Solving Critical Events through Mobile Edge Computing: An Approach for Smart Cities

2016 ◽  
Author(s):  
Marco Sapienza ◽  
Ermanno Guardo ◽  
Marco Cavallo ◽  
Giuseppe La Torre ◽  
Guerrino Leombruno ◽  
...  
Keyword(s):  
Smart Cities ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Critical Events

Virtual Mobile Edge Computing Based on IoT Devices Resources in Smart Cities

2020 ◽  
Author(s):  
Mohammed Laroui ◽  
Hatem Ibn Khedher ◽  
Hassine Moungla ◽  
Hossam Afifi ◽  
Ahmed E. Kamal
Keyword(s):  
Smart Cities ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Iot Devices

scholarly journals Spatio-temporal Bayesian Learning for Mobile Edge Computing Resource Planning in Smart Cities

2021 ◽  
Vol 21 (3) ◽  
pp. 1-21
Author(s):  
Laha Ale ◽  
Ning Zhang ◽  
Scott A. King ◽  
Jose Guardiola
Keyword(s):  
Smart City ◽  
Smart Cities ◽  
Resource Planning ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Core Network ◽  
Process Data ◽  
Real World Data ◽  
Spatio Temporal ◽  
Iot Devices

A smart city improves operational efficiency and comfort of living by harnessing techniques such as the Internet of Things (IoT) to collect and process data for decision-making. To better support smart cities, data collected by IoT should be stored and processed appropriately. However, IoT devices are often task-specialized and resource-constrained, and thus, they heavily rely on online resources in terms of computing and storage to accomplish various tasks. Moreover, these cloud-based solutions often centralize the resources and are far away from the end IoTs and cannot respond to users in time due to network congestion when massive numbers of tasks offload through the core network. Therefore, by decentralizing resources spatially close to IoT devices, mobile edge computing (MEC) can reduce latency and improve service quality for a smart city, where service requests can be fulfilled in proximity. As the service demands exhibit spatial-temporal features, deploying MEC servers at optimal locations and allocating MEC resources play an essential role in efficiently meeting service requirements in a smart city. In this regard, it is essential to learn the distribution of resource demands in time and space. In this work, we first propose a spatio-temporal Bayesian hierarchical learning approach to learn and predict the distribution of MEC resource demand over space and time to facilitate MEC deployment and resource management. Second, the proposed model is trained and tested on real-world data, and the results demonstrate that the proposed method can achieve very high accuracy. Third, we demonstrate an application of the proposed method by simulating task offloading. Finally, the simulated results show that resources allocated based upon our models’ predictions are exploited more efficiently than the resources are equally divided into all servers in unobserved areas.

scholarly journals Efficient of Floading Approach for Resource Allocation and Optimization in Mobile Edge Computing Environment

2021 ◽  
Vol 12 (6) ◽  
pp. 2581-2593
Author(s):  
C. Anuradha, M. Ponnavaikko
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Smart Cities ◽  
Edge Computing ◽  
Cloud Services ◽  
Security And Privacy ◽  
The Internet ◽  
Mobile Edge Computing ◽  
Major Barrier

Cloud computing provides a platform for services and resources over the internet for users. The large pool of data resources and services has enabled the emergence of several novel applications such as smart grids, smart environments, and virtual reality. However, the state-of-the-art of cloud computing faces a delay constraint, which becomes a major barrier for reliable cloud services. This constraint is mostly highlighted in the case of smart cities (SC) and the Internet of Things (IoT). Therefore, the recent cloud computing paradigm has poor performance and cannot meet the low delay, navigation, and mobility support requirements.Machine-to-machine (M2M) connectivity has drawn considerable interest from both academia and industry with a growing number of machine-type communication devices (MTCDs). The data links with M2M communications are usually small but high bandwidth, unlike conventional networking networks, demanding performance management of both energy consumption and computing. The main challenges faced in mobile edge computing are task offloading, congestion control, Resource allocation, security and privacy issue, mobility and standardization .Our work mainly focus on offloading based resource allocation and security issues by analyzing the network parameters like reduction of latency and improvisation of bandwidth involved in cloud environment. The cloudsim simulation tool has been utilized to implement the offload balancing mechanism to decrease the energy consumption and optimize the computing resource allocation as well as improve computing capability.

Mobile Edge Computing for Content Distribution and Mobility Support in Smart Cities

2021 ◽  
pp. 473-500
Author(s):  
Pedro F. do Prado ◽  
Maycon L. M. Peixoto ◽  
Marcelo C. Araújo ◽  
Eduardo S. Gama ◽  
Diogo M. Gonçalves ◽  
...  
Keyword(s):  
Content Distribution ◽  
Smart Cities ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Mobility Support

scholarly journals Exploiting Mobile Edge Computing for Enhancing Vehicular Applications in Smart Cities

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1073 ◽  
Author(s):  
Hesham El-Sayed ◽  
Moumena Chaqfeh
Keyword(s):  
Traffic Safety ◽  
Intelligent Transportation Systems ◽  
Vehicular Networks ◽  
Data Dissemination ◽  
Smart Cities ◽  
Transportation Systems ◽  
Edge Computing ◽  
Communication Overhead ◽  
Mobile Edge Computing ◽  
Communication Frameworks

Mobile edge computing (MEC) has been recently proposed to bring computing capabilities closer to mobile endpoints, with the aim of providing low latency and real-time access to network information via applications and services. Several attempts have been made to integrate MEC in intelligent transportation systems (ITS), including new architectures, communication frameworks, deployment strategies and applications. In this paper, we explore existing architecture proposals for integrating MEC in vehicular environments, which would allow the evolution of the next generation ITS in smart cities. Moreover, we classify the desired applications into four major categories. We rely on a MEC architecture with three layers to propose a data dissemination protocol, which can be utilized by traffic safety and travel convenience applications in vehicular networks. Furthermore, we provide a simulation-based prototype to evaluate the performance of our protocol. Simulation results show that our proposed protocol can significantly improve the performance of data dissemination in terms of data delivery, communication overhead and delay. In addition, we highlight challenges and open issues to integrate MEC in vehicular networking environments for further research.

Load Balancing for Energy-Harvesting Mobile Edge Computing

2021 ◽  
Vol E104.A (1) ◽  
pp. 336-342
Author(s):  
Ping ZHAO ◽  
Jiawei TAO ◽  
Abdul RAUF ◽  
Fengde JIA ◽  
Longting XU
Keyword(s):  
Energy Harvesting ◽  
Load Balancing ◽  
Edge Computing ◽  
Mobile Edge Computing

Computation Offloading Game in Multiple UAV-Enabled Mobile Edge Computing Networks

2020 ◽  
Author(s):  
Yanling Ren ◽  
Zhibin Xie ◽  
Zhenfeng Ding ◽  
xiyuan sun ◽  
Jie Xia ◽  
...  
Keyword(s):  
Edge Computing ◽  
Computation Offloading ◽  
Mobile Edge Computing
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