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Journal articles on the topic 'Auction-based task allocation'

Author: Grafiati

Published: 14 March 2023

Last updated: 31 July 2025

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1

Baroudi, Uthman, Mohammad Alshaboti, Anis Koubaa, and Sahar Trigui. "Dynamic Multi-Objective Auction-Based (DYMO-Auction) Task Allocation." Applied Sciences 10, no. 9 (2020): 3264. http://dx.doi.org/10.3390/app10093264.

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In this paper, we address the problem of online dynamic multi-robot task allocation (MRTA) problem. In the existing literature, several works investigated this problem as a multi-objective optimization (MOO) problem and proposed different approaches to solve it including heuristic methods. Existing works attempted to find Pareto-optimal solutions to the MOO problem. However, to the best of authors’ knowledge, none of the existing works used the task quality as an objective to optimize. In this paper, we address this gap, and we propose a new method, distributed multi-objective task allocation

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Rub, Abd Ur, and Lu Xuanmin. "Multiple Task Assignment Algorithms for Unmanned Aerial Vehicles." Journal of Engineering Research and Reports 26, no. 7 (2024): 26–42. http://dx.doi.org/10.9734/jerr/2024/v26i71191.

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To fulfill the requirement of assigning task the auction algorithm is widely used. There are many classical auction algorithms those performances not up to the mark while dealing with multi-UAVs dynamic task assignment. SWARM UAVs are made up of a large number of small UAVs with limited mission resources that can operate in an autonomous, appropriate and universal manner. Based on the in-depth research of the traditional auction algorithm CAA, this paper proposes an iterative method that can improve the task allocation efficiency of multi-UAV, namely the two-stage auction algorithm. At the sam

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Lu, Ye, Rui Zhou, Xing Li, Zhiheng Liu, and Bin Di. "Heterogeneous multi-task allocation algorithm based on multi-round distributed auction." Journal of Applied Artificial Intelligence 1, no. 2 (2024): 101–13. http://dx.doi.org/10.59782/aai.v1i2.293.

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Aiming at the distributed decision-making needs of different combat tasks such as multi-machine collaborative detection, attack, and evaluation, a multi-round distributed auction algorithm for heterogeneous tasks with time constraints is established. The algorithm flexibly solves the distributed collaborative task allocation problem with complex constraints between drones with different capabilities. In the distributed task allocation framework, each drone can realize distributed asynchronous computing and asynchronous communication, and can ensure good solution efficiency and accuracy. The ta

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Zhang, Jiandong, Yuyang Chen, Qiming Yang, et al. "Dynamic Task Allocation of Multiple UAVs Based on Improved A-QCDPSO." Electronics 11, no. 7 (2022): 1028. http://dx.doi.org/10.3390/electronics11071028.

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With the rapid changes in the battlefield situation, the requirement of time for UAV groups to deal with complex tasks is getting higher, which puts forward higher requirements for the dynamic allocation of the UAV group. However, most of the existing methods focus on task pre-allocation, and the research on dynamic task allocation technology during task execution is not sufficient. Aiming at the high real-time requirement of the multi-UAV collaborative dynamic task allocation problem, this paper introduces the market auction mechanism to design a discrete particle swarm algorithm based on par

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Liang, Yajie, Kun Zhou, and Caicong Wu. "Dynamic Task Allocation Method for Heterogenous Multiagent System in Uncertain Scenarios of Agricultural Field Operation." Journal of Physics: Conference Series 2356, no. 1 (2022): 012049. http://dx.doi.org/10.1088/1742-6596/2356/1/012049.

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This study focuses on the problem of dynamic task allocation for a heterogeneous multiagent system (MAS) in uncertain scenarios and its application in agricultural field operation. Previous studies lacked robustness or efficiency for uncertain environments especially in agricultural field, such as agent removal, agent inclusion, changes of agent capabilities, and task changes. We present herein a novel concept of the potential field of capability influence (PFCI), and based on which, we can estimate potential overloaded tasks. This provides an opportunity to improve the allocation of the remai

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Fang, Baofu, Lu Chen, Hao Wang, Shuanglu Dai, and Qiubo Zhong. "Research on Multirobot Pursuit Task Allocation Algorithm Based on Emotional Cooperation Factor." Scientific World Journal 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/864180.

