In this paper, we propose an improved energy optimization clustering algorithm (EOCA) for the multi-hop underwater acoustic cooperative sensor networks (UWA-CSN) due to the limited energy supply of the underwater sensor nodes. The proposed EOCA scheme considers multiple factors, such as the number of neighbor nodes, the residual energy of each node, the motion of the sensor nodes caused by the ocean currents, and the distance between the sink node and each underwater sensor node. All these factors are considered within the framework of the multi-hop cooperative communication. Moreover, in the proposed energy optimization scheme, we define a residual energy based maximum effective communication range (reE-MECR) according to the residual energy of each underwater sensor node, which can adaptively control the energy consumption of data transmission for each hop. Compared with the existing clustering scheme, the experimental results demonstrate that the proposed EOCA scheme can not only prolong the life time of the multi-hop UWA-CSN, but also keep a good communication and networking performance, including the package delivery ratio, the energy consumption efficiency, and the network coverage area. The proposed scheme can obtain both the clustering benefit and the cooperative communication benefit for the multi-hop UWA-SN in terms of energy saving in underwater environments.

Use of magnetic fields for underwater wireless communication recently has been found very promising due to its advantages of being low power, low cost, no multipath, no Doppler effect and instant communication. Keeping in view the importance and the potential of magnetic field communication for underwater wireless sensor networks, this paper presents a review of the fundamental concepts and theory of magnetic field communication by identifying the main blocks, describing the limitation of each block and mentioning the advancements being done. The three fundamental blocks identified are: 1) the transmit and receive coils, 2) matching network, and 3) channel characteristics of magnetic fields. The first block explains how a single dimensional coil is used to establish a directional communication link between a transmitter & receiver and then how the directionality problem is solved with the help of multi-dimensional coils and meta-material coils. Further, the matching network, explains the difference between the induction and resonance phenomena and their effect on the range and capacity. In the third block, the factors affecting the magnetic fields are mentioned that are critical for underwater wireless communication with magnetic fields. Finally, the paper introduces the recent advancements at physical, MAC and network layers.

The orthogonal matching pursuit (OMP) algorithm has been widely adopted in sparse channel estimation, both in radio and underwater acoustic communications. Traditionally, the accuracy of path delay estimation in an OMP based channel estimator depends on the adopted delay grid. The finer the delay grid, the higher the estimation accuracy, but meanwhile the higher the computational complexity. In this letter, we propose a modified OMP based channel estimation scheme for orthogonal frequency division multiplexing with equispaced pilots, which utilizes the characteristic of the Hermitian inner product between the pilot-compensated frequency observation vector and the columns of the dictionary matrix to provide a closed-form estimate for the path delays. In addition, a two-step scheme facilitated by Goertzel's algorithm is proposed to search the samples in the closed-form estimation to further reduce the computational complexity. Simulation results and complexity analysis demonstrate the advantages of the proposed scheme in both estimation accuracy and computational complexity.

The limited energy supply of underwater nodes is one of the key issues for the multi-hop underwater acoustic sensor networks (UWA-SN). In this paper, the fusion scheme based on ant colony optimization algorithm (ACOA), artificial fish swarm algorithm (AFSA) and dynamic coded cooperation (DCC) strategy, named as ACOA-AFSA fusion DCC routing algorithm, has been proposed for routing in the multi-hop UWA-SN, aiming at simultaneously reducing the energy consumption and enhancing the robustness. In the proposed ACOA-AFSA fusion DCC routing algorithm, the randomness of the AFSA and the positive feedback mechanism of the ACOA enable the algorithm to find the global optimal routing more efficient and accurate. In addition, most existing routing protocols consider the large-scale networks (more than 100 nodes), while the medium-and small-scale networks (less than 100 nodes) are more practical in nowadays’ multi-hop UWA-SN. The network scale affects the optimal design of routing protocols in terms of energy saving. Considering the practical situation, we compare the proposed scheme with other four existing artificial intelligence (AI) routing algorithms in the medium-and small-scale multi-hop UWA-SN, which is instructive for application of AI in practical multi-hop UWA-SN. The simulation results show that the proposed ACOA-AFSA fusion DCC routing algorithm can reduce the energy consumption by 40.1compared to that with non-cooperative strategy in the multi-hop UWA-SN with 20 nodes. The proposed routing algorithm also consumes less energy than other four existing AI routing algorithms in the mediumscale multi-hop UWA-SN with 50 nodes or 100 nodes. However, for the small-scale case with 10 nodes, the advantage is not very obvious. In the meantime, the proposed ACOA-AFSA fusion DCC algorithm is compatible with the ACOA-AFSA fusion non-cooperative algorithm, and the complexity is acceptable, which is very appealing to the time-varying marine environments.