3,545 research outputs found
A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments
In recent years, due to the unnecessary wastage of electrical energy in
residential buildings, the requirement of energy optimization and user comfort
has gained vital importance. In the literature, various techniques have been
proposed addressing the energy optimization problem. The goal of each technique
was to maintain a balance between user comfort and energy requirements such
that the user can achieve the desired comfort level with the minimum amount of
energy consumption. Researchers have addressed the issue with the help of
different optimization algorithms and variations in the parameters to reduce
energy consumption. To the best of our knowledge, this problem is not solved
yet due to its challenging nature. The gap in the literature is due to the
advancements in the technology and drawbacks of the optimization algorithms and
the introduction of different new optimization algorithms. Further, many newly
proposed optimization algorithms which have produced better accuracy on the
benchmark instances but have not been applied yet for the optimization of
energy consumption in smart homes. In this paper, we have carried out a
detailed literature review of the techniques used for the optimization of
energy consumption and scheduling in smart homes. The detailed discussion has
been carried out on different factors contributing towards thermal comfort,
visual comfort, and air quality comfort. We have also reviewed the fog and edge
computing techniques used in smart homes
Adaptive learning-based resource management strategy in fog-to-cloud
Technology in the twenty-first century is rapidly developing and driving us into a new smart computing world, and emerging lots
of new computing architectures. Fog-to-Cloud (F2C) is among one of them, which emerges to ensure the commitment for
bringing the higher computing facilities near to the edge of the network and also help the large-scale computing system to be
more intelligent. As the F2C is in its infantile state, therefore one of the biggest challenges for this computing paradigm is to
efficiently manage the computing resources. Mainly, to address this challenge, in this work, we have given our sole interest for
designing the initial architectural framework to build a proper, adaptive and efficient resource management mechanism in F2C.
F2C has been proposed as a combined, coordinated and hierarchical computing platform, where a vast number of
heterogeneous computing devices are participating. Notably, their versatility creates a massive challenge for effectively handling
them. Even following any large-scale smart computing system, it can easily recognize that various kind of services is served for
different purposes. Significantly, every service corresponds with the various tasks, which have different resource requirements.
So, knowing the characteristics of participating devices and system offered services is giving advantages to build effective and
resource management mechanism in F2C-enabled system. Considering these facts, initially, we have given our intense focus for
identifying and defining the taxonomic model for all the participating devices and system involved services-tasks.
In any F2C-enabled system consists of a large number of small Internet-of-Things (IoTs) and generating a continuous and
colossal amount of sensing-data by capturing various environmental events. Notably, this sensing-data is one of the key
ingredients for various smart services which have been offered by the F2C-enabled system. Besides that, resource statistical
information is also playing a crucial role, for efficiently providing the services among the system consumers. Continuous
monitoring of participating devices generates a massive amount of resource statistical information in the F2C-enabled system.
Notably, having this information, it becomes much easier to know the device's availability and suitability for executing some tasks
to offer some services. Therefore, ensuring better service facilities for any latency-sensitive services, it is essential to securely
distribute the sensing-data and resource statistical information over the network. Considering these matters, we also proposed
and designed a secure and distributed database framework for effectively and securely distribute the data over the network.
To build an advanced and smarter system is necessarily required an effective mechanism for the utilization of system resources.
Typically, the utilization and resource handling process mainly depend on the resource selection and allocation mechanism. The
prediction of resources (e.g., RAM, CPU, Disk, etc.) usage and performance (i.e., in terms of task execution time) helps the
selection and allocation process. Thus, adopting the machine learning (ML) techniques is much more useful for designing an
advanced and sophisticated resource allocation mechanism in the F2C-enabled system. Adopting and performing the ML
techniques in F2C-enabled system is a challenging task. Especially, the overall diversification and many other issues pose a
massive challenge for successfully performing the ML techniques in any F2C-enabled system. Therefore, we have proposed and
designed two different possible architectural schemas for performing the ML techniques in the F2C-enabled system to achieve
an adaptive, advance and sophisticated resource management mechanism in the F2C-enabled system. Our proposals are the
initial footmarks for designing the overall architectural framework for resource management mechanism in F2C-enabled system.La tecnologia del segle XXI avança ràpidament i ens condueix cap a un nou món intel·ligent, creant nous models d'arquitectures informàtiques. Fog-to-Cloud (F2C) és un d’ells, i sorgeix per garantir el compromís d’acostar les instal·lacions informàtiques a prop de la xarxa i també ajudar el sistema informàtic a gran escala a ser més intel·ligent. Com que el F2C es troba en un estat preliminar, un dels majors reptes d’aquest paradigma tecnològic és gestionar eficientment els recursos informàtics. Per fer front a aquest repte, en aquest treball hem centrat el nostre interès en dissenyar un marc arquitectònic per construir un mecanisme de gestió de recursos adequat, adaptatiu i eficient a F2C.F2C ha estat concebut com una plataforma informàtica combinada, coordinada i jeràrquica, on participen un gran nombre de dispositius heterogenis. La seva versatilitat planteja un gran repte per gestionar-los de manera eficaç. Els serveis que s'hi executen consten de diverses tasques, que tenen requisits de recursos diferents. Per tant, conèixer les característiques dels dispositius participants i dels serveis que ofereix el sistema és un requisit per dissenyar mecanismes eficaços i de gestió de recursos en un sistema habilitat per F2C. Tenint