Web Service-Based Remote Monitoring System for Smart Home Space

1
Web Service-Based Remote Monitoring System for
Smart Home Space
Project for CSE535 Mobile Computing

• Sheng Cai Joshua Ferguson Xinhui Hu Wei Wu

2
Introduction

• Smart Home Space
• wireless sensor network deployed in home
  environment
• monitoring the home space
• Motivation
• remotely obtain the sensed data
• retrieve the data pervasively
• apply our system framework for extended functions

3
System Framework
Database
Home Space
Home Gateway
Sensor
Internet
Web Service
Sensor
Mobile User
Sensor

• ByWei Wu Joshua Ferguson

4
System Framework

• Web service
• user based, require user name and password to
  login
• generate users request for specific type of
  data, e.g. temperature
• query the requested information and display it
• Home gateway
• handoff between home network inside the home
  space and internet outside
• combined with database
• functions of database
• store the data transferred from sensors
• send users request
• ByWei Wu Joshua Ferguson

5
Application1Energy-efficient Data gathering
schemes

• We provide data gathering schemes in order to
  reduce energy consumption and finally increase
  the lifetime of the sensors.
• Data gathering scheme based on user request
• Data gathering scheme without user request
• ByXinhui Hu

6
Application2Temperature control

• Adaptive and Intelligent
• Using the owners control orders and customs
  recorded in the database, the controller could
  automatically executes to make the environment to
  fit owners customs.

By Sheng Cai
7
Energy Efficient Building Environment Control
Strategies UsingReal-time Occupancy Measurements

• Varick L. Erickson, Yiqing Lin, Ankur
  Kamthe,Rohini Brahme,Amit Surana, Alberto E.
  Cerpa, Michael D. Sohn and Satish Narayanan.
• Energy Efficient Building Environment Control
  Strategies Using Real-time Occupancy
  Measurements, BuildSys09, November 3, 2009,
  Berkeley, CA, USA.
• Presented by
  Sheng Cai

8
Introduction

• Heating ventilating and air conditioning (HVAC)
  systems account for 50 of the total energy
  budget in buildings.
• Current systems often rely on building regulation
  maximum occupancy numbers for maintaining proper
  temperatures.

9
How to save energy?

• 1 Get room occupancy data in the building.
• 2 Make a model which has the capability to
  predict room usage patterns .
• 3 Modify the control system by the prediction
  model for energy saving.

10
Contribution

• 1 SCOPES
• a wireless camera sensor network for
  collecting occupancy data in a multi-function
  building.
• 2 Multivariate Gaussian Model
• a model for predicting occupancy patterns in
  buildings by using SCOPESs data.

11
1 SCOPES
The red and green lines indicate the locations of
the transition boundaries. The gray areas
indicate the area occupancies that can be derived
from the transition data.
12
occupancy data
When a person crosses any one of these transition
points, the cameras capture and process the image
data and, thus indicating the transition.
SCOPES system detected 80 of all recorded
transitions.
13
2 Occupancy Models

• Multivariate Gaussian Model
• Let Oh denote all occupancies that occur per
  second during hour h where 1 h 24, HB, HM, L,
  and Of refers to the Hallwayback,
  Hallwaymiddle,Lab, and Office areas occupancies.
• n represents the number of observations in hour
  h. µHB, µHM, µL, and µOf denote the average
  occupancy.
• We calculate a vector of means µh
  µHB,µHM,µL,µOf and covariance matrix from Oh.
• probability density function

14
Multivariate Gaussian Model Results
15
Multivariate Gaussian Model Results
RMSE root mean squared error NRMSE normalized
root mean squared error
16
Potential Energy Savings

• 14 reduction in HVAC energy usage by having an
  optimal control strategy based on occupancy
  estimates and usage patterns.
• 20 occupancy estimation errors have negligible
  impact (0.28) on HVAC energy savings estimation
  of 14.

17
Conclusions

• Dynamic occupancy levels and patterns in
  buildings can be estimated, and the energy
  efficiency gains possible by utilizing actual
  facility usage information for building controls.

18
future research

• Room occupancy data in the longer durations
  (days, months) could provide a more exact model.
• Doing online HVAC control algorithm by using
  real-time data supported by a wireless camera
  sensor network.

19
Presented by Joshua Ferguson

• HiCon A Hierarchical Context Monitoring and
  Composition Framework for Next-Generation
  Context-Aware Services
• K. Cho, I. Hwang, S. Kang, B. Kim, J. Lee, S.
  Lee, S. Park, Y. Rhee, and J. Song, HiCon A
  Hierarchical Context Monitoring and Composition
  Framework for Next Generation Context-Aware
  Services, IEEE Network, July 2008.

