scispace - formally typeset
Search or ask a question
Author

Sherif Abdelwahab

Other affiliations: Hewlett-Packard, Alcatel-Lucent
Bio: Sherif Abdelwahab is an academic researcher from Oregon State University. The author has contributed to research in topics: Cloud computing & Wireless sensor network. The author has an hindex of 9, co-authored 14 publications receiving 491 citations. Previous affiliations of Sherif Abdelwahab include Hewlett-Packard & Alcatel-Lucent.

Papers
More filters
Journal ArticleDOI
TL;DR: The main objective of this article is to explore the potential of NFV in enhancing 5G radio access networks' functional, architectural, and commercial viability, including increased automation, operational agility, and reduced capital expenditure.
Abstract: 5G wireless technology is paving the way to revolutionize future ubiquitous and pervasive networking, wireless applications, and user quality of experience. To realize its potential, 5G must provide considerably higher network capacity, enable massive device connectivity with reduced latency and cost, and achieve considerable energy savings compared to existing wireless technologies. The main objective of this article is to explore the potential of NFV in enhancing 5G radio access networks' functional, architectural, and commercial viability, including increased automation, operational agility, and reduced capital expenditure. The ETSI NFV Industry Specification Group has recently published drafts focused on standardization and implementation of NFV. Harnessing the potential of 5G and network functions virtualization, we discuss how NFV can address critical 5G design challenges through service abstraction and virtualized computing, storage, and network resources. We describe NFV implementation with network overlay and SDN technologies. In our discussion, we cover the first steps in understanding the role of NFV in implementing CoMP, D2D communication, and ultra densified networks.

186 citations

Journal ArticleDOI
TL;DR: This paper presents a multilayer architecture of CARS by describing each layer's functionalities and responsibilities, as well as its interactions and interfaces with its upper and lower layers.
Abstract: The recent emergence and success of cloud-based services has empowered remote sensing and made it very possible Cloud-assisted remote sensing (CARS) enables distributed sensory data collection, global resource and data sharing, remote and real-time data access, elastic resource provisioning and scaling, and pay-as-you-go pricing models CARS has great potentials for enabling the so-called Internet of Everything (IoE), thereby promoting smart cloud services In this paper, we survey CARS First, we describe its benefits and capabilities through real-world applications Second, we present a multilayer architecture of CARS by describing each layer’s functionalities and responsibilities, as well as its interactions and interfaces with its upper and lower layers Third, we discuss the sensing services models offered by CARS Fourth, we discuss some popular commercial cloud platforms that have already been developed and deployed in recent years Finally, we present and discuss major design requirements and challenges of CARS

117 citations

Journal ArticleDOI
TL;DR: The analysis and numerical evaluation suggest that the proposed Replisom system has significant potential in reducing the delay, energy consumption, and cost for cloud offloading of IoT applications given the massive number of devices with tiny memory sizes.
Abstract: Augmenting the long-term evolution (LTE)-evolved NodeB (eNB) with cloud resources offers a low-latency, resilient, and LTE-aware environment for offloading the Internet of Things (IoT) services and applications. By means of devices memory replication, the IoT applications deployed at an LTE-integrated edge cloud can scale its computing and storage requirements to support different resource-intensive service offerings. Despite this potential, the massive number of IoT devices limits the LTE edge cloud responsiveness as the LTE radio interface becomes the major bottleneck given the unscalability of its uplink access and data transfer procedures to support a large number of devices that simultaneously replicate their memory objects with the LTE edge cloud. We propose Replisom ; an LTE-aware edge cloud architecture and an LTE-optimized memory replication protocol which relaxes the LTE bottlenecks by a delay and radio resource-efficient memory replication protocol based on the device-to-device communication technology and the sparse recovery in the theory of compressed sampling. Replisom effectively schedules the memory replication occasions to resolve contentions for the radio resources as a large number of devices simultaneously transmit their memory replicas. Our analysis and numerical evaluation suggest that this system has significant potential in reducing the delay, energy consumption, and cost for cloud offloading of IoT applications given the massive number of devices with tiny memory sizes.

