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Smart Home Systems Based on Internet of Things

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Smart Home Systems Based on
Internet of Things
Smart home systems achieved great popularity in the last decades as they
increase the comfort and quality of life. Most smart home systems are controlled by
smartphones and microcontrollers. A smartphone application is used to control and
monitor home functions using wireless communication techniques. We explore the
concept of smart home with the integration of IoT services and cloud computing
to it, by embedding intelligence into sensors and actuators, networking of smart
things using the corresponding technology, facilitating interactions with smart
things using cloud computing for easy access in different locations, increasing
computation power, storage space and improving data exchange efficiency. In this
chapter we present a composition of three components to build a robust approach
of an advanced smart home concept and implementation.
Keywords: smart home, IoT, cloud computing, event processing, home appliances,
rule-based event processing
. Introduction
Classic smart home, internet of things, cloud computing and rule-based event
processing, are the building blocks of our proposed advanced smart home integrated
compound. Each component contributes its core attributes and technologies to the
proposed composition. IoT contributes the internet connection and remote manage-
ment of mobile appliances, incorporated with a variety of sensors. Sensors may
be attached to home related appliances, such as air-conditioning, lights and other
environmental devices. And so, it embeds computer intelligence into home devices to
provide ways to measure home conditions and monitor home appliances’ functional-
ity. Cloud computing provides scalable computing power, storage space and applica-
tions, for developing, maintaining, running home services, and accessing home
devices anywhere at anytime. The rule-based event processing system provides the
control and orchestration of the entire advanced smart home composition.
Combining technologies in order to generate a best of breed product, already
appear in recent literature in various ways. Christos Stergioua etal. [1] merge cloud
computing and IoT to show how the cloud computing technology improves the
functionality of the IoT.Majid Al-Kuwari [2] focus on embedded IoT for using
analyzed data to remotely execute commands of home appliances in a smart home.
Trisha Datta etal. [3] propose a privacy-preserving library to embed traffic shap-
ing in home appliances. Jian Mao etal. [4] enhance machine learning algorithms to
play a role in the security in a smart home ecosystem. Faisal Saeed etal. [5] propose
using sensors to sense and provide in real-time, fire detection with high accuracy.
IoT and Smart Home Automation
In this chapter we explain the integration of classic smart home, IoT and cloud
computing. Starting by analyzing the basics of smart home, IoT, cloud computing
and event processing systems. We discuss their complementarity and synergy, detail-
ing what is currently driving to their integration. We also discuss what is already
available in terms of platforms, and projects implementing the smart home, cloud
and IoT paradigm. From the connectivity perspective, the added IoT appliances and
the cloud, are connected to the internet and in this context also to the home local
area network. These connections complement the overall setup to a complete unified
and interconnected composition with extended processing power, powerful 3rd
party tools, comprehensive applications and an extensive storage space.
In the rest of this chapter we elaborate on each of the four components. In
Section 1, we describe the classic smart home, in Section 2, we introduce the
internet of things [IoT], in Section 3, we outline cloud computing and in Section 4,
we present the event processing module. In Section 5, we describe the composition
of an advanced smart home, incorporating these four components. In Section 6, we
provide some practical information and relevant selection considerations, for build-
ing a practical advanced smart home implementation. In Section 7, we describe our
experiment introducing three examples presenting the essence of our integrated
proposal. Finally, we identify open issues and future directions in the future of
advanced smart home components and applications.
. Classic smart home overview
Smart home is the residential extension of building automation and involves the
control and automation of all its embedded technology. It defines a residence that has
appliances, lighting, heating, air conditioning, TVs, computers, entertainment sys-
tems, big home appliances such as washers/dryers and refrigerators/freezers, security
and camera systems capable of communicating with each other and being controlled
remotely by a time schedule, phone, mobile or internet. These systems consist of
switches and sensors connected to a central hub controlled by the home resident
using wall-mounted terminal or mobile unit connected to internet cloud services.
Smart home provides, security, energy efficiency, low operating costs and
convenience. Installation of smart products provide convenience and savings of
time, money and energy. Such systems are adaptive and adjustable to meet the
ongoing changing needs of the home residents. In most cases its infrastructure is
flexible enough to integrate with a wide range of devices from different providers
and standards.
The basic architecture enables measuring home conditions, process instru-
mented data, utilizing microcontroller-enabled sensors for measuring home condi-
tions and actuators for monitoring home embedded devices.
The popularity and penetration of the smart home concept is growing in a good
pace, as it became part of the modernization and reduction of cost trends. This is
achieved by embedding the capability to maintain a centralized event log, execute
machine learning processes to provide main cost elements, saving recommenda-
tions and other useful reports.
. Smart home services
.. Measuring home conditions
A typical smart home is equipped with a set of sensors for measuring home
conditions, such as: temperature, humidity, light and proximity. Each sensor is
Smart Home Systems Based on Internet of Things
dedicated to capture one or more measurement. Temperature and humidity may
be measured by one sensor, other sensors calculate the light ratio for a given area
and the distance from it to each object exposed to it. All sensors allow storing the
data and visualizing it so that the user can view it anywhere and anytime. To do
so, it includes a signal processer, a communication interface and a host on a cloud
.. Managing home appliances
Creates the cloud service for managing home appliances which will be hosted
on a cloud infrastructure. The managing service allows the user, controlling the
outputs of smart actuators associated with home appliances, such as such as
lamps and fans. Smart actuators are devices, such as valves and switches, which
perform actions such as turning things on or off or adjusting an operational sys-
tem. Actuators provides a variety of functionalities, such as on/off valve service,
positioning to percentage open, modulating to control changes on flow conditions,
emergency shutdown (ESD). To activate an actuator, a digital write command is
issued to the actuator.
