wireless multimedia sensor networks a brief overview and some … · 2010-04-26 · wireless...
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Wireless Multimedia Sensor NetworksA brief overview and some challenges
A. Mostefaoui
Franche Comte Computing Lab.(LIFC)
Contact: [email protected]
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A. Mostefaoui 2
OutlineOutline
1. Introduction : Multimedia Sensor Networks : new challenges or reemerging roles?
2. Data adaptation vs energy consumption : prolonging the network lifetime.
3. data fusion for surveillance purposes.
4. System architecture.
5. Conclusion and future works.
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IntroductionIntroduction
• Multimedia Sensor Networks ?– Set of sensors with the ability to collect
multimedia data :– video, audio, image,
– scalar data (temperature, humidity…)
Cyclops video board on Mica motes
128x128140x140
240x240
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IntroductionIntroduction
• Multimedia Sensor Networks ?
– Communicate through a wireless network
Network
Server
Sink
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IntroductionIntroduction
• Characteristics:
– High data rate (Mbps)– Real time requirements (processing and
transmission)– QoS issues – Rich media (interpretation issues)
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IntroductionIntroduction
• Why now ?– Availability of cheap multimedia hardware
• e.g., CMOS camera
– Advances in hardware: miniaturization of components (chips, embedded systems, radiofrequency communication…)
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IntroductionIntroduction
• Applications:
– Large scale video surveillance system
– Health care applications
– Home automation applications
– Etc.
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ChallengesChallenges
“In addition to the ability to retrieve multimedia data, Wireless Multimedia Sensor Networks will also be able to store, process in real-time, correlate and fuse multimedia data originated from
heterogeneous sources”
A.F. Akyildiz et al.*
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Challenges (Challenges (wsnwsn vsvs wmsnwmsn))
• Coverage issues
– WSN: sensing range is usually considered as a circle
– WMSN: • we deal with “views” (cone) with zooming
capabilities and rotation “orientation”possibilities.
• Better information could be captured from far multimedia sensors (global understanding of the observed scene)
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Challenges (Challenges (wsnwsn vsvs wmsnwmsn))
• Fusion issues
– WSN: averaging scalar sensor measurements.
– WMSN: • Merging two images or videos does not
make sense!
• We are more concerned with content merging (observed scene)
• How to extract data content from images and videos?
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Challenges (Challenges (wsnwsn vsvs wmsnwmsn))
• QoS issues
– WSN: data uncertainty (corrupted measurements due to environmental conditions for instance)
– WMSN:
• Data acquisition: high resolution does not necessary lead to a better understanding of the data content! …
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Challenges (Challenges (wsnwsn vsvs wmsnwmsn))
• QoS issues
– WMSN: • Example :
333x358 16M colors, no light 167x180 16 colors, light 167x180 BW (2 colors), light
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Challenges (Challenges (wsnwsn vsvs wmsnwmsn))
• QoS issues– WMSN:
• Real-time delivery: – avoidance of jitter
– Critical event detection
• End-user real-time processing: in order to extract content features for instance.
•320x200•30 fps•256 gray scale•15Mbps raw
•320x200•2 fps•4 gray scale•256 Kbps raw
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Challenges (Challenges (wsnwsn vsvs wmsnwmsn))
• Heterogeneity issues
– WSN: scalar heterogeneity (humidity, pressure, temperature, etc.)
– WMSN: • Scalar sensors,
• Multimedia sensors (video, audio),
• Intermediate storage/processing hubs,
• Etc.
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Challenges (Challenges (wsnwsn vsvs wmsnwmsn))
• Heterogeneity issues
– WMSN: reference architecture
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Challenges (Challenges (wsnwsn vsvs wmsnwmsn))
• Energy optimization issues
– WSN: energy consumption is the main optimization concern.
– WMSN :
• Also true…
• But: more depended of the target application (video surveillance applications, …)
• Cross-layer optimization including acquisition, coding, transmission.
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Data adaptation Data adaptation vsvs energy consumptionenergy consumption
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The problemThe problem
• In wireless Ad Hoc environments:
– In addition to their own workload, nodes have to perform network functionalities (routing and relaying)
– Nodes are energy constrained.
– Each node, participating in the wireless ad hoc network, wants to prolong its own lifetime by reducing its “relaying tasks”
– If each node acts as above, the network will broke down!
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MotivationMotivation
G
F
A
B
C
D
E Network Tolopogy
Initial situation
...
Time
Time
B C D E F GA
"deal"
Scenario 1
Scenario 2
Energy Provision
Network workload (relaying)
Application workload
Initial situation
...
B C D E F GAB C D E F GA
B C D E F GA B C D E F GA
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ObjectiveObjective
meets the node interest with the network one. We are looking for a “deal” (an approach) such that:
– Each node, participating in the network, has the guaranty that its participation is conditioned by its own energy provision.
– Ensure a long (max) lifetime to the network.
– Ensure fairness to all nodes of the network
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Our approachOur approach
• A totally distributed algorithm (i.e., each node is running this algorithm).
• Nodes communicate only with their neighbors.
• Analytically proven to ensure fairness to all nodes.
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NotationNotation
Let us use the following notation:
• Ei(k): energy provision of node i at time slot (k).
• Ti,j(k): number of class j tasks that node I holds at time slot (k).
• ei,j: energy spent by node i to execute one task from class j.
• Ni,j(k): number of class j tasks executed by node i during time slot
(k).
