1. EcoHome
Control Things Using AI Techniques
Murtaza Ahmed Kazi
Student at SZABIST
Bs in Computing
Karachi, Sind, Pakistan
kazimurtaza@gmail.com
Abstract— the concept of Smart Homes using AI plays an important
role in the planning of future housing-based models of care. More
and more research groups are working in this area, Such as MIT,
Siemens, Cisco, IBM, Xerox, Microsoft and Etc. In these groups,
nearly 20 home labs have been set. In These home labs, more than 30
appliances are used, over 5Network protocols are produced and more
than 3 AI Techniques have been used.
Keywords— Artificial Intelligence; Voice Recognition; Image &
Pattern Recognition; Expert System; Android; Arduino
I. INTRODUCTION
Smart house; devices integrated with an intelligent agent. Letting the
agent control our environment would not only bring efficiency but
comfort, like security management, and electrical appliance control
and other all sorts of controls one could think of. Agent could
observe the patterns and would perform actions on prediction if given
the platform. Many believe that we should use voice recognition to
communicate with the agent, control devices operation with your
voice such as, “living-room light1 on”, agent could easily split the
string and classify it as living-room being the location and light1 as
device and on/off as action to perform on it. This means agent would
require understand discrete words, but that is not the difficult part as
explained above, the part which could cause problem is teaching all
possible input, but keeping in mind since our model world is a typical
home, we could easily classify it for an agent, how many rooms and
devices there are and what are their identifiers, such as living room,
kitchen, master bedroom, second bedroom and others.
II. VOICE RECOGNITION[1]
The part where voice is converted to text could become a challenge.
We could easily use tools available to us, rather than reinventing the
wheel, voice tool which could be used is already available in cheapest
of android phones, “goggle voice” or “goggle now” - it is open
source and could be used in an android app with two lines of code, so
we would not need to make a language processing module. Voice
could eliminate all unnatural form of communication. And voice
recognition is not evolved enough to sort out speech patterns, which
could be a limiting factor, But luckily for us, Android phones already
comes with voice recognition and processing framework, which
could be used to take input from user and send to a agent which
would be connected to all the devices, where as a agent could be a
computer or a micro-controller such as Arduino or Netdino(with SD-
Card, could hold knowledge base which has all the knowledge of all
devices in the house and its operation once it gets the input from
owners phone it could then send out the preferred action to the
device.
III. EXPERT SYSTEMS[2]
one could say this could be perfect environment for a expert system,
it could manage the home in comfortable way it could prove to be
quiet efficient to perform some of the goals like managing
temperature of the house while conserving energy, by using
temperature preferred by the owner, present temperature, and
humidity data or conserving energy using motion sensors to check if
no one is around to turn of the room. System could easily keep this
data and adapt it by learning process, sensors such as temperature and
humidity and motion could be used to easily provide such
information to the system. This system could have operation such as;
prediction of owner’s preferences, learning efficient ways of doing
operation like for example switching on devices in morning before
the owner has set the alarm for, this way things could be ready for
him when he does wake up. All is only possible when everything is
connected centrally and all information could be accessed by the
agent like alarm clock, motion, temperature, time, lights, and owner’s
preferences and other.
Expert system and combination of rules, knowledge base and an
inference mechanism for applying the rules, knowledge base would
be made of knowledge of house such as – motion sensing, devices
and its types, other systems, thermal details and previous
consumptions of things and its inhabitants their names relationships,
age, priority habits and preferences. And system could not generate
this knowledge base by just observing actions, we need to classify
what should already be taught to the agent and what things, the agent
can learn on its own.