.. Controlling home access
Home access technologies are commonly used for public access doors. A com-
mon system uses a database with the identification attributes of authorized people.
When a person is approaching the access control system, the persons identification
attributes are collected instantly and compared to the database. If it matches the
database data, the access is allowed, otherwise, the access is denied. For a wide
distributed institute, we may employ cloud services for centrally collecting persons’
data and processing it. Some use magnetic or proximity identification cards, other
use face recognition systems, finger print and RFID.
In an example implementation, an RFID card and an RFID reader have been
used. Every authorized person has an RFID card. The person scanned the card via
RFID reader located near the door. The scanned ID has been sent via the internet to
the cloud system. The system posted the ID to the controlling service which com-
pares the scanned ID against the authorized IDs in the database.
. The main components
To enable all of the above described activities and data management, the system
is composed of the following components, as described in Figure .
a. Sensors to collect internal and external home data and measure home condi-
tions. These sensors are connected to the home itself and to the attached-
to-home devices. These sensors are not internet of things sensors, which are
attached to home appliances. The sensors’ data is collected and continually
transferred via the local network, to the smart home server.
b. Processors for performing local and integrated actions. It may also be con-
nected to the cloud for applications requiring extended resources. The sensors’
data is then processed by the local server processes.
c. A collection of software components wrapped as APIs, allowing external appli-
cations execute it, given it follows the pre-defined parameters format. Such an
API can process sensors data or manage necessary actions.
IoT and Smart Home Automation
d. Actuators to provision and execute commands in the server or other control
devices. It translates the required activity to the command syntax; the device
can execute. During processing the received sensors’ data, the task checks if
any rule became true. In such case the system may launch a command to the
proper device processor.
e. Database to store the processed data collected from the sensors [and cloud
services]. It will also be used for data analysis, data presentation and visualiza-
tion. The processed data is saved in the attached database for future use.
. Internet of things [IoT] overview
The internet of things (IoT) paradigm refers to devices connected to the inter-
net. Devices are objects such as sensors and actuators, equipped with a telecom-
munication interface, a processing unit, limited storage and software applications.
It enables the integration of objects into the internet, establishing the interaction
between people and devices among devices. The key technology of IoT includes
radio frequency identification (RFID), sensor technology and intelligence technol-
ogy. RFID is the foundation and networking core of the construction of IoT.Its
processing and communication capabilities along with unique algorithms allows
the integration of a variety of elements to operate as an integrated unit but at the
same time allow easy addition and removal of components with minimum impact,
making IoT robust but flexible to absorb changes in the environment and user
preferences. To minimize bandwidth usage, it is using JSON, a lightweight version
of XML, for inter components and external messaging.
. Cloud computing and its contribution to IoT and smart home
Cloud computing is a shared pool of computing resources ready to provide a
variety of computing services in different levels, from basic infrastructure to most
sophisticated application services, easily allocated and released with minimal
efforts or service provider interaction [6, 7]. In practice, it manages computing,
storage, and communication resources that are shared by multiple users in a virtual-
ized and isolated environment. Figure  depicts the overall cloud paradigm.
Figure 1.
Smart home paradigm with optional cloud connectivity.
Smart Home Systems Based on Internet of Things
IoT and smart home can benefit from the wide resources and functionalities of
cloud to compensate its limitation in storage, processing, communication, support
in pick demand, backup and recovery. For example, cloud can support IoT service
management and fulfillment and execute complementary applications using the
data produced by it. Smart home can be condensed and focus just on the basic and
critical functions and so minimize the local home resources and rely on the cloud
capabilities and resources. Smart home and IoT will focus on data collection, basic
processing, and transmission to the cloud for further processing. To cope with secu-
rity challenges, cloud may be private for highly secured data and public for the rest.
IoT, smart home and cloud computing are not just a merge of technologies. But
rather, a balance between local and central computing along with optimization of
resources consumption. A computing task can be either executed on the IoT and
smart home devices or outsourced to the cloud. Where to compute depends on the
overhead tradeoffs, data availability, data dependency, amount of data transporta-
tion, communications dependency and security considerations. On the one hand,
the triple computing model involving the cloud, IoT and smart home, should
minimize the entire system cost, usually with more focus on reducing resource
consumptions at home. On the other hand, an IoT and smart home computing
service model, should improve IoT users to fulfill their demand when using cloud
applications and address complex problems arising from the new IoT, smart home
and cloud service model.
Some examples of healthcare services provided by cloud and IoT integration:
properly managing information, sharing electronic healthcare records enable high-
quality medical services, managing healthcare sensor data, makes mobile devices
suited for health data delivery, security, privacy, and reliability, by enhancing
medical data security and service availability and redundancy and assisted-living
services in real-time, and cloud execution of multimedia-based health services.
. Centralized event processing, a rule-based system
Smart home and IoT are rich with sensors, which generate massive data flows
in the form of messages or events. Processing this data is above the capacity of
a human being’s capabilities [8–10]. Hence, event processing systems have been
developed and used to respond faster to classified events. In this section, we focus
Figure 2.
Cloud computing paradigm.
IoT and Smart Home Automation
on rule management systems which can sense and evaluate events to respond to
changes in values or interrupts. The user can define event-triggered rule and to con-
trol the proper delivery of services. A rule is composed of event conditions, event
pattern and correlation-related information which can be combined for modeling
complex situations. It was implemented in a typical smart home and proved its
suitability for a service-oriented system.
