Sameh Sorour
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View article: Spatiotemporal Analysis of Parallelized Computing at the Extreme Edge
Spatiotemporal Analysis of Parallelized Computing at the Extreme Edge Open
Extreme Edge Computing (EEC) pushes computing even closer to end users than traditional Multi-access Edge Computing (MEC), harnessing the idle resources of Extreme Edge Devices (EEDs) to enable low-latency, distributed processing. However,…
View article: Motivating Learners in Multiorchestrator Mobile Edge Learning: A Stackelberg Game Approach
Motivating Learners in Multiorchestrator Mobile Edge Learning: A Stackelberg Game Approach Open
Mobile Edge Learning (MEL) is a learning paradigm that enables distributed training of Machine Learning models over heterogeneous edge devices (e.g., IoT devices). Multi-orchestrator MEL refers to the coexistence of multiple learning tasks…
View article: Motivating Learners in Multi-Orchestrator Mobile Edge Learning: A\n Stackelberg Game Approach
Motivating Learners in Multi-Orchestrator Mobile Edge Learning: A\n Stackelberg Game Approach Open
Mobile Edge Learning (MEL) is a learning paradigm that enables distributed\ntraining of Machine Learning models over heterogeneous edge devices (e.g., IoT\ndevices). Multi-orchestrator MEL refers to the coexistence of multiple learning\nta…
View article: Energy-Efficient Multi-Orchestrator Mobile Edge Learning
Energy-Efficient Multi-Orchestrator Mobile Edge Learning Open
Mobile Edge Learning (MEL) is a collaborative learning paradigm that features distributed training of Machine Learning (ML) models over edge devices (e.g., IoT devices). In MEL, possible coexistence of multiple learning tasks with differen…
View article: Fusion of Airborne and Terrestrial Sensed Data for Real-time Monitoring of Traffic Networks
Fusion of Airborne and Terrestrial Sensed Data for Real-time Monitoring of Traffic Networks Open
The data is organized in three data sets. Data set 1: includes the LIDAR point cloud data in LAS format, data set 2: includes the LIDAR point cloud data in ASCII data format , data set 3: include the trajectory data organized in CSV file f…
View article: HIERARCHICAL PRIORITY–BASED CONTROL OF SIGNALIZED INTERSECTIONS IN SEMI-CONNECTED CORRIDORS
HIERARCHICAL PRIORITY–BASED CONTROL OF SIGNALIZED INTERSECTIONS IN SEMI-CONNECTED CORRIDORS Open
In this project, we developed an efficient distributed yet coordinated algorithm to control signalized intersections in connected and semi-connected corridors. The research enhances traffic signal optimization formulations to allow for the…
View article: Task Allocation for Asynchronous Mobile Edge Learning with Delay and Energy Constraints
Task Allocation for Asynchronous Mobile Edge Learning with Delay and Energy Constraints Open
This paper extends the paradigm of "mobile edge learning (MEL)" by designing an optimal task allocation scheme for training a machine learning model in an asynchronous manner across mutiple edge nodes or learners connected via a resource-c…
View article: Decentralized Autonomous EV Mobility Meta Data
Decentralized Autonomous EV Mobility Meta Data Open
The data is organized in four sets: data aet 1: includes the dispatching optimization, data set 2: includes data for testing average trip time, data set 3: include the dimensioning analysis data and simulation, and data set 4 includes the …
View article: PDR and SSRI Contour Data & Regression Data
PDR and SSRI Contour Data & Regression Data Open
The data set includes field collected Packet delivery rate (PDR) and Received signal strength indication (RSSI) data that were used for two analysis: development of DSCR communication contour lines at intersection and regression analysis t…
View article: Returning the Favor: What Wireless Networking Can Offer to AI and Edge Learning
Returning the Favor: What Wireless Networking Can Offer to AI and Edge Learning Open
Machine learning (ML) and artificial intelligence (AI) have recently made a significant impact on improving the operations of wireless networks and establishing intelligence at the edge. In return, rare efforts were made to explore how ada…
View article: Jointly Optimizing Dataset Size and Local Updates in Heterogeneous Mobile Edge Learning
Jointly Optimizing Dataset Size and Local Updates in Heterogeneous Mobile Edge Learning Open
This paper proposes to maximize the accuracy of a distributed machine learning (ML) model trained on learners connected via the resource-constrained wireless edge. We jointly optimize the number of local/global updates and the task size al…
View article: Decrasing_n7_compare.fig
Decrasing_n7_compare.fig Open
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View article: main2-2.m
main2-2.m Open
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View article: North_003.tab
North_003.tab Open
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View article: myfunction.m
myfunction.m Open
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View article: Gaussiandist.m
Gaussiandist.m Open
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View article: Conditions.m
Conditions.m Open
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View article: Energy-Aware Cross-Layer Offloading in Fog-RANs Using Network Coded Device Cooperation
Energy-Aware Cross-Layer Offloading in Fog-RANs Using Network Coded Device Cooperation Open
This paper studies a Fog Radio Access Network (F-RAN) architecture that utilises the increasing storage and device-to-device communication capacities of users' smart devices (referred to as F-UEs) in order to reduce the time that the centr…
View article: West_002.tab
West_002.tab Open
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View article: Test_data.tab
Test_data.tab Open
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View article: threeD_GG.fig
threeD_GG.fig Open
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View article: Decrasing_n7_compare.pdf
Decrasing_n7_compare.pdf Open
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View article: Conditions-1.m
Conditions-1.m Open
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View article: merged.eps
merged.eps Open
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View article: threeD_DG_GG_Compare.fig
threeD_DG_GG_Compare.fig Open
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View article: parameterfun-1.m
parameterfun-1.m Open
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View article: VaryCharging.fig
VaryCharging.fig Open
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View article: South_003.tab
South_003.tab Open
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View article: blackout-1.png
blackout-1.png Open
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View article: special case Gaussian T.fig
special case Gaussian T.fig Open
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