Download New Aggregation Techniques for Sensor

Sensor Networks
From The Network Perspective
Anxiao (Andrew) Jiang
Paradise Mini-Workshop
05/18/2006
Examples of Sensor Network Applications
Microclimate monitoring of redwood forest (UC Berkeley)
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•
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70% of H2O cycle is through trees, not ground
Complex interactions of tree growth and environment
Need to understand dynamic processes within the trees
Berkeley/SF
Examples of Sensor Network Applications
Microclimate monitoring of redwood forest (UC Berkeley)
Ad Hoc Multihop Network
Examples of Sensor Network Applications
Habitat Monitoring on Great Duck Island (UC Berkeley)
Acadia National Park
Mt. Desert Island, ME
Great Duck Island
Nature Conservancy
Burrow mote and petrel
Examples of Sensor Network Applications
Habitat Monitoring on Great Duck Island (UC Berkeley)
Patch
Network
Sensor Node
Sensor Patch
Gateway
Transit Network
Client Data Browsing
and Processing
Basestation
Base-Remote Link
Internet
Data Service
Examples of Sensor Network Applications
Habitat Monitoring on Great Duck Island (UC Berkeley)
Examples of Sensors
• Light Sensors
• Environmental Sensors
humidity + temp
Pressure + temp
Weather Mote
antenna
Functions of a sensor:
mote
Collecting data
Computation
battery
Communication
Examples of Sensors
Examples of Sensors
MICA mote
Properties of Sensor Networks
Sensing, computation, communication
Ad hoc, self-configuration
Long lived, large, unattended
In network processing, save energy
Asks for adaptive and fault tolerant algorithms
Implications: Computation
Computation with limited memory, limited energy, locality
Distributed computing, peer to peer, collaborative
Collective behavior
Noisy measurements, dynamic conditions, failures
Data collection, aggregation, computation, communication
Implications: Networking
New networks, new properties, new interfaces
Implications: Networking
An example of sensornet architecture
David Culler, et al., UC Berkeley
“Small” Technology, Broad Agenda
(Culler, 2005)
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Social factors
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Applications
•
Programming the Ensemble
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Distributed services
•
Networking
•
Operating system
•
Architecture
•
Components
– security, privacy, information sharing
– long lived, self-maintaining, dense instrumentation of previously unobservable
phenomena
– interacting with a computational environment
– describe global behavior, synthesis local rules that have correct, predictable global
behavior
– localization, time synchronization, resilient aggregation
– self-organizing multihop, resilient, energy efficient routing
– despite limited storage and tremendous noise
– extensive resource-constrained concurrency, modularity
– framework for defining boundaries
– rich interfaces and simple primitives allowing cross-layer optimization
– low-power processor, ADC, radio, communication, encryption, sensors, batteries
Routing
Routing in General Networks
Shortest path routing
Compact routing: smaller routing tables, bounded stretch factor
Hierarchical routing: BGP, OSPF
Large sensor networks: We can use geographic locations.
Routing
Greedy Forwarding
source
Assumptions:
Every node knows its location
and its neighbors’ locations
The source knows the location
of the destination
Greedy Forwarding:
A node always forwards
the message to a neighbor
whose Euclidean distance
to the destination is
smaller.
destination
Routing
Greedy Forwarding Can Fail
destination
When the message reaches node x, no next hop can be
selected for Greedy Forwarding, because both w and y are
further away from D than x is.
Routing: Face Routing
Planarization of a graph: Mark some edges as unusable, so that the
remaining graph is a connected planar graph.
Routing: Face Routing
If the graph is a unit disk graph (UDG), there are localized ways to planarize it:
Gabriel Graph:
Relative Neighborhood Graph:
Restricted Delaunay Graph:
Routing: Face Routing
Face routing is nearly stateless. (Nearly) no routing tables.
face
face
face
face
face
source
destination
GFG: [Bose01]
GPSR (Greedy Perimeter Stateless Routing): [Karp00]
Routing: Virtual Coordinates
When node locations are unknown, we can do embedding.
Rubber-band algorithm for embedding
Geographic Routing without Location Information, Rao et al., MobiCom’03
Routing: Virtual Coordinates
Rubber-band algorithm for embedding
Perimeter nodes do not change their coordinates.
