Current Issue : October - December Volume : 2019 Issue Number : 4 Articles : 5 Articles
In remote areas, wireless multimedia sensor networks (WMSNs) have limited energy,\nand the data processing of wildlife monitoring images always suffers from energy consumption\nlimitations. Generally, only part of each wildlife image is valuable. Therefore, the above mentioned\nissue could be avoided by transmitting the target area. Inspired by this transport strategy, in this\npaper, we propose an image extraction method with a low computational complexity, which can\nbe adapted to extract the target area (i.e., the animal) and its background area according to the\ncharacteristics of the image pixels. Specifically, we first reconstruct a color space model via a CIELUV\n(LUV) color space framework to extract the color parameters. Next, according to the importance of\nthe Hermite polynomial, a Hermite filter is utilized to extract the texture features, which ensures\nthe accuracy of the split extraction of wildlife images. Then, an adaptive mean-shift algorithm is\nintroduced to cluster texture features and color space information, realizing the extraction of the\nforeground area in the monitoring image. To verify the performance of the algorithm, a demonstration\nof the extraction of field-captured wildlife images is presented. Further, we conduct a comparative\nexperiment with N-cuts (N-cuts), the existing aggregating super-pixels (SAS) algorithm, and the\nhistogram contrast saliency detection (HCS) algorithm. A comparison of the results shows that the\nproposed algorithm for monitoring image target area extraction increased the average pixel accuracy\nby 11.25%, 5.46%, and 10.39%, respectively; improved the relative limit measurement accuracy by\n1.83%, 5.28%, and 12.05%, respectively; and increased the average mean intersection over the union\nby 7.09%, 14.96%, and 19.14%, respectively....
Wireless sensor networks (WSNs) enable many applications such as intelligent control,\nprediction, tracking, and other communication network services, which are integrated into many\ntechnologies of the Internet-of-Things. The conventional localization frameworks may not function\nwell in practical environments since they were designed either for two-dimensional space only, or\nhave high computational costs, or are sensitive to measurement errors. In order to build an accurate\nand efficient localization scheme, we consider in this paper a hybrid received signal strength and\nangle-of-arrival localization in three-dimensional WSNs, where sensors are randomly deployed with\nthe transmit power and the path loss exponent unknown. Moreover, in order to avoid the difficulty of\nsolving the conventional maximum-likelihood estimator due to its non-convex and highly complex\nnatures, we derive a weighted least squares estimate to estimate jointly the location of the unknown\nnode and the two aforementioned channel components through some suitable approximations.\nSimulation results confirm the effectiveness of the proposed method....
In this paper, a deep learning (DL)-based physical (PHY) layer authentication framework is\nproposed to enhance the security of industrial wireless sensor networks (IWSNs). Three algorithms,\nthe deep neural network (DNN)-based sensor nodesâ?? authentication method, the convolutional neural\nnetwork (CNN)-based sensor nodesâ?? authentication method, and the convolution preprocessing\nneural network (CPNN)-based sensor nodesâ?? authentication method, have been adopted to implement\nthe PHY-layer authentication in IWSNs. Among them, the improved CPNN-based algorithm requires\nfew computing resources and has extremely low latency, which enable a lightweight multi-node\nPHY-layer authentication. The adaptive moment estimation (Adam) accelerated gradient algorithm\nand minibatch skill are used to accelerate the training of the neural networks. Simulations are\nperformed to evaluate the performance of each algorithm and a brief analysis of the application\nscenarios for each algorithm is discussed. Moreover, the experiments have been performed with\nuniversal software radio peripherals (USRPs) to evaluate the authentication performance of the\nproposed algorithms. Due to the trainings being performed on the edge sides, the proposed method\ncan implement a lightweight authentication for the sensor nodes under the edge computing (EC)\nsystem in IWSNs....
A novel energy-efficient data gathering scheme that exploits spatial-temporal correlation is proposed for clustered wireless sensor\nnetworks in this paper. In the proposed method, dual prediction is used in the intracluster transmission to reduce the temporal\nredundancy, and hybrid compressed sensing is employed in the intercluster transmission to reduce the spatial redundancy.\nMoreover, an error threshold selection scheme is presented for the prediction model by optimizing the relationship between\nthe energy consumption and the recovery accuracy, which makes the proposed method well suitable for different application\nenvironments. In addition, the transmission energy consumption is derived to verify the efficiency of the proposed method.\nSimulation results show that the proposed method has higher energy efficiency compared with the existing schemes, and the sink\ncan recover measurements with reasonable accuracy by using the proposed method....
Existing cascading models are unable to depict the sink-convergence characteristic of WSNs (wireless sensor networks). In this\nwork, we build a more realistic cascading model for WSNs, in which two load-redistribution schemes (i.e., idle redistribution and\neven redistribution) are introduced. In addition, failed nodes are allowed to recover after a certain time delay rather than being\ndeleted from the network permanently. Simulation results show that the network invulnerability is positively correlated to the\ntolerance coefficient and negatively correlated to the exponential coefficient. Under the idle-redistribution scheme, the network\ncan have stronger invulnerability against cascading failures.The extension of the recovery time will exacerbate the fluctuation of\nthe cascading process....
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