Software-defined networking (SDN) is recognized as the next-generation networking paradigm. The software-defined architecture for underwater acoustic sensor networks (SDUASNs) has become a hot topic. However, the current researches on SDUASNs is still in its infancy, which mainly focuses on network architecture, data transmission and routing. There exists some shortcomings that the scale of the SDUASNs is difficult to expand, and the security maintenance is seldom dabble. Therefore, a scalable software-definition architecture for underwater acoustic sensor networks (SSDUASNs) is introduced in this paper. It realizes an organic combination of the knowledge level, control level, and data level. The new nodes can easily access the network, which could be conducive to large-scale deployment. Then, the basic security authentication mechanism called BSAM is designed based on our architecture. In order to reflect the advantages of flexible and programmable in SSDUASNs, security authentication mechanism with pre-push (SAM-PP) is proposed in the further. In the current UASNs, nodes authentication protocol is inefficient as high consumption and long delay. In addition, it is difficult to adapt to the dynamic environment. The two mechanisms can effectively solve these problems. Compared to some existing schemes, BSAM and SAM-PP can effectively distinguish between legal nodes and malicious nodes, save the storage space of nodes greatly, and improve the efficiency of network operation. Moreover, SAM-PP has a further advantage in reducing the authentication delay.

The changes of temperature, salinity and ocean current in underwater environment, have adverse effects on the communication range of sensors, and make them become temporary failure. These temporarily misbehaving sensors are called dumb nodes. In this paper, an energy-efficient connectivity re-establishment (EECR) scheme is proposed. It can reconstruct the topology of underwater acoustic sensor networks (UASNs) with the existing of dumb nodes. Due to the dynamic of underwater environment, the generation and recovery of dumb nodes also change dynamically, resulting in intermittent interruption of network topology. Therefore, a multi-band transmission mode for dumb nodes is designed firstly. It ensures that the current stored data of dumb nodes can be sent out in time. Subsequently, a connectivity re-establishment scheme of sub-nodes is designed. The topology reconstruction is adaptively implemented by changing the current transmission path. This scheme does't need to arrange the sleep nodes in advance. So it can reduce the message expenses and energy consumption greatly. Simulation results show that the proposed method has better network performance under the same conditions than the classical algorithms named LETC and A1. What's more, our method has a higher network throughput rate when the nodes' dumb behavior has a shorter duration.

This paper presents a novel approach for mobile positioning in IEEE 802.11a wireless LANs with acceptable computational complexity. The approach improves the positioning accuracy by utilizing the time and frequency domain channel information obtained from the orthogonal frequency-division multiplexing (OFDM) signals. The simulation results show that the proposed approach outperforms the multiple signal classification (MUSIC) algorithm, Ni's algorithm and achieve a positioning accuracy of 1 m with a 97% probability in an indoor scenario.

In this letter, the performance of a state-of-the-art deep learning (DL) algorithm in [5] is analyzed and evaluated for orthogonal frequency-division multiplexing (OFDM) receivers, in the presence of harmonic spur interference. Moreover, a novel spur cancellation receiver structure and algorithm are proposed to enhance the traditional OFDM receivers, and serve as a performance benchmark for the DL algorithm. It is found that the DL algorithm outperforms the traditional algorithm and is much more robust to spur carrier frequency offset.

How to represent images into highly compact binary codes is a critical issue in many computer vision tasks. Existing deep hashing methods typically focus on designing loss function by using pairwise or triplet labels. However, these methods ignore the attention mechanism in the human visual system. In this letter, we propose a novel Deep Attention Residual Hashing (DARH) method, which directly learns hash codes based on a simple pointwise classification loss function. Compared to previous methods, our method does not need to generate all possible pairwise or triplet labels from the training dataset. Specifically, we develop a new type of attention layer which can learn human eye fixation and significantly improves the representation ability of hash codes. In addition, we embedded the attention layer into the residual network to simultaneously learn discriminative image features and hash codes in an end-to-end manner. Extensive experiments on standard benchmarks demonstrate that our method preserves the instance-level similarity and outperforms state-of-the-art deep hashing methods in the image retrieval application.

This letter investigates the training convergence in range-based cooperative positioning with stochastic positional knowledge. Firstly, a closed-form of squared position-error bound (SPEB) is derived with error-free ranging. Using the derived closed-form, it is proved that the SPEB reaches its minimum when at least 2 out of N (> 2) agents send training sequences. Finally, numerical results are provided to elaborate the theoretical analysis with zero-mean Gaussian ranging errors.

This letter presents a novel opportunistic cooperative positioning approach for orthogonal frequency-division multiple access (OFDMA) systems. The basic idea is to allow idle mobile terminals (MTs) opportunistically estimating the arrival timing of the training sequences for uplink synchronization from active MTs. The major advantage of the proposed approach over state-of-the-arts is that the positioning-related measurements among MTs are performed without the paid of training overhead. Moreover, Cramer-Rao lower bound (CRLB) is utilized to derive the positioning accuracy limit of the proposed approach, and the numerical results show that the proposed approach can improve the accuracy of non-cooperative approaches with the a-priori stochastic knowledge of clock bias among idle MTs.