Optimal deployment of mobile cloudlets for mobile applications in edge computing

Achievement of precision measurement is highly desired in a current industrial revolution where a significant increase in living standards increased municipal solid waste. The current industry 4.0 standards require accurate and efficient edge computing sensors towards solid waste classification. Thus, if waste is not managed properly, it would bring about an adverse impact on health, the economy, and the global environment. All stakeholders need to realize their roles and responsibilities for solid waste generation and recycling. To ensure recycling can be successful, the waste should be correctly and efficiently separated. The performance of edge computing devices is directly proportional to computational complexity in the context of nonorganic waste classification. Existing research on waste classification was done using CNN architecture, e.g., AlexNet, which contains about 62,378,344 parameters, and over 729 million floating operations (FLOPs) are required to classify a single image. As a result, it is too heavy and not suitable for computing applications that require inexpensive computational complexities. This research proposes an enhanced lightweight deep learning model for solid waste classification developed using MobileNetV2, efficient for lightweight applications including edge computing devices and other mobile applications. The proposed model outperforms the existing similar models achieving an accuracy of 82.48% and 83.46% with Softmax and support vector machine (SVM) classifiers, respectively. Although MobileNetV2 may provide a lower accuracy if compared to CNN architecture which is larger and heavier, the accuracy is still comparable, and it is more practical for edge computing devices and mobile applications.

Download Full-text

Security and Cost-Aware Computation Offloading via Deep Reinforcement Learning in Mobile Edge Computing

Wireless Communications and Mobile Computing ◽

10.1155/2019/3816237 ◽

2019 ◽

Vol 2019 ◽

pp. 1-20 ◽

Cited By ~ 6

Author(s):

Binbin Huang ◽

Yangyang Li ◽

Zhongjin Li ◽

Linxuan Pan ◽

Shangguang Wang ◽

...

Keyword(s):

Reinforcement Learning ◽

Mobile Devices ◽

Mobile Applications ◽

Mobile User ◽

Edge Computing ◽

Computation Offloading ◽

Security Threats ◽

Mobile Edge Computing ◽

Computing Environment ◽

Markov Decision

With the explosive growth of mobile applications, mobile devices need to be equipped with abundant resources to process massive and complex mobile applications. However, mobile devices are usually resource-constrained due to their physical size. Fortunately, mobile edge computing, which enables mobile devices to offload computation tasks to edge servers with abundant computing resources, can significantly meet the ever-increasing computation demands from mobile applications. Nevertheless, offloading tasks to the edge servers are liable to suffer from external security threats (e.g., snooping and alteration). Aiming at this problem, we propose a security and cost-aware computation offloading (SCACO) strategy for mobile users in mobile edge computing environment, the goal of which is to minimize the overall cost (including mobile device’s energy consumption, processing delay, and task loss probability) under the risk probability constraints. Specifically, we first formulate the computation offloading problem as a Markov decision process (MDP). Then, based on the popular deep reinforcement learning approach, deep Q-network (DQN), the optimal offloading policy for the proposed problem is derived. Finally, extensive experimental results demonstrate that SCACO can achieve the security and cost efficiency for the mobile user in the mobile edge computing environment.

Download Full-text

Mobile Edge Computing

Global Journal of Computer Science and Technology ◽

10.34257/gjcstevol19is2pg29 ◽

2019 ◽

pp. 29-31

Author(s):

Matthew N. O. Sadiku ◽

Chandra M. M. Kotteti ◽

Sarhan M. Musa

Keyword(s):

Mobile Devices ◽

Mobile Applications ◽

Edge Computing ◽

Mobile Edge Computing ◽

Battery Power ◽

And Storage

Mobile applications are becoming increasingly computational intensive, while many mobile devices still have limited battery power and cannot support computational intensive tasks. Mobile edge computing (MEC) computing is an extension of edge computing, and it refers to computing at the edge of a network. In mobile edge computing, computing and storage nodes are placed at the Internet's edge near mobile devices. It places the edge clouds at the candidate locations. This paper presents a brief introduction to MEC.

Download Full-text

Dynamic Service Migration in Ultra-Dense Multi-Access Edge Computing network for High Mobility Scenarios

10.21203/rs.3.rs-27930/v1 ◽

2020 ◽

Author(s):

Haowei Lin ◽

Xiaolong Xu ◽

Juan Zhao ◽

Xinheng Wang

Keyword(s):

Cloud Computing ◽

Mobile Applications ◽

High Mobility ◽

Edge Computing ◽

The Other ◽

Mobile Users ◽

Computing Power ◽

Service Migration ◽

The Stability ◽

Multi Access

Abstract The Multi-Access Edge Computing (MEC) has higher computing power than user equipment and lower latency than remote cloud computing, making new types of services and mobile applications keep emerging. However, the movement of users could induce service migration or interruption in the MEC network. Especially for highly mobile users, they accelerate the frequency of services' migration and handover, impacting on the stability of the total MEC network. In this paper, we propose a hierarchical multi-access edge computing architecture, setting up the Infrastructure for dynamic service migration in the ultra-dense MEC networks. Moreover, we propose a new mechanism for users with high mobility in the ultra-dense MEC network, efficiently arranging service migrations for users with high mobility and ordinary users together. Then, we propose an algorithm for evaluating migrated services to contribute to choose the suitable MEC servers for migrated services. The results show that the proposed mechanism can efficiently arrange service migrations and more quickly restore the services even in the blockage. On the other hand, the proposed algorithm is able to make a supplement to the existing algorithms for selecting MEC servers because it can better reflect the capability of migrated services.