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Multirobot task allocation is a hot issue in the field of robot research. A new emotional model is used with the self-interested robot, which gives a new way to measure self-interested robots’ individual cooperative willingness in the problem of multirobot task allocation. Emotional cooperation factor is introduced into self-interested robot; it is updated based on emotional attenuation and external stimuli. Then a multirobot pursuit task allocation algorithm is proposed, which is based on emotional cooperation factor. Combined with the two-step auction algorithm recruiting team leaders and te

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Braquet, Martin, and Efstathios Bakolas. "Greedy Decentralized Auction-based Task Allocation for Multi-Agent Systems." IFAC-PapersOnLine 54, no. 20 (2021): 675–80. http://dx.doi.org/10.1016/j.ifacol.2021.11.249.

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Zhenyu Wu, Minhua Xiao, Bo Jin, and Lin Feng. "Dynamic task allocation based on distance of superior probability auction." Journal of Convergence Information Technology 7, no. 2 (2012): 10–17. http://dx.doi.org/10.4156/jcit.vol7.issue2.2.

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9

He, Jianhua, Siqi Tao, Yang Deng, Libin Chen, and Zhiying Mou. "Research on Multi-Sensor Resource Dynamic Allocation Auction Algorithm." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 37, no. 2 (2019): 330–36. http://dx.doi.org/10.1051/jnwpu/20193720330.

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This paper designs a multi-sensor resource dynamic allocation method based on auction algorithm. Tasks are prioritized according to the needs of the engineering field. Task priority is used as the basis for multi-sensor resource allocation order, taking into account the target's threat value and information needs. The sensor and task pairing function is established and used to measure the sensor resource dynamic allocation, we also use Analytic Hierarchy Process to determine the weight of each performance parameter in the pairing function (such as detection probability, intercept probability,

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10

Fazal, Nayyer, Muhammad Tahir Khan, Shahzad Anwar, Javaid Iqbal, and Shahbaz Khan. "Task allocation in multi-robot system using resource sharing with dynamic threshold approach." PLOS ONE 17, no. 5 (2022): e0267982. http://dx.doi.org/10.1371/journal.pone.0267982.

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Task allocation is a fundamental requirement for multi-robot systems working in dynamic environments. An efficient task allocation algorithm allows the robots to adjust their behavior in response to environmental changes such as fault occurrences, or other robots’ actions to increase overall system performance. To address these challenges, this paper presents a Task Allocation technique based on a threshold level which is an accumulative value aggregated by a centralized unit using the Task-Robot ratio and the number of the available resource in the system. The threshold level serves as a refe

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LIANG, Zhiwei, Jie SHEN, Xiang YANG, Juan LIU, and Songhao ZHU. "Task Allocation Algorithm Based on Auction in RoboCup Rescue Robot Simulation." Robot 35, no. 4 (2013): 410. http://dx.doi.org/10.3724/sp.j.1218.2013.00410.

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12

Deng, Yueyue, Pierre-Philippe J. Beaujean, Edgar An, and Edward Carlson. "Task Allocation and Path Planning for Collaborative Autonomous Underwater Vehicles Operating through an Underwater Acoustic Network." Journal of Robotics 2013 (2013): 1–15. http://dx.doi.org/10.1155/2013/483095.

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Dynamic and unstructured multiple cooperative autonomous underwater vehicle (AUV) missions are highly complex operations, and task allocation and path planning are made significantly more challenging under realistic underwater acoustic communication constraints. This paper presents a solution for the task allocation and path planning for multiple AUVs under marginal acoustic communication conditions: a location-aided task allocation framework (LAAF) algorithm for multitarget task assignment and the grid-based multiobjective optimal programming (GMOOP) mathematical model for finding an optimal

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Chen, Chao, Weidong Bao, Tong Men, Xiaomin Zhu, Ji Wang, and Rui Wang. "NECTAR-An Agent-Based Dynamic Task Allocation Algorithm in the UAV Swarm." Complexity 2020 (September 16, 2020): 1–14. http://dx.doi.org/10.1155/2020/6747985.

