ZUBIN BHUYANCSI 11014

STCN Seminar

Energy Efficient Data Gathering Protocolsin WSN

Outline

Introduction WSN basics

Protocols EAR, 2002 CHIRON, 2009 ETR, 2009 REAR, 2011

Proposition of a novel Energy Efficient DGP Conclusion Reference

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Introduction

WSN nodes have the ability to sense and process data wirelessly communicate with other nodes and a

sink node have the ability to collect data from other nodes gateway or a base station [1] (Liu, et al, IEEE ICC 2007 proc.)

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ENVIRONMENT

EVENTS

Introduction

Challenges & Constraints:

Power Consumption Aggressive energy-scavenging policy required

Low Cost Computation constraints Communication: Low Data Rates <<10Kbps Self-organization and Localization

Redundancy in deployment Fault Tolerance

Scalability…. and many more!!

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R.C. Shah, J.M Rabaey, “Energy Aware Routing for Low Energy Ad Hoc Sensor Networks”, IEEE WCNC’02, pp. 350-355, March 2002

EAR: Energy Aware Routing Protocol

Destination initiated routing Directional flooding to determine

various routes (based on location) Collect energy metrics along the way Every route has a probability of being

chosen Probability 1/energy cost

The choice of path is made locally at every node for every packet

Energy Aware Routing6

Energy Aware Routing:Functioning

Each node is addressable through class-based addressing, includes Location Type of the node

Three phases of the protocol1. Setup phase or interest propagation

o Localized flooding to find all the routes from source to destination and their energy costs

2. Data Communication phase or data propagationo paths are chosen probabilistically for data transmission

3. Route maintenanceo Localized flooding to keep paths alive and update

route cost information

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Setup Phase:

Controller

Sensor

Directional flooding

10 nJ

30 nJ

(0.75*10) + (0.25*30) = 15 nJp1 = 0.75

p2 = 0.25

Local Rule

Energy Aware Routing † :Functioning

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† Slide borrowed from Rahul C. Shah, Jan Rabaey, Berkeley Wireless Research Center, Dept. of EECS University of California, Berkeleyhttp://bwrc.eecs.berkeley.edu/publications/2002/presentations/WCNC2002/wcnc.ppt

The metric can also include: Information about the data buffered for a

neighbor Regeneration rate of energy at a node Correlation of data

initial

remainingrxtx E

EEEC )(

Energy Aware Routing:Energy Cost

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1.01.0

0.4

0.6

Controller

Sensor0.3

0.7

Each node makes a local decision

Data Communication Phase:

Energy Aware Routing:Functioning

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Energy Aware Routing:Simulation Results

Energy Usage Comparison

Diffusion Routing Energy Aware Routing

Peak energy usage was ~50 mJ for 1 hour simulation

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Energy Aware Routing:Advantage

Spread traffic over different paths; keep paths alive without redundancy

Mitigates the problem of hot-spots in the network

Has built in tolerance to nodes moving out of range or dying

Continuously check different paths Simulation result shows improvement of

21.5% energy saving 44% increase in network lifetime over

Directed Diffusion

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Kuong-Ho Chen,   Jyh-Ming Huang,   Chieh-Chuan Hsiao, “CHIRON: An energy-efficient chain-based hierarchical routing protocol in wireless sensor networks”, IEEE Wireless Telecommunications Symposium, 2009

CHIRON: An Energy-Efficient Chain-Based Hierarchical Routing Protocol in WSN

CHIRON

Energy efficient hierarchical chain-based routing protocol

Main idea: Split the sensing field into a smaller

areas Create multiple shorter chains to reduce

the data transmission delay and redundant path

Therefore effectively conserve the node energy and prolong the network lifetime

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CHIRON:Phases of operation

Operation of CHIRON protocol consists of four phases:1. Group Construction Phase. 2. Chain Formation Phase.3. Leader Node Election Phase.4. Data Collection and Transmission Phase.

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CHIRON:Phases I

1. Group Construction Phase: Divide the sensing field into a

number of smaller areas R: the transmission range of the

BS. (1 … n) θ: the beam width of the

directional antenna of BS (1….m) Gθ, R: Group id. By changing R

and θ, n*m groups can be defined

After the sensor nodes are scattered, the BS gradually sweeps the whole sensing area by changing Tx power level, R, θ.

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CHIRON:Phases II

2. Chain Formation Phase: The nodes within each group Gx,y will be linked

together to form a chain Cx,y

Chain formation process is same as that in PEGASIS scheme

the node farthest away from the BS is initiated to create the group chain

Greedily add nearest node of last chained node to the chain

Repeat until all nodes are put together

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CHIRON:Phases III

3. Leader Node Election Phase: Node with maximum

residual energy becomes leader

For first round, the node farthest away from the BS is assigned to be the group chain leader

Thereafter, for each data transmission round, the node with the maximum residual energy is elected.

