LOCATIONAWARE RESOURCE MANAGEMENT IN SMART HOME ENVIRONMENTS

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LOCATION-AWARE RESOURCE MANAGEMENT IN SMART
HOME ENVIRONMENTS

• Sajal K. Das, Director
• Center for Research in Wireless Mobility
  Networking (CReWMaN)
• Department of Computer Science and Engineering
  (CSE_at_UTA)
• The University of Texas at Arlington, USA
• E-mail das_at_cse.uta.edu
• URL http//crewman.uta.edu
• Funded by US National Science Foundation

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What is a Smart Environment ?

• Saturated with computing and communication
  capabilities to make
• intelligent decisions in an automated,
  context-aware manner
• ? pervasive or ubiquitous computing vision.
• Technology transparently weaved into the fabric
  of our daily lives
• ? technology that disappears. (Weiser 1991)
• Portable devices around users networked with
  body LANs, PANs
• (personal area networks) and wireless sensors
  for reliable commun.
• Environment that takes care of itself or users ?
  intelligent
• assistants provide proactive interaction with
  information Web.
• Examples Smart home, office, mall, hotel,
  hospital, park, airport

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Smart/Pervasive Healthcare

• Consider a heart attack or an accident victim
• Desired actions
• Coordinate with the ambulance, hospital,
  personal physician, relatives and friends,
  insurance, etc.
• Control the traffic for smooth ambulance pass
  through
• Prepare the ER (Emergency Room) and the ER
  personnel
• Provide vital medical records to physician
• Allow the physician to be involved remotely
• On a Timely, Automated, Transparent basis
• PICO (Pervasive Information Community
  Organization)
• http//www.cse.uta.edu/pico_at_cs
  e
• M. Kumar, S. K. Das, et al., PICO A
  Middleware Platform for Pervasive Computing,
  IEEE Pervasive Computing, Vol. 2, No. 3,
  July-Sept 2003.

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Pervasive Healthcare
Heart attack victim
Victim- Ambulance Community

• Spouse
• Police
• Traffic control
• Insurance Co.

Larger community to save patient
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PICO Framework

• Creates mission-oriented, dynamic computing
  communities of software agents that perform tasks
  on behalf of the users and devices autonomously
  over existing heterogeneous network
  infrastructures, including the Internet.
• Provides transparent, automated services what
  you want, when you want, where you want, and how
  you want.
• Proposes community computing concept to provide
  continual, dynamic, automated and transparent
  services to users.

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PICO Building Blocks

• Camileuns (Physical devices)
• (Context-aware, mobile, intelligent, learned,
  ubiquitous nodes)
• Computer-enabled devices small wearable to
  supercomputers
• Sensors, actuators, network elements
• Communication protocols

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PICO Building Blocks

• Delegents (Intelligent Delegates)
• Intelligent SW agents and middleware
• Location/context-aware, goal-driven services
• Dynamic community of collaborating delegents
• Proxy-capable exist on the networking
  infrastructure
• Resource discovery and migration strategies
• QoS (quality of service) management

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Camileuns Delegents Chameleons
Streetlamp
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PICO Architecture
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Smart Homes Objectives

• Use smart and pro-active technology
• Cognizant of inhabitants daily life and contexts
• Absence of inhabitants explicit awareness
• Learning and prediction as key components
• Pervasive communications and computing capability
• Optimize overall cost of managing homes
• Minimize energy (utility) consumption
• Optimize operation of automated devices
• Maximize security
• Provide inhabitants with sufficient comfort /
  productivity
• Reduction of inhabitants explicit activities
• Savings of inhabitants time
• The profound technologies are those which
  disappear (Weiser, 1991)

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Smart Home Prototypes /Projects

• Aware Home (GA-Tech) Determination of Indoor
  location and activities
• Intelligent Home (Univ. Mass.) Multi-agent
  systems technology for designing an intelligent
  home
• Neural Network House (Univ. Colorado, Boulder)
  Adaptive control of home environment (heating,
  lighting, ventilation)
• House_n (MIT) Building trans-generational,
  interactive, sustainable and adaptive environment
  to satisfy the needs of people of all age
• Easy Living (Microsoft Research) Computer
  vision for person-tracking and visual user
  interaction
• Internet Home (CISCO) Effects of Internet
  revolution in homes
• Connected Family (Verizon) Smart technologies
  for home-networking

