Socially Aware Energy-Efficient Mobile Edge Collaboration for Video Distribution

Wang, Dapeng; Liu, Qianru; Wang, Honggang; Wu, Dalei; Wang, Ruyan

doi:10.1109/tmm.2017.2733300

Cited by 102 publications

(48 citation statements)

References 42 publications

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“…The literature [14] proposed a Stackelberg game-based pricing mechanism to inspire core users to distribute videos to the multicast users via device-to-device (D2D) communication. Nonmonetary incentives include entertainment game incentives [16], social relations incentives [15,17], and virtual integration incentives [18]. The entertainment game incentive [16] uses game entertainment and attractiveness to encourage the sensing users to complete the sensing task.…”

Section: Incentive Mechanismmentioning

confidence: 99%

“…Social relation incentives refer to a type of social networking relationship where the sensing users already exist or server platform is built, and the sensing users are motivated to maintain a sense of belonging in social relations [15]. The literature [17] exploits the coalition game by employing the user's social preference list to dynamically establish virtual communities. Virtual integral incentive [18] refers to the fact that the sensing users will receive the virtual integral from the sensing task.…”

Section: Incentive Mechanismmentioning

confidence: 99%

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Achieving Incentive, Security, and Scalable Privacy Protection in Mobile Crowdsensing Services

Xiong

Chen

et al. 2018

Wireless Communications and Mobile Computing

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Mobile crowdsensing as a novel service schema of the Internet of Things (IoT) provides an innovative way to implement ubiquitous social sensing. How to establish an effective mechanism to improve the participation of sensing users and the authenticity of sensing data, protect the users' data privacy, and prevent malicious users from providing false data are among the urgent problems in mobile crowdsensing services in IoT. These issues raise a gargantuan challenge hindering the further development of mobile crowdsensing. In order to tackle the above issues, in this paper, we propose a reliable hybrid incentive mechanism for enhancing crowdsensing participations by encouraging and stimulating sensing users with both reputation and service returns in mobile crowdsensing tasks. Moreover, we propose a privacy preserving data aggregation scheme, where the mediator and/or sensing users may not be fully trusted. In this scheme, differential privacy mechanism is utilized through allowing different sensing users to add noise data, then employing homomorphic encryption for protecting the sensing data, and finally uploading ciphertext to the mediator, who is able to obtain the collection of ciphertext of the sensing data without actual decryption. Even in the case of partial sensing data leakage, differential privacy mechanism can still ensure the security of the sensing user's privacy. Finally, we introduce a novel secure multiparty auction mechanism based on the auction game theory and secure multiparty computation, which effectively solves the problem of prisoners' dilemma incurred in the sensing data transaction between the service provider and mediator. Security analysis and performance evaluation demonstrate that the proposed scheme is secure and efficient.

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Section: Incentive Mechanismmentioning

confidence: 99%

Section: Incentive Mechanismmentioning

confidence: 99%

Achieving Incentive, Security, and Scalable Privacy Protection in Mobile Crowdsensing Services

Xiong

Chen

et al. 2018

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

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“…(12), duration(u i , u j ) denotes the encounter duration of user u i and u j , and t u i ,u j denotes the total transmission delay between user u i and u j , which is related to the data transmission rate R u i ,u j and data size F, as shown in Eq. (15).…”

Section: Local Centralitymentioning

confidence: 99%

Socially aware D2D cooperative communications for enhancing Internet of Things application

Yan

Wang

Zhang

et al. 2018

J Wireless Com Network

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To satisfy the rapidly growing requirements of wireless data services recently, fog computing which is as an extension of cloud computing is proposed for offloading the Internet of Things (IoT) data services and applications. It depends more on the collaboration of near-located devices, instead of sending the information to remote servers. In this paper, we conceive the idea of utilizing device-to-device (D2D) communications as the infrastructures for computation and communication. In particular, D2D communication technology is one of the most important and innovative revolutions in the development of future cellular networks, which greatly enlarges the system capacity and extensively enriches the service category and application diversity. In computation part, by analyzing the interest difference of users, a Virtual Fog Community (VFC) is established in this paper, which exploits the coalition game based on the transfer effectiveness to adaptively divide users into multiple fog communities. In communication part, by perceiving the importance degrees of users and accordingly employing multidimensional network status parameters such as the local centrality and closeness degree, the best cooperative user can be reasonably selected based on the physical and social attributes of users. Therefore, the corresponding inter-community and intra-community D2D cooperative transmission mechanisms are devised to dramatically optimize the network resource utilization. Simulation results show that the proposed mechanism fully exploits the social relationship between users and effectively enhances the network operational efficiency at the cost of relatively low delay.

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“…Nowadays, people's demand for mobile video services is growing rapidly, which brings a great burden to base stations, and the characteristics of D2D communication technology bring hope to reduce the burden of base stations. Therefore, in recent years, there have been more and more studies combining D2D communication technology with video services [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Network Coding for Efficient Video Multicast in Device-to-Device Communications

Wang

Pan³

et al. 2020

Sensors

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Device-to-Device (D2D) communication is one of the critical technologies for the fifth-generation network, which allows devices to communicate directly with each other while increasing transmission rate, but this communication is vulnerable to interference. When video transmission is carried out in an environment with interference, problems such as high packet loss rate, poor quality of the video, and blurred screen may exist. These problems can be effectively solved by redundant coding operations at the source node, but the extra coding operation imposes a heavy computational burden on the source node. In order to alleviate the computational overhead of the source node, reduce transmission delay, and guarantee transmission quality, this paper proposes an efficient video multicast transmission scheme based on Random Linear Network Coding (RLNC) in D2D networks. In the scheme, the receiving devices in the transmission participate in the process of generating repair packets that are used to remedy the loss of encoded packets during transmission. The source node multicasts the encoded video file. The receiving nodes re-encode the received data packets with RLNC and then send them to the network again. The nearby nodes can decode the original data through the encoded or re-encoded data packets. The performance of the proposed scheme is evaluated through both simulation and real experiments. The experimental results show that compared with the traditional RLNC scheme, this scheme could balance the computation overhead of the mobile devices and reduce the encoding and decoding delay by about 8%. When the packet loss rate is high, the proposed scheme can obtain better video quality than the traditional replication-based scheme.

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Socially Aware Energy-Efficient Mobile Edge Collaboration for Video Distribution

Cited by 102 publications

References 42 publications

Achieving Incentive, Security, and Scalable Privacy Protection in Mobile Crowdsensing Services

Achieving Incentive, Security, and Scalable Privacy Protection in Mobile Crowdsensing Services

Socially aware D2D cooperative communications for enhancing Internet of Things application

Network Coding for Efficient Video Multicast in Device-to-Device Communications

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