Frequency Resource Allocation and Interference Management in Mobile Edge Computing for an Internet of Things System

Na, Woongsoo; Jang, Seonmin; Lee, Yoonseong; Park, Laihyuk; Dao, Nhu‐Ngoc; Cho, Sungrae

doi:10.1109/jiot.2018.2885348

Cited by 36 publications

(8 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…General IoT resource management challenges include session management and setup [130], interference management, and channel dynamic access [131]. Conventional resource allocation and management methods in IoT networks mainly make use of optimization techniques.…”

Section: For Resource Allocation and Management In Iotmentioning

confidence: 99%

Application of Deep Learning for Quality of Service Enhancement in Internet of Things: A Review

2021

View full text Add to dashboard Cite

The role of the Internet of Things (IoT) networks and systems in our daily life cannot be underestimated. IoT is among the fastest evolving innovative technologies that are digitizing and interconnecting many domains. Most life-critical and finance-critical systems are now IoT-based. It is, therefore, paramount that the Quality of Service (QoS) of IoTs is guaranteed. Traditionally, IoTs use heuristic, game theory approaches and optimization techniques for QoS guarantee. However, these methods and approaches have challenges whenever the number of users and devices increases or when multicellular situations are considered. Moreover, IoTs receive and generate huge amounts of data that cannot be effectively handled by the traditional methods for QoS assurance, especially in extracting useful features from this data. Deep Learning (DL) approaches have been suggested as a potential candidate in solving and handling the above-mentioned challenges in order to enhance and guarantee QoS in IoT. In this paper, we provide an extensive review of how DL techniques have been applied to enhance QoS in IoT. From the papers reviewed, we note that QoS in IoT-based systems is breached when the security and privacy of the systems are compromised or when the IoT resources are not properly managed. Therefore, this paper aims at finding out how Deep Learning has been applied to enhance QoS in IoT by preventing security and privacy breaches of the IoT-based systems and ensuring the proper and efficient allocation and management of IoT resources. We identify Deep Learning models and technologies described in state-of-the-art research and review papers and identify those that are most used in handling IoT QoS issues. We provide a detailed explanation of QoS in IoT and an overview of commonly used DL-based algorithms in enhancing QoS. Then, we provide a comprehensive discussion of how various DL techniques have been applied for enhancing QoS. We conclude the paper by highlighting the emerging areas of research around Deep Learning and its applicability in IoT QoS enhancement, future trends, and the associated challenges in the application of Deep Learning for QoS in IoT.

show abstract

Section: For Resource Allocation and Management In Iotmentioning

confidence: 99%

Application of Deep Learning for Quality of Service Enhancement in Internet of Things: A Review

2021

View full text Add to dashboard Cite

show abstract

“…As a result, VEC servers and PVs cooperate to process task in an edge-edge collaboration manner. To alleviate the workload on ESs, Na et al in [55] proposed to utilize edge gateways (EGs) at the edge layer to assist task processing. A resource orchestration scheme among EGs and/or between ES and EGs is also proposed, aiming to maximize the efficiency of IoT systems.…”

Section: Edge-edge Collaborationmentioning

confidence: 99%

“…4) Edge-to-Edge: The edge-to-edge offloading is actually operated under the edge-edge collaboration manner, as discussed in Section II-D, which can alleviate the workload of some overloaded EN by offloading (or migrating) some workloads to a peer. The typical issues of the edge-to-edge offloading mainly include: (i) task scheduling, which can orchestrate the task processing among different ENs [55]- [58]; (ii) service migration, by which services are dynamically migrated across multiple heterogeneous ENs [98], [99]; (iii) offload forwarding, in which an EN is regarded as a relay to forward workloads to neighboring ENs [100]. 5) Device-to-Device: The device-to-device offloading can be operated under both the things-edge collaboration manner and the things-edge-cloud collaboration manner, as discussed in Section II-B and Section II-C, which offloads the workloads from one end-device to a peer by making full use of idle resources.…”

Section: B1 Directionmentioning

confidence: 99%

Resource Scheduling in Edge Computing: A Survey

Luo

et al. 2021

Preprint

View full text Add to dashboard Cite

With the proliferation of the Internet of Things (IoT) and the wide penetration of wireless networks, the surging demand for data communications and computing calls for the emerging edge computing paradigm. By moving the services and functions located in the cloud to the proximity of users, edge computing can provide powerful communication, storage, networking, and communication capacity. The resource scheduling in edge computing, which is the key to the success of edge computing systems, has attracted increasing research interests. In this paper, we survey the state-of-the-art research findings to know the research progress in this field. Specifically, we present the architecture of edge computing, under which different collaborative manners for resource scheduling are discussed. Particularly, we introduce a unified model before summarizing the current works on resource scheduling from three research issues, including computation offloading, resource allocation, and resource provisioning. Based on two modes of operation, i.e., centralized and distributed modes, different techniques for resource scheduling are discussed and compared. Also, we summarize the main performance indicators based on the surveyed literature. To shed light on the significance of resource scheduling in real-world scenarios, we discuss several typical application scenarios involved in the research of resource scheduling in edge computing. Finally, we highlight some open research challenges yet to be addressed and outline several open issues as the future research direction.

show abstract

“…anks to this, the multimedia services such as self-driving, video surveillance, and augmented reality are also more and more applied in edge IoT. However, the transmission in edge IoT is still facing the problems of low throughput and unreliability caused by the complex and changeable networks [3]. e transmission requirements of multimedia services are often not met.…”

Section: Introductionmentioning

confidence: 99%

Failure-Aware and Delay-Predicted Multipath Virtual Queue Scheduling for Multimedia Transmission in Edge IoT

Qin

Wang

Gao

et al. 2020

Mobile Information Systems

View full text Add to dashboard Cite

The spread of Edge Internet of Things (IoT) radically changes our lifestyle. However, the multimedia services in edge IoT are still stuck by inefficiency. The dynamic typologies perplex the transmission of massive real-time data. To solve this problem, multipath transmission control protocol (MPTCP) which has a natural advantage in transmission robustness and bandwidth aggregation is becoming a good choice. In this paper, failure-aware and delay-predicted multipath virtual queue scheduling (FD-MVQS) is proposed to optimize the MPTCP performance in edge IoT. FD-MVQS constructs a two-plane cooperative scheduling system. In the control plane, the transmission failure estimation and chaos theory-based arrival delay prediction methods are introduced to provide the foundation for prescheduling. In the data plane, the multipath virtual queue scheduling is designed to allocate segments to different subflows. Simulation results showed that the proposed FD-MVQS performed better than standard and typical multipath transmission solutions in throughput, delay, and segment disorder.

show abstract

Frequency Resource Allocation and Interference Management in Mobile Edge Computing for an Internet of Things System

Cited by 36 publications

References 24 publications

Application of Deep Learning for Quality of Service Enhancement in Internet of Things: A Review

Application of Deep Learning for Quality of Service Enhancement in Internet of Things: A Review

Resource Scheduling in Edge Computing: A Survey

Failure-Aware and Delay-Predicted Multipath Virtual Queue Scheduling for Multimedia Transmission in Edge IoT

Contact Info

Product

Resources

About