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MAVHome at CSE_at_UTA

• MavHome Managing an Adaptive Versatile Home
• Unique project focuses on the entire home
• Creates an intelligent home that acts as a
  rational agent
• Perceives the state of the home through sensors
  and acts on the environment through effectors
  (device controllers).
• Optimizes goal functions Maximize inhabitants
  comfort and productivity, Minimize house
  operation cost, Maximize security.
• Able to reason about and adapt to its inhabitants
  to accurately route messages and multimedia
  information.
• http//ranger.uta.edu/smarthome
  
• S. K. Das, et al., The Role of Prediction
  Algorithms in the MavHome Smart Home
  Architecture, IEEE Wireless Communications,
  Vol. 9, No. 6, pp. 77 84, Dec. 2002.

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MavHome Vision
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MavHome Bob Scenario

• 645 am MavHome turns up heat to achieve optimal
  temperature for waking (learned)
• 700 am Alarm rings, lights on in bed-room,
  coffee maker in the kitchen (prediction)
• Bob steps into bathroom, turns on light MavHome
  records this interaction (learning), displays
  morning news on bathroom video screen, and turns
  on shower (proactive)
• While Bob shaves, MavHome senses he is 2 lbs
  overweight, adjusts his menu (reasoning and
  decision making)
• When Bob finishes grooming, bathroom light turns
  off, kitchen light and menu/schedule display
  turns on, news program moves to the kitchen
  screen
• (follow-me multimedia communication)
• At breakfast, Bob notices the floor is dirty,
  requests janitor robot to clean house
  (reinforcement learning)
• Bob leaves for office, MavHome secures the house
  and operates lawn sprinklers despite knowing 70
  predicted chance of rain (over rule)
• In the afternoon, MavHome places grocery order
  (automation)

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MAVHome Multi-Disciplinary Research Project

• Seamless collection and aggregation (fusion) of
  sensory data
• Active databases and monitoring
• Profiling, learning, data mining, automated
  decision making
• Learning and Prediction of inhabitants location
  and activity
• Wireless, mobile, and sensor networking
• Pervasive computing and communications
• Location- and context-aware middleware services
• Cooperating agents MavHome agent design
• Multimedia communication for entertainment and
  security
• Robot assistance
• Web monitoring and control

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MAVHome Agent Architecture

• Hierarchy of rational agents to meet
  inhabitants needs and optimize house goals
• Four cooperating layers in an agent
• Decision Layer
• Select actions for the agent
• Information Layer
• Gathers, stores, generates knowledge
  for decision making
• Communication Layer
• Information routing between agents and
  users/external sources
• Physical layer
• Basic hardware in house

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Indoor Location Management

• Location Awareness
• Location (current and future) is the most
  important
• context in any smart computing paradigm
• Why Location Tracking ?
• Intelligent triggering of active databases
• Efficient operation of automated devices
• Guarantees accurate time-frame of service
  delivery
• Supports aggressive teleporting and
  location-aware multimedia services -- seamless
  follow of media along inhabitants route
• Efficient resource usage by devices -- Energy
  consumption only along predicted locations and
  routes that the inhabitant is most likely to
  follow

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Location Representation

• Location Information
• Geometric Location information in explicit
  co-ordinates
• Symbolic - Topology-relative location
  representation
• Blessings of Symbolic Representation
• Universal applicability in location tracking
• Easy processing and storage
• Development of a predictive framework

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Indoor Location Tracking Systems
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Inhabitants Movement Profile

• Efficient Representation of Mobility Profile
• In-building movement sampled as collection of
  sensory information
• Symbolic domain helps in efficient representation
  of sensor-ids
• Role of Text Compression
• Lempel Ziv type of text compression aids in
  efficient learning of inhabitants mobility
  profiles (movement patterns)
• Captures and processes sampled message in chunks
  and report in encoded (compressed) form
• Idea Delay the update if current string-segment
  is already in history (profile) essentially a
  prefix matching technique using variable-to-fixed
  length encoding in a dictionary minimizes
  entropy
• Probability computation Prediction by partial
  match (PPM) style blending method start from
  the highest context and escape into lower
  contexts

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MavHome Floor Plan and Mobility Profile
Graph-Abstraction
Sample Floor-plan

• Bobs movement profile a j k k o o j h h a a j
  k k o o j a a j k k o o j a a j k k

• Incremental parsing results in phrases
• a, j, k, ko, o, jh, h, aa, jk, koo,
  ja, aj, kk, oo, jaa, jkk, ...

