A Reference Architecture for Cloud–Edge Meta-Operating Systems Enabling Cross-Domain, Data-Intensive, ML-Assisted Applications: Architectural Overview and Key Concepts

Trakadas, Panagiotis; Masip-Bruin, X.; Facca, Federico Michele; Spantideas, Sotirios T.; Giannopoulos, Anastasios E.; Kapsalis, Nikolaos C.; Martins, Rui P.; Bosani, Enrica; Ramon, Joan; Prats, Raül González; Ntroulias, George; Lyridis, Dimitrios V.

doi:10.3390/s22229003

Cited by 20 publications

(12 citation statements)

References 72 publications

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“…A maritime environment is generally characterized by multiple and diverse entities dispersed over large geographical areas, including, among others, ships, vessels, ports, unmanned surface vehicles (USVs), unmanned underwater vehicles (UUVs), sensors, and actuators [71,72]. A challenging task in such a heterogeneous environment is proper data collection and processing for the optimization of various tasks related to the maritime sector, such as just-in-time arrival for vessels and ships in ports [17], pollution monitoring, search and rescue (SAR) operations, etc. Therefore, IoT devices can collect and transmit data to edge servers for proper ML training and process optimization.…”

Section: Maritime Applicationsmentioning

confidence: 99%

“…However, on one hand, the collection of heterogeneous data from all involved nodes of the continuum might increase the pre-processing load, and on the other hand, centralized ML training might jeopardize latency requirements in critical applications. Therefore, as will also be Future Internet 2023, 15, 383 3 of 27 described in Section 2, the support of distributed and decentralized ML approaches is a key concept in IEC systems [16][17][18].…”

mentioning

confidence: 99%

“…will also be described in Section 2, the support of distributed and decentralized ML approaches is a key concept in IEC systems [16][17][18].…”

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confidence: 99%

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A Survey on IoT-Edge-Cloud Continuum Systems: Status, Challenges, Use Cases, and Open Issues

Gkonis,

Giannopoulos,

Trakadas

et al. 2023

Future Internet

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The rapid growth in the number of interconnected devices on the Internet (referred to as the Internet of Things—IoT), along with the huge volume of data that are exchanged and processed, has created a new landscape in network design and operation. Due to the limited battery size and computational capabilities of IoT nodes, data processing usually takes place on external devices. Since latency minimization is a key concept in modern-era networks, edge servers that are in close proximity to IoT nodes gather and process related data, while in some cases data offloading in the cloud might have to take place. The interconnection of a vast number of heterogeneous IoT devices with the edge servers and the cloud, where the IoT, edge, and cloud converge to form a computing continuum, is also known as the IoT-edge-cloud (IEC) continuum. Several key challenges are associated with this new computing systems’ architectural approach, including (i) the design of connection and programming protocols aimed at properly manipulating a huge number of heterogeneous devices over diverse infrastructures; (ii) the design of efficient task offloading algorithms aimed at optimizing services execution; (iii) the support for security and privacy enhancements during data transfer to deal with the existent and even unforeseen attacks and threats landscape; (iv) scalability, flexibility, and reliability guarantees to face the expected mobility for IoT systems; and (v) the design of optimal resource allocation mechanisms to make the most out of the available resources. These challenges will become even more significant towards the new era of sixth-generation (6G) networks, which will be based on the integration of various cutting-edge heterogeneous technologies. Therefore, the goal of this survey paper is to present all recent developments in the field of IEC continuum systems, with respect to the aforementioned deployment challenges. In the same context, potential limitations and future challenges are highlighted as well. Finally, indicative use cases are also presented from an IEC continuum perspective.

