A Transmission Prediction Neighbor Mechanism Based on a Mixed Probability Model in an Opportunistic Complex Social Network

The amount of data has skyrocketed in Fifth-generation (5G) networks. How to select an appropriate node to transmit information is important when we analyze complex data in 5G communication. We could sophisticate decision-making methods for more convenient data transmission, and opportunistic complex social networks play an increasingly important role. Users can adopt it for information sharing and data transmission. However, the encountering of nodes in mobile opportunistic network is random. The latest probabilistic routing method may not consider the social and cooperative nature of nodes, and could not be well applied to the large data transmission problem of social networks. Thus, we quantify the social and cooperative relationships symmetrically between the mobile devices themselves and the nodes, and then propose a routing algorithm based on an improved probability model to predict the probability of encounters between nodes (PEBN). Since our algorithm comprehensively considers the social relationship and cooperation relationship between nodes, the prediction result of the target node can also be given without encountering information. The neighbor nodes with higher probability are filtered by the prediction result. In the experiment, we set the node’s selfishness randomly. The simulation results show that compared with other state-of-art transmission models, our algorithm has significantly improved the message delivery rate, hop count, and overhead.

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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 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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The Social Relationship Based Adaptive Multi-Spray-and-Wait Routing Algorithm for Disruption Tolerant Network

Mobile Information Systems ◽

10.1155/2017/1819495 ◽

2017 ◽

Vol 2017 ◽

pp. 1-13 ◽

Cited By ~ 11

Author(s):

Jianfeng Guan ◽

Qi Chu ◽

Ilsun You

Keyword(s):

Social Relationship ◽

Routing Algorithm ◽

Buffer Management ◽

Delivery Rate ◽

Message Delivery ◽

Dead End ◽

The Social ◽

Disruption Tolerant Network ◽

Spray And Wait ◽

Blind Spots

The existing spray-based routing algorithms in DTN cannot dynamically adjust the number of message copies based on actual conditions, which results in a waste of resource and a reduction of the message delivery rate. Besides, the existing spray-based routing protocols may result in blind spots or dead end problems due to the limitation of various given metrics. Therefore, this paper proposes a social relationship based adaptive multiple spray-and-wait routing algorithm (called SRAMSW) which retransmits the message copies based on their residence times in the node via buffer management and selects forwarders based on the social relationship. By these means, the proposed algorithm can remove the plight of the message congestion in the buffer and improve the probability of replicas to reach their destinations. The simulation results under different scenarios show that the SRAMSW algorithm can improve the message delivery rate and reduce the messages’ dwell time in the cache and further improve the buffer effectively.

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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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FCNS: A Fuzzy Routing-Forwarding Algorithm Exploiting Comprehensive Node Similarity in Opportunistic Social Networks

Symmetry ◽

10.3390/sym10080338 ◽

2018 ◽

Vol 10 (8) ◽

pp. 338 ◽

Cited By ~ 16

Author(s):

Kanghuai Liu ◽

Zhigang Chen ◽

Jia Wu ◽

Leilei Wang

Keyword(s):

Social Networks ◽

Data Transmission ◽

Contextual Information ◽

Relay Node ◽

Feedback Mechanism ◽

Delivery Ratio ◽

Fuzzy Evaluation ◽

Message Delivery ◽

Transmission Process ◽

Node Similarity

At the dawn of big data and 5G networks, end-to-end communication with large amounts of data between mobile devices is difficult to be implemented through the traditional face-to-face transmission mechanism in social networks. Consequently, opportunistic social networks proposed that message applications should choose proper relay nodes to perform effective data transmission processes. At present, several routing algorithms, based on node similarity, attempt to use the contextual information related to nodes and the special relationships between them to select a suitable relay node among neighbors. However, when evaluating the similarity degree between a pair of nodes, most existing algorithms in opportunistic social networks pay attention to only a few similar factors, and even ignore the importance of mobile similarity in the data transmission process. To improve the transmission environment, this study establishes a fuzzy routing-forwarding algorithm (FCNS) exploiting comprehensive node similarity (the mobile and social similarities) in opportunistic social networks. In our proposed scheme, the transmission preference of the node is determined through the fuzzy evaluation of mobile and social similarities. The suitable message delivery decision is made by collecting and comparing the transmission preference of nodes, and the sustainable and stable data transmission process is performed through the feedback mechanism. Through simulations and the comparison of social network algorithms, the delivery ratio in the proposed algorithm is 0.85 on average, and the routing delay and network overhead of this algorithm are always the lowest.

