Energy-Efficient Cloudlet Management for Privacy Preservation in Wireless Metropolitan Area Networks

Social network has provided a promising way for massive users to share their ideas and communicate with each other. A key issue in social network is to find out the prospective friends of users so as to extend the users' social cycles. Fortunately, users' thumbs-up data on web news or blogs have become an important evaluation basis in friend finding. Typically, through analyzing the thumbs-up data from different users, we can find out the friends or neighbors of a user. However, the thumbs-up data are often sensitive to users as they can disclose the private information of users, which violate the civil privacy-protection laws enacted by governments. In view of this challenge, we introduce the Simhash technique in information retrieval domain into social network and further bring forth a privacy-aware prospective friend-finding solution in social network based on the sensitive thumbs-up data. At last, we conduct a range of experiments based on well-known Movielens dataset. Experimental data demonstrate the advantages of our solution.

Section: Discussionmentioning

confidence: 99%

Privacy-aware friend finding in social network based on thumbs-up data

Sun

Kou

Huang

2019

“…However, the time consumption of task offloading is a problem waiting to be processed. The time consumption affect the experience of mobile users directly [33,34]. In order to make full use of the computing resources in edge computing reasonably, an appropriate resource schedule need to be designed.…”

Section: Related Workmentioning

confidence: 99%

A time-efficient data offloading method with privacy preservation for intelligent sensors in edge computing

Liu

Jiang

et al. 2019

Over the past years, with the development of hardware and software, the intelligent sensors, which are deployed in the wearable devices, smart phones, and etc., are leveraged to collect the data around us. The data collected by the sensors is analyzed, and the corresponding measures will be implemented. However, due to the limited computing resources of the sensors, the overload resource usage may occur. In order to satisfy the requirements for strong computing power, edge computing, which emerges as a novel paradigm, provides computing resources at the edge of networks. In edge computing, the computing tasks could be offloaded from the sensors to the other sensors for processing. Despite the advantages of edge computing, during the offloading process of computing tasks between sensors, private data, including identity information and address, may be leaked, which threatens personal security. Hence, it is important to avoid privacy leakage in edge computing. In addition, the time consumption of offloading computing tasks affects the using experience of customers, and low time consumption makes contributions to the development of applications which are strict with time. To satisfy the above requirements, a time-efficient offloading method (TEO) with privacy preservation for intelligent sensors in edge computing is proposed. Technically, the time consumption and the offloading of privacy data are analyzed in a formalized way. Then, an improved of Strength Pareto Evolutionary Algorithm (SPEA2) is leveraged to optimize the average time consumption and average privacy entropy jointly. At last, abundant experimental evaluations are conducted to verify efficiency and reliability of our method.

“…There are many kinds of edge servers [43]. Cloudlet is a type of edge server, which has been widely used in LAN and WMAN [25][26][27][44][45][46]. Computation offloading was originally studied in MCC [28][29][30][31][32][33].…”

Section: Related Workmentioning

confidence: 99%

An energy- and cost-aware computation offloading method for workflow applications in mobile edge computing

Peng

Zhu

Zhang

et al. 2019

Self Cite

Mobile edge computing is becoming a promising computing architecture to overcome the resource limitation of mobile devices and bandwidth bottleneck of the core networks in mobile cloud computing. Although offloading applications to the cloud can extend the performance for the mobile devices, it may also lead to greater processing latency. Usually, the mobile users have to pay for the cloudlet resource or cloud resource they used. In this paper, we bring a thorough study on the energy consumption, time consumption, and cost of using the resource of cloudlet and cloud for workflow applications in mobile edge computing. Based on theoretical analysis, a multi-objective optimization model is established. In addition, a corresponding multi-objective computation offloading method based on non-dominated sorting genetic algorithm II is proposed to find the optimal offloading strategy for all the workflow applications. Finally, extensive experimental evaluations are performed to show that our proposed method is effective and energy-and cost-aware for workflow applications in MEC.