adhoc netwrk ppt(1)

7/28/2019 adhoc netwrk ppt(1)

1/175

Unicast Routing

Ad Hoc networks

(under standardization in the IETF

MANET WG)


2/175

What is an ad hoc networkA wireless multi-hop infrastructure-lessnetwork whose devices act as source/destination of messages & as relay forpackets generated by a node s and addressed

to a node z (iff they are on a s-z route)Pros: No need for infrastructure low cost,enables communication where it is usuallynot needed or is not viable

Must be: Self-organizing, self-configuring,self-maintaining


3/175

Application scenariosCollaboration between users in officeenvironments

Disaster recovery applications

Military networksPersonal Area Networks

Home Networking

Wireless Sensor Networks (WSNs)Inter-vehicular communication


4/175

Features of ad hoc networksHighly dynamic networks device mobility, energysaving sleep/awake modes

Need for low energy/resource-consuming, simpleprotocols

Bandwidth and resource constrained environmentTraffic: All-pairs in general ad hoc networks, from sensors to sink(s) in

sensor networks

In many case not high

Scale: Application dependent 10-100 nodes in traditional ad hoc networks

1000-10000 in sensor networks


5/175

Features of highly resource constrained ad hocnetworks (WSNs)

Highly dynamic networks due to device mobility (only insome specific applications), to the fact the active node setchanges in time for sake of energy saving (always to beconsidered)

Need to design low energy/resource-consuming, simpleprotocols very critical, energy consumption a realbottleneck

Traffic from sensors to sink(s)

Scalability is a major issueCode must be simple (small storage capability, verysimple, inexpensive, resource constrained devices)

First solutions we will see for traditional ad hoc networksdo not scale to high numbers and are not energy-saving


6/175

Routing-Traditional approach(from Reti 1)

Intra-AS routing in the Internet

Link State Approaches

(info on the topology graph gathered atnodes which run shortest path algorithms-Dijkstra- to decide the routes to thedifferent destinationse.g. OSPF routingprotocol)

Distance Vector approaches (e.g. RIP)


7/175

Bellman-FordGiven a graph G=(N,A) and a node sfind the shortest path

froms

to every node in N.A shortest walk from sto i subject to the constraint that the walkcontains at most h arcs and goes through node sonly once, is denotedshortest(


8/175

Bellman-Ford

Dh+1i=mink [wi,k + Dhk]

Can be computed locally.

What do I need?

For each neighbor k, I need to know-the cost of the link to it (known info)

-The cost of the best route from the neighbor k to the destination(this is an info that each of my neighbor has to send to me viamessages)

In the real world: I need to know the best routes among each

pair of nodes we apply distributed Bellman Ford to get the bestroute for each of the possible destinations


9/175

Distance Vector Routing Algorithm-Distributed Bellman Ford

iterative:continues until nonodes exchange info.

self-terminating: nosignal to stop

asynchronous:nodes need notexchange info/iteratein lock step!

Distributed, based onlocal info:

each nodecommunicates onlywith directly-attachedneighbors

Distance Table data

structureeach node has its own

row for each possibledestination

column for each directly-attached neighbor to node

example: in node X, for dest.Y via neighbor Z:

D (Y,Z)X

distance from X toY, via Z as next hop

c(X,Z) + min {D (Y,w)}Z

w

=

=

Cost associated to the (X,Z) link

Info maintained at Z. Min mustbe communicated


10/175

Distance Vector: link cost changesLink cost changes:

good news travels fastbad news travels slow-count to infinity problem!

X Z

14

50

Y60

algorithmcontinues

on!

Y detects link costIncrease but think can

Reach X through Z at atotal cost of 6 (wrong!!)

The path is Y-Z-Y-X


11/175

Count-to-infinityan everyday lifeexample

Which is the problem here?the info exchanged by the protocol!! the best route to X I

have has the following cost (no additional info on the

route)

A Roman example-assumption: there is only one route going from Colosseo to

Altare della Patria: Via dei Fori Imperiali. Let us now consider

a network, whose nodes are Colosseo., Altare della Patria,

Piazza del Popolo

Colosseo Altare PatriaPiazza del

Popolo

1 Km 1 Km


12/175

Colosseo Al.Patria P.Popolo1Km 1Km

The Colosseo. and Alt. Patria nodes exchange the following info

Colosseo says the shortest route from me to P. Popolo is 2 Km

Alt. Patria says the shortest path from me to P. Popolo is 1Km

Based on this exchange from Colosseo you go to Al. Patria, and from there toPiazza del Popolo OKNow due to the big dig they close Via del Corso(Al. PatriaP.Popolo)

Al. Patria thinks I have to find another route from me to P.Popolo.

Look there is a route from Colosseo to P.Popolo that

takes 2Km, I can be at Colosseo in 1Km I have founda 3Km route from me to P.Popolo!! Communicates the new cost to

Colosseo that updates OK I can go to P.Popolo via Al. Patria in 4Km

VERY WRONG!! Why is it so? I didnt know that the route from

Colosseo to P.Popolo was going through Via del Corso from Al.Patriato P.Popolo (which is closed)!!

Count-to-infinityeveryday life example (2/2)


13/175

Routing in ad hoc networks-Goals

Minimal control overhead

Minimal processing overhead

Multi-hop path routing capability

Dynamic topology maintenance

No loops

Self-starting


14/175

2 Primary Approaches

Proactive Based on traditional distance-vector and link-state

protocols Each node maintains route to each other network node

Periodic and/or event triggered routing update exchange

Higher overhead in most scenarios

Longer route convergence time Examples: DSDV, TBRPF, OLSR


15/175

Highly Dynamic Destination-SequencedDistance-Vector (DSDV) RoutingProactive, distance vector approach (uses distributedasynchronous Bellman Ford). Updates on routes coststransmitted periodically or when significant new informationis available.

Difference wrt Bellman Ford: in ad hoc networks there arefrequent changes in the topology, solutions must try to avoidloops (approaches such as Poison reverse non effective inbroadcast channels, we seek solutions which are simple andfully distributed)

Metrics: fresh routes better than stale routes, number ofhops used to select among the fresh routes

How to identify fresh routes? By means of sequencenumbers identifying the freshness of the communicated

information. When changes occur, the sequence numberincrease.


