The combination of blockchain technology and Internet of Things (IoT) technology has brought many significant advantages and new development directions. With the development of embedded technology and 5G communication technology, the performance limitations and network limitations that are traditionally believed to restrict the application of blockchain technology to IoT devices have been broken. The development of “blockchain + 5G + IoT” provides reliable data from the source for the blockchain, linking the credible mapping of physical assets and digital assets. However, at the beginning of the blockchain design, the application of the IoT was not fully considered, so there have been some obvious defects in applying the blockchain technology in the IoT. In the Byzantine fault tolerance (BFT) consensus algorithm of traditional blockchain, the entire blockchain network will become paralyzed when more than 1/3 of the nodes in the network are offline. However, in IoT applications, this situation is likely to occur and greatly limits the security and stability of the application of blockchain technology in the IoT. In order to solve this problem, we proposed an IoT adaptive dynamic blockchain networking method based on discrete heartbeat signals. The feature of the method is to set a different monitoring time for each group of nodes, that is, discrete heartbeat signals monitoring. When the number of nodes gradually decreases, the IoT adaptive dynamic blockchain network can dynamically adapt to this process. Even when more than 1/3 of the IoT are offline, the adaptive dynamic IoT blockchain network can maintain stable running. This method also has the advantages of a short network expectation recovery time and avoids instantaneous system paralysis caused by the thundering herd effect. This research improves the security and stability of the application of blockchain technology in the IoT, and provides the necessary technical foundation for the better combination of blockchain technology and IoT technology.

Improving the quality of experience (QoE) of video streaming is a significant task in the wireless network scenario. Buffer starvation in the transmission process will cause playback freeze, and a certain number of packets must be prefetched before the service restarts. Taking into account the shortcomings of buffer in video streaming services, this paper proposes a deep learning-based starvation probability calculation model and a reinforcement learning-based packet prefetching model. The deep learning approach extracts the correlation between different timing inputs through the recurrent neural network module to return an explicit result and the precise distribution of the number of buffer starvation. The reinforcement learning approach leverages a better trade-off between start-up/rebuffering delay and buffer starvation by adjusting the packet prefetching strategy, so that the long-term objective quality of experience (QoE) of the video stream is optimized. Our framework can be applied to actual scenarios including finite video streaming and long video streaming transmission.

In the process of wireless network video streaming, especially in more complex scenarios (such as video transmission of 5G-powered drones), analyzing the quality of experience (QoE) of the video streaming is a very crucial task. Thus attention should be paid to the dynamic interaction between QoE indicators including buffer starvation probability and traffic load. This paper proposes a video streaming scheduling model based on reinforcement learning. By learning the correlation between user behavior and traffic patterns, a series of resource allocation strategies that optimize QoE indicators are obtained. Since there is a certain degree of randomness in the network status at each moment in the transmission process, the model introduces exploration rewards to solve the noise problem of random environments. At the same time, this mechanism enables the model to fully explore the environment even when the reward is sparse, so as to obtain an effective scheduling strategy. Simulation experiments have proved that our model can improve the long-term QoE of video streaming in different network environments.

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Opportunistic routing has been shown to achieve the high throughput of the wireless mesh network with lossy channels. Different from deterministic routing mechanisms in which a frame is <em>transmitted</em> and forwarded along with a fixed and predetermined <em>path</em>, the opportunistic routing technique allows multiple nodes hearing the frame to form the forwarder set containing promising candidates for the frame forwarding. Existing opportunistic routing protocols typically choose among forwarding candidates based on the decision made from the transmitter disregarding the current loads in candidates. In this paper, the opportunistic frame forwarding mechanism with considering backlog of frames among forwarders is proposed and analyzed. Specifically, in addition to take into account the delivery probability, our proposal restricts members of the forwarder set for a given transmitter to those wireless nodes whose transmission range covers one another and makes the true forwarder picked from the forward set of a given transmitting frame being the one who gains access to wireless channel for the frame before others do. Therefore, the efficient and load-balanced opportunistic routing for wireless mesh networks can be achieved. Analytic results show that the proposed method compared to the deterministic routing methodology can achieve the high frame delivery ratio.