Mobility-Aware Video Streaming in MIMO-Capable Heterogeneous Wireless Networks

Multiple input and multiple output (MIMO) is a well-known technique for the exploitation of the spatial multiplexing (MUX) and spatial diversity (DIV) gains that improve transmission quality and reliability. In this paper, we propose a quality-adaptive scheme for handover and forwarding that supports mobile-video-streaming services in MIMO-capable, heterogeneous wireless-access networks such as those for Wi-Fi and LTE. Unlike previous handover schemes, we propose an appropriate metric for the selection of the wireless technology and the MIMO mode, whereby a new address availability and the wireless-channel quality, both of which are in a new wireless-access network so that the handover and video-playing delays are reduced, are considered. While an MN maintains its original care-of address (oCoA), the video packets destined for the MN are forwarded with the MIMO technique (MUX mode or DIV mode) on top of a specific wireless technology from the previous Access Router (pAR) to the new Access Router (nAR) until they finally reach the MN; however, to guarantee a high video-streaming quality and to limit the video-packet-forwarding hops between the pAR and the nAR, the MN creates a new CoA (nCOA) within the delay threshold of the QoS/quality of experience (QoE) satisfaction result, and then, as much as possible, the video packet is forwarded with the MUX. Through extensive simulations, we show that the proposed scheme is a significant improvement upon the other schemes

Security-aware fair transmission scheme for 802.11 based cognitive IoT

Cognitive IoT is exponentially increased because of various real time and robust applications with sensor networks and big data analysis. Each IoT protocol of network layer can be RPL, COAP and so on based on IETF standards. But still collision problems and security-aware fair transmission on top of scalable IoT devices were not solved enough. In the open wireless LAN system based cognitive IoTs, IoT node that is continuously being stripped of its transmission opportunity will continue to accumulate packets to be sent in the butter and spoofing attacks will not allow the data transfer opportunities to be fair. Therefore, in this paper, we propose a method to reduce the average wait time of all packets in the system by dynamically controlling the contention window (CW) in a wireless LAN based cognitive IoT environment where there are nodes that do not have fair transmission opportunities due to spoofing attacks. Through the performance evaluation, we have proved that the proposed technique improves up to 80% in terms of various performance evaluation than the basic WLAN 802.11 based IoT

Light-weight Routing Protocol in IoT-based Inter-Device Telecommunication Wireless Environment

The primary task for IoT-based hyper-connectivity communications lies in the development of direct communications technique among IoT devices in RPL (Routing Protocol for Low-Power and Lossy Networks) environment without the aid from infras such as access points, base stations etc. In a low-power and lossy wireless network, IoT devices and routers cannot keep the original path toward the destination since they have the limited memory, except for a limited number of the default router information.. Different from the previous light-weight routing protocols focusing on the reduction of the control messages, the proposed scheme provides the light-weight IPv6 address auto-configuration, IPv6 neighbor discovery and routing protocol in a IoT capable infra-less wireless networks with the bloom filer and enhanced rank concepts. And for the first time we evaluate our proposed scheme based on the modeling of various probability distributions in the IoT environments with the lossy wireless link. Specifically, the proposed enhanced RPL based light-weight routing protocol improves the robustness with the multi-paths locally established based on the enhanced rank concepts even though lossy wireless links are existed. We showed the improvements of the proposed scheme up to 40% than the RPL based protocol

EETA: An Energy Efficient Transmission Alignment for Wireless Sensor Network Applications

Energy conserving MAC protocols performing adaptive duty-cycling mechanism have been widely studied to improve the energy efficiency in Wireless Sensor Networks (WSNs). In particular, several asynchronous Low Power Listening (LPL) MAC protocols such as B-MAC, X-MAC and ContikiMAC transmit a long preamble or consecutive data packets for an efficient rendezvous between senders and receivers. However, the rendezvous results in the challenging problem of unnecessary channel utilization since the senders occupy a large portion of the medium. Furthermore, when a traffic generation time overlaps with other neighbouring nodes, they frequently encounter spatially-correlated contention incurring excessive channel contention. In this paper, we propose a novel traffic distribution scheme called an Energy Efficient Transmission Alignment (EETA), that shifts a traffic generation time of the application layer. By using a MAC layer feedback including contention information, the cross-layer framework determines whether the node delays its transmission or not. EETA is robust from the heavy contending environment due to its traffic distribution feature. We evaluate the performance of EETA through diverse experiments on the TelosB platform. The results show that EETA improves the overall energy efficiency by up to 35%, and reduces the latency by up to 48% compared to the existing scheme

A Field Study on Concurrent Spare Parts Recommendation in an Airborne Weapon System

As the complexity of weapon systems has grown exponentially during the past few years, initial operation capability has been a crucial factor for military forces. Concurrent spare parts (CSPs) is the quantity of spare parts ensuring initial operating period specified by demanding forces acquiring newly deployed weapon systems. Because of the growth of system complexity, recommending precise CSP is not trivial. The Republic of Korea developed an improved CSP recommendation system and deployed the system for naval weapon systems. In this paper, we increase the prediction accuracy of CSP up to 23.1 per cent and 7.16 per cent higher in terms of budget constraint and operational availability (Ao) constraint. The main improvement is achieved by facilitating simulations using the real field data from Korean air force. Also, we propose two validation approaches and show the possibility of extension to the general weapon systems. From the experimental study, we show that the CSP recommendation system can be deployed for navy and air forces

