Introduction to Mobile Edge Computing

Mukherjee, Anwesha; De, Debashis; Ghosh, Soumya K.; Buyya, Rajkumar

doi:10.1007/978-3-030-69893-5_1

Cited by 9 publications

(13 citation statements)

References 64 publications

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“…Fog computing thereby minimizes network traffic and processing overhead on the cloud. Edge computing has brought resources at the network edge and reduced the latency 2 . An edge computing and IoT data sensing architecture for the entire crop lifespan was established 22 .…”

Section: Related Workmentioning

confidence: 99%

“…To store the large amount of sensor data, cloud computing is generally used. However, the use of the remote cloud for analyzing and storing the entire data enhances the service provisioning latency, network traffic, and computation overhead on the cloud 2 . Moreover, the entire data storage over the cloud raises a major concern in data security.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the entire data storage over the cloud raises a major concern in data security. Edge and fog computing can provide solutions to reduce the propagation and data transmission latencies 2 . Small cells with high computation ability such as small cell cloud enhanced eNodeB (SCceNB), femtolet can also be used for data processing 3 .…”

Section: Introductionmentioning

confidence: 99%

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Fly: Femtolet‐based edge‐cloud framework for crop yield prediction using bidirectional long short‐term memory

Dey,

Bera,

Paul

et al. 2024

Softw Pract Exp

Self Cite

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Crop yield prediction is a crucial area in agriculture that has a large impact on the economy of a country. This article proposes a crop yield prediction framework based on Internet of Things and edge computing. We have used a fifth generation network device referred to as femtolet as the edge device. The femtolet is a small cell base station that has high storage and high processing ability. The sensor nodes collect the soil and environmental data, and then the collected data is sent to the femtolet through the microcontrollers. The femtolet retrieves the weather‐related data from the cloud, and then processes the sensor data and weather‐related data using Bi‐LSTM. The femtolet after processing the data sends the generated results to the cloud. The user can access the results from the cloud to predict the suitable crop for his/her land. This is observed that the suggested framework provides better accuracy, precision, recall, and F1‐score compared to the state‐of‐the‐art crop yield prediction frameworks. This is also demonstrated that the use of femtolet reduces the latency by ˜25% than the conventional edge‐cloud framework.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fly: Femtolet‐based edge‐cloud framework for crop yield prediction using bidirectional long short‐term memory

Dey,

Bera,

Paul

et al. 2024

Softw Pract Exp

Self Cite

View full text Add to dashboard Cite

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“…MEC offers cloud services at the edge of the cellular network. With ultra-low latency and significant bandwidth, the use of edge computing to support IoT systems is the backbone for advancing intelligent systems and use cases, such as smart homes, competent healthcare, intelligent traffic control, intelligent farming, and smart cities [23].…”

Section: Literature Reviewmentioning

confidence: 99%

Distributed Edge Computing for Resource Allocation in Smart Cities Based on the IoT

et al. 2022

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Smart cities using the Internet of Things (IoT) can operate various IoT systems with better services that provide intelligent and efficient solutions for various aspects of urban life. With the rapidly growing number of IoT systems, the many smart city services, and their various quality of service (QoS) constraints, servers face the challenge of allocating limited resources across all Internet-based applications to achieve an efficient per-formance. The presence of a cloud in the IoT system of a smart city results in high energy con-sumption and delays in the network. Edge computing is based on a cloud computing framework where computation, storage, and network resources are moved close to the data source. The IoT framework is identical to cloud computing. The critical issue in edge computing when executing tasks generated by IoT systems is the efficient use of energy while maintaining delay limitations. In this paper, we study a multicriteria optimization approach for resource allocation with distributed edge computing in IoT-based smart cities. We present a three-layer network architecture for IoT-based smart cities. An edge resource allocation scheme based on an auctionable approach is proposed to ensure efficient resource computation for delay-sensitive tasks.

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“…In some studies, edge computing between mobile users and cloud servers have been analyzed to reduce energy consumption and network delays in mobile devices [2][3][4]. In a cloud-based mobile computing system, the efficient distribution issue of mobile users' tasks among multiple cloudlets was considered [5].…”

Section: Related Workmentioning

confidence: 99%

Clustering Method of Mobile Cloud Computing According to Technical Characteristics of Cloudlets

Alakberov¹

2022

IJCNIS

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The rapid increase in the number of mobile phones and IoT devices connected to the network reduces the bandwidth of the Internet communication channel, and as a result, delays occur in the delivery of data processed in remote clouds. Edge computing systems (cloudlet, fog computing, etc.) are used to eliminate resource shortages, energy consumption, and communication channel delays in mobile devices. Edge computing systems place processing devices (computers) close to users. Cloudlet-based mobile cloud computing is widely used to reduce delays in communication channels and energy consumption in mobile devices. Selection of the most suitable cloudlet allowing users to run applications fast in cloud is still a considerable problem. This paper proposes a strategy for the selection of high-performance cloudlets providing fast solutions, considering the complexity of application (file type). It offers a method for cloudlet selection out of large number of cloudlets with different technical capabilities providing faster processing of user application. The timing of user applications in cloudlets with different technical capabilities (operating frequency, number of cores, volume of RAM, etc.) also varies. The proposed method provides faster solution for the user application. User applications are grouped by type of application, and a set of cloudlets are clustered by the number of groups. Clustering is performed first by the parameters corresponding to the operating frequency of the cloudlets, then by the number of cores and the volume of RAM. The proposed method reduces energy consumption of mobile devices by providing faster processing of applications. Thus, the proposed strategy provides an energy consumption reduction on mobile devices, faster processing of results and decrease of network delays.

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Introduction to Mobile Edge Computing

Cited by 9 publications

References 64 publications

Fly: Femtolet‐based edge‐cloud framework for crop yield prediction using bidirectional long short‐term memory

Fly: Femtolet‐based edge‐cloud framework for crop yield prediction using bidirectional long short‐term memory

Distributed Edge Computing for Resource Allocation in Smart Cities Based on the IoT

Clustering Method of Mobile Cloud Computing According to Technical Characteristics of Cloudlets

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