A QoS Monitor Selection Mechanism for Cellular Data Networks

Ridhawi, Ismaeel Al; Samaan, Nancy; Karmouch, Ahmed

doi:10.1109/glocom.2015.7417185

Cited by 5 publications

(3 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An example of an ontology structure is given in Figure 3. Details in regards to ontology syntax, semantics and the mathematical functions used for measuring semantic similarity and distance can all be found in [38] and is out of the scope of this article. A node's capability score is derived by comparing ontological property descriptions of node 6 and service request 8 as follows:…”

Section: A Node Capabilitesmentioning

confidence: 99%

Providing secure and reliable communication for next generation networks in smart cities

Ridhawi

Otoum

Aloqaily³

et al. 2020

Sustainable Cities and Society

Self Cite

104

View full text Add to dashboard Cite

show abstract

Section: A Node Capabilitesmentioning

confidence: 99%

Providing secure and reliable communication for next generation networks in smart cities

Ridhawi

Otoum

Aloqaily³

et al. 2020

Sustainable Cities and Society

Self Cite

104

View full text Add to dashboard Cite

show abstract

“…Nodes with higher residual power (

p_{residual}^{{M N}_{i}}

) and low power transmission requirements (

p_{transmit}^{{italicM italicN}_{i} \to {MEC}_{j}}

) are more likely to be selected as cache nodes. We assume that residual power is calculated as a percentage score according to McGuire and Al Ridhawi MN i to the MEC node that the MN is communicating with for services (ie, MEC j ).…”

Section: Cache Node Selectionmentioning

confidence: 99%

“…Given that two nodes are within communication distance of each other at previous time points, it is highly likely that those nodes will be within the communication distance at a future time point . Therefore, the relative velocities of the previous time points are considered using a forgetting weight factor (

δ^{t_{k} - i}

), where more significance to recent relative velocity measurements is given.…”

Section: Cache Node Selectionmentioning

confidence: 99%

A collaborative mobile edge computing and user solution for service composition in 5G systems

Ridhawi

Aloqaily

Kotb

et al. 2018

Trans Emerging Tel Tech

Self Cite

View full text Add to dashboard Cite

Mobile edge computing (MEC) is an emergent technology that has revolutionized traditional cloud service solutions. Mobile edge computing extends cloud computing by providing processing, storage, and networking capabilities at the edge of the mobile network. Delay‐sensitive and context‐aware applications are able to execute within close proximity of mobile users. Additionally, today's cloud services are not tailored to user specifications, but rather diversified toward a group of users. To guarantee delivery of user‐specific services in 5G networks, service composition techniques should be incorporated. This article envisions a real‐time, context‐aware, service‐composition collaborative framework that lies at the edge of the network, comprising MEC and user devices for fast composite service delivery. The proposed solution decomposes cloud data into a set of files and services, which are then replicated to MEC nodes. Frequently requested files and services are further cached onto user mobile devices for faster access. Both MEC nodes and mobile users advertise their services onto the collaborative edge/user space, where services are delivered either composite or unrendered according to users' requests. Service composition is achieved through a learning‐based workflow‐net approach that relies on previous composition results to build service composition models to be used for new compositions. The presented solution provides guaranteed and fast delivery of the requested cloud composite services to end users while sustaining QoS requirements and load balancing among edge and mobile nodes.

show abstract