Sensor Networks From The Network Perspective Paradise Mini-Workshop Anxiao (Andrew) Jiang...

Sensor Networks From The Network Perspective

Paradise Mini-Workshop

Anxiao (Andrew) Jiang

05/18/2006

Examples of Sensor Network Applications

Microclimate monitoring of redwood forest (UC Berkeley)

Berkeley/SFBerkeley/SF

• 70% of H2O cycle is through trees, not ground• Complex interactions of tree growth and environment• Need to understand dynamic processes within the trees


Microclimate monitoring of redwood forest (UC Berkeley)

Ad Hoc Multihop Network


Habitat Monitoring on Great Duck Island (UC Berkeley)

Acadia National ParkMt. Desert Island, ME

Great Duck IslandNature Conservancy

Burrow mote and petrel



Transit Network

Basestation

Gateway

Sensor Patch

Patch Network

Base-Remote Link

Data Service

Internet

Client Data Browsingand Processing

Sensor Node

Examples of Sensors

• Light Sensors• Environmental Sensors

humidity + tempPressure + temp

battery

mote

antenna

Weather Mote

Functions of a sensor:

Communication

Collecting data

Computation

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

http://www.jschlesinger.com/corporate/index.html

Implications: Networking

An example of sensornet architecture

David Culler, et al., UC Berkeley

“Small” Technology, Broad Agenda (Culler, 2005)

• Social factors– security, privacy, information sharing

• Applications– long lived, self-maintaining, dense instrumentation of previously unobservable

phenomena– interacting with a computational environment

• Programming the Ensemble– describe global behavior, synthesis local rules that have correct, predictable global

behavior• Distributed services

– localization, time synchronization, resilient aggregation• Networking

– self-organizing multihop, resilient, energy efficient routing– despite limited storage and tremendous noise

• Operating system– extensive resource-constrained concurrency, modularity– framework for defining boundaries

• Architecture– rich interfaces and simple primitives allowing cross-layer optimization

• Components– 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

destination

Greedy Forwarding:

A node always forwards the message to a neighbor whose Euclidean distance to the destination is smaller.

Assumptions:

Every node knows its locationand its neighbors’ locations

The source knows the locationof the destination

Routing

Greedy Forwarding Can Fail

destination

When the message reaches node x, no next hop can beselected for Greedy Forwarding, because both w and y arefurther 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.


If the graph is a unit disk graph (UDG), there are localized ways to planarize it:

Gabriel Graph: Relative Neighborhood Graph: Restricted Delaunay Graph:


source

destination

faceface

faceface

face

Face routing is nearly stateless. (Nearly) no routing tables.

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

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 iterations10 )(ii iterations100 )(iii iterations1000

Rubber-band algorithm for embedding

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


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.

A A AB B BC

C

C

We cannot tell which of the following localizations is true. All are possible …

For a large network, generally, valid localizations (localizations that conform tothe 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.

Note: truthful localization is not always feasible …


Hardness of Localization

Network model: Unit disk graph model.

Hardness results:

No good known approximation algorithm.


Practical Techniques: Trilateration, triangulation, multileration …


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.


Hole Detection (for Quasi-UDG model)

Kroller, Fekete, Pfisterer, Fischer, Deterministic boundary recognition and topology extraction for large sensor networks, SODA 2006.


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. Whatcan 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.

AB

B’s location server

Location Service

GLS --- a distributed geographic location service

Reference: A Scalable Location Service for Geographic Ad Hoc Routing, by Li, Jannotti, De Couto, Karger, Morris, MobiCom’00

Locality-sensitive: when querying the location of a nearby node, the query path is short.

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.

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 Hashingto 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:

sensornet

Key: elephant sighting

Key: elephant sightingValue: # of elephants: one Time: 9:02am Location: xxxx

Hash(elephant sighting)=(23,36)

23,36

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-resolutionStorage for sensor networks, SenSys 2003.

Directed Diffusion: Query by Interest Dissemination

Directed Diffusion: A Scalable and Robust Communication Paradigm for Sensor Networks, Intanagonwiwat, Govindan, Estrin, MobiCom’00.

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.

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

MIN

COUNT

SUM

AVERAGE

MEDIAN

COUNT DISTINCT

HISTOGRAM


Aggregate data in a tree

(Implemented in TinyDB project at Berkeley and Cougar project at Cornell)

basestation (resource rich)

users inject queries





users inject query: MAX

3 9

612

11

8

8 19

121

7

10

5

2

4

27

10

12

local data




11 9

9

27

10


users inject query: MAX

3 9

612

8

8 19

121

7

10

5

2

4

27

10

12

local data

8

8

12

312

1

12

10

27

12 10

271912

27

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.

Aggregation: Data Structures for More Complex Queries


Example of Q-Digest:


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.


Most sensing device models share two factors in common:


An example from:

Exposure in Wireless Ad-Hoc Sensor Networks, Meguerdichian et al., MobiCom’01.

Sensor Networks From The Network Perspective Paradise Mini-Workshop Anxiao (Andrew) Jiang...

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