Current Issue : October - December Volume : 2018 Issue Number : 4 Articles : 5 Articles
With the development of Internet of Things (IoT), the number of mobile terminal devices is increasing rapidly. Because of\nhigh transmission delay and limited bandwidth, in this paper, we propose a novel three-layer network architecture model which\ncombines cloud computing and edge computing (abbreviated as CENAM). In edge computing layer, we propose a computational\nscheme of mutual cooperation between the edge devices and use the Kruskal algorithm to compute the minimum spanning\ntree of weighted undirected graph consisting of edge nodes, so as to reduce the communication delay between them. Then we\ndivide and assign the tasks based on the constrained optimization problem and solve the computation delay of edge nodes by\nusing the Lagrange multiplier method. In cloud computing layer, we focus on the balanced transmission method to solve the\ndata transmission delay from edge devices to cloud servers and obtain an optimal allocation matrix, which reduces the data\ncommunication delay. Finally, according to the characteristics of cloud servers, we solve the computation delay of cloud computing\nlayer. Simulation shows that the CENAMhas better performance in data processing delay than traditional cloud computing....
Mobile crowd sensing has been a very important paradigm for collecting sensing data from a large number of mobile nodes\ndispersed over a wide area. Although it provides a powerful means for sensing data collection, mobile nodes are subject to privacy\nleakage risks since the sensing data from a mobile node may contain sensitive information about the sensor node such as physical\nlocations.Therefore, it is essential for mobile crowd sensing to have a privacy preserving scheme to protect the privacy of mobile\nnodes. A number of approaches have been proposed for preserving node privacy in mobile crowd sensing. Many of the existing\napproaches manipulate the sensing data so that attackers could not obtain the privacy-sensitive data. The main drawback of these\napproaches is that the manipulated data have a lower utility in real-world applications. In this paper, we propose an approach\ncalled ...
The new radio technology for the fifth-generation wireless system has been extensively studied all over the world. Specifically, the\nair interface protocols for 5G radio access network will be standardized by the 3GPP in the coming years. In the next-generation 5G\nnew radio (NR) networks, millimeter wave (mmWave) communications will definitely play a critical role, as new NR air interface\n(AI) is up to 100GHz just likemmWave.The rapid growth of mmWave systems poses a variety of challenges in physical layer (PHY)\nsecurity. This paper investigates those challenges in the context of several 5G new radio communication technologies, including\nmultiple-input multiple-output (MIMO) and nonorthogonal multiple access (NOMA). In particular, we introduce a ray-tracing\n(RT) based 5G NR network channel model and reveal that the secrecy capacity in mmWave band widely depends on the richness\nof radio frequency (RF) environment through numerical experiments....
The collection of sensory data is crucial for cyber-physical systems. Employing mobile agents (MAs) to collect data from sensors\noffers a new dimension to reduce and balance their energy consumption but leads to large data collection latency due to MAs�\nlimited velocity.Most existing research effort focuses on the offline mobile data collection (MDC), where the MAs collect data from\nsensors based on preoptimized tours. However, the efficiency of these offlineMDC solutions degrades when the data generation of\nsensors varies. In this paper, we investigate the on-demand MDC; that is, MAs collect data based on the real-time data collection\nrequests from sensors. Specifically, we construct queuing models to describe the First-Come-First-Serve-based MDC with a single\nMA and multiple MAs, respectively, laying a theoretical foundation.We also use three examples to show how such analysis guides\nonline MDC in practice....
It is no doubt that the sub-field of Artificial Intelligence, which uses the tenets\nof Machine learning and data mining has progressively gained popularity in\nthe past years to become one of fundamental yet revolutionary technologies. It\nis the basis of systems that can learn and improve using algorithms and big\ndata with minimal programming or none. It is envisaged that mobile computing\nwill empower end-users to seamlessly access and consume digital content\nservices regardless of spatial or temporal orientations. Such are already\nthe features of smart phones that at production are bundled with trending and\nnecessary services. Of the many capabilities that advancement in technology\nhave actualized in smart devices, gaming, video streaming, online library access,\nand m-commerce access services are the commonly among smart device\nowners. Given the near-exponential growth in ownership of smart devices,\nthere is a need to identify and prioritize mobile services, and such was focus of\nthis study. In specific, the study used Decision Tree, a popular machine\nlearning algorithm, to predict the adoption of mobile services among smart\ndevice owners. Besides this purpose, the study identified the core uses of\nsmart phones, and data used in the study was from an open source and was\nretrieved from Pew Research Centre Internet and Technology website. The\ndataset had 140 variables and 2001 themes, from which only the key attributes\nwere selected for analysis. The study established that the level of education\nwas the significant predictor of the mobile phones usage while race of the user\nemerged as the least predictor of smart device usage. The findings indicated\nthat smart mobile phones were mostly used for entertainment, getting locations,\ndirection and for recommendation purposes....
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