Convergence of Recommender Systems and Edge Computing: A Comprehensive Survey

Sun, Chuan; Li, Hui; Li, Xiuhua; Wen, Junhao; Xiong, Qingyu; Zhou, Wei

doi:10.1109/access.2020.2978896

Cited by 32 publications

(14 citation statements)

References 92 publications

(107 reference statements)

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“…consumer's smartphone) after completing the computing task in the cloud server [231]. Although these architectures allow to achieve a good efficiency, they are prone to serious noticeable delays in the system's feedback and user's reaction because of the network bandwidth and latency between the cloud and edge [232]. By contrast, implementing the recommender algorithms directly on the edge can allow real-time computing and identify consumers' interests/preferences more accurately and thereby increasing their satisfaction and trust of the generated recommendations [233,234].…”

Section: Edge/fog Recommender Systemsmentioning

confidence: 99%

A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects

Himeur¹,

Alsalemi²,

Al‐Kababji³

et al. 2021

Preprint

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Recommender systems have significantly developed in recent years in parallel with the witnessed advancements in both internet of things (IoT) and artificial intelligence (AI) technologies. Accordingly, as a consequence of IoT and AI, multiple forms of data are incorporated in these systems, e.g. social, implicit, local and personal information, which can help in improving recommender systems' performance and widen their applicability to traverse different disciplines. On the other side, energy efficiency in the building sector is becoming a hot research topic, in which recommender systems play a major role by promoting energy saving behavior and reducing carbon emissions. However, the deployment of the recommendation frameworks in buildings still needs more investigations to identify the current challenges and issues, where their solutions are the keys to enable the pervasiveness of research findings, and therefore, ensure a large-scale adoption of this technology. Accordingly, this paper presents, to the best of the authors' knowledge, the first timely and comprehensive reference for energy-efficiency recommendation systems through (i) surveying existing recommender systems for energy saving in buildings; (ii) discussing their evolution; (iii) providing an original taxonomy of these systems based on specified criteria, including the nature of the recommender engine, its objective, computing platforms, evaluation metrics and incentive measures; and (iv) conducting an in-depth, critical analysis to identify their limitations and unsolved issues. The derived challenges and areas of future implementation could effectively guide the energy research community to improve the energy-efficiency in buildings and reduce the cost of developed recommender systems-based solutions.

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Section: Edge/fog Recommender Systemsmentioning

confidence: 99%

A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects

Himeur¹,

Alsalemi²,

Al‐Kababji³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…This could help in providing great intelligent, personalized services, and further supporting real-time implementations. To that end, increasing attention was put recently to develop edge/fogbased RSs [32].…”

Section: B Edge/fog-based Rssmentioning

confidence: 99%

Intelligent edge-based recommender system for internet of energy applications

Sayed,

Himeur,

Alsalemi

et al. 2021

Preprint

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Preserving energy in households and office buildings is a significant challenge, mainly due to the recent shortage of energy resources, the uprising of the current environmental problems, and the global lack of utilizing energy-saving technologies. Not to mention, within some regions, COVID-19 social distancing measures have led to a temporary transfer of energy demand from commercial and urban centers to residential areas, causing an increased use and higher charges, and in turn, creating economic impacts on customers. Therefore, the marketplace could benefit from developing an internet of things (IoT) ecosystem that monitors energy consumption habits and promptly recommends action to facilitate energy efficiency. This paper aims to present the full integration of a proposed energy efficiency framework into the Home-Assistant platform using an edge-based architecture. End-users can visualize their consumption patterns as well as ambient environmental data using the Home-Assistant user interface. More notably, explainable energysaving recommendations are delivered to end-users in the form of notifications via the mobile application to facilitate habit change. In this context, to the best of the authors' knowledge, this is the first attempt to develop and implement an energy-saving recommender system on edge devices. Thus, ensuring better privacy preservation since data are processed locally on the edge, without the need to transmit them to remote servers, as is the case with cloudlet platforms.

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“…Similarly, there are applications involving speech processing and recognition, biomedical image data processing, video games, etc. that require the kind of processing capabilities that even the modern day mobile phones cannot provide [4][5][6]. An obvious solution is to offload the processing of compute-intensive applications to powerful remote servers.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Approach for Mobility-Aware and Energy-Efficient Resource Allocation in MEC

et al. 2020

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Mobile Edge Computing (MEC) has emerged as an alternative to cloud computing to meet the latency and Quality-of-Service (QoS) requirements of mobile devices. In this paper, we address the problem of server resource allocation in MEC. Due to the dynamic load conditions on MEC servers, their resources need to be used intelligently to meet the QoS requirements of the users and to minimize server energy consumption. We present a novel resource allocation algorithm, called Power Migration Expand (PowMigExpand). Our algorithm assigns user requests to the optimal server and allocates optimal amount of resources to User Equipment (UE) based on our comprehensive utility function. PowMigExpand also migrates UE requests to new servers, when needed due to the mobility of users. We also present a low cost Energy Efficient Smart Allocator (EESA) algorithm that uses deep learning for energy efficient allocation of requests to optimal servers. The proposed algorithms consider varying load of incoming requests and their heterogeneous nature, energy efficient activation of servers, and Virtual Machine (VM) migration for smart resource allocation and, thus, is the first comprehensive approach to address the complex and multidimensional resource allocation problem using deep learning. We compare our proposed algorithms with other resource allocation approaches and show that our approach can handle the dynamic load conditions better. The proposed algorithms improve the service rate and the overall utility with minimum energy consumption. On average, it reduces 26% energy consumption of MESs and improves the service rate by 23%, compared with other algorithms. We also get more than 70% accuracy for EESA in allocating the resources of multiple servers to multiple users.INDEX TERMS Mobile edge computing, resource allocation, computational offloading, deep learning, energy efficient.

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Convergence of Recommender Systems and Edge Computing: A Comprehensive Survey

Cited by 32 publications

References 92 publications

A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects

A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects

Intelligent edge-based recommender system for internet of energy applications

A Deep Learning Approach for Mobility-Aware and Energy-Efficient Resource Allocation in MEC

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