• Possible contexts jk (order-2), j
  (order-1), ? (order-0)

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Trie Representation and Phrase Frequencies
Phrases a, j, k, ko, o, jh, h, aa, jk, koo, ja,
aj, kk, oo, jaa, jkk, ...
Phrases and frequencies of different orders

• Probability of jaa
• Absence in order-2 and order-1 escape
  probability in each order ½
• Probability of jaa in order-0 1/30
• Combined probability of phrase jaa
• (½) (½ )(1/30) 0.0048

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Probability Computation of Phrases

• Probability of k
• ½ at the context of order-2
• Escaping into next lower order (order-1) with
  probability ½
• Probability of k at the order-1 (context of
  kk) 1/(11) ½
• Probability of escape from order-1 to lowest
  order (order-0) ½
• Probability of k at order-0 (context of ? ) 4
  / 30
• Combined probability of phrase k ½ ½ ½
  ½ (4/30) 0.509

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Phrase Probabilities

• Bobs movement profile a j k k o o j h h a a j
  k k o o j a a j k k o o j a a j k k

Probabilities of individual locations can be
estimated by dividing the phrase probabilities
into their constituent symbols according to
symbol-frequency and adding up all such
frequencies for a particular symbol
(location) Total probability for location k
is 0.5905 0.0809
0.0048/2 0.0048/3 0.6754
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Probability Computation of Individual Locations

• Bobs movement profile a j k k o o j h h a a j
  k k o o j a a j k k o o j a a j k k
• Phrases a, j, k, ko, o, jh, h, aa, jk, koo, ja,
  aj, kk, oo, jaa, jkk, ...
• Probabilistic prediction of locations (symbols)
  based on their ranking
• Prime Advantages of Lempel-Ziv type compression
  most likely location is predicted
• Prediction starts from k and proceeds along a,
  h, o and j

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Characterizing Mobility from Information Theory

• Movement history A string v1v2v3 of symbols
  from alphabet ?
• Inhabitant mobility model V Vi, a
  (piece-wise) stationary, ergodic stochastic
  process where Vi assumes values vi??
• Stationarity Vi is stationary if any of its
  subsequence is invariant with respect to shifts
  in time-axis
• Essentially the movement history v1, v2, , vn
  reaches the system as C(w1), C(w2), , C(wn)
  where wi s are non-overlapping segments of
  history vi and C(wi)s are their encoded forms
• Minimizes H(X) and asymptotically outperforms any
  finite-order Markov model
• The number of phrases is bounded by the relation

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Entropy Estimation

• Bobs movement profile a j k k o o j h h a a
  j k k o o j a a j k k o o j a a j k k
• For a particular depth d of an LZ trie, let
  H(Vi) represent entropy at ith level.
• Running-average of overall entropy is

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LeZi-Update Location Prediction Scheme

• A paradigm shift from position based update to
  route based update
• Encoder Collects symbols and stores in the
  dictionary in a compressed form
• Decoder Decodes the encoded symbols and
  update phrase frequencies

Encoder
Decoder
Init dictionary, phrase w loop wait for next
symbol v if (w.v in dictionary) w w.v
else encode ltindex(w), vgt add w.v to
dictionary w null forever
Initialize dictionary empty loop wait for
next codewordlti, sgt decode phrase
dictionaryi.s add phrase to dictionary
increment frequency of every prefix
of every suffix of phrase forever
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Predictive Framework Route Tracking

• Probability of a set of route sequences depends
  exponentially on relative entropy between actual
  route-distribution and its type-class
• Route-sequences away from actual distribution
  have exponentially smaller probabilities
• Typical-Set Set of sequences with very small
  relative entropy
• Small subset of routes having a large probability
  mass that controls inhabitants movement behavior
  in the long run
• Concept of Asymptotic Equipartition Property
  (AEP) helps capture inhabitants typical set of
  routes

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Probability Computation of Typical Routes

• From AEP, typical routes classified as ? 2
  -1.789 L(?) - ? ? Pr?
• where L(?) is the length of phrase ? and ? is
  a very small value
• Threshold-probability of inclusion of a phrase
  into typical-set
• depends on its length L(?)

