Joint Device Scheduling and Resource Allocation for ISCC-Based Multiview-Multitask Inference

This article investigates an integrated sensing-communication-computation (ISCC)-based multiview-multitask (MVMT) edge artificial intelligence inference system. Each device senses a narrow view of a target area and processes the echo signal to generate real-time sensory data. An edge server receives...

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Bibliographic Details
Published in:IEEE internet of things journal Vol. 11; no. 24; pp. 40814 - 40830
Main Authors: Wang, Diao, Wen, Dingzhu, He, Yinghui, Chen, Qimei, Zhu, Guangxu, Yu, Guanding
Format: Journal Article
Language:English
Published: Piscataway IEEE 15.12.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Accuracy
Algorithms
Artificial intelligence
Communication
Computational modeling
Device scheduling
edge artificial intelligence (AI) inference
Edge computing
Feature extraction
Human motion
Human performance
Inference
integrated sensing-communication-computation (ISCC)
Mixed integer
Motion perception
multitask optimization
Optimization
Real time
Recognition
Resource allocation
Resource management
Resource scheduling
Scheduling
Sensors
Servers
Smart buildings
Sorting algorithms
Task complexity
Task scheduling
ISSN:2327-4662, 2327-4662
Online Access:Get full text
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Summary:This article investigates an integrated sensing-communication-computation (ISCC)-based multiview-multitask (MVMT) edge artificial intelligence inference system. Each device senses a narrow view of a target area and processes the echo signal to generate real-time sensory data. An edge server receives and combines multiple views of data from multiple devices to complete several downstream inference tasks. Compared with existing designs where dedicated sensory data are obtained, transmitted, and processed for each task, this ISCC-based MVMT framework enjoys reduced costs of sensing, on-device computation, and communication overhead due to data sharing among different tasks. The challenges of improving all tasks' inference accuracy lie in the tight coupling of sensing, communication, and computation among different devices and sensory view competition among different tasks. These two challenges intertwine, making the multitask optimization problem mixed-integer nonconvex programming. To tackle this problem, we propose a joint device scheduling and resource allocation (JDSRA) scheme, which alternatively solves a subproblem of joint device scheduling and time allocation and a subproblem of resource allocation till convergence. Particularly, in addition to a dynamic-programming-based optimal device scheduling algorithm, a low-complexity suboptimal algorithm is proposed based on sorting a derived closed-form indicator, which represents the increase of all tasks' inference accuracy per time unit consumption. Besides, a low-complexity optimal resource allocation algorithm is proposed by parallelly solving multiple simple convex subproblems. Numerical results based on jointly completing three tasks of human motion recognition, human height recognition, and localization in smart home scenarios are conducted to verify the performance of our proposed schemes.
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ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2024.3456569