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Computational Intelligence Applications in Smart Grids: Enabling Methodologies for Proactive and Self Organizing Power Systems
This book considers the emerging technologies and methodologies of the application of computational intelligence to smart grids.
From a conceptual point of view, the smart grid is the convergence of information and operational technologies applied to the electric grid, allowing sustainable options to customers and improved levels of security. Smart grid technologies include advanced sensing systems, two-way high-speed communications, monitoring and enterprise analysis software, and related services used to obtain location-specific and real-time actionable data for the provision of enhanced services for both system operators (i.e. distribution automation, asset management, advanced metering infrastructure) and end-users (i.e. demand side management, demand response).
In this context, a crucial issue is how to support the evolution of existing electrical grids from static hierarchal systems to self-organizing, highly scalable and pervasive networks. Modern trends are oriented toward the employment of computational intelligence techniques for deploying advanced control, protection and monitoring architectures that move away from the older centralized paradigm to systems distributed across the field with an increasing pervasion of intelligence devices. The large-scale deployment of computational intelligence technologies in smart grids could lead to a more efficient tasks distribution amongst energy resources and, consequently, to a sensible improvement of the electrical grid flexibility
Big Data and the Internet of Things
Advances in sensing and computing capabilities are making it possible to
embed increasing computing power in small devices. This has enabled the sensing
devices not just to passively capture data at very high resolution but also to
take sophisticated actions in response. Combined with advances in
communication, this is resulting in an ecosystem of highly interconnected
devices referred to as the Internet of Things - IoT. In conjunction, the
advances in machine learning have allowed building models on this ever
increasing amounts of data. Consequently, devices all the way from heavy assets
such as aircraft engines to wearables such as health monitors can all now not
only generate massive amounts of data but can draw back on aggregate analytics
to "improve" their performance over time. Big data analytics has been
identified as a key enabler for the IoT. In this chapter, we discuss various
avenues of the IoT where big data analytics either is already making a
significant impact or is on the cusp of doing so. We also discuss social
implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski
(eds.) Big Data Analysis: New algorithms for a new society, Springer Series
on Studies in Big Data, to appea
Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications
In the era when the market segment of Internet of Things (IoT) tops the chart
in various business reports, it is apparently envisioned that the field of
medicine expects to gain a large benefit from the explosion of wearables and
internet-connected sensors that surround us to acquire and communicate
unprecedented data on symptoms, medication, food intake, and daily-life
activities impacting one's health and wellness. However, IoT-driven healthcare
would have to overcome many barriers, such as: 1) There is an increasing demand
for data storage on cloud servers where the analysis of the medical big data
becomes increasingly complex, 2) The data, when communicated, are vulnerable to
security and privacy issues, 3) The communication of the continuously collected
data is not only costly but also energy hungry, 4) Operating and maintaining
the sensors directly from the cloud servers are non-trial tasks. This book
chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog
Computing is a service-oriented intermediate layer in IoT, providing the
interfaces between the sensors and cloud servers for facilitating connectivity,
data transfer, and queryable local database. The centerpiece of Fog computing
is a low-power, intelligent, wireless, embedded computing node that carries out
signal conditioning and data analytics on raw data collected from wearables or
other medical sensors and offers efficient means to serve telehealth
interventions. We implemented and tested an fog computing system using the
Intel Edison and Raspberry Pi that allows acquisition, computing, storage and
communication of the various medical data such as pathological speech data of
individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate
estimation, and Electrocardiogram (ECG)-based Q, R, S detection.Comment: 29 pages, 30 figures, 5 tables. Keywords: Big Data, Body Area
Network, Body Sensor Network, Edge Computing, Fog Computing, Medical
Cyberphysical Systems, Medical Internet-of-Things, Telecare, Tele-treatment,
Wearable Devices, Chapter in Handbook of Large-Scale Distributed Computing in
Smart Healthcare (2017), Springe
Optimizing the Structure and Scale of Urban Water Infrastructure: Integrating Distributed Systems
Large-scale, centralized water infrastructure has provided clean drinking water, wastewater treatment, stormwater management and flood protection for U.S. cities and towns for many decades, protecting public health, safety and environmental quality. To accommodate increasing demands driven by population growth and industrial needs, municipalities and utilities have typically expanded centralized water systems with longer distribution and collection networks. This approach achieves financial and institutional economies of scale and allows for centralized management. It comes with tradeoffs, however, including higher energy demands for longdistance transport; extensive maintenance needs; and disruption of the hydrologic cycle, including the large-scale transfer of freshwater resources to estuarine and saline environments.While smaller-scale distributed water infrastructure has been available for quite some time, it has yet to be widely adopted in urban areas of the United States. However, interest in rethinking how to best meet our water and sanitation needs has been building. Recent technological developments and concerns about sustainability and community resilience have prompted experts to view distributed systems as complementary to centralized infrastructure, and in some situations the preferred alternative.In March 2014, the Johnson Foundation at Wingspread partnered with the Water Environment Federation and the Patel College of Global Sustainability at the University of South Florida to convene a diverse group of experts to examine the potential for distributed water infrastructure systems to be integrated with or substituted for more traditional water infrastructure, with a focus on right-sizing the structure and scale of systems and services to optimize water, energy and sanitation management while achieving long-term sustainability and resilience
DIAMOnDS - DIstributed Agents for MObile & Dynamic Services
Distributed Services Architecture with support for mobile agents between
services, offer significantly improved communication and computational
flexibility. The uses of agents allow execution of complex operations that
involve large amounts of data to be processed effectively using distributed
resources. The prototype system Distributed Agents for Mobile and Dynamic
Services (DIAMOnDS), allows a service to send agents on its behalf, to other
services, to perform data manipulation and processing. Agents have been
implemented as mobile services that are discovered using the Jini Lookup
mechanism and used by other services for task management and communication.
Agents provide proxies for interaction with other services as well as specific
GUI to monitor and control the agent activity. Thus agents acting on behalf of
one service cooperate with other services to carry out a job, providing
inter-operation of loosely coupled services in a semi-autonomous way. Remote
file system access functionality has been incorporated by the agent framework
and allows services to dynamically share and browse the file system resources
of hosts, running the services. Generic database access functionality has been
implemented in the mobile agent framework that allows performing complex data
mining and processing operations efficiently in distributed system. A basic
data searching agent is also implemented that performs a query based search in
a file system. The testing of the framework was carried out on WAN by moving
Connectivity Test agents between AgentStations in CERN, Switzerland and NUST,
Pakistan.Comment: 7 pages, 4 figures, CHEP03, La Jolla, California, March 24-28, 200
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