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Register a new userORCA: Enabling an Owner-centric and Data-driven Management Paradigm for Future Heterogeneous Edge-IoT Systems
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Added by
Jianli Pan
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Integrating Internet of Things (IoT) and edge computing for Edge-IoT systems, converged with machine intelligence, has the potentials of enabling a wide range of applications in smart homes, factories and cities. Edge-IoT can connect many diverse devices and the IoT asset owners can run heterogeneous IoT systems supported by various vendors or service providers (SPs), using either cloud or local edge computing (or both) for resource assistance. The existing methods typically manage the systems as separate vertical silos, or in a vendor/SP-centric way, which suffers from significant challenges. In this paper, we present a novel owner-centric management paradigm named ORCA to address the gaps left by the owner-centric paradigm and empower the IoT assets owners to effectively identify and mitigate potential issues in their own network premises, regardless the vendors/SPs situations. ORCA aims to be scalable and extensible in assisting IoT owners to perform intelligent management through a behavior-oriented and data-driven approach. ORCA is an ongoing project and the preliminary results indicate that it can significantly empower the IoT systems owners to better manage their IoT assets.
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Edge computing is an emerging solution to support the future Internet of Things (IoT) applications that are delay-sensitive, processing-intensive or that require closer intelligence. Machine intelligence and data-driven approaches are envisioned to build future Edge-IoT systems that satisfy IoT devices demands for edge resources. However, significant challenges and technical barriers exist which complicate the resource management for such Edge-IoT systems. IoT devices running various applications can demonstrate a wide range of behaviors in the devices resource demand that are extremely difficult to manage. In addition, the management of multidimensional resources fairly and efficiently by the edge in such a setting is a challenging task. In this paper, we develop a novel data-driven resource management framework named BEHAVE that intelligently and fairly allocates edge resources to heterogeneous IoT devices with consideration of their behavior of resource demand (BRD). BEHAVE aims to holistically address the management technical barriers by: 1) building an efficient scheme for modeling and assessment of the BRD of IoT devices based on their resource requests and resource usage; 2) expanding a new Rational, Fair, and Truthful Resource Allocation (RFTA) model that binds the devices BRD and resource allocation to achieve fair allocation and encourage truthfulness in resource demand; and 3) developing an enhanced deep reinforcement learning (EDRL) scheme to achieve the RFTA goals. The evaluation results demonstrate BEHAVEs capability to analyze the IoT devices BRD and adjust its resource management policy accordingly.
The rapid deployment of Internet of Things (IoT) applications leads to massive data that need to be processed. These IoT applications have specific communication requirements on latency and bandwidth, and present new features on their generated data such as time-dependency. Therefore, it is desirable to reshape the current IoT architectures by exploring their inherent nature of communication and computing to support smart IoT data process and analysis. We introduce in this paper features of IoT data, trends of IoT network architectures, some problems in IoT data analysis, and their solutions. Specifically, we view that software-defined edge computing is a promising architecture to support the unique needs of IoT data analysis. We further present an experiment on data anomaly detection in this architecture, and the comparison between two architectures for ECG diagnosis. Results show that our method is effective and feasible.
Fog or Edge computing has recently attracted broad attention from both industry and academia. It is deemed as a paradigm shift from the current centralized cloud computing model and could potentially bring a Fog-IoT architecture that would significantly benefit the future ubiquitous Internet of Things (IoT) systems and applications. However, it takes a series of key enabling technologies including emerging technologies to realize such a vision. In this article, we will survey these key enabling technologies with specific focuses on security and scalability, which are two very important and much-needed characteristics for future large-scale deployment. We aim to draw an overall big picture of the future for the research and development in these areas.
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We present a detailed review of various Home Energy Management Schemes (HEM,s). HEM,s will increase savings, reduce peak demand and Pto Average Ratio (PAR). Among various applications of smart grid technologies, home energy management is probably the most important one to be addressed. Various steps have been taken by utilities for efficient energy consumption.New pricing schemes like Time of Use (ToU), Real Time Pricing (RTP), Critical Peak Pricing (CPP), Inclining Block Rates (IBR) etc have been been devised for future smart grids.Home appliances and/or distributed energy resources coordination (Local Generation) along with different pricing schemes leads towards efficient energy consumption. This paper addresses various communication and optimization based residential energy management schemes and different communication and networking technologies involved in these schemes.
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