Effective path prediction and data transmission in opportunistic social networks

Wu, Jia; Zou, Wenhao; Long, Huiyun

doi:10.1049/cmu2.12254

Cited by 11 publications

(5 citation statements)

References 27 publications

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“…According to the characteristics of opportunistic social network nodes [16], 2 T ≤ q is tenable, so the following inequalities can be obtained:

\begin{matrix} S false(i goodbreak+ 1 false) - S false(i false) \geq \frac{\begin{matrix} 4 {Q_{t o t a l}}^{3} Δ q - 6 {Q_{t o t a l}}^{2} T Δ q + 2 {Q_{t o t a l}}^{2} T Δ q \\ - 2 {Q_{t o t a l}}^{2} T Δ q^{2} + {(T Δ q)}^{2} \end{matrix}}{4 {Q_{t o t a l}}^{2} {(Q_{t o t a l} + Δ q)}^{2}} \\ = \frac{normalΔ q \times false(4 {Q_{t o t a l}}^{3} - 6 {Q_{t o t a l}}^{2} T + 2 {Q_{t o t a l}}^{2} T - 2 Q_{t o t a l} T normalΔ q + T^{2} normalΔ q false)}{4 {Q_{t o t a l}}^{2} {(Q_{t o t a l} + Δ q)}^{2}} \\ = \frac{normalΔ q \times ((), 2 {Q_{t o t a l}}^{2} - 2 Q_{t o t a l} T - T normalΔ q) \times ((), 2 Q_{t o t a l} - T)}{4 {Q_{t o t a l}}^{2} (Q_{t ...})} \end{matrix}

…”

Section: System Model Designmentioning

confidence: 99%

“…According to the characteristics of opportunistic social network nodes [16], 2T ≤ q is tenable, so the following inequalities can be obtained:…”

Section: Common Featuresmentioning

confidence: 99%

“…At the same time, it is necessary to consider the decision of work preemption and work interruption within the time limit of task completion. In the existing research, there are many schemes on how to establish and update task priority [16], but for work preemption and work interruption, more model indicators are needed to measure, the reason why is that human beings participate in network activities. As the abstract node in network activities, mobile devices also have matching social attributes because of human social activities, which represent the relationship between different users and make the indicators related to scheduling have the characteristics of 'multiple layers' and 'all aspects' [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

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User‐optimized data transmission scheduling based on edge community service in opportunistic social network

Wang

Jin

2022

IET Communications

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In order to improve transmission performance, opportunistic social networks are widely used in 5G mobile network communication for network congestion. However, transferring large amounts of data in an instant will lead to data redundancy problems, which will affect the transmission quality. The authors propose a method called user‐optimized data transmission scheduling based on edge community service in opportunistic social networks (ECSUO). This method constructs the edge community service model by introducing mobile edge computing into the opportunistic social network. Then several scheduling factors are set to coordinate the dynamic priority assessment. The simulation results show that ECSUO performs better than other classical opportunistic complex network algorithms in a variety of transmission quality evaluation indexes, and this method also has good applicability when applied to different transmission models.

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“…According to the characteristics of opportunistic social network nodes [16], 2 T ≤ q is tenable, so the following inequalities can be obtained:

\begin{matrix} S false(i goodbreak+ 1 false) - S false(i false) \geq \frac{\begin{matrix} 4 {Q_{t o t a l}}^{3} Δ q - 6 {Q_{t o t a l}}^{2} T Δ q + 2 {Q_{t o t a l}}^{2} T Δ q \\ - 2 {Q_{t o t a l}}^{2} T Δ q^{2} + {(T Δ q)}^{2} \end{matrix}}{4 {Q_{t o t a l}}^{2} {(Q_{t o t a l} + Δ q)}^{2}} \\ = \frac{normalΔ q \times false(4 {Q_{t o t a l}}^{3} - 6 {Q_{t o t a l}}^{2} T + 2 {Q_{t o t a l}}^{2} T - 2 Q_{t o t a l} T normalΔ q + T^{2} normalΔ q false)}{4 {Q_{t o t a l}}^{2} {(Q_{t o t a l} + Δ q)}^{2}} \\ = \frac{normalΔ q \times ((), 2 {Q_{t o t a l}}^{2} - 2 Q_{t o t a l} T - T normalΔ q) \times ((), 2 Q_{t o t a l} - T)}{4 {Q_{t o t a l}}^{2} (Q_{t ...})} \end{matrix}

