Fall 2006

Routing Techniques in Wireless Sensor Routing Techniques in Wireless Sensor NetworksNetworks

Professor Choong Seon Hong

Kyung Hee UniversityKyung Hee University

cshong@khu.ac.krcshong@khu.ac.kr

2Fall 2006

IntroductionIntroduction

Routing Process of delivering a message across one or more

networks via the most appropriate path WSNs routing is different from traditional IP routing One of the main goals of WSNs is to prolonged the lifetime

of the network Maintain connectivity by employing energy management

techniques as energy sources in WSNs are limited and irreplaceable

Extensive collaboration between sensor nodes is required to perform high quality sensing and to behave as fault tolerant systems

Sensor network can be categorized as time-driven or event-driven. So, routing techniques must be adaptive to applications

3Fall 2006

Design IssuesDesign Issues

Routing protocols should be designed with the following principles Sensor nodes should be self-organizing. Also the operation

of the sensor networks is unattended, so the organization and configuration should be performed automatically

Most application sensor nodes are stationary. However, in some applications nodes may allowed to change their location

Sensor networks are application specific. For example the challenging problem of low-latency precision tactical surveillance is different from that required for a periodic weather-monitoring task

Data collected by many sensors in WSNs are based on common phenomena. So, there is high probability that these data have some redundancy. So efficient routing technique should perform an in-network processing

4Fall 2006

Design Issues (Cont’d)Design Issues (Cont’d)

Sensor networks are data-centric. Once an event of interest is detected, data can be sent to sink. So, periodic sleep can be exercised to conserve more energy

Exercising periodic sleep requires appropriate duty cycle calculation and synchronization

Data aggregation is useful only when it does not hinder the collaborative effort of the sensor nodes

An ideal sensor network has attribute –based addressing and location awareness

5Fall 2006

Design Issues (Cont’d)Design Issues (Cont’d)

As sensor network is highly dense. For a certain period of time optimal number of sensor will be responsible of sensing and disseminating data

Dynamic routing capability based on power availability, position and reachability

As sensor networks works under broadcast mechanism, probabilistic or location aware flooding technique should be used rather than simple flooding.

6Fall 2006

Routing Challenges Routing Challenges

Ad hoc deployment Adaptive to topology changes

Computation capabilities Light-weight and simple

Communication range Short range Multihop routing

Fault Tolerance Physical damage Lack of power

Scalability High density deployment Routing should be scalable according to network size,

topology and density

7Fall 2006

Routing Challenges (Cont’d)Routing Challenges (Cont’d)

Hardware constraints Small in size Extremely low power

Transmission media Contention based protocols (e.g. CSMA) do not suite

Connectivity Nodes are expected to be highly connected

Control overhead Control packet overhead increase linearly with density Tread off between energy conservation, route setup cost,

routing performance matrices (e.g. latency, hop count) Quality of service

In some applications, data should be delivered within a certain period of time from the moment they are sensed

8Fall 2006

Taxonomy of Current Proposed Routing ProtocolsTaxonomy of Current Proposed Routing Protocols

Routing protocols in WSN

Network Structure Protocol operation

Flat network routing

Negotiationbased

routing

Hierarchical network routing

Locationbased routing

Multipathbased

routing

Query based

routing

QoS based

routing

Coherentbased

routing

SPIN, Directed

Diffusion, Rumor, MCFA,

GBR

LEACH,PEGASIS,

TEEN,APTEEN,

MECN

GAF,GEAR,SPAN

SPINDirectedDiffusion

SequentialAssignment

Routing(SAR)

Maximum Lifetime Routing

SequentialAssignment

Routing

9Fall 2006

Flat Vs Hierarchical RoutingFlat Vs Hierarchical Routing

Hierarchical Routing: Reservation based Scheduling Collision avoided Reduced duty cycle due to

periodic sleeping Simple but non-optimal Routing Requires local and global

Synchronization Overhead of cluster formation

throughout the network Lower latency because multiple

hops networks formed by cluster head always available

Energy dissipation is uniform Energy dissipation can be

controlled Fair Channel allocation

Flat Routing: Contention based scheduling Collision overhead present Variable duty cycle by

controlling sleep time of nodes Node on multihop path

aggregates incoming data from neighbors

Routing is complex but optimal Links formed on the fly without

synchronization Routes formed only in regions

with data for transmission Latency in waking up

intermediate nodes and setting up multipath

Energy dissipation depends on traffic pattern

Energy dissipation adapts to traffic patterns

Fairness not guaranteed

10Fall 2006

Why Hierarchical and Data-Centric Routing is BetterWhy Hierarchical and Data-Centric Routing is Better