74 citations

Journal ArticleDOI
TL;DR: A distributed sensing resource discovery and virtualization algorithms that efficiently deploy virtual sensor networks on top of a subset of the selected IoT devices and an uncoordinated, distributed algorithm that relies on the selected sensors to estimate a set of parameters without requiring synchronization among the sensors are designed.
Abstract: We propose Cloud of Things for sensing-as-a-service: a global architecture that scales up cloud computing by exploiting the global sensing resources of the Internet of Things (IoT) to enable remote sensing. Cloud of Things enables in-network distributed processing of sensors data offered by the globally available IoT devices and provides a global platform for meaningful and responsive data analysis and decision making. We propose a distributed sensing resource discovery and virtualization algorithms that efficiently deploy virtual sensor networks on top of a subset of the selected IoT devices. We show, through analysis and simulations, the potential of the proposed solutions to realize virtual sensor networks with minimal physical resources, reduced communication overhead, and low complexity. We also design an uncoordinated, distributed algorithm that relies on the selected sensors to estimate a set of parameters without requiring synchronization among the sensors. Our simulations show that the proposed estimation algorithm, when compared to conventional alternating direction method of multipliers (ADMMs), reduces communication overhead significantly without compromising the estimation error. In addition, the convergence time, though increases slightly, is still linear as in the case of conventional ADMM.

61 citations

Journal ArticleDOI
TL;DR: This work analytically and empirically verify that Bird-VNE outperforms existing VNE algorithms with respect to computational efficiency, closeness to optimality, and its ability to avoid potential migrations in mobile wireless networks.
Abstract: We develop an efficient virtual network embedding (VNE) algorithm, termed B ird -VNE, for mobile wireless networks. B ird -VNE is an approximation algorithm that ensures a close to optimal virtual embedding profit and acceptance rate while minimizing the number of virtual network migrations resulting from the mobility of wireless nodes. B ird -VNE employs a constraint satisfaction framework by which we analyze the constraint propagation properties of the VNE problem and design constraint processing algorithms that efficiently narrow the solution space and avoid backtracking as much as possible without compromising the solution quality. Our evaluation results show that the likelihood that B ird -VNE results in backtracking is small, thus demonstrating its effectiveness in reducing the search space. We analytically and empirically verify that B ird -VNE outperforms existing VNE algorithms with respect to computational efficiency, closeness to optimality, and its ability to avoid potential migrations in mobile wireless networks.

32 citations


Cited by
More filters
Journal ArticleDOI
TL;DR: This paper provides an up-to-date picture of CloudIoT applications in literature, with a focus on their specific research challenges, and identifies open issues and future directions in this field, which it expects to play a leading role in the landscape of the Future Internet.

1,880 citations

Journal ArticleDOI
TL;DR: This paper describes major use cases and reference scenarios where the mobile edge computing (MEC) is applicable and surveys existing concepts integrating MEC functionalities to the mobile networks and discusses current advancement in standardization of the MEC.
Abstract: Technological evolution of mobile user equipment (UEs), such as smartphones or laptops, goes hand-in-hand with evolution of new mobile applications. However, running computationally demanding applications at the UEs is constrained by limited battery capacity and energy consumption of the UEs. A suitable solution extending the battery life-time of the UEs is to offload the applications demanding huge processing to a conventional centralized cloud. Nevertheless, this option introduces significant execution delay consisting of delivery of the offloaded applications to the cloud and back plus time of the computation at the cloud. Such a delay is inconvenient and makes the offloading unsuitable for real-time applications. To cope with the delay problem, a new emerging concept, known as mobile edge computing (MEC), has been introduced. The MEC brings computation and storage resources to the edge of mobile network enabling it to run the highly demanding applications at the UE while meeting strict delay requirements. The MEC computing resources can be exploited also by operators and third parties for specific purposes. In this paper, we first describe major use cases and reference scenarios where the MEC is applicable. After that we survey existing concepts integrating MEC functionalities to the mobile networks and discuss current advancement in standardization of the MEC. The core of this survey is, then, focused on user-oriented use case in the MEC, i.e., computation offloading. In this regard, we divide the research on computation offloading to three key areas: 1) decision on computation offloading; 2) allocation of computing resource within the MEC; and 3) mobility management. Finally, we highlight lessons learned in area of the MEC and we discuss open research challenges yet to be addressed in order to fully enjoy potentials offered by the MEC.