16/175

Highly Dynamic Destination-SequencedDistance-Vector (DSDV) RoutingPeriodically destination nodes transmit updates with a newsequence number (and such updates are propagated by theother nodes). Updates contain information on the costs toachieve the different destinations and the freshness of thedelivered information

Data broadcast include multiple entries each with: Destination address

Number of hops required to reach the destination

Sequence number of the information received regarding that

destination as originally stamped by the destinationIn the header the data broadcast also include: Address (HW address/Net address) of the sender of the message

Sequence number created by the transmitter

Two types of updates (full dump or incremental-only

changes- to decrease bandwidth consumption.

Hi hl D i D i i S d


17/175

Highly Dynamic Destination-SequencedDistance-Vector (DSDV) RoutingHow can the costs be modified? Cost=number of hops, target:

using fresh routes as short as possible a link cost changesfrom 1 to inf and from inf to 1

How do we detect that a link is broken? At layer 2 (no hellomessages received for some time, or attempts to retransmit aframe exceeds the MAC protocol threshold) or at layer 3 (do

not receive periodic updates by a neighbor)Link cost increase (1 inf): The nodes incident to that link (A,B) discover it (see above)

Routes going through that link get assigned an inf cost in nodes A and Brouting tables

A new sequence number is generated by the mobile node. Mobile nodesdifferent from the destination use odd SN, the destination even SN.

Updates with routes with infinite cost are immediately transmitted bynodes

Link cost decrease (inf1): Immediately transmits updates


18/175

Highly Dynamic Destination-SequencedDistance-Vector (DSDV) RoutingWhen a node receives updates it sees if costs to reach thedifferent destinations can be improved: routes with more recent sequence numbers to a given destination are

used

if more routes available with the same SN the shortest is used

Newly recorded routes are scheduled for immediateadvertisement (inf finite value)

Routes with improved metric are scheduled for advertisementat a time which depends on the estimated average settling

time for routes to that particular destination (based onprevious history)

As soon as a route cost changes the node may delayinforming its neighbors but immediately starts using the newinformation for its forwarding


19/175

Highly Dynamic Destination-SequencedDistance-Vector (DSDV) Routing-Correctness

Assuming routing tables are stable and a change occurs let G(x) denotes the routes graph from the sources to x BEFORE the

change (assume no loop)

change occurs at iwhen 1) the link from ito its parent p(i) in G(x)

breaks isets to inf that route (no loop can occur) 2) node ireceivesfrom one of its neighbors k a route to x with sequence number SNxkand metric m which is selected to replace the current metric ihas toreach x (this occurs only if SNxkgreater than the previous SN I hadstored Snxi or if the two SN are equal but the new route has a lowerhop cost in the first case if selecting k leads to a loop then SNx

k


20/175


21/175

Why traditional approacheshave limits?Proactive protocols are costly in terms of overhead(the bandwidth and energy are critical resources)

The cost of maintaining routes updated may not

make sense in an environment in which Medium-high mobility

Medium-high dynamicity (awake/asleep states)

Often make the opt. Route change (requiring updates)while

Traffic is generally low (so the cost of maintaining alwaysupdated routes is not balanced by their use)

If this is the scenario what can we do?


22/175

2 Primary Approaches (cont.)

Reactive (on-demand) Source build routes on-demand by flooding

Maintain only active routes Route discovery cycle

Typically, less control overhead, better scalingproperties

Drawback: route acquisition latency Example: AODV, DSR


23/175

Ad hoc On-Demand DistanceVector (AODV) RoutingReactive (nodes that do not lie on active paths neithermaintain any routing information nor participate in anyperiodic routing table exchange; a node does not have todiscover/maintain a route to a destination till it is on a path

to it or has to send messages to it)Route discovery cycleused for route finding

Maintenance ofactiveroutes

Sequence numbers used for loop prevention and as routefreshness criteria

Descendant of DSDV (standard distance vector approachmapped to ad hoc networks), in AODV no periodic updatesbut pure on-demand operation.

Provides unicast and multicast communication


24/175

AODV: Route Discovery

1. Node Sneeds a route to D AND does nothave routing info for it in its table

A

B

C D

S


25/175


1. Node Sneeds a route to D

2. Creates a Route Request (RREQ)

Enters Ds IP addr, seq#,Ss IP addr, seq#

hopcount (=0), broadcast ID

A

B

C D

S


26/175


1. Node Sneeds a route to D2. Creates a Route Request (RREQ)

Enters Ds IP addr, seq#,Ss IP addr, seq#

hopcount (=0), broadcast ID

3. Node Sbroadcasts RREQ to neighbors

A

B

C D

SRREQ


27/175


4. NodeAreceives RREQ Makes reverse route entry for S

dest=S, nexthop=S, hopcnt=1,expiration time for reverse path

Source node, Source node SN,D,broadcastID also maintained It has no route to D, so it rebroadcasts RREQ (hopcount

increased)

If it has already received that request (same source andbroadcast ID) it discards the RREQ

if it knows a valid path to D it will send back a reply to thesource

A

B

C D

SRREQ


28/175


4. NodeAreceives RREQ Makes reverse route entry for S

dest=S, nexthop=S, hopcnt=1 It has no route to D, so it rebroadcasts RREQ

A

B

C D

S

RREQ


29/175


5. Node Creceives RREQ Makes reverse route entry for S

dest=S, nexthop=A, hopcnt=2 It has a route to D, and the seq# for route to D

is >= Ds seq# in RREQ

A

B

C D

S

RREQ


30/175


5. Node Creceives RREQ (cont.) Ccreates a Route Reply (RREP)

Enters Ds IP addr, seq#

Ss IP addr, hopcount to D(= 1), lifetime of theforward route

Unicasts RREP toA

A

B

C D

S

RREQ


31/175


5. Node Creceives RREQ (cont.) Ccreates a Route Reply (RREP)

Enters Ds IP addr, seq#Ss IP addr, hopcount to D(= 1).

Unicasts RREP toA

A

B

C D

S RREP


32/175


6. NodeAreceives RREP Makes forward route entry to D

dest = D, nexthop = C, hopcount = 2 Unicasts RREP to S

A

B

C D

SRREP

RREP


33/175


7. Node Sreceives RREP Makes forward route entry to D

dest = D, nexthop =A, hopcount = 3

Also the latest SN of the destination is updated when receivingthe RREP

Nodes not along the path determined by the RREP will timeout

after ACTIVE_ROUTE_TIMEOUT (3000ms) and will deletethe reverse pointer

A

B

C D

SRREP


34/175


7. Node Sreceives RREP Makes forward route entry to D

dest = D, nexthop =A, hopcount = 3 Sends data packets on route to D

A

B

C D

S


35/175

What if.

