EdgeOSH: A Home Operating System for Internet of Everything

Cao, Jie; Xu, Lanyu; Abdallah, Raef; Shi, Weisong

doi:10.1007/978-3-030-02083-5_2

Cited by 8 publications

(7 citation statements)

References 16 publications

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“…This is especially true when one considers the deployment of the IoT system at scale. Our vision is that distributed systems research and development has a significant role to play toward the democratization of the IoT, which is well illustrated by prior vision research papers for the field (see [45]- [47]).…”

Section: Discussionmentioning

confidence: 92%

LATTICE: A Framework for Optimizing IoT System Configurations at the Edge

Issarny

Billet

Bouloukakis

et al. 2019

2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS)

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The Internet of Things is expected to contribute to a "smarter world" by connecting the physical to the virtual, i.e., enabling advanced knowledge engineering over the big data gathered about the physical world. However, such a promise comes along with high resource consumption, spanning the network, storage and computational resources, not to mention possible security and privacy threats. As a result, it tends to be admitted that the IoT smartness will not be accommodated at scale by a centralized cloud-based approach. Instead, the deployment of IoT systems needs to leverage a highly distributed system architecture, which optimizes the distribution of the computation-from the edge to the cloud-according to the unique business requirements in terms of financial cost, latency, availability, etc. Toward that goal, this paper introduces the LATTICE framework, which aims at taming the complexity of configuring edge-based IoT systems. LATTICE builds upon ontologies that have proven useful to characterize the constituents of IoT systems in the required domain-specific way. However, LATTICE also revisits the exploitation of ontologies-i.e., the formal description of the real world, spanning the physical and cyber entities-across the development life-cycle of the IoT systems. As a first evidence, this paper introduces an automated approach to the optimization of IoT system configurations at the edge, provided the ontological description of the target IoT system.

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Section: Discussionmentioning

confidence: 92%

LATTICE: A Framework for Optimizing IoT System Configurations at the Edge

Issarny

Billet

Bouloukakis

et al. 2019

2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS)

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“…However, the ever-increasing number of smart devices, the multiple applications with low latency requirements, the big data generated by smart devices, and the extremely private home data, make it a tread to apply edge computing instead of cloud computing to smart homes. There are many works focusing on edge resource scheduling towards the smart home environment [220], [285], [293]. EdgeOSH, a home operating system, was proposed in [293] to provide functions of the program interface and data management.…”

Section: G Smart Homementioning

confidence: 99%

Resource Scheduling in Edge Computing: A Survey

Luo

et al. 2021

Preprint

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With the proliferation of the Internet of Things (IoT) and the wide penetration of wireless networks, the surging demand for data communications and computing calls for the emerging edge computing paradigm. By moving the services and functions located in the cloud to the proximity of users, edge computing can provide powerful communication, storage, networking, and communication capacity. The resource scheduling in edge computing, which is the key to the success of edge computing systems, has attracted increasing research interests. In this paper, we survey the state-of-the-art research findings to know the research progress in this field. Specifically, we present the architecture of edge computing, under which different collaborative manners for resource scheduling are discussed. Particularly, we introduce a unified model before summarizing the current works on resource scheduling from three research issues, including computation offloading, resource allocation, and resource provisioning. Based on two modes of operation, i.e., centralized and distributed modes, different techniques for resource scheduling are discussed and compared. Also, we summarize the main performance indicators based on the surveyed literature. To shed light on the significance of resource scheduling in real-world scenarios, we discuss several typical application scenarios involved in the research of resource scheduling in edge computing. Finally, we highlight some open research challenges yet to be addressed and outline several open issues as the future research direction.

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“…Edge computing becomes a suitable network platform for smart home, due to meet the requirement that enables applications to be processed locally through the network [48]. Cao et al [49] propose an edge operating system for SM and further discuss challenges.…”

Section: A Application Domains Of Edge Computing 1) Smart Homementioning

confidence: 99%

Convergence of Recommender Systems and Edge Computing: A Comprehensive Survey

Sun

et al. 2020

IEEE Access

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Under the explosive growth of information available on the Web, recommender systems have been used as an effective technology to filter useless information and attempt to recommend the most useful items. The proliferation of smart phones, smart wearable devices and other Internet of Thing (IoT) devices has gradually driven many novel emerging services which are latency-sensitive and computation-intensive with a higher quality-of-service. Under such circumstances, the data sources contain four key characteristics (i.e., sparsity, heterogeneity, mobility, volatility). The conventional recommender systems based on cloud computing are incapable of digging the information of user demands. Mobile edge computing is a novel computing paradigm via pushing computation/storage resource from the remote cloud servers to the network edge servers to provide more intelligent and personalized service. This paper comprehensively reviews the state of the art literature on the convergence of recommender systems and edge computing, and identify the future directions along this dimension. This paper can provide an array of new perspectives on the convergence for researchers, practitioners, and tap into the richness of this interdisciplinary research area.

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EdgeOSH: A Home Operating System for Internet of Everything

Cited by 8 publications

References 16 publications

LATTICE: A Framework for Optimizing IoT System Configurations at the Edge

LATTICE: A Framework for Optimizing IoT System Configurations at the Edge

Resource Scheduling in Edge Computing: A Survey

Convergence of Recommender Systems and Edge Computing: A Comprehensive Survey

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