DVFO: Learning-Based DVFS for Energy-Efficient Edge-Cloud Collaborative Inference
Due to limited resources on edge and different characteristics of deep neural network (DNN) models, it is a big challenge to optimize DNN inference performance in terms of energy consumption and end-to-end latency. In addition to dynamic voltage frequency ...
Distributed DNN Inference With Fine-Grained Model Partitioning in Mobile Edge Computing Networks
Model partitioning is a promising technique for improving the efficiency of distributed inference by executing partial deep neural network (DNN) models on edge servers (ESs) or Internet-of-Things (IoT) devices. However, due to heterogeneous resources of ...
A Chebyshev Polynomial-Based Authentication Scheme Using Blockchain Technology for Fog-Based Vehicular Network
The increasing number of vehicles has resulted in a tremendous growth of data in vehicular communications. Cloud-based models are inefficient for handling this large data due to high latency and bandwidth requirements. To address this, fog computing-based ...
Efficient Deployment of Partial Parallelized Service Function Chains in CPU+DPU-Based Heterogeneous NFV Platforms
The introduction of network function virtualization (NFV) leads to service function chain (SFC) deployment problems, promoting the idea of composing network services as virtualized network functions (VNFs). Meanwhile, the rapid development of edge ...
Finite SNR Diversity-Multiplexing Trade-Off in Hybrid ABCom/RCom-Assisted NOMA Networks
The upcoming sixth generation (6G) driven Internet-of-Things (IoT) will face the great challenges of extremely low power demand, high transmission reliability and massive connectivities. To meet these requirements, we propose a novel hybrid ambient ...
Wi-Fi-Based Indoor Localization With Interval Random Analysis and Improved Particle Swarm Optimization
The rise of the Internet of Things has spurred the growth of wireless applications, particularly Wi-Fi-based indoor localization, which is gaining prominence owing to its cost-effectiveness. Nevertheless, the accuracy of Wi-Fi-based indoor localization is ...
Mobility-Aware Computation Offloading in Satellite Edge Computing Networks
Satellite edge computing, as an extension of ground edge computing, is a key technology for achieving seamless global computing coverage. However, the low earth orbit (LEO) satellites have limited computing resources and are moving at a high speed. This ...
Colosseum as a Digital Twin: Bridging Real-World Experimentation and Wireless Network Emulation
- Davide Villa,
- Miead Tehrani-Moayyed,
- Clifton Paul Robinson,
- Leonardo Bonati,
- Pedram Johari,
- Michele Polese,
- Tommaso Melodia
Wireless network emulators are being increasingly used for developing and evaluating new solutions for Next Generation (NextG) wireless networks. However, the reliability of the solutions tested on emulation platforms heavily depends on the precision of ...
Multi-Access Edge Computing for Real-Time Applications With Sporadic DAG Tasks – A Graphical Game Approach
We consider a multi-operator multi-access edge computing (MEC) network for applications with dependent tasks. Each task includes jobs executed based on logical precedence modelled as a directed acyclic graph, where each vertex is a job, each edge – ...
LoRa Meets IP: A Container-Based Architecture to Virtualize LoRaWAN End Nodes
In this work, a container-based architecture for the integration of Long Range Wide Area Network (LoRaWAN) end nodes—e.g., used to monitor industrial machines or mobile entities in specific environments—with Internet Protocol (IP)-based ...
Online and Predictive Coordinated Cloud-Edge Scrubbing for DDoS Mitigation
To mitigate Distributed Denial-of-Service (DDoS) attacks towards enterprise networks, we study the problem of scheduling DDoS traffic through on-premises scrubbing at the local edge and on-demand scrubbing in the remote clouds. We model this problem as a ...
Efficient Parallel Split Learning Over Resource-Constrained Wireless Edge Networks
The increasingly deeper neural networks hinder the democratization of privacy-enhancing distributed learning, such as federated learning (FL), to resource-constrained devices. To overcome this challenge, in this paper, we advocate the integration of edge ...
A Blockchain-Empowered Incentive Mechanism for Cross-Silo Federated Learning
In cross-silo federated learning (FL), organizations cooperatively train a global model with their local datasets. However, some organizations may act as free riders such that they only contribute a small amount of resources but can obtain a high-accuracy ...
Spatial and Temporal Detection With Attention for Real-Time Video Analytics at Edges
The detection of objects via neural networks plays a key role in various video analytics, but consumes huge resources. Due to the limited computing capability at edges, such real-time detections should be precisely used for the objects that need the most ...