A node receives further RREPs for the samerequest? (e.g. more neighbors of a node hadpaths to the destination in cache)? upon reception of another RREP the node

updates its routing information and propagatesthe RREP only if the RREP contains either agreater destination SN, or the same destination SN

with a smaller hopcount


36/175

Other info maintained

Each node maintains the list of activeneighbors, neighbors sending to a givendestination through it useful for route maintenance

Routing table entries: dest,next hop,hopcount, dest SN, active neighbors for thisroute, expiration time for route table entry

(updates each time the route is used fortransmitting data routes entries aremaintained if the route is active)


37/175

AODV: Route Maintenance

1. Link between Cand Dbreaks2. Node Cinvalidates route to Din route table3. Node Ccreates Route Error (RERR) message

Lists all destinations which are now unreachable

Sends to upstream neighbors

Increases of one the SN of the destination

A

B

C D

S RERR


38/175


4. NodeAreceives RERR Checks whether Cis its next hop on route to D

Deletes route to D Forwards RERR to S

A

B

C D

SRERR

RERR


39/175


5. Node Sreceives RERR Checks whetherAis its next hop on route to D

Deletes route to D Rediscovers route if still needed (in that case it

sends a RREQ with a SN which is equal to thelast known destination Sequence Number +1)

A

B

C D

SRERR


40/175

AODV: Optimizations

Expanding Ring Search

Prevents flooding of network during route

discovery Control Time To Live (TTL) of RREQ to search

incrementally larger areas of network

Advantage: Less overhead when successful Disadvantage: Longer delay if route not found

immediately


41/175

AODV: Optimizations (cont.)

Local Repair Repair breaks in active routes locally instead of

notifying source

Use small TTL because destination probably hasntmoved far

If first repair attempt is unsuccessful, send RERR tosource

Advantage: repair links with less overhead, delay andpacket loss

Disadvantage: longer delay and greater packet losswhen unsuccessful


42/175

AODV: Summary

Reactive/on-demand

Sequence numbers used for route

freshness and loop preventionRoute discovery cycle

Maintain only active routes

Optimizations can be used to reduceoverhead and increase scalability


43/175

Dynamic Source Routing (DSR)

Reactive

Route discovery cycleused for route

findingMaintenance ofactiveroutes

Utilizes source routing


44/175

DSR: Route Discovery

1. Node Sneeds a route to D

2. Broadcasts RREQ packet

1. RREQ identifies the target of the route discovery,contains a route record in which the traversed route isaccumulated, contains a pair uniquely identifying the request

A

B

C D

S

RREQ: S


45/175


1. Node Sneeds a route to D2. Broadcasts RREQ packet

3. NodeAreceives packet, has no route to DAND is NOT D Rebroadcasts packet after adding its address to

source route

A

B

C D

S

RREQ: S


46/175


1. Node Sneeds a route to D2. Broadcasts RREQ packet

3. NodeAreceives packet, has no route to D Rebroadcasts packet after adding its address to

source route

A

B

C D

S

RREQ: S, A


47/175


4. Node Creceives RREQ, has no route to D Rebroadcasts packet after adding its address to

source route

A

B

C D

S

RREQ: S, A


48/175



source route

A

B

C D

S RREQ: S, A, C


49/175



source route5. Node Dreceives RREQ, unicasts RREP to C Puts source route accumulated in RREQ into

RREP

A

B

C D

S RREQ: S, A, C


50/175



source route5. Node Dreceives RREQ, unicasts RREP to C Puts source route accumulated in RREQ into

RREP

A

B

C D

S RREP: S, A, C, D


51/175


6. Node Creceives RREP Unicasts toA

A

B

C D

S RREP: S, A, C, D


52/175


6. NodeAreceives RREP Unicasts to S

A

B

C D

SRREP: S, A, C, D


53/175


8. Node Sreceives RREP Uses route for data packet transmissions

A

B

C D

SRREP: S, A, C, D

General node operation upon


54/175

General node operation uponreceiving RREQ

If the pair has recently been seen, DISCARD

If the node ID is already listed in thesource route DISCARD avoids loops

If Im the destination, send a RREP

Otrherwise, append my ID in the sourceroute and rebroadcast (orange casesalready seen in the previous slides)


55/175

Route maintenance

The two endpoints of a failed link aretransmitted to the source in a route errorpacket

Upon a receiving a RERR packet a nodeinvalidates all the routes going through thatlink

If the route is invalidated and it is needed, anew route must be discovered


56/175

Optimizations (1)Extensive use of caching (caching source routes

means that I already know all the route tointermediate destinations, discovery a betterroute to an intermediate destination also bringsme to improve the route to the final destination).

Transmitting packets or sending back repliesmake me learn routes.

A node that knows a route to a given destination(has a source route in cache) can immediately

answer a RREQ Broadcast storm? Each nodes waits for a time which is

C*(h-1+r), r random in (0,1), h length of the route Imadvertising. Only if I havent received other routes listen to other routes tx in the meanwhile-I transmitmine.


57/175

Optimization (2)

Operation in promiscuous mode (I keepdiscovering new routes by transmission of routesby my neighbours)

RREQ overhead minimization: first set a TTL=1, if

I do not get answer I set it to infinityPath shortening: if Y receives a packet by node X

but in the source route we have X, B,,C,Y, Ysignals the path can be shortened (unsolicited

RREP)What if the network is disconnected? Exponentialback-off to decrease the quantity of RREQ sent


58/175

AODV and DSR Differences

DSR uses source routing;AODV uses next hop entry

DSR uses route cache;

AODV uses route tableDSR route cache entries do not havelifetimes;

AODV route table entries do have lifetimes

DSR nodes respond to each RREQ duplicate;AODV nodes only respond to first RREQ,unlessone arrives along a better path


59/175


60/175

Geographically-Enabled Routing

Outline Problems with proactive approaches

Problems with reactive approachesA new way of naming\locating the

destination node: geographic routing

Two seminal protocols

DREAM & LAR Geo-enable routing costs: I need to know

where I am, where the destination is.

