Opportunistic Social Networks Research Articles

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

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.

An efficient data packet iteration and transmission algorithm in opportunistic social networks

Effective data transmission is a key technology in researching opportunistic networks. Increased data packet transmission among nodes can easily cause the death of nodes, especially in social networks environment. However, effective packet transmission is seldom discussed in existing algorithms in opportunistic networks research. In this study, an efficient data packet iteration and transmission (EDPIT) algorithm, which selects data packets via iteration, is proposed to save energy and overhead during transmission. The effective transmission among nodes in this algorithm improves the transmission rate of data packets. With satisfactory results from simulation and comparison with some existing algorithms, the EDPIT algorithm is found to not only reduce energy consumption but also improve the delivery ratio and overhead in opportunistic social networks.

An Adaptive Routing-Forwarding Control Scheme Based on an Intelligent Fuzzy Decision-Making System for Opportunistic Social Networks

Routing selection in opportunistic social networks is a complex and challenging issue due to intermittent communication connections among mobile devices and dynamic network topologies. The structural characteristics of opportunistic social networks indicate that the social attributes of mobile nodes play a significant role on data dissemination. To this end, in this paper, we propose an adaptive routing-forwarding control scheme (FPRDM) based on an intelligent fuzzy decision-making system. On the foundation of the conception of fuzzy inference logic, two techniques are used in the proposed routing algorithm. Information fusion of social characteristics of message users and node identification are implemented based on the fuzzy recognition strategy, and the fuzzy decision-making mechanism is applied to control message replication and optimize data transmission. Simulation results demonstrate that, in the best case, the proposed scheme presents an average delivery ratio of 0.8, reduces the average end-to-end delay by nearly 45% as compared with the Epidemic routing protocol, and lowers the network overhead by about 75% as compared to the Spray and Wait routing algorithm.

Incentive evolutionary game model for opportunistic social networks

Opportunistic social networks transmit messages through the way of nodes cooperation. Because it is a kind of fully open networks, message forwarding is vulnerable to interference from abnormal nodes such as selfish and malicious nodes, who may result in disorder of network transmission. In order to suppress the bad impact on the network from those abnormal nodes, an incentive evolutionary game model for stimulating cooperation among nodes is proposed in this paper. By constructing our game model in routers, normal nodes and abnormal nodes are both encouraged to participate in network collaboration by self-evolution of gaming. Then after evolution, the entire network can reach a general cooperative state of nodes. In order to guide the evolution of gaming, a credit based incentive approach is proposed. The reward and punishment factor is designed in the revenue payment function of the game. The effectiveness is that the cooperative behavior is rewarded, and the non cooperative behavior is punished. Besides, nodes can update their policies of interaction with others by imitating their neighbors. The proposed model is implemented in several popular routers of opportunistic networks, and various scenarios are simulated. The experiments evaluate the performance of proposed model in terms of effectiveness, performance and scalability. The results show that our model is effective to improve the network performance when there are selfish nodes or malicious nodes, and It can be extended to a variety of network sizes. Besides, our incentive evolutionary game model can be easily applied to the current popular routers of opportunistic networks.

An effective positive transmission routing algorithm based on social relationships in opportunistic social networks

Opportunistic social network enables users to form an instant network for information sharing, which is a type of multi-hop wireless network in nature, thus relies on cooperation between nodes to complete message transmission. At present, most traditional routing algorithms require relay nodes to unconditionally forward received data. However, due to the limited of network resources, not all nodes are willing to forward data for other nodes, which is manifested as refusing to accept or even discarding messages, leading to deterioration of network performance. In this regard, this paper proposes an effective positive transmission routing algorithm (EPTR) based on social relationships in opportunistic social networks. This technology optimizes the method of community division by modularity, which not only considers the tightness of networks, but also discusses the social attributes of nodes. In this scheme, nodes with high relationship strength are selected to participate in data forwarding process, which makes packets are transmitted in the direction of increasing forwarding capability of nodes to the destination community. Simulation results show that the proposed algorithm can discover most abnormal nodes including malicious nodes and selfish nodes. Besides, compared with EIMCT algorithm, ITPCM algorithm and Spray and Wait algorithm, the proposed scheme has the highest transmission success rate and the lowest network load.

