LOCASS: Local Optimal Caching Algorithm With Social Selfishness for Mixed Cooperative and Selfish Devices

Yang, Yang; Wu, Yecheng; Chen, Nanxi; Wang, Kunlun; Chen, Shanzhi; Yao, Sha

doi:10.1109/access.2018.2835368

Cited by 9 publications

(6 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Content caching has evolved as an integral part of wireless networks. Edge caching in wireless networks employs data and social network analysis and machine learning methods for estimating content popularity [3], determining request frequency [8], optimal content placement [2], and collaborative caching [15] between endusers by storing content at user devices for later serving on-demand using the D2D communications [4,5].…”

Section: Related Workmentioning

confidence: 99%

“…A more recent approach for D2D caching exploits the knowledge of pairwise social interactions [13]. In [15,18] a game-theoretic approach is proposed for mobile users to cache contents, both for themselves and for their friends.…”

Section: Related Workmentioning

confidence: 99%

“…The rapid growth in the use of smart mobile devices over the last decade has resulted in an explosive increase in mobile data traffic. The major contributing factor is the increasing number of one-tomany transmissions in the form of multimedia messaging to groups and content posted with fans over social networks [2,11,15]. The Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Social Groups Based Content Caching in Wireless Networks

Mustafa

Khan

et al. 2021

Proceedings of the 19th ACM International Symposium on Mobility Management and Wireless Access

View full text Add to dashboard Cite

The unprecedented growth of wireless mobile traffic, mainly due to multimedia traffic over online social platforms has strained the resources in the mobile backhaul network. A promising approach to reduce the backhaul load is to proactively cache content at the network edge, taking into account the overlaid social network. Known caching schemes require complete knowledge of the social graph and mainly focus on one-to-one interactions forgoing the prevalent mode of content sharing among circles of 'friends'. We propose Bingo, a proactive content caching scheme that leverages the presence of interest groups in online social networks. The mobile network operator (MNO) can choose to incrementally deploy Bingo at select network nodes (base stations, packet core, data center) based on user profiles and revenue numbers. We approximate the group memberships of users using the available user-content request logs without any prior knowledge of the overlaid social graph. Bingo can cater to the evolving nature of online social groups and file popularity distribution for making caching decisions. We use synthetically generated group structures and simulate user requests at the base station for empirical evaluation against traditional and recent caching schemes. Bingo achieves up to 30% − 34% gain over the best baseline. CCS CONCEPTS• Networks → Network algorithms.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Social Groups Based Content Caching in Wireless Networks

Mustafa

Khan

et al. 2021

Proceedings of the 19th ACM International Symposium on Mobility Management and Wireless Access

View full text Add to dashboard Cite

show abstract

“…The IoV has been proposed to sense the data and process it for smart transport system [5], [6]. It consists of parked and moving vehicles, roads side units (RSUs), traffic lights and handheld devices used by the passengers and the general public [7]. The nodes participating in the IoV have single ownership, person or organization [8], [9].…”

Section: Introductionmentioning

confidence: 99%

IPS: Incentive and Punishment Scheme for Omitting Selfishness in the Internet of Vehicles (Iov)

Rehman¹,

Ghani

Zubair³

et al. 2019

IEEE Access

View full text Add to dashboard Cite

Internet of Vehicles (IoV) is a new emerging concept and is an extended notion of Vehicular Ad-hoc networks (VANETs). In IoV the vehicles (nodes) are connected to the internet and able to transmit information. However, due to resources constraint nature of vehicles, they may not want to cooperate in order to save its own resources such as memory, energy, and buffer, etc. This behavior may lead to poor system performance. IoV needs an efficient solution to motivate the nodes in terms of cooperation to avoid selfish behavior. A novel mechanism Incentive and Punishment Scheme (IPS) has been proposed in this article where vehicles with higher weight and cooperation are elected as Heads during the election process. Vickrey, Clarke, and Groves (VCG) model has been used to scrutinize the weight of these heads. Vehicle participating in the election process can increase its incentives (reputation) by active participation (forwarding data). Vehicles with repeated selfish behavior are punished. The monitoring nodes monitor the performance of their neighbor nodes after the election process. A mathematical model and algorithms has been developed for the election, monitoring and incentive processes. The proposed approach has been simulated through VDTNSim environment to analyze the performance of the proposed IPS. The performance results demonstrate that the proposed schemes outperform the existing schemes in terms of packet delivery ratio, average delivery delay, average cost, and overhead. INDEX TERMS Internet of vehicles, smart objects, VCG model, selfish behavior, incentive techniques.

show abstract

“…Common objectives for joint optimal caching include maximizing the cache hit probability [17], maximizing the caching capacity of a network [18], and minimizing the content provisioning delay [19], [20] or cost [21]. Alternatively, the problem of content placement can also be formulated as a non-cooperative game [22] or an auction [23] between cache servers, content providers, and/or network operators, and the solution can be found from the resulting equilibrium or outcome in a decentralized manner [24], [25]. Probabilistic content placement was adopted in many recent works in the literature of optimizationbased MEC with known content popularity [26]- [30].…”

mentioning

confidence: 99%

The Design of Dynamic Probabilistic Caching with Time-Varying Content Popularity

Gao

Zhang

Zhao

et al. 2021

IEEE Trans. on Mobile Comput.

View full text Add to dashboard Cite

In this paper, we design dynamic probabilistic caching for the scenario when the instantaneous content popularity may vary with time while it is possible to predict the average content popularity over a time window. Based on the average content popularity, optimal content caching probabilities can be found, e.g., from solving optimization problems, and existing results in the literature can implement the optimal caching probabilities via static content placement. The objective of this work is to design dynamic probabilistic caching that: i) converge (in distribution) to the optimal content caching probabilities under time-invariant content popularity, and ii) adapt to the time-varying instantaneous content popularity under time-varying content popularity. Achieving the above objective requires a novel design of dynamic content replacement because static caching cannot adapt to varying content popularity while classic dynamic replacement policies, such as LRU, cannot converge to target caching probabilities (as they do not exploit any content popularity information). We model the design of dynamic probabilistic replacement policy as the problem of finding the state transition probability matrix of a Markov chain and propose a method to generate and refine the transition probability matrix. Extensive numerical results are provided to validate the effectiveness of the proposed design.

show abstract

LOCASS: Local Optimal Caching Algorithm With Social Selfishness for Mixed Cooperative and Selfish Devices

Cited by 9 publications

References 29 publications

Social Groups Based Content Caching in Wireless Networks

Social Groups Based Content Caching in Wireless Networks

IPS: Incentive and Punishment Scheme for Omitting Selfishness in the Internet of Vehicles (Iov)

The Design of Dynamic Probabilistic Caching with Time-Varying Content Popularity

Contact Info

Product

Resources

About