Proactive Solutions:


61/175

Proactive Solutions:Drawbacks

Updates overhead, especially inpresence of high mobility

Overhead for enforcing loop freedomLarge routing tables

Low scalability

Is it really necessary to maintain aconsistent view of the networktopology?


62/175

Reactive Protocols: DrawbacksThe discovery phase introduces long delaysRoute discovery and maintenance is very sensitive tonode mobility

Route caching is memory greedy

The size of the header of a data packet can becomecumbersome in approaches such as DSR (noscalability)

Operating in promiscuous mode is energy-consuming.

Relying on flooding based route discovery is resourceconsuming.

Is the dependency on the network topologyavoidable?

Location-Enabled Ad Hoc


63/175

Location Enabled Ad HocRouting

Nodes are equipped with positioningsystem devices (e.g., Global PositioningSystem receivers) that make themaware of their positionThis enables directional routing

Possible solutions differ on how the

location information of the destinationnodes is achieved


64/175

Strengths

No need to update big routing tables, no

need to piggyback routes to the packet

Destination position must be known at the

source.

No need to know the nodes on the way to the

destination: they can be moving while the

packet travels


65/175

Drawbacks

Needs extra hardware

Depends on the extra hardware limitation

(and resource requirements)

Scalability is an issue (indeed the problem

here translates to how to maintain correct

estimates of the nodes positions)


66/175

DREAM

Distance routing effect algorithm formobility [Basagni+, 1998]

A proactive, effective way to spreadlocation information

Directional routing

Disseminating Location


67/175

Disseminating LocationInformation: Problems

Need to periodically update thelocation of a moving node.

Efficient broadcast of location information Determining how far each location packet

should travel

Determining how often a location packetshould be sent

Disseminating Location


68/175

Disseminating LocationInformation: Solutions

Distance effect

Rate of updates is bound to the mobilityof the node


69/175

The Distance Effect


70/175

The Distance Effect

Closer nodes look like they are movingfaster

Need to receive more location updatesfrom closer node

Each location update packet is

associated with an age that determineshow far that packet must travel


71/175

DREAM: Rate of updates

Triggered by the mobility of the nodes

The faster the node the more updates itsends

A plus: slow moving nodes impose littleoverhead


72/175

DREAM: Directional Routing

Source S determines the location ofdestination D at time t0 based on its locationtable

Based on the current time t1 and t0 Sdetermines the area in which D can be found(hence, Ds direction)

S transmits the data packet to all its

neighbors in Ds directionEach neighbor does the same till D is reached

DREAM: Routing a Data


73/175

DREAM: Routing a DataPacket


74/175

DREAM, Strengths

First of its kind: after that, the deluge!

Robustness: multiple routes to thedestination

directional flooding


75/175

DREAM, Weaknesses

It is flooding, although only directional

It is not that scalable, geographic infoupdates have to be periodically

transmitted (even if mechanisms to

limit such overhead are enforced)


76/175

Location-Aided Routing (LAR)

Exploits location information to limit scope ofRREQ flood

Expected Zone: region that is expected to holdthe current location of the destination Expected region determined based on potentially old

location information, and knowledge of thedestinations speed

RREQs limited to a Request Zonethat contains

the Expected Zone and location of the sendernode


77/175

LAR: Expected Zone

D

D'

r

D = last known location of node

D, at time t0

D' = location of node D at current

time t1, unknown to node S

r = (t1 t0) * estimate of Ds speed

Expected Zone


78/175

LAR

The request zone is the smallest rectangle thatincludes the current location of the source andthe expected zone

Only nodes within the request zone forward

route requests Node A does not forward RREQ, but node B does

Request zone explicitly specified in the RREQ

Each node must know its physical location todetermine whether it is within the request zone

LAR R t Z


79/175

LAR: Request Zone

D

D'

r

S

Request Zone

Network Space

BA

(XS, YS)(XD+ r, YS)

(XS, YD + r) (XD+ r, YD+ r)


80/175

LAR, Possible Failures

If route discovery using the smallerrequest zone fails to find a route, thesender initiates another route discovery

(after a timeout) using a larger requestzone The larger request zone may be the entire

network

Rest of route discovery protocol similar toDSR


81/175

LAR, the Routing

The basic proposal assumes that, initially, locationinformation for node X becomes known to Y only duringa route discovery

This location information is used for a future routediscovery

Variations

Location information can also be piggybacked on anymessage from Y to X

Y may also proactively distribute its location information


82/175

LAR, Pros and Cons

Advantages

Reduces the scope of RREQ flood

Reduces overhead of route discoveryDisadvantages

Nodes need to know their physical locations


83/175


84/175

In a sensor network we seek..

For solutions which scaleWhich are energy savingWhich are well integrated with awake/asleep schedulesWhich do not require to maintain routing tablesWhich are simple

Solutions such as AODV and DSR have been proven to work well iffthey exploit intensively caching and promiscuous mode operation(energy inefficient work by L. Feeney et al, 2001) and have beenshown not to scale to the volumes of nodes expected in sensor networks

(work by E. Belding Royer and S.J. Lee)What can we use? communication sensors sink Info such as localization and some level of synchronization often needed

by the application (if I sense an event I have to say WHERE and WHEN itoccurred, otherwise the information is not very interesting)


85/175

An example: GeRaF

Integrates

geographic routing

awake/asleep schedule MAC

How do nodes alternate between

awake and asleep states? According toa duty cycle (time ON/time ON+ OFF)

ON ON

OFF OFF

GeRaF basic idea


86/175

GeRaF basic idea

Geographic routing:each node needs toknow its location, thedestination (sink)

location, and thelocation of whom istransmitting(communicated in thepacket)Greedy approach:

tries to select relaysso to advance asmuch as possibletoward the destinationat each hop


87/175

GeRaF: operations

Main problem to be solved: how to make acontention-based scheme/routing work in thepresence of sleep modes Flat solution

Integrated MAC/routing/awake-asleep but awake-asleep schedule and routing decoupled each nodedoes not know its neighbor and their schedules low overhead

Tightly integrated with the routing layer (no

clear separation really) Without requiring routing tables/routing table updates

Based on the knowledge of the nodes location and onthe knowledge of the sink location

