Current Issue : January - March Volume : 2019 Issue Number : 1 Articles : 5 Articles
The increasingly severe network security situation brings unanticipated challenges to mobile networking. Traditional HMM\n(HiddenMarkovModel) based algorithms for predicting the network security are not accurate, and to address this issue, a weighted\nHMM based algorithm is proposed to predict the security situation of the mobile network.The multi scale entropy is used to address\nthe low speed of data training in mobile network, whereas the parameters of HMM situation transition matrix are also optimized.\nMoreover, the auto correlation coefficient can reasonably use the association between the characteristics of the historical data to\npredict future security situation. Experimental analysis on DARPA2000 shows that the proposed algorithm is highly competitive,\nwith good performance in prediction speed and accuracy when compared to existing design....
This paper attempts to identify the requirement and the development of machine learning-based mobile big data (MBD) analysis\nthrough discussing the insights of challenges in the mobile big data. Furthermore, it reviews the state-of-the-art applications of data\nanalysis in the area of MBD. Firstly, we introduce the development of MBD. Secondly, the frequently applied data analysis methods\nare reviewed. Three typical applications of MBD analysis, namely, wireless channel modeling, human online and offline behavior\nanalysis, and speech recognition in the Internet of Vehicles, are introduced, respectively. Finally, we summarize the main challenges\nand future development directions of mobile big data analysis....
This paper proposes an adaptive duty-cycled hybrid X-MAC (ADX-MAC) protocol for\nenergy-efficient forest fire prediction. The Asynchronous sensor network protocol, X-MAC protocol,\nacquires additional environmental status details from each forest fire monitoring sensor for a given\nperiod, and then changes the duty-cycle sleep interval to efficiently calculate forest fire occurrence\nrisk according to the environment. Performance was verified experimentally, and the proposed\nADX-MAC protocol improved throughput by 19% and was 24% more energy efficient compared\nto the X-MAC protocol. The duty-cycle was shortened as forest fire probability increased, ensuring\nforest fires were detected at faster cycle rate...
The evolution of virtual reality technology allows users to immerse themselves into virtual environments, providing a new\nexperience that is impossible in the real world. The appearance of cyber-physical systems and the Internet of things makes humans\nto understand and control the real world in detail. The integration of virtual reality into cyber-physical systems and the Internet of\nthings may induce innovative education services in the near future. In this paper, we propose a novel, a virtual reality-based cyberphysical\neducation system for efficient education in a virtual reality on a mobile platform, called VR-CPES. VR-CPES can integrate\nthe real world into virtual reality using cyber-physical systems technology, especially using digital twin. We extract essential\nservice requirements of VR-CPES in terms of delay time in the virtual reality service layer. In order to satisfy the requirements of\nthe network layer, we design a new, real-time network technology inter working software, defined as network and time-sensitive\nnetwork. A gateway function for the inter working is developed to make protocol level transparency. In addition, a path selection\nalgorithm is proposed to make flexible flow between physical things and cyber things. Finally, a simulation study will be conducted\nto validate the functionalities and performance in terms of packet loss and delay as defined in the requirements....
Accuracy performance ofWiFi fingerprinting positioning systems deteriorates severely when signal attenuations caused by human\nbody are not considered. Previous studies have proposedWiFi fingerprinting positioning based on user orientation using compasses\nbuilt in smartphones. However, compasses always cannot provide required accuracy of user orientation estimation due to the\nsevere indoormagnetic perturbations.More importantly, we discover that not only user orientations but also smartphone carrying\npositions may affect signal attenuations caused by human body greatly. Therefore, we propose a novel WiFi fingerprinting\npositioning approach considering both user orientations and smartphone carrying positions. For user orientation estimation, we\ndeploy Rotation Matrix and Principal Component Analysis (RMPCA) approach. For carrying position recognition, we propose a\nrobust Random Forest classifier based on the developed orientation invariant features. Experimental results show that the proposed\nWiFi positioning approach may improve positioning accuracy significantly....
Loading....