Balancing Energy Consumption and Reputation Gain of UAV Scheduling in Edge Computing

Due to the extensive use of unmanned aerial vehicles (UAVs) in civil and military environment, effective deployment and scheduling of a swarm of UAVs are rising to be a challenging issue in edge computing. This is especially apparent in the area of Internet of Things (IoT) where massive UAVs are connected for communications. One of the characteristics of IoT is that an operator can interact with more than one UAVs for the effective scheduling under multi-task requests. Based on this scenario, we clarify the issue on how to maintain the energy efficiency of UAVs and guarantee the reputation gain during the scheduling deployment. In this paper, we first formulate the energy consumption and reputation into the decision model of UAVs scheduling. A game-theoretic scheme is then developed for the optimal decision searching. With the developed model, a range of important parameters of UAV scheduling are thoroughly investigated. Our numerical results show that the proposed scheduling strategy is able to increase the reputation and decrease the energy consumption of UAVs simultaneously. In addition, in the game process, the profit of an operator can be maximized and the network economy research can be explored.

[1]  Yongming Huang,et al.  Power-Efficient Communication in UAV-Aided Wireless Sensor Networks , 2018, IEEE Communications Letters.

[2]  Weihua Gui,et al.  Heterogeneous cooperative belief for social dilemma in multi-agent system , 2018, Appl. Math. Comput..

[3]  Szu-Yin Lin,et al.  A dynamic data driven-based semi-distributed reputation mechanism in unknown networks , 2015, Electron. Commer. Res. Appl..

[4]  Guanghui Wen,et al.  Incentivizing Honest Mining in Blockchain Networks: A Reputation Approach , 2020, IEEE Transactions on Circuits and Systems II: Express Briefs.

[5]  Jiabin Wu,et al.  Belief-updating rule and sequential reciprocity , 2019, Games Econ. Behav..

[6]  Agathoniki Trigoni,et al.  Supporting Search and Rescue Operations with UAVs , 2010, 2010 International Conference on Emerging Security Technologies.

[7]  Swaroop Darbha,et al.  Scheduling Tasks for Human Operators in Monitoring and Surveillance Applications , 2016 .

[8]  Song Guo,et al.  Incentive Scheme for Cyber Physical Social Systems Based on User Behaviors , 2020, IEEE Transactions on Emerging Topics in Computing.

[9]  Tarik Taleb,et al.  A green strategic activity scheduling for UAV networks: A sub-modular game perspective , 2016, IEEE Communications Magazine.

[10]  Hanno Hildmann,et al.  Review: Using Unmanned Aerial Vehicles (UAVs) as Mobile Sensing Platforms (MSPs) for Disaster Response, Civil Security and Public Safety , 2019, Drones.

[11]  Serge Chaumette,et al.  Chaos-enhanced mobility models for multilevel swarms of UAVs , 2018, Swarm Evol. Comput..

[12]  Guoliang Cai,et al.  A New Finance Chaotic Attractor , 2007 .

[13]  Valentine Crespi,et al.  Decentralized sensing and tracking for UAV scheduling , 2004, SPIE Defense + Commercial Sensing.

[14]  Mohsen Guizani,et al.  Edge Computing in the Industrial Internet of Things Environment: Software-Defined-Networks-Based Edge-Cloud Interplay , 2018, IEEE Communications Magazine.

[15]  Dan Levin,et al.  Bridging Level-K to Nash Equilibrium , 2019, Review of Economics and Statistics.

[16]  Yusheng Ji,et al.  2016 Energy-Efficient Resource Allocation for Multi-User Mobile Edge Computing , 2016 .

[17]  Jie Liu,et al.  Modeling and simulation of dynamic ant colony’s labor division for task allocation of UAV swarm , 2018 .

[18]  Mary L. Cummings,et al.  Developing Operator Models for UAV Search Scheduling , 2010 .

[19]  Zhen Wang,et al.  Rigorous or tolerant: The effect of different reputation attitudes in complex networks , 2017, Future Gener. Comput. Syst..

[20]  Dong In Kim,et al.  Toward Secure Blockchain-Enabled Internet of Vehicles: Optimizing Consensus Management Using Reputation and Contract Theory , 2018, IEEE Transactions on Vehicular Technology.

[21]  Yong Deng,et al.  A novel matrix game with payoffs of Maxitive Belief Structure , 2018, Int. J. Intell. Syst..

[22]  Rui Dai,et al.  Quality-aware UAV coverage and path planning in geometrically complex environments , 2018, Ad Hoc Networks.

[23]  Mohsen Guizani,et al.  Evaluating Reputation Management Schemes of Internet of Vehicles Based on Evolutionary Game Theory , 2019, IEEE Transactions on Vehicular Technology.

[24]  Prakash Ranganathan,et al.  UAV swarm communication and control architectures: a review , 2019, Journal of Unmanned Vehicle Systems.

[25]  Zhu Han,et al.  Game-Theoretic Approaches for Wireless Communications with Unmanned Aerial Vehicles , 2018, IEEE Wireless Communications.

[26]  Peter Norman,et al.  On Bayesian Persuasion with Multiple Senders , 2018, Economics Letters.

[27]  Zhiwei Feng,et al.  The Multiobjective Trajectory Optimization for Hypersonic Glide Vehicle Based on Normal Boundary Intersection Method , 2016 .

[28]  Maria Cristina Pinotti,et al.  Range-Free Localization Algorithm Using a Customary Drone , 2018, 2018 IEEE International Conference on Smart Computing (SMARTCOMP).

[29]  Zhu Han,et al.  Weighted sum-rate maximization for cooperative multiceli multiuser massive MIMO systems based on discretized Pareto boundary approximation , 2018, 2018 27th Wireless and Optical Communication Conference (WOCC).

[30]  R. Srikant,et al.  DARWIN: distributed and adaptive reputation mechanism for wireless ad-hoc networks , 2007, MobiCom '07.

[31]  Mohamed Ayoub Messous,et al.  Implementing an emerging mobility model for a fleet of UAVs based on a fuzzy logic inference system , 2017, Pervasive Mob. Comput..

[32]  R. Trestian,et al.  Reputation-based network selection mechanism using game theory , 2011, Physical Communication.

[33]  Hakim Ghazzai,et al.  A Generic Spatiotemporal UAV Scheduling Framework for Multi-Event Applications , 2019, IEEE Access.

[34]  G. Q. Li,et al.  An UAV scheduling and planning method for post-disaster survey , 2014 .

[35]  Huimin Lu,et al.  Task Allocation Without Communication Based on Incomplete Information Game Theory for Multi-robot Systems , 2019, J. Intell. Robotic Syst..

[36]  Timo Hämäläinen,et al.  Requirements for Energy Efficient Edge Computing: A Survey , 2018, NEW2AN.

[37]  Pascal Morin,et al.  Modeling and Energy Evaluation of Small Convertible UAVs , 2013 .

[38]  Fatemeh Afghah,et al.  Use of a quantum genetic algorithm for coalition formation in large-scale UAV networks , 2019, Ad Hoc Networks.

[39]  Sudipta Sarangi,et al.  Exploring Payoffs and Beliefs in Game Theory , 2000 .

[40]  Mohammed Atiquzzaman,et al.  Energy efficient device discovery for reliable communication in 5G-based IoT and BSNs using unmanned aerial vehicles , 2017, J. Netw. Comput. Appl..