Opportunistic Social Networks Research Articles

User trust and evaluation mechanism based on community classification calculation in opportunistic social networks

With the development of Internet of Things technology, 5G communication has gradually entered people’s daily lives. The number of network users has also increased dramatically, and it has become the norm for the same user to enjoy the services provided by multiple network service providers and to complete the exchange and sharing of a large amount of information at the same time. However, the existing opportunistic social network routing is not sufficiently scalable in the face of large-scale network data. Moreover, only the transaction information of network users is used as the evaluation evidence, ignoring other information, which may lead to the wrong trust assessment of nodes. Based on this, this study proposes an algorithm called Trust and Evaluation Mechanism for Users Based on Opportunistic Social Network Community Classification Computation (TEMCC). Firstly, communication communities are established based on community classification computation to solve the problem of the explosive growth of network data. Then a trust mechanism based on the Bayesian model is established to identify and judge the trustworthiness of the recommended information between nodes. This approach ensures that more reliable nodes can be selected for interaction and complete data exchange. Through simulation experiments, the delivery rate of this scheme can reach 0.8, and the average end-to-end delay is only 190 ms.

Novel data transmission technology based on complex IoT system in opportunistic social networks

Novel data transmission technology based on complex IoT system in opportunistic social networks

Optimal matching method based on rare plants in opportunistic social networks

The spread of seeds affects the regeneration, pattern, genetic structure, invasion, and settlement of plant populations. However, seed transmission is a relatively weak research link. The spread of plants is not controlled by the communicator. wind transmission of seeds is a relatively weak research link. The spread of plants is not controlled by the communicator. The way plants transmit and accept seeds is similar to how user nodes accept data transmission requests in social networks. In this study, we selected rare and endangered Pterospermum heterophyllum as the research object and applied them to a social network. All plants were considered nodes and all seeds as transmitted data by history record. Through use of historical records, the seeds that established infection and immunity algorithms in opportunistic social networks were recorded, healthy plants were identified, and the number of plants with diseases was reduced to achieve the selectivity of dominant plants in the social network.

Data transmission scheme based on node model training and time division multiple access with IoT in opportunistic social networks

Data transmission scheme based on node model training and time division multiple access with IoT in opportunistic social networks

Node Social Nature Detection OSN Routing Scheme Based on IoT System

In pace with the rapid growth of mobile communication technologies, such as 5G, the Internet of Things (IoT), as an emerging network standard, is gradually entering people’s daily lives. In this development situation, the specific applications of the IoT are reflected in the IoT systems. However, in such an open and complex environment, the nature of nodes that are easily accessible in the IoT system is unknowable. Therefore, how to use the social natures of nodes to detect the true nature of the nodes and construct a secure routing strategy is the key to the current research opportunistic social network (OSN). This article proposes a node social nature detection OSN routing scheme based on IoT system (NSND). The algorithm first proposes that there is a social relationship between nodes, and combines the characteristics of the social state of the nodes to give a formal definition of the node relationship. Then construct an OSN system to describe the types of nodes. Finally, various nodes are designed to affect the calculation method. Simulation experiments show that the NSND algorithm proposed in this article effectively utilizes the social nature of nodes. It can not only reduce the transmission impact of malicious nodes but also improve the activity of kind nodes. Its various network performance indicators are better than other algorithms.

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

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.

Effective Data Optimization and Evaluation Based on Social Communication with AI-Assisted in Opportunistic Social Networks

Billions of people around the world send and receive data over online networks daily. Sufficient and redundant data are transmitted over social platforms with AI-assisted in 5G networks. In opportunistic social networks, the main challenge faced by traditional methods is that numerous user nodes participate in data transmission, causing a lot of message copy redundancy and node cache consumption. As a result, the transmission delay of the algorithm is high, the node energy consumption is too large, and even information is lost. To solve these problems, this study establishes an artificial intelligence-based optimization multiple evaluation method. The main purpose of this method is to avoid information loss caused by data loss when reducing data noise, reasonably select communication nodes in opportunistic social network scenarios, optimize data transmission performance, and avoid network congestion. Moreover, our method can effectively identify and exclude potential malicious nodes, reducing the situation that packets are intercepted and discarded. The experiment confirms that the optimized transmission evaluation scheme can effectively reduce routing overheads and energy consumption of a user node, improve the delivery ratio of node data transmission, and ensure the reliability and security of data transmission.