Follow spam detection based on Cascaded Social Information

In the last decade we have witnessed the explosive growth of online social networking services (SNSs) such as Facebook, Twitter, RenRen and LinkedIn. While SNSs provide diverse benefits for example, forstering inter-personal relationships, community formations and news propagation, they also attracted uninvited nuiance. Spammers abuse SNSs as vehicles to spread spams rapidly and widely. Spams, unsolicited or inappropriate messages, significantly impair the credibility and reliability of services. Therefore, detecting spammers has become an urgent and critical issue in SNSs. This paper deals with Follow spam in Twitter. Instead of spreading annoying messages to the public, a spammer follows (subscribes to) legitimate users, and followed a legitimate user. Based on the assumption that the online relationships of spammers are different from those of legitimate users, we proposed classification schemes that detect follow spammers. Particularly, we focused on cascaded social relations and devised two schemes, TSP-Filtering and SS-Filtering, each of which utilizes Triad Significance Profile (TSP) and Social status (SS) in a two-hop subnetwork centered at each other. We also propose an emsemble technique, Cascaded-Filtering, that combine both TSP and SS properties. Our experiments on real Twitter datasets demonstrated that the proposed three approaches are very practical. The proposed schemes are scalable because instead of analyzing the whole network, they inspect user-centered two hop social networks. Our performance study showed that proposed methods yield significantly better performance than prior scheme in terms of true positives and false positives.OAIID:RECH_ACHV_DSTSH_NO:T201620357RECH_ACHV_FG:RR00200001ADJUST_YN:EMP_ID:A001118CITE_RATE:3.364FILENAME:Follow spam detection based on cascaded social information.pdfDEPT_NM:컴퓨터공학부EMAIL:[email protected]_YN:YFILEURL:https://srnd.snu.ac.kr/eXrepEIR/fws/file/be43ae94-4659-467d-bc0f-17dc45d3e775/linkCONFIRM:

Optical dispersion control in surfactant-free DNA thin films by vitamin B2 doping

A new route to systematically control the optical dispersion properties of surfactant-free deoxyribonucleic acid (DNA) thin solid films was developed by doping them with vitamin B2, also known as riboflavin. Surfactant-free DNA solid films of high optical quality were successfully deposited on various types of substrates by spin coating of aqueous solutions without additional chemical processes, with thicknesses ranging from 18 to 100 nm. Optical properties of the DNA films were investigated by measuring UV-visible-NIR transmission, and their refractive indices were measured using variable-angle spectroscopic ellipsometry. By doping DNA solid films with riboflavin, the refractive index was consistently increased with an index difference Δn ≥ 0.015 in the spectral range from 500 to 900 nm, which is sufficiently large to make an all-DNA optical waveguide. Detailed correlation between the optical dispersion and riboflavin concentration was experimentally investigated and thermo-optic coefficients of the DNA-riboflavin thin solid films were also experimentally measured in the temperature range from 20 to 85 °C, opening the potential to new bio-thermal sensing applications

Corrections to “A YouTube Spam Comments Detection Scheme Using Cascaded Ensemble Machine Learning Model”

In the above article [1], the author would like to correct the Acknowledgment as follows

AI-Based Degradation Index from the Microstructure Image and Life Prediction Models Based on Bayesian Inference

In this study, we propose a consistent and explainable degradation indexing method and a non-destructive-based degradation and creep-life prediction method from extensive destructive test (creep-rupture) data of a nickel-based superalloy (DA-5161 SX), an extreme-environment material. High-temperature components made of nickel-based superalloys that operate in extreme environments (e.g., gas turbine blades) deteriorate over time and shorten the life of the device. To ensure the safety and efficiency of the equipment, it is important to predict the lifetime of high-temperature parts, and a consistent and explanatory degradation index and a reliable predictive model that can predict the degree of degradation and life without destructive testing of high-temperature parts are needed. As the degradation of nickel-based superalloys progresses, degradation indices reflecting the geometrical characteristics are required that focus on the fact that the shape of the gamma-prime phase becomes longer and larger. A representative value of the degradation index was selected through parameter inference based on a Bayesian method, and the high-dimensional degradation index of previous studies was simplified to only one dimension. The robustness of the degradation index quantification model was verified by confirming that the degradation index obtained from 20% of the test images had the lowest change rate of the degradation index obtained from 80% of the training images at 6.9%. The basis for predicting the life of high-temperature parts without destructive testing was established in the degradation index and life prediction model by connecting environmental conditions and degradation indices/the LMP (Larson–Miller parameter) to represent creep life in regression models. Gaussian process regression (GPR) models based on sampling-based Bayesian inference performed well in terms of both RMSE in the degradation index and the LMP prediction model, demonstrating robust behavior in performance variation. This may be used as a key health factor that indicates the soundness of diagnostic solutions in the future, and it is expected to be a foundational technology for decision-making models for maintenance, repair, and disposal