location-aware resource management in smart home environments
DESCRIPTION
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@UTA) The University of Texas at Arlington, USA E-mail: [email protected] - PowerPoint PPT PresentationTRANSCRIPT
SAJAL K. DAS CReWMaN
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@UTA)
The University of Texas at Arlington, USA
E-mail: [email protected]
URL: http://crewman.uta.edu
[Funded by US National Science Foundation]
SAJAL K. DAS CReWMaN
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 HealthcareConsider a heart attack or an accident victimDesired 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@cse
M. Kumar, S. K. Das, et al., “PICO: A Middleware Platform for Pervasive Computing,” IEEE Pervasive Computing, Vol. 2, No. 3, July-Sept 2003.
Heart attack victim
Heart attack victim
Pervasive Healthcare
Ambulance
Ambulance
Victim-AmbulanceCommunity
Largercommunityto save patient
Physician
Hospital HospitalCardiacSurgeon
Nurse
• Spouse• Police• Traffic control • Insurance Co.
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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 BlocksCamileuns (Physical devices)
(Context-aware, mobile, intelligent, learned, ubiquitous nodes)
Computer-enabled devices: small wearable to supercomputers
Sensors, actuators, network elements Communication protocols
CamileunsAccess pointInternet Gateway Access point
Gateway
Bluetooth802.11bCellular…
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PICO Building BlocksDelegents (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
Community
Delegents
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Visitor’sDelegent
Camileuns + Delegents = Chameleons
Surveillance
Traffic Monitor
Information Kiosk
Police Community
Automobile Community
Streetlamp
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PICO Architecture
PICO Middleware Services
Community
Delegents
CamileunsAccess point/Gateway Access point/
Gateway
Bluetooth802.11bCellular…
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Smart Homes: Objectives Use smart and pro-active technology
Cognizant of inhabitant’s daily life and contexts Absence of inhabitant’s 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 inhabitant’s explicit activities Savings of inhabitant’s 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@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
Face recognition, automated door entry
Smart sprinklers
Lighting control
Door/lock controllers,Surveillance system
Robot vacuum cleaner
Robot lawnmower
Intelligent appliancesClimate control
Intelligent Entertainment
Automated blinds
Remote site monitoring and controlAssistance for disabilities
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MavHome: Bob Scenario 6:45 am: MavHome turns up heat to achieve optimal temperature for waking (learned)
7:00 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)
When Bob returns, grocery order has arrived and hot tub is ready (just-in-time).
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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 inhabitant’s 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 inhabitant’s 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
House Agent
Rooms/robots
Agent Agent Agent Network / mobile network …
Agent Agent Agent Network / mobile network …
Appliances/robots
Transducers/actuators
User Interface
External resources
Physical
• Sensors• Actuators• Networks• Agents
Communication
• Routing• Multimedia download
Information
• Data Mining• Action Prediction• Mobility Prediction• Active database
Decision
• MDP/policy•Reinforcement learning• Multiagent systems/ communication
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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 inhabitant’s 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
Research Prototypes Underlying Technology Location Data Granularity
Active Badge
(Univ. of Cambridge)
Infrared Symbolic Room-level
Active Bats
(Univ. of Cambridge)
Ultrasonic Geometric 9 cm
Cricket (MIT) RF and Ultrasound Symbolic 4 x 4 feet
RADAR (Microsoft) IEEE 802.11 WLANs Symbolic 3 – 4.3 m
Smart Floor
(Georgia Tech)
Pressure Sensors Geometric Position of sensors
Easy Living (Microsoft)
Vision Triangulation Symbolic variable
Motion Star Scene Analysis Geometric 1 m
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Inhabitant’s 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
inhabitant’s 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
Bob’s 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, ...
Sample Floor-plan Graph-Abstraction
Possible contexts: jk (order-2), j (order-1), (order-0)
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Trie Representation and Phrase Frequencies
jk (order-2) j (order-1) (order-0)
k|jk (1)
|jk (1)
a|j (1)
aa|j (1)
k|j (1)
kk|j (1)
h|j (1)
|j (2)
a(4) aa(2) aj(1)
j(2) ja(1) jaa(1)
jk(1) jh(1) k(4)
ko(1) koo(1) kk(2)
o(4) oo(2) h(2)
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
a (7) j (7) o (6)h (2) k (8)
j (1)a (2)
k (1)a (1)
h (1)a (2) k (2) k (2)o (2)
o (1)
o (2)
Phrases and frequencies of different orders
Phrases: a, j, k, ko, o, jh, h, aa, jk, koo, ja, aj, kk, oo, jaa, jkk, ...