Residual power information of nodes can be piggybacked with fused data

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CHIRON:Phases IV

4. Data collection & Transmission Phase:

Nodes transmit along the chain to chain leader

Then, starting from the farthest group multi-hop leader-by-leader aggregated transmission is made to BS

Neighbouring leader is elected as relaying node if it is nearer to BS than any other CL

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CHIRON:Performance comparisons

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CHIRON:Performance comparisons

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Soyoung Hwang, Gwang-Ja Jin, Changsub Shin, Bongsoo Kim, “Energy-Aware Data Gathering in Wireless Sensor Networks”, 6th IEEE Consumer Communications and Networking Conference, 2009

ETR: Energy Aware Tree Routing Protocol

ETR: Energy Aware Tree Routing Protocol

Tree structure used to route data Multi-hop route Three phases:

Route setup Data Delivery Path maintenance

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ETR:Phase I

Route Setup: In the first phase, a hierarchical topology is created Sink node is assigned Level 0 It broadcasts route setup message with its

address and level On receiving route setup message a node

sets its level to {parent_level+1} and the sender as parent

The steps are repeated until all nodes are included

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ETR:Phase I

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Route Setup: Node selects another node as its parent node if it has lowest level from received route setup messages.

ETR:Phase II

Data delivery: Data is routed to the sink node. sensor node transmits a data message

including its own address, a destination address set to its parent

On receiving parent transmits acknowledgement

If a parent fails, node selects neighbour with highest residual energy as parent

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ETR:Phase III

Path maintenance: Considers residual energy of nodes Data messages have Residual Energy

information of the node Any data transmitted is received by all

neighbouring nodes A candidate is selected as parent based

on this list of neigbours

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ETR:Performance

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Average residual energy

Network lifeime

Jin Wang, Tinghuai Ma, Jinsung Cho, and Sungoung Lee, “An Energy Efficient and Load Balancing Routing Algorithm for Wireless Sensor Networks”, ComSIS Vol. 8, No. 4, Special Issue, October 2011

REAR: Ring-based Energy Aware Routing

REAR

Motivation:

Hotspot issue still an open problem Nodes on the shortest path or close to the BS

deplete energy quickly REAR aims to achieve both energy balancing

and energy efficiency for all nodes Multi-hop route is built by BS in a centralized

way: BS has more powerful resources such as memory,

computation and communication Algorithm considers:

Primary metric: Hop number and distance Secondary metric: Residual energy

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REAR:Algorithm

1. If the source to BS distance d < ∑d(ni), use direct transmission

2. else, broadcast a multi-hop request to BS

3. BS determines the final multi-hop route with the optimal number n and distances {d1, …., dn}

4. BS builds ring structure with different ring size

5. Classify nodes into different levels based on ring size

6. BS will determine the final multi-hop route as follows: Choose some nodes from level n such that di,j ∈ (dn, dn + Δ) Within these, BS will choose those which belong to level (n+1)

to make progress from source to BS BS will choose the one from level (n+1) with maximal

remaining energy as the final next hop node Source node will start the transmission of its data when it

receives the complete multi-hop route information

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REAR:WSN structure

BS oriented ring-structure

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REAR:Experimental Results

Average hop number decreases as the transmission radius R increases When 140≤R ≤220 REAR outperforms greedy algorithm

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REAR:Experimental Results

R = 110m Area = 20 m2

Averaging done over 100 different network topology simulation result REAR algorithm has the longest lifetime

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A Proposal: Novel WSN routing protocol based on energy dissipation history

Network Survivability †

Critical node to maintain network connectivity

Critical node as it is the only one of its type

•Delay the death of highly active nodes ensuring long network lifetime•Load balancing•Predict nodes that may die early

† Images from Rahul C. Shah, Jan Rabaey, Berkeley Wireless Research Center, Dept. of EECS University of California, Berkeleyhttp://bwrc.eecs.berkeley.edu/publications/2002/presentations/WCNC2002/wcnc.ppt

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Routing based on Energy Usage History in WSN

Highly active nodes should not be used for common or periodic/routine chain transmissions

Aim to reroute data transmission paths along nodes that are less active

Energy Usage Index(EUI)calculated before every transmission Use ‘energy spent per second’ for last λ seconds

EUI, Residual Energy Level piggybacked on data packets.

Neighbouring nodes can overhear transmissions and will know about other nodes’ EUI

Prevention is better than cure: Identify highly active nodes beforehand

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Routing based on Energy Usage History in WSN

Past-information about energy dissipation of nodes may improve network lifetime

EWMA: applies weighting factors which decrease exponentially

EUIt = α x Et + (1 - α) x EUIt-1 Weighting for each older data point decreases

exponentially, giving much more importance to recent observations while still not discarding older observations entirely.

