Distributed task allocation in Mobile Device Cloud exploiting federated learning and subjective logic

2020 ◽  
pp. 101972
Author(s):  
Palash Roy ◽  
Sujan Sarker ◽  
Md. Abdur Razzaque ◽  
Md. Mamun-or-Rashid ◽  
Mohmmad Mehedi Hassan ◽  
...  
Keyword(s):  
Mobile Device ◽  
Task Allocation ◽  
Subjective Logic ◽  
Distributed Task Allocation ◽  
Mobile Device Cloud

scholarly journals Quality-of-Experience-Aware Incentive Mechanism for Workers in Mobile Device Cloud

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Sajeeb Saha ◽  
Md. Ahsan Habib ◽  
Tamal Adhikary ◽  
Md. Abdur Razzaque ◽  
Md. Mustafizur Rahman ◽  
...  
Keyword(s):  
Mobile Device ◽  
Quality Of Experience ◽  
Incentive Mechanism ◽  
Mobile Device Cloud

Distributed task allocation for multiple heterogeneous UAVs based on consensus algorithm and online cooperative strategy

2018 ◽  
Vol 90 (9) ◽  
pp. 1464-1473 ◽  
Author(s):  
Weinan Wu ◽  
Naigang Cui ◽  
Wenzhao Shan ◽  
Xiaogang Wang
Keyword(s):  
Task Allocation ◽  
Consensus Algorithm ◽  
Communication Link ◽  
Communication Mode ◽  
Cooperative Strategy ◽  
Bayesian Theorem ◽  
Content Type ◽  
Distributed Framework ◽  
Allocation Process ◽  
Distributed Task Allocation

Purpose The purpose of this paper is to develop a distributed task allocation method for cooperative mission planning of multiple heterogeneous unmanned aerial vehicles (UAVs) based on the consensus algorithm and the online cooperative strategy. Design/methodology/approach In this paper, the allocation process is conducted in a distributed framework. The cooperative task allocation problem is proposed with constraints and uncertainties in a real mission. The algorithm based on the consensus algorithm and the online cooperative strategy is proposed for this problem. The local chain communication mode is adopted to restrict the bandwidth of the communication link among the UAVs, and two simulation tests are given to test the optimality and rapidity of the proposed algorithm. Findings This method can handle both continuous and discrete uncertainties in the mission space, and the proposed algorithm can obtain a feasible solution in allowable time. Research limitations/implications This study is only applied to the case that the total number of the UAVs is less than 15. Practical implications This study is expected to be practical for a real mission with uncertain targets. Originality/value The proposed algorithm can go beyond previous works that only deal with continuous uncertainties, and the Bayesian theorem is adopted for estimation of the target.

scholarly journals Multi-Auv Distributed Task Allocation Based on the Differential Evolution Quantum Bee Colony Optimization Algorithm

2017 ◽  
Vol 24 (s3) ◽  
pp. 65-71
Author(s):  
Jianjun Li ◽  
Ru Bo Zhang
Keyword(s):  
Differential Evolution ◽  
Optimization Algorithm ◽  
Task Allocation ◽  
Artificial Bee Colony ◽  
Differential Evolution Algorithm ◽  
Optimal Solution ◽  
Allocation Model ◽  
Bee Colony ◽  
Bee Colony Optimization ◽  
Distributed Task Allocation

Abstract The multi-autonomous underwater vehicle (AUV) distributed task allocation model of a contract net, which introduces an equilibrium coefficient, has been established to solve the multi-AUV distributed task allocation problem. A differential evolution quantum artificial bee colony (DEQABC) optimization algorithm is proposed to solve the multi-AUV optimal task allocation scheme. The algorithm is based on the quantum artificial bee colony algorithm, and it takes advantage of the characteristics of the differential evolution algorithm. This algorithm can remember the individual optimal solution in the population evolution and internal information sharing in groups and obtain the optimal solution through competition and cooperation among individuals in a population. Finally, a simulation experiment was performed to evaluate the distributed task allocation performance of the differential evolution quantum bee colony optimization algorithm. The simulation results demonstrate that the DEQABC algorithm converges faster than the QABC and ABC algorithms in terms of both iterations and running time. The DEQABC algorithm can effectively improve AUV distributed multi-tasking performance.

A simulation study of two distributed task allocation procedures

1988 ◽  
Vol 14 (1) ◽  
pp. 54-61 ◽  
Author(s):  
V.M. Milutinovic ◽  
J.J. Crnkovic ◽  
C.E. Houstis
Keyword(s):  
Simulation Study ◽  
Task Allocation ◽  
Distributed Task Allocation
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