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

In this article, we address the social-awareness property and unmanned-aerial-vehicle (UAV)-assisted information diffusion in emergency scenarios, where 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 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 the 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 vertices, 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 show that our proposed approach can achieve a better tradeoff among the information diffusion speed, channel switch cost, and complexity.

[1]  Yolande Berbers,et al.  ACODYGRA: an agent algorithm for coloring dynamic graphs , 2004 .

[2]  Ryu Miura,et al.  On A Novel Adaptive UAV-Mounted Cloudlet-Aided Recommendation System for LBSNs , 2019, IEEE Transactions on Emerging Topics in Computing.

[3]  Xingqin Lin,et al.  The Sky Is Not the Limit: LTE for Unmanned Aerial Vehicles , 2017, IEEE Communications Magazine.

[4]  Gernot Heiser,et al.  An Analysis of Power Consumption in a Smartphone , 2010, USENIX Annual Technical Conference.

[5]  Trung Quang Duong,et al.  An Energy-Efficient Clustering and Routing Framework for Disaster Relief Network , 2019, IEEE Access.

[6]  Geoffrey Ye Li,et al.  Hypergraph Theory: Applications in 5G Heterogeneous Ultra-Dense Networks , 2017, IEEE Communications Magazine.

[7]  Enrico Natalizio,et al.  UAV-assisted disaster management: Applications and open issues , 2016, 2016 International Conference on Computing, Networking and Communications (ICNC).

[8]  Fumiyuki Adachi,et al.  Transceiver Design and Multihop D2D for UAV IoT Coverage in Disasters , 2019, IEEE Internet of Things Journal.

[9]  Qingqing Wu,et al.  Joint Trajectory and Communication Design for Multi-UAV Enabled Wireless Networks , 2017, IEEE Transactions on Wireless Communications.

[10]  Nei Kato,et al.  On a Novel Deep-Learning-Based Intelligent Partially Overlapping Channel Assignment in SDN-IoT , 2018, IEEE Communications Magazine.

[11]  Nan Qi,et al.  Interference-Aware Online Distributed Channel Selection for Multicluster FANET: A Potential Game Approach , 2019, IEEE Transactions on Vehicular Technology.

[12]  Sergiy Butenko,et al.  Graph Domination, Coloring and Cliques in Telecommunications , 2006, Handbook of Optimization in Telecommunications.

[13]  Sasthi C. Ghosh,et al.  A Decentralize Algorithm for Perturbation Minimization in 5G D2D Communication , 2019, 2019 15th Annual Conference on Wireless On-demand Network Systems and Services (WONS).

[14]  Ryu Miura,et al.  AC-POCA: Anticoordination Game Based Partially Overlapping Channels Assignment in Combined UAV and D2D-Based Networks , 2017, IEEE Transactions on Vehicular Technology.

[15]  Zhu Han,et al.  Radio Resource Allocation for Device-to-Device Underlay Communication Using Hypergraph Theory , 2016, IEEE Transactions on Wireless Communications.

[16]  Zhu Han,et al.  Caching based socially-aware D2D communications in wireless content delivery networks: a hypergraph framework , 2016, IEEE Wireless Communications.

[17]  Walid Saad,et al.  Unmanned Aerial Vehicle With Underlaid Device-to-Device Communications: Performance and Tradeoffs , 2015, IEEE Transactions on Wireless Communications.

[18]  Dan Wu,et al.  Matching-Coalition Based Cluster Formation for D2D Multicast Content Sharing , 2019, IEEE Access.

[19]  Theodoros A. Tsiftsis,et al.  Device-to-Device Communications Underlying UAV-Supported Social Networking , 2018, IEEE Access.

[20]  Justin Manweiler,et al.  Avoiding the Rush Hours: WiFi Energy Management via Traffic Isolation , 2011, IEEE Transactions on Mobile Computing.

[21]  Vipin Kumar,et al.  Multilevel Graph Partitioning Schemes , 1995, ICPP.

[22]  S H Strogatz,et al.  Random graph models of social networks , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[23]  Ahmed E. Kamal,et al.  Post-Disaster 4G/5G Network Rehabilitation Using Drones: Solving Battery and Backhaul Issues , 2018, 2018 IEEE Globecom Workshops (GC Wkshps).

[24]  Leonard M. Freeman,et al.  A set of measures of centrality based upon betweenness , 1977 .

[25]  Walid Saad,et al.  Social Community-Aware Content Placement in Wireless Device-to-Device Communication Networks , 2018, IEEE Transactions on Mobile Computing.

[26]  Christian Bettstetter,et al.  Drone delivery systems: job assignment and dimensioning , 2018, Auton. Robots.

[27]  Nei Kato,et al.  An Intelligent Traffic Load Prediction-Based Adaptive Channel Assignment Algorithm in SDN-IoT: A Deep Learning Approach , 2018, IEEE Internet of Things Journal.

[28]  Lingyang Song,et al.  Joint Trajectory and Power Optimization for UAV Relay Networks , 2018, IEEE Communications Letters.

[29]  Qiang Liu,et al.  Cooperative channel allocation and scheduling in multi-interface wireless mesh networks , 2019, Peer-to-Peer Netw. Appl..

[30]  Ayse Kortun,et al.  Real-Time Deployment and Resource Allocation for Distributed UAV Systems in Disaster Relief , 2019, 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC).

[31]  Xilong Liu,et al.  Resource Allocation in UAV-Assisted M2M Communications for Disaster Rescue , 2019, IEEE Wireless Communications Letters.

[32]  Jie Yang,et al.  DSF-NOMA: UAV-Assisted Emergency Communication Technology in a Heterogeneous Internet of Things , 2019, IEEE Internet of Things Journal.

[33]  Zhiguo Ding,et al.  Joint User Pairing, Mode Selection, and Power Control for D2D-Capable Cellular Networks Enhanced by Nonorthogonal Multiple Access , 2019, IEEE Internet of Things Journal.

[34]  Lin Cai,et al.  UAV-Assisted Dynamic Coverage in a Heterogeneous Cellular System , 2017, IEEE Network.

[35]  Lei Feng,et al.  Energy-Efficient Resource Allocation Based on Hypergraph 3D Matching for D2D-Assisted mMTC Networks , 2018, 2018 IEEE Global Communications Conference (GLOBECOM).

[36]  Lijun Chang,et al.  Effective and Efficient Dynamic Graph Coloring , 2017, Proc. VLDB Endow..

[37]  Antonio Iera,et al.  The Social Internet of Things (SIoT) - When social networks meet the Internet of Things: Concept, architecture and network characterization , 2012, Comput. Networks.

[38]  Alain Bretto,et al.  Hypergraph Theory: An Introduction , 2013 .

[39]  D. J. A. Welsh,et al.  An upper bound for the chromatic number of a graph and its application to timetabling problems , 1967, Comput. J..

[40]  Weidang Lu,et al.  UAV-Assisted Emergency Networks in Disasters , 2019, IEEE Wireless Communications.

[41]  Xuesong Qiu,et al.  Resource Allocation for 5G D2D Multicast Content Sharing in Social-Aware Cellular Networks , 2018, IEEE Communications Magazine.

[42]  Yanjing Sun,et al.  Hierarchical Matching With Peer Effect for Low-Latency and High-Reliable Caching in Social IoT , 2019, IEEE Internet of Things Journal.

[43]  Xiaoli Xu,et al.  Trajectory Design for Completion Time Minimization in UAV-Enabled Multicasting , 2018, IEEE Transactions on Wireless Communications.