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With the popularization of mobile communication equipment, human activities have an increasing impact on the structure of networks, and so the social characteristics of opportunistic networks become increasingly obvious. Opportunistic networks are increasingly used in social situations. However, existing routing algorithms are not suitable for oppo...
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Aiming at the problem of the lack of user social attribute characteristics in the process of dividing overlapping communities in multilayer social networks, in this paper, we propose a multilayer social network overlapping community detection algorithm based on trust relationship. By combining structural trust and social attribute trust, we transfo...
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... An efficient data packet iteration and transmission algorithm, which selects data packets via iteration, to save energy and overhead during transmission is proposed by Wu et al. [14]. An effective data transmission algorithm based on social relationships, which considers the community characteristics of opportunistic mobile social networks is proposed by Yan et al. [15]. ...
Cognitive radio is an intelligent communication system that is aware of its environment and can dynamically adapt its operating parameters with the aim of providing an efficient use of the scarce spectrum. The main advantage of cognitive radio technology is its ability to adapt and cooperate with all other wireless technologies such as fifth generation technology, 5G. 5G enabled cognitive radio technology provides accelerated communication performance in accordance with spectrum efficiency and energy efficiency. 5G enabled cognitive radio proposes system interoperability and integration of communication system through cognition. Social networking is a common communication media among internet users connected by one or more relationships. Large numbers of internet users share their experiences and thoughts through social networking web sites. Semantic analysis is defined as the process of drawing meaning from text. In this paper, a fuzzy logic based semantic analysis is performed for the estimation of comment content in 5G enabled cognitive radio based social networks. In social networks, the positive comments posted by the users have the positive influence for the members to examine related comments. The comment content posted by the users is decided to be positive or negative with the help of fuzzy logic based semantic analysis approach. In this regard, the relevant interpretation can be positive or negative based on the input parameters in the fuzzy logic system. Our 5G enabled cognitive radio technology based semantic analysis approach with fuzzy logic system can be utilized in many social networks, taking superior accuracy results of 93% into account.
... The recommendation algorithm based on social relationships [7][8][9][10][11] has alleviated user data sparsity and cold-start issues, while improving recommendation accuracy. Yuan et al. introduced [12] social network information propagation and singular value decomposition into a recommendation algorithm, significantly improving the quality of the recommendation. ...
Environmental e-commerce is a sustainability-oriented e-commerce model. To address the problem of data sparsity and the lack of diversity in traditional e-commerce recommendation algorithms, a new collaborative filtering recommendation algorithm based on multiple social relationships is proposed in environmental e-commerce. In real social networks, there were many relationships between users. On the basis of the traditional matrix decomposition model, the proposed algorithm integrates multiple social relationships between users into the user feature matrix, and then the multiple social relationships between users and the user rating preference similarity were used to jointly predict the user’s rating value for commodity, thus the personalized recommendation for users was achieved. In order to verify the superiority of the proposed algorithm, in this paper, two open datasets were used to compare the performance of several recommendation algorithms. The experimental results show that compared with the traditional social recommendation algorithms, the proposed algorithm improves recommendation accuracy and diversity. In real environmental e-commerce recommendation systems, the proposed algorithm can provide users with more personalized recommendation results, and reduce the arbitrariness of customer purchases and frequent returns in reality.
... Ref. [27] proposed an algorithm to divide nodes in social networks into multiple communities and obtained several effective communities based on multiple attribute strategies, so as to enable them to carry out effective routing propagation through the community. ...
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.
... The e®ective data transmission algorithm based on social relations (ESR) divides the nodes into di®erent communities through historical connection records to improve the community structure, thereby increasing the e±ciency of data transmission. 28 TCCB is an e®ective data-forwarding strategy based on time proximity and centrality proposed by Zhou et al. 29 Its key is to capture data within a valid time and use time correlation to infer the possible future social contacts. TCCB mainly increases the data transmission rate when the transmission cost is similar. ...
... . An e®ective algorithm based on social relations (ESR). 28 . Fuzzy routing and forwarding algorithm (FCNS). ...
