3rd International Workshop on Edge Computing and Artificial Intelligence based Sensor-Cloud System

Session ECAISS


10:30 AM — 12:00 PM HKT
Dec 13 Tue, 9:30 PM — 11:00 PM EST

An Adaptive Data Rate-Based Task Offloading Scheme in Vehicular Networks

Chaofan Chen, Wendi Nie, Yaoxin Duan, Victor C.S. Lee, Kai Liu and Huamin Li

As an important application of Internet of Things (IoT), Internet of Vehicles (IoVs) can provide various valuable services which may require computation-intensive tasks under strict time constraints. Most traditional vehicles may not be able to process all these computation-intensive tasks locally because of the limitation of computing resources. Therefore, task offloading has been proposed, which allows vehicles to offload computation- intensive tasks to Mobile Edge Computing (MEC) servers. With the arising and development of intelligent vehicles, the concept of Vehicle as a Resource (VaaR) has been proposed as an important supplement to MEC, which enables intelligent vehicles to share computation resources with nearby vehicles. Most studies in VaaR generally assume that the transmission data rate of offloading tasks from one vehicle to another is fixed. However, in VaaR, due to the high mobility of vehicles, the communication distance between vehicles may change over time, resulting in changing data rate. Therefore, it is challenging to make offloading decisions (i.e., selecting proper vehicles as computation resource providers) while considering adaptive data rate. In this paper, we study task offloading in vehicular networks while considering adaptive data rate. We propose an Adaptive Data Rate-based Offloading algorithm named ADRO, which can not only achieve minimum energy consumption while satisfying time constraints, but also take adaptive data rate into consideration. Compre- hensive experiments have been conducted to demonstrate the efficiency of the ADRO algorithm.

HCA Operator: A Hybrid Cloud Auto-scaling Tooling for Microservice Workloads

Y uyang Wang, Fan Zhang, Samee U. Khan

Elastic cloud platform, e.g. Kubernetes, enables dy- namically scale in or out computing resources in accordance with the workloads fluctuation. As the cloud evolves to hybrid, where public and private clouds co-exist as the underline substrate, autoscaling applications within a hybrid cloud is no longer straightforward. The difficulty lies in all aspects, e.g. global load balancing, hybrid-cloud monitoring and alerting, storage sharing and replication, security and privacy, etc. However, it will significantly pay off if hybrid-cloud autoscaling is supported and boundless computing resources can be utilized per request. In this paper, we design Hybrid Cloud Autoscaler Operator (HCA Operator), a customized Kubernetes Controller that leverages the Kubernetes Custom Resource to auto-scale microservice applications across hybrid clouds. HCA Operator load balances across hybrid clouds, monitors metrics, and autoscales to des- tination clusters that exist in other clouds. We discuss the implementation details and perform experiments in a hybrid cloud environment. The experimental results demonstrate that if the workload changes quickly, our Operator can properly auto- scale the microservice applications across hybrid cloud in order to meet the Service Level Agreement (SLA) requirements.

Multi-UAV Joint Observation, Communication, and Policy in MEC

Shuai Liu, Y uebin Bai

The use of multi-agent reinforcement learning meth- ods (MARL) in mobile edge computing (MEC) environments enables multiple unmanned aerial vehicles (multi-UA V) to in- telligently provide relay or computational offloading services to mission targets. UA V?€?s observation range and communication methods between UA Vs have a significant impact on multi-UA V collaboration strategy. For this purpose, we study the multi- UA V observation range dynamic control method and the optimal inter-UA V communication method. Our approach is to design a multi-UA V joint observation, communication, policy, and service collaboration protocol and study the optimization method of the protocol. We propose an expert-guided deep reinforcement learn- ing framework to optimize this protocol. Each UA V?€?s optimal radar observation range and inter-UA V communication method are learned using an information entropy value decomposition method. Through our observation and communication method, multi-UA V are able to obtain the most valuable information. Experiments demonstrate that our method can improve MEC?€?s service coverage by 9.38%-21.88% compared to the classical MARL algorithm. Our method improves the radar observation efficiency and communication efficiency by 3.05%-38.9% and 8.55%-22.03%, respectively. The results show that this method improves multi-UA V energy utilization.

Federated Learning for Heterogeneous Mobile Edge Device:A Client Selection Game

Tongfei Liu, Hui Wang, Maode Ma

In the federated learning (FL) paradigm, edge devices use local datasets to participate in machine learning model training, and servers are responsible for aggregating and maintaining public models. FL cannot only solve the bandwidth limitation problem of centralized training, but also protect data privacy. However, it is difficult for heterogeneous edge devices to obtain optimal learning performance due to limited computing and communication resources. Specifically, in each round of the global aggregation process by the FL, clients in a ?€?strong group?€? have a greater chance to contribute their own local training results, while those clients in a ?€?weak group?€? have a low opportunity to participate, resulting in a negative impact on the final training result. In this paper, we consider a federated learning multi-client selection (FL-MCS) problem, which is an NP-hard problem. To find the optimal solution, we model the FL global aggregation process for clients participation as a potential game. In this game, each client will selfishly decide whether to participate in the FL global aggregation process based on its efforts and rewards. By the potential game, we prove that the competition among clients eventually reaches a stationary state, i.e. the Nash equilibrium point. We also design a distributed heuristic FL multi-client selection algorithm to achieve the maximum reward for the client in a finite number of iterations. Extensive numerical experiments prove the effectiveness of the algorithm.