• At our context

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Capturing Typical Routes

• At this point of time and context, the
  inhabitant is most likely to move around the
  routes along Bedroom 2, Corridor, Dining room and
  Living room
• Typical Set of route segments comprises of
  k, kk, koo, jaa, aa

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Bobs Movement along Typical Routes

• Typical Route k o o k j a a
• Bedroom 2, Corridor, Dining room and Living room

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Energy Consumption

• Static Energy Plan
• Devices remain on from morning until the
  inhabitant leaves for office and again after
  return at the end of the day.
• Let Pi power of ith device M maximum number
  of devices t device-usage time p(t)
  uniform PDF.
• Expected average energy consumption

• Using typical values of power, number and
  usage-time for lights, air-conditioning and
  devices like television, music-system,
  coffee-maker from standard home, static energy
  plan yields 1213 KWH average daily energy
  consumption.
• Worst-Case
  scenario

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Energy Consumption

• Optimal (Manual) Energy Plan
• Every device turned on and off manually during
  residents entrance and exit in a particular
  zone.
• Pi,j power of ith device in jth zone ? max
  devices in a zone R zones t device-usage
  time in a zone p(t) uniform PDF.
• Expected average energy consumption

• Using standard power usage, optimal energy
  plan results in 22.5 KWH of average daily
  energy consumption.
• Optimal Scenario
• But lacks automation and needs constant manual
  intervention

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Energy Consumption

• Predictive Energy Plan
• Devices turned on and off based on the
  prediction of residents typical routes and
  locations (Incorrect prediction incurs overhead)
• Devices turned on in advance existence of time
  lag (?t)
• s predictive success-rate. As s ? 1,
• Eenergypredict ? Eenergyopt

• For the scenario, predictive scheme yields 3-4
  KWH consumption
• Successful prediction reduction of
  manual operations and saving of inhabitants
  invaluable time inhabitants comfort

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Discrete Event Simulator

• Event types Daily actions of a user, e.g.,
  sleeping, dining, cooking, etc.
• Event Queue
• Priority Queue for buffering events
• Events ranked according to time stamp.
• Event Initializer
• Generates the first event and pushes it into
  the event queue
• Event Processing
• Carried out with every event
• Calls the event generator to generate next
  event and pushes it into the queue
• Calls various action modules depending
  upon the type of event

Simulation Structure
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Simulation Assumptions

• Simulation Duration 70 days
• Different life-styles at weekdays and weekends
• Mobility initiated as the inhabitant wakes up in
  the morning and starts daily-routine
• Inhabitants residence-time at every zone
  uniformly distributed between a maximum and a
  minimum value
• Negligible delay between sensory data
  acquisition and actuator activation
• Prediction occurs while leaving every zone
• In inhabitants absence, the house has minimal
  activity to conserve energy resources

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Granularities of Prediction

• Predicting next zone
• Inhabitants immediate next zone / location
• A coarse level movement pattern in different
  locations
• Predicting typical routes / paths
• Inhabitants typical routes along with zones
• More granular indicating inhabitants movement
  patterns
• Predicting next sensor
• Every next sensor predicted from current sensor
• Large number of predictions lead to system
  overhead
• Predicting next device
• Predict every next device the inhabitant is going
  to use
• Details of inhabitants activities can be
  observed

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A Snapshot of Simulation
Master bedroom
Restroom
kitchen
kitchen
kitchen
kitchen
kitchen
Dining Room
Dining Room
Dining Room
Dining Room
Dining Room
kitchen
Success Rate
Restroom
Wash room
Closet
Corridor
closet
0
Bedroom
Bedroom
Energy Savings
Living Room
Static
Optimal
Predicted
Predicted
Actual
Correct Prediction
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Learning Curve and Predictive Accuracy