In sensor networks, hierarchical control structures contribute to improved efficiency of resource use by creating contexts for:

Managing the whole network with near optimal number of nodes for energy conservation

Managing wireless communications among multiple nodes to reduce channel contention

Forming routing backbones to reduce network diameter Periodic sleep and awake technique can be exercised to

conserve energy Data-centric routing can make efficient use of resources in the

following way To combine data from different sensors to eliminate redundant

transmission From multiple sources to a destination data-centric approach

allows in-network consolidation of data Most of the sensor network application data is requested based

on certain attribute, so data-centric routing is suitable for sensor network

11Fall 2006

Desired Characteristics of Hierarchical RoutingDesired Characteristics of Hierarchical Routing

Constructing clustering algorithm should have the following properties:

The algorithm must choose the best nodes as clusterheads and gateways considering energy and topological position in the network

Number of cluster head should be kept as minimum as possible without destroying the reliability of the network operation

Distributed hierarchical control structure (it means cluster head and gateway must be well distributed in the network)

Cluster setup overhead should be kept as low as possible The algorithm must execute based on local coordination

and should not depend on global topology information as localized protocols are rewarded with good performance in terms of energy consumption and backbone size

12Fall 2006

Flat and Query Based Routing ProtocolsFlat and Query Based Routing Protocols

Joanna Kulik, Wendi Rabiner Heinzelman, and Hari Balakrishnan: SPIN: Sensor Protocol for Information via Negotiation, Selected Papers from Mobicom'99 Volume 8 ,  Issue 2/3  (March-May 2002)

Pages: 169 – 185

13Fall 2006

What do we expect in a WSN?What do we expect in a WSN?

Can monitor and control the physical environment from remote locations (sink nodes)

Improve sensing accuracy by distributed processing Can aggregate sensor data to provide multi-dimensional view Focus on critical events (e.g. intruder entering) Function accurately when individual sensors fail

Internet and Satellite

Sink

Task manager nodeUser

Sensor nodesSensor field

A

B

CD

E

Problem: Information dissemination

14Fall 2006

Challenges !Challenges !

Energy-limited nodes Sense data Transmit data Route data

Computation Signal processing Algorithms Sophisticated network protocols

Communication Bandwidth-limited Energy-intensive

Goal: Minimize energy dissipation

15Fall 2006

What sparked off SPIN?What sparked off SPIN?

Study of the conventional protocols led to SPIN’s development –protocol characterized as “classic flooding”

B

D E

FG

C

A

Flooding Send data to all neighbors

16Fall 2006

Classic Flooding LimitationsClassic Flooding Limitations

Implosion

A

B C

D

(a)

(a)

(a)

(a)

A B

C (r,s)(q,r)

q sr

Data overlap

Resource blindness Nodes do not modify their activities

based on the energy available to them

17Fall 2006

Other Data Dissemination AlgorithmsOther Data Dissemination Algorithms

Gossiping Forward data to a random neighbor Avoids implosion (but no for overlap)

B

D

C

A

Ideal Dissemination Shortest-path routes Avoids overlap Minimum energy

B

D E

FG

C

A Classic Flooding

18Fall 2006

SPIN FamilySPIN Family

Negotiation Using “meta-data” – only useful information is

sent -> take care of implosion and overlap

Resource-adaptation Check the energy level before plunging into data

transmissions Cut down activity to save energy

Sensor Protocol for Information via Negotiation

19Fall 2006

Meta-DataMeta-Data

Exchanging sensor data may be expensive, but exchanging data about sensor may not be.

Sensors use meta-data to describe the sensor data briefly If x is the meta-data descriptor for data X

sizeof (x) < sizeof (X)

If x==ysensor-data-of (x) = sensor-data-of (y)

If X==Ymeta-data-of (X) = meta-data-of (Y)

Meta-data format is application specific

20Fall 2006

SPIN MessagesSPIN Messages

ADV- advertise data REQ- request specific data DATA- requested data

A B

A B

A B

ADV

REQ

DATA

• ADV and REQ messages contain only meta-data so they are smaller in size.