Non-perimeter nodes update their coordinates
through multiple iterations. In each iteration, it
takes its coordinates as the average coordinates
of its neighbors.
(i) 10 iterations
(ii) 100 iterations
(iii) 1000 iterations
Routing: Virtual Coordinates in 3-D
Idea: Embed the network in a high-dimensional space, and/or
define new ways of ‘greedy forwarding.’
Theorem: Any graph containing a 3-connected planar spanning sub-graph can
be embedde in the 3-dimensional Euclidean space, where greedy
forwarding guarantees delivery using a new distance function.
On A Conjecture Related to Geometric Routing, Papadimitriou et al., manuscript.
Routing: Location Free Routing
Use geometric properties, not node locations.
New naming and routing system for nodes.
GLIDER
Fang, Gao, Guibas, de Silva, Zhang, GLIDER: Gradient Landmark-based Distributed
Routing for Sensor Networks, INFOCOM 2005.
Routing: Location Free Routing
Use geometric properties, not node locations.
New naming and routing system for nodes.
MAP
Blue: MAP
Green: GPSR
(geographical
forwarding)
Bruck, Gao, Jiang, MAP: Medial Axis Based Geometric Routing In Sensor Networks,
MobiCom 2005.
Network Localization
Definition: Determine the (relative) positions of nodes based on
known information.
Location information that can be learned in a wireless network:
Connectivity
Sometimes, also …
Distance between adjacent nodes
Angle between adjacent edges
Knowing the locations of nodes are important for:
The meaning of data
Routing
Tasking
Network Localization
Note: truthful localization is not always feasible …
A most simple example where truthful localization is not feasible:
Known information: there are three nodes A, B, C;
A and B are adjacent;
B and C are adjacent.
We cannot tell which of the following localizations is true. All are possible …
C
C
A
B
C
A
B
A
B
For a large network, generally, valid localizations (localizations that conform to
the known information) are similar to the truthful localization.
So we are interested in finding just one valid localization.
Valid localization: localization that conform to the known information.
Network Localization
Hardness of Localization
Network model: Unit disk graph model.
Hardness results:
No good known approximation algorithm.
Network Localization
Practical Techniques:
Trilateration, triangulation, multileration …
Network Localization
Practical Techniques: Mass-Spring Method
Edges are springs, whose lengths equal their measured distance.
Multidimensional Scaling (MDS)
Given the estimated distance matrix, take the largest d eigenvalues
and eigenvectors of the distance matrix to get the d-dimensional
approximate embedding.
More: Dynamic algorithms, robust to errors …
Detect Important Geometric Features
Hole Detection
Topological Hole Detection in Wireless Sensor Networks and Its
Applications, Funke, DIALM-POMC’05.
Detect Important Geometric Features
Hole Detection (for Quasi-UDG model)
Kroller, Fekete, Pfisterer, Fischer, Deterministic boundary recognition and topology extraction
for large sensor networks, SODA 2006.
Detect Important Geometric Features
Cut Detection (For linear cut only)
Shrivastava, Suri and Toth, Detecting cuts in sensor networks, ISPN 2005.
Location Service
Basic Scenario:
Node A needs to send messages to node B via location-based
routing. Node A only knows node B’s ID, not its location. What
can we do?
We need Location Service to make it work.
Location Service
Scenario: Node A needs to send messages to node B via
location-based routing. Node A only knows node B’s
ID, not its location.
Solution: Node B has a location server, whose position is common
known to all nodes. Node B sends its location to that server.
Node A retrieves node B’s location from that server.
A
B
B’s location server
Location Service
GLS --- a distributed geographic location service
Properties of GLS:
Decentralized.
There is no special node in the network; no infrastructure is needed.
Each node acts as a location server for a small number of other nodes.
Scalable.
It enables construction of network that scales to a large number of nodes.
Fault-tolerant.
There is no dependence on specially designated nodes.
When network is partitioned, it operates effectively in each network component.
Locality-sensitive: when querying the location of a nearby node, the query path
is short.
