1,777 research outputs found
Fog Computing: A Taxonomy, Survey and Future Directions
In recent years, the number of Internet of Things (IoT) devices/sensors has
increased to a great extent. To support the computational demand of real-time
latency-sensitive applications of largely geo-distributed IoT devices/sensors,
a new computing paradigm named "Fog computing" has been introduced. Generally,
Fog computing resides closer to the IoT devices/sensors and extends the
Cloud-based computing, storage and networking facilities. In this chapter, we
comprehensively analyse the challenges in Fogs acting as an intermediate layer
between IoT devices/ sensors and Cloud datacentres and review the current
developments in this field. We present a taxonomy of Fog computing according to
the identified challenges and its key features.We also map the existing works
to the taxonomy in order to identify current research gaps in the area of Fog
computing. Moreover, based on the observations, we propose future directions
for research
Technical considerations towards mobile user QoE enhancement via Cloud interaction
This paper discusses technical considerations of a Cloud infrastructure which interacts with mobile devices in order to migrate part of the computational overhead from the mobile device to the Cloud. The aim of the interaction between the mobile device and the Cloud is the enhancement of parameters that affect the Quality of Experience (QoE) of the mobile end user through the offloading of computational aspects of demanding applications. This paper shows that mobile user’s QoE can be potentially enhanced by offloading computational tasks to the Cloud which incorporates a predictive context-aware mechanism to schedule delivery of content to the mobile end-user using a low-cost interaction model between the Cloud and the mobile user. With respect to the proposed enhancements, both the technical considerations of the cloud infrastructure are examined, as well as the interaction between the mobile device and the Cloud
Edge Offloading in Smart Grid
The energy transition supports the shift towards more sustainable energy
alternatives, paving towards decentralized smart grids, where the energy is
generated closer to the point of use. The decentralized smart grids foresee
novel data-driven low latency applications for improving resilience and
responsiveness, such as peer-to-peer energy trading, microgrid control, fault
detection, or demand response. However, the traditional cloud-based smart grid
architectures are unable to meet the requirements of the new emerging
applications such as low latency and high-reliability thus alternative
architectures such as edge, fog, or hybrid need to be adopted. Moreover, edge
offloading can play a pivotal role for the next-generation smart grid AI
applications because it enables the efficient utilization of computing
resources and addresses the challenges of increasing data generated by IoT
devices, optimizing the response time, energy consumption, and network
performance. However, a comprehensive overview of the current state of research
is needed to support sound decisions regarding energy-related applications
offloading from cloud to fog or edge, focusing on smart grid open challenges
and potential impacts. In this paper, we delve into smart grid and
computational distribution architec-tures, including edge-fog-cloud models,
orchestration architecture, and serverless computing, and analyze the
decision-making variables and optimization algorithms to assess the efficiency
of edge offloading. Finally, the work contributes to a comprehensive
understanding of the edge offloading in smart grid, providing a SWOT analysis
to support decision making.Comment: to be submitted to journa
Managed Forgetting to Support Information Management and Knowledge Work
Trends like digital transformation even intensify the already overwhelming
mass of information knowledge workers face in their daily life. To counter
this, we have been investigating knowledge work and information management
support measures inspired by human forgetting. In this paper, we give an
overview of solutions we have found during the last five years as well as
challenges that still need to be tackled. Additionally, we share experiences
gained with the prototype of a first forgetful information system used 24/7 in
our daily work for the last three years. We also address the untapped potential
of more explicated user context as well as features inspired by Memory
Inhibition, which is our current focus of research.Comment: 10 pages, 2 figures, preprint, final version to appear in KI -
K\"unstliche Intelligenz, Special Issue: Intentional Forgettin
Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms
The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications
Sparks of GPTs in Edge Intelligence for Metaverse: Caching and Inference for Mobile AIGC Services
Aiming at achieving artificial general intelligence (AGI) for Metaverse,
pretrained foundation models (PFMs), e.g., generative pretrained transformers
(GPTs), can effectively provide various AI services, such as autonomous
driving, digital twins, and AI-generated content (AIGC) for extended reality.
With the advantages of low latency and privacy-preserving, serving PFMs of
mobile AI services in edge intelligence is a viable solution for caching and
executing PFMs on edge servers with limited computing resources and GPU memory.
However, PFMs typically consist of billions of parameters that are computation
and memory-intensive for edge servers during loading and execution. In this
article, we investigate edge PFM serving problems for mobile AIGC services of
Metaverse. First, we introduce the fundamentals of PFMs and discuss their
characteristic fine-tuning and inference methods in edge intelligence. Then, we
propose a novel framework of joint model caching and inference for managing
models and allocating resources to satisfy users' requests efficiently.
Furthermore, considering the in-context learning ability of PFMs, we propose a
new metric to evaluate the freshness and relevance between examples in
demonstrations and executing tasks, namely the Age of Context (AoC). Finally,
we propose a least context algorithm for managing cached models at edge servers
by balancing the tradeoff among latency, energy consumption, and accuracy
Tag-assisted social-aware opportunistic device-to-device sharing for traffic offloading in mobile social networks
Within recent years, the service demand for rich multimedia over mobile networks has kept being soaring at a tremendous pace. To solve the critical problem of mobile traffic explosion, substantial efforts have been made from researchers to try to offload the mobile traffic from infrastructured cellular links to direct short-range communications locally among nearby users. In this article, we discuss the potential of combining users’ online and offline social impacts to exploit the device-to-device (D2D) opportunistic sharing for offloading the mobile traffic. We propose Tag-Assisted Social-Aware D2D sharing framework, TASA, with corresponding optimization models, architecture design, and communication protocols. Through extensive simulations based on real data traces, we demonstrate that TASA can offload up to 78.9% of the mobile traffic effectively
Rentable Internet of Things Infrastructure for Sensing as a Service (S2aaS)
Sensing as a Service (S2aaS) model [1] [2] is inspired by the traditional
Everything as a service (XaaS) approaches [3]. It aims to better utilize the
existing Internet of Things (IoT) infrastructure. S2aaS vision aims to create
'rentable infrastructure' where interested parties can gather IoT data by
paying a fee for the infrastructure owners
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