Example of GeRaF operation


88/175

Example of GeRaF operationRTS invites all awake neighbors to become

a relayNodes in best position should win

Nodes within tx range are divided in areas

depending on how close they are to the finaldestination (the closest the better as relay)

Need of location awareness

GeraF operation


89/175

GeraF operationNode i sends RTS with the identity of the area it

is polling now (starting from the closer to the sink,among the slices in which its tx range has beendivided)

Each node, upon receiving the RTS, decides

whether it belongs to the polled area or not(based on location info)

Only nodes in the polled area answer with a CTS No node answers node i polls next area (no node

available for forwarding in the area-there are no nodesor they are sleeping)

One answer, CTS correctly received, send DATA

Multiple answer COLLISION, sender sends a collision

packet, MAC needed to solve collision (next slide)

GeraF operation (how to


90/175

handle collision)

1)A node receiving a collision packet tosses acoin and with probability p transmits a CTS iffit was participating to the previous phase (it

had previously sent a CTS resulting incollision) if only one node answers node i sends data

If no node answers node i asks these nodes to

toss a coin again.. if more nodes answer COLLISION. Collision

packet is sent. GO TO 1) (only the nodes whichhave lead to collision survive to the next phase)


91/175

What If

All areas are polled unsuccessfully?

Try again after some time (exponentialbackoff)

Can I always reach the destination inthis way?


92/175

What if (answer)

No. Here is

An example

Solutions? (mechanisms have to be added torecognize the problem, do backtracking andtry another route)

sink

Tx radius

How to solve dead-endsA problem only at low density


93/175

A problem only at low density

We can set a maximum number of attempts

to find a relay. When a node fails to find arelay it starts decreasing its duty cycle/or theprobability to propose itself as relay overtime nodes along paths to dead-ends are lessand less selected as next hop relays andother paths able to bring to the destinationare instead found

Still..we may have problems

sink

Tx radius

GeRaf performance


94/175

GeRaf performance

square area 320m x 320m

Transmission range=40m

100-1000 randomly deployed nodes (avgdegree 5-50)

Duty cycle =0.01,0.1,0.5

Comparable costs for tx/rx/idle

Poisson packet arrivalChannel data rate 38Kbps

Casari, Marcucci,Nati, Petrioli, Zorzi IEEE MILCOM 2005

GeRaf performance d=0 1


95/175

GeRaf performance, d=0.1Casari, Marcucci,Nati, Petrioli, Zorzi IEEE MILCOM 2005



96/175




97/175


Con i meccanismi per evitare dead end si sale a % di successfullyDelivered packets nel caso di 200 nodi pari a 93-97% (evitandoNel tempo cammini che portino a dead ends). Non si risolve ilProblema nel caso n=100. Soluzione completa in uno schema che

abbiamo proposto: ALBA.


98/175


99/175

Localization in sensornetworks

Thanks to Prof. Mani Srivastava

These slides have been derived

From his tutorial on sensor networks

Given at Rome Un. On July 2003

LocalizationUseful info


100/175

Useful info Helps with some protocols (e.g. GeraF)

Needed for being able to identify where events occurWhy not just GPS (Global Positioning System) atevery node? Large size

High power consumption Works only when LOS to satellites (not in indoor, heavy

foliage)

Over kill often only relative position is needed (e.g.enough to know that relative to a coordinate systemcentered in the sink the event occurred in a position(x,y). Starting from relative info if some nodes haveglobal coordinates global coordinates of events can beinferred.

Works only on earth

Localization


101/175

Localization

Basic step is to evaluate distance betweentwo nodes (ranging). Different techniquesdepending on the available HW: AoA (e.g. directional antennas)

RSS ToA

Range free approaches (number of hops

between nodes used to estimate thedistance between them without using anyextra HW)

Techniques for LocationSensing (AoA)


102/175

Sensing (AoA)Measure direction of landmarks Simple geometric relationships can be used to

determine the location by finding the intersectionsof the lines-of-position

e.g. Radiolocation based on angle of arrival (AoA)

measurements of beacon nodes (e.g. basestations) can be done using directive antennas + a compass

need at least two measurementsBS

BS

BS

MS

12

3

Measure distance to landmarks, or RangingTechniques for Location Sensing (RSS or ToA)


103/175

, g g

e.g. Radiolocation using signal-strength or time-of-flight also done with optical and acoustic signals

Distance via received signal strength use a mathematical model that describes the path loss attenuation with

distance each measurement gives a circle on which the MS must lie

use pre-measured signal strength contours around fixed basestation(beacon) nodes

can combat shadowing location obtained by overlaying contours for each BS

Distance via Time-of-arrival (ToA) distance measured by the propagation time

distance = time * c

each measurement gives a circle on which the MS must lie active vs. passive

active: receiver sends a signal that is bounced back so that the receiver knowsthe round-trip time

passive: receiver and transmitter are separate

time of signal transmission needs to be known

N+1 BSs give N+1 distance measurements to locate in N dimensions

d l d SS


104/175

Radiolocation via ToA and RSSI

x1

x2

x3

d1

d3

d2

MS

BS

BS

BS

But what if the circles

do not intersect due tomeasurement errors (e.g.

due to fading etc.)?will have to identify the best guess given errors

L i i 3D


105/175

Location in 3D

L ti i 3D


106/175

Location in 3D

L ti i 3D


107/175

Location in 3D

A possible solution: AbsoluteL li ti


108/175

Localization

Beacon

Unkown Location

Randomly Deployed Sensor Network

Beacon nodes

A small fraction of the nodesis aware of their locations

Rest need to collaborate to

estimate their locations

Atomic Multilateration


109/175

22

,, )()( uiuiiuiu yyxxrf

2

,min),( iuuu fyxF

),( uu yx - initial position estimate for node u

u

1

2

3

1,ur

3,ur

2,ur

Beacon nodewith known

location

Our objective function is:

Metrica di interesseErrore quadratico medio

Nodi che hanno

almeno 3 vicini

(in 2D, se si usaAd esempio RSS)

beacon

possono stimare

la loro posizione(triangolarization)

In presenza di errori

Iterative Multilateration


110/175

Iterative MultilaterationNodes that estimate their locations can become

beacons and help other nodes discover theirlocations.