Quantitative social relations based on trust routing algorithm in opportunistic social network

The trust model is widely used in the opportunistic social network to solve the problem of malicious nodes and information flooding. The previous method judges whether the node is a cooperative node through the identity authentication, forwarding capability, or common social attribute of the destination node. In real applications, this information does not have integrity and does not take into account the characteristics and dynamic adaptability of nodes, network structures, and the transitivity of social relationships between nodes. Therefore, it may not be effective in solving node non-cooperation problems and improving transmission success rate. To address this problem, the proposed node social features relationship evaluation algorithm (NSFRE) establishes a fuzzy similarity matrix based on various features of nodes. Each node continuously and iteratively deletes the filtered feature attributes to form a multidimensional similarity matrix according to the confidence level and determines the weights under different feature attributes. Then, the social relations of nodes are further quantified. The experimental results show that, compared with the traditional routing algorithm, NSFRE algorithm can effectively improve the transmission success rate, reduce transmission delay, ensure the safe and reliable transmission of information in the network, and require low buffer space and computing capacity.

Fair and Social-Aware Message Forwarding Method in Opportunistic Social Networks

In opportunistic social networks, most of the existing store-carry-forward routing mechanisms have severe problems with unfair traffic distribution among nodes and unfair delivery success ratio for individuals. To address these issues, we propose a novel protocol, called fair and social-aware message forwarding (FSMF), for opportunistic social networks. FSMF first introduces a Markov chain model of users’ social ties to evaluate users’ social relationship and then formulates the problems of unfair traffic distribution and unfair delivery success ratio. Moreover, FSMF limits the number of message copies and restricts the number of forwarding copies according to users’ social ties in order to improve fairness. The simulation results show that our protocol has a lower standard deviation of traffic distribution and delivery success ratio.

Exploiting Temporal and Spatial Regularities for Content Dissemination in Opportunistic Social Network

Recently, content dissemination has become more and more important for opportunistic social networks. The challenges of opportunistic content dissemination result from random movement of nodes and uncertain positions of a destination, which seriously affect the efficiency of content dissemination. In this paper, we firstly construct time-varying interest communities based on the temporal and spatial regularities of users. Next, we design a content dissemination algorithm on the basis of time-varying interest communities. Our proposed content dissemination algorithm can run in O(nlog⁡n) time. Finally, the comparisons between the proposed content dissemination algorithm and state-of-the-art content dissemination algorithms show that our proposed content dissemination algorithm can (a) keep high query success rate, (b) reduce the average query latency, (c) reduce the hop count of a query, and (d) maintain low system overhead.

Disseminating authorized content via data analysis in opportunistic social networks

Authorized content is a type of content that can be generated only by a certain Content Provider (CP). The content copies delivered to a user may bring rewards to the CP if the content is adopted by the user. The overall reward obtained by the CP depends on the user's degree of interest in the content and the user's role in disseminating the content copies. Thus, to maximize the reward, the content provider is motivated to disseminate the authorized content to the most interested users. In this paper, we study how to effectively disseminate the authorized content in Interest-centric Opportunistic Social Networks (IOSNs) such that the reward is maximized. We first derive Social Connection Pattern (SCP) data to handle the challenging opportunistic connections in IOSNs and statistically analyze the interest distribution of the users contacted or connected. The SCP is used to predict the interests of possible contactors and connectors. Then, we propose our SCP-based Dissemination (SCPD) algorithm to calculate the optimum number of content copies to disseminate when two users meet. Our dataset based simulation shows that our SCPD algorithm is effective and efficient to disseminate the authorized content in IOSNs.