• 85 90 accuracy in predicting next sensor,
  zone and typical route
• Route prediction accuracy slightly lower than
  location prediction, yet provides more
  fine-grained view about inhabitants movements
• Only 4-5 days to be cognizant of inhabitants
  life-style and movements
• Higher granularity keeps device prediction
  accuracy low (63)

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Memory Requirements
Variation of Success-rate with table-size

• 85 success rate with only 34 KB memory for
  inhabitants profile
• Small size typical set (5.5 -- 11 of total
  routes) as typical routes

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Energy Savings
Reduction in Average Energy Consumption

• Energy along predicted routes / locations only
  minimum wastage
• Average energy consumption 1.4 (optimal /
  manual energy plan)
• 65 72 energy savings in comparison with
  current homes

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Reduction in Manual Operations

• Prediction accuracy ? reduction of manual
  operations of devices ? brings comfort and
  productivity, saves time
• 80 85 reduction in manual switching
  operations

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Future Work

• Route prediction and resource management in
  multi-inhabitant (possibly cooperative) homes
• Design and analysis of location-aware wireless
  multimedia communication in smart homes
• Integration of smart homes with wide area
  cellular networks (3G wireless) for complete
  mobility management solution
• QoS routing in resource-poor wireless and sensor
  networks
• Security and privacy issues

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Selected References

• A. Roy, S. K. Das Bhaumik, A. Bhattacharya, K.
  Basu, D. Cook and S. K. Das, Location Aware
  Resource Management in Smart Homes, Proc. of
  IEEE Intl Conf. on Pervasive Computing (PerCom),
  pp. 481-488, Mar 2003.
• S. K. Das, D. J. Cook, A. Bhattacharya, E.
  Hierman, and T. Z. Lin, The Role of Prediction
  Algorithms in the MavHome Smart Home
  Architecture, IEEE Wireless Communications,
  Vol. 9, No. 6, pp. 77 84, Dec. 2002.
• A. Bhattacharya and S. K. Das, LeZi-Update An
  Information Theoretic Framework for Personal
  Mobility Tracking in PCS Networks, ACM Journal
  on Wireless Networks, Vol. 8, No. 3, pp. 121-135,
  Mar-May 2002.
• A. Bhattacharya and S. K. Das, LeZi-Update An
  Information Theoretic Approach to Track the
  Mobile Users in PCS Networks, Proc. ACM Intl.
  Conference on Mobile Computing and Networking
  (MobiCom99), pp. 1-12, Aug 1999 (Best Paper
  Award).

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Selected References

• D. J. Cook and S. K. Das, Smart Environments
  Algorithms, Protocols and Applications, John
  Wiley, to appear, 2004.
• A. Bhattacharya, A Predictive Framework for
  Personal Mobility Management in Wireless
  Infrastructure Networks, Ph.D. Dissertation,
  CSE Dept, UTA (Best PhD Dissertation Award), May
  2002.
• A. Roy, Location Aware Resource Optimization in
  Smart Homes, MS Thesis, CSE Dept, UTA (Best MS
  Thesis Award), Aug 2002.
• S. K. Das, A. Bhattacharya, A. Roy and A. Misra,
  Managing Location in Universal Location-Aware
  Computing, in Handbook in Wireless Networks
  (Eds, B. Furht and M. Illyas), Chapter 17, CRC
  Press, June 2003.

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Technology Forecasts (?)

• Heavier-than air flying machines are not
  possible
• 
  Lord Kelvin, 1895
• I think there is a world market for maybe five
  computers
• IBM Chairman
  Thomas Watson, 1943
• 640,000 bytes of memory ought to be enough for
  anybody
• 
  Bill Gates, 1981
• The Internet will catastrophically collapse in
  1996
• 
  Robert Metcalfe
• Long before the year 2000, the entire
  antiquated structure of college degrees, majors
  and credits will be a shambles
• 
  Alvin Toffler

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Concluding Remarks
A teacher can never truly teach unless he is
still learning himself. A lamp can never light
another lamp unless it continues to burn its own
flame. The teacher who has come to the end of his
subject, who has no living traffic with his
knowledge but merely repeats his lesson to his
students, can only load their minds, he cannot
quicken them. Rabindranath Tagore
(Nobel Laureate, 1913)