LOCATIONAWARE RESOURCE MANAGEMENT IN SMART HOME ENVIRONMENTS - PowerPoint PPT Presentation


Title: LOCATIONAWARE RESOURCE MANAGEMENT IN SMART HOME ENVIRONMENTS


1
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

2
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

3
(No Transcript)
4
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.

5
Pervasive Healthcare
Heart attack victim
Victim- Ambulance Community
  • Spouse
  • Police
  • Traffic control
  • Insurance Co.

Larger community to save patient
6
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.

7
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

8
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

9
Camileuns Delegents Chameleons
Streetlamp
10
PICO Architecture
11
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)

12
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

13
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.

14
MavHome Vision
15
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)

16
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

17
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

18
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

19
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

20
Indoor Location Tracking Systems
21
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

22
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)

23
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

24

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

25
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
26
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

27
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

28
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

29
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
30
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

31
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

32
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

33
Bobs Movement along Typical Routes
  • Typical Route k o o k j a a
  • Bedroom 2, Corridor, Dining room and Living room

34
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

35
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

36
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

37
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
38
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

39
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

40
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
41
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)

42
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

43
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

44
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

45
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

46
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).

47
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.

48
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

49
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)
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Title: LOCATIONAWARE RESOURCE MANAGEMENT IN SMART HOME ENVIRONMENTS


1
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

2
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

3
(No Transcript)
4
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.

5
Pervasive Healthcare
Heart attack victim
Victim- Ambulance Community
  • Spouse
  • Police
  • Traffic control
  • Insurance Co.

Larger community to save patient
6
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.

7
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

8
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

9
Camileuns Delegents Chameleons
Streetlamp
10
PICO Architecture
11
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)

12
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

13
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.

14
MavHome Vision
15
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)

16
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

17
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

18
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

19
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

20
Indoor Location Tracking Systems
21
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

22
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)

23
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

24

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

25
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
26
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

27
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

28
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

29
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
30
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

31
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

32
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

33
Bobs Movement along Typical Routes
  • Typical Route k o o k j a a
  • Bedroom 2, Corridor, Dining room and Living room

34
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

35
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

36
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

37
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
38
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

39
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

40
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
41
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)

42
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

43
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

44
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

45
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

46
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).

47
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.

48
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

49
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)
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