UAV-Assisted Emergency Communications in Social IoT: A Dynamic Hypergraph Coloring Approach

Abstract

In this paper, we address social-awareness property and unmanned aerial vehicle (UAV) assisted information diffusion in emergency scenarios, where the UAVs can disseminate alert messages to a set of terrestrial users within their coverage, and then these users can continuously disseminate the received data packets to their socially connected users in a device-to-device (D2D) multicast manner. In this regard, we have to solve both the dynamic cluster formation and spectrum sharing problems in stochastic environments, since both the UAVs and terrestrial users may arrive or depart suddenly. For the cluster formation problem, considering that the data rate of a multicast cluster is determined by the member with worst link condition, we formulate it as a many-to-one matching game and adopt the rotation-swap algorithm to maximize the expected number of users receiving the alerting messages in each time slot. For the dynamic spectrum sharing problem, aiming at eliminating the interference while minimizing the channel switching cost, we propose a dynamic hypergraph coloring approach to model the cumulative interference and maintain the mutual interference at a low level by exploring a small number of vertexes, when the graph is dynamically updated, i.e., the insertion/deletion of vertex/edge. Moreover, we prove some crucial properties including global stability, convergence, and complexity. Finally, simulation results shows that our proposed approach can achieve a better trade-off among the information diffusion speed, channel switch cost, and complexity.

Efficient Radio Channel Allocation in Integrated mmWave/Sub-6 GHz UAV-Assisted Disaster Relief Networks

“…In particular, the convergence speed and robustness of most game theory methods are not sufficient to deal with the changes of dynamic topology. Hence, in order to cope with this problem, some graph coloring theory-based channel allocation approaches [7,8,24,25] have been proposed. As mentioned above, the work in [7] does not apply to highly dynamic graph, while that in [8] does not converge fast enough to respond to topology changes.…”

“…As mentioned above, the work in [7] does not apply to highly dynamic graph, while that in [8] does not converge fast enough to respond to topology changes. To tackle the dynamic spectrum sharing problem, the authors of [24] proposed a dynamic hypergraph coloring approach to suppress the mutual channel interferences at a threshold value. Based on the proposed cooperative D2D communications framework, the authors of [25] adopted graph-based dynamic channel allocation with an attempt to improve the spectral efficiency.…”

“…However, the graph coloring theory-based approaches proposed in [7,8,24,25] can only well perform in the case where the BSs can work properly. In the post-disaster areas where BSs are severely damaged, it is necessary to utilize the emerging technologies (e.g., UAV-assisted emergency networks) to restore the communicating services.…”

“…Based on local information in decision-making, the authors of [9] studied the integration of graph coloring and learning to adapt to channel allocation under dynamic topology. Although the work in [9] improved the convergence speed, along with those in [7,8,[24][25][26][27][28], it did not apply to channel allocation in integrated mmWave/sub-6 GHz UAV-assisted disaster relief networks.…”

“…ereafter, Algorithm 1 initializes all the elements in X to 0 (see line 5). Next, it chooses the mmWave link from L(t) corresponding to q 1 and then estimates the average system energy efficiency at interval t (i.e., E 1 (t)) based on formula (24) and sets x 1 to 1 (see line 6). Finally, it finds out the corresponding mmWave link based on x 1 and adds it to the vertex set of interference graph (see lines 7∼9).…”