1,829 citations

Journal ArticleDOI
TL;DR: The definition of MEC, its advantages, architectures, and application areas are provided; where the security and privacy issues and related existing solutions are also discussed.
Abstract: Mobile edge computing (MEC) is an emergent architecture where cloud computing services are extended to the edge of networks leveraging mobile base stations. As a promising edge technology, it can be applied to mobile, wireless, and wireline scenarios, using software and hardware platforms, located at the network edge in the vicinity of end-users. MEC provides seamless integration of multiple application service providers and vendors toward mobile subscribers, enterprises, and other vertical segments. It is an important component in the 5G architecture which supports variety of innovative applications and services where ultralow latency is required. This paper is aimed to present a comprehensive survey of relevant research and technological developments in the area of MEC. It provides the definition of MEC, its advantages, architectures, and application areas; where we in particular highlight related research and future directions. Finally, security and privacy issues and related existing solutions are also discussed.

1,815 citations

Journal ArticleDOI
TL;DR: In this paper, the authors present a survey of the research on computation offloading in mobile edge computing (MEC), focusing on user-oriented use cases and reference scenarios where the MEC is applicable.
Abstract: Technological evolution of mobile user equipments (UEs), such as smartphones or laptops, goes hand-in-hand with evolution of new mobile applications. However, running computationally demanding applications at the UEs is constrained by limited battery capacity and energy consumption of the UEs. Suitable solution extending the battery life-time of the UEs is to offload the applications demanding huge processing to a conventional centralized cloud (CC). Nevertheless, this option introduces significant execution delay consisting in delivery of the offloaded applications to the cloud and back plus time of the computation at the cloud. Such delay is inconvenient and make the offloading unsuitable for real-time applications. To cope with the delay problem, a new emerging concept, known as mobile edge computing (MEC), has been introduced. The MEC brings computation and storage resources to the edge of mobile network enabling to run the highly demanding applications at the UE while meeting strict delay requirements. The MEC computing resources can be exploited also by operators and third parties for specific purposes. In this paper, we first describe major use cases and reference scenarios where the MEC is applicable. After that we survey existing concepts integrating MEC functionalities to the mobile networks and discuss current advancement in standardization of the MEC. The core of this survey is, then, focused on user-oriented use case in the MEC, i.e., computation offloading. In this regard, we divide the research on computation offloading to three key areas: i) decision on computation offloading, ii) allocation of computing resource within the MEC, and iii) mobility management. Finally, we highlight lessons learned in area of the MEC and we discuss open research challenges yet to be addressed in order to fully enjoy potentials offered by the MEC.

1,759 citations

Journal ArticleDOI
TL;DR: This paper analyzes the MEC reference architecture and main deployment scenarios, which offer multi-tenancy support for application developers, content providers, and third parties, and elaborates further on open research challenges.
Abstract: Multi-access edge computing (MEC) is an emerging ecosystem, which aims at converging telecommunication and IT services, providing a cloud computing platform at the edge of the radio access network MEC offers storage and computational resources at the edge, reducing latency for mobile end users and utilizing more efficiently the mobile backhaul and core networks This paper introduces a survey on MEC and focuses on the fundamental key enabling technologies It elaborates MEC orchestration considering both individual services and a network of MEC platforms supporting mobility, bringing light into the different orchestration deployment options In addition, this paper analyzes the MEC reference architecture and main deployment scenarios, which offer multi-tenancy support for application developers, content providers, and third parties Finally, this paper overviews the current standardization activities and elaborates further on open research challenges

1,351 citations