNs). This paper optimizes the CHN selection based on the residual energy of nodes and the distance factor. The selected CHN gathers data sent by the CMNs and transmits them to the sink node by multiple hops. Optimal multi-hop paths from the CHNs to the SN are found by an improved ACO algorithm. This paper presents the ACO algorithm through the improvement of the heuristic information, the evaporation parameter for the pheromone update mechanism, and the ant searching scope. Simulation results indicate the high effectiveness and efficiency of the proposed algorithm in reducing the energy consumption, prolonging the network lifetime, and decreasing the packet loss ratio. underwater wireless sensor networks; ant colony optimization algorithms; clustering routing algorithms; energy efficiency; network lifetime +
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employ a 2D network model, whereas UWSNs adopt a 3D network model, which is a great challenge to researchers. Thirdly, the replacement of sensor nodes is more difficult in UWSNs than in TWSNs. In conserving energy, multi-hop data transmission in long-distance communication for UWSNs is more effective than single-hop transmission [10]. Additionally, to alleviate the problems of data collision and traffic load balance, it is important to design a reliable network topology [11]. Many studies have shown that the clustering routing algorithm is capable of saving energy, avoiding collisions, and balancing traffic load because it employs the clustering topology and uses a multi-hop mechanism during the inter-cluster data transmission [12; 13]. In clustering routing algorithms, the network is divided into many clusters and each cluster consists of one cluster head node (CHN) and several cluster member nodes (CMNs) [14]. When clusters are formed, the CHNs allocate channel resources for CMNs and the CMNs transmit data according to the allocation, which can decrease collisions [15]. After receiving the data from the CMNs in the same cluster, the CHN is responsible for aggregating the data, which can reduce data redundancy and decrease the number of data packets to be sent to the sink node (SN), thereby conserving energy [16]. Meanwhile, the decreased number of data packets helps reduce collisions when the CHNs transmit them to the SN. Additionally, the multi-hop mechanism is used when CHNs send data to the SN, which can save energy compared to the single-hop mechanism. Moreover, the clustering routing algorithm usually employs a CHN rotation mechanism, which avoids the excessive energy consumption of CHNs, balances the energy dissipation, and prolongs the network lifetime [17]. Many studies indicate that clustering routing algorithms are superior in controlling data traffic and reducing data transmission, and are thus capable of saving energy, extending network lifespan, and decreasing the packet loss ratio [18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28]. The low-energy adaptive clustering hierarchy (LEACH) algorithm, the earliest clustering routing algorithm, employs a probabilistic method to select CHNs and does not consider the residual energy of nodes, which causes some low-energy nodes to become CHNs [18]. This goes against the energy balance and the energy efficiency, for these inefficient CHNs may die prematurely. Therefore, researchers proposed improved clustering routing algorithms.