A traffic hotline discovery method over cloud of things using big taxi GPS data

High reliable street-level landmarks are the basis of IP geolocation, but landmark evaluation methods having been proposed cannot evaluate the street-level landmarks. Therefore, in this paper, a street-level landmark evaluation method based on nearest router is proposed. The location organization declared is regarded as an area not a point. Firstly, the declared location of preevaluated landmark is verified by IP location databases. Secondly, the preevaluated landmarks are grouped according to their nearest router. Then, the distance constraint is obtained using delay value between landmark and its nearest router by delay-distance correlation. And relation model is established among distance constraint, organization’s region radius, and distance between two landmarks. Finally, the reliability value of landmarks is calculated in each group based on relational model and binomial distribution. Landmarks evaluation experiment is taken based on 7082 preevaluated landmarks, and the results show that geolocation errors decrease obviously using evaluated landmarks. The mean error of 100 targets in Shanghai is reduced from 7.832km to 2.185km.

“…Adding the radius of region (recorded as " "), relation model among distance constraint, region radius, and distance between and is established as inequality (6).…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Street-Level Landmark Evaluation Based on Nearest Routers

Li¹,

Sun²,

Hu³

et al. 2018

“…Big data needs a huge mass of computing resources and storage resources, to promote the development of cloud technology [19][20][21]. Data placement in cloud environment has been widely concerned to improve the quality of cloud services.…”

Section: Related Workmentioning

confidence: 99%

Data Placement for Privacy-Aware Applications over Big Data in Hybrid Clouds

Zhao

Ruan

et al. 2017

Self Cite

Nowadays, a large number of groups choose to deploy their applications to cloud platforms, especially for the big data era. Currently, the hybrid cloud is one of the most popular computing paradigms for holding the privacy-aware applications driven by the requirements of privacy protection and cost saving. However, it is still a challenge to realize data placement considering both the energy consumption in private cloud and the cost for renting the public cloud services. In view of this challenge, a cost and energy aware data placement method, named CEDP, for privacy-aware applications over big data in hybrid cloud is proposed. Technically, formalized analysis of cost, access time, and energy consumption is conducted in the hybrid cloud environment. Then a corresponding data placement method is designed to accomplish the cost saving for renting the public cloud services and energy savings for task execution within the private cloud platforms. Experimental evaluations validate the efficiency and effectiveness of our proposed method.

“…Using the principle and technology of neural network to realize intrusion detection has become a new direction in the development of intrusion detection technology in recent years. It has emulated the theory and method of the biological information processing mode to obtain the intelligent information processing function [10]. The intrusion detection system based on neural network belongs to the category of abnormal intrusion detection, including data acquisition module, data training, and detection module and a response module.…”

Section: Introductionmentioning

confidence: 99%

Application of Temperature Prediction Based on Neural Network in Intrusion Detection of IoT

Liu

Zhang

Liu

et al. 2018

The security of network information in the Internet of Things faces enormous challenges. The traditional security defense mechanism is passive and certain loopholes. Intrusion detection can carry out network security monitoring and take corresponding measures actively. The neural network-based intrusion detection technology has specific adaptive capabilities, which can adapt to complex network environments and provide high intrusion detection rate. For the sake of solving the problem that the farmland Internet of Things is very vulnerable to invasion, we use a neural network to construct the farmland Internet of Things intrusion detection system to detect anomalous intrusion. In this study, the temperature of the IoT acquisition system is taken as the research object. It has divided which into different time granularities for feature analysis. We provide the detection standard for the data training detection module by comparing the traditional ARIMA and neural network methods. Its results show that the information on the temperature series is abundant. In addition, the neural network can predict the temperature sequence of varying time granularities better and ensure a small prediction error. It provides the testing standard for the construction of an intrusion detection system of the Internet of Things.