Self-Healing in Emerging Cellular Networks: Review, Challenges, and Research Directions

Asghar, Ahmad Bilal; Farooq, Hasan; Imran, Ali

doi:10.1109/comst.2018.2825786

Cited by 90 publications

(48 citation statements)

References 117 publications

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“…The cellular network could encounter communication congestion in urban areas while the coverage at rural areas is very poor. Furthermore, both DSRC-based and cellular networks have difficulty in supporting highly mobile vehicles, due to the frequent handovers [35].…”

Section: A Sag-integrated Vehicular Networkmentioning

confidence: 99%

Energy and Information Management of Electric Vehicular Network: A Survey

Chen

Wang

Zhang

et al. 2020

IEEE Commun. Surv. Tutorials

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The connected vehicle paradigm empowers vehicles with the capability to communicate with neighboring vehicles and infrastructure, shifting the role of vehicles from a transportation tool to an intelligent service platform. Meanwhile, the transportation electrification pushes forward the electric vehicle (EV) commercialization to reduce the greenhouse gas emission by petroleum combustion. The unstoppable trends of connected vehicle and EVs transform the traditional vehicular system to an electric vehicular network (EVN), a clean, mobile, and safe system. However, due to the mobility and heterogeneity of the EVN, improper management of the network could result in charging overload and data congestion. Thus, energy and information management of the EVN should be carefully studied. In this paper, we provide a comprehensive survey on the deployment and management of EVN considering all three aspects of energy flow, data communication, and computation. We first introduce the management framework of EVN. Then, research works on the EV aggregator (AG) deployment are reviewed to provide energy and information infrastructure for the EVN. Based on the deployed AGs, we present the research work review on EV scheduling that includes both charging and vehicle-to-grid (V2G) scheduling. Moreover, related works on information communication and computing are surveyed under each scenario. Finally, we discuss open research issues in the EVN.

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Section: A Sag-integrated Vehicular Networkmentioning

confidence: 99%

Energy and Information Management of Electric Vehicular Network: A Survey

Chen

Wang

Zhang

et al. 2020

IEEE Commun. Surv. Tutorials

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“…To improve both, Resilient and Survivable networks (ResiliNets) are very important [24]. ResiliNets uses a formula as shown in (1). Defend -The network must be in a position to defend itself from any kinds of attack or natural disasters.…”

Section: Resiliencementioning

confidence: 99%

Self-Healing and Resilience of Complex Network

ADC¹,

Sabapathi²

2019

IJRTE

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Currently mobile operators spend more money towards maintaining the mobile network in fsinding faults, which will degrade the cellular services. If any fault occurs in the network it must be healed automatically and made to work. This self-healing network gives solutions for fixing or helps in deciding the problems to be carried out autonomously with no human interference. Also the networks ability to recover after any failure is said to be resilience. This paper discusses the strategy of resilience of complex network, its principles and metrics of resilience. The main aim of self-healing and resilience is to heal the network automatically and the network must recover after any fault.

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“…quality of service (QoS) for their subscribers. There are essentially two types of expenditures for the cellular network operators to bear: capital expenditure (CAPEX) which refers to acquisition and modernization of network entities, and operational expenditure (OPEX) which refers to the amount spent on management and maintenance of the cellular network's operations [7]. One of the major reasons for heightening OPEX and revenue loss is the escalation of network faults that result in outages.…”

Section: Introductionmentioning

confidence: 99%

“…In fact, network maintenance and operation cost roughly one-fourth of the total revenue, out of which a significant portion is dedicated to cell outage-full, indicates a complete dysfunction of a cell and partial, means cellular service deterioration-management [7]. The faults and outages are likely to magnify in 5G networks due to the implementation of small cells; making it arduous to manually manage the outages by heavily depending on the human experts, as is done in current cellular networks [7]. Network faults can occur due to hardware malfunctions, software problems, functional resource failures, loss due to overload situations, or communication failures [8].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Powered Mobile Edge Computing-Based Anomaly Detection in Cellular Networks

Hussain

Imran

et al. 2020

IEEE Trans. Ind. Inf.

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Escalating cell outages and congestion-treated as anomalies-cost a substantial revenue loss to the cellular operators and severely affect subscriber quality of experience. Stateof-the-art literature applies feed-forward deep neural network at core network (CN) for the detection of above problems in a single cell; however, the solution is impractical as it will overload the CN that monitors thousands of cells at a time. Inspired from mobile edge computing and breakthroughs of deep convolutional neural networks (CNNs) in computer vision research, we split the network into several 100-cell regions each monitored by an edge server; and propose a framework that pre-processes raw call detail records having user activities to create an image-like volume, fed to a CNN model. The framework outputs a multilabeled vector identifying anomalous cell(s). Our results suggest that our solution can detect anomalies with up to 96% accuracy, and is scalable and expandable for industrial Internet of things environment.

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Self-Healing in Emerging Cellular Networks: Review, Challenges, and Research Directions

Cited by 90 publications

References 117 publications

Energy and Information Management of Electric Vehicular Network: A Survey

Energy and Information Management of Electric Vehicular Network: A Survey

Self-Healing and Resilience of Complex Network

Artificial Intelligence-Powered Mobile Edge Computing-Based Anomaly Detection in Cellular Networks

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