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EWMA weights,N = 15

Routing based on Energy Usage History in WSN

Energy Usage Index (EUI): Indicates at what rate a node is using up its energy

Distance from BS (DB): parameter that restricts the delay in propagation

Residual Energy (RE): Current energy level These three parameters are used to select next-hop

node for the route Nodes know only about their next-hop neighbours info Node Ni forwards to neighbour NJ if ∀ neighbour of

current node Ni, NJ has

min(Total Cost Index = α x EUI + β x DB + γ x RE)

α, β, γ parameters can be adjusted as required.

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High energy dissipationzones: Areas of high activity

Dip

Routing based on Energy Usage History in WSN

Highly active nodes are not over-burdened with extra transmission load by its neighbors

Graphical representation of spatial energy dissipation in a random WSN node dispersion

BS

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Routing based on Energy Usage History in WSN: Possible directions of further investigation

How to use it in a clustered-based approach?

Can EUI be calculated for a sub-region, partition, cluster?

Can α, β, γ parameters be automatically adapted (by cluster heads, neighbours)?

Simulation and comparison with other protocols.

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CONCLUSION

Network performance is application dependent Need to clearly identify metrics of interest

Trade-off: Accuracy vs. Latency vs. Lifetime vs. …..

Research directions Routing graphs: selecting a tree, transmission

schedule, maintenance policy Power aware routing: enhanced link sharing,

load balancing, improving lifetitme Optimality in Algorithms

Open Problems everywhere!!

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References

[1] Ming Liu, Yuan Zheng, Jiannong Cao, Guihai Chen, Lijun Chen,Haigang Gong, “An Energy-Aware Protocol for Data Gathering Applications in Wireless Sensor Networks”, IEEE Communications Society subject matter experts for publication in the ICC 2007 proceedings

[2] R.C. Shah, J.M Rabaey, “Energy Aware Routing for Low Energy Ad Hoc Sensor Networks”, IEEE WCNC’02, pp. 350-355, March 2002

[3] Kuong-Ho Chen,   Jyh-Ming Huang,   Chieh-Chuan Hsiao, “CHIRON: An energy-efficient chain-based hierarchical routing protocol in wireless sensor networks”, IEEE Wireless Telecommunications Symposium, 2009

[4] Jin Wang, Tinghuai Ma, Jinsung Cho, and Sungoung Lee, “An Energy Efficient and Load Balancing Routing Algorithm for Wireless Sensor Networks”, ComSIS Vol. 8, No. 4, Special Issue, October 2011

[5] K.Ramanan, E.Baburaj, “Data Gathering Algorithms For Wireless Sensor Networks: A Survey”, International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol.1, No.4, December 2010

[6] S. Jamal N. Al-karaki, Ahmed E. Kamal, ”Routing Techniques In Wireless Sensor Networks: A Survey”, IEEE Wireless Communications • December 2004

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References

[8] S. M. Jung, Y. J. Han, and T. M. Chung, “The Concentric Clustering Scheme for Efficient Energy Consumption in the PEGASIS,” Proceedings of the 9th International Conference on Advanced Communication Technology, Vol. 1, pp. 260-265, 2007

[9] Soyoung Hwang, Gwang-Ja Jin, Changsub Shin, Bongsoo Kim, “Energy-Aware Data Gathering in Wireless Sensor Networks”, 6th IEEE Consumer Communications and Networking Conference, 2009

Few images and slides have been take from the links given below:

[10] http://www.cs.ucf.edu/~turgut/COURSES/EEL6788_ACN_Fall05/Lecture7-Oct05-05.ppt

[11] http://bwrc.eecs.berkeley.edu/publications/2002/presentations/WCNC2002/wcnc.ppt

[12] http://www.cs.binghamton.edu/~kang/teaching/cs580s/routing-survey.ppt

[13] http://www.senmetrics.org/papers/Senmetrics-keyNote-Helmy-2.ppt

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Thank You

Introduction: Taxonomy

WSN protocols are classified according to their data delivery model into the following categories [Kulik, et al, 2002]:

1. Continuous LEACH: For routing data to base stations in static

WSN TEEN and PEGASIS: Improvements over LEACH

2. Observer-initiated Directed Diffusion:

Data/information are named using attribute-value pairs

Interest based queries

3. Event-driven SPIN: Set of negotiation based protocols

4. Hybrid

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Energy conservation policies

[2] Jones, Sivalingam, Agrawal, and Chen survey article in ACM WINET, July 2001[3] Lindsey, Sivalingam, and Raghavendra book chapter in Wiley Handbook of Mobile Computing, Ivan Stojmenovic, Editor, 2002

Physical Layer •Low power circuit (CMOS, etc.) design•Optimum hardware, software function division•Energy effective waveform/ code design•Adaptive RF power control

MAC sub-layer • Energy effective MAC protocol

• Collision free, reduce retransmission and transceiver on-times

• Intermittent, synchronized operation

• Rendezvous protocols

Link Layer • FEC versus ARQ schemes; Link packet length adapt.

