Routing in Sensor Networks: Directed Diffusion and other proposals

brainybootsMobile - Wireless

Nov 21, 2013 (3 years and 8 months ago)

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Routing in Sensor Networks:

Directed Diffusion and other proposals



Presented By


Romit Roy Choudhury & Pradeep Kyasanur

Class Presentation
-

CS 598ig



2

Sensor Networking


Why ??


Monitoring activities


A basic need


How many people cross Green St. every day?


How much poisenous gas in the atmosphere?


How many enemy tanks crossed through the jungle?



Human monitoring possible/feasible ?


Not always



Automated smart montoring required


Network small computing elements to achieve this

3

San Fransisco’s Moscone Center equipped with sensor network

4

AdHoc and Sensors …


Ad Hoc network lacking killer applications


Difficult to force co
-
operation among HUMAN users


Mobility/connectivity unreliable for a business model


Difficult to bootstrap


critical mass required



Sensor networks more realizable


More defined applications


Single owner/administration


easier to implement


Sensing already an established process


just add
networking to it.

5

However …


Ad Hoc and Sensor Networks are both multi
-
hop wireless architectures


Thereby shares several technical issues and challenges


Solutions in one domain often applicable to others.



However, key differences exist


Energy constraint in sensor networks


Traffic models and characteristics


Other issues like coverage, fault
-
tolerance, etc.


6

This Talk …


Directed Diffusion


Focusing on the shift from the ad hoc paradigm


The attention to energy conservation



Other routing proposals


SPIN, LEACH, Rumor Routing, etc.



Energy Efficient disaster recovery


Focusing on an application of adhoc/sensor network



Quick note on other issues in sensor networking


Coverage, Fault
-
toerance, synch, aggregation, disseminations

7




Directed Diffusion

8

The Problem


A region requires event
-
monitoring
(harmful gas,
vehicle motion, seismic
vibration, temperature, etc.)



Deploy sensors forming a
distributed network



On event, sensed and/or
processed information
delivered to the inquiring
destination


Event

Sensor sources

Sensor sink

Directed

Diffusion

A sensor field

9

The Proposal


Proposes an
application
-
aware

paradigm to
facilitate efficient aggregation, and delivery of
sensed data to inquiring destination



Challenges:


Scalability


Energy efficiency


Robustness / Fault tolerance in outdoor areas


Efficient routing (multiple source destination pairs)


10

Directed Diffusion


Typical IP based networks


Requires unique host ID addressing


Application is end
-
to
-
end, routers unaware



Directed diffusion


uses publish/subscribe


Inquirer expresses an interest,
I,
using attribute values



Sensor sources that can service
I,
reply with data

11

Data Naming


Expressing an Interest


Using attribute
-
value pairs


E.g.,





Other interest
-
expressing schemes possible


E.g., hierarchical (different problem)

Type = Wheeled vehicle

// detect vehicle location

Interval = 20 ms


// send events every 20ms
Duration = 10 s


// Send for next 10 s

Field = [x1, y1, x2, y2]

// from sensors in this area

12

Gradient Set Up


Inquirer (sink) broadcasts exploratory interest,
i1


Intended to discover routes between source and sink



Neighbors update interest
-
cache and forwards
i1




Gradient for
i1

set up to upstream neighbor


No source routes


Gradient


a weighted reverse link


Low gradient


Few packets per unit time needed

13

Low

Exploratory Gradient

Event

Low

Low

Exploratory Request

Gradient

Bidirectional gradients established on all links through flooding

14

Event
-
data propagation


Event
e1

occurs, matches
i1
in sensor cache


e1
identified based on waveform pattern matching



Interest reply diffused down gradient (unicast)


Diffusion initially exploratory (low packet
-
rate)



Cache filters suppress previously seen data


Problem of bidirectional gradient avoided

15

Reinforcement





From exploratory gradients, reinforce optimal
path for high
-
rate data download


Unicast



By

requesting higher
-
rate
-
i1
on the optimal path



Exploratory gradients still exist


useful for faults

Event

Sink

A sensor field

Reinforced gradient

Reinforced gradient

16

Path Failure / Recovery


Link failure detected by reduced rate, data loss


Choose next best link (i.e., compare links based on
infrequent exploratory downloads)


Negatively reinforce lossy link


Either send
i1

with base (exploratory) data rate


Or, allow neighbor’s cache to expire over time

Event

Sink

Src

A

C

B

M

D

Link A
-
M lossy

A reinforces B

B reinforces C …

D need not

A (

) reinforces M

M (

) reinforces D

17






M gets same data from both D and P, but P
always

delivers late due to looping


M negatively
-
reinforces (nr) P, P nr Q, Q nr M


Loop {M


Q


P} eliminated


Conservative nr useful for fault resilience

Loop Elimination

A

Q

P

D

M

18

Simulation Setup & Metrics



ns2, 50 nodes in 160x160 sqm., range 40m


Node density maintained, 802.11 MAC


Random 5 sources in 70x70, random 5 sinks


Average Dissipated Energy


Per node energy dissipation / # events seen by sinks


Average Delay


Latency of event transmission to reception at sink


Distinct event delivery ratio


Ratio of # events sent to # events received by sink

19

Average Dissipated Energy











In
-
network aggregation reduces DD redundancy


Flooding poor because of multiple paths from source to sink

flooding

Diffusion

Multicast

20

Delay











DD finds least delay paths, as OM


encouraging


Flooding incurs latency due to high MAC contention, collision

flooding

Diffusion

Multicast

21











Delivery ratio degrades with higher % node failures


Graceful degradation indicates efficient negative reinforcement

Event Delivery Ratio under node failures

0 %

10%

20%

22

Conclusion


Directed diffusion, a paradigm proposed for
event monitoring sensor networks


Energy efficiency achievable


Diffusion mechanism resilient to fault tolerance


Conservative negative reinforcements proves useful



A careful MAC protocol, designed for such
specifics, can yield further performance gains