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NotationNotation
– k > S
)(
1,
)(,
)(1
1,1
)(,1
...k
N
m
jjN
kjN
k
m
jj
kj
E
eT
E
eT ∑∑== ==
Remaining tasks of class J at instant (k)
Energy needed for node 1 to execute a task from class J
Remaining energyprovision at instant (k) for node 1
Node 1 ratio value at instant (k)
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AnalysisAnalysis
Let us use the following notation:
ki
m
jji
kji
ki E
eT
x∑
== 1,
)(,
Remaining tasks of class J at instant k
Energy needed for node i to execute a task from class J
Remaining energyprovision at instant k for node i
Node i ratio value atinstant k
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Energy Aware Assignment Algorithm
if do1. Exchange with each 2. Compute if do
if do3. Compute
4. Send tasks of class to node v endif
endifendif
φ≠kiV
)(kix )(k
in Vv ∈∑∈
+ −+=)(
)( )()()()()1(
kiv Vn
ki
kv
kiv
ki
ki xxAxx
)( )()1( ε>−+ kv
ki xx
)0( )()( <− ki
kv xx
−=
θ
α,
)()()()(
,)(
,i
kv
kik
ikiv
kvi e
xETA
)(,kviα θ
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Energy Aware Assignment Algorithm
• Diffusion matrix:– We used a symmetric doubly stochastic matrix
• We proved that the above algorithm converges after a certain number of iterations.
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Experimental settingsExperimental settings
Energy consumption:
• Data processing:– Four types of tasks: baseline (system tasks), processing-
intensive (FFT), storage intensive and communication-intensive.
– Two types of computer (Dell Latitude C600 and Crossbow Stargate platform)
• Data transmission:– Lucent WaveLAN IEEE 802.11 Silver card.
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Experimental settingsExperimental settings
Settings :– Types of nodes: two types
• PC like nodes (Dell Latitude C600)
• PDA like nodes (Crossbow Stargate Platform)
• 5~20 nodes in the network.
– Network topology: three topologies • linear topology,
• fully-connected topology
• mixed topology:
– Initial energy provision:• Varied in order to get a variation in the standard deviation
between nodes.
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Data adaptation Data adaptation vsvs energy consumptionenergy consumption
Convergence (equity)
Totally connected topology Linear topology
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Preliminary resultsPreliminary results
Results:
Network lifetime improvement
50%49.01%55.5%
Mixed topologyLinear topologyFully connectedtopology
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Continuous 3D representation for Continuous 3D representation for
multimedia sensor networksmultimedia sensor networks
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The challengeThe challenge
Traditional surveillance infrastructure
What happen when we are dealing with dozens or hundreds of video sensors ?
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Our approachOur approach
– Targeted to the video surveillance system
– 3D real-time reconstruction : the views of the sensor nodes are merged in the 3D scene of the observed region
– Allows a comprehensive representation of the scene
– Better supports spatio-temporal requests :
• “Where is the busiest place in the building?”
• “Where is the place where people spend most of their time?”
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The ProblemsThe Problems
– Deporting image processing to sensors• Reduce the network load by sending only some data about
detected objects in the image
– Fusion of these data
– 3D representation of the resulted information for the human observer
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System architectureSystem architecture
• Three main components:
– The Capture devise
– The fusion server
– The end-user server
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System architectureSystem architecture
• The capture devise – Capturing and processing the video
• Hardware architecture (32 bits processors)
• processing (simple testing methods)– Difference between images
– Borders extraction
– Vectorisation
– Reduces the amount of data to sendto the fusion server • Set of segment coordinates.
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System architectureSystem architecture
• Image capture
• Difference with the reference image
• Thresholding to obtainblack and white image
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System architectureSystem architecture
• Filtering to extract borders.
• Vectorisation to obtain a setof segment coordinates.
• Sending the coordinates tothe fusion server
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System architectureSystem architecture
• The fusion server– Merges the data sent by all the capture devices
– Places the detected objects into the 3D environment
– Use a 3D database• 3D Model of the scene
• Position and directionof each camera– Needs a calibration step
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System architectureSystem architecture
• 3D interpretation:– Using simple 3D hypothesis
• People standing on the floor
– 2D data are placed in the 3D environment• Possibility of merging data (average position….)
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System architectureSystem architecture
• The end-user server:– Interface representing in 3D
• The geometrical model of the scene
• The detected objects– Viewed from camera positions
– Calculated from all other positionsin the building
– Using Virtual Reality software• Virtools (Dassault system)
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System architectureSystem architecture
• The end-user server:
Virtual image of the scene from Virtual image of the scene from camera#1 point of viewcamera#1 point of view
Virtual image of the scene from Virtual image of the scene from camera#2 point of viewcamera#2 point of view
Virtual image of the scene from Virtual image of the scene from another free point of viewanother free point of view
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Preliminary ResultsPreliminary Results
• Capture device image processing capabilities
• 27 frames/second for 320x200 video images
• 2 frames/second for 640x480 video images
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Conclusion and future workConclusion and future work
• Key points:– Data processing located on the sensorsallows a
significant network traffic reduction
– 3D real-time reconstruction allows:• Comprehensive understanding of the observed scenes
• Complex spatio-temporal requests
• Advanced user interaction
• Limitations:– Not always relevant (depends on the target
application)
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Thank you for your attention!...Thank you for your attention!...
Questions are welcomed ;Questions are welcomed ;--) )