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The advancement of UAV technology makes the use of UAVs more and more widespread, and the swarm is the main mode of UAV applications owing to its robustness and adaptability. Meanwhile, task allocation plays an essential role in a swarm to obtain overall high performance and unleash the potential of each UAVs owing to the complexity of the large-scale swarm. In this paper, we pay attention to the real-time allocation problem of dynamic tasks. We design models for the task assigning problem to construct the constraints model and assigning objectives. In addition, we introduce a novel agent-base

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14

Chen, Yiting, Runfeng Chen, Yuchong Huang, Zehao Xiong, and Jie Li. "Distributed Task Allocation for Multiple UAVs Based on Swarm Benefit Optimization." Drones 8, no. 12 (2024): 766. https://doi.org/10.3390/drones8120766.

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The auction mechanism stands as a pivotal distributed solution approach for addressing the task allocation problem in unmanned aerial vehicle (UAV) swarms, with its rapid solution capability well-suited to meet the real-time requirements of aerial mission planning for UAV swarms. Building upon the auction mechanism, this paper proposes a distributed task allocation method for multi-UAV grounded in swarm benefits optimization. The method introduces individual benefit variation to quantify the effect of a task on the benefit of a single UAV, thereby enabling direct optimization of swarm benefit

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15

Arif, Muhammad Usman, and Sajjad Haider. "A Flexible Framework for Diverse Multi-Robot Task Allocation Scenarios Including Multi-Tasking." ACM Transactions on Autonomous and Adaptive Systems 16, no. 1 (2021): 1–23. http://dx.doi.org/10.1145/3502200.

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In a multi-robot operation, multi-tasking resources are expected to simultaneously perform multiple tasks, thus, reducing the overall time/energy requirement of the operation. This paper presents a task allocation framework named Rostam that efficiently utilizes multi-tasking capable robots. Rostam uses a task clustering mechanism to form robot specific task maps. The customized maps identify tasks that can be multi-tasked by individual robots and mark them for simultaneous execution. The framework then uses an Evolutionary Algorithm along with the customized maps to make quality task allocati

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16

Edalat, Neda, Chen-Khong Tham, and Wendong Xiao. "An auction-based strategy for distributed task allocation in wireless sensor networks." Computer Communications 35, no. 8 (2012): 916–28. http://dx.doi.org/10.1016/j.comcom.2012.02.004.

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17

Rinaldi, Marco, Stefano Primatesta, Giorgio Guglieri, and Alessandro Rizzo. "Auction-based Task Allocation for Safe and Energy Efficient UAS Parcel Transportation." Transportation Research Procedia 65 (2022): 60–69. http://dx.doi.org/10.1016/j.trpro.2022.11.008.

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18

Shi, Jieke, Zhou Yang, and Junwu Zhu. "An auction-based rescue task allocation approach for heterogeneous multi-robot system." Multimedia Tools and Applications 79, no. 21-22 (2018): 14529–38. http://dx.doi.org/10.1007/s11042-018-7080-4.

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19

Wang, Qiang, Xingye Han, Weizhen He, and Yongsheng Cheng. "Research on multi-UAV hierarchical task allocation in large-scale scenarios." Journal of Physics: Conference Series 2478, no. 10 (2023): 102023. http://dx.doi.org/10.1088/1742-6596/2478/10/102023.

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Abstract The task allocation problem of multiple UAVs in large-scale scenarios has the characteristics of large amount of computation, poor real-time performance, non-convergence or slow convergence. Therefore, this paper designs a hierarchical task allocation method to solve the task allocation problem when multiple UAVs perform large-scale tasks. The problem is divided into two sub-problems, task clustering and task ranking, by using a hierarchical structure, which effectively reduces the scale of solution. The high-level task clustering is used to determine which UAV performs which tasks, a

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20

Kim, Min-Geol, Suk-Hoon Shin, Eun-Bog Lee, and Sung-Do Chi. "Modified Consensus Based Auction Algorithm for Task Allocation of Multiple Unmanned Aerial Vehicle." Journal of the Korea Society for Simulation 23, no. 4 (2014): 197–202. http://dx.doi.org/10.9709/jkss.2014.23.4.197.

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21

Kong, Yan, Minjie Zhang, and Dayong Ye. "An Auction-Based Approach for Group Task Allocation in an Open Network Environment." Computer Journal 59, no. 3 (2015): 403–22. http://dx.doi.org/10.1093/comjnl/bxv061.

