Underwater acoustic sensor networks (UASNs) are widely used in a variety of ocean applications, such as exploring ocean resources or monitoring abnormal ocean environments. However, data collection schemes in UASNs are significantly different than those in wireless sensor networks due to high power consumption, high propagation delay, and so on. What's more, previous research has overlooked practical conditions, such as water delamination characteristics and autonomous underwater vehicles (AUV) limited energy. To solve these problems, we propose a data collection scheme based on a stratification model (DCSM) for 3D UASNs. In our scheme, the network is divided into two layers. The top layer, called the Ekman layer, is dynamic; we employ a forwarding set-based multi-hop forwarding algorithm in data collection. Then, a neighbor density clustering-based AUV data gathering algorithm is adopted in the second layer, which is relatively static. By employing different data collection algorithms in different layers, we can integrate the advantages of a multi-hop transmission scheme and AUV-aided data collection scheme to reduce network consumption and improve network lifetime. The simulation results also confirm the proposed method has good performance.

In this paper, a high-availability data collection scheme based on multiple autonomous underwater vehicles (AUVs) (HAMA) is proposed to improve the performance of the sensor network and guarantee the high availability of the data collection service. Multi-AUVs move in the network and their trajectory is pre-defined. Data from the nodes near the trajectory of an AUV are sent to the AUV directly and the other nodes transmit their data to the nodes that are closer to the trajectory. A malfunction discovery and repair mechanisms are applied to ensure that the network operates appropriately when an AUV fails to communicate with the nodes while collecting data. Compared with existing methods, the HAMA method increases the packet delivery ratio and the network lifetime

The localization of sensor nodes is a significant issue in wireless sensor networks (WSNs) because many applications cannot provide services without geolocation data, especially during disaster management. In recent years, a promising unknown-nodes positioning method has been developed that localizes unknown nodes, employing a GPS-enabled mobile anchor node moving in the network, and broadcasting its location information periodically to assist localization. In contrast to most studies on path planning that assume infinite energy of the mobile anchor node, the anchor node in this study, consumes different amounts of energy during phases of startup, turning, and uniform motion considering the aftermath of disasters. To enable a trade-off between location accuracy and energy consumption, a path-planning algorithm combining a Localization algorithm with a Mobile Anchor node based on Trilateration (LMAT) and SCAN algorithm (SLMAT) is proposed. SLMAT ensures that each unknown node is covered by a regular triangle formed by beacons. Furthermore, the number of corners along the planned path is reduced to save the energy of the mobile anchor node. In addition, a series of experiments have been conducted to evaluate the performance of the SLMAT algorithm. Simulation results indicate that SLMAT outperforms SCAN, LMAT, HILBERT, and Z-curve in terms of localization accuracy and energy consumption.

Energy constraint is a critical issue in the development of wireless sensor networks (WSNs) because sensor nodes are generally powered by batteries. Recently, wireless rechargeable sensor networks (WRSNs), which introduce wireless mobile chargers (MCs) to replenish energy for nodes, have been proposed to resolve the root cause of energy limitations in WSNs. However, existing wireless charging algorithms cannot fully leverage the mobility of MCs because unity between the energy replenishment process and mobile data collection has yet to be realized. Thus, in this paper, a joint energy replenishment and data collection algorithm for WRSNs is proposed. In this algorithm, the network is divided into multiple clusters based on a K-means algorithm. Two MCs visit the anchor point in each cluster by moving along the shortest Hamiltonian cycle in opposite directions. The positions of anchor points are calculated by the base station (BS) based on the energy distribution in each cluster. A spare MC is assigned to the network in case either of the two MCs depletes its energy before reaching the BS. After the two MCs’ current tours are over, a semi-Markov model is proposed for energy prediction so anchor points can be updated in the next round. Simulation results demonstrate the semi-Markov-based energy prediction model is highly precise, and the proposed algorithm can replenish energy for network energy effectively.

Aiming at the problems of low mobility, low positioning accuracy, high communication overhead and high energy consumption of sensor nodes in underwater acoustic sensor networks, MPL algorithm is proposed in this paper. The algorithm is divided into two stages: mobile prediction and node location. In the node location phase, firstly, in order to reduce the communication overhead and energy consumption, a TOA-based ranging strategy is proposed. Then, after dimension reduction processing, the gray wolf algorithm is used to find the optimal location of the secondary nodes with low location accuracy. Finally, the node location is obtained and the node movement prediction stage is entered. In the coastal area, tidal phenomenon is the main factor leading to node movement, so combining the tidal model with the node stress, a more practical node movement model is constructed. Therefore, in the movement prediction stage, the velocity and position of each time point in the prediction window are predicted according to the node movement model, and then the underwater positioning is completed. Finally, the proposed MPL algorithm is simulated and analyzed; the simulation results show that the proposed MPL algorithm has higher localization performance. Compared with LSLS, SLMP, GA-SLMP algorithm, the proposed MPL algorithm not only effectively reduces the network communication overhead and energy consumption, but also improves the network location coverage and node location accuracy.

Wireless rechargeable sensor networks (WRSN) have attracted considerable attention in recent years due to the constant energy supply for battery-powered sensor nodes. However, current technologies only enable the mobile charger to replenish energy for one single node at a time. This method has poor scalability and is not suitable for large-scale WRSNs. Recently, wireless energy transfer technology based on multi-hop energy transfer has made great progress. It provides fundamental support to alleviate the scalability problem. In this paper, the node energy replenishment problem is formulated into an optimization problem. The optimization objective is to minimize the number of non-functional nodes. We propose the uneven cluster-based mobile charging (UCMC) algorithm for WRSNs. An uneven clustering scheme and a novel charging path planning scheme are incorporated in the UCMC algorithm. The simulation results verify that the proposed algorithm can achieve energy balance, reduce the number of dead nodes, and prolong the network lifetime.