21Fall 2006

SPIN ProtocolsSPIN Protocols

SPIN on Point-to-Point Networks SPIN-PP (Point-to-Point) SPIN-EC (Energy Conserving)

SPIN on Broadcast Networks SPIN-BC (BroadCast) SPIN-RL (ReLiable)

22Fall 2006

SPIN on Point-to-Point NetworksSPIN on Point-to-Point Networks

Linear cost with number of neighborsSPIN-PP

3-stage handshake protocol (ADV-REQ-DATA) Advantages

• Straightforward.• Scalability (single-hop neighbors).

SPIN-EC SPIN-PP + low-energy threshold Modifies behavior based on current energy

resources

23Fall 2006

SPIN-PP DemonstrationSPIN-PP Demonstration

B

AADVREQDATA

ADV

AD

VADV

ADV

AD

V ADV

REQ

RE

Q

REQ

RE

Q

REQ

DATADA

TA

DATA

DA

TA

DATA

24Fall 2006

SPIN on Broadcast NetworksSPIN on Broadcast Networks

One transmission reaches all neighbors SPIN-BC

Same 3-stage handshake protocol as SPIN-PP Uses only broadcast communication

• Same transmission cost as unicast

• Coordination among nodes

• Broadcast message suppression

SPIN-RL SPIN-BC + Reliability Periodically re-broadcast ADVs and REQs

25Fall 2006

SPIN-BC DemonstrationSPIN-BC Demonstration

Nodes with data

Nodes without data

Nodes waiting to send REQ

ADV

A

B

DC

ERequest suppression

Mode

A

B

DC

E

REQ

A

B

DC

E

A

B

DC

EDATA

A

B

DC

EDATA

G

A

B

DC

E

FADV

ADV

ADV

26Fall 2006

SPIN EvaluationSPIN Evaluation

Strengths Simplicity -> save energy in communication (more efficient

(60%) than classic flooding) Latency: converges relatively quickly Straightforward: ADV REQ DATA Scalability: Topological changes are localized (single-hop

neighbors) Robust: immune to node failures. Reliability: adapted to work in lossy or mobile networks.

Weaknesses Nodes always participating (broadcast media) Cannot guarantee the delivery of data (intrusion detection)

27Fall 2006

SPIN ConclusionsSPIN Conclusions

Successfully use meta-data negotiation solves the implosion and overlap problems

Resource-adaptive enhancements take care of resource blindness problem

Reliability enhancements SPIN outperforms gossiping SPIN consumes less energy than flooding SPIN distributes more data per unit energy than

flooding

28Fall 2006

D. Braginsky and D. Estrin, "Rumor routing algorithm for sensor networks," in Proceedings of the First Workshop on Sensor

Networks and Applications (WSNA), Atlanta, GA, October 2002

Flat and Query Based Routing ProtocolsFlat and Query Based Routing Protocols

29Fall 2006

Rumor RoutingRumor Routing

Designed for query/event ratios between query and event flooding

Motivation Sometimes a non-optimal route is satisfactory

Advantages Tunable best effort delivery Tunable for a range of query/event ratios

Disadvantages Optimal parameters depend heavily on topology (but can be

adaptively tuned) Does not guarantee delivery

30Fall 2006

Basis for AlgorithmBasis for Algorithm

Observation: Two lines in a bounded rectangle have a 69% chance of intersecting

Create a set of straight line gradients from event, then send query along a random straight line from source.

Event

Source

31Fall 2006

Creating PathsCreating Paths

Nodes having observed an event send out agents which leave routing info to the event as state in nodes

Agents attempt to travel in a straight line

If an agent crosses a path to another event, it begins to build the path to both

Agent also optimizes paths if they find shorter ones.

32Fall 2006

Algorithm BasicsAlgorithm Basics

All nodes maintain a neighbor list.Nodes also maintain a event table

When it observes an event, the event is added with distance 0.

Agents Packets that carry local event info across the

network. Aggregate events as they go.

33Fall 2006

Agent PathAgent Path

Agent tries to travel in a “somewhat” straight path. Maintains a list of recently seen nodes. When it arrives at a node, it adds the node’s

neighbors to the list. For the next tries to find a node not in the recently

seen list. Avoids loops important to find a path regardless of “quality”

34Fall 2006

Following PathsFollowing Paths

A query originates from source, and is forwarded along until it reaches it’s TTL

Forwarding Rules: If a node has not seen the query before, it is sent

to a random neighbor If a node has a route to the event, forward to

neighbor along the route Otherwise, forward to random neighbor using

straightening algorithm

35Fall 2006

Energy ComparisonEnergy Comparison

Rumor Routing (1000 queries) Total energy = Es + Q*(Eq + N*(1000-Qf)/1000) Es = avg. energy to set up path Eq = avg. energy to route a query Qf = successful queries Q number of queries are routed N = total number of nodes

Query Flooding Total energy = Q*N

Event Flooding Total energy = E*N

36Fall 2006

Fault ToleranceFault Tolerance

After agents propagated paths to events, some nodes were disabled.