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22

Xie, Bing, Shaofei Chen, Jing Chen, and LinCheng Shen. "A mutual-selecting market-based mechanism for dynamic coalition formation." International Journal of Advanced Robotic Systems 15, no. 1 (2018): 172988141875584. http://dx.doi.org/10.1177/1729881418755840.

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This article presents a novel market-based mechanism for a dynamic coalition formation problem backgrounded under real-time task allocation. Specifically, we first analyze the main factors of the real-time task allocation problem, and formulate the problem based on the coalition game theory. Then, we employ a social network for communication among distributed agents in this problem, and propose a negotiation mechanism for agents forming coalitions on timely emerging tasks. In this mechanism, we utilize an auction algorithm for real-time agent assignment on coalitions, and then design a mutual-

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23

Xue, Kai, Zhiqin Huang, Ping Wang, and Zeyu Xu. "An Exact Algorithm for Task Allocation of Multiple Unmanned Surface Vehicles with Minimum Task Time." Journal of Marine Science and Engineering 9, no. 8 (2021): 907. http://dx.doi.org/10.3390/jmse9080907.

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Task allocation of unmanned surface vehicles (USVs) with low task cost is an important research area which assigns USVs from starting points to different target points to complete tasks. Most of the research lines of task allocation are using heuristic algorithms to obtain suboptimal solutions to reduce both the max task cost and total task cost. In practice, reducing the maximum is more important to task time, which is from the departure of USVs to the last USV arriving at the designated position. In this paper, an exact algorithm is proposed to minimize the max task time and reduce the total

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24

Alhaqbani, Amjaad, Heba Kurdi, and Kamal Youcef-Toumi. "Fish-Inspired Task Allocation Algorithm for Multiple Unmanned Aerial Vehicles in Search and Rescue Missions." Remote Sensing 13, no. 1 (2020): 27. http://dx.doi.org/10.3390/rs13010027.

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The challenge concerning the optimal allocation of tasks across multiple unmanned aerial vehicles (multi-UAVs) has significantly spurred research interest due to its contribution to the success of various fleet missions. This challenge becomes more complex in time-constrained missions, particularly if they are conducted in hostile environments, such as search and rescue (SAR) missions. In this study, a novel fish-inspired algorithm for multi-UAV missions (FIAM) for task allocation is proposed, which was inspired by the adaptive schooling and foraging behaviors of fish. FIAM shows that UAVs in

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Lu, Qiwen, Yifeng Qiu, Chaotao Guan, et al. "Coordinated Multi-UAV Reconnaissance Scheme for Multiple Targets." Applied Sciences 13, no. 19 (2023): 10920. http://dx.doi.org/10.3390/app131910920.

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This study addresses dynamic task allocation challenges in coordinated surveillance involving multiple unmanned aerial vehicles (UAVs). A significant concern is the increased UAV flight distance resulting from the assignment of new missions, leading to decreased reconnaissance efficiency. To tackle this issue, we introduce a collaborative multi-target and multi-UAV reconnaissance scheme. Initially, the multitasking constrained multi-objective optimization framework (MTCOM) is employed to optimize task allocation and reconnaissance time in static scenarios. Subsequently, in case of emergency, w

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Choi, S. H., and W. K. Zhu. "Performance Optimisation of Mobile Robots for Search-and-Rescue." Applied Mechanics and Materials 232 (November 2012): 403–7. http://dx.doi.org/10.4028/www.scientific.net/amm.232.403.

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This paper presents a team performance optimisation system for multiple mobile robots in search-and-rescue operations, in which refugees are first discovered and subsequently robots are dispatched to transport themto shelters. Coordination of mobile robots involves two fundamental issues, namely task allocation and motion planning. While task allocation assigns jobs to robots, motion planning generates routes for robots to execute the assigned jobs. Task allocation and motion planning together play a pivotal role in optimisation of the robot team performance. These two issues become more chall

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27

Jain, Vibha, and Bijendra Kumar. "Combinatorial auction based multi-task resource allocation in fog environment using blockchain and smart contracts." Peer-to-Peer Networking and Applications 14, no. 5 (2021): 3124–42. http://dx.doi.org/10.1007/s12083-021-01161-y.

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28

Elango, Murugappan, Subramanian Nachiappan, and Manoj Kumar Tiwari. "Balancing task allocation in multi-robot systems using K -means clustering and auction based mechanisms." Expert Systems with Applications 38, no. 6 (2011): 6486–91. http://dx.doi.org/10.1016/j.eswa.2010.11.097.

