Effective path prediction and data transmission in opportunistic social networks

Effective Data Transmission Based on Path Prediction in Opportunistic Social Networks

10.21203/rs.2.24596/v1 ◽

2020 ◽

Author(s):

Xuan ZHANG ◽

Jia WU ◽

Genghua YU

Keyword(s):

Social Networks ◽

Data Transmission ◽

Information Exchange ◽

Prediction Method ◽

Transmission Delay ◽

Network Efficiency ◽

Trajectory Prediction ◽

Path Prediction ◽

Opportunistic Transmission ◽

Surrounding Areas

Abstract With the development of social media, social networks have become an important platform for people to share and communicate. In social network communication, each participant can only pass information correctly if we find the goal of our communication. In other words, when people carry mobile devices for data transmission, they need to find a definite transmission destination to ensure the normal conduct of information exchange activities. In social networks, this is manifested in the process of data transmission by nodes, which requires analysis and judgment of surrounding areas, and finds suitable nodes for effective data classification and transmission. However, the node cache space in social networks is limited, and the process of waiting for the target node will cause end-to-end transmission delay. In order to improve such a transmission environment, this paper proposes a node trajectory prediction method named EDPPM algorithm. This algorithm can guarantee that nodes with high probability are given priority to obtain data information, which realized an effective data transmission mechanism. Through experiments and comparison of opportunistic transmission algorithms in social networks, such as Epidemic algorithm, Spray and Wait algorithm, and PRoPHET algorithm, our algorithm can improve the cache utilization of nodes, reduce data transmission delay, and improve the overall network efficiency.

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Routing Algorithm Based on User Adaptive Data Transmission Scheme in Opportunistic Social Networks

Electronics ◽

10.3390/electronics10101138 ◽

2021 ◽

Vol 10 (10) ◽

pp. 1138

Author(s):

Yu Lu ◽

Liu Chang ◽

Jingwen Luo ◽

Jia Wu

Keyword(s):

Social Networks ◽

Social Network ◽

Data Transmission ◽

Routing Algorithm ◽

Relay Node ◽

Network Routing ◽

Transmission Factor ◽

Transmission Scheme ◽

Network Delay ◽

Significant Burden

With the rapid popularization of 5G communication and internet of things technologies, the amount of information has increased significantly in opportunistic social networks, and the types of messages have become more and more complex. More and more mobile devices join the network as nodes, making the network scale increase sharply, and the tremendous amount of datatransmission brings a more significant burden to the network. Traditional opportunistic social network routing algorithms lack effective message copy management and relay node selection methods, which will cause problems such as high network delay and insufficient cache space. Thus, we propose an opportunistic social network routing algorithm based on user-adaptive data transmission. The algorithm will combine the similarity factor, communication factor, and transmission factor of the nodes in the opportunistic social network and use information entropy theory to adaptively assign the weights of decision feature attributes in response to network changes. Also, edge nodes are effectively used, and the nodes are divided into multiple communities to reconstruct the community structure. The simulation results show that the algorithm demonstrates good performance in improving the information transmission’s success rate, reducing network delay, and caching overhead.

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An effective data transmission scheme based on IoT system in opportunistic social networks

International Journal of Communication Systems ◽

10.1002/dac.5062 ◽

2021 ◽

Author(s):

Zhuoqi Huang ◽

Xiaoli Li ◽

Jia Wu

Keyword(s):

Social Networks ◽

Data Transmission ◽

Transmission Scheme

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An Efficient Data Transmission Strategy for Edge-Computing-Based Opportunistic Social Networks

Lecture Notes in Computer Science - Network and Parallel Computing ◽

10.1007/978-3-030-79478-1_28 ◽

2021 ◽

pp. 323-335

Author(s):

Jingwen Luo ◽

Jia Wu ◽

Yuzhou Wu

Keyword(s):

Social Networks ◽

Data Transmission ◽

Edge Computing ◽

Transmission Strategy ◽

Efficient Data

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An Adaptive Delay-Tolerant Routing Algorithm for Data Transmission in Opportunistic Social Networks

Electronics ◽

10.3390/electronics9111915 ◽

2020 ◽

Vol 9 (11) ◽

pp. 1915

Author(s):

Shupei Chen ◽

Zhigang Chen ◽

Jia Wu ◽

Kanghuai Liu

Keyword(s):

Social Networks ◽

Data Transmission ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Routing Algorithm ◽