Some observations: Can work for small networks, if ranging is accurate

Energy efficient

Still requires quite a lot of initial beacons

Suffers from error accumulation

Bad geometry yields bad results => unpredictableperformance

Still a useful primitive for Distributed Collaborative Multilateration

Primo approccio semplice, vasta letteratura

Ref: based on slides by Andreas Savvides

R f l li ti


111/175

Range free localization

Non usa ranging, ma solo informazioniche si possono ottenere tramite algo dirouting tradizionali

Idee su come possa funzionare?

Qualche idea sullapproccio


112/175

Qualche idea sull approccio Servono degli anchor, nodi che

conoscono la propria posizione in unsistema di coordinate comune

X1,Y1

X2,Y2

X3,Y3



113/175

X1,Y1

X2,Y2

X3,Y3

Tutti I nodi calcolano il numero min. di hop tra

loro e gli anchor Anche gli anchor lo fanno tra loro



114/175

Anchor A: conosco la posizione esatta mia edegli altri anchor, il num. di hop, posso stimare la

lunghezza media di un hop

Questa informazione e usata per stimare ledistanze da tutti I nodi agli anchor. Sulla base ditali distanze, le corrette coordinate degli anchor,per triangolarizzazione ciascun nodo stima leproprie coordinate

Pro: Non serve extra HW

Cons: si perde in precisione


115/175

Scalability Problems andCl t i


116/175

Clustering

What happens to protocols when the numberof network nodes grows? Especially crucial in WSNs

A traditional networking solution: Hierarchicalorganization of the nodes

Network nodes are grouped into clusters

Some nodes, locally the best, are selected

to coordinate the clustering process:Clusterheads

How to Select the Best Nodes


117/175

How to Select the Best Nodes

Independence of the clusterheads

Dominance of the clusterheads

Possibility to express preferences

Distributed operations

Fast and simple implementation

Previous Approaches


118/175

Previous Approaches

Heuristics based on Independent Sets

Minimum ID approach (Gerla & al.)

Maximum degree (Ephremides & al.)Heuristics based on Dominating Sets

The concept of spine

Minimum connected dominating set

Maximum Independent Set(MIS)


119/175

(MIS)

A subset Vof the vertices V of a graphG=(V,E) is independent when for eachu,v V the edge {u,v} E

MIS is an Optimization Problem

Input: A Graph G=(V,E) with n vertices

Output: A subset V of V that isindependent and has maximum size

MIS: Hardness


120/175

MIS: Hardness

No known algorithm computer a MIS inpolynomial time

Need for approximate solutions

And approximation algorithm is an algorithmthat produces a solution that is not optimal,but that approximates it

We sacrifice optimality in favor of a goodsolution that can be computed efficiently

MIS is HARD to Approximate


121/175

MIS is HARD to Approximate

Bad news

Not only MIS is computationally hard

It is also hard to approximate:Approximate solutions are not so good

They are unboundedly far from the optimum

We consider the simple greedy heuristicfor the MIS

Greedy Heuristic for MIS 1


122/175

Greedy Heuristic for MIS, 1

Select the vertex with minimum degreeand put it in the MIS

The degree of a vertex is the number of itsneighbors Cardinality of its adjacency list

Keep going till all the vertices are either in

the MIS or COVERED by a vertices in theMIS

Greedy Heuristic for MIS 2


123/175

Greedy Heuristic for MIS, 2

MIS(V,E,d)// d is the vector of degrees

mis =

while V dov = vertex with min degree

mis = mis U {v}

V = V {{v} U N(v)}return mis

Greedy MIS: Maximal Solution


124/175

Greedy MIS: Maximal Solution

The greedy solution provides a maximalindependent set An independent set is maximal when, if you add a

vertex, the set is no longer independentYou cannot make an maximal independent set bigger

This solution is also a minimal dominating set A dominating set D subset of V is a set such that a

vertex v V is either in D or it has a neighbor in D

Solutions we will see are variant of thisapproach

On MDSwhat if we look at UDGgraphs?


125/175

graphs?

Bad news: Still computationally hard

Better news: Minimum DS It is

approximable up to a constant It means that the ratio between the size of

a DS computed by MIS greedy on UDGsand the size of a MDS is < c, c a constant

This constant is 5

Greedy MIS for MDS on UDGis 5 approximable 1


126/175

is 5-approximable, 1

Key fact: In a UDG disk (radius 1) thereare at most 5 independent nodes

Consider an Optimal solution and aGreedy solutionSince Opt is dominant, it dominatesGreedy

Assign every vertex of Greedy to onedominator in Opt (choose one if more)

Greedy MIS for MDS on UDGis 5 approximable 2


127/175

is 5-approximable, 2

For each u in Opt consider its assignedvertices v1(u), v2(u), , vk(u) of Greedy

How big is k?Well, all vi(u) must be distant 1 from uand they also have to be independent

Greedy: at most 5 times bigger thanOpt

MIS and Dominating Sets andWireless Networks


128/175

Wireless Networks

UDGs model ad hoc networks

IS and DS are useful for clustering ad hoc

networks Gives the network a hierarchical organization

Decreases the amount of information at each node

Enhances scalability

Helps in resource assignment

Advantages of hierarchicalorganization


129/175

organization

routing always through the clusterhead

data aggregation at the clusterhead

easy to locally synchronize nodeswithin the cluster, using TDMA MACprotocol for intra-cluster communicationand different MAC protocols (e.g.CDMA) for inter-cluster communications

MWIS-Based Clustering


130/175

MWIS-Based Clustering

MWIS = Maximal Weight IndependentSetClustering selection based on generic

weights (real numbers > 0)

Mobility/node related parameters

Generalizes previous IndependentSet solutions

Two Protocols


131/175

Two Protocols

Distributed Clustering Algorithm (DCA) Quasi-mobile networks, periodical

reclustering. Allow complexity analysis, fast

and simpleDistributed and Mobility-AdaptiveClustering (DMAC) Algorithm Same rules/procedures for clustering set

up and maintenance, adaptive to nodesmobility and node/link failures

DCA: Distributed ClusteringAlgorithm 1


132/175

Algorithm, 1

Assumptions

Knowledge of IDs and weights of one-hop

neighbors

Broadcast transmission of a packet in finitetime (a step)

Nodes do not move during clustering

DCA 2


133/175

DCA, 2

(Only) Two messages:

CH(v): Sent by a clusterhead v

JOIN(u,t): Sent by ordinary node u when itjoins the cluster of clusterhead t

Three (simple) procedures:

