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Deep Reinforcement Learning for Delay-Oriented IoT Task Scheduling in SAGIN
IEEE Transactions on Wireless Communications, 2021In this article, we investigate a computing task scheduling problem in space-air-ground integrated network (SAGIN) for delay-oriented Internet of Things (IoT) services. In the considered scenario, an unmanned aerial vehicle (UAV) collects computing tasks
Conghao Zhou +6 more
semanticscholar +1 more source
IEEE Internet of Things Journal, 2021
Internet of Things (IoT) is a huge network and establishes ubiquitous connections between smart devices and objects. The flourishing of IoT leads to an unprecedented data explosion, traditional data storing or processing techniques have the problem of ...
Xingjuan Cai +4 more
semanticscholar +1 more source
Internet of Things (IoT) is a huge network and establishes ubiquitous connections between smart devices and objects. The flourishing of IoT leads to an unprecedented data explosion, traditional data storing or processing techniques have the problem of ...
Xingjuan Cai +4 more
semanticscholar +1 more source
Partial Computation Offloading and Adaptive Task Scheduling for 5G-Enabled Vehicular Networks
IEEE Transactions on Mobile Computing, 2022A variety of novel mobile applications are developed to attract the interests of potential users in the emerging 5G-enabled vehicular networks. Although computation offloading and task scheduling have been widely investigated, it is rather challenging to
Zhaolong Ning +8 more
semanticscholar +1 more source
IEEE Internet of Things Journal, 2022
By delegating the infrastructure management, such as provisioning or scaling to third-party providers, serverless edge computing has recently been widely adopted in several applications, especially Internet of Things (IoT) applications.
Qinqin Tang +6 more
semanticscholar +1 more source
By delegating the infrastructure management, such as provisioning or scaling to third-party providers, serverless edge computing has recently been widely adopted in several applications, especially Internet of Things (IoT) applications.
Qinqin Tang +6 more
semanticscholar +1 more source
IEEE Transactions on Cloud Computing, 2022
Processing data from Internet of Things (IoT) applications at the cloud centers has known limitations relating to latency, task scheduling, and load balancing.
Ismail M. Ali +5 more
semanticscholar +1 more source
Processing data from Internet of Things (IoT) applications at the cloud centers has known limitations relating to latency, task scheduling, and load balancing.
Ismail M. Ali +5 more
semanticscholar +1 more source
A Dynamic Task Scheduling Strategy for Multi-Access Edge Computing in IRS-Aided Vehicular Networks
IEEE Transactions on Emerging Topics in Computing, 2022Multi-access Edge Computing (MEC) has played an important role in realizing intelligent beyond 5G (B5G) vehicular networks. The computation tasks of intelligent applications can be offloaded to and processed by near-end-user MEC servers to meet strict ...
Yishi Zhu, Bomin Mao, N. Kato
semanticscholar +1 more source
Mobility-Aware Joint Task Scheduling and Resource Allocation for Cooperative Mobile Edge Computing
IEEE Transactions on Wireless Communications, 2021Mobile edge computing (MEC) has emerged as a new paradigm to assist low latency services by enabling computation offloading at the network edge. Nevertheless, human mobility can significantly impact the offloading decision and performance in MEC networks.
Umber Saleem +4 more
semanticscholar +1 more source
INFORMS Journal on Computing, 1996
We consider the problem of nonpreemptively scheduling periodic tasks on a minimum number of processors, assuming that the tasks have to be executed strictly periodically. We show that the problem is NP-complete in the strong sense, even in the case of a single processor, but that it is solvable in polynomial time if the periods and execution times are
Korst, J.H.M. +2 more
openaire +2 more sources
We consider the problem of nonpreemptively scheduling periodic tasks on a minimum number of processors, assuming that the tasks have to be executed strictly periodically. We show that the problem is NP-complete in the strong sense, even in the case of a single processor, but that it is solvable in polynomial time if the periods and execution times are
Korst, J.H.M. +2 more
openaire +2 more sources
Federated Deep Reinforcement Learning for Task Scheduling in Heterogeneous Autonomous Robotic System
Global Communications Conference, 2022In this paper, we investigate the problem of task scheduling in automated warehouses with hetero-geneous autonomous robotic systems. We formulate the task scheduling for a heterogeneous autonomous robots (HAR) system in each warehouse as a queueing ...
T. Ho, K. Nguyen, M. Cheriet
semanticscholar +1 more source
AoI-centric Task Scheduling for Autonomous Driving Systems
IEEE Conference on Computer Communications, 2022An Autonomous Driving System (ADS) uses a plethora of sensors and many deep learning based tasks to aid its perception, prediction, motion planning, and vehicle control. To ensure road safety, those tasks should be synchronized and use the latest sensing
Chengyuan Xu +5 more
semanticscholar +1 more source