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The 5G mobile communication system is attracting attention as one of the most suitable communication models for broadcasting and managing disaster situations, owing to its large capacity and low latency. High-quality videos taken by a drone, which is an embedded IoT device for shooting in a disaster environment, play an important role in managing the disaster. However, the 5G mmWave frequency band is susceptible to obstacles and has beam misalignment problems, severing the connection and greatly affecting the degradation of TCP performance. This problem becomes even more serious in high-mobility drones and disaster sites with many obstacles. To solve this problem, we propose a deep-learning-based TCP (DL-TCP) for a disaster 5G mmWave network. DL-TCP learns the node's mobility information and signal strength, and adjusts the TCP congestion window by predicting when the network is disconnected and reconnected. As a result of the experiment, DL-TCP provides better network stability and higher network throughput than the existing TCP NewReno, TCP Cubic, and TCP BBR.

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Device-to-device communications have been considered as an indispensable enabler, which reduces the traffic burden associated with fifth-generation (5G) mobile networks. In such communications, cognitive spectrum sensing identifies the available spectrum resources for direct interconnections among user devices. Although various sensing techniques have been proposed during the last decade, improving the sensing efficiency (SE), such as energy reduction and positive sensing ratio, remains an open challenge. The problem becomes severe in 5G networks, wherein battery-constrained Internet-of-things devices (IoTDs) are densely interconnected. In this paper, we optimize the SE based on adaptive medium learning with a probabilistic decay feature. The wireless channels that are potentially available for IoTDs are sorted and sensed in the descending order of their probabilities, which indicate the estimated percentage of the availability of the sensed channels. The probabilities learn from the preceding sensing-results, and they decay with time. Numerical results show that the proposed sensing approach achieves significant SE improvement compared to existing algorithms.

The existing network infrastructure may not work well in a disaster environment caused by a fire or an earthquake. Instead of relying on the existing infrastructure, communicating through a mobile ad hoc network (MANET) is recommended because MANET can configure a network without an infrastructured communication system. In addition, firefighters conducting emergency activities in harsh environments surrounded by flames and smoke need a communication system to assist their rapid firefighting operations. Existing work is not suitable for indoor firefighter communications because they did not consider the indoor disaster environment well. In this proposed scheme, dual channels (i.e., 2.4 GHz and sub-GHz bands) are used for an efficient routing table configuration. Data frame and HELLO message are exchanged through the 2.4 GHz band, while the neighbor list of each node is exchanged through the sub-GHz band. Each node can configure the routing table based on the exchanged neighbor list. A performance evaluation is conducted to compare the proposed technique with enhanced versions of optimized link state routing (OLSR) and destination-sequenced distance vector routing (DSDV). The results show that the proposed scheme outperforms the other two MANET routing algorithms (i.e., OLSRmod and DSDV-mod) in terms of the packet delivery ratio (PDR), end-to-end delay, and initial routing table configuration time approximately 27.8%, 4.7%, and 166.7%, respectively.