Reference: A Scalable Location Service for Geographic Ad Hoc Routing,
by Li, Jannotti, De Couto, Karger, Morris, MobiCom’00
Location Service
GLS --- a distributed geographic location service
For each square that node u is in and its three sibling squares, u will have a location server.
Update: Send a node’s
location to its servers.
Query: Find a nearby
location server of the
destination node.
Use Consistent Hashing
to reduce the size of
routing tables.
Data Centric Storage
Data-centric storage: Sensed data are stored at a node determined by the
name associated with the sensed data.
GHT: A Geographic Hash Table for Data-Centric Storage, Ratnasamy et al., 2002.
Basic elements in GHT:
GHT hashes keys into geographic coordinates, and stores a key-value
pair at the sensor node geographically nearest the hash of its key.
Data Centric Storage
GHT: Geographic Hash Table
Basic elements in GHT:
Hash(elephant sighting)=(23,36)
Key: elephant sighting
23,36
Key: elephant sighting
Value: # of elephants: one
Time: 9:02am
Location: xxxx
sensornet
The system replicates stored data locally to ensure persistence when
nodes fail.
Multi-Resolution Storage
Ganesan, Greenstein, Perelyubskiy, Estrin and Heidemann, An evalution of multi-resolution
Storage for sensor networks, SenSys 2003.
Directed Diffusion:
Query by Interest Dissemination
Basic process of directed diffusion:
Users spread their interests for data in the network; the interests
received by a node pull the related data toward it; good routing
paths are reenforced over time.
Directed Diffusion: A Scalable and Robust Communication Paradigm for Sensor Networks,
Intanagonwiwat, Govindan, Estrin, MobiCom’00.
Query of Sensor Networks
Aggregation
Sensor applications depend on the ability to extract data from the network.
Often, the data consists of aggregations (or summaries) rather than raw
sensor readings.
Examples of data aggregates:
MAX
MEDIAN
MIN
COUNT DISTINCT
COUNT
HISTOGRAM
SUM
AVERAGE
Query of Sensor Networks
Aggregate data in a tree
(Implemented in TinyDB project at Berkeley and Cougar project at Cornell)
users inject queries
basestation (resource rich)
Query of Sensor Networks
Aggregate data in a tree
(Implemented in TinyDB project at Berkeley and Cougar project at Cornell)
users inject queries
basestation (resource rich)
Query of Sensor Networks
Aggregate data in a tree
(Implemented in TinyDB project at Berkeley and Cougar project at Cornell)
users inject query: MAX
basestation (resource rich)
8
19
12
10
4
8
11
12
27
2
7
6
5
1
3
12
9
10
local data
Query of Sensor Networks
Aggregate data in a tree
(Implemented in TinyDB project at Berkeley and Cougar project at Cornell)
users inject query: MAX
27
basestation (resource rich)
8
8
19
19
27
12
27
12
8
12
4
8
10 27
10
12
12 11
9
12
3
3
2
7
9
1
12
9
27
1
12
6
10
5 10
10
local data
Query of Sensor Networks
Aggregation: Data Structures for More Complex Queries
• Use Q-Digest to Support
Aggregation Queries
– Quantile query
• i-th  value
– Reverse quantile query
• Value  i-th
– Consensus query
• Most frequent?
– Histogram
Medians and Beyond: New Aggregation Techniques for Sensor Networks, Shrivastava et
al., SenSys’04.
Query of Sensor Networks
Aggregation: Data Structures for More Complex Queries
Example of Q-Digest:
Query of Sensor Networks
More:
Massive data, distributed, noisy measurement, errors, complex queries,
historical data, real time data, streaming algorithms, dynamic update,
privacy ……
Sensing Coverage and Exposure
Coverage / Exposure: Sensors observe targets.
Observe a single target, multiple targets, or a sensor field.
From the target’s point of view  Exposure.
From the sensor’s point of view  Coverage.
Minimize / maximize exposure, maximize coverage.
Sensing Coverage and Exposure
Most sensing device models share two factors in common:
Sensing Coverage and Exposure
An example from:
Exposure in Wireless Ad-Hoc Sensor Networks, Meguerdichian et al.,
MobiCom’01.