en compte aquests fets, inicialment ens hem centrat en identificar i definir el model taxonòmic per a tots els dispositius i sistemes implicats en l'execució de tasques de serveis. Qualsevol sistema habilitat per F2C inclou en un gran nombre de dispositius petits i connectats (conegut com a Internet of Things, o IoT) que generen una quantitat contínua i colossal de dades de detecció capturant diversos events ambientals. Aquestes dades són un dels ingredients clau per a diversos serveis intel·ligents que ofereix F2C. A més, el seguiment continu dels dispositius participants genera igualment una gran quantitat d'informació estadística. En particular, en tenir aquesta informació, es fa molt més fàcil conèixer la disponibilitat i la idoneïtat dels dispositius per executar algunes tasques i oferir alguns serveis. Per tant, per garantir millors serveis sensibles a la latència, és essencial distribuir de manera equilibrada i segura la informació estadística per la xarxa. Tenint en compte aquests assumptes, també hem proposat i dissenyat un entorn de base de dades segura i distribuïda per gestionar de manera eficaç i segura les dades a la xarxa. Per construir un sistema avançat i intel·ligent es necessita un mecanisme eficaç per a la gestió de l'ús dels recursos del sistema. Normalment, el procés d’utilització i manipulació de recursos depèn principalment del mecanisme de selecció i assignació de recursos. La predicció de l’ús i el rendiment de recursos (per exemple, RAM, CPU, disc, etc.) en termes de temps d’execució de tasques ajuda al procés de selecció i assignació. Adoptar les tècniques d’aprenentatge automàtic (conegut com a Machine Learning, o ML) és molt útil per dissenyar un mecanisme d’assignació de recursos avançat i sofisticat en el sistema habilitat per F2C. L’adopció i la realització de tècniques de ML en un sistema F2C és una tasca complexa. Especialment, la diversificació general i molts altres problemes plantegen un gran repte per realitzar amb èxit les tècniques de ML. Per tant, en aquesta recerca hem proposat i dissenyat dos possibles esquemes arquitectònics diferents per realitzar tècniques de ML en el sistema habilitat per F2C per aconseguir un mecanisme de gestió de recursos adaptatiu, avançat i sofisticat en un sistema F2C. Les nostres propostes són els primers passos per dissenyar un marc arquitectònic general per al mecanisme de gestió de recursos en un sistema habilitat per F2C.Postprint (published version
Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions
Traditional power grids are being transformed into Smart Grids (SGs) to
address the issues in existing power system due to uni-directional information
flow, energy wastage, growing energy demand, reliability and security. SGs
offer bi-directional energy flow between service providers and consumers,
involving power generation, transmission, distribution and utilization systems.
SGs employ various devices for the monitoring, analysis and control of the
grid, deployed at power plants, distribution centers and in consumers' premises
in a very large number. Hence, an SG requires connectivity, automation and the
tracking of such devices. This is achieved with the help of Internet of Things
(IoT). IoT helps SG systems to support various network functions throughout the
generation, transmission, distribution and consumption of energy by
incorporating IoT devices (such as sensors, actuators and smart meters), as
well as by providing the connectivity, automation and tracking for such
devices. In this paper, we provide a comprehensive survey on IoT-aided SG
systems, which includes the existing architectures, applications and prototypes
of IoT-aided SG systems. This survey also highlights the open issues,
challenges and future research directions for IoT-aided SG systems
A smart resource management mechanism with trust access control for cloud computing environment
The core of the computer business now offers subscription-based on-demand
services with the help of cloud computing. We may now share resources among
multiple users by using virtualization, which creates a virtual instance of a
computer system running in an abstracted hardware layer. It provides infinite
computing capabilities through its massive cloud datacenters, in contrast to
early distributed computing models, and has been incredibly popular in recent
years because to its continually growing infrastructure, user base, and hosted
data volume. This article suggests a conceptual framework for a workload
management paradigm in cloud settings that is both safe and
performance-efficient. A resource management unit is used in this paradigm for
energy and performing virtual machine allocation with efficiency, assuring the
safe execution of users' applications, and protecting against data breaches
brought on by unauthorised virtual machine access real-time. A secure virtual
machine management unit controls the resource management unit and is created to
produce data on unlawful access or intercommunication. Additionally, a workload
analyzer unit works simultaneously to estimate resource consumption data to
help the resource management unit be more effective during virtual machine
allocation. The suggested model functions differently to effectively serve the
same objective, including data encryption and decryption prior to transfer,
usage of trust access mechanism to prevent unauthorised access to virtual
machines, which creates extra computational cost overhead
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
Intelligent Energy Management with IoT Framework in Smart Cities Using Intelligent Analysis: An Application of Machine Learning Methods for Complex Networks and Systems
Smart buildings are increasingly using Internet of Things (IoT)-based
wireless sensing systems to reduce their energy consumption and environmental
impact. As a result of their compact size and ability to sense, measure, and
compute all electrical properties, Internet of Things devices have become
increasingly important in our society. A major contribution of this study is
the development of a comprehensive IoT-based framework for smart city energy
management, incorporating multiple components of IoT architecture and
framework. An IoT framework for intelligent energy management applications that
employ intelligent analysis is an essential system component that collects and
stores information. Additionally, it serves as a platform for the development
of applications by other companies. Furthermore, we have studied intelligent
energy management solutions based on intelligent mechanisms. The depletion of
energy resources and the increase in energy demand have led to an increase in
energy consumption and building maintenance. The data collected is used to
monitor, control, and enhance the efficiency of the system
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