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Si, JiaShuai, and MingRui Hao. "Online Weapon-target Assignment based on Distributed Auction Mechanism." Journal of Physics: Conference Series 2456, no. 1 (2023): 012044. http://dx.doi.org/10.1088/1742-6596/2456/1/012044.

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Abstract To solve the problem of online weapon-target assignment (OWTA) in the integration of large-scale search and attack in unknown environment, an OWTA algorithm based on distributed auction mechanism is presented. Aiming at the problem that the traditional combinatorial optimization algorithm needs to set up the global battlefield situation in advance, considering the consumability of resources in the attack process, the integrated search and attack task flow is established. Considering the communication restricted environment, the unmanned aerial vehicles (uavs) are grouped, with central

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Zhu, Dan, and Jianguo Gao. "Path planning and optimization for transmission line barrier operations in complex terrain based on multimachine collaborative control." International Journal of Low-Carbon Technologies 20 (2025): 36–46. https://doi.org/10.1093/ijlct/ctae254.

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Abstract To address the challenges of path planning for transmission line barrier operations in complex terrains, this paper proposes a solution based on multimachine collaborative control. A path-planning algorithm that integrates multiple classical algorithms and incorporates multiobjective optimization methods is constructed to handle the complexities of challenging terrains. Auction algorithm and genetic algorithm are adopted to achieve optimal load balance by dynamically adjusting task allocation. Simulation results show that the proposed method effectively enhances task execution efficie

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Wang, Jun, Xiao Lin Lu, Si Yuan Guo, Lu Yu, and Wei Liu. "Network Resource Allocation for Scalable Video Streaming over P2P Networks Based on Game Theory." Applied Mechanics and Materials 687-691 (November 2014): 1974–78. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.1974.

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In order to adapt to the heterogeneity of terminals and networks, Scalable Video Coding (SVC) encodes raw video stream with different scales of temporal, spatial and quality into layers. Considering the P2P network characteristic, it is a challenging task to design an appropriate P2P steaming network resource allocation mechanism combining with SVC. In this paper, SVC is applied in P2P streaming based on game theory; considering free-riding, bandwidth conflicts in P2P multi-overlay and one chunk with multiple providers, we design a bidirectional serial auction model that jointly optimize the b

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Wang, Bo, and Mingchu Li. "Resource Allocation Scheduling Algorithm Based on Incomplete Information Dynamic Game for Edge Computing." International Journal of Web Services Research 18, no. 2 (2021): 1–24. http://dx.doi.org/10.4018/ijwsr.2021040101.

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With the advent of the 5G era, the demands for features such as low latency and high concurrency are becoming increasingly significant. These sophisticated new network applications and services require huge gaps in network transmission bandwidth, network transmission latency, and user experience, making cloud computing face many technical challenges in terms of applicability. In response to cloud computing's shortcomings, edge computing has come into its own. However, many factors affect task offloading and resource allocation in the edge computing environment, such as the task offload latency

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Tang, Jian, Kejun Zhu, Haixiang Guo, Chengzhu Gong, Can Liao, and Shuwen Zhang. "Using auction-based task allocation scheme for simulation optimization of search and rescue in disaster relief." Simulation Modelling Practice and Theory 82 (March 2018): 132–46. http://dx.doi.org/10.1016/j.simpat.2017.12.014.

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CHU, Jing, Yiqiu TIAN, Qi YUE, and Yong HUANG. "Task allocation and path planning for multi-robot systems in intelligent warehousing." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 42, no. 5 (2024): 929–38. https://doi.org/10.1051/jnwpu/20244250929.

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Faced with today's increasingly complex market demands, traditional manual warehouse systems are becoming inadequate, necessitating the urgent intelligent transformation and upgrading of warehouse systems. In this context, this paper aims to design a task allocation and path planning strategy for a multi-robot warehouse system to efficiently accomplish mixed single-robot and multi-robot types of warehouse tasks. The study proposes a warehouse task allocation strategy that incorporates traffic flow impact factors into the auction algorithm, optimizing task allocation by predicting robot density

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35

Hooshangi, Navid, Ali Asghar Alesheikh, Mahdi Panahi, and Saro Lee. "Urban search and rescue (USAR) simulation system: spatial strategies for agent task allocation under uncertain conditions." Natural Hazards and Earth System Sciences 21, no. 11 (2021): 3449–63. http://dx.doi.org/10.5194/nhess-21-3449-2021.

