Edge and fog computing for IoT: A survey on current research activities &amp; future directions

Laroui, Mohammed; Nour, Boubakr; Moungla, Hassine; Cherif, Moussa Ali; Afifi, Hossam; Guizani, Mohsen

doi:10.1016/j.comcom.2021.09.003

Cited by 168 publications

(82 citation statements)

References 100 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Os dados coletados pelos sensores precisam ser processados para serem transformados em informação. De acordo com o local de processamento pode-se classificar a arquitetura do sistema desenvolvido de três maneiras: em borda (Edge Computing) [18][19][20], em névoa (Fog Computing) [20,21] ou em nuvem (Cloud Computing) [22]. A mais conhecida, computação em nuvem, é aquela onde os dados captados são transmitidos para serviços em servidores de Internet, local onde são processados.…”

Section: Arquiteturas De Processamentounclassified

Construção e Validação de um Sistema IoT de Baixo Custo para Detecção de Vazamento de Água em Residências

Barbosa de Assis Jr.,

Lopes e Silva,

Da Rocha Henriques

et al. 2022

Anais Do XIII Computer on the Beach - COTB'22

View full text Add to dashboard Cite

The rational use of water is essential to development and economic growth. Data from United Nations (UN) indicates that until 2050 will occur a more significant water scarcity due to the increasing demand for it in emerging countries, mainly with the populational growth of these locals. A conscious consumption by society can softy this effect preserving natural reserves and increasing water security. This premise is used to support the development of this work, which proposes the creation of a low-cost computational system that detects water leakages in residences using Internet of Things concepts. The system includes data acquisition, transmission, processing, and finally displays the information to the user. Empirical assessments about the data transmitting process and a comparison of different architectures and leakage detection techniques are in development to demonstrate proposal viability. The project presents the prototype tested in a real environment.

show abstract

Section: Arquiteturas De Processamentounclassified

Construção e Validação de um Sistema IoT de Baixo Custo para Detecção de Vazamento de Água em Residências

Barbosa de Assis Jr.,

Lopes e Silva,

Da Rocha Henriques

et al. 2022

Anais Do XIII Computer on the Beach - COTB'22

View full text Add to dashboard Cite

show abstract

“…interoperability with legacy systems. Edge computing enables service providers to virtualize the cloud beyond their data center [5]. Indeed, computations and workloads created in the cloud can now be offloaded towards edge servers that are located near to users (essentially at the edge network, one hop from the application).…”

Section: Introductionmentioning

confidence: 99%

Matching-based Service Offloading for Compute-less Driven IoT Networks

Nour¹,

Cherkaoui²

2022

Preprint

Self Cite

View full text Add to dashboard Cite

With the advent of the Internet of Things (IoT) and 5G networks, edge computing is offering new opportunities for business model and use cases innovations. Service providers can now virtualize the cloud beyond the data center to meet the latency, data sovereignty, reliability, and interoperability requirements. Yet, many new applications (e.g., augmented reality, virtual reality, artificial intelligence) are computationintensive and delay-sensitivity. These applications are invoked heavily with similar inputs that could lead to the same output. Compute-less networks aim to implement a network with a minimum amount of computation and communication. This can be realized by offloading prevalent services to the edge and thus minimizing communication in the core network and eliminating redundant computations using the computation reuse concept. In this paper, we present matching-based services offloading schemes for compute-less IoT networks. We adopt the matching theory to match service offloading to the appropriate edge server(s). Specifically, we design, WHISTLE, a vertical many-tomany offloading scheme that aims to offload the most invoked and highly reusable services to the appropriate edge servers. We further extend WHISTLE to provide horizontal one-to-many computation reuse sharing among edge servers which leads to bouncing less computation back to the cloud. We evaluate the efficiency and effectiveness of WHISTLE with a real-world dataset. The obtained findings show that WHISTLE is able to accelerate the tasks completion time by 20%, reduce the computation up to 77%, and decrease the communication up to 71%. Theoretical analyses also prove the stability of the designed schemes.

show abstract

“…In parallel, wireless mobile technologies, such as the fifth-generation (5G), promise to connect billions of heterogeneous devices to the network edge [2]. This connection opens various opportunities to verticals toward the rise of new applications and business models under the banner of the Internet of Things (IoT) [3]. Edge nodes seek to collect a massive amount of data and open new avenues for ML applications.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Data Splitting Scheme for Federated Edge Learning in IoT Networks

Nour¹,

Cherkaoui²

2022

Preprint

Self Cite

View full text Add to dashboard Cite

Federated Edge Learning (FEEL) is a promising distributed learning technique that aims to train a shared global model while reducing communication costs and promoting users' privacy. However, the training process might significantly occupy a long time due to the nature of the used data for training, which leads to higher energy consumption and therefore impacts the model convergence. To tackle this issue, we propose a datadriven federated edge learning scheme that tends to select suitable participating nodes based on quality data and energy. First, we design an unsupervised data-aware splitting scheme that partitions the node's local data into diverse samples used for training. We incorporate a similarity index to select quality data that enhances the training performance. Then, we propose a heuristic participating nodes selection scheme to minimize the communication and computation energy consumption, as well as the amount of communication rounds. The obtained results show that the proposed scheme substantially outperforms the vanilla FEEL in terms of energy consumption and the number of communication rounds.

show abstract

Edge and fog computing for IoT: A survey on current research activities & future directions

Cited by 168 publications

References 100 publications

Construção e Validação de um Sistema IoT de Baixo Custo para Detecção de Vazamento de Água em Residências

Construção e Validação de um Sistema IoT de Baixo Custo para Detecção de Vazamento de Água em Residências

Matching-based Service Offloading for Compute-less Driven IoT Networks

Unsupervised Data Splitting Scheme for Federated Edge Learning in IoT Networks

Contact Info

Product

Resources

About