…”

Section: System Model Designmentioning

confidence: 99%

“…According to the characteristics of opportunistic social network nodes [16], 2T ≤ q is tenable, so the following inequalities can be obtained:…”

Section: Common Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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User‐optimized data transmission scheduling based on edge community service in opportunistic social network

Wang

Jin

2022

IET Communications

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“…However, since the communication range of the existing method is very lim-ited, the information transmission needs to be realized through mutual communication between nodes [6,7]. Due to the development of high-speed communication modes brought by 5G networks, the resulting problem needs to be solved by suitable opportunistic social routing [9]. The current 5G network architecture provides ultra-high bandwidth and ultra-high-density connections, realizing a leap in the quality of network transmission and meeting the needs of high-speed communication [10].…”

Section: Introductionmentioning

confidence: 99%

An effective data communication community establishment scheme in opportunistic networks

Hao

Gou

2023

IET Communications

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The network transmission speed has been greatly improved, thanks to the power of 5G technology. The millisecond‐level communication delay has made a qualitative leap in communication quality. However, the sharp increase in the number of nodes connected to the internet has resulted in an explosion of traffic. Ensuring stable network transmission in the face of large data volumes has become an urgent problem to be solved. Existing research mainly optimizes for low data volumes of nodes, and cannot dynamically adapt to node transmission load to cope with explosive growth. This is because the node's cache is always at a high level, which causes community communication to be blocked. To address this issue, the authors have designed an effective data communication community establishment scheme. By adding a data information flow (DIF) attribute to each user and using the dynamic feedback adjustment (DFA) mechanism, the authors can combine the entire communication community and control the user's data information flow concentration at a relatively low level. This degree of optimization ensures that the user node maintains a relatively stable state for fast communication. The authors design an effective data communication community establishment scheme. By adding a data information flow (DIF) attribute to each user, using the dynamic feedback adjustment mechanism and combining the entire communication community, the user's data information flow concentration is controlled at a relatively low level.

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“…On the one hand, edge nodes can directly handle some tasks that do not require the capacity of ECS; On the other hand, they can preprocess tasks and data that need to be transmitted to the cloud server to reduce its bandwidth pressure. At the same time, edge computing can also realize the security and controllability of sensitive data and user privacy by reducing the opportunity of user data transmission on the core network and deploying encryption and anonymization mechanisms at the edge [14]. Edge computing can process and make decisions locally to reduce delay and bandwidth consumption.…”

Section: Introductionmentioning

confidence: 99%

A user cache management and cooperative transmission mechanism based on edge community computing in opportunistic social networks

et al. 2022

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With the advent of the 5G era, people's demand for information and bandwidth is increasing. The signal source will mainly be high-frequency radio waves with high bandwidth. However, the coverage of high-frequency radio waves is very limited, which leads to the emergence of micro base stations. Opportunistic social networks are just suitable for this new transmission method, it is easy for users to simultaneously request content with high popularity in a short time, which will cause a heavy load on the micro base station and a high transmission delay. This paper establishes the community mechanism and proposes a user cache management and cooperative transmission mechanism (CMCT) based on edge community computing by using the predictability of users' requests for popular content. The free user equipment space is used to manage cache distribution through the edge community computing to perform cooperative transmission, thereby reducing the content delivery delay and the load of the micro base station. Simulation experiments show that the CMCT algorithm can reduce end-to-end delay and overhead, and improve data transmission efficiency.

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Effective path prediction and data transmission in opportunistic social networks

Cited by 11 publications

References 27 publications

User‐optimized data transmission scheduling based on edge community service in opportunistic social network

User‐optimized data transmission scheduling based on edge community service in opportunistic social network

An effective data communication community establishment scheme in opportunistic networks

A user cache management and cooperative transmission mechanism based on edge community computing in opportunistic social networks

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