Social-aware cooperative caching mechanism in mobile social networks

Wang, Dapeng; Liu, Bingxu; Yang, Qing; Wang, Ruyan

doi:10.1016/j.jnca.2019.102457

Cited by 50 publications

(60 citation statements)

References 30 publications

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“…User characteristics have a significant impact on whether participants can complete tasks in a timely manner and submit high-quality data [39]- [41]. This paper considers the fitness between users and tasks and the user reputation.…”

Section: User Characteristics Awarenessmentioning

confidence: 99%

Bilateral Satisfaction Aware Participant Selection With MEC for Mobile Crowd Sensing

2020

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To meet some real-time mobile crowd sensing (MCS) scenarios, there is a tendency to enhance the MCS system with mobile edge computing (MEC). One of the key challenges is how to select some satisfied participants in such an edge-cloud collaboration MCS system to effectively and real-timely handle dynamic and heterogeneous sensing tasks. In this paper, we propose a bilateral satisfaction aware participant selection mechanism in the edge-cloud collaboration MCS system. The participant selection process is coordinated by the cloud service platform and the MEC server. The cloud service platform sends the required data types to the MEC server and evaluates the user reputation through the user history task records. The MEC server generates a set of tasks and evaluates user fitness based on the user's real-time location. Then the MEC server obtains the user sensing cost based on the user status, and develops the task price model based on the user supply index and data demand index. Finally, the participant selection process is transformed into a game between users and the MEC server about the task reward, and the user who accepts the optimal task price is selected as the participant. The results show that the proposed participant selection strategy can effectively reduce the amount of data processed by the cloud platform, shorten the task completion time, and increase bilateral satisfaction. INDEX TERMS Crowd sensing, edge computing, participant selection, user characteristic, bilateral satisfaction.

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Section: User Characteristics Awarenessmentioning

confidence: 99%

Bilateral Satisfaction Aware Participant Selection With MEC for Mobile Crowd Sensing

2020

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“…Besides, for each packet, its packet delay can be obtained by (6). It is noticed that this paper only calculates the delay of the packets which have been processed.…”

Section: Performance Analysismentioning

confidence: 99%

“…In the simulations, the minimum Poisson factor λ min varies in a range [2,6]. As mentioned in III.C, the arriving multimedia packets in each QoS flow q at each time slot t follows a Poisson distribution with expected value λ q = ν q • λ.…”

Section: Performance Analysismentioning

confidence: 99%

“…Cisco's forecasts [1] predicted that mobile data traffic will increase sevenfold between 2017 and 2022, which will bring significant challenges to wireless networks. Besides, with the enhancement of 5G technologies, e.g., network slice [2], device-to-device communication [3]- [5], edge caching [6], Internet of things [7], [8] and so forth, many streaming media services emerge [9], [10]. Specifically, various multimedia applications from massive mobile devices pose diverse requirements (e.g., ultra-low latency, high bitrate, etc.)…”

Section: Introductionmentioning

confidence: 99%

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Deep-Q-Network-Based Multimedia Multi-Service QoS Optimization for Mobile Edge Computing Systems

et al. 2019

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By offloading storage and computing resources to the edge of networks, mobile edge computing (MEC) is emerged as a promising architecture to reduce the transmission delay and bandwidth waste for mobile multimedia services. This paper focuses on a multi-service scenario in the MEC systems, where the MEC server can provide three multimedia services including live streaming, buffered streaming and low latency enhanced mobile broadband applications for edge users at the same time. In order to satisfy various quality of service (QoS) requirements for different multimedia applications, the 5G QoS model is applied. Notably, the packets from the multimedia applications with the same or similar requirements are mapped into the same QoS flow, and each QoS flow is processed individually. Therefore, how to effectively schedule the limited radio resource for QoS flows is an intractable problem. To address the problem above, a QoS evaluation model is designed, and a QoS maximization problem is formulated. Furthermore, a deep reinforcement learning method, deep-Q-network, is adopted to make decisions to allocate radio resource dynamically. Compared with round-robin and priority-based scheduling algorithms, the simulation results validate that the proposed algorithm outperforms other resource scheduling algorithms for multi-service scenario. INDEX TERMS Deep-Q-network, deep reinforcement learning, mobile edge computing, multimedia, quality of service.

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“…Therefore, combining the opportunistic network [4] with the mobile social network [5,6] to form the opportunistic social network [7,8] has both the social characteristics of the social network and the data forwarding characteristics of the opportunistic network, which is a form of the social network's transition to offline materialization. The opportunistic social network adopts a "storage-carry-forward" [9,10] routing model and proposes that data forwarding can be achieved through the "opportunity exchange" [11][12][13] method.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Multi-Scenario Routing Algorithm Based on Fuzzy Control Theory in Opportunistic Social Network

Chen

et al. 2020

Symmetry

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With the flourishing of big data and the 5G era, the amount of data to be transmitted in the communication process is increasing, and end-to-end communication in traditional social networks has been unable to meet the current communication needs. Therefore, in order to improve the success rate of data forwarding, social networks propose that the sender of the message should reasonably choose the next hop node. However, existing routing and forwarding algorithms do not take into account nodes that are live in different scenarios, and the applicable next hop node metrics are also different. These algorithms only consider the forwarding preferences of the nodes during working hours and do not consider the forwarding preferences of the nodes during non-working hours. We propose a routing algorithm based on fuzzy decision theory, which aims at a more accurate decision on selecting the next hop. A routing and forwarding algorithm based on fuzzy decision is proposed in this paper. This algorithm symmetrical divides scenes in opportunistic social networks into working time and non-working time according to real human activity. In addition, metrics are designed symmetrically for these two scenarios. Simulation results demonstrate that, in the best case, the proposed scheme presents an average delivery ratio of 0.95 and reduces the average end-to-end delay and average overhead compared with the epidemic routing algorithm, the EIMSTalgorithm, the ICMT algorithm, and the FCNSalgorithm.

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Social-aware cooperative caching mechanism in mobile social networks

Cited by 50 publications

References 30 publications

Bilateral Satisfaction Aware Participant Selection With MEC for Mobile Crowd Sensing

Bilateral Satisfaction Aware Participant Selection With MEC for Mobile Crowd Sensing

Deep-Q-Network-Based Multimedia Multi-Service QoS Optimization for Mobile Edge Computing Systems

A Comprehensive Multi-Scenario Routing Algorithm Based on Fuzzy Control Theory in Opportunistic Social Network

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