The system can process large amounts of events, execute functions to monitor,
navigate and optimize processes in real-time. It discovers and analyzes anomalies or
exceptions and creates reactive/proactive responses, such as warnings and prevent-
ing damage actions. Situations are modeled by a user-friendly modeling interface
for event-triggered rules. When required, it breaks them down into simple, under-
standable elements. The proposed model can be seamlessly integrated into the
distributed and service-oriented event processing platform.
The evaluation process is triggered by events delivering the most recent state
and information from the relevant environment. The outcome is a decision graph
representing the rule. It can break down complex situations to simple conditions,
and combine them with each other, composing complex conditions. The output
is a response event raised when a rule fires. The fired events may be used as
input for other rules for further evaluation. Event patterns are discovered when
multiple events occur and match a pre-defined pattern. Due to the graphical
model and modular approach for constructing rules, rules can be easily adapted
to domain changes. New event conditions or event patterns can be added or
removed from the rule model. Rules are executed by event services, which sup-
ply the rule engine with events and process the evaluation result. To ensure the
availability of suitable processing resources, the system can run in a distributed
mode, on multiple machines and facilitate the integration with external systems,
as well. The definition of relationships and dependencies among events that are
relevant for the rule processing, are performed using sequence sets, generated
by the rule engine. The rule engine constructs sequences of events relevant to a
specific rule condition to allow associating events by their context data. Rules
automatically perform actions in response when stated conditions hold. Actions
generate response events, which trigger response activities. Event patterns can
match temporal event sequences, allowing the description of home situations
where the occurrences of events are relevant. For example, when the door is kept
open too long.
The following challenges are known with this model: structure for the processed
events and data, configuration of services and adapters for processing steps, includ-
ing their input and output parameters, interfaces to external systems for sensing
data and for responding by executing transactions, structure for the processed
events and data, data transformations, data analysis and persistence. It allows to
model which events should be processed by the rule service and how the response
events should be forwarded to other event services. The process is simple: data is
collected and received from adapters which forward events to event services that
consume them. Initially the events are enriched to prepare the event data for the
rule processing. For example, the response events are sent to a service for sending
notifications to a call agent, or to services which transmit event delay notifications
and event updates back to the event management system.
. Event processing languages
Event processing is concerned with real-time capturing and managing pre-
defined events. It starts from managing the receptors of events right from the event
occurrence, even identification, data collection, process association and activation
Smart Home Systems Based on Internet of Things
of the response action. To allow rapid and flexible event handling, an event process-
ing language is used, which allows fast configuration of the resources required to
handle the expected sequence of activities per event type. It is composed of two
modules, ESP and CEP.ESP efficiently handles the event, analyzes it and selects the
appropriate occurrence. CEP handles aggregated events. Event languages describe
complex event-types applied over the event log.
. Rediscovering workflow from events
In some cases, rules relate to discrepancies in a sequence of events in a work-
flow. In such cases, it is mandatory to precisely understand the workflow and its
associated events. To overcome this, we propose a reverse engineering process to
automatically rediscover the workflows from the events log collected over time,
assuming these events are ordered, and each event refers to one task being executed
for a single case. The rediscovering process can be used to validate workflow
sequences by measuring the discrepancies between prescriptive models and actual
process executions. The rediscovery process consists of the following three steps:
(1) construction of the dependency/frequency table. (2) Induction of dependency/
frequency graphs. (3) Generating WF-nets from D/F-graphs.
. Advanced smart home
In this section, we focus on the integration of smart home, IoT and cloud com-
puting to define a new computing paradigm. We can find in the literature section
[1114] surveys and research work on smart home, IoT and cloud computing sepa-
rately, emphasizing their unique properties, features, technologies, and drawbacks.
However, our approach is the opposite. We are looking at the synergy among these
three concepts and searching for ways to integrate them into a new comprehensive
paradigm, utilizing its common underlying concepts as well as its unique attributes,
to allow the execution of new processes, which could not be processed otherwise.
Figure  depicts the advanced smart-home main components and their inter-
connectivity. On the left block, the smart home environment, we can see the typical
Figure 3.
Advanced smart home—integrating smart home, IoT and cloud computing.
IoT and Smart Home Automation
devices connected to a local area network [LAN]. This enables the communica-
tion among the devices and outside of it. Connected to the LAN is a server and
its database. The server controls the devices, logs its activities, provides reports,
answers queries and executes the appropriate commands. For more comprehensive
or common tasks, the smart home server, transfers data to the cloud and remotely
activate tasks in it using APIs, application programming interface processes. In
addition, IoT home appliances are connected to the internet and to the LAN, and so
expands smart home to include IoT.The connection to the internet allows the end
user, resident, to communicate with the smart home to get current information and
remotely activate tasks.
To demonstrate the benefits of the advanced smart home, we use RSA, a robust
asymmetric cryptography algorithm, which generates a public and private key
and encrypts/decrypts messages. Using the public key, everyone can encrypt a
message, but only these who hold the private key can decrypt the sent message.
Generating the keys and encrypting/decrypting messages, involves extensive
calculations, which require considerable memory space and processing power.