20
Historic Problem

• Current App developers 're-invent'
  context-monitoring schemes
• Futile work Why do this?
• No established framework to use
• Additionally, context-monitoring has many
  geographical scales

21
Overview of Paper

• Framework design
• Focused on upward scalability
• Body, Local Region, Global
• Implementation of each, with an application for
  two
• BAN Games, Taxi Cab Management, Core Functions
• (very) Brief conclusion

22
Fun Part First Applications BANs

• SympaThings Household objects respond to
  personal emotions
• Running Bomber Cooperative game for exercising
  on treadmill

23
Fun Part First Applications Local Region

• Ubiquitous taxiCab Taxi Dispatching Service
• Matches Taxi dispatching to passenger arrival
  patterns in predictive manner
• U-BattleWatcher Monitors soldier contextual
  information, and tracks location of enemy forces

24
Framework Overview

• Each level of context is centered around one
  central object
• BAN PocketMon, Region HiperMon, Global EGI
  (Efficient Global Infrastructure)
• The Context Gateway provides two-way access to
  upper level context information

25
Framework Overview (cont...)
26
PocketMon

• Architecture

Sample PCMQ (Personal Context Monitoring Query)
CONTEXT (location Library) AND (activity
sleeping) AND (time evening) ALARM F ?
T DURATION 120 days
27
HiperMon

• Responsible for collecting many contexts and
  matching queries to them
• Necessarily capable of high-performance massive
  context processing

28
HiperMon (cont)

• Uses mainframe-like dataflow evaluation methods
  to effectively process thousands of queries.
• Imagine every restaurant in a large-sized city
  registered a query or two. Could range in the
  10,000s of concurrent and continuous queries,
  just for restaurants.
• Implemented using Border Monitoring Query
• Sets of queries are evaluated to find data
  boundaries
• The continuous stream of information generated
  by individual context switches is then tracked,
  and when boundaries are crossed, the associated
  services are notified.

29
EGI

• Cluster heads abstract the context delivery
  requirements of their children
• Global and Local proxies are responsible for
  coordination of communication in their respective
  domain.

30
Evaluation - PocketMon

• Throughput is the maximum number of queries that
  can be handled without creating a growing
  backlog
• Conventional Context Monitoring Methods
  Classification of context states for each query.
  Apples to oranges comparison of frameworks.

31
Evaluation PocketMon (cont)
Data Scale of Sensors Sample Rate/second
1 8 300
2 16 600
3 24 900
4 32 1200
5 40 1500
6 48 1800
7 56 2100
32
Evaluation UbiCab on HiperMon

• Performed on a simple 1.4 Ghz CPU and 4GB RAM.
• Streams are context evaluations, in the form of
  tuples.
• Findings suggest UbiCab can coordinate taxis in a
  small/mid sized city using HiperMon

33
Evaluation HiperMon (cont)
Workload Parameters Traffic condition (per 1 km2) Traffic condition (per 1 km2) Traffic condition (per 1 km2)
Workload Parameters Heavy Normal Light
of Taxis 100 100 100
of People 20,000 10,000 5000
of Taxi calls/min. 8 2 1.5
34
Criticism

• No evaluation of Global level scaling of
  framework.
• Lack of convincing evaluation at PocketMon
  scale.
• Needs review or comparison against other
  similarly multi-scale frameworks.

35
Take-Aways

• Centralized context information definitely
  streamlines efficiency, but at what cost?
• Privacy? Public control?
• How can we balance providing useful regional
  context services while effectively anonymizing
  users when required?
• Food for thought. . .
• Google/Microsoft have both implemented some sort
  of Region-level context monitoring combined with
  their mapping services.

36
Web-based Remote Monitoring of Live EEG
Philip D. Healy, Ruairi D. OReilly, Geraldine
B. Boylan, John P. Morrison 2010 12th IEEE
International Conference on e-Health Networking
Applications and Services (Healthcom)

• Presented by Wei Wu

37
Introduction

• The prompt analysis of EEG data captured in ICU
  is essential
• only a limited windows of opportunity for
  treatment may be available
• e.g. hypoxic-ischemic encephalopathy (HIE)
• EEG is a prognostic value in the case of it
• commence within 6 hours of birth sensitive in
  delay
• Solution
• provided that the process of transferring the EEG
  data between locations does not introduce delays
• our system web-based EEG remote monitoring
  system that allows live recordings to be viewed
  in near-real-time while acquisition is ongoing
• The only tool required for viewing EEG data is a
  modern web browser
• It is ubiquitous, data analysis is not limited by
  geographic location.