Hey, influencer! Message delivery to social central nodes in social opportunistic networks

This paper presents a new strategy to efficiently deliver messages to influencers in social opportunistic networks. An influencer node is an important node in the network with a high social centrality and, as a consequence, it can have some characteristics such as high reputation, trustfulness and credibility, that makes it an interesting recipient. Social network analysis has already been used to improve routing in opportunistic networking, but there are no mechanisms to efficiently route and deliver messages to these network influencers. The delivery strategy proposed in this article uses optimal stopping statistical techniques to choose among the different delivery candidate nodes in order to maximise the social centrality of the node chosen for delivery. For this decision process, we propose a routing–delivery strategy that takes into account node characteristics such as how central a node is in terms of its physical encounters. We show, by means of simulations based on real traces and message exchange datasets, that our proposal is efficient in terms of influencer selection, overhead, delivery ratio and latency time. With the proposed strategy, a new venue of applications for opportunistic networks can be devised and developed using the leading figure of social influencers.

Routing in Mobile Opportunistic Social Networks with Selfish Nodes

When the connection to Internet is not available during networking activities, an opportunistic approach exploits the encounters between mobile human-carried devices for exchanging information. When users encounter each other, their handheld devices can communicate in a cooperative way, using the encounter opportunities for forwarding their messages, in a wireless manner. But, analyzing real behaviors, most of the nodes exhibit selfish behaviors, mostly to preserve the limited resources (data buffers and residual energy). That is the reason why node selfishness should be taken into account when describing networking activities: in this paper, we first evaluate the effects of node selfishness in opportunistic networks. Then, we propose a routing mechanism for managing node selfishness in opportunistic communications, namely, SORSI (Social-based Opportunistic Routing with Selfishness detection and Incentive mechanisms). SORSI exploits the social-based nature of node mobility and other social features of nodes to optimize message dissemination together with a selfishness detection mechanism, aiming at discouraging selfish behaviors and boosting data forwarding. Simulating several percentages of selfish nodes, our results on real-world mobility traces show that SORSI is able to outperform the social-based schemes Bubble Rap and SPRINT-SELF, employing also selfishness management in terms of message delivery ratio, overhead cost, and end-to-end average latency. Moreover, SORSI achieves delivery ratios and average latencies comparable to Epidemic Routing while having a significant lower overhead cost.

An Effective Transmission Strategy Exploiting Node Preference and Social Relations in Opportunistic Social Networks

With the development of network technology and the advent of 5G communication era, high-frequency radio waves with high bandwidth will become the main choice of signal sources. While promoting the development of a high-speed communication network, a high-frequency radio wave has the problem of limited coverage, which makes opportunistic social network become the mainstream communication method. Since the transmission of a large amount of data in a short time will cause the problem of data redundancy, opportunistic social networks suggest that the most appropriate next hop should be selected to achieve efficient data transmission. At present, there are several routing algorithms based on social relations, which attempt to select the most suitable next-hop node among neighbor nodes by making use of relevant context information and historical interaction between nodes. However, existing data transmission methods in opportunistic social networks mainly focus on the influence of a few social attributes on the similarity between nodes but ignore the transmission preference caused by individual characteristics of nodes. To improve the transmission efficiency, this paper establishes an effective data transmission strategy (ENPSR) exploiting node preference and social relations in opportunistic social networks. In our scheme, individual transmission preferences are obtained by measuring the social attributes and historical information of nodes in the transmission process. The appropriate message delivery decision is determined by the prediction scheme, and the continuous and stable data transmission are realized through the recommendation mechanism. According to the simulation experiments, the average delivery ratio of ENPSR algorithm is 0.85, which is 20% higher than that of the epidemic algorithm.

Interest Characteristic Probability Predicted Method in Social Opportunistic Networks

In social opportunistic networks, mobile devices can be regarded as socialization nodes. Furthermore, they carry and store useful information. Mobile devices can select destination nodes and deliver messages through social opportunistic networks because messages can be securely and conveniently stored, carried, and transmitted with nodes. According to such characteristics, communities can be established. However, many communities may deliver messages only depending on one or two nodes. If those nodes are not enough cache and over-flooding, data transmission in communities may wait for a long time. To find many cooperation neighbor nodes can share much useful information. This paper proposes a method that characteristic interests with neighbors are selected or pushed service by users. According to establish interest characteristic probability predicted method, interest neighbors can be selected and receive or send information. Compared with the social opportunistic network algorithms, the new research method achieves better results in accuracy selected and delivery ratio, transmission delay, and routing overhead. According to the simulation experiments, the average delivery ratio of new research method is 0.88, which is 40% higher than that of the Epidemic algorithm; the end-to-end delay reduces 38% with Spray and wait for algorithm.