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Abstract. Task allocation under uncertain conditions is a key problem for agents attempting to achieve harmony in disaster environments. This paper presents an agent-based simulation to investigate task allocation considering appropriate spatial strategies to manage uncertainty in urban search and rescue (USAR) operations. The proposed method is based on the contract net protocol (CNP) and implemented over five phases: ordering existing tasks considering intrinsic interval uncertainty, finding a coordinating agent, holding an auction, applying allocation strategies (four strategies), and imple

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Roberts-Elliott, Laurence, Gautham P. Das, and Grzegorz Cielniak. "Kriging-Variance-Informed Multi-Robot Path Planning and Task Allocation for Efficient Mapping of Soil Properties." Robotics 14, no. 6 (2025): 77. https://doi.org/10.3390/robotics14060077.

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One of the most commonly performed environmental explorations is soil sampling to identify soil properties of agricultural fields, which can inform the farmer about the variable rate treatment of fertilisers in precision agriculture. However, traditional manual methods are slow, costly, and yield low spatial resolution. Deploying multiple robots with proximal sensors can address this challenge by parallelising the sampling process. Yet, multi-robot soil sampling is under-explored in the literature. This paper proposes an auction-based multi-robot task allocation that efficiently coordinates th

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Wen, Shanghua, Wendi Wu, Ning Li, et al. "Auction-Based Behavior Tree Evolution for Heterogeneous Multi-Agent Systems." Applied Sciences 14, no. 17 (2024): 7896. http://dx.doi.org/10.3390/app14177896.

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Collaboration in Multi-Agent Systems (MASs) is crucial but challenging in robotics, especially in heterogeneous MASs where robots have different capabilities. Nowadays, the key issue in research on collaboration in MASs is to fully utilize the capabilities of heterogeneous agents. To address this issue, we propose Auction-Based Behavior Tree Evolution (ABTE), a novel two-layer framework designed to learn BTs for heterogeneous MASs. In the first layer, we call it the command layer, and robots receive their tasks through the auction algorithm, enhanced by our innovative three-way handshaking com

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Ahmad, Shabeer, Jinling Zhang, Adil Khan, Umar Ajaib Khan, and Babar Hayat. "JO-TADP: Learning-Based Cooperative Dynamic Resource Allocation for MEC–UAV-Enabled Wireless Network." Drones 7, no. 5 (2023): 303. http://dx.doi.org/10.3390/drones7050303.

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Providing robust communication services to mobile users (MUs) is a challenging task due to the dynamicity of MUs. Unmanned aerial vehicles (UAVs) and mobile edge computing (MEC) are used to improve connectivity by allocating resources to MUs more efficiently in a dynamic environment. However, energy consumption and lifetime issues in UAVs severely limit the resources and communication services. In this paper, we propose a dynamic cooperative resource allocation scheme for MEC–UAV-enabled wireless networks called joint optimization of trajectory, altitude, delay, and power (JO-TADP) using anarc

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Li, Xun, Zhi Zhang, Dan-Dan Wu, Michel Medema, and Alexander Lavozik. "A multi-robot allocation model for multi-object based on Global Optimal Evaluation of Revenue." International Journal of Advanced Robotic Systems 18, no. 6 (2021): 172988142110606. http://dx.doi.org/10.1177/17298814211060650.

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The problem of global optimal evaluation for multi-robot allocation has gained attention constantly, especially in a multi-objective environment, but most algorithms based on swarm intelligence are difficult to give a convergent result. For solving the problem, we established a Global Optimal Evaluation of Revenue method of multi-robot for multi-tasks based on the real textile combing production workshop, consumption, and different task characteristics of mobile robots. The Global Optimal Evaluation of Revenue method could traversal calculates the profit of each robot corresponding to differen

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Zhu, Kejun, Jian Tang, Haixiang Guo, Chengzhu Gong, and Jinling Li. "Using a combinatorial auction-based approach for simulation of cooperative rescue operations in disaster relief." International Journal of Modeling, Simulation, and Scientific Computing 09, no. 04 (2018): 1850035. http://dx.doi.org/10.1142/s1793962318500356.

