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Designing an efficient deployment method to guarantee optimal monitoring quality is one of the key topics in underwater sensor networks. At present, a realistic approach of deployment involves adjusting the depths of nodes in water. One of the typical algorithms used in such process is the self-deployment depth adjustment algorithm (SDDA). This algorithm mainly focuses on maximizing network coverage by constantly adjusting node depths to reduce coverage overlaps between two neighboring nodes, and thus, achieves good performance. However, the connectivity performance of SDDA is irresolute. In this paper, we propose a depth adjustment algorithm based on connected tree (CTDA). In CTDA, the sink node is used as the first root node to start building a connected tree. Finally, the network can be organized as a forest to maintain network connectivity. Coverage overlaps between the parent node and the child node are then reduced within each sub-tree to optimize coverage. The hierarchical strategy is used to adjust the distance between the parent node and the child node to reduce node movement. Furthermore, the silent mode is adopted to reduce communication cost. Simulations show that compared with SDDA, CTDA can achieve high connectivity with various communication ranges and different numbers of nodes. Moreover, it can realize coverage as high as that of SDDA with various sensing ranges and numbers of nodes but with less energy consumption. Simulations under sparse environments show that the connectivity and energy consumption performances of CTDA are considerably better than those of SDDA. Meanwhile, the connectivity and coverage performances of CTDA are close to those depth adjustment algorithms base on connected dominating set (CDA), which is an algorithm similar to CTDA. However, the energy consumption of CTDA is less than that of CDA, particularly in sparse underwater environments.

Epilepsy is a disease in which patients undergo seizures caused by brain functionality disorder. Clinically, it is usually diagnosed by experienced clinicians according to continuous electroencephalography (cEEG), which is time consuming even for experienced doctors. Meanwhile, amplitude integrated electroencephalography (aEEG) has shown potential to detect epileptic seizures. Therefore, the paper proposes a hybrid seizure detection algorithm by combining cEEG-based seizure detection algorithm and aEEG-based seizure detection algorithm to detect seizures. In cEEG-based seizure detection algorithm, cEEG signals are divided into 5 s epoch with 4 s overlap and multi-domain features are extracted from each epoch. Then random forest classification is applied to do seizure detection. In aEEG-based seizure detection algorithm, morphological filter is applied to do spike detection and determine whether there are seizures after transforming the cEEG signals into aEEG signals. In order to evaluate the generality of the proposed method, experiments are performed on two independent datasets, including a publicly available EEG dataset (CHB-MIT) and an epileptic dataset collected by using the EEG device developed by the Hangzhou Neuro Science and Technology Co., Ltd. In the CHB-MIT dataset, the accuracy (AC), specificity (SP), sensitivity based on the event (SE), and false positive ratio based on the event (FPRE) obtained by the hybrid method are 99.36%, 82.98%, 99.41%, and 0.57 times/h, respectively. In the dataset we collected, the AC, SP, SE, and FPRE obtained by the hybrid method are 99.23%, 89.47%, 99.23%, and 0.71 times/h, respectively. The experimental results show that the performance of the proposed method is competitive with state-ofthe-art methods and results. Furthermore, basing on the hybrid method, this paper has developed a portable automatic seizure detection system, which can reduce the burden of clinicians in processing the large amounts of cEEG signals by detecting seizure automatically.INDEX TERMS Seizure detection, multi-domain feature, spike detection, hybrid method.

If a person comes into contact with pathogens on public facilities, there is a threat of contact (skin/wound) infections. More urgently, there are also reports about COVID-19 coronavirus contact infection, which once again reminds that contact infection is a very easily overlooked disease exposure route. Herein, we propose an innovative implantation strategy to fabricate a multi-walled carbon nanotube/polyvinyl alcohol (MWCNT/PVA, MCP) interpenetrating interface to achieve flexibility, anti-damage, and non-contact sensing electronic skin (E-skin). Interestingly, the MCP E-skin had a fascinating non-contact sensing function, which can respond to the finger approaching 0–20 mm through the spatial weak field. This non-contact sensing can be applied urgently to human-machine interactions in public facilities to block pathogen. The scratches of the fruit knife did not damage the MCP E-skin, and can resist chemical corrosion after hydrophobic treatment. In addition, the MCP E-skin was developed to real-time monitor the respiratory and cough for exercise detection and disease diagnosis. Notably, the MCP E-skin has great potential for emergency applications in times of infectious disease pandemics. Electronic Supplementary Material Supplementary material (fabrication of MCP E-skin, laser confocal tomography, parameter optimization, mechanical property characterization, finite element simulation, sensing mechanism, signal processing) is available in the online version of this article at 10.1007/s12274-021-3831-z.

Considering that deployment strategies for underwater sensor networks should contribute to fully connecting the networks, a Guaranteed Full Connectivity Node Deployment (GFCND) algorithm is proposed in this study. The GFCND algorithm attempts to deploy the coverage nodes according to the greedy iterative strategy, after which the connectivity nodes are used to improve network connectivity and fully connect the whole network. Furthermore, a Location Dispatch Based on Command Nodes (LDBCN) algorithm is proposed, which accomplishes the location adjustment of the common nodes with the help of the SINK node and the command nodes. The command nodes then dispatch the common nodes. Simulation results show that the GFCND algorithm achieves a comparatively large coverage percentage and a fully connected network; furthermore, the LDBCN algorithm helps the common nodes preserve more total energy when they reach their destination locations.

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Epidemic spreading and cascading failure are two important dynamical processes over complex networks. They have been investigated separately for a long history. But in the real world, these two dynamics sometimes may interact with each other. In this paper, we explore a model combined with SIR epidemic spreading model and local loads sharing cascading failure model. There exists a critical value of tolerance parameter that whether the epidemic with high infection probability can spread out and infect a fraction of the network in this model. When the tolerance parameter is smaller than the critical value, cascading failure cuts off abundant of paths and blocks the spreading of epidemic locally. While the tolerance parameter is larger than the critical value, epidemic spreads out and infects a fraction of the network. A method for estimating the critical value is proposed. In simulation, we verify the effectiveness of this method in Barabási-Albert (BA) networks.

A local-area and energy-efficient (LAEE) evolution model for wireless sensor networks is proposed. The process of topology evolution is divided into two phases. In the first phase, nodes are distributed randomly in a fixed region. In the second phase, according to the spatial structure of wireless sensor networks, topology evolution starts from a network of very small size which contains the sink, grows with an energy-efficient preferential attachment rule in the new node's local-area, and stops until all nodes are connected into network. Both analysis and simulation results show that the degree distribution of LAEE follows the power law. The comparison shows that this topology construction model has better tolerance against energy depletion or random failure than other nonscale-free WSN topologies.