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Section: Maritime Applicationsmentioning

confidence: 99%

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confidence: 99%

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A Survey on IoT-Edge-Cloud Continuum Systems: Status, Challenges, Use Cases, and Open Issues

Gkonis,

Giannopoulos,

Trakadas

et al. 2023

Future Internet

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“…Smart homes have become increasingly popular with the widespread adoption of Internet of Things (IoT) devices [1][2][3][4][5], and have paved the way for improving multiple aspects of homes by utilizing the enormous amounts of data generated every day. One key challenge in this domain is predicting energy consumption to optimize energy management, reduce waste, and save costs [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Data Aging Matters: Federated Learning-Based Consumption Prediction in Smart Homes via Age-Based Model Weighting

et al. 2023

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Smart homes, powered mostly by Internet of Things (IoT) devices, have become very popular nowadays due to their ability to provide a holistic approach towards effective energy management. This is made feasible via the deployment of multiple sensors, which enables predicting energy consumption via machine learning approaches. In this work, we propose FedTime, a novel federated learning approach for predicting smart home consumption which takes into consideration the age of the time series datasets of each client. The proposed method is based on federated averaging but aggregates local models trained on each smart home device to produce a global prediction model via a novel weighting scheme. Each local model contributes more to the global model when the local data are more recent, or penalized when the data are older upon testing for a specific residence (client). The approach was evaluated on a real-world dataset of smart home energy consumption and compared with other machine learning models. The results demonstrate that the proposed method performs similarly or better than other models in terms of prediction error; FedTime achieved a lower mean absolute error of 0.25 compared to FedAvg. The contributions of this work present a novel federated learning approach that takes into consideration the age of the datasets that belong to the clients, experimenting with a publicly available dataset on grid import consumption prediction, while comparing with centralized and decentralized baselines, without the need for data centralization, which is a privacy concern for many households.

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“…In addition to the cloud-edge-end hierarchical architecture we are investigating, there is a rising trend in integrated continuum architectures. For instance, Trakadas et al in [ 24 ] introduced the meta-operating system reference architecture (RAMOS) to tackle the data surge resulting from IoT proliferation, aiming to establish a dynamic, distributed, and trusted continuum for future data-intensive applications at the edge. Yet, creating a continuum from IoT to the edge and cloud still poses an ongoing challenge [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

Computation Offloading and Resource Allocation Based on P-DQN in LEO Satellite Edge Networks

Yang,

Fang,

Gao

et al. 2023

Sensors

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Traditional low earth orbit (LEO) satellite networks are typically independent of terrestrial networks, which develop relatively slowly due to the on-board capacity limitation. By integrating emerging mobile edge computing (MEC) with LEO satellite networks to form the business-oriented “end-edge-cloud” multi-level computing architecture, some computing-sensitive tasks can be offloaded by ground terminals to satellites, thereby satisfying more tasks in the network. How to make computation offloading and resource allocation decisions in LEO satellite edge networks, nevertheless, indeed poses challenges in tracking network dynamics and handling sophisticated actions. For the discrete-continuous hybrid action space and time-varying networks, this work aims to use the parameterized deep Q-network (P-DQN) for the joint computation offloading and resource allocation. First, the characteristics of time-varying channels are modeled, and then both communication and computation models under three different offloading decisions are constructed. Second, the constraints on task offloading decisions, on remaining available computing resources, and on the power control of LEO satellites as well as the cloud server are formulated, followed by the maximization problem of satisfied task number over the long run. Third, using the parameterized action Markov decision process (PAMDP) and P-DQN, the joint computing offloading, resource allocation, and power control are made in real time, to accommodate dynamics in LEO satellite edge networks and dispose of the discrete-continuous hybrid action space. Simulation results show that the proposed P-DQN method could approach the optimal control, and outperforms other reinforcement learning (RL) methods for merely either discrete or continuous action space, in terms of the long-term rate of satisfied tasks.

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A Reference Architecture for Cloud–Edge Meta-Operating Systems Enabling Cross-Domain, Data-Intensive, ML-Assisted Applications: Architectural Overview and Key Concepts

Cited by 20 publications

References 72 publications

A Survey on IoT-Edge-Cloud Continuum Systems: Status, Challenges, Use Cases, and Open Issues

A Survey on IoT-Edge-Cloud Continuum Systems: Status, Challenges, Use Cases, and Open Issues

Data Aging Matters: Federated Learning-Based Consumption Prediction in Smart Homes via Age-Based Model Weighting

Computation Offloading and Resource Allocation Based on P-DQN in LEO Satellite Edge Networks

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