Network Coding based Information Security in Multi-hop Wireless Networks

Multi-hop Wireless Networks (MWNs) represent a class of networks where messages are forwarded through multiple hops of wireless transmission. Applications of this newly emerging communication paradigm include asset monitoring wireless sensor networks (WSNs), command communication mobile ad hoc networks (MANETs), community- or campus-wide wireless mesh networks (WMNs), etc. Information security is one of the major barriers to the wide-scale deployment of MWNs but has received little attention so far. On the one hand, due to the open wireless channels and multi-hop wireless transmissions, MWNs are vulnerable to various information security threats such as eavesdropping, data injection/modification, node compromising, traffic analysis, and flow tracing. On the other hand, the characteristics of MWNs including the vulnerability of intermediate network nodes, multi-path packet forwarding, and limited computing capability and storage capacity make the existing information security schemes designed for the conventional wired networks or single-hop wireless networks unsuitable for MWNs. Therefore, newly designed schemes are highly desired to meet the stringent security and performance requirements for the information security of MWNs. In this research, we focus on three fundamental information security issues in MWNs: efficient privacy preservation for source anonymity, which is critical to the information security of MWNs; the traffic explosion issue, which targets at preventing denial of service (DoS) and enhancing system availability; and the cooperative peer-to-peer information exchange issue, which is critical to quickly achieve maximum data availability if the base station is temporarily unavailable or the service of the base station is intermittent. We have made the following three major contributions. Firstly, we identify the severe threats of traffic analysis/flow tracing attacks to the information security in network coding enabled MWNs. To prevent these attacks and achieve source anonymity in MWNs, we propose a network coding based privacy-preserving scheme. The unique “mixing” feature of network coding is exploited in the proposed scheme to confuse adversaries from conducting advanced privacy attacks, such as time correlation, size correlation, and message content correlation. With homomorphic encryption functions, the proposed scheme can achieve both privacy preservation and data confidentiality, which are two critical information security requirements. Secondly, to prevent traffic explosion and at the same time achieve source unobservability in MWNs, we propose a network coding based privacy-preserving scheme, called SUNC (Source Unobservability using Network Coding). Network coding is utilized in the scheme to automatically absorb dummy messages at intermediate network nodes, and thus, traffic explosion induced denial of service (DoS) can be naturally prevented to ensure the system availability. In addition to ensuring system availability and achieving source unobservability, SUNC can also thwart internal adversaries. Thirdly, to enhance the data availability when a base station is temporarily unavailable or the service of the base station is intermittent, we propose a cooperative peer-to-peer information exchange scheme based on network coding. The proposed scheme can quickly accomplish optimal information exchange in terms of throughput and transmission delay. For each research issue, detailed simulation results in terms of computational overhead, transmission efficiency, and communication overhead, are given to demonstrate the efficacy and efficiency of the proposed solutions

Exploiting Deep Features for Remote Sensing Image Retrieval: A Systematic Investigation

Remote sensing (RS) image retrieval is of great significant for geological information mining. Over the past two decades, a large amount of research on this task has been carried out, which mainly focuses on the following three core issues: feature extraction, similarity metric and relevance feedback. Due to the complexity and multiformity of ground objects in high-resolution remote sensing (HRRS) images, there is still room for improvement in the current retrieval approaches. In this paper, we analyze the three core issues of RS image retrieval and provide a comprehensive review on existing methods. Furthermore, for the goal to advance the state-of-the-art in HRRS image retrieval, we focus on the feature extraction issue and delve how to use powerful deep representations to address this task. We conduct systematic investigation on evaluating correlative factors that may affect the performance of deep features. By optimizing each factor, we acquire remarkable retrieval results on publicly available HRRS datasets. Finally, we explain the experimental phenomenon in detail and draw conclusions according to our analysis. Our work can serve as a guiding role for the research of content-based RS image retrieval

MiR-196a-5p facilitates progression of estrogen-dependent endometrial cancer by regulating FOXO1

Background and Purpose. Estrogen-dependent endometrial cancer mainly occurs in younger pre-menopausal and post-menopausal women and threatens their health. Recently, microRNAs (miRNAs) have been considered as novel targets in endometrial cancer treatment. Therefore, we aimed to explore the effect of miRNA (miR)-196a-5p in estrogen-dependent endometrial cancer. Methods. 17β-estradiol (E2; 2.5, 5, 10 and 20 nM) was used to treat RL95-2, HEC-1B and ECC-1 cells followed by cell viability assessment using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT). The level of miR-196a-5p was measured by reverse transcription-quantitative PCR (RT-qPCR). We then transfected miR-196a-5p mimic/inhibitor and Forkhead box protein O1 (FOXO1) small interfering RNA (siRNA) into E2-treated cells. Apoptotic cells were measured by flow cytometry. Wound healing and Transwell assays were implemented to assess migration and invasion. Bioinformatics and luciferase reporter assays were applied to confirm the interaction between miR-196a-5p and FOXO1. Immunoblotting determined the levels of FOXO1, Bcl-2, Bax, Caspase 3. Results. E2 promoted cell viability and miR-196a-5p expression in RL95-2 and ECC-1 cells. miR-196a-5p mimic enhanced cell viability, migration and invasion but suppressed apoptosis and FOXO1, whilst miR-196a-5p inhibitor blocked these processes. In addition, miR-196a-5p upregulated Bcl-2, but down regulated Bax and Caspase 3 expression, an effect that was reversed by miR-196a-5p inhibitor. We determined that miR-196a-5p targeted FOXO1, and that si-FOXO1 blocked the effects of miR-196a-5p inhibitor on viability, apoptosis, migration and invasion of E2-treated RL95-2 and ECC-1 cells. Conclusions. Our findings suggested potential diagnostic and therapeutic applications for miR-196a-5p and its FOXO1 target in patients suffering from estrogen-dependent endometrial cancer