Dependency-Aware Task Reconfiguration and Offloading in Multi-Access Edge Cloud Networks
Multi-access Edge Cloud (MEC) networks are powerful for providing emerging computation-intensive and latency-sensitive applications with low latency leveraging ubiquitous edge devices. These networks enable complex applications to be split into multiple ...
Towards Intelligent Adaptive Edge Caching Using Deep Reinforcement Learning
The tremendous expansion of edge data traffic poses great challenges to network bandwidth and service responsiveness for mobile computing. Edge caching has emerged as a promising method to alleviate these issues by storing a portion of data at the network ...
Familiar Paths are the Best: Incentive Mechanism Based on Path-Dependence Considering Space-Time Coverage in Crowdsensing
Location Dependent Mobile Crowdsensing (LDMC) often needs to collect data at different time points in various regions to ensure the coverage of sensing data. An incentive mechanism is needed to encourage participants to move to sparse areas and improve ...
QoS-Aware Content Delivery in 5G-Enabled Edge Computing: Learning-Based Approaches
The increasing demand for high-volume multimedia services through mobile user equipment (UEs) has imposed a significant burden on mobile networks. To cope with this growth in demand, it is necessary to extend the 5G network's ability to meet ...
Game Analysis and Incentive Mechanism Design for Differentially Private Cross-Silo Federated Learning
Cross-silo federated learning (FL) is a distributed learning method where clients collaboratively train a global model without exchanging local data. However, recent works reveal that potential privacy leakage occurs when clients upload their local ...
Feature Matching Data Synthesis for Non-IID Federated Learning
Federated learning (FL) has emerged as a privacy-preserving paradigm that trains neural networks on edge devices without collecting data at a central server. However, FL encounters an inherent challenge in dealing with non-independent and identically ...
FedCache: A Knowledge Cache-Driven Federated Learning Architecture for Personalized Edge Intelligence
Edge Intelligence (EI) allows Artificial Intelligence (AI) applications to run at the edge, where data analysis and decision-making can be performed in real-time and close to data sources. To protect data privacy and unify data silos distributed among end ...
Adversarial Bandits With Multi-User Delayed Feedback: Theory and Application
The multi-armed bandit (MAB) models have attracted significant research attention due to their applicability and effectiveness in various real-world scenarios such as resource allocation in uncertain environments, online advertising, and dynamic pricing. ...
PtrTasking: Pointer Network Based Task Scheduling for Multi-Connectivity Enabled MEC Services
Interactive services of mobile edge computing demand low latency in task handling, which cannot be easily satisfied due to limited per-user computing power on an edge server. Fortunately, a user can establish links with multiple base stations and the co-...
Bandwidth-Efficient Mobile Volumetric Video Streaming by Exploiting Inter-Frame Correlation
Volumetric videos offer viewers more immersive experiences, enabling a variety of applications. However, state-of-the-art streaming systems still need hundreds of Mbps bandwidth to transmit volumetric videos, exceeding the common bandwidth capabilities of ...
CPPer-FL: Clustered Parallel Training for Efficient Personalized Federated Learning
In this paper, a clustered parallel training algorithm is designed for personalized federated learning (Per-FL), called CPPer-FL. CPPer-FL improves the communication and training efficiency of Per-FL from two perspectives, namely, less burden for the ...
MoEI: Mobility-Aware Edge Inference Based on Model Partition and Service Migration
Deep neural networks are the cornerstone of many mobile intelligent systems, and their inference processes bring about computation-intensive tasks. Device-edge cooperative inference in mobile edge computing provides a fine-grained processing method to ...
PPRP: Preserving Location Privacy for Range-Based Positioning in Mobile Networks
In this paper, we propose a privacy-preserving range-based positioning scheme, named PPRP, which can preserve the location privacy of both user equipment (UE) and anchors (ACs) in mobile networks. Specifically, PPRP is established on a decentralized trust-...
AoI-Guaranteed Bandit: Information Gathering Over Unreliable Channels
In many IoT applications, information needs to be gathered from multiple heterogeneous sources to the base station for real-time processing and follow-up actions. Undoubtedly, information freshness, measured by age of information (AoI), is critical in ...
REWAFL: Residual Energy and Wireless Aware Participant Selection for Efficient Federated Learning Over Mobile Devices
Participant selection (PS) helps to accelerate federated learning (FL) convergence, which is essential for the practical deployment of FL over mobile devices. While most existing PS approaches focus on improving training accuracy and efficiency rather ...
An Adaptive Cooperative Caching Strategy for Vehicular Networks
Edge caching has emerged as an effective solution to the challenges posed by massive content delivery in the vehicular network. In vehicular networks, vehicles and roadside units (RSUs) can serve as intermediate relays with caching capabilities. However, ...