Deep Learning Empowered Task Offloading for Mobile Edge Computing in Urban Informatics

Zhang, Ke; Zhu, Yongxu; Leng, Supeng; He, Yejun; Maharjan, Sabita; Zhang, Yan

doi:10.1109/jiot.2019.2903191

Cited by 280 publications

(85 citation statements)

References 29 publications

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“…Value-iteration methods are often carried out off-policy, meaning that the policy used to generate behavior for training data can be unrelated to the policy being evaluated and improved, called the estimation policy [11,12]. Popular value-iteration methods used in dynamic task scheduling are Q-Learning [7,9,10,[15][16][17] and Deep Q-Network (DQN) [3,8,[18][19][20]. Apart from these two, Greedy methods [19], Monte Carlo Methods [21] and Temporal Difference (TD) Learning [22,23] also have been used.…”

Section: Value-iteration Methodsmentioning

confidence: 99%

“…Deep Q-Networks have also been studied along with reinforcement learning in Task Offloading for Mobile Edge Computing(MEC) applications. In [20], the task offloading in the heterogeneous vehicular network is studied. The system consists an application of several MEC servers, base station, mobile vehicles and roadside units.…”

Section: Deep Q-network (Dqn) / Deep Q-learning (Dql)mentioning

confidence: 99%

“…The system consists an application of several MEC servers, base station, mobile vehicles and roadside units. Researchers have included Deep Q-learning to select MEC servers and determine transmission mode [20] as measures to enhance task offloading reliability. In similar research, DQN is proposed to address dynamic, flexible job shop scheduling problem (DFJSP) [3].…”

Section: Deep Q-network (Dqn) / Deep Q-learning (Dql)mentioning

confidence: 99%

See 2 more Smart Citations

Reinforcement Learning in Dynamic Task Scheduling: A Review

2020

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Scheduling is assigning shared resources over time to efficiently complete the tasks over a given period of time. The term is applied separately for tasks and resources correspondingly in task scheduling and resource allocation. Scheduling is a popular topic in operational management and computer science. Effective schedules ensure system efficiency, effective decision making, minimize resource wastage and cost, and enhance overall productivity. It is generally a tedious task to choose the most accurate resources in performing work items and schedules in both computing and business process execution. Especially in real-world dynamic systems where multiple agents involve in scheduling various dynamic tasks is a challenging issue. Reinforcement Learning is an emergent technology which has been able to solve the problem of the optimal task and resource scheduling dynamically. This review paper is about a research study that focused on Reinforcement Learning techniques that have been used for dynamic task scheduling. The paper addresses the results of the study by means of the state-of-theart on Reinforcement learning techniques used in dynamic task scheduling and a comparative review of those techniques.

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Section: Value-iteration Methodsmentioning

confidence: 99%

Section: Deep Q-network (Dqn) / Deep Q-learning (Dql)mentioning

confidence: 99%

Section: Deep Q-network (Dqn) / Deep Q-learning (Dql)mentioning

confidence: 99%

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Reinforcement Learning in Dynamic Task Scheduling: A Review

2020

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“…Considering the highly dynamic topology and time-varying spectrum states in cognitive radio based vehicular networks, a DRL-based optimal data transmission scheduling scheme is designed in [20] to minimize transmission costs while ensuring data QoS requirements. For computation offloading in vehicular netowrks, Zhang et al [21] propose a DRL-based optimal task offloading scheme with varying states of multiple edge servers and multiple vehicular offloading modes.…”

Section: A Related Work and Chanllengesmentioning

confidence: 99%

Deep-Reinforcement-Learning-Based Mode Selection and Resource Allocation for Cellular V2X Communications

Zhang

Peng

Yan

et al. 2020

IEEE Internet Things J.

212

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Cellular vehicle-to-everything (V2X) communication is crucial to support future diverse vehicular applications. However, for safety-critical applications, unstable vehicle-to-vehicle (V2V) links and high signalling overhead of centralized resource allocation approaches become bottlenecks. In this paper, we investigate a joint optimization problem of transmission mode selection and resource allocation for cellular V2X communications. In particular, the problem is formulated as a Markov decision process, and a deep reinforcement learning (DRL) based decentralized algorithm is proposed to maximize the sum capacity of vehicle-to-infrastructure users while meeting the latency and reliability requirements of V2V pairs. Moreover, considering training limitation of local DRL models, a two-timescale federated DRL algorithm is developed to help obtain robust model. Wherein, the graph theory based vehicle clustering algorithm is executed on a large timescale and in turn the federated learning algorithm is conducted on a small timescale. Simulation results show that the proposed DRL-based algorithm outperforms other decentralized baselines, and validate the superiority of the twotimescale federated DRL algorithm for newly activated V2V pairs.

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“…As shown above, the rise of new technologies (e.g. Internet of Vehicles, [34][35][36][37] Heterogeneous Sensor Networks, [38][39][40][41][42][43] Edge Computing, 44,45 Smart City, 46 Blockchain) [47][48][49] makes CIA unsuitable. In this section, we will answer two fundamental problems: which security properties are fundamental?…”

Section: Connotation Of Information Securitymentioning

confidence: 99%

Hierarchically defining Internet of Things security: From CIA to CACA

Liu

Fang

et al. 2020

International Journal of Distributed Sensor Networks

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With the rapid development of Internet of Things technology (e.g. wireless sensor networks), security has become a global issue. Confidentiality, integrity, and availability (known as the CIA triangle) is widely used to define and model information security. However, this CIA triangle is insufficient to address rapidly changing security requirements. In this article, we divide information systems into four layers: physical layer, operational layer, data layer, and content layers (PODC). Corresponding, hierarchy of information security is proposed. Furthermore, we define the basic security properties for each layer and show that the four properties (i.e. confidentiality, availability, controllability, and authentication, called CACA) are minimally complete and independent for information security. Based on PODC and CACA, a new definition of information security is proposed, which acts as a secure foundation for information systems.

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Deep Learning Empowered Task Offloading for Mobile Edge Computing in Urban Informatics

Cited by 280 publications

References 29 publications

Reinforcement Learning in Dynamic Task Scheduling: A Review

Reinforcement Learning in Dynamic Task Scheduling: A Review

Deep-Reinforcement-Learning-Based Mode Selection and Resource Allocation for Cellular V2X Communications

Hierarchically defining Internet of Things security: From CIA to CACA

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