Abstract With the continuous progress of Internet technology and the popularization of social platforms, the speed and scope of information dissemination have become more rapid and extensive. We proposed a variable local-world scale-free hypernetwork model, so that the establishment of new connections can be more in line with the evolution mechanism of the real social network. Theory and simulation prove that hyperdegree distribution is not affected by the neighbor order, and the theory agrees with the simulation. Combining the SIS model based on the reaction process (RP) strategy and the mean-field theory, we derive a theoretical expression for the density of informed nodes in steady state. The effects of hypernetwork structure parameters and dissemination parameters on information dissemination under two strategies are comparatively analyzed through simulation experiments. Most noteworthy, as neighbor order $n$ increases, it decreases the information dissemination rate under RP strategy and increases the information dissemination rate under contact process strategy, but both steady-state values remain the same. The results of the study provide a new perspective for a deeper understanding of the establishment of new relationships and information dissemination in social networks, as well as a theoretical basis for other practical applications that require local-world preference of connections.

As a promising candidate for future Internet architecture, Named Data Networking (NDN) can achieve significant potential advantages over current TCP/IP based Internet in content distribution and mobility support, etc. However, the communication mode in NDN that one Interest packet for pulling one data packet is likely to incur Interest packets flooding and to cause extremely large-scale Pending Interest Table (PIT) of the NDN router, which may substantially degrade the performance of NDN. Moreover, the absence of predefined connections also induces another challenge for NDN to manage the successive and concurrent requests from consumers efficiently. In this paper, we propose flow-based NDN (f-NDN) architecture capable of supporting flow transmission mode for addressing the aforementioned challenges. In the context of f-NDN, a data flow is defined as an aggregate of data packets with the same name prefix, and a flow Interest packet is introduced to pull a data flow rather than a single data packet. The PIT, CS, and FIB are re-designed to enable f-NDN to operate at the granularity of flows. Bitmap structure aided error handling mechanism is further presented for f-NDN to deal with the flow transmission's uncertain failures. The built-in flow support in f-NDN allows us to conceive a flow-level multi-path transmission regime for balancing the traffic in NDN network and reducing the time consumed for pulling the entire content. Weight-based flow Interest splitting algorithm and optimal rate control algorithm are both proposed for optimizing multi-path transmission. We illustrate the capability of the proposed architecture in supporting flow transmission and multipath by implementing it in both simulator and prototype systems. The evaluation results are also presented to show its achievable performance.

Named Data Networking (NDN) is an emerging future network architecture that changes the network communication model from push mode to pull mode, which leads to the requirement of a new mechanism of congestion control. To fully exploit the capability of NDN, a suitable congestion control scheme must consider the characteristics of NDN, such as connectionless, in-network caching, content perceptibility, etc. In this paper, firstly, we redefine the congestion control objective for NDN, which considers requirements diversities for different contents. Then we design and develop an efficient congestion control mechanism based on deep reinforcement learning (DRL), namely DRL-based Congestion Control Protocol (DRL-CCP). DRL-CCP enables consumers to automatically learn the optimal congestion control policy from historical congestion control experience. Finally, a real-world test platform with some typical congestion control algorithms for NDN is implemented, and a series of comparative experiments are performed on this platform to verify the performance of DRL-CCP.