IV. PATTERN RECOGNITION[3]
Once decided what things an agent should learn from, now let’s look
how an agent would observe, Pattern recognition; supplying of data
to system, one could use this to recognize the habits of the owner
and, using pattern recognition to learn how many people are in house
to adjust the temperature could be a example and gathering
information from sensors and making patterns for owner, like when
he usually wakes up, so agent could bring the temperature to the
preferred point or turning on the water heater or if owner has
suddenly fallen to ground and is not answering back to the agent
perhaps the owner needs some sort of help and call emergency
services, while no such sensors are available we could use other
sensors in combination to determine things, such as camera,
proximity sensor, motion sensor, light sensor, temperature sensors
and global position system , dirty location provided by networks and
wifi with goggle maps using overlays to find accurate location to a
specified room of the owner. For example a house could be in one of
two states occupied or unoccupied, by knowing this the system could
modify its operation like if the home is unoccupied it would switch of

2. all devices and turn on the security system. Integrating a camera,
could open up a whole channel of information for agent to compute
although having maximum data could make a very efficient agent,
but will also over crowd it and slow down its computation, suppose
we have no hardware limitations for now and we integrate camera
with agent, which could possibly recognize faces, habits and position
of its inhabitants quiet efficiently, But processing image is quiet
expensive computation wise and agent has to be at least a desktop
machine. One example of Image processing; could be that camera
checks the person who just entered the room and scans its faces and
finds no one with such face in its knowledge base could call police
and alert owners or others and starts playing loud music and tells the
person to evacuate and its picture has been recorded and sent to local
police.
V. NETWORK[4]
And at last we only require one thing which will be able to connect
all these devices together much like “bus” in motherboards of our
computers, creating smart house network, option available are;
Powerline (XlO, EIB Powerline etc); Busline: EIB, Cebus, Lonwork,
Batibus EHS etc) and Radio Frequency (RF) (e.g. Bluetooth, Wifi,
Zigbi and most major smart home manufacturers), choosing one
totally depends on your situation powerlines and buslines are not
appropriate updates for a house which is already made that situation
could use radio frequency more efficiently preferably wifi as others
have short range and capacity or zigbi which has the ability to form a
mesh network.
VI. EXAMPLES[5]
Some of the examples could be; Security Management System
controlled by AI, keeping track of its owners and protecting them in
case of all sorts of emergencies, and being a personal assistant of all
its owners, keeping track of their calendar and schedules and
reminding tasks which are given by owner for another owner, and
controlling appliances by itself or by owner request, using voice as
communication medium between agent and owner, keeping track of
electricity, gas and water usage and forwarding it to a local server for
detailed charts and statistical computation and representations, and
parental control – letting parents know where their kids are what are
they doing. And some smart devices could be; a pillow with in-built
speakers, which could be used for music and reading book while
owner falls asleep, and keep check sensors for data such as
respiration, temperature, pulse and if something is wrong could warn
emergency services or others living in the house. And refrigerator
which keeps data about all the items in side and keep track of
expiration data and could place order when something is finished
could use camera with image processing to keep track of items.
VII. CONCLUSION
Voice Recognition, Image recognition, pattern recognition, Expert
system all require one essential component, Learning, observing
patterns and changing knowledge base and rules to meet owners
feedback/preferences and efficient standards. Learning could be done
in two ways one is that a user requests something to be canceled or
second using of patterns recognition to learn something by observing
user pattern.
REFERENCES
[1] Enhancingresidential automationsystems with artificial intelligence,T.
Abinger W. KastnerG. Luber G. Neugschwandtner, Automation
Systems Group, Vienna Universityof Technology,Siemens AG, NX
Scientific Conference 2008,November2008.
[2] Solvinghome automationproblems usingartifical intelliengence,
Grayson Evans - Directorof Engineering, MTI Inc.,Portland, OR.
pp. 396, August 1991.
[3] Solvinghome automationproblems usingartifical intelliengence,
Grayson Evans - Directorof Engineering, MTI Inc.,Portland, OR.
pp. 398, August 1991.
[4] Smart home research, Li Jiang, Da-YouLiu, Bo Yang, College of
Computer Science andTechnology, Jilin University,pp 659, August
2004.
[5] Smart home research, Li Jiang, Da-YouLiu, Bo Yang, College of
Computer Science andTechnology, Jilin University,pp 660, August
2004.