Learning-based Computation Offloading in LEO Satellite Networks

Juan Luo, Quanwei Fu, Fan Li, Ying Qiao, Ruoyu Xiao

Satellite networks can provide network coverage in remote areas without terrestrial infrastructure and offer ground users an offload option. However, using satellite networks to provide computation offload services requires consideration not only of the dynamics of the satellite system, but also of how ground users offload tasks and how the limited resources of the satellite are allocated. Therefore, in this paper, we propose a computation offloading algorithm based on the optimal allocation of satellite resources (CO-SROA) and formulate an objective function to minimize the delay and energy consumption for ground users to process the computation tasks. The algorithm decomposes the optimization problem into two subproblems. One is the optimal allocation of satellite resources with determinate offloading decisions in a single time slot, which is solved based on the Lagrange multiplier method. The other is the long-term user offloading decision problem, which is solved by formulating it as a Markovian decision process and using a deep reinforcement learning (DRL) algorithm. Simulation results show that the CO- SROA can achieve better long-term returns in terms of delay and energy consumption.

The Short-Term Passenger Flow Prediction Method for Urban Rail Transit Based on CNN-LSTM with Attention Mechanism

Yang Liu, Chen Mu, Pingping Zhou

This paper studies the short-term passenger flow prediction of urban rail transit for optimally adjusting the real- time departure of rail trains. Aiming at the problem that the traditional deep learning model does not consider the spatial- temporal information enough, the short-term passenger flow prediction model of urban rail transit based on CNN-LSTM with attention mechanism is proposed. Firstly, the stations are divided into seven categories according to the significant difference of daily passenger flow in urban rail stations so as to further analyze the distribution pattern of daily inbound and outbound passenger flow in different categories of stations; secondly, the short sequence feature abstraction ability of CNN is used to extract the spatial characteristics of historical passenger flow in each time period in different categories of stations; finally, the attention mechanism is used to assign different weights to the extracted characteristic information, and the temporal characteristic information is obtained from the LSTM comprehensive short- term sequence to realize the short-term passenger flow prediction of urban rail transit. Experiments show that the prediction model has the encouraging prediction performance and accuracy.

Linguistic-Enhanced Transformer with CTC Embedding for Speech Recognition

Xulong Zhang, Jianzong Wang, Ning Cheng, Mengyuan Zhao, Zhiyong Zhang, Jing Xiao

The recent emergence of joint CTC-Attention model shows significant improvement in automatic speech recognition (ASR). The improvement largely lies in the modeling of linguistic information by decoder. The decoder joint-optimized with an acoustic encoder renders the language model from ground- truth sequences in an auto-regressive manner during training. However, the training corpus of the decoder is limited to the speech transcriptions, which is far less than the corpus needed to train an acceptable language model. This leads to poor robustness of decoder. To alleviate this problem, we propose linguistic-enhanced transformer, which introduces refined CTC information to decoder during training process, so that the decoder can be more robust. Our experiments on AISHELL- 1 speech corpus show that the character error rate (CER) is relatively reduced by up to 7%. We also find that in joint CTC- Attention ASR model, decoder is more sensitive to linguistic information than acoustic information.

Viewing Flowers at their Most Beautiful Moments: A Crowd Sensing Application

Weifeng Xiong, Fangwan Huang, Zhiyong Yu, Xianwei Guo, Binwei Lin, Qiquan Cai

To assist people's itinerary planning for viewing flowers, it is very meaningful to visualize the different stages of specific flowers with high spatio-temporal resolution. To achieve this goal, this paper realized a crowdsensing application called Hanami, which means ?€?flower viewing?€?. The implementation of this application contains three modules: data sensing, flower classifier, and visualization. Particularly, the flower classification module utilized a residual network to identify the types and stages of flowers from crowdsensed photos. For the visualization module, a bilayer clustering view method was designed to aggregate the points on the map, which can be further clustered by different features of flowers. Experimental evaluation showed that Hanami can help users view flowers at their most beautiful moments.

Lightweight YOLOV4 algorithm for underwater whale detection

Lili He, Defeng Du, Hongtao Bai, Kai Wang

At present, it is difficult to implement on-line detection on underwater equipment due to the large model of biometric algorithm. In this paper, a YOLOv4 lightweight whale detection algorithm suitable for embedded equipment is proposed. MobileNetv3 was used as the backbone network of YOLOv4 to reduce the network scale, and the neck and head network were optimized by Depthwise Separable Convolutional to achieve lightweight feature extraction. Experimental results on whale data set show that compared with YOLOv4 algorithm, the number of network parameters is reduced by 87.2%, and the detection speed is improved by 1.65 times under GPU-only and 12.56 times under CPU-only. The method presented in this paper can theoretically implement underwater whale on-line detection in embedded devices.

Anti-jamming Channel Allocation in UAV-Enabled Edge Computing: A Stackelberg Game Approach

Y uan Xinwang, Xie Zhidong, Tan Xin

In edge computing, terminal devices can offload intensive computing tasks to edge servers to obtain high- performance computing services with low latency. Introducing unmanned aerial vehicles (UA Vs) as relays can improve transmis- sion efficiency. However, with the continuous expansion of UA Vs?€? scale, resource management issues need to be solved urgently. In this paper, we study the anti-jamming channel assignment problem in the presence of multiple intelligent jammers for UA V- enabled edge computing scenarios. Each user is an offload-receive (or offload-relay-receive) pair of a computing task. Considering the mutual interference between users and the malicious jamming from smart jammers, we construct a multi-layer Stackelberg game model: the jammer is the leader, and the user is the follower. The user and the jammer consider their utility respectively, achieve the equilibrium state through iterations, and then obtain the optimal channel access scheme. Finally, we analyze the convergence and superiority of the proposed algorithm through simulation and performance comparison.

Session Chair

Pengfei Wang, Dalian University of Technology, China

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