Delivery probability degraded linearly up to 20% node failure, then dropped sharply

37Fall 2006

Some ThoughtsSome Thoughts

The straightening algorithm used is essentially only a random walk … can something better be done.

The tuning of parameters for different network sizes and different node densities is not clear.

38Fall 2006

Can we analyzeCan we analyze

The inherent concept looks powerful. Even though not presented in this way … this algorithm is

just an example of gossip routing. There are two types of gossip, gossip of events and gossip

of queries. It maybe possible to find the probability of intersection of

these two. That might lead to a set of techniques for parameter

estimation, or an optimal setting.

39Fall 2006

C. Intanagonwiwat, R. Govindan, and D. Estrin, Directed diffusion: a scalable and robust communication paradigm

for sensor networks, Proceedings of ACM MobiCom '00, Boston, MA, (2000) 56-67

40Fall 2006

Directed Diffusion

Directed Diffusion is a prominent example of data-centric routing based on application layer data and purely local interaction

A sensing task is disseminated throughout the network

This dissemination sets up gradients within the network

There may be multiple gradient pathsThe sensor network reinforces one or

small number of these paths

41Fall 2006

Basic ElementsBasic Elements

Interest: Query or interrogation which specifies what an user wants

Data Messages: Collected or processed information of a physical phenomenon

Gradient: Direction state in each node that receives an interest

Reinforcement: Selection of a particular neighbor for drawing real data

42Fall 2006

NamingNaming

Task descriptions are named by a list of attribute-value pairs: Type = Four-legged animal //detect animal location Interval = 20 ms //send back events every 20 ms Duration = 10 seconds // for next 10 seconds Rect = [-100, 100, 200, 400] //from sensors within

rectangle

Intuitively, the task description specifies an interest for data matching the attributes. For this reason, such a task description is called an interest

43Fall 2006

Interest DisseminationInterest Dissemination

sinkSink disseminates interest for a four-legged animal (~36 bytes).Initial interval is large.

C’s Interest cacheInterests Gradients

B

C

source

Type = four-legged animal Interval = 1s

Rect = [-100,200,200,400] Timestamp = 01:10:40 Expires at = 01:20:40

44Fall 2006

Interest DisseminationInterest Dissemination

sink

Every node contains an interest cache, with separate entries for distinct interests. Entries do not contain info about sinks and therefore scale well.Overlapping entries may be aggregated for efficiency.

C’s Interest cacheInterests Gradients

B

C

source

45Fall 2006

Interest DisseminationInterest Dissemination

sinkEach interest cache entry contains a list of gradients; events that match interest entries are propagated back to the sink via these gradients. Gradient entries contain locally unique neighbor IDs, data rates, and interval attributes.

C’s Interest cacheInterests Gradients

B

C

source

Sink: 1s | B: 1s

In the absence of information about which sensor nodes are likely to be able to satisfy an interest, interests are broadcasted to all neighbors.

source: 1s

However, a node may suppress a received interest if it recently re-sent a matching request.

46Fall 2006

Data PropagationData Propagation

sinkInitial interests request data at slow rates (e.g. 1 event per second).

C’s Interest cacheInterests Gradients

B

C

source

Sink: 1s | B: 1s

1 eps

1 eps

1 eps

1 eps

1 eps

C’s Data cache

EVENT

A sensor node that is able to furnish a query-result searches its interest cache for a matching entry; if it finds one, it begins sending data messages (~64 bytes) towards the sink via its gradient list at the highest specified rate.

source: 1s

Type = four-legged animal // type of animal seen Instance = elephant //instance of this typeLocation = [125,220] // node location Confidence = 0.85 //confidence in the match Timestamp = 01:20:40 //even generation time

47Fall 2006

Data PropagationData Propagation

sinkUpon receiving a data message, nodes check their interest caches. If no match is found, the data message is silently dropped.

C’s Interest cacheInterests Gradients

B

C

source

Sink: 1s | B: 1s

1 eps

1 eps

1 eps

1 eps

1 eps

C’s Data cache

EVENT

If a match is found, the node checks its data cache, which keeps track of recently seen data items. If no data cache entry matches the message, a new entry is made in the data cache and the message is re-sent to the node’s neighbors.

match

If a data cache entry matches the data message, the message is silently dropped, thus, preventing loops.

source: 1s

48Fall 2006

ReinforcementReinforcement

sinkAfter the sink starts receiving these low data rate events, it reinforces one particular neighbor in order to “draw down” higher quality (higher data rate) events.

