Joint Mode Selection and Task Assignment in Multi-Mode Sensor Network

Sensors with multiple functions and working modes constitute the nodes of the multi-mode sensor network. In this work, the mathematical model of task assignment problem in multi-mode sensor network is established. The model consists of task priorities, capability matching relationships, costs of tas...

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Bibliographic Details
Published in:Chinese Control Conference pp. 1846 - 1852
Main Authors: Li, Shikang, Tao, Rentuo, Xu, Xianzhe, Chen, Yawei
Format: Conference Proceeding
Language:English
Published: Technical Committee on Control Theory, Chinese Association of Automation 24.07.2023
Subjects:
Benchmark testing
Costs
Greedy algorithms
integer linear programming
Mathematical models
Monte Carlo methods
multi-mode sensor network
particle swarm optimization
Sensors
Task analysis
Task assignment
ISSN:1934-1768
Online Access:Get full text
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Summary:Sensors with multiple functions and working modes constitute the nodes of the multi-mode sensor network. In this work, the mathematical model of task assignment problem in multi-mode sensor network is established. The model consists of task priorities, capability matching relationships, costs of tasks, resources of sensors and mode constraints. A two-stage scheme composed of joint mode selection and task assignment is proposed and evaluated. First, the cooperative working modes of sensors are determined with quantum-behaved particle swarm optimization (QPSO). Next, task assignment is performed with modified Hungarian algorithm under market-based assumption. Our proposed two-stage approach exhibits higher flexibility and achieves better tasks completion ratio compared with the benchmark of greedy policy, while the time complexity is much less than the conventional assignment scheme with PSO algorithm. According to the Monte Carlo simulation, the assignment of 20 sensors and 1000 tasks is completed in 0.225 seconds with the proposed strategy, while the completion percentage of tasks is 7% higher than those achieved with the greedy algorithm.
ISSN:1934-1768
DOI:10.23919/CCC58697.2023.10239828