Therefore, it is usually processed on powerful computers built to cope with the
required resources. However, due to its limited resources, running RSA in an IoT
device is almost impossible, and so, it opens a security gap in the Internet, where
attackers may easily utilize. To cope with it, we combine the power of the local
smart home processors to compute some RSA calculations and forward more
complicated computing tasks to be processed in the cloud. The results will then
be transferred back to the IoT sensor to be compiled and assembled together, to
generate the RSA encryption/decryption code, and so close the mentioned IoT
security gap. This example demonstrates the data flow among the advanced smart
home components. Where, each component performs its own stack of operations
to generate its unique output. However, in case of complicated and long tasks
it will split the task to sub tasks to be executed by more powerful components.
Referring to the RSA example, the IoT device initiates the need to generate an
encryption key and so, sends a request message to the RSA application, running
in the smart home computer. The smart home computer then asks the “prime
numbers generation” application running on cloud, to provide p and q prime
numbers. Once p and q are accepted, the encryption code is generated. In a later
stage, an IoT device issues a request to the smart home computer to encrypt a
message, using the recent generated RSA encryption key. The encrypted message
is then transferred back to the IoT device for further execution. A similar scenario
may be in the opposite direction, when an IoT device gets a message it may request
the smart home to decrypt it.
To summarize, the RSA scenarios depict the utilization of the strength of the
cloud computing power, the smart home secured computing capabilities and at
the end the limited power of the IoT device. It proves that without this automatic
cooperation, RSA would not be able to be executed at the IoT level.
A more practical example is where several detached appliances, such as an
oven, a slow cooker and a pan on the gas stove top, are active in fulfilling the
resident request. The resident is getting an urgent phone call and leaves home
immediately, without shutting off the active appliances. In case the relevant IoTs
have been tuned to automatically shut down based on a predefined rule, it will be
taken care at the IoT level. Otherwise, the smart home realizes the resident has
left home [the home door has been opened and then locked, the garage has been
opened, the resident’s car left, the main gate was opened and then closed, no one
was at home] and will shut down all active devices classified as risk in case of
absence. It will send an appropriate message to the mailing list defined for such
an occasion.
Smart Home Systems Based on Internet of Things
. Practical aspects and implementation considerations for IoT and
smart home
Smart home has three components: hardware, software and communication
protocols. It has a wide variety of applications for the digital consumer. Some of the
areas of home automation led IoT enabled connectivity, such as: lighting control,
gardening, safety and security, air quality, water-quality monitoring, voice assis-
tants, switches, locks, energy and water meters.
Advanced smart home components include: IoT sensors, gateways, protocols,
firmware, cloud computing, databases, middleware and gateways. IoT cloud can be
divided into a platform-as-a-service (PaaS) and infrastructure-as-a-service (IaaS).
Figure  demonstrates the main components of the proposed advanced smart home
and the connection and data flow among its components.
The smart home application updates the home database in the cloud to allow
remote people access it and get the latest status of the home. A typical IoT plat-
form contains: device security and authentication, message brokers and message
queuing, device administration, protocols, data collection, visualization, analysis
capabilities, integration with other web services, scalability, APIs for real-time
information flow and open source libraries. IoT sensors for home automation are
known by their sensing capabilities, such as: temperature, lux, water level, air
composition, surveillance video cameras, voice/sound, pressure, humidity, accel-
erometers, infrared, vibrations and ultrasonic. Some of the most commonly used
smart home sensors are temperature sensors, most are digital sensors, but some are
analog and can be extremely accurate. Lux sensors measure the luminosity. Water
level ultrasonic sensors.
Float level sensors offer a more precise measurement capability to IoT develop-
ers. Air composition sensors are used by developers to measure specific components
in the air: CO monitoring, hydrogen gas levels measuring, nitrogen oxide measure,
hazardous gas levels. Most of them have a heating time, which means that it
requires a certain time before presenting accurate values. It relies on detecting gas
Figure 4.
Advanced smart home composition.
IoT and Smart Home Automation
components on a surface only after the surface is heated enough, values start to
show up. Video cameras for surveillance and analytics. A range of cameras, with a
high-speed connection. Using Raspberry Pi processor is recommended as its camera
module is very efficient due to its flex connector, connected directly to the board.
Sound detectors are widely used for monitoring purposes, detecting sounds and
acting accordingly. Some can even detect ultra-low levels of noise, and fine tune
among various noise levels.
Humidity sensors sense the humidity levels in the air for smart homes. Its accuracy
and precision depend on the sensor design and placement. Certain sensors like the
DHT22, built for rapid prototyping, will always perform poorly when compared to
high-quality sensors like HIH6100. For open spaces, the distribution around the sensor
is expected to be uniform requiring fewer corrective actions for the right calibration.
Smart home communication protocols: bluetooth, Wi-Fi, or GSM.Bluetooth
smart or low energy wireless protocols with mesh capabilities and data encryption
algorithms. Zigbee is mesh networked, low power radio frequency-based protocol
for IoT.X10 protocol that utilizes powerline wiring for signaling and control.
Insteon, wireless and wireline communication. Z-wave specializes in secured
home automation. UPB, uses existing power lines. Thread, a royalty-free protocol
for smart home automation. ANT, an ultra-low-power protocol for building low-
powered sensors with a mesh distribution capability. The preferred protocols are
bluetooth low energy, Z-wave, Zigbee, and thread. Considerations for incorporat-
ing a gateway may include: cloud connectivity, supported protocols, customization
complexity and prototyping support. Home control is composed of the following:
state machine, event bus, service log and timer.
Modularity: enables the bundle concept, runtime dynamics, software compo-
nents can be managed at runtime, service orientation, manage dependencies among
bundles, life cycle layer: controls the life cycle of the bundles, service layers: defines
a dynamic model of communication between various modules, actual services: this
is the application layer. Security layer: optional, leverages Java 2 security architec-
ture and manages permissions from different modules.
