Sensor nodes equipped with various sensory devices can sense a wide range of information regarding human or things, thereby providing a foundation for Internet of Thing (IoT). Fast and energy-efficient data collection to the control center (CC) is of significance yet very challenging. To deal with this challenge, a low redundancy data collection (LRDC) scheme is proposed to reduce delay as well as energy consumption for monitoring network by using matrix completion technique. Due to the correlation of the location-dependent sensing data, some data without being collected can still be recovered by the matrix completion technology, thereby reducing the data amount for data collection and transmission, reducing the network energy consumption, and accelerating the process of data acquisition. Based on matrix completion technique, LRDC scheme can select only part of the nodes to sense data and transmit less data to CC. By doing so, the data collected by the network can be greatly reduced, which can effectively improve the network lifetime. In addition, LRDC scheme also proposes a method for quickly compensate sample data in cases of packet loss, whereby part of redundant data is sent in advance to the area closer to CC. If the data required for matrix completion is lost, these redundant data can be quickly obtained by CC, so the LRDC scheme has low delay characteristics. Simulation results demonstrate that LRDC scheme can achieve better performance than the traditional strategy, and it can reduce the maximum energy consumption of the network by 27.6-57.9% and reduce the delay by 0.7-17.9%.
Oral squamous cell carcinoma (OSCC) is a lethal malignancy and its prognosis remains dismal. Thus, a deeper understanding of the mechanisms is needed to provide a new insight for new therapies. It has been reported that long noncoding RNA (lncRNA) maternally expressed gene 3 (MEG3) was downregulated in OSCC tissues, however, its functional mechanism remains uncertain. Here, we found that the overexpression of MEG3 suppressed migration and promoted apoptosis in OSCC cell lines, while inhibition of MEG3 exhibited opposite effect. We also found that MEG3 could effectively sponge miR‐548d‐3p and decrease its expression level. Moreover, miR‐548d‐3p repressed the expression of SOCS5 and SOCS6 through binding their 3’UTR, thereby modulating the JAK–STAT signaling pathway and functioning as an oncogene in OSCC cells. Importantly, overexpression of MEG3 enhanced the expression of SOCS5 and SOCS6 to regulate JAK–STAT pathway, whereas miR‐548d‐3p overexpression decreased the effects of MEG3 on levels of SOCS5/SOCS6. Furthermore, upregulated expression of miR‐548d‐3p could abrogate the effect of MEG3 overexpression on migration and apoptosis in OSCC cell lines. In addition, the overexpression of MEG3 inhibited tumor migration and facilitated apoptosis in vivo. Together, our results revealed that MEG3 could modulate JAK–STAT pathway via miR‐548d‐3p/SOCS5/SOCS6 to suppresses migration and promote apoptosis in OSCC. Our research indexed a new functional mechanism of MEG3 in OSCC, and this mechanism may be a potential prognostic factor and therapeutic target. © 2019 IUBMB Life, 2019
Advanced communications and networks greatly enhance the user experience and have a major impact on all aspects of people's lifestyles. Widely deployed sensor nodes provide support for these services. However, although energy harvesting and transfer technology provides a solution to allow the long-term survival of wireless sensor nodes for wireless sensor networks, the single collection scheme causes a lot of energy waste. Thus, efficient energy utilization and fast data collection are still serious challenges for energy harvesting wireless sensor networks. To overcome these challenges, an adaptive collection scheme based on matrix completion (ACMC) is proposed to reduce delay and to improve the energy utilization of the network. In the ACMC scheme, compared with traditional data collection schemes, the data collection schemes vary with the available energy, collecting large amounts of data when the available energy is sufficient to obtain high-quality data-based applications. Otherwise, adaptive selecting the collected data based on previously collected data, the amount of data collected can be effectively reduced based on the application requirements, thereby improving the energy utilization of the network. The ACMC scheme also proposes a method for reducing the delay by increasing the duty cycle of the nodes that are far from the CC. At the same time, the transmission reliability of these nodes increases due to the increase in the transmission frequency. Thus, the ACMC scheme can also further reduce the delay of the network. The experimental results of the ACMC scheme in planar networks show better performance than the traditional data collection schemes and can improve the energy utilization of the network by 4.26%-6.68% while reducing the maximum delay by 9.4%.
Mobile edge computing (MEC) is envisioned as a promising platform for supporting emerging computation-intensive applications on capacity and resource constrained mobile devices (MDs). In this platform, the task with high computing resource demand can be offloaded to edge nodes for computing. Moreover, the computing result can be cached to edge nodes. When other MDs request the task that has been cached, the edge nodes can directly return the result to MD. However, the storage capacity of edge nodes is limited, the effective task prediction and caching scheme is one of the key issues for MEC. In this article, a matrix completion technology based content popularity prediction joint cache placement (MCTCPP-CP) scheme is proposed to tackle this issue for MEC. On the one hand, the MCTCPP-CP scheme is the first scheme using matrix completion (MC) technology to content popularity prediction. It proved by experiments that the accuracy of using MC technology to estimate caching content is improved compared with the previous methods. On the other hand, a cache placement decision approach based on the benefit of unit storage is proposed. Extensive numerical studies demonstrate the superior performance of our MCTCPP-CP scheme. The key performance indicators such as task duration, hit rate, estimated error are better than previous schemes by about: 0.13% to 14.01%, 17.28% to 37.65%, and 8.17%. INTRODUCTIONIn the past decades, the number of mobile devices (MDs) has grown at an unprecedented, 1-3 billions of devices (eg, MDs, wearable devices, sensors, and other IoT computing nodes) are connected to the internet for a wide variety of mobile applications. [4][5][6][7] One of the important applications is computation-intensive tasks, such as in-vehicle videos and virtual reality (VR). 8 To process such computation-intensive tasks, many computing capacity and energy supply are required. [9][10][11] Unfortunately, due to the limited computation resources such as limited battery life and insufficient computing capacity, the users' quality of experience will be reduced when executing the computation-hungry applications in MDs. [12][13][14] To mitigate the excessive computing, task offloading has been widely recognized as an efficient Abbreviations: ANA, antinuclear antibodies; APC, antigen-presenting cells; IRF, interferon regulatory factor.
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