Data Transmission Strategy Based on Node Motion Prediction IoT System in Opportunistic Social Networks

Data Transmission Strategy Based on Node Motion Prediction IoT System in Opportunistic Social Networks

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

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.

Node Screening Method Based on Federated Learning with IoT in Opportunistic Social Networks

With the advent of the 5G era, the number of Internet of Things (IoT) devices has surged, and the population’s demand for information and bandwidth is increasing. The mobile device networks in IoT can be regarded as independent “social nodes”, and a large number of social nodes are combined to form a new “opportunistic social network”. In this network, a large amount of data will be transmitted and the efficiency of data transmission is low. At the same time, the existence of “malicious nodes” in the opportunistic social network will cause problems of unstable data transmission and leakage of user privacy. In the information society, these problems will have a great impact on data transmission and data security; therefore, in order to solve the above problems, this paper first divides the nodes into “community divisions”, and then proposes a more effective node selection algorithm, i.e., the FL node selection algorithm based on Distributed Proximal Policy Optimization in IoT (FABD) algorithm, based on Federated Learning (FL). The algorithm is mainly divided into two processes: multi-threaded interaction and a global network update. The device node selection problem in federated learning is constructed as a Markov decision process. It takes into account the training quality and efficiency of heterogeneous nodes and optimizes it according to the distributed near-end strategy. At the same time, malicious nodes are screened to ensure the reliability of data, prevent data loss, and alleviate the problem of user privacy leakage. Through experimental simulation, compared with other algorithms, the FABD algorithm has a higher delivery rate and lower data transmission delay and significantly improves the reliability of data transmission.

Modified Data Delivery Strategy Based on Stochastic Block Model and Community Detection in Opportunistic Social Networks

The nodes in the opportunistic network make up communities according to the relevance between them. Some of the structural characteristics of an opportunistic network can be reflected by the structure of the communities that exist in the network. Therefore, finding community from the network is of great significance for people to better study, use, and transform the network. The overlap of communities is considered to be an important feature of communities. Almost all community discovery algorithms were based on nonoverlapping communities in the past. A node in a nonoverlapping community belongs to only one community. However, there are overlapping and interrelated characteristics between communities, so it is not in line with the actual environment of the network. As a result, the previous algorithms have many shortcomings in the face of practical application scenarios, coupled with the limitation of the computing capacity of mobile devices; data transmission for low delay and the low energy consumption is difficult to meet the requirements. In the study, we formulate the problem of dividing nodes into several communities in the opportunistic social network as how to build communities dynamically according to weight distribution. Then, we propose a modified data delivery strategy based on stochastic block model and community detection (DDBSC). The simulation results show that, compared with other algorithms in the experiments, the strategy proposed in this paper exhibits good performance in terms of overhead, energy consumption, and delivery rate.

Disease Control and Prevention in Rare Plants Based on the Dominant Population Selection Method in Opportunistic Social Networks.