Init (start up)

OnReceivingCH(v), OnReceivingJOIN(u,v)(message triggered)

DCA


134/175

DCA

Ogni nodo conosce i suoi vicini ed il loro pesoUn nodo init se ha il peso pi grande deipesi dei suoi nodi viciniGli init node diventano clusterhead e invitanoi loro vicini a far parte del loro clusterUn nodo x aspetta di ricevere messaggi daivicini di peso maggiore prima di prendere unadecisione Se un vicino di peso maggiore lo invita a far parte

del suo cluster allora x entra a far parte del clusterdel vicino di peso maggiore che lo contatta(inviando un messaggio di Join) nodo ordinario

Altrimenti diventa clusterhead lui stesso e inviaun messaggio di CH

DCA


135/175

DCA

Due tipi di messaggi

CH(v) usato da un nodo v per rendereconsapevoli i suoi vicini del fatto che ha

assunto il ruolo di clusterhead

JOIN(v,u) usato dal nodo v percomunicare ai suoi vicini che sar parte di

un cluster il cui clusterhead il nodo u

DCA


136/175

DCA

Variabili Cluster(v) indica linsieme dei nodi che fanno parte

del cluster di cui clusterhead v

Clusterhead una variabile che identifica ilclusterhead del mio cluster

Ch(u) vero quando o ha mandato un messaggioCH (u==v) oppure quando ha ricevuto un

messaggio di CH dal nodo u La variabile booleana Join (u,t) vera se il nodo v

ha ricevuto un JOIN(u,v) dal nodo u

DCA-Procedure (eseguite dalnodo v)


137/175

nodo v)

Init

Se tutti i nodi vicini hanno un peso

minore di vinvia CH(v);

Cluster(v)=Cluster(v)U{v};

Ch(v)=true;Clusterhead=v;



138/175

nodo v)

On receiving CH(u)

Ch(u)=true;

Se u ha un peso maggiore di me e ivicini di peso maggiore di v con pesomaggiore di u hanno tutti mandato unJoin, allora

Clusterhead=u;invia JOIN(v,Clusterhead);



139/175

nodo v)On receiving JOIN(u,t)

Join(u,t)=true;Se v un clusterhead allora se t==v

Cluster(v)=Cluster(v)U{u};Se ho ricevuto Join da tutti i vicini pi piccoli EXIT

Altrimenti si verifica se tutti i vicini di peso maggiore hanno presouna decisione sul ruolo.

Se questo il caso e tutti i vicini di peso maggiore hannomandato JOIN

mandiamo un CH(v);Cluster(v)=Cluster(v)U{v};Clusterhead =v;Se si ricevuto JOIN da tutti i vicini minori EXIT.

Altrimenti se uno o pi vicini di peso maggiore hanno mandato unCH

Clusterhead=il vicino di peso maggiroe con peso pigrande tra quelli che sono diventati clusterhead e mi hannoinvitato.

manda JOIN(v,Clusterhead);

EXIT;

Example


140/175

Example

4(9)

5(8)

7(5)

6(1)

2(3)

1(6)

8(1)

3(2)

Cluster 1Cluster 2

Cluster 3

clusterhead clusterhead

clusterhead

I Step II Step III Step IV Step V Step

DCA: Provable Properties


141/175

DCA: Provable Properties

Consider

t: V {1,2,3, , 2k}

V = set of network nodes, k = number of

clustersProposition: Each node v in V sends exactlyone message by t(v) steps

Corollary 1: DCA message complexity is n

=|V|Corollary 2: DCA terminates correctly in atmost 2k steps (


142/175

Node/Link Failures: DMAC

DMAC is for clustering set up ANDmaintenance

Nodes can move during the clustering

Each node reacts to Reception of a message

Presence of a new link

Link failure

Same assumptions of DCA, plus knowledge ofneighbors roles (no role = ordinary role)

Quando muore un nodo ordinario se ne tiene solo tracciaQuando muore un gw bisogna riselezionare un nuovo gwQuando muore un CH un nodo che stava in quel clusterhead

vede se c un altro vicino CH a cui riaffiliarsi ed in casosi affilia a quello di peso maggioreAltrimenti se ha il peso maggiore tra i vicini restanti diventa CHLarrivo di un nuovo nodo o la comparsa di un link puoCostringere a manutenzioni del backbone (per mantenere la

proprieta che il nodo di peso maggiore CH)

DMAC: The Procedures


143/175

DMAC: The Procedures

INIT

Link-dependent procedures:

Link_Failure New_Link

Message-triggered procedures:

OnReceivingCH(v) OnReceivingJOIN(u,t)

Joining Clusterheads: DynamicBackbone


144/175

Backbone

A theorem from Chlamtac and Farago:

If a network is connected, and DCA is

used, then if and only if eachclusterhead is linked to all theclusterheads at most three hops away,the resulting backbone network is

connected

Example


145/175

Example

4(9)

5(8)

7(5)

6(1)

2(3)

1(6)

8(1)

3(2)

Cluster 1Cluster 2

Cluster 3

clusterhead clusterhead

clusterhead

I Step II Step III Step IV Step V Step


146/175

4 Backbone Formation Protocols


147/175

4 Backbone Formation Protocols

3 representatives of major approaches Selection of independent set of nodes and

backbone construction (DCA)

Rich dominating set formation and pruning(WuLi)

Two-phase algorithm with theoreticalguarantees (WAF)

1 proposal after the performancecomparison (DCA-S)

Distributed Clustering Algorithm(DCA)


148/175

(DCA)

Distributed and localized implementationof the greedy for independent set

Takes node status into account for node

selectionIndependent nodes are joined into aconnected backbone (connectivity is

guaranteed) via gatewaysLow degree of parallelism (dependencychains)

A DCA Backbone


149/175

A DCA Backbone

WuLi: Wu and Li protocol


150/175


Distributed and localized protocols forforming a connected dominating set

Build a rich connected dominating setApplies localized rules for pruningunnecessary nodes/links

High degree of parallelism (alllocalized)



151/175



Build a rich connected dominating set

Applies localized rules for pruningunnecessary nodes/links


If a vertex v has two neighbors which are notin visibility range it enters the set C



152/175

u u a d p o o o




High degree of parallelism (alllocalized)What ia needed is, from the neighbors, whetherthey are in C and their list of neighbors.