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DEVELOPMENT OF A MULTIPATH ROUTING ALGORITHM IN SOFTWARE-DEFINED COMMUNICATION NETWORKS

T-Comm ◽

10.36724/2072-8735-2021-15-9-17-23 ◽

2021 ◽

Vol 15 (9) ◽

pp. 17-23

Author(s):

Alexey S. Volkov ◽

◽

Aleksandr E. Baskakov ◽

Keyword(s):

Communication Networks ◽

Data Transmission ◽

Storage System ◽

Routing Algorithm ◽

Weighted Graph ◽

Transmission Network ◽

Message Delivery ◽

Network Resource ◽

Auxiliary Graph ◽

Reservation Protocol

The paper describes the development of routing algorithm in software-defined communication networks using the principle of multi-path message delivery. The use of the OpenFlow protocol as the main one for connecting data- and control-plane devices between each other, that is, programmable switches with the controller, allows us to take the network topology presented in undirected weighted graph form as the initial data for the algorithm. There are known solutions to the problem of finding ways to transmit data in a communication network, as a rule, using the network resource reservation protocol, but additional restrictions are imposed on the network, since RSVP has a low degree of scalability, respectively, inappropriate consumption of computing resources and storage system resources of individual routers. In view of the above, an algorithm has been developed for finding a set of paths on a graph with the construction of an auxiliary graph based on the original one. Conditions are given under which an auxiliary graph can be constructed from the initial one. The algorithm takes into account the possibility of constructing several paths passing through one vertex, while meeting the requirements for the delay of the input data stream. To expand the functionality and possible areas of application of the algorithm for finding a set of paths, a criterion for the required total throughput by a set of data transmission paths is introduced. Conditions for constructing paths from a vertex to set of vertices are given. The algorithm presented in the work has an order of magnitude less time complexity, which allows you to quickly respond to changes in the data transmission network, while the most significant differences in the time spent on building a set of paths are noticeable with an increase in nodes in the data transmission network and the number of possible paths.

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Group Reconstruction and Max-Pooling Residual Capsule Network

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2019/310 ◽

2019 ◽

Cited By ~ 1

Author(s):

Xinpeng Ding ◽

Nannan Wang ◽

Xinbo Gao ◽

Jie Li ◽

Xiaoyu Wang

Keyword(s):

Routing Algorithm ◽

Large Data ◽

Data Networks ◽

Complex Data ◽

Low Level ◽

Max Pooling ◽

The Arts ◽

Routing Mechanism ◽

High Level ◽

Complex Data Structure

In capsule networks, the mapping of low-level capsules to high-level capsules is achieved by a routing-by-agreement algorithm. Since the capsule is made up of collections of neurons and the routing mechanism involves all the capsules instead of simply discarding some of the neurons like Max-Pooling, the capsule network has stronger representation ability than the traditional neural network. However, considering too much low-level capsules' information will cause its corresponding upper layer capsules to be interfered by other irrelevant information or noise capsules. Therefore, the original capsule network does not perform well on complex data structure. What's worse, computational complexity becomes a bottleneck in dealing with large data networks. In order to solve these shortcomings, this paper proposes a group reconstruction and max-pooling residual capsule network (GRMR-CapsNet). We build a block in which all capsules are divided into different groups and perform group reconstruction routing algorithm to obtain the corresponding high-level capsules. Between the lower and higher layers, Capsule Max-Pooling is adopted to prevent overfitting. We conduct experiments on CIFAR-10/100 and SVHN datasets and the results show that our method can perform better against state-of-the-arts.

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An Adaptive Routing Algorithm Based on Relation Tree in DTN

Sensors ◽

10.3390/s21237847 ◽

2021 ◽

Vol 21 (23) ◽

pp. 7847

Author(s):

Diyue Chen ◽

Hongyan Cui ◽

Roy E. Welsch

Keyword(s):

Negative Feedback ◽

Routing Algorithm ◽

Adaptive Routing ◽

Routing Algorithms ◽

Source Node ◽

Message Delivery ◽

Average Delay ◽

Hop Count ◽

Simulation Results ◽

Delay Tolerant

It is found that nodes in Delay Tolerant Networks (DTN) exhibit stable social attributes similar to those of people. In this paper, an adaptive routing algorithm based on Relation Tree (AR-RT) for DTN is proposed. Each node constructs its own Relation Tree based on the historical encounter frequency, and will adopt different forwarding strategies based on the Relation Tree in the forwarding phase, so as to achieve more targeted forwarding. To further improve the scalability of the algorithm, the source node dynamically controls the initial maximum number of message copies according to its own cache occupancy, which enables the node to make negative feedback to network environment changes. Simulation results show that the AR-RT algorithm proposed in this paper has significant advantages over existing routing algorithms in terms of average delay, average hop count, and message delivery rate.

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