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Next Century Challenges: Scalable Coordination in
sensor NetworksMOBICOMM (1999)
Deborah Estrin, Ramesh Govindan, John Heidemann, Satish Kumar
Presented by
Mohammed Alam (shahed)
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OUTLINE
Introduction Challenges to Sensor Networks Localized Algorithms for Coordination Directed Diffusion Related Work Summary Discussion
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NETWORKED SENSORS
Sensor devices coordinating to achieve larger sensing task. EXAMPLE:
Tracking inventory Tracking motion of vehicles Temperature Noise level
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EXAMPLES of Sensors
29 Palms Fixed/Mobile ExperimentTracking vehicles with a UAV-delivered sensor network
TINY OS
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Design Challenges
Sheer number of devices Rule out traditional network device management Ratio of communicating nodes to users much
larger (1000 :1). Impossible to concentrate on specific sensors.
Power constraint Device failure common
Battery supply limited
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Design Challenges
Frequent change in position Sensors added Sensors moved Sensors removed
Out of power Damaged Unreachable
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Proposed Design Features
Data Centric Sensors do not need identity (no IP address) Application focus on data having attributes Communication primitive : “request” for data
Application Specific Intermediate nodes cache and aggregate
application specific data Forwarding requests (like routers)
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Proposed Solution
Localized Algorithm
Distributed algorithm
Sensors interact in restricted area
Collectively achieve global objective
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Localized algorithm
Achieved using clustering of sensors (Localized Clustering algorithm).
Advantages: Scalability Improved robustness Efficient resource utilization (battery power)
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Clustering in Sensor Networks
Child Sensor
Parent Sensor
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Goal of Localized Clustering algorithm
Elect cluster-head sensor such that each sensor has a cluster-head as parent.
no asymmetric connections
Cluster adapts to network dynamics and changing energy level of nodes
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Localized Clustering algorithm
Assume link level procedure on sensor
Adjusts Communication range by tweaking transmission power to minimum value for full network connectivity.
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Localized Clustering algorithm
Assume a multi-level cluster formation
Associate sensors at a level with radius
Radius: Number of physical hops sensor advertisement will travel
Sensors at higher level = larger radii.
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Localized Clustering algorithm
1 2 3 4
Level1
Level 0
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Localized Clustering algorithm
1 2 3 4
Level1
Level 0
Send advertisements
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Localized Clustering algorithm
1 2 3 4
Level1
Level 0
Send advertisements
Start promotion timers
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Localized Clustering algorithm
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2
3
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Level1
Level 0
promote
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Localized Clustering algorithm
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2
3
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Level1
Level 0
Notify potential children
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Localized Clustering algorithm
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2
3
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Level1
Level 0
Select parent
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Localized Clustering algorithm
1 2
3
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Level1
Level 0
Demote (no child)
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Localized Clustering algorithm
1 2
3
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Level1
Level 0
Select parent
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Localized Clustering algorithm
All sensors start at level 0. Sensors send periodic advertisement to
sensors within radius hops. Advertisements carry:
Hierarchy level Parent ID (if any) Remaining energy in sensor
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Localized Clustering algorithm
After sending advertisements: Sensors wait for wait time (proportional to radius). At end of wait time, if sensor does not have
parent Level 0 sensor starts promotion timer. Promotion timer inversely proportional to remaining
energy and number of level 0 advertisements received.
Smaller time out value for sensors in dense regions with more power.
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Localized Clustering algorithm
After promotion timer expires: Sensor promotes itself to level 1. Sends periodic advertisements at level 1 radius. Advertisement lists potential child sensors:
Sensors whose advertisement received in level 0.
The child sensors in lower level chooses the closest parent.
All sensors keep checking (parent, child) after wait time period.
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Localized Clustering algorithm
If battery power of parent sensor less than certain threshold compared to children Parent sensor drops a level down. Election takes place so that a new parent
selected with more power.
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Difficulty of Localized Algorithms
Should provide desired global behavior with indirect global knowledge Converting centralized algorithm to distributed. Difficulty in designing adaptability to different
environments and converge to global behavior over range
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Solutions to overcome disadvantage
Adaptive Fidelity Algorithm Quality of answer traded against battery life,
network bandwidth or number of active sensors
Develop Techniques for characterizing performance of Localized Algorithms sacrifice resource utilization, responsiveness
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Directed Diffusion
Set of abstractions that describe communication pattern in localized algorithms.
Sensors name data that it generates. Data contains attributes.
Other nodes express interests based on attributes.
Network nodes propagate interests.
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Directed Diffusion
Interest on data creates gradients that direct diffusion of data.
Gradients are data dissemination path from source to sink (requesting information) nodes.
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Example of Directed Diffusion
SINK
SOURCE
Gradient
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Related Work
Ad-hoc Networks Proactive vs. reactive routing protocols
Energy-efficiency issues
Distributed Robotics Robots cooperate to discover entire map
Internet Multicast and web caching Lightweight session
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Current Developments
Smartdust project: cubic millimeter sensors Sensors float in air like dust
WINS (wireless integrated wireless Sensors) WSN (Wireless Sensing Network) Odyssey Habitat monitoring
Great Duck Island
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Summary
Manage sensor networks using localized algorithm
Advantages of localized algorithm Robustness, Energy efficient, manage sheer
numbers Cluster approach for localization Directed Diffusion for communication among
sensors
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QUESTIONS
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DISCUSSION
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References
http://robotics.eecs.berkeley.edu/~pister/29Palms0103/ http://www.eecs.berkeley.edu/IPRO/Summary/
01abstracts/szewczyk.1.html http://nms.lcs.mit.edu/projects/leach/ http://citeseer.nj.nec.com/context/1822734/0 http://www.cens.ucla.edu/Estrin/index.shtml http://www.greatduckisland.net/images.php www.mdpi.net/sensors/papers/s20700286.pdf