With the popularization of the Internet of Things technology and the improvement of 5G communication technology, the influence of mobile devices on the network structure is increasing. The devices in the network are usually regarded as social users that transmit information. Because the movement of users is dynamic and random, it is more difficult for complex networks to grasp the changing rules of their topological structure. The data transmission model established by considering only the historical behavior of users can no longer meet the demand for fast transmission of large-capacity data. Based on this, this paper proposes a dynamic personalized data transmission model (GRDPS) that considers the recurrent neural network and attention mechanism. First, it uses a recurrent neural network to build users’ personalized preferences and model the user’s historical behavior. Then, GRDPS introduces an attention mechanism to dynamically weight historical user behaviors based on the user’s current message transmission. It is different from the previous methods of modeling user historical behaviors. Based on the requirements of user dynamics, GRDPS effectively considers the temporal characteristics of user historical behaviors and automatically learns the evolution law of user behaviors. Based on the demand of user randomness, GRDPS fully considers the characteristic correlation between the user’s historical behavior and current transmission demand. Finally, GRDPS combines these two points to obtain a personalized ranking of users. The simulation results show that the delivery rate of GRDPS is up to 0.95. Moreover, its data transmission delay and network overhead are better than other methods in the experiment.
... The existing social-aware routing algorithms make full use of the social characteristics of nodes to design routing algorithms suitable for more scenarios. Yeqing et al. [27] proposed an effective data transmission algorithm based on social relationship (ESR). The ESR algorithm divides communities according to the social characteristics of nodes and reduces useless nodes in the community to achieve the purpose of improving information forwarding efficiency. ...
With the vigorous development of big data and the 5G era, in the process of communication, the number of information that needs to be forwarded is increasing. The traditional end-to-end communication mode has long been unable to meet the communication needs of modern people. Therefore, it is particularly important to improve the success rate of information forwarding under limited network resources. One method to improve the success rate of information forwarding in opportunistic social networks is to select appropriate relay nodes so as to reduce the number of hops and save network resources. However, the existing routing algorithms only consider how to select a more suitable relay node, but do not exclude untrusted nodes before choosing a suitable relay node. To select a more suitable relay node under the premise of saving network resources, a routing algorithm based on intuitionistic fuzzy decision-making model is proposed. By analyzing the real social scene, the algorithm innovatively proposes two universal measurement indexes of node attributes and quantifies the support degree and opposition degree of node social attributes to help node forward by constructing intuitionistic fuzzy decision-making matrix. The relay nodes are determined more accurately by using the multi-attribute decision-making method. Simulation results show that, in the best case, the forwarding success rate of IFMD algorithm is 0.93, and the average end-to-end delay, network load, and energy consumption are the lowest compared with Epidemic algorithm, Spray and Wait algorithm, NSFRE algorithm, and FCNS algorithm.
... In some scenarios, the Prophet Routing can have high delivery ratio and low overhead. There are other routing algorithms [14]- [16] which make the tradeoff between delivery success and network overhead, and have good performance in some specific scenarios. However, those traditional routing algorithms have a premise hypothesis which is all intermediate nodes in opportunistic networks should be willing to forward messages for other nodes. ...
In a real edge network, many nodes may be selfish and unwilling to forward messages for other nodes. In this case, an incentive mechanism is needed to encourage the selfish nodes to participate in message forwarding. In this paper, we analyze the interaction between the source node and the relay node in edge opportunistic networks, and propose an incentive mechanism based on game theory to encourage the cooperation between nodes. Firstly, we define the interaction steps between the source and the relay node, which include that the source node decides the price of forwarding a message, the relay node responses the forwarding plan to the source node, and all nodes can get reward for their participant in message forwarding. We provide two-stage incentives to nodes, that is, the nodes can get reward from both receiving and forwarding messages. Since the nodes may be selfish, both the source node and the relay node want to maximize their utilities. Then, we model the cooperation between the source and the relay node as Bertrand Game, and the utility functions of the source and the relay node are defined. Furthermore, we find that the Nash Equilibrium is existed and unique, and present the best pricing scheme for the source node and the best forwarding plan for the relay node. The simulation results show that the proposed incentive mechanism can encourage the cooperation between selfish nodes, and improve the performance of routing algorithm in terms of delivery ratio and delay.
... In recent years, due to the rapid development of 5G networks and big data [1], each of us has mobile communication devices, such as smartphones and iPads with Bluetooth, WiFi, etc. These communication devices carried by people have become an integral part of people's daily lives [2]. In this case, we can consider these mobile devices carried or used by people as nodes in opportunistic social networks. ...
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.