23

Contribution


Application
-
awareness


a beneficial tradeoff


Data aggregation can improve energy efficiency


Better bandwidth utilization


Network addressing is data centric


Probably correct approach for sensor type applications


Notion of gradient (exploratory and reinforced)


Flexible architecture


enables configuration based on
application requirements, tradeoffs


Implementation on Berkley motes


Network API, Filter API

24

Critique


Choice of path does not maximize aggregation


Least delay path does not


max aggregation


Exploratory paths improve fault tolerance


But at the cost of additional msg./energy overhead


Overhead analysis omits the exploratory paths


Data overlap can be suppressed


2 sources, reporting overlapping data can be combined


Idle energy = 10% of receive, 5% of transmit


Explains the poor energy performance of flooding


Not realistic numbers


optimistic assumption

25


Rumor Routing


LEACH


SPIN


Some other proposals for sensor routing

26

Rumor Routing

27

LEACH


Proposes clustering of sensors + cluster leaders


Can aggregate data in single (local) cluster


Rotating cluster head balances energy consumption


Cluster formation distributed and energy efficient

Cluster
-
head

always awake

Member nodes can

sleep when not Txing

28

LEACH


The Protocol


Time is divided into rounds


A node self
-
elects itself as the cluster head


Higher residual energy, higher probability to be head


Close
-
by sensors join this cluster
-
head


Cluster head does TDMA scheduling and gathers data


Gathered data compressed based on spatial correlation


Transmits data to Base Station (@ higher power)


In the next round, another cluster head elected


Probabilistic load balancing


Network lifetime can increase manifolds

29

SPIN: Information Via Negotiation


Flooding


many sensors transmit same data



Redundant


Make sensors disseminate spatially/temporally
disjoint data sets


Name data with meta
-
data to define space/time property


Sensors compare overheard data with self
-
sensed data


Combine data to minimize overlap


Make sensors resource
-
adaptive


When low battery


perform minimum activities

30

The SPIN 3
-
Step Protocol


B

A

31

The SPIN 3
-
Step Protocol


B

A

Notice the color of the data packets sent by node B


32

The SPIN 3
-
Step Protocol


B

A

SPIN effective when DATA sizes are large :

REQ, ADV overhead gets amortized


33



Energy Efficient Routing in Ad Hoc Disaster
Recovery Networks:


An Application Perspective

34

Motivation


Disaster recovery


emerging application for
adhoc/sensor networks


During Sep 11 attacks


survivors were detected
through mobile phone signals


People often buried below earthquake disaster



New RFID or smart badge technologies


Each person wears a badge that is a transceiver


Sends out very low rate signals about human location


Information collected at peripheral central stations

35

Problem


Given some pkt generation rate at each badge


Design routing strategy that maximizes network
lifetime



Problem formulated as a LPP


Maximize minimum lifetime


subject to the flow constraints on each node


Subject to the capacity constraints of the links


36

Approach


Existing simplex techniques can be used to
solve the problem


Computation intensive due to several iterations for
convergence



Paper proposes binary search on network
lifetime


In plain words, a network lifetime (T) is chosen and
applied to see if there exists a feasible flow assignment


If not, (T/2) is tried, else (2T) … until convergence

37

Summary


Complexity of O(n
3
logT)


n
3
for finding a feasible assignment of flows


Log T for the binary search



However, distributed version of this protocol


Only available for a single origin node


For multiple badges


future work

38

Other Research Challenges in Sensors


Coverage


Union of all sensing ranges need to cover entire region


Time synchronization


Data Aggregation


Calculating functions over a spatial distribution of sensors


Data Dissemination


Rumour routing, Ant colonies, swarm intelligence


Motion tracking, object guiding


Sensors + Actuators


mobile robots !!!

39









Thank You

40

Message Complexity

Grid topology

N = 25

n = 5 Sources

m = 3 sinks

Nodes talk with

Adj. or diagonal

nodes

Flooding
:
Unrestricted broadcast

Each interest broadcast by each node


nN messages

A msg received twice over a link


total # receptions =
2n
(# of links)

Total msg. cost =
nN + 4n(

N


1)(2

N


1)
=
O( nN )


41

Message Complexity II

Omniscient Multicast
:
Multicast trees rooted at each source

(Cost of tree establishment not counted.)


Overhead of 2 receptions on each link of tree,
T
j

Total msg. cost =
2 |{distinct links l: l


U
j = 1 to n
(T
j
)}|

Expressing all trees in terms of a common tree, T
1
, we get

Message Complexity =
O(n

N), asymptotically, and m «

N

Directed Diffusion
:
Similar approach using rooted trees


Message Complexity =
O(n

N), asymptotically, and m «

N

But, cost lower than OM, cause DD can perform duplicate
suppression on common link. More gain when more sources