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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/(1+1) = 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
jk (order-2) j (order-1) (order-0)
k|jk (1)
|jk (1)
a|j (1)
aa|j (1)
k|j (1)
kk|j (1)
h|j (1)
|j (2)
a(4) aa(2) aj(1)
j(2) ja(1) jaa(1)
jk(1) jh(1) k(4)
ko(1) koo(1) kk(2)
o(4) oo(2) h(2)
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Phrase Probabilities
0.0048
0.0048
0.0048
0.0048
0.0905
0.0809
0.0048
ja
jaa
jk
jh
a
aa
aj
0.5905
0.0809
0.0048
0.0048
0.0195
0.0095
0.0809
0.0095
k
kk
ko
koo
o
oo
h
j
Phrase Probability Phrase Probability
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
Bob’s 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 …
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Probability Computation of Individual Locations
Location Probability
k
a
h
o
j
0.6754
0.1794
0.0833
0.0346
0.0207
Bob’s 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
a
j
hk
o
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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:
nlnllnn vVvVvVvVvVvV ,...,,Pr,...,,Pr 22112211
)(log)()( xpxpXH
nn
nOnc
logloglog)(
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Entropy Estimation Bob’s 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: d
i ii VVVVHd
VH1 121 ,...,,|
1)(
a (7) j (7) o (6)h (2) k (8)
j (1)a (2)
k (1)a (1)
h (1)a (2) k (2) k (2)o (2)
o (1)
o (2)
5795.26
30lg
30
6
8
30lg
30
8
2
30lg
30
2
7
30lg
30
7
7
30lg
30
7)( 1
VH
9361.0
2
4log
4
2
2
4log
4
2
30
85log
5
1
2
5log
5
2
2
5log
5
2
30
73log
3
1
2
3log
3
2
30
7)|( 12
VVH
789.12
)|()()( 121
VVHVHVH
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LeZi-Update: Location Prediction Scheme
Init dictionary, phrase w
loop
wait for next symbol v
if (w.v in dictionary)
w := w.v
else
encode <index(w), v>
add w.v to dictionary
w := null
forever
Initialize dictionary := empty
loop
wait for next codeword<i, s>
decode phrase := dictionary[i].s
add phrase to dictionary
increment frequency of every prefix
of every suffix of phrase
forever
EncoderDecoder
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
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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 inhabitant’s movement behavior in the long run
Concept of Asymptotic Equipartition Property (AEP) helps capture inhabitant’s 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: L() Threshold Probability
1 0.289
2 0.080
3 0.002
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Capturing Typical Routes
0.0048
0.0048
0.0048
0.0048
0.0905
0.0809
0.0048
ja
jaa
jk
jh
a
aa
aj
0.5905
0.0809
0.0048
0.0048
0.0195
0.0095
0.0809
0.0095
k
kk
ko
koo
o
oo
h
j
Phrase Probability Phrase Probability
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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Bob’s Movement along Typical Routes
a
j
k
o
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:
M
i i
b
a
b
a
M
i i
M
i istat Pab
dtab
tPdtttpPenergyE
111 2)(
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 ~ 12–13 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 resident’s 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:
n
j iji
q
p
n
j ijiopt P
pqdtttpPenergyE
1 1,
1 1, 2
)(
Using standard power usage, optimal energy plan results in ~ 2–2.5 KWH of average daily energy consumption.
Optimal Scenario
But lacks automation and needs constant manual intervention
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Energy ConsumptionPredictive Energy Plan:
Devices turned on and off based on the prediction of resident’s 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,
E[energypredict] E[energyopt]
sPpq
energyEn
j iji
tpredict
1 1,2
For the scenario, predictive scheme yields ~3-4 KWH consumption
Successful prediction reduction of manual operations and saving of inhabitant’s invaluable time inhabitant’s comfort
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Discrete Event Simulator
Simulation Structure
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
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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
Inhabitant’s 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 inhabitant’s absence, the house has minimal activity to conserve energy resources
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Granularities of Prediction
Predicting next zone Inhabitant’s immediate next zone / location A coarse level movement pattern in different locations
Predicting typical routes / paths Inhabitant’s typical routes along with zones More granular indicating inhabitant’s 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 inhabitant’s activities can be observed
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A Snapshot of Simulation
Master bedroom
Closet
closet
Bedroom Bedroom
Restroom
Restroom Wash
room
kitchen
Living Room
kitchenkitchenkitchen
0
20
10
30
40
50
60
70
80
90
100
Success Rate
Corridor
kitchen
Dining RoomDining RoomDining RoomDining Room
4
2
6
8
10
12
14
Energy Savings
Static
Optimal
Predicted
Predicted Actual Correct Prediction
Dining Room
kitchen
GarageGarageGarage
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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 inhabitant’s movements
Only 4-5 days to be cognizant of inhabitant’s 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 3–4 KB memory for inhabitant’s 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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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 Int’l 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 Int’l. Conference on Mobile Computing and Networking (MobiCom’99), pp. 1-12, Aug 1999 (Best Paper Award).
Selected References
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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.
Selected References
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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 RemarksConcluding Remarks
““AA teacher teacher can never truly teach unless he is can never truly teach unless he is
still learning himself. A lamp can never light still learning himself. A lamp can never light
another lamp unless it continues to burn its another lamp unless it continues to burn its
own flame. The teacher who has come to the own flame. The teacher who has come to the
end of his subject, who has no living traffic end of his subject, who has no living traffic
with his knowledge but merely repeats his with his knowledge but merely repeats his
lesson to his students, can only load their lesson to his students, can only load their
minds, he cannot quicken them”.minds, he cannot quicken them”.
Rabindranath TagoreRabindranath Tagore (Nobel Laureate, (Nobel Laureate,
1913)1913)