38
Related Work

• An early method of remote monitoring of EEG and
  other physiological data
• involved analogue transmission over telephone
  lines
• allow for real-time monitoring, constrained by
  poor bandwidth to a limited number of channels
• A neurosurgery ICU monitoring system was
  developed at UCLA
• provided a web interface to physiological signal
  data
• remotely monitor the dynamic generation of plots
  that are viewed as images embedded in HTML pages
• lack the immediacy available
• The BRIAN system
• allows for interactive, but nonreal-time,
• remote monitoring using compressed digital
  transmission

39
System Overview

• The system is comprised of three components
• an upload application
• transfer data from the acquisition location to
  the data server
• running on the file server, streams data from the
  EEG data file to the data server
• file server is not accessible outside the
  hospital LAN because it contains sensitive
  patient information
• a data server
• act as a repository of recorded data, and
  provides the interfaces necessary for both humans
  and software to interact with the data
• streamed to the viewing application running on
  the neurophysiologist's PC
• a viewing application
• monitor individual recordings

viewing application
upload application
40
Data Acquisition and Uploading

• A more "hands off" approach
• the required data extracted from the EEG data
  file output by the acquisition software
  performed on a file server
• the strategy currently used for data acquisition
  in the upload application
• All EEG data found in the file server when it is
  first opened are uploaded immediately
• Thereafter, the upload application continually
  monitors the data file's access time in order to
  detect the addition of new data. When a change is
  observed, the newly added data are uploaded.

41
Data Storage

• Data server is a standalone application acts as
  a bridge
• Confidentiality preservation
• avoid the acquisition of unnecessary patient data
• upload application does not read demographic
  patient information from data files, and the data
  server has no facility for storing it
• recordings are identified either by their
  recording ID or the location and time that
  acquisition commenced
• Data storage
• an embedded SQL database for storing recording
  information
• recording attributes such as the acquisition
  location, commencement time
• a collection of data files containing sample
  values
• sample values is maintained for each channel of
  each recording

42
Data Storage cont.

• A web interface
• hosted by an embedded HTTP server
• the user must first log into the web application
  hosted on the server by providing a user name and
  password
• upon successful login, the user is presented with
  a list of the recordings available on the server
• for each recording, three options are available
• view detailed information about the recording
• download the recording in EDF
• open the recording in the viewing application
• A REST interface
• also hosted by the embedded HTTP server
• to support the functionality of the upload and
  viewing applications
• for the upload application, POST requests are
  supported for creating recordings and appending
  sample data to them
• for the viewing application, GET requests are
  provided for retrieving recording information and
  downloading arbitrary intervals of sample data

43
Remote Monitoring

• Compared with a simple plot, display of EEG tends
  is more complex
• switch between several different montages when
  analyzing a recording
• two viewing modes are provided
• a review mode switch instantaneously between one
  screenful of data at the user's discretion
• a "playback" mode simulate the acquisition
  process
• A plugin technology
• Flash, Java and Silverlight
• Flash was chosen for its ubiquity and excellent
  graphics support
• Display the data
• once the upload of a recording to the data server
  has commenced, the recording may be viewed by
  users with access to the data server
• next, the application begins to download signal
  data from the data server, check its changes

44
Conclusion and Future Work

• A general technical solution for soliciting
  expert feedback on data being streamed from
  critically ill patients
• Provide several secondary benefits
• allow engagement in activities such as archiving,
  data mining
• Remote monitoring reduces diagnosis turnaround
  time
• Future Work
• reduce the delay between the acquisition of data
  and its availability in the viewing application
• compression scheme
• end-to-end encryption
• addition of video support

45
Location Based Sleep Scheduling for Target
Tracking Applications in Smart Space Environments

• By Ben Harrison, Alan Marshall
• IEEE Communications Society subject matter
  experts in the IEEE ICC 2009
• Xinhui Hu

46
Outline
Motivation
Related work
LocMAC
Results
Conclusion
47
Motivation
Smart Space
How to monitor our patients behaviors?
48
Motivation
Smart Space
Please use us!
49
Motivation
Smart Space
I am out of battery!
50
Related work

• SMAC uses a fixed sleep schedule for the entire
  network.
• DMAC expands upon SMAC by adding a limited
  dynamic sleep cycle, based on network latency and
  power availability on a node by node basis.
• PMAC adjusts sleep schedule for the whole network
  based on its current network throughput.

51
LocMAC

• LocMAC is a new location aware power saving MAC
  layer for target tracking applications in Smart
  Space Environments.
• LocMAC propagates user location updates, and
  perform Location Based Sleep Scheduling (LBSS)
  based on users location.

52
LocMAC operation

• Users of a LocMAC network are connected by mobile
  nodes which provide users speed.
• Nodes of a LocMAC network maintain a list of
  neighbors including neighbors location and their
  targets.
• When a node receives a target update it will
  determine if the target is within the range of
  service and activate any sensors required.

53
LocMAC operation

• Take the age of the position information into
  account for sensors sleeping interval
• Sleep period

54
Results

• Compared to SMAC
• each node wakes at least once as a target moves
  through the diameter of its service area (iii)
• each node wakes at least once as a target moves
  through its service range (iv)
• at least once every 15 seconds1 (v).

55
Results

• Performance of LocMAC with different mobility
  patterns.
• Performance of LocMAC with different velocities.

56
Conclusion

• LocMAC guarantees service to a mobile node within
  a wireless WSN while wasting less power from
  unnecessary wakeups compared to fixed sleep
  schedule MAC layers.
• For future improvement, LocMAC should consider
  targets with different applications Qos
  requirement.

57

• Question?