## 1 Introduction  
Nowadays, marine surveillance, water contamination detection and monitoring, and oceanographic data collection are indispensable to the exploration, protection and exploitation of aquatic environment [1]. Because of the huge amount of unexploited resources in the ocean, there is an urgent need for research in the field of sensors and sensor networks [2]. Underwater Wireless Sensor Networks (UWSNs) has become a main approach to gain information from previously inaccessible waters. Traditional wireless sensor networks (WSNs) consist of a large number of sensor nodes randomly distributed in a detection field, and these nodes are usually either stationary or moving in limited ranges. However, in many practical scenarios, the movement of nodes is relatively large, such as nodes in UWSNs, delay-tolerant networks, vehicular networks, etc. Nodes in UWSNs can be categorized as stationary nodes and moving nodes. Stationary nodes are anchored to the water bottom while moving nodes can move in a preset velocity, such as Autonomous Underwater Vehicles (AUVs). Nevertheless, only a few researchers take passive mobility of nodes into account. More specifically, nodes may move along internal currents or vortices. Underwater nodes have no access to GPS signals, and the network topology is completely time varying due to irregular mobilities of water currents, which is essentially different from terrestrial WSNs. Meanwhile, due to dynamic topology changes and poor communication conditions underwater, data packets cannot be delivered to the sink nodes deployed on the water surface rapidly. A major challenge of UWSNs is real-time requirements. For instance, fishery surveillance and real-time monitoring of precious assets such as petroleum pipelines. Specifically, report delay of sea properties such as temperatures may lead to serious loss of temperature sensitive sea animals, e.g., sea cucumbers, because they dissolve fast in high temperatures. Moreover, the detection of leakages of coal oil in early stage prevents water contamination and further resource waste. Therefore, we adopt the concept of the value of information (VoI) which evaluates information in terms of timeliness [3]. Additionally, UWSNs are energy constrained due to the fact that they cannot be recharged or replaced, so their ability to route data diminishes when sensor nodes run out of energy. Network lifetime remains the performance bottleneck which perhaps is one main obstacle in the wide scale deployment of wireless sensor networks [4; 5]. In this case, energy consumption is also a fundamental issue in UWSNs. In conclusion, it is significant to consider the timeliness and energy consumption of data forwarding in UWSNs, along with the passive mobility of sensor nodes. Motivated by the timeliness demand and the energy constraint of UWSNs, we aim to explore data forwarding in UWSNs with passive mobility, jointly considering the timeliness of packets and the energy consumption of the sensor nodes. Due to irregular dynamics of water, the node movement is unpredictable, i.e., the future status has little relevance to its historical trajectories. Consequently, the determination of the relay node of a sensor node depends on its current status and its neighborhood relationship. A reinforcement learning method is proposed in this paper. To the best of knowledge, we are the first to jointly consider timeliness and energy consumption of data forwarding in UWSNs with passive mobility. The main contributions of this paper are as follows. We first formulate the problem of data forwarding, by jointly considering timeliness and energy consumption under a novel passive mobility model for UWSNs. We then propose a reinforcement learning-based method for the problem. We finally evaluate the performance of the proposed method through simulations. Experimental results demonstrated the validity of the proposed method and they also demonstrated the efficiency, compared with two benchmark methods. The rest of this paper is organized as follows.