Date post: | 20-Dec-2015 |
Category: |
Documents |
View: | 216 times |
Download: | 2 times |
Fall 2006
Routing Techniques in Wireless Sensor Routing Techniques in Wireless Sensor NetworksNetworks
Professor Choong Seon Hong
Kyung Hee UniversityKyung Hee University
[email protected]@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 !