The spread of seeds of rare and dangerous plants affects the regeneration, pattern, genetic structure, invasion, and settlement of plant populations. However, seed transmission is a relatively weak research link. The spread of plant seeds is not controlled by the communicator. Rather, this event results from the interaction between the host and the external environment determined by the mother. The way plants transmit and accept seeds is similar to how user nodes accept data transmission requests in social networks. Plants select the characteristics including seed size, maturity time, and gene matching, which are consistent with the size, delay, and keywords of the data received by the user. In this study, we selected rare and endangered Pterospermum heterophyllum as the research object and applied them to a social network. All plants were considered nodes and all seeds as transmitted data. This method avoids the influence of errors in actual sampling and statistical laws. By using historical information to record the reception of seeds, the Infection and Immunity Algorithm (IAIA) in opportunistic social networks was established. This method selects healthy plants through plant social populations and reduces the number of diseased plants. The experimental results show that the IAIA algorithm has a good effect in distinguishing dominant seedlings from seedlings with disease genes and realizes the selection of dominant plants in social networks.

Efficient path-sense transmission based on IoT system in opportunistic social networks

Efficient path-sense transmission based on IoT system in opportunistic social networks

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

SummaryWith the advent of the era of Big Data, users are increasingly demanding network bandwidth and data storage in the Internet of Things (IoT) system. While current research on the opportunistic social network does not take the redundant influence of high‐speed data transmission into account, which leads to problems such as lower delivery ratio, we present an effective data transmission scheme based on IoT system (EDTS). To divide the community, which consists of devices with relatively close connections, we measure the social relationship of nodes by quantifying the remaining cache, social relation, and interest similarity in opportunistic social networks. An efficient strategy is designed in this paper to update the community by analyzing the change of edge weights between two nodes in the community, deciding whether the community should be further divided, and whether the nodes should join the larger community. The proposed scheme also involves data transmission methods based on community, which involve inter‐community forwarding and intra‐community forwarding. Simulation results show that EDTS outperforms other comparative algorithms with regard to average delivery ratio, end‐to‐end delay, and routing overhead.

A Reputation Value-Based Task-Sharing Strategy in Opportunistic Complex Social Networks

As the Internet of Things (IoT) smart mobile devices explode in complex opportunistic social networks, the amount of data in complex networks is increasing. Large amounts of data cause high latency, high energy consumption, and low-reliability issues when dealing with computationally intensive and latency-sensitive emerging mobile applications. Therefore, we propose a task-sharing strategy that comprehensively considers delay, energy consumption, and terminal reputation value (DERV) for this context. The model consists of a task-sharing decision model that integrates latency and energy consumption, and a reputation value-based model for the allocation of the computational resource game. The two submodels apply an improved particle swarm algorithm and a Lagrange multiplier, respectively. Mobile nodes in the complex social network are given the opportunity to make decisions so that they can choose to share computationally intensive, latency-sensitive computing tasks to base stations with greater computing power in the same network. At the same time, to prevent malicious competition from end nodes, the base station decides the allocation of computing resources based on a database of reputation values provided by a trusted authority. The simulation results show that the proposed strategy can meet the service requirements of low delay, low power consumption, and high reliability for emerging intelligent applications. It effectively realizes the overall optimized allocation of computation sharing resources and promotes the stable transmission of massive data in complex networks.

The Optimization of Classroom Teaching in Colleges and Universities Based on Network Topology

In order to improve the teaching effect of programming courses, this article applies network topology to the construction of programming course teaching platform system and constructs a teaching framework combined with intelligent teaching mode. In addition, this article takes the opportunistic social network composed of mobile smart terminal devices as the application background and briefly introduces the concept, characteristics, and application scenarios of the opportunistic social network. In addition, the typical message forwarding algorithm currently studied in this paper is analyzed. Taking into account the social attributes and forwarding willingness of nodes, in order to increase the message transmission rate and reduce message transmission delay and network overhead, two message transmission strategies are proposed to improve the operating effect of the programming course teaching platform. Finally, this paper verifies the effectiveness of the network topology in the teaching of the algorithm and programming of the intelligent teaching mode through experimental research.