WuLi-dominance propertyda On Calculating Connected Dominating Set for Efficient Routing in Ad Hoc Wireless Networks, Wu and Li


153/175

Assume V is the set of vertices that are marked T in V, i.e., the set of vertices whichinitially enter the CDS since they have at least two neighbors which are notneighbors of each other. The reduced graph G is the subgraph of G induced by V.The following two theorems show that G is a dominating set of G and it isconnected.

THEOREM 1: Given a G = (V, E) that is connected, but not completely connected,the vertex subset V, derived from the marking process, forms a dominating set of G.PROOF: Randomly select a vertex v in G. We show that v is either in V (a set ofvertices in V that are marked T) or adjacent to a vertex in V . Assume v is marked F,if there is at least one neighbor marked T, the theorem is proved. When all itsneighbors are marked F, we consider the following two cases: (1) All the othervertices in G are neighbors of v. Based on the marking process and the fact thatm(v)=F, all these neighbors must be pairwise connected, i.e., G is completelyconnected. This contradicts to the assumption that G is not completely connected.(2) There is at least one vertex u in G that is not adjacent to vertex v. Construct ashortest path, {v,vi,v2, . . . . u}, between vertices v and u. Such a path always existssince G is a connected graph. Note that v2 is u when v and u are 2-distance apart inG. Also, v and v2 are not directly connected; otherwise, {v, v2,. .. u} is a shorterpath between v and u. Based on the marking process, vertex vi, with both v and v2as its neighbors, must be marked T. Again this contradicts the assumption that vsneighbors are all marked F. CVD

WuLi (first step produces a CDS)


154/175

( p p )

THEOREM 2: The reduced graph G = G -V is aconnected graph.PROOF: We prove this theorem by contradiction.Assume G is disconnected and v and u are twodisconnected vertices in G. Assume dis

G(v,u) =k+1 >

1 and{v,v1,v2,...,vk,u} is a shortest path betweenvertices v and u in G. Clearly, all v1,v2, . . ..vk aredistinct and among them there is at least one vi suchthat m(vi) = F (otherwise, v and u are connected inG). On the other hand, the two adjacent vertices of

vi, vi-1 and vi+i, are not connected in G (otherwise,{v, ..vi, vi+1, . . . . vk,u} would be a shorter path).Therefore, m(vi) =T based on the marking process.


Rule 1: for each pair of nodes u and v in Cthe one with the smallest ID, say v, can be removed


155/175

p





from C if v and all its neighbors are covered by u

Rule 2: Assume nodes u,v, and w are in C andassume that vs ID is the smallest. If u and w areneighors of v and are in each other transmissionrange and if each neighbor of v is covered by uand w, then v can be removed from C.

Mantengono la propriet di connessione e di dominanza



156/175

p




High degree of parallelism (alllocalized)What ia needed is, from the neighbors, whetherthey are in C and their list of neighbors.

A WuLi Backbone


157/175

WAF: Wan, Alzoubi andFrieder


158/175

Two phases Leader election: One node is chosen

among all network nodes to be the root ofa tree

Nodes at different levels of the trees canbe chosen to form a connected dominatingset

The leader election tree is quite

expensiveVery low degree of parallelism

A WAF Backbone


159/175

DCA-S: DCA Sparsified


160/175

p

Build a connected dominating set (say, withDCA) and consider its spanned sub-graph H(include gateways)Erds: If a graph does not have small cycles

then it is sparseFind and break small cycles (small=log n) In practice we search and break cycles with 3 and

4 links

Breaking cycles does not compromiseconnectivity

Simulation Results


161/175

Metrics (all averages)

1. Protocol duration

2. Operation overhead (in bytes)

3. Energy consumption (per node)

4. Backbone size

5. Route length

6. Backbone robustness (node deaths fordisconnections)

Simulation Results, 2


162/175

,

Parameters of ns2-based simulations

Nodes: 300, IST EYES prototype Tx range: 30m

Initial (residual) energy: 1J

Tx, Rx, idle power: 24, 14.4, 0.015 (mW)

Area: 200 x 200m

Six scenarios with increasing densities(avg. degrees: 3.5 to 20)

Protocol Duration


163/175

WuLi is fastest Simple operation; parallelism

DCA: Reasonably fast

Possible dependencies and gateway selectionDCA-S: As DCA The sparsification phase is executed by fewer

nodes and requires little info exchange

WAF: Slower Non-trivial leader election

Protocol Duration, 2


164/175

Protocol Overhead


165/175

Protocol Overhead, 2


166/175

Average number of protocol bytes pernode

WuLi: Best performing Simple list exchangeDCA(-S): Almost twice as much Bit more info needed (weight, IDs, )

WAF Leader election complexity

Energy Consumption


167/175

Important metric per backbone set upand maintenance

Similar to overhead results

WuLi and DCA perform quite well

DCA-S performs similarly: No difference

in breaking cycles with 3 or 4 linksWAF: High consumption due to firstphase

Energy Consumption, 2


168/175

Backbone Size


169/175

Important metric: Aggregation andawake/asleep cycles

Small backbone + role rotation: key for

WSNs

Decrease with n increasing (biggerclusters)

WAF: Slimmer backbone (tree like)

DCA-S, 4 < DCA-S, 3 < DCA < WuLi

Backbone Size, 2


170/175

Backbone Robustness


171/175

Number of nodes needed to disconnect thebackbone

Useful for planning backbone re-orgs

Increases with network densityWuLi and DCA: More robust Resilient to up to 25 death when n = 300

WAF: Quite a disaster (tree-like topologies)DCA-S: In the middle

Backbone Robustness, 2


172/175

Route Length


173/175

Flat topology (visibility graph) as abase

Expected increase: Hierarchy routes arelonger

DCA & WuLi: 7 to 34.7% longer routes

DCA-S: Up to 9% more than DCAWAF: Up to 33.4% longer than DCA

Route Length, 2


174/175

To Go


175/175

Hierarchical organization is effective forprolonging network lifetime

Four protocols for backbone formation: DCA, WuLi, WAF and DCA-S

Nice theoretical features hard to implement

Simple solutions (WuLi, DCA): Good startingpoint for efficient implementations

DCA-S: Slimmer backbone at a reasonable cost

Documents

adhoc netwrk ppt(1)