A Mobile-assisted Edge Computing Framework for Emerging IoT Applications

2021 ◽  
Vol 17 (4) ◽  
pp. 1-24
Author(s):  
Deke Guo ◽  
Siyuan Gu ◽  
Junjie Xie ◽  
Lailong Luo ◽  
Xueshan Luo ◽  
...  
Keyword(s):  
Profit Maximization ◽  
Edge Computing ◽  
Task Completion ◽  
Service Provisioning ◽  
Specific Service ◽  
Peak Period ◽  
Auction Mechanism ◽  
Iot Applications ◽  
Delay Sensitive ◽  
Computing Framework

Edge computing (EC) is a promising paradigm for providing ultra-low latency experience for IoT applications at the network edge, through pre-caching required services in fixed edge nodes. However, the supply-demand mismatch can arise while meeting the peak period of some specific service requests. The mismatch between capacity provision and user demands can be fatal to the delay-sensitive user requests of emerging IoT applications and will be further exacerbated due to the long service provisioning cycle. To tackle this problem, we propose the mobile-assisted edge computing framework to improve the QoS of fixed edge nodes by exploiting mobile edge nodes. Furthermore, we devise a CRI (Credible, Reciprocal, and Incentive) auction mechanism to stimulate mobile edge nodes to participate in the services for user requests. The advantages of our mobile-assisted edge computing framework include higher task completion rate, profit maximization, and computational efficiency. Meanwhile, the theoretical analysis and experimental results guarantee the desirable economic properties of our CRI auction mechanism.

Two-tier end-edge collaborative computation offloading for edge computing

2021 ◽  
pp. 1-12
Author(s):  
Yong Xiao ◽  
Ling Wei ◽  
Junhao Feng ◽  
Wang En
Keyword(s):  
Computing System ◽  
End Users ◽  
Edge Computing ◽  
Computation Offloading ◽  
Iot Applications ◽  
Delay Sensitive ◽  
Task Processing ◽  
Queue Lengths ◽  
The Stability ◽  
The Cost

Edge computing has emerged for meeting the ever-increasing computation demands from delay-sensitive Internet of Things (IoT) applications. However, the computing capability of an edge device, including a computing-enabled end user and an edge server, is insufficient to support massive amounts of tasks generated from IoT applications. In this paper, we aim to propose a two-tier end-edge collaborative computation offloading policy to support as much as possible computation-intensive tasks while making the edge computing system strongly stable. We formulate the two-tier end-edge collaborative offloading problem with the objective of minimizing the task processing and offloading cost constrained to the stability of queue lengths of end users and edge servers. We perform analysis of the Lyapunov drift-plus-penalty properties of the problem. Then, a cost-aware computation offloading (CACO) algorithm is proposed to find out optimal two-tier offloading decisions so as to minimize the cost while making the edge computing system stable. Our simulation results show that the proposed CACO outperforms the benchmarked algorithms, especially under various number of end users and edge servers.

scholarly journals DESIGN AND DEVELOPMENT AI-ENABLED EDGE COMPUTING FOR INTELLIGENT-IOT APPLICATIONS

2019 ◽  
Vol 2019 (02) ◽  
pp. 84-94 ◽  
Author(s):  
Dr. Sivaganesan D.
Keyword(s):  
Artificial Intelligence ◽  
Internet Of Things ◽  
Edge Computing ◽  
The Internet ◽  
Service Provisioning ◽  
Iot Applications ◽  
The People ◽  
Smart Farming ◽  
The Internet Of Things ◽  
Day By Day

The advancements in the technologies and the increase in the digital miniaturization day by day are causing devices to become smarter and smarter and the emergence of the internet of things and the cloud has made things even better with insightful suggestions for organization as well as the way the people work and lead their life. The limitations in the cloud paradigm in terms of processing complexity, the latency in the service provisioning and improper resource scheduling, remains as a reason leading to shifting of applications from cloud to edge. More over the emergence of the artificial intelligence in the edge computing has turned out to be center of attention as it improves the speed and the range of the IOT applications. The paper also puts forth the design of the AI-enabled Edge computing for developing a Smart Farming.

Service Provisioning for Multi-source IoT Applications in Mobile Edge Computing

2022 ◽  
Vol 18 (2) ◽  
pp. 1-25
Author(s):  
Jing Li ◽  
Weifa Liang ◽  
Zichuan Xu ◽  
Xiaohua Jia ◽  
Wanlei Zhou
Keyword(s):  
Minimization Problem ◽  
Optimization Problems ◽  
Cost Minimization ◽  
Edge Computing ◽  
Service Provisioning ◽  
Mobile Edge Computing ◽  
Iot Applications ◽  
Iot Devices ◽  
Cost Minimization Problem ◽  
The Cost

We are embracing an era of Internet of Things (IoT). The latency brought by unstable wireless networks caused by limited resources of IoT devices seriously impacts the quality of services of users, particularly the service delay they experienced. Mobile Edge Computing (MEC) technology provides promising solutions to delay-sensitive IoT applications, where cloudlets (edge servers) are co-located with wireless access points in the proximity of IoT devices. The service response latency for IoT applications can be significantly shortened due to that their data processing can be performed in a local MEC network. Meanwhile, most IoT applications usually impose Service Function Chain (SFC) enforcement on their data transmission, where each data packet from its source gateway of an IoT device to the destination (a cloudlet) of the IoT application must pass through each Virtual Network Function (VNF) in the SFC in an MEC network. However, little attention has been paid on such a service provisioning of multi-source IoT applications in an MEC network with SFC enforcement. In this article, we study service provisioning in an MEC network for multi-source IoT applications with SFC requirements and aiming at minimizing the cost of such service provisioning, where each IoT application has multiple data streams from different sources to be uploaded to a location (cloudlet) in the MEC network for aggregation, processing, and storage purposes. To this end, we first formulate two novel optimization problems: the cost minimization problem of service provisioning for a single multi-source IoT application, and the service provisioning problem for a set of multi-source IoT applications, respectively, and show that both problems are NP-hard. Second, we propose a service provisioning framework in the MEC network for multi-source IoT applications that consists of uploading stream data from multiple sources of the IoT application to the MEC network, data stream aggregation and routing through the VNF instance placement and sharing, and workload balancing among cloudlets. Third, we devise an efficient algorithm for the cost minimization problem built upon the proposed service provisioning framework, and further extend the solution for the service provisioning problem of a set of multi-source IoT applications. We finally evaluate the performance of the proposed algorithms through experimental simulations. Simulation results demonstrate that the proposed algorithms are promising.

EdgeLSTM: Towards Deep and Sequential Edge Computing for IoT Applications

2021 ◽  
pp. 1-14
Author(s):  
Di Wu ◽  
He Xu ◽  
Zhongkai Jiang ◽  
Weiren Yu ◽  
Xuetao Wei ◽  
...  
Keyword(s):  
Edge Computing ◽  
Iot Applications
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