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Nodes in socially aware networks (SANs) may act selfishly on individual bases due to resource constraints and socially selfish behavior arising from the social preferences of nodes. In response to such selfish behaviors exhibited by nodes, this paper proposes a social trust confirmation-based selfish node detection algorithm (STCDA). This algorithm first utilizes a subjective forwarding willingness detection mechanism to discern selfishness. If a node's energy is insufficient or its message rejection rate is too high—that is, the node cannot or is unwilling to forward messages—it indicates that the node is selfish. Otherwise, it is evaluated more thoroughly through the node's social trust detection mechanisms. It calculates the social trust level of nodes based on the benefits of forwarding messages, thereby distinguishing between individually selfish nodes and socially selfish nodes in the network. If further evaluation is needed, the final judgment will be made using the message confirmation feedback detection mechanism. This checks the message information forwarded by nodes in the network. If nodes fail to forward messages after receiving them—excluding reasons such as message expiration or temporary insufficient cache space—it indicates that the nodes are selfish. Results from experimental simulations show that this algorithm performs better than traditional algorithms. Under conditions of 80% selfish nodes, a message TTL of 300 min, and 10 MB of cache space, it improves the message delivery rate by 5.87% and reduces the average delay by 6.2% compared to the existing comprehensive confirmation-based selfish node detection algorithm. [ABSTRACT FROM AUTHOR]

In socially aware networking, nodes typically behave selfishly due to resource constraints and social correlations, resulting in low network performance. To incentivize selfish nodes to actively participate in message forwarding, this paper proposes a resource-based dynamic pricing and forced forwarding incentive algorithm (DFIA). Firstly, the algorithm introduces virtual currency as a transaction medium and then designs a pricing function based on factors such as the node's resource status, participation contribution, location relevance, and social connectivity. It ensures that the forwarding service is transacted at a reasonable price through bargaining rules. Secondly, a forced forwarding strategy is implemented to compel selfish nodes, which are unwilling to participate in other nodes' message forwarding, to forward a certain number of non-local messages. Meanwhile, in order to prevent nodes from discarding messages and to ensure successful forwarding to the destination, specific rules are used to allocate contribution values to nodes that successfully participate in message forwarding. Lastly, to avoid false quotation behavior, blockchain technology is employed. Transaction information is packaged into blocks and added to the blockchain after consensus validation by other nodes in the network, ensuring the transparency and immutability of transaction data. Simulation results indicate that compared with the existing incentive algorithms, this algorithm not only enhances message delivery probability but also effectively reduces average latency. [ABSTRACT FROM AUTHOR]

In the social internet of things, community structure exists objectively and affects the transmission of network messages. If the social context such as community is fully utilized, the efficiency of data forwarding will be effectively improved. A community-based routing algorithm (MSAR) is proposed by studying the multiple social relationships. First, we propose four measures of social relationships. They are social closeness degree, in-community activeness, cross-community activeness and community interaction. Then, the design of routing algorithm considers two stages. One is in-community forwarding and the other is cross-community forwarding. The measurement of node forwarding capability depends on closeness degree and in-community activeness in the in-community forwarding stage. In the cross-community stage, the measurement of node forwarding capability depends on closeness degree, cross-community activeness and community interaction. The relay node with higher cross-community forwarding utility will be selected. This prevents messages from being limited to the local community. Therefore, messages can always travel in the direction of the destination node's community. Finally, a lot of simulation experiments and analyses are carried out. The analysis results show that the proposed algorithm has good performance in the following two aspects, the average latency and the message delivery rate respectively. [ABSTRACT FROM AUTHOR]

The authors first review the recently proposed Das's biometric-based remote user authentication scheme, and then show that Das's scheme is still insecure against some attacks and has some problems in password change phase. In order to overcome the design flaws in Das's scheme, an improvement of the scheme is further proposed. Cryptanalysis shows that our scheme is more efficient and secure against most of attacks; moreover, our scheme can provide strong mutual authentication by using verifying biometric, password as well as random nonces generated by the user and server.

The rapid development of the social network has brought great convenience to people's lives. A large amount of cross‐media big data, such as text, image, and video data, has been accumulated. A cross‐media search can facilitate a quick query of information so that users can obtain helpful content for social networks. However, cross‐media data suffer from semantic gaps and sparsity in social networks, which bring challenges to cross‐media searches. To alleviate the semantic gaps and sparsity, we propose a cross‐media search method based on complementary attention and generative adversarial networks (CAGS). To obtain high‐quality feature representations, we build a complementary attention mechanism containing the focused and unfocused features of images to realize the consistent association of cross‐media data in social networks. By designing the cross‐media adversarial learning process, we can obtain a common semantic representation of cross‐media data and further alleviate the semantic gap and sparsity issues for social networks. Finally, we perform a similarity calculation to realize an accurate cross‐media search. We construct four search tasks utilizing two standard cross‐media data sets to verify the search performance of the proposed CAGS. [ABSTRACT FROM AUTHOR]