A risk defense method based on microscopic state prediction with partial information observations in social networks

In this paper, we propose an optimization framework of vehicular fog computing and a cooperation vehicular network model. We aim to improve the performance of vehicular fog computing and solve the problem that the data of the vehicle collaborative network is difficult to obtain. This paper applies the hypergraph theory to study the underlying structure, considering the social characteristics of the vehicles and vehicle communication. Since the vehicles join the network in accordance with the Poisson process law, the model is analyzed by using Poisson stochastic process and mean field theory. This paper uses MATLAB to simulate the evolution process of cooperative networks. The results show that the vehicle's super-degree in vehicular fog computing has scale-free characteristics. Through this model, the vehicle cooperation situation can be analyzed, and the vehicle dynamics can be accurately predicted to further improve the performance of vehicular fog computing. The model can be transformed into some complex network models by adjusting the parameters. It has strong universality and has certain reference significance for the research on the related characteristics of VANETs and the theoretical research of the cooperative network.2 of 17 to higher network delays. For vehicles that are using autonomous driving in the Internet of Vehicles, high latency is likely to cause serious traffic accidents. Cellular network can provide limited enhanced communication, mainly controlled by network operators, which is inefficient from the application point of view [8]. Mobile cloud computing can bring rich computing resources to mobile users [9], but it is costly and time consuming to upload real-time information [10].Fog computing can effectively solve the above problems, by deploying a fog server with storage and computing near the user to receive and process user data [11]. The deployment location of the fog server is closer to the user, which can save a lot of data transmission time. The deployment of fog computing nodes in the connected vehicle network has become a popular trend in the research of VANETs. We finally proposed a novel concept of vehicular fog computing (VFC), which uses vehicles as infrastructure to make full use of vehicular communication and computing resources. Specifically, VFC is an architecture that has cloud-based features and provides data, computing, storage and application services to end users. VFC is also differentiated from other existing technologies by being close to end users, and having dense geographical distribution and support for mobility; its bottom layer consists of cooperative vehicles, so that the vehicle is both a fog computing provider and a vehicular fog computing user. It can use computing resources nearby and reduce the use of communication resources. In the fog computing architecture, vehicles and mobile devices are end users, and there is a fog server layer between these edge users and the cloud. The low-level equipment of vehicular fog computing vehicles has high-speed moving ch...

Section: The Architecture Of Vehicular Fog Computingmentioning

confidence: 99%

Vehicle Cooperative Network Model Based on Hypergraph in Vehicular Fog Computing

Ran

Bai

2020

“…However, a large subregion would increase the energy dissipation of image transmission within the region. Therefore, we provide the following inequality: (11) where N comp is the average number of compressed images per normal node in a round; T round is the time consumed by one round; F camera is the image transmission frequency of the camera node; N area is the number of sub-regions; S area is the area of the network region.…”

Section: Determining the Number Of Subregionsmentioning

confidence: 99%

“…WMSNs integrate and expand the application of traditional WSNs and are widely used in security and environment monitoring, intelligent transportation and homes, and other applications requiring multimedia information. WMSNs are a typical application motivated by the combination of Wireless Sensor Networks (WSNs) and edge computing [11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

A Distributed Image Compression Scheme for Energy Harvesting Wireless Multimedia Sensor Networks

Han

Zhang

et al. 2020

As an emerging technology, edge computing will enable traditional sensor networks to be effective and motivate a series of new applications. Meanwhile, limited battery power directly affects the performance and survival time of sensor networks. As an extension application for traditional sensor networks, the energy consumption of Wireless Multimedia Sensor Networks (WMSNs) is more prominent. For the image compression and transmission in WMSNs, consider using solar energy as the replenishment of node energy; a distributed image compression scheme based on solar energy harvesting is proposed. Two level clustering management is adopted. The camera node-normal node cluster enables camera nodes to gather and send collected raw images to the corresponding normal nodes for compression, and the normal node cluster enables the normal nodes to send the compressed images to the corresponding cluster head node. The re-clustering and dynamic adjustment methods for normal nodes are proposed to adjust adaptively the operation mode in the working chain. Simulation results show that the proposed distributed image compression scheme can effectively balance the energy consumption of the network. Compared with the existing image transmission schemes, the proposed scheme can transmit more and higher quality images and ensure the survival of the network.

“…Moreover, in order to perfect the system, they also design a task allocation scheme, a reward scheme, and a processing flow to deal with the pictures uploaded. Considering the security of the data such as photos, Wu et al, propose a data protection and recovery method based on the incentive [35] and a prediction-based risk defense strategy [36] for the data. Zhuo et al [37] propose a tripartite architecture for data aggregation and analysis to mitigate the pressure of resource constrained requesters in mobile crowdsourcing by means of cloud support; they achieve privacy-preserving of data and identity of workers.…”

Section: Photo Crowdsourcingmentioning

confidence: 99%

Storage Management Strategy in Mobile Phones for Photo Crowdsensing

Wang

Liang

et al. 2020

In mobile crowdsensing, some users jointly finish a sensing task through the sensors equipped in their intelligent terminals. In particular, the photo crowdsensing based on Mobile Edge Computing (MEC) collects pictures for some specific targets or events and uploads them to nearby edge servers, which leads to richer data content and more efficient data storage compared with the common mobile crowdsensing; hence, it has attracted an important amount of attention recently. However, the mobile users prefer uploading the photos through Wifi APs (PoIs) rather than cellular networks. Therefore, photos stored in mobile phones are exchanged among users, in order to quickly upload them to the PoIs, which are actually the edge services. In this paper, we propose a utility-based Storage Management strategy in mobile phones for Photo Crowdsensing (SMPC), which makes a sending/deleting decision on a user's device for either maximizing photo delivery ratio (SMPC-R) or minimizing average delay (SMPC-D). The decision is made according to the photo's utility, which is calculated by measuring the impact of reproducing or deleting a photo on the above performance goals. We have done simulations based on the random-waypoint model and three real traces: roma/taxi, epfl, and geolife. The results show that, compared with other storage management strategies, SMPC-R gets the highest delivery ratio and SMPC-D achieves the lowest average delay.