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Abstract Underwater wireless sensor networks (UWSNs) have played an increasingly important role in the fields of ocean exploration, underwater environment monitoring, underwater navigation target tracking, terrain-assisted navigation, and so on. However, UWSNs are a dynamic network, which are unlike terrestrial wireless sensor networks, and the complexity of underwater environment poses many challenges for the positioning of sensor nodes. Aiming at the difficulty of location update, this paper proposes a UWSN positioning technology based on iterative optimization and data position correction (IO-DPC). Firstly, particle swarm optimization technology is used to perform the rough positioning stage of sensor nodes, and iterative optimization is performed for many times. Then, data position correction work is carried out, and a scheme is designed for correcting underwater sensor data position when base station receives the data packet. An underwater ocean current model is established, and IO-DPC technology is simulated in various underwater simulation environments. Experimental results show that IO-DPC technology has higher positioning accuracy than other traditional technologies.

Abstract Considering the insufficient global energy consumption optimization of the existing routing algorithms for Underwater Wireless Sensor Network (UWSN), a new algorithm, named improved energy-balanced routing (IEBR), is designed in this paper for UWSN. The algorithm includes two stages: routing establishment and data transmission. During the first stage, a mathematical model is constructed for transmission distance to find the neighbors at the optimal distances and the underwater network links are established. In addition, IEBR will select relays based on the depth of the neighbors, minimize the hops in a link based on the depth threshold, and solve the problem of data transmission loop. During the second stage, the links built in the first stage are dynamically changed based on the energy level (EL) differences between the neighboring nodes in the links, so as to achieve energy balance of the entire network and extend the network lifetime significantly. Simulation results show that compared with other typical energy-balanced routing algorithms, IEBR presents superior performance in network lifetime, transmission loss, and data throughput.

Abstract In multi-antenna simultaneous wireless information and power transfer (SWIPT) system, beamforming strategy has been widely analyzed due to the increasing signal strength. It can not only increase the signal strength in the direction of the antenna array but also reduce the interference strength, which is a good option for SWIPT system to achieve directional transmission of information and energy. However, the traditional beamforming strategy only uses single beamforming vector, and it does not consider the differences between information and energy in SWIPT system. Actually, interference can also be collected as energy. Based on the traditional beamforming strategy, the resources in SWIPT system are not properly utilized. Therefore, this paper proposes a joint beamforming strategy in multi-input and single-output (MISO) SWIPT system. We extend the traditional single beamforming vector into two beamforming vectors to realize independent control of information and energy based on time switching (TS) receiving mode. In information receiving phase, information beamforming vector is used to carry specific user’s information for information alignment. Since there is an orthogonal relationship between information beamforming vector and channel gain vector, we can achieve to eliminate interference and realize error-free information transmission. In energy receiving phase, energy beamforming vector is used to carry user’s energy. Energy beamforming vector and channel gain vector do not require the orthogonality so that the interference can also be collected as energy. In this paper, we model it as a transmission power optimization (TPO) problem, which is a complex non-convex problem. We firstly transform it into a convex problem, and then, it can be solved using CVX toolbox. Simulation results show that the proposed strategy could increase the energy collection at the same transmission power and also decrease the transmission power at the same energy collection.