Edge Computing in the Industrial Internet of Things Environment: Software-Defined-Networks-Based Edge-Cloud Interplay

Kaur, Kuljeet; Garg, Sahil; Aujla, Gagangeet Singh; Kumar, Neeraj; Rodrigues, Joel J. P. C.; Guizani, Mohsen

doi:10.1109/mcom.2018.1700622

Cited by 327 publications

(112 citation statements)

References 11 publications

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“…In our simulations, we set T = 100ms and consider five Type-I devices and five Type-II devices. Specifically, we set the distance between the AP and Type-I devices as d 1 = (d i , i ∈ N 1 ) = [3,5,7,9,11]m. Without loss of generality, we consider d 2 = (d i , i ∈ N 2 ) = d 1 .…”

Section: Asymptotic Optimality Analysis Under Outdated Nsimentioning

confidence: 99%

“…Generally, the data will be transmitted to and processed at a nearby radio access point (AP) to facilitate network model training and intelligent decisions, e.g., machine learning for feature extraction [5] and fuzzy Q-learning for load balancing [6]. To this end, endowing APs with powerful mobile edge computing (MEC) functionalities will be a major form of IIoT scenarios [7]- [9].…”

Section: Introductionmentioning

confidence: 99%

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Online Optimization of Wireless Powered Mobile-Edge Computing for Heterogeneous Industrial Internet of Things

Lyu

Tian

2019

IEEE Internet Things J.

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A spurt of progress in wireless power transfer (WPT) and mobile edge computing (MEC) provides a promising approach for Industrial Internet of Things (IIoT) to enhance the quality and productivity of manufacturing. Scheduling in such a scenario is challenging due to congested wireless channels, time-dependent energy constraints, complicated device heterogeneity, and prohibitive signaling overheads. In this paper, we first propose an online algorithm, called energy-aware resource scheduling (ERS), to maximize the system utility comprising throughput and fairness, with consideration on both system sustainability and stability. Based on Lyapunov optimization and convex optimization techniques, the proposed algorithm achieves asymptotic optimality for heterogeneous IIoT systems without prior knowledge of network state information (NSI). Subsequently, we extend the ERS algorithm to a more realistic scenario where the overhead and delay of NSI feedbacks are nonnegligible. The optimal scheduling decisions of the scenario are provided, and the optimality loss on system utility under outdated NSI is analyzed. Simulations verify our theoretical claims and demonstrate the gains of our proposed ERS algorithm over alternative benchmark schemes.

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Section: Asymptotic Optimality Analysis Under Outdated Nsimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Online Optimization of Wireless Powered Mobile-Edge Computing for Heterogeneous Industrial Internet of Things

Lyu

Tian

2019

IEEE Internet Things J.

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“…Therefore, to handle these challenges in VCPS, an SDN-based control scheme based on virtualization is designed for traffic flow management. In this scheme, three types of data flows are considered for evaluation [12]. The first type is the active data flow, wherein flows matched successfully are included.…”

Section: Virtualized Control Scheme For Flow Management In Sdnmentioning

confidence: 99%

SeDaTiVe: SDN-Enabled Deep Learning Architecture for Network Traffic Control in Vehicular Cyber-Physical Systems

et al. 2018

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The rapid growth in the transportation sector has led to the emergence of smart vehicles which are equipped with information and communication technologies (ICT). These modern smart vehicles are connected to the Internet to have various services such as road condition information, infotainment, and energy management. This kind of scenario can be viewed as vehicular cyber-physical system (VCPS) where the vehicles are at physical layer and services are at cyber layer. However, network traffic management is the biggest issue in the modern VCPS scenario as the mismanagement of the network resources can degrade the quality of service (QoS) for the end users. To deal with this issue, we propose a software defined network (SDN)-enabled approach, named SeDaTiVe, which used deep learning architecture to control the incoming traffic in the network in VCPS environment. The advantage of using deep learning in network traffic control is that it learns the hidden patterns in the data packets and creates an optimal route based on the learned features. Moreover, a virtual controller based scheme for flow management using SDN in VCPS is designed for effective resource utilization for providing QoS. The simulation scenario comprising of 1000 vehicles seeking various services in the network is considered to generate the dataset using SUMO. The data obtained from the simulation study is evaluated using NS-2 which proves that the proposed scheme effectively handles the real-time incoming requests in VCPS. The results also depict the improvement in performance on various evaluation metrics like delay, throughput, packet delivery ratio, and network load by using the proposed scheme over the traditional SDN and TCP/IP protocol suite.Index Terms-Traffic control, deep learning, software-defined network, vehicular cyber-physical system. ! • A. Jindal is with the

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“…Nevertheless, with the penetration of millions of smart vehicles in the global market, a gigantic bunch of vehicular on-board facilities have been furnished which collect and exchange huge amounts of data, resulting in significant growth of the network traffic [2]. Although the conventional cloud computing architecture is known for providing a diverse range of services to vehicular networks, the long network distance between mobile devices and remote data centers hinders its performance while resulting in a high delay sensitivity [3]. Thus, the concept of vehicular fog computing (VFC) has evolved, in which computation, storage, and networking are hosted in close proximity to vehicles [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Blockchain-Based Lightweight Authentication Mechanism for Vehicular Fog Infrastructure

Kaur

Garg

Kaddoum

et al. 2019

2019 IEEE International Conference on Communications Workshops (ICC Workshops)

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With the increasing development of advanced communication technologies, vehicles are becoming smarter and more connected. Due to the tremendous growth of various vehicular applications, a huge amount of data is generated through advanced on-board devices and is deemed critical to improve driving safety and enhance vehicular services. However, cloud based models often fall short in applications where latency and mobility are critical. In order to fully realize the potential of vehicular networks, the challenges of efficient communication and computation need to be addressed. In this direction, vehicular fog computing (VFC) has emerged which extends the concept of fog computing to conventional vehicular networks. It is a geographically distributed paradigm that has the potential to conduct time-critical and data-intensive tasks by pushing intelligence (i.e. computing resources, storage, and application services) in the vicinity of end vehicles. However secure and reliable transmission are of significant importance in highly-mobile vehicular networks in order to ensure the optimal Quality of Service (QoS). In this direction, several authentication mechanisms have been proposed in the literature but most of them are found unfit due to absence of decentralization, anonymity, and trust characteristics. Thus, an effective cross-datacenter authentication and key-exchange scheme based on blockchain and elliptic curve cryptography (ECC) is proposed in this paper. Here, the distributed ledger of blockchain is used for maintaining the network information while the highly secure ECC is employed for mutual authentication between vehicles and road side units (RSUs). Additionally, the proposed scheme is lightweight and scalable for the considered VFC setup. The performance evaluation results against the existing state-of-the-art reveal that the proposed scheme accomplishes enhanced security features with reduced computational and communicational overheads. Further, its extensive evaluation on the widely applicable Automated Validation of Internet Security Protocols and Applications (AVISPA) tool guarantee its safeness against different attack vectors.

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Edge Computing in the Industrial Internet of Things Environment: Software-Defined-Networks-Based Edge-Cloud Interplay

Cited by 327 publications

References 11 publications

Online Optimization of Wireless Powered Mobile-Edge Computing for Heterogeneous Industrial Internet of Things

Online Optimization of Wireless Powered Mobile-Edge Computing for Heterogeneous Industrial Internet of Things

SeDaTiVe: SDN-Enabled Deep Learning Architecture for Network Traffic Control in Vehicular Cyber-Physical Systems

Blockchain-Based Lightweight Authentication Mechanism for Vehicular Fog Infrastructure

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