With the development of recent innovative applications (e.g., augment reality, self-driving, and various cognitive applications), more and more computation-intensive and data-intensive tasks are delay-sensitive. Mobile edge computing in ultra-dense network is expected as an effective solution for meeting the low latency demand. However, the distributed computing resource in edge cloud and energy dynamics in the battery of mobile device makes it challenging to offload tasks for users. In this paper, leveraging the idea of software defined network, we investigate the task offloading problem in ultra-dense network aiming to minimize the delay while saving the battery life of user’s equipment. Specifically, we formulate the task offloading problem as a mixed integer non-linear program which is NP-hard. In order to solve it, we transform this optimization problem into two sub-problems, i.e., task placement sub-problem and resource allocation sub-problem. Based on the solution of the two sub-problems, we propose an efficient offloading scheme. Simulation results prove that the proposed scheme can reduce 20% of the task duration with 30% energy saving, compared with random and uniform task offloading schemes.

Task Offloading for Mobile Edge Computing in Software Defined Ultra-Dense Network

Low illumination underwater light field images reconstruction using deep convolutional neural networks

Cognitive Internet of Vehicles

Movie recommendation in mobile environment is critically important for mobile users. It carries out comprehensive aggregation of user’s preferences, reviews, and emotions to help them find suitable movies conveniently. However, it requires both accuracy and timeliness. In this paper, a movie recommendation framework based on a hybrid recommendation model and sentiment analysis on Spark platform is proposed to improve the accuracy and timeliness of mobile movie recommender system. In the proposed approach, we first use a hybrid recommendation method to generate a preliminary recommendation list. Then sentiment analysis is employed to optimize the list. Finally, the hybrid recommender system with sentiment analysis is implemented on Spark platform. The hybrid recommendation model with sentiment analysis outperforms the traditional models in terms of various evaluation criteria. Our proposed method makes it convenient and fast for users to obtain useful movie suggestions.

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A Sentiment-Enhanced Hybrid Recommender System for Movie Recommendation: A Big Data Analytics Framework

Advanced communications and networks greatly enhance the user experience and have a major impact on all aspects of people’s lifestyles. Widely deployed sensor nodes provide support for these services. However, although energy harvesting and transfer technology provides a solution to allow the long-term survival of wireless sensor nodes for wireless sensor networks, the single collection scheme causes a lot of energy waste. Thus, efficient energy utilization and fast data collection are still serious challenges for energy harvesting wireless sensor networks. To overcome these challenges, an adaptive collection scheme based on matrix completion (ACMC) is proposed to reduce delay and to improve the energy utilization of the network. In the ACMC scheme, compared with traditional data collection schemes, the data collection schemes vary with the available energy, collecting large amounts of data when the available energy is sufficient to obtain high-quality data-based applications. Otherwise, adaptive selecting the collected data based on previously collected data, the amount of data collected can be effectively reduced based on the application requirements, thereby improving the energy utilization of the network. The ACMC scheme also proposes a method for reducing the delay by increasing the duty cycle of the nodes that are far from the CC. At the same time, the transmission reliability of these nodes increases due to the increase in the transmission frequency. Thus, the ACMC scheme can also further reduce the delay of the network. The experimental results of the ACMC scheme in planar networks show better performance than the traditional data collection schemes and can improve the energy utilization of the network by 4.26%–6.68% while reducing the maximum delay by 9.4%.

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An Adaptive Collection Scheme-Based Matrix Completion for Data Gathering in Energy-Harvesting Wireless Sensor Networks

With the popularization of social networks, increasing numbers of users choose to use Weibo to get information. However, as the number of users grows, the information on Weibo is also multiplying, making it increasingly difficult for users to find the right information they are interested in. Therefore, how to recommend high-quality friends to follow the Weibo is one of the focuses of studies in Weibo-based personalized services. Based on existing Weibo social networking topologies and content-based hybrid recommendation algorithms, the study proposed a hybrid recommendation algorithm based on social relations and time-sequenced topics, which has been verified using Real Sina Weibo datasets. The results show that the improved hybrid recommendation algorithm works well and achieves better mean average precision (MAP) than existing other counterparts.

Zhixiao Tu

Papers

TempoRec: Temporal-Topic Based Recommender for Social Network Services