... • We use the Money Management Center (MMC) [26] as a third party to manage virtual currency, making the trading node's account transparent, therefore, false quotations of malicious nodes are avoided to some extent [27]. ...
A large number of routing algorithms in Opportunistic Networks are based on the assumption that nodes are free to help other nodes forward messages. However, when the Opportunistic Network is applied to an urban environment, the nodes will have certain social attributes. In many cases, a node can decide whether to execute the routing policy or not. Due to the limited resources and poor social relationships, nodes may be unwilling to forward messages from other nodes and have strong motivation to implement selfish policies. As a result, increased network latency reduces message delivery rates and affects the overall network performance. In order to solve this problem, we propose a perceptual routing protocol to promote node cooperation from the perspective of game theory. Specifically, We introduce the concept of virtual currency and construct a price function, and nodes can obtain a certain virtual currency through cooperation. In the process of message forwarding, we consider the change of link degree and energy of node (the energy exists in the form of electricity in this article), and use them as factors of the trading node quotation. The trading node finally makes it through the multiple rounds of bargaining games, so that the proposed game between both sides reaches the Nash equilibrium. Experiments show that the algorithm outperforms Epidemic, EPSR, MINEIRO and ICRP algorithms in terms of delivery rate, average latency and energy consumption. According to the simulation experiments, the average delivery ratio of GIR algorithm is 0.68, which is 13% higher than that of the epidemic algorithm. In terms of average delay, 7% is better than ICRP algorithm.
... The simulation adopts the opportunistic network environment (ONE) to evaluate ETNS by performance comparison with ESR [52] (effective algorithm based on social relationships), FCNS [48] (fuzzy routing-forwarding algorithm), EWDCR [42] (Effective weight distribution and communities reconstitution algorithm) and epidemic algorithm [33]. ESR, FCNS and EWDCR are the latest routing algorithms for opportunistic social networks , while epidemic algorithm is a typical and traditional method. ...
With the development of big data and high-speed communication networks, traditional end-to-end transmission mechanisms in social networks are difficult to achieve large amounts of data communication between mobile devices. Therefore, the implementation of effective data transmission in social networks requires "opportunity". Opportunistic social networks suggest choosing the most appropriate next hop nodes for effective data transmission. Most existing routing algorithms attempt to use the interest points of nodes and the social relationships between them to choose optimal relay nodes among neighbors. However, most community-based algorithms take node attributes and social relations into account but fail to consider the energy consumption of inefficient nodes which accounts for a large part of routing cost. To improve the transmission strategy, this work proposes an effective transmission strategy based on node socialization (ETNS), which divides nodes in the network into several different communities. The proposed scheme also involves a community reduction method that removes some inefficient nodes according to the attributes of optimal relay nodes. Simulation results show that the packet delivery ratio of ETNS is 13% higher than epidemic algorithm, and ETNS also has lower transmission delay and routing overhead.
... In the new round of technological change and industrial upgrading, the integration of the internet and various fields for development has become an irresistible trend. With the rapid development of internet of vehicles, internet of things and 5G network, people's lifestyle has been transformed into a social form based on the internet [6]. The popularization of mobile devices, as well as people's demand for data communication on the internet at any time and any where, has jointly spawned the era of data explosion. ...
Opportunistic network enables users to form an instant network for data sharing, which is a type of Ad-hoc network in nature, thus depends on cooperation between nodes to complete message transmission. Because of intermittent communication and frequent changes of topology structure in opportunistic networks, the duration of node encounters is limited, as well as the length of established connections. If the amount of interactive data is large and the communication bandwidth is small, the messages that need to be transmitted are not guaranteed to be delivered successfully every time. In this regard, this paper establishes a transmission prediction mechanism exploiting comprehensive node forwarding capability (TPMEC) in opportunistic networks. When quantifying the forwarding capability of nodes, the algorithm not only considers the cooperative tendency, but also discusses the encounter strength between nodes. At the same time, in order to find out all key nodes during the transmission process, the algorithm adopts the theory of matrix decomposition to predict and supplement the missing forwarding capability value of nodes, thus improving the efficiency of message transmission. Simulation results show that compared with ITPCM algorithm, ETNS algorithm, Spray and Wait algorithm and PRoPHET algorithm, the proposed scheme has the highest transmission success ratio and the lowest routing overhead.