OpenHAB is a framework, combining home automation and IoT gateway for
smart homes. Its features: rules engine, logging mechanism and UI abstraction.
Automation rules that focus on time, mood, or ambiance, easy configuration, com-
mon supported hardware:
Domoticz architecture: very few people know about the architecture of
Domoticz, making it extremely difficult to build applications on it without taking
unnecessary risks in building the product itself. For example, the entire design of
general architecture feels a little weird when you look at the concept of a sensor to
control to an actuator. Building advanced applications with Domoticz can be done
using OO based languages.
Deployment of blockchain into home networks can easily be done with
Raspberry Pi. A blockchain secured layer between devices and gateways can be
implemented without a massive revamp of the existing code base. Blockchain is a
technology that will play a role in the future to reassure them with revolutionary
and new business models like dynamic renting for Airbnb.
. Smart home and IoT examples
We can find in the literature and practical reports, many implementations of vari-
ous integrations among part of the main three building blocks, smart home, IoT and
cloud computing. For example, refer to [1214]. In this section we outline three imple-
mentations, which clearly demonstrate the need and the benefits of interconnecting
Smart Home Systems Based on Internet of Things
or integrating all three components, as illustrated in Figure . Each component is
numbered, 1–6. In the left side, we describe for each implementation, the sequence of
messages/commands among components, from left to right and from bottom up. Take
for example the third implementation, a control task constantly runing at the home
server (2) discovers the fact that all residents left home and automatically, initiates
actuators to shut down all IoT appliances (3), then it issues messages to the relevant
users/residents, updating them about the situation and the applied actions it took (6).
The use of (i) in the implementations explanation, corresponds to the circled
numbers in Figure .
. Discovery of water leaks and its prevention
First step is deploying water sensors under every reasonable potential leak
source and an automated master water valve sensor for the whole house, which now
means the house is considered as an IoT.
In case the water sensor detects a leak of water (3), it sends an event to the hub
(2), which triggers the “turn valve off ” application. The home control application
then sends a “turn off” command to all IoT (3) appliances defined as sensitive to
water stopping and then sends the “turn off” command to the main water valve
(1). An update message is sent via the messaging system to these appearing in the
notification list (6). This setup helps defending against scenarios where the source
of the water is from the house plumbing. The underlying configuration assumes
an integration via messages and commands between the smart home and the IoT
control system. It demonstrates the dependency and the resulting benefits of
combining smart home and IoT.
. Smoke detectors
Most houses already have the typical collection of smoke detectors (1), but there
is no bridge to send data from the sensor to a smart home hub. Connecting these
sensors to a smart home app (2), enables a comprehensive smoke detection system.
It is further expanded to notify the elevator sensor to block the use of it due to fire
condition (1), and so, it is even further expanded to any IoT sensor (3), who may be
activated due to the detected smoke alert.
In [5] they designed a wireless sensor network for early detection of house fires.
They simulated a fire in a smart home using the fire dynamics simulator and a
language program. The simulation results showed that the system detects fire early.
Figure 5.
Advanced smart home implementations chart.
IoT and Smart Home Automation
© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms
of the Creative Commons Attribution License (
by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,
provided the original work is properly cited.
Author details
Computer Science Department, Ashkelon Academic College, Ashkelon, Israel
*Address all correspondence to:
. Incident management to control home appliances
Consider the scenario where you leave home while some of the appliances are
still on. In case your absence is long enough, some of the appliances may over heat
and are about to blowout. To avoid such situations, we connect all IoT appliances’
sensors to the home application (2), so that when all leave home it will automati-
cally adjust all the appliances’ sensors accordingly (3), to avoid damages. Note that
the indication of an empty home is generated by the Smart Home application, while
the “on” indication of the appliance, is generated by IoT.Hence, this scenario is
possible due to the integration between smart home and IoT systems.
. Conclusions and summary
In this chapter we described the integration of three loosely coupled compo-
nents, smart home, Iot, and cloud computing. To orchestrate and timely manage
the vast data flow in an efficient and balanced way, utilizing the strengths of each
component we propose a centralized real time event processing application.
We describe the advantages and benefits of each standalone component and
its possible complements, which may be achieved by integrating it with the other
components providing new benefits raised from the whole compound system.
Since these components are still at its development stage, the integration among
them may change and provide a robust paradigm that generates a new generation of
infrastructure and applications.
As we follow-up on the progress of each component and its corresponding
impact on the integrated compound, we will constantly consider additional compo-
nents to be added, resulting with new service models and applications.
Smart Home Systems Based on Internet of Things
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... The proposed architecture is based on work done by [5], Internet of Things (IoT), Context-awareness (CA), Cloud Computing (CC) and Rule-based Event Processing Systems (RbEPS). Researchers frequently classify the problem of control as one of end-user programming, which causes them to think about research and assessment in terms of device control. ...
... In the construction of smart homes, the Internet of Things (IoT) plays a significant role. IoT allows for remote management of mobile users/devices/sensors by utilizing an internet connection [5,7,8]. Practically anything in a home might be associated with the Internet via IoT, allowing for remote monitoring and control of all connected objects regardless of time or location [9,10]. ...
... CC provides scalable infrastructures and platforms for accessing home devices and developing, managing, and executing home services anywhere at any time, in terms of processing power and/or storage space. The RbEPS allows building and controlling a full advanced smart home [5]. Scaling system capabilities, interestingly, might easily transcend some unseen threshold, leaving families feeling at the mercy of, rather than in charge of, technology [6]. ...