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In practice, we experience low efficiency of search and rescue (SAR) frequently in disaster relief. Here, we will optimize the SAR through agent-based simulation. In the kind of cases described here, rescue teams are characterized by different capabilities, and the tasks often require different capabilities to complete. To this end, a combinatorial auction-based task allocation scheme is used to develop a cooperative rescue plan for the heterogeneous rescue teams. Then, we illustrate the proposed cooperative rescue plan in different scenarios with the case of landslide disaster relief. The sim

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Yuan, Youdong, Ping Yang, Hanbing Jiang, and Tiange Shi. "A Multi-Robot Task Allocation Method Based on the Synergy of the K-Means++ Algorithm and the Particle Swarm Algorithm." Biomimetics 9, no. 11 (2024): 694. http://dx.doi.org/10.3390/biomimetics9110694.

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Addressing challenges in the traditional K-means algorithm, such as the challenge of selecting initial clustering center points and the lack of a maximum limit on the number of clusters, and where the set of tasks in the clusters is not reasonably sorted after the task assignment, which makes the cooperative operation of multiple robots inefficient, this paper puts forward a multi-robot task assignment method based on the synergy of the K-means++ algorithm and the particle swarm optimization (PSO) algorithm. According to the processing capability of the robots, the K-means++ algorithm that lim

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Sun, Cheng, Yuwen Yao, and Enhui Zheng. "Enhancing Unmanned Aerial Vehicle Task Assignment with the Adaptive Sampling-Based Task Rationality Review Algorithm." Drones 8, no. 9 (2024): 422. http://dx.doi.org/10.3390/drones8090422.

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As the application areas of unmanned aerial vehicles (UAVs) continue to expand, the importance of UAV task allocation becomes increasingly evident. A highly effective and efficient UAV task assignment method can significantly enhance the quality of task completion. However, traditional heuristic algorithms often perform poorly in complex and dynamic environments, and existing auction-based algorithms typically fail to ensure optimal assignment results. Therefore, this paper proposes a more rigorous and comprehensive mathematical model for UAV task assignment. By introducing task path decision

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Zhao, Yan, Guo Qing Long, and Shi You Dong. "Dynamic Mission Control Method for Multi-UAV System." Applied Mechanics and Materials 490-491 (January 2014): 942–46. http://dx.doi.org/10.4028/www.scientific.net/amm.490-491.942.

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On the problem of mission control of the multi-UAV system in a dynamic environment, the method of Dynamic Task Allocation and Coordination (DTAC), based on the combination of market mechanism and alliance recruitment, is proposed. On the basis of the DTAC model of multi-UAV system, constructed with the application of market negotiation, the physical object of the market negotiation mechanism is substituted by that of alliance recruitment. Furthermore, the recruiter is empowered to coordinate within the alliance, which reduces the auction times and increases the efficiency of DTAC. The result o

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Yu, Wan-Yu, Xiao-Qiang Huang, Hung-Yi Luo, Von-Wun Soo, and Yung-Lung Lee. "Auction-Based Consensus of Autonomous Vehicles for Multi-Target Dynamic Task Allocation and Path Planning in an Unknown Obstacle Environment." Applied Sciences 11, no. 11 (2021): 5057. http://dx.doi.org/10.3390/app11115057.

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The autonomous vehicle technology has recently been developed rapidly in a wide variety of applications. However, coordinating a team of autonomous vehicles to complete missions in an unknown and changing environment has been a challenging and complicated task. We modify the consensus-based auction algorithm (CBAA) so that it can dynamically reallocate tasks among autonomous vehicles that can flexibly find a path to reach multiple dynamic targets while avoiding unexpected obstacles and staying close as a group as possible simultaneously. We propose the core algorithms and simulate with many sc

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Li, Lan, Xiaoyong Zhang, Kaiyang Liu, Fu Jiang, and Jun Peng. "An Energy-Aware Task Offloading Mechanism in Multiuser Mobile-Edge Cloud Computing." Mobile Information Systems 2018 (2018): 1–12. http://dx.doi.org/10.1155/2018/7646705.