Opportunistic Networks ◽

Membership Functions ◽

Inference System ◽

Fuzzy Input ◽

Delay Tolerant

In opportunistic networks, the requirement of QoS (quality of service) poses several major challenges to wireless mobile devices with limited cache and energy. This implies that energy and cache space are two significant cornerstones for the structure of a routing algorithm. However, most routing algorithms tackle the issue of limited network resources from the perspective of a deterministic approach, which lacks an adaptive data transmission mechanism. Meanwhile, these methods show a relatively low scalability because they are probably built up based on some special scenarios rather than general ones. To alleviate the problems, this paper proposes an adaptive delay-tolerant routing algorithm (DTCM) utilizing curve-trapezoid Mamdani fuzzy inference system (CMFI) for opportunistic social networks. DTCM evaluates both the remaining energy level and the remaining cache level of relay nodes (two-factor) in opportunistic networks and makes reasonable decisions on data transmission through CMFI. Different from the traditional fuzzy inference system, CMFI determines three levels of membership functions through the trichotomy law and evaluates the fuzzy mapping from two-factor fuzzy input to data transmission by curve-trapezoid membership functions. Our experimental results show that within the error interval of 0.05~0.1, DTCM improves delivery ratio by about 20% and decreases end-to-end delay by approximate 25% as compared with Epidemic, and the network overhead from DTCM is in the middle horizon.

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QoS-Aware Service Selection and Multicast Framework for Wireless Mesh Networks

International Journal of Business Data Communications and Networking ◽

10.4018/ijbdcn.2016010101 ◽

2016 ◽

Vol 12 (1) ◽

pp. 1-15 ◽

Cited By ~ 1

Author(s):

A. Narayana Rao ◽

Ch. D. V. Subba Rao

Keyword(s):

Data Transmission ◽

Mesh Networks ◽

Service Selection ◽

Video Data ◽

Mesh Network ◽

Delivery Ratio ◽

Wired Networks ◽

Wireless Mesh ◽

Security And Reliability ◽

Effective Path

Wireless Mesh Network (WMN) is a multi-hop, multi-path network that has become the most favored method in delivering end-to-end data, voice and video. Data transmission through WMN has the security and reliability, same as the conventional wired networks. Since, WMN has a decentralized topology, maintaining QoS is very crucial. Hence in this work, we propose to develop a WMN that selects services based on high QoS. In order to avoid redundancy in data transmission, in this work we propose to develop an efficient framework for multicasting by determining the most effective path for transmitting the same data towards multiple destination nodes. By simulation results, we show that the proposed technique provides better QoS in terms of throughput and packet delivery ratio.

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MSSN: An Attribute-Aware Transmission Algorithm Exploiting Node Similarity for Opportunistic Social Networks

Information ◽

10.3390/info10100299 ◽

2019 ◽

Vol 10 (10) ◽

pp. 299

Author(s):

Mei Guo ◽

Min Xiao

Keyword(s):

Social Networks ◽

Mobile Devices ◽

Data Transmission ◽

Routing Algorithms ◽

Delivery Ratio ◽

Relay Nodes ◽

Similarity Degree ◽

The Social ◽

Node Similarity ◽

End To End

Recently, with the development of big data and 5G networks, the number of intelligent mobile devices has increased dramatically, therefore the data that needs to be transmitted and processed in the networks has grown exponentially. It is difficult for the end-to-end communication mechanism proposed by traditional routing algorithms to implement the massive data transmission between mobile devices. Consequently, opportunistic social networks propose that the effective data transmission process could be implemented by selecting appropriate relay nodes. At present, most existing routing algorithms find suitable next-hop nodes by comparing the similarity degree between nodes. However, when evaluating the similarity between two mobile nodes, these routing algorithms either consider the mobility similarity between nodes, or only consider the social similarity between nodes. To improve the data dissemination environment, this paper proposes an effective data transmission strategy (MSSN) utilizing mobile and social similarities in opportunistic social networks. In our proposed strategy, we first calculate the mobile similarity between neighbor nodes and destination, set a mobile similarity threshold, and compute the social similarity between the nodes whose mobile similarity is greater than the threshold. The nodes with high mobile similarity degree to the destination node are the reliable relay nodes. After simulation experiments and comparison with other existing opportunistic social networks algorithms, the results show that the delivery ratio in the proposed algorithm is 0.80 on average, the average end-to-end delay is 23.1% lower than the FCNS algorithm (A fuzzy routing-forwarding algorithm exploiting comprehensive node similarity in opportunistic social networks), and the overhead on average is 14.9% lower than the Effective Information Transmission Based on Socialization Nodes (EIMST) algorithm.

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