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The main objective of this paper is to propose a simple, low cost, reliable and scalable architecture for building Smart Home Systems (SHSs) that can be used to remotely automate and control home appliances, using microcontroller. The proposed architecture aims to take advantage of emerging technologies to make it easier to develop Smart Home systems and to provide more management by expanding its capabilities suitably. The suggested design intends to make it easier and more convenient for many applications to access context data, as well as providing a new schematic guide for creating as complete and comprehensive Smart Home Systems and data processing as possible. Related topics like smart homes and their intelligent systems will be addressed by examining prior work and proposing the authors' opinions in order to suggest the new architecture. The proposed advanced architecture's building blocks include Classic Smart Homes, Internet of Things (IoT), Context-awareness (CA), Cloud Computing (CC), and Rule-based Event Processing Systems (RbEPS). Finally, the proposed architecture is validated and evaluated by constructing a smart home system.
... This offers a wide range of powerful application programming interfaces (APIs) to interface with connected smart devices. Cloud computing sets the path for the development of linked technologies in this way [6]. A key feature of cloud computing is the fact that minimum efforts and management cloud models give us rapid services. ...
... The system does not provide the optimum result for the worst case 6 Mhamane & Shriram [41] QR Code The vacant seat of the unavailable passengers is allocated to the waiting passengers. The method helps to solve the problem of waiting time. ...
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The Development in technology has brought about a common norm of communicating and interacting with appliances remotely using portable devices like laptops and smartphones that have Internet connections. This is possible with the use of the Internet of Things, popularly referred to as IoT. This paper presents a system by which classroom held interactions specifically seat allotment is made a simpler process by secured automation coupled with the Internet of Things to develop a system that enables a person or group of people to remotely monitor seat allotment, de-allotment with precision over a wide distance. Traditional means of allotment are quite slow and tiresome especially when dealing with a large number of students. Hence there is a need to develop a system to automatically allot, track, and monitor real-time seats in a classroom. In this research, an IoT seat allotment system is proposed; the system is developed and tested within a smart classroom environment using the Federal University of Agriculture Abeokuta (FUNAAB), Nigeria as a case study. The system is implemented on Arduino IDE using C++ programming language and a prototype of the mobile IoT seat allotment application is developed using Java programming language. Through experimental analysis, it was discovered that the IoT Seat allotment achieved the ranges of 30 % to 50 % reduced time, and higher accuracy when compared with the traditional seat allotment method.
... 'Smart home can be de?ned as a residence equipped with a communication network, high-tech household devices, appliances, and sensors that can be remotely accessed, monitored, and controlled and that provide services responding to the residents' needs' (Yang et al., 2017). Thus, the modern smart home is a place equipped with various devices, lighting, heating, air conditioning, RTV equipment, household appliances and security systems that can communicate with each other and are controlled by using an application on a smartphone or tablet to by remotely turning on or off a given hardware contraption (Domb, 2019). The operation of a smart home is based on the use of a wireless home network (WiFi, Bluetooth, RFID) that allows many devices to be connected with an appropriate application developed and made available by manufacturers of smart devices. ...
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Year after year, smart home systems continue to increase in popularity among consumer households. Consumption of smart home systems is also predicted to increase steeply in the coming years. Therefore, the article aims at assessing the attitude of young consumers towards a smart home and its devices. The article is based on primary and secondary sources of information. While primary information indicated the attitude of young consumers towards smart homes in their capacity as potential buyers, secondary information allowed for the definition of the issues discussed pertaining to smart homes and their devices. Using direct research, the online survey technique was employed on a sample of 588 consumers aged 18-34 years living in Poland in 2021. The respondents’ declarations showed that they intend using smart home services in the future, and that they thought using smart home devices would be enjoyable. In addition, respondents agreed with the statements that smart home devices are easy to use and valuable in everyday life, and that using smart home devices helps complete household chores faster. In their opinion, using a smart device at home can increase security and safety by detecting gas and smoke emissions and by creating an alert in the event of unauthorised home intrusion.
... Many hope that Active Assisted Living (AAL) technologies will help build medical and social infrastructure to support ageing populations around the world, while preserving their dignity and wellbeing [9,14]. Such technologies include smart wearables that can monitor hydration levels, blood metrics, temperature, activity, and location, and report these back to the caring community [1,6,13], as well as smart access systems which can automatically identify friends, family, and caring professionals and allow them access to an older person's home [3,19,21], among many others. However, many AAL technologies, contemporary or speculative, demand extensive access to the private lives of their older users, even restricting their autonomy or imposing new regimes against their will [2,11]. ...
Conference Paper
A variety of technologies are being developed to help older people live healthier, more independent, and safer lives, for longer. While many of these technologies are positively impacting the lives of older adults, they also have the potential to dictate specific behaviours or restrict their autonomy rather than empower them. The vulnerability theory of privacy proposes that vulnerable populations are not only more likely to be susceptible to privacy violations, but are also disproportionately affected by said violations. In this position paper, we adapt the vulnerability theory of privacy to the older adult population, and identify a further potential exacerbatory cycle. The risk of a `slippery slope' of privacy violation occurs when AAL technologies enable an elevated and quantified visibility of (mis)behaviour and irregular activity that could seem to justify the deployment of further AAL technology. We present `ratchet-wise rehabilitation' as an alternative vision to the `slippery slope' and identify research and design challenges throughout the paper.