A relationship matrix resolving model for identifying vital nodes based on community in opportunistic social networks

AbstractThe introduction of the 5G Internet and big data communication into human society underpins the opportunistic social networks (OSNs) to survive. The storage‐carry‐forward mechanism refers to a data transmission strategy of OSNs, seeking any data transmission opportunities to communicate in the mobile process. Accordingly, identifying influential nodes is considered critical to OSNs and an essential prerequisite for developing efficient collaborative transmission algorithms. According to conventional OSNs, however, the node identification methods exhibit several defects (eg, low recognition accuracy, high time complexity, and poor performance in complexity networks). To address the mentioned defects, this study builds a relationship matrix resolving model to identify vital nodes by complying with community, which is capable of effectively identifying influential nodes in the network. The proposed model iterates by drawing upon the community features in the network to yield the optimal solution that satisfies the set condition, that is, the influential nodes in the community. As revealed from the experimental results, the method exhibits high performance.

Triad link prediction method based on the evolutionary analysis with IoT in opportunistic social networks

The data volume of various smart devices has exploded with the advent of the 5G communication era and the rapid development of Internet of Things technology. A large amount of data has higher requirements on the transmission protocol in the network. Most of the existing research focuses on studying a single node as the basic information transmission unit. However, social networks have complex topological structures, and such methods are difficult to grasp the evolutionary rules of the network. It usually leads to higher resource waste and problem complexity. A triad (a network subgraph containing three nodes) can not only provide accurate local topology information, but also its conversion rules are easy to describe, which can reduce the complexity of the problem. The mutual conversion between triads constitutes the evolution of the entire network. Based on this, this article first proposes a triad-based social network evolution analysis method (SNEA). SNEA includes a prediction algorithm (TPMPA) that learns the evolution of the triad transition probability matrix through time series and a quantization algorithm (TTHQA) that quantifies the impact of triad transformations on the network-based on random walks. SNEA integrates the advantages of the two algorithms to dynamically grasp the evolutionary rules of the network. Then this paper proposes a triad link prediction algorithm (TLPA) to quantitatively evaluate the results of the evolution analysis of the SNEA method. The TLPA algorithm reduces the blindness of message forwarding and unnecessary waste of resources by predicting the probability of a connection between nodes in the network. Experimental results show that compared with Epidemic, SECM, CRDNT, ICMT algorithms, our method has a prominent advantage in improving message delivery rate and reducing resource consumption.

Effective path prediction and data transmission in opportunistic social networks

With the development of social media, social networks have become an important platform for people to share and communicate. In social network communication, 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. 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. The EDPPM algorithm guarantees 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, the proposed scheme outperforms ProPhet by 47% in terms of cache utilization of nodes, 55% in terms of data transmission delay, and 32% in terms of network efficiency; compared with Epidemic by 53% in terms of cache utilization of nodes, 62% in terms of data transmission delay, and 47% in terms of network efficiency; same thing with regard to the Spray and wait by 27% in terms of cache utilization of nodes, 31% in terms of data transmission delay, and 17% in terms of network efficiency.

A Routing Query Algorithm Based on Time-Varying Relationship Group in Opportunistic Social Networks

With the fast development of IoT and 5G technologies, opportunity social networks composed of portable mobile devices have become a hot research topic in recent years. However, arbitrary node movement in opportunity networks and the absence of end-to-end pathways make node communication unstable. At the same time, the problem of ignoring human social preferences and relying on wrong message relay nodes lead to a low data transmission rate and high network overhead. Based on the above issues, we propose a time-varying relationship groups-based routing query algorithm for mobile opportunity networks (Time-varying Relationship Groups, TVRGs). Firstly, we construct the relationship groups based on the time-varying characteristics according to the intimacy between users. Secondly, we calculate the importance of nodes by their connectivity time and communication frequency. Finally, we find the suitable message relay nodes according to the similarity of node weights and their action trajectories and design the routing query algorithm accordingly. The simulation results show that the algorithm can vastly improve the message query success rate, effectively improve the data transmission efficiency, and reduce the average delay and system overhead compared with the existing routing algorithms.