Seed Selection for Data Offloading Based on Social and Interest Graphs

Recommendation system is one of the most common applications in the field of big data. The traditional collaborative filtering recommendation algorithm is directly based on user-item rating matrix. However, when there are huge amounts of user and commodities data, the efficiency of the algorithm will be significantly reduced. Aiming at the problem, a collaborative filtering recommendation algorithm based on multirelational social networks is proposed. The algorithm divides the multi-relational social networks based on the multi-subnet complex network model into communities by using information dissemination method, which divides the users with similar degree into a community. Then the user-item rating matrix is constructed by choosing the k-nearest neighbor set of users within the community, in this case, the collaborative filtering algorithm is used for recommendation. Thus, the execution efficiency of the algorithm is improved without reducing the accuracy of recommendation.

Section: Related Workmentioning

confidence: 99%

Collaborative Filtering Recommendation Algorithm Based on Multi-Relationship Social Network

Bin¹,

Sun²,

Cao³

et al. 2019

“…Li et al [Li, Li, Chen et al (2018)] propose opportunistic data offloading solutions to mitigate traffic on mobile networks. The explosive increase in the number of wireless data is becoming increasingly burdensome in wireless networks today.…”

Section: Related Workmentioning

confidence: 99%

Development of Cloud Based Air Pollution Information System Using Visualization

Han¹,

Seo²,

Kim³

et al. 2019

Air pollution caused by fine dust is a big problem all over the world and fine dust has a fatal impact on human health. But there are too few fine dust measuring stations and the installation cost of fine dust measuring station is very expensive. In this paper, we propose Cloud-based air pollution information system using R. To measure fine dust, we have developed an inexpensive measuring device and studied the technique to accurately measure the concentration of fine dust at the user's location. And we have developed the smartphone application to provide air pollution information. In our system, we provide collected data based analytical results through effective data modeling. Our system provides information on fine dust value and action tips through the air pollution information application. And it supports visualization on the map using the statistical program R. The user can check the fine dust statistics map and cope with fine dust accordingly.

“…There are plenty of challenges in highly stochastic and dynamic mobility communication, such as network congestion, performance degradation, etc [Hossain, Chow, Leung et al (2010); Li, Li, Chen et al (2018)]. Currently, numerous researchers have focused on handover decision process, and a number of solutions have been proposed in relevant literatures.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Handover Decision Inspired by Biological Mechanism in Vehicle Ad-hoc Networks

Duan¹,

Wei²,

Tian³

et al. 2019

In vehicle ad-hoc networks (VANETs), the proliferation of wireless communication will give rise to the heterogeneous access environment where network selection becomes significant. Motivated by the self-adaptive paradigm of cellular attractors, this paper regards an individual communication as a cell, so that we can apply the revised attractor selection model to induce each connected vehicle. Aiming at improving the Quality of Service (QoS), we presented the bio-inspired handover decision-making mechanism. In addition, we employ the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) for any vehicle to choose an access network. This paper proposes a novel framework where the bio-inspired mechanism is combined with TOPSIS. In a dynamic and random mobility environment, our method achieves the coordination of performance of heterogeneous networks by guaranteeing the efficient utilization and fair distribution of network resources in a global sense. The experimental results confirm that the proposed method performs better when compared with conventional schemes.