C’s Interest cacheInterests Gradients

B

C

source

Sink: 1s | B: 1s

1 eps

1 eps

1 eps

1 eps

1 eps

C’s Data cache

EVENT

It does this explicitly by re-sending the original interest message, but with a smaller interval value, to the empirically low delay path node.Nodes update their caches and can then propagate reinforcement messages according to local policies. For example, the node might choose that neighbor from whom it first received the latest event matching the interest

S: .01s

100 eps

100 eps

source: 1s

49Fall 2006

ConsiderationsConsiderations

Embedding application semantics in communication logic allows for optimizations such as loop prevention and downconversion (for instance, interpolating high rate messages for a low rate receiver)

Negative reinforcement is used to prune superfluous gradients

50Fall 2006

Negative ReinforcementNegative Reinforcement

Could use time outs or explicit degrade messages as negative reinforcement mechanisms

Orthogonal to the mechanism, NR controls can be propagated according to a number of different rules E.g.: negatively reinforce that neighbor from which

no new events have been received within a window of N events or T time units

51Fall 2006

Network TopologyNetwork Topology

This paradigm works with multiple sources (but sinks may draw redundant data) and multiple sinks hosting identical interests (in which case the second sink can immediately draw down high quality via its cache)

52Fall 2006

Local RepairLocal Repair

Reinforcement rules can be applied by intermediate nodes to repair faulty links: Node C can discover better

path by requesting higher rates from non-faulty neighbors

Reinforcement must be applied carefully to prevent all downstream nodes from doing the same, which will result in discovery of a good path, but will waste resources

53Fall 2006

Design ParametersDesign Parameters

Diffusion Element

Design Choices

Interest Propagation

FloodingConstrained or directional floodingDirectional propagation based on previously cached data

Data Propagation

Reinforcement to single path deliveryMulti-path delivery with selective quality along different pathsMulti-path delivery with probabilistic forwarding

Data Caching and Aggregation

For robust data delivery in the face of node failureFor coordinated sensing and data reductionFor directing interests

Reinforcement Rules for deciding when to reinforceRules for how many neighbors to reinforceNegative reinforcement mechanism and rules

54Fall 2006

Evaluation: MetricsEvaluation: Metrics

Average Dissipated Energy Measures the ratio of total dissipated energy per node in

the network to the number of distinct events seen by sinks

Computes average work done by a node as well as the overall lifetime of sensor nodes

Average Delay Measures the average one-way latency between

transmitting an event and receiving it at a sink

Event Delivery Ratio Ratio of the number of distinct events received to the

number originally sent

55Fall 2006

ConclusionsConclusions

Diffusion is data-centric All communication is neighbor-to-neighbor, not end-

to-end No routers—each node can interpret all messages No globally unique IDs (but locally unique IDs

needed) Application-specific semantics embedded in

communication Observations

Congestion? Network locality could be used to conserve energy and get

rid of un-necessary transmissions?

56Fall 2006

Hierarchical Routing ProtocolsHierarchical Routing Protocols

Heinzelman, W.R.; Chandrakasan, A.; Balakrishnan, H.; LEACH: Low Energy Adaptive Cluster Hierarchy, System Sciences,

2000. Proceedings of the 33rd Annual Hawaii International Conference on , 4-7 Jan. 2000 vol.2

57Fall 2006

LEACH (1)LEACH (1)

In order to spread this energy usage over multiple nodes, the cluster-head nodes are not fixed:

Dynamic clusters: (a) cluster-head nodes= C at time t1

(b) cluster-head nodes = C’ at time t1+d.

58Fall 2006

LEACH (2)LEACH (2) Self-organizing, adaptive clustering protocol that uses

randomization to distribute the energy load evenly among the sensors in the network.