A Survey of Human Mobility Models

Human mobility models are key components of various research fields including transportation, mobile networks, disaster management, urban planning, and epidemic modeling. Understanding human mobility has a major role in the realistic evaluation of new approaches to challenges in these fields. For the perspective of networked systems, simulations of the networks with human participants such as opportunistic social networks are highly dependent on human mobility. In this article, we summarize the state of the art for scientific research on human mobility and survey the currently used human mobility models. We discuss the commonly used metrics and data collection techniques. Furthermore, we include a taxonomy of the mobility models according to their main characteristics and classify them. We lastly discuss the general trends, applicability, further research directions and open problems of human mobility modeling.

Effective Data Transmission Strategy Based on Node Socialization in Opportunistic Social Networks

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 paper 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. The simulation results show that the packet delivery ratio of ETNS is 13% higher than the epidemic algorithm, and ETNS also has lower transmission delay and routing overhead.

Effective Data Communication Based on Social Community in Social Opportunistic Networks

The online social network in wireless communication has become increasingly important owing to the fact that approximately billions of people send and receive information online using mobile devices. Communication may face a challenge arising from the conflict between traditional theories and new research methods in data transmission. The massive amount of transmitted data results in energy over-consumption, low delivery ratio, and transmission delays. This study attempts to solve these drawbacks by proposing a method that effectively divides social communities according to the human activity characteristics in opportunistic networks. The established effective information transmission based on structuralization areas scheme allows information to be transmitted between resource nodes and communities. A good performance is achieved with nodes that extend network lifetime and reduce energy consumption in the communication environment.

An Effective Data Transmission Algorithm Based on Social Relationships in Opportunistic Mobile Social Networks

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 opportunistic social networks, because traditional opportunistic network routing does not consider participation in human activities, which usually causes a high ratio of transmission delay and routing overhead. Therefore, this research proposes an effective data transmission algorithm based on social relationships (ESR), which considers the community characteristics of opportunistic mobile social networks. This work uses the idea of the faction to divide the nodes in the network into communities, reduces the number of inefficient nodes in the community, and performs another contraction of the structure. Simulation results show that the ESR algorithm, through community transmission, is not only faster and safer, but also has lower transmission delay and routing overhead compared with the spray and wait algorithm, SCR algorithm and the EMIST algorithm.

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

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.

Weight distribution and community reconstitution based on communities communications in social opportunistic networks

In social communication, mobile devices can be regarded as socialization nodes in social networks. Furthermore, they carry and store useful information. Mobile devices can select destination nodes and deliver messages through opportunistic networks because messages can be securely and conveniently stored, carried, and transmitted with nodes. However, many communities may deliver messages often depending on one or two nodes. If those nodes are not enough cache and over-flooding, data transmission in communities may wait for a long time. In this study, weight distribution between nodes and communities reconstitution would be established to solve this problem in social opportunistic networks. With satisfactory results from simulation and comparison with some existing algorithms, the new method is found to not only decrease tendency of energy consumption but also improve the delivery ratio, overhead and End-to-end delay in social opportunistic networks.

Information cache management and data transmission algorithm in opportunistic social networks

In social networks, nodes should analyze communication area during data transmission and find suitable neighbors to perform effective data classification transmission. This is similar to finding certain transmission destinations during data transmission with mobile devices. However, cache space with node in social opportunistic networks is limited, and waiting for destination node could also cause end-to-end delay. To improve the transmission environment, this study established a node recognition method for assessment probability, to satisfy the priority adjustment for the high probability nodes of cache, and then cache space should be reconstructed. To avoid accidentally deleting cached data, the cache task of the node is shared through the neighbor node cooperation, and the effective data transmission is performed. Through experiments and the comparison of social network algorithms, the proposed scheme improves delivery ratio by 82% and reduces delay by 74% with the traditional algorithms on average.