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Mobile-edge cloud computing, an emerging and prospective computing paradigm, can facilitate the complex application execution on resource-constrained mobile devices by offloading computation-intensive tasks to the mobile-edge cloud server, which is usually deployed in close proximity to the wireless access point. However, in the multichannel wireless interference environment, the competition of mobile users for communication resources is not conducive to the energy efficiency of task offloading. Therefore, how to make the offloading decision for each mobile user and select its suitable channel

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Gao, Li-Ping, Tao Jin, and Chao Lu. "A Long-Term Quality Perception Incentive Strategy for Crowdsourcing Environments with Budget Constraints." International Journal of Cooperative Information Systems 29, no. 01n02 (2020): 2040005. http://dx.doi.org/10.1142/s0218843020400055.

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Quality control is a critical design goal for crowdsourcing. However, when measuring the long-term quality of workers, the existing strategies do not make effective use of workers’ historical information, whereas others regard workers’ conditions as fixed values, even if they do not consider the impact of workers’ quality. This paper proposes a long-term quality perception incentive model (called QAI model) in a crowdsourcing environment with budget constraints. In this work, QAI divides the entire long-term activity cycle into multiple stages based on proportional allocation rules. Each stage

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Predescu, Alexandru, Diana Arsene, Bogdan Pahonțu, Mariana Mocanu, and Costin Chiru. "A Serious Gaming Approach for Crowdsensing in Urban Water Infrastructure with Blockchain Support." Applied Sciences 11, no. 4 (2021): 1449. http://dx.doi.org/10.3390/app11041449.

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This paper presents the current state of the gaming industry, which provides an important background for an effective serious game implementation in mobile crowdsensing. An overview of existing solutions, scientific studies and market research highlights the current trends and the potential applications for citizen-centric platforms in the context of Cyber–Physical–Social systems. The proposed solution focuses on serious games applied in urban water management from the perspective of mobile crowdsensing, with a reward-driven mechanism defined for the crowdsensing tasks. The serious game is des

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Wang, Tonghe, Songpu Ai, Junwei Cao, and Yuming Zhao. "A Blockchain-Based Distributed Computational Resource Trading Strategy for Internet of Things Considering Multiple Preferences." Symmetry 15, no. 4 (2023): 808. http://dx.doi.org/10.3390/sym15040808.

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The architecture of cloud–edge collaboration can improve the efficiency of Internet of Things (IoT) systems. Recent studies have pointed out that using IoT terminal devices as destinations for computing offloading can promote further optimized allocation of computational resources. However, in practice, this idea encounters the problem that participants might lack the motivation to take over computational tasks from others. Although the edge and the terminal are provided with symmetrical positions in collaborative offloading, their computational resources and capabilities are asymmetric. To mi

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Periyasami, Karthikeyan, Arul Xavier Viswanathan Mariammal, Iwin Thanakumar Joseph, and Velliangiri Sarveshwaran. "Combinatorial Double Auction Based Meta-scheduler for Medical Image Analysis Application in Grid Environment." Recent Advances in Computer Science and Communications 13, no. 5 (2020): 999–1007. http://dx.doi.org/10.2174/2213275911666190320161934.

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Background: Medical image analysis application has complex resource requirement. Scheduling Medical image analysis application is the complex task to the grid resources. It is necessary to develop a new model to improve the breast cancer screening process. Proposed novel Meta scheduler algorithm allocate the image analyse applications to the local schedulers and local scheduler submit the job to the grid node which analyses the medical image and generates the result sent back to Meta scheduler. Meta schedulers are distinct from the local scheduler. Meta scheduler and local scheduler have the a

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Chelladurai, Aarthi, M. D. Deepak, Przemysław Falkowski-Gilski, and Parameshachari Bidare Divakarachari. "Multi-Joint Symmetric Optimization Approach for Unmanned Aerial Vehicle Assisted Edge Computing Resources in Internet of Things-Based Smart Cities." Symmetry 17, no. 4 (2025): 574. https://doi.org/10.3390/sym17040574.

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Smart cities are equipped with a vast number of IoT devices, which help to collect and analyze data to improve the quality of life for urban people by offering a sustainable and connected environment. However, the rapid growth of IoT systems has issues related to the Quality of Service (QoS) and allocation of limited resources in IoT-based smart cities. The cloud in the IoT system also faces issues related to higher consumption of energy and extended latency. This research presents an effort to overcome these challenges by introducing opposition-based learning incorporated into Golden Jackal O

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