Nodes organize themselves into local clusters, with one node acting as the local base station or cluster-head

LEACH includes randomized rotation of the high-energy cluster-head position such that it rotates among the various sensors in order to not drain the battery of a single sensor

Cluster-header nodes broadcast their status to the other sensors in the network. Each sensor node determines to which cluster it wants to belong by choosing the cluster-head that requires the minimum communication energy

Each cluster-head creates a schedule for the nodes in its cluster Each non-cluster-head node to be turned off at all times except

during its transmit time Cluster-head node aggregates the data and then transmits the

compressed data to the base station

59Fall 2006

LEACH (3)LEACH (3)

Normalized total system energy

dissipated versus the percent of

nodes that are cluster-heads

Total system energy dissipated using direct communication, MTE routing and LEACH for

the 100-node random network

60Fall 2006

LEACH (4)LEACH (4)

System lifetime using direct transmission, MTE routing,

static clustering, and LEACH with 0.5 J/node

Sensors that remain alive and those that are dead after 1200

rounds with 0.5 J/node for LEACH

61Fall 2006

LEACH (5)LEACH (5)

Total system energy dissipated using (a) direct communication and LEACH and (b) MTE routing

and LEACH for the random network

62Fall 2006

LEACH Algorithm Details (1)LEACH Algorithm Details (1)

The operation of LEACH is broken up into rounds, where each round begins with a set-up phase, when the clusters are organized, followed by a steady-state phase, when data transfers to the base station occur.

In order to minimize overhead, the steady-state phase is long compared to the set-up phase

63Fall 2006

LEACH Algorithm Details (2)LEACH Algorithm Details (2)

Advertisement Phase Initially, when clusters are being created, each node decides whether or not to

become a cluster-head for the current round This decision is based on the suggested percentage of cluster heads for the

network and the number of times the node has been a cluster-head so far This decision is made by the node n choosing a random number between 0

and 1. If the number is less than a threshold T(n). The node becomes a cluster-head

for the current round. The threshold is set as:

where P = the desired percentage of cluster heads, r = the current round, and G is

the set of nodes that have not been cluster-heads in the last 1/P rounds

T(n) = 1-P*(r mod 1/P)

P

0

If n ЄG

otherwise

64Fall 2006

LEACH Algorithm Details (3)LEACH Algorithm Details (3)

Advertisement Phase (cont.)

During round 0 (r=0), each node has a probability P of becoming a cluster-head.

Nodes that are cluster-heads in round 0 cannot be cluster-heads for the next 1/P rounds

After 1/P -1 rounds, T =1 for any nodes that have not yet been cluster-heads, and after 1/P rounds, all nodes are once again eligible to become cluster-heads.

Assuming that all nodes begin with the same amount of energy and being a cluster-head removes approximately the same amount of energy for each node

Each non-cluster-head node decides the cluster to which it will belong for this round. This decision is based on the received signal strength of the advertisement

T(n) = 1-P*(r mod 1/P)

P

0

If n ЄP

otherwise

65Fall 2006

LEACH Algorithm Details (4)LEACH Algorithm Details (4)

Cluster Set-Up Phase After each node has decided to which cluster it belongs, it must

inform the cluster-head node that it will be a member of the cluster All cluster-head nodes must keep their receivers on

Schedule Creation Based on the number of nodes in the cluster, the cluster-head node

creates a TDMA schedule telling each node when it can transmit This schedule is broadcast back to the nodes in the cluster

Data transmission Send it during their allocated transmission time to the cluster head Each non-cluster-head node can be turned off until the node’s

allocated transmission time, thus minimizing energy dissipation in these nodes

All the data has been received, the cluster head node performs signal processing functions to compress the data into a single signal

66Fall 2006

LEACH Algorithm Details (5)LEACH Algorithm Details (5)

Multiple Clusters Transmission in one cluster will affect communication in a nearby

cluster Each cluster communicates using different CDMA codes Neighboring clusters’ radio signals will be filtered out and not corrupt

the transmission of nodes in the cluster

Hierarchical Clustering Cluster-head nodes would communicate with “super-cluster-head”

nodes and so no until the top layer of the hierarchy For large network, this hierarchy could save a tremendous amount of

energy

A

C

B

67Fall 2006

LEACH ConclusionLEACH Conclusion

LEACH: a clustering-based routing protocol that minimizes global energy usage by distributing the load to all the nodes at different points in time

LEACH outperforms static clustering algorithms by requiring nodes to volunteer to be high-energy cluster-heads and adapting the corresponding clusters based on the nodes that nodes choose to be cluster-head at a given time

Distributing the energy among the nodes in the network is effective in reducing energy dissipation from a global perspective and enhancing system lifetime

68Fall 2006

DiscussionDiscussion

Routing is one of the challenging and flourishing frontier of WSNs research

One of the major reason is target localization Energy efficiency, still prevailing as the main

optimization issueWe will study few of the recent protocols and

analysis in the next class

69Fall 2006

Thanks !Thanks !