... In addition, they can remind individuals about turning off water or lights or taking medicines. They can also be used to monitor falls [26,28]; the more advanced designs of smart homes make the lives of seniors and the sick much easier. Numerous activities that previously had to be done manually have been gradually automated for convenience [29]. ...
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In the 21st century, society has been undergoing a technology-driven transformation which heralds a new revolution that has potential to strengthen the position of an individual and community but may also lead to the marginalization of certain groups. The Internet of Things takes advantage of the technology’s potential to improve digital competencies and the quality of life in society. The purpose of this paper is to obtain information about the digital competencies and needs of contemporary seniors and pre-senior age people, as well as socially sensitive groups from Poland’s rural areas. To strength the level of internet infrastructure in rural areas, power line communication (PLC) systems that utilize high-voltage line(s) between transformer substations are presented as a cost-effective communication tool. PowerLink IP has made PLC systems today more attractive and efficient than ever before. Based on nation-wide representative surveys conducted in deliberately selected groups, we collected information on digital competencies and formulated recommendations pertaining to the structure and contents of an innovative internet portal as regards offering, sharing, and the availability of commercial and social services targeted at seniors and other dependent groups. The recommended portal combines the needs of target groups with interests of entrepreneurs, self-government authorities, and NGOs.
... It requires many apps to maintain them; hence, knowledge is needed to prevent different apps installed at home to control the connected devices. Fig.3 Integrating Smart Home, IoT and Cloud computing [20]. ...
The rapid growth of the Internet of Things (IoT) has led to the development of many smart devices that have empowered homes to become Smart Homes (SH). Smart Homes play an essential role in their applications in various fields such as security, healthcare, water, and energy usage, etc. This rapid growth of IoT has led to many positive impacts in society and challenges to its applicability.
This article develops the principles of building an intelligent home microprocessor subsystem to control the multi-channel irrigation of houseplants. The relevance of this topic has also been substantiated. Currently, there is a small number of devices in demand with a comfortable user interface and timer, which allows to adjust the watering at any time of day. The advantages over other available analogs and the need to create a customized system have been investigated. The developed structural- schematic diagram of the irrigation control system of houseplants based on the Arduino Nano microcontroller and a diagram of the algorithm of the subsystem has been proposed and given. As a result, there has been an example of the development of a subsystem that aims to improve and simplify the care of houseplants, which will save time and water resources.
Natural language interfaces are gaining popularity as an alternative interface for non-technical users. Natural language interface to database (NLIDB) systems have been attracting considerable interest recently that are being developed to accept user’s query in natural language (NL), and then converting this NL query to an SQL query, the SQL query is executed to extract the resultant data from the database. This Text-to-SQL task is a long-standing, open problem, and towards solving the problem, the standard approach that is followed is to implement a sequence-to-sequence model. In this paper, I recast the Text-to-SQL task as a machine translation problem using sequence-to-sequence-style neural network models. To this end, I have introduced a parallel corpus that I have developed using the WikiSQL dataset. Though there are a lot of work done in this area using sequence-to-sequence-style models, most of the state-of-the-art models use semantic parsing or a variation of it. None of these models’ accuracy exceeds 90%. In contrast to it, my model is based on a very simple architecture as it uses an open-source neural machine translation toolkit OpenNMT, that implements a standard SEQ2SEQ model, and though my model’s performance is not better than the said models in predicting on test and development datasets, its training accuracy is higher than any existing NLIDB system to the best of my knowledge.
Internet of Things (IoT) smart home automation is gradually becoming an attractive aspect of everyday life around the world. This chapter discusses the most significant IoT network challenges and their implications for smart home security. The challenges for the networks are presented, with reference to existing practices and available standards and initiatives, along with the relevant global standards. Network security continues to be a substantial concern within the smart home community. In order to combat the network attacks, a number of security improvement strategies have been created and distributed within the Internet community. A number of solutions have been proposed that recognize and block the related threats at the network level and can contribute to the security of IoT‐based smart homes. However, there is still a big gap to be bridged to offer seamless network security across all usable devices in IoT‐based smart homes.
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Energy harvesting is the demand of present day wireless communication for improving the energy efficiency of a network and a way to green communication as well. Wireless sensor networks (WSNs) suffer from energy depletion of nodes and energy harvesting is a promising solution to enhance the life-time of sensor nodes in the area having lesser human intervention. In this work, different energy harvesting techniques have been presented and electromagnetic-based energy harvester model is deployed with WSN to evaluate its performance. Low energy adaptive clustering hierarchy protocol has been used as a routing protocol for sensor nodes. The performance of the proposed model is evaluated with variation in hardware characteristics of energy harvester and analyzed for sensor characteristics such as the number of dead nodes, alive nodes as well. The energy harvester model and WSN have been implemented on the MATLAB platform.
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IoT devices frequently generate large volumes of streaming data and in order to take advantage of this data, their temporal patterns must be learned and identified. Streaming data analysis has become popular after being successfully used in many applications including forecasting electricity load, stock market prices, weather conditions, etc. Artificial Neural Networks (ANNs) have been successfully utilized in understanding the embedded interesting patterns/behaviors in the data and forecasting the future values based on it. One such pattern is modelled and learned in the present study to identify the occurrence of a specific pattern in a Water Management System (WMS). This prediction aids in making an automatic decision support system, to switch OFF a hydraulic suction pump at the appropriate time. Three types of ANN, namely Multi-Input Multi-Output (MIMO), Multi-Input Single-Output (MISO), and Recurrent Neural Network (RNN) have been compared, for multi-step-ahead forecasting, on a sensor’s streaming data. Experiments have shown that RNN has the best performance among three models and based on its prediction, a system can be implemented to make the best decision with 86% accuracy.