Download Full-text

Multiaccess Edge Computing-Based Simulation as a Service for 5G Mobile Applications: A Case Study of Tollgate Selection for Autonomous Vehicles

Wireless Communications and Mobile Computing ◽

10.1155/2020/9869434 ◽

2020 ◽

Vol 2020 ◽

pp. 1-15

Author(s):

Junhee Lee ◽

Sungjoo Kang ◽

Jaeho Jeon ◽

Ingeol Chun

Keyword(s):

Medical Devices ◽

Mobile Applications ◽

Autonomous Vehicles ◽

Base Station ◽

Edge Computing ◽

Computation Offloading ◽

Cloud Infrastructure ◽

5G Network ◽

Selection For

As the data rate and area capacity are enormously increased with the advent of 5G wireless communication, the network latency becomes a severe issue in a 5G network. Since there are various types of terminals in a 5G network such as vehicles, medical devices, robots, drones, and various sensors which perform complex tasks interacting with other devices dynamically, there is a need to handle heavy computing resource intensive operations. Placing a multiaccess edge computing (MEC) server at the base station, which is located at the edge, can be one of the solutions to it. The application running on the MEC platform needs a specific simulation technique to analyze complex systems inside the MEC network. We proposed and implemented a simulation as a service (SIMaaS) for the MEC platform, which is to offload the simulation using a Cloud infrastructure based on the concept of computation offloading. In the case study, the Monte-Carlo simulations are conducted using the proposed SIMaaS to select the optimal highway tollgate where vehicles are allowed to enter. It shows how clients of the MEC platform use SIMaaS to obtain certain goals.

Download Full-text

A Neuro-Fuzzy Hybrid Framework for Augmenting Resources of Mobile Device

International Journal of Information Technology & Decision Making ◽

10.1142/s0219622021500413 ◽

2021 ◽

pp. 1-37

Author(s):

S. Anitha ◽

T. Padma

Keyword(s):

Energy Consumption ◽

Mobile Devices ◽

Mobile Device ◽

Mobile Applications ◽

Execution Time ◽

Mobile App ◽

Edge Computing ◽

Bandwidth Utilization ◽

Modeling Framework ◽

Neuro Fuzzy

Due to the drastic exploitation of mobile devices and mobile apps in the day-to-day activities of people, the enhancement in hardware and software tools for mobile devices is also rising rapidly to cater to the requirements of mobile users. However, the progress of resource-intensive mobile applications is still inhibited by the limited battery power, restricted memory, and scarce resources of mobile devices. By employing mobile cloud computing, mobile edge computing, and fog computing, many researchers are providing their frameworks and offloading algorithms to augment the resources of mobile devices. In the existing solutions, offloading resource-intensive tasks is adopted only for specific scenarios and also not supporting the flexible exploitation of IoT-based smart mobile applications. So, a novel neuro-fuzzy modeling framework is proposed to augment the inadequate resources of a mobile device by offloading the resource-intensive tasks to external entities, and also a Bat optimization algorithm is exploited to schedule as many tasks as possible to the augmentation entities thereby improving the total execution time of all tasks and minimizing the resource exploitation of the mobile device. In this research work, external augmentation entities like distant cloud, edge cloud, and microcontroller devices are providing Resource augmentation as a Service (RaaS) to mobile devices. An IoT-based smart transport mobile app is implemented based on the proposed framework which depicts a significant reduction in execution time, energy consumption, bandwidth utilization, and average delay. Performance analysis depicts that the neuro-fuzzy hybrid model with Bat optimization provides a significant improvement compared with proximate computing and web service frameworks on the Quality of Service (QoS) parameters namely energy consumption, execution time, bandwidth utilization, and latency. Thus, the proposed framework exhibits a feasible solution of RaaS to resource-constrained mobile devices by exploiting edge computing.

Download Full-text

Parked Vehicle Edge Computing: Exploiting Opportunistic Resources for Distributed Mobile Applications

IEEE Access ◽

10.1109/access.2018.2879578 ◽

2018 ◽

Vol 6 ◽

pp. 66649-66663 ◽

Cited By ~ 11

Author(s):

Xumin Huang ◽

Rong Yu ◽

Jianqi Liu ◽

Lei Shu

Keyword(s):

Mobile Applications ◽

Edge Computing ◽

Distributed Mobile Applications

Download Full-text

Efficient Computation Offloading for Service Workflow of Mobile Applications in Mobile Edge Computing

Mobile Information Systems ◽

10.1155/2021/5578465 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Youwei Yuan ◽

Lu Qian ◽

Gangyong Jia ◽

Longxuan Yu ◽

Zixuan Yu ◽

...

Keyword(s):

Mobile Applications ◽

Blocking Probability ◽

Limited Resource ◽

Edge Computing ◽

Computation Offloading ◽

Efficient Computation ◽

Computing Services ◽

Scheduling Method ◽

Np Hard Problem ◽

Environment Service

Edge computing has become a promising solution to overcome the user equipment (UE) constraints such as low computing capacity and limited energy. A key edge computing challenge is providing computing services with low service congestion and low latency, but the computing resources of edge servers were limited. User task randomness and network inhomogeneity brought considerable challenges to limited-resource MEC systems. To solve these problems, the presented paper proposed a blocking- and delay-aware schedule strategy for MEC environment service workflow offloading. First, the workflow was modeled in mobile applications and the buffer queue in servers. Then, the server collaboration area was divided through a collaboration area division method based on clustering. Finally, an improved particle swarm optimization scheduling method was utilized to solve this NP-hard problem. Many simulation results verified the effectiveness of the proposed scheme. This method was superior to existing methods, which effectively reduces the blocking probability and execution delay and ensures the quality of the experience of the user.

Download Full-text