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Fires usually occur in homes because of carelessness and changes in environmental conditions. They cause threats to the residential community and may result in human death and property damage. Consequently, house fires must be detected early to prevent these types of threats. The immediate notification of a fire is the most critical issue in domestic fire detection systems. Fire detection systems using wireless sensor networks sometimes do not detect a fire as a consequence of sensor failure. Wireless sensor networks (WSN) consist of tiny, cheap, and low-power sensor devices that have the ability to sense the environment and can provide real-time fire detection with high accuracy. In this paper, we designed and evaluated a wireless sensor network using multiple sensors for early detection of house fires. In addition, we used the Global System for Mobile Communications (GSM) to avoid false alarms. To test the results of our fire detection system, we simulated a fire in a smart home using the Fire Dynamics Simulator and a language program. The simulation results showed that our system is able to detect early fire, even when a sensor is not working, while keeping the energy consumption of the sensors at an acceptable level.
Conference Paper
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To enhance the convenience of life, Internet of things today is a famous research topic. However, different home appliances provide different functions and services. Hence, in this research, the IOT base Smart Home Appliances by using Cloud Intelligent Tetris Switch is proposed which including the Cloud Intelligent Tetris Switch, Cloud Home as a Service (HaaS) Server, and IOT based Appliances. The Cloud Intelligent Tetris Switch is proposed to achieve the power control and local data exchanging. In addition, the dynamic extendable module is embedded. The IOT based Appliances provide the service of identification. Similar to the EPC network, the corresponding home appliance description data with RFID unique number can be obtained from the Internet and manufacture. The Cloud Home as a Service (HaaS) Server is proposed to provide the user interface for client users, storage all the information or data corresponding to the specific house, and query the function information of individual home appliance.
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Cloud computing and Internet of Things (IoT) are two very different technologies that are both already part of our life. Their adoption and use are expected to be more and more pervasive, making them important components of the Future Internet. A novel paradigm where Cloud and IoT are merged together is foreseen as disruptive and as an enabler of a large number of application scenarios.In this paper, we focus our attention on the integration of Cloud and IoT, which is what we call the CloudIoT paradigm. Many works in literature have surveyed Cloud and IoT separately and, more precisely, their main properties, features, underlying technologies, and open issues. However, to the best of our knowledge, these works lack a detailed analysis of the new CloudIoT paradigm, which involves completely new applications, challenges, and research issues. To bridge this gap, in this paper we provide a literature survey on the integration of Cloud and IoT. Starting by analyzing the basics of both IoT and Cloud Computing, we discuss their complementarity, detailing what is currently driving to their integration. Thanks to the adoption of the CloudIoT paradigm a number of applications are gaining momentum: we provide an up-to-date picture of CloudIoT applications in literature, with a focus on their specific research challenges. These challenges are then analyzed in details to show where the main body of research is currently heading. We also discuss what is already available in terms of platforms-both proprietary and open source-and projects implementing the CloudIoT paradigm. Finally, we identify open issues and future directions in this field, which we expect to play a leading role in the landscape of the Future Internet.
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This paper presents the design of a smart home system based on Internet of Things (IOT) and service component technologies. The current situation of IOT has been analyzed in detail. An approach based on SOA and component technology has been proposed and applied, which can help to realize every-changing dynamic semantic integration of the web services. Furthermore, the software architecture and main modules are explained as well. Finally, this paper discussed the heterogeneous information fusion in the Internet of Things. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of [CEIS 2011]
Conference Paper
The number and variety of Internet-connected devices have grown enormously in the past few years, presenting new challenges to security and privacy. Research has shown that network adversaries can use traffic rate metadata from consumer IoT devices to infer sensitive user activities. Shaping traffic flows to fit distributions independent of user activities can protect privacy, but this approach has seen little adoption due to required developer effort and overhead bandwidth costs. Here, we present a Python library for IoT developers to easily integrate privacy-preserving traffic shaping into their products. The library replaces standard networking functions with versions that automatically obfuscate device traffic patterns through a combination of payload padding, fragmentation, and randomized cover traffic. Our library successfully preserves user privacy and requires approximately 4 KB/s overhead bandwidth for IoT devices with low send rates or high latency tolerances. This overhead is reasonable given normal Internet speeds in American homes and is an improvement on the bandwidth requirements of existing solutions.
Mobile Cloud Computing is a new technology which refers to an infrastructure where both data storage and data processing operate outside of the mobile device. Another recent technology is Internet of Things. Internet of Things is a new technology which is growing rapidly in the field of telecommunications. More specifically, IoT related with wireless telecommunications. The main goal of the interaction and cooperation between things and objects which sent through the wireless networks is to fulfill the objective set to them as a combined entity. In addition, there is a rapid development of both technologies, Cloud Computing and Internet of Things, regard the field of wireless communications. In this paper, we present a survey of IoT and Cloud Computing with a focus on the security issues of both technologies. Specifically, we combine the two aforementioned technologies (i.e Cloud Computing and IoT) in order to examine the common features, and in order to discover the benefits of their integration. Concluding, we present the contribution of Cloud Computing to the IoT technology. Thus, it shows how the Cloud Computing technology improves the function of the IoT. Finally, we survey the security challenges of the integration of IoT and Cloud Computing.