Network Layer • Multi-hop route determination

• Energy aware route algorithm

• Route cache, directed diffusion

Application Layer • Video applications: compression and frame-dropping

• In-network data aggregation and fusion

C. Intanagonwiwat, R. Govindan and D. Estrin, “Directed Diffusion: A scalable and robust communication paradigm for sensor networks”, IEEE/ACM Mobicom, 2000

Directed Diffusion protocol

Directed Diffusion

Query-driven data delivery model Diffusing data by using a naming scheme

named using attribute-value pairs Interest, data propagation and data

aggregation are determined by local interactions

Sink requests data by broadcasting interests

Interest diffuses through the WSN hop-by-hop according to contents of the interest

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Directed Diffusion:Interest & Gradient

Interest is generally given by the sink node For each active task, sink periodically broadcasts an interest

message to each of its neighbors Sink periodically refreshes each interest by re-sending the same

interest with monotonically increasing timestamp attribute for

reliability purposes Every node maintains an interest cache where each item in the

cache corresponds to a distinct interest Interest entries in the cache do not contain information about the

sink Definition of distinct interests may allow interest aggregation The interest entry contains several gradient fields, up to one per

neighbor

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Directed Diffusion:Functioning

Setting up Gradient: When a node receives an interest, it

determines if the interest exists in the cache:

1. If no matching exist, the node creates an interest entry

This entry has single gradient towards the neighbor from

which the interest was received with specified data rate

Individual neighbors can be distinguished by locally unique

identifiers

2. If the interest entry exists, but no gradient for the sender of

interest

Node adds a gradient with the specified value

Updates the entry’s timestamp and duration fields

3. If there exists both entry and a gradient,

The node updates the entry’s timestamp and duration fields

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Directed Diffusion:Functioning

Data propagation Data message is unicast individually to the relevant neighbors A node receiving a data message from its neighbors checks to see if matching

interest entry in its cache exists according the matching rules described

1. If no match exist, the data message is dropped

2. If match exists, the node checks its data cache associated with the

matching interest entry

If a received data message has a matching data cache entry, the

data message is dropped

Otherwise, the received message is added to the data cache and the

data message is re-sent to the neighbors Data cache keeps track of the recently seen data items, preventing loops By checking the data cache, a node can determine the data rate of the

received events

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Directed Diffusion:Functioning

Destination

Source

Setting up gradients

Destination

Source

Sending data

oEvery node maintains an interest cacheoData message is unicast individually to the relevant neighbouroRecent data is cached to prevent loopingoReinforcement of one neighbor to draw higher quality

achieved by data driven local rules: observed losses, delay variancesoNegative reinforcement of certain paths: low resource levels, etc

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A. Manjeshwar , D. P. Agarwal, “TEEN: a Routing Protocol for Enhanced Efficiency in Wireless Sensor Networks,” 1st Int’l. Wksp. on Parallel and Distrib. Comp. Issues in WirelessNetworks and Mobile Comp., 2001

Threshold sensitive Energy Efficient Network protocol

Threshold sensitive Energy Efficient Network protocol (TEEN)

Hierarchical, cluster-based data-centric protocol

Designed to respond to sudden changes For time-critical applications Reactive network Nodes sense continuously, but data

transmission is done infrequently Control over energy consumption and

accuracy

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TEEN : Multi-level hierarchical clustering

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Clusters

1st Level Cluster Head

Simple Node

2nd Level Cluster Head

Base Station

TEEN: Functioning

Every node in a cluster takes turns to become the CH for a time interval called cluster period

At every cluster change time the cluster-head broadcasts to its members Hard threshold (HT) : A member only sends data to CH

only if data values are in the range of interest Soft threshold (ST) : A member only sends data if its

value changes by at least the soft threshold HT is the minimum possible value of an attribute. Node transmits data only when the value of that

attribute changed by an amount equal to or greater than the ST

Tx(Ni): Δ (SV) ≥ ST

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TEEN: Features & Discussion

Good for time-critical applications Energy saving

Less energy than proactive approaches Transmission consumes more energy than

sensing Inappropriate for periodic monitoring Ambiguity between packet loss and

unimportant data (indicating no drastic change)

The ST can be varied, depending on the criticality/accuracy required

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APTEEN (Adaptive Threshold sensitive Energy Efficient Network protocol)

Extends TEEN to support both periodic sensing & reacting to time critical events

Unlike TEEN, a node must sample & transmit a data if it has not sent data for a time period equal to CT (count time) specified by CH

Network lifetime: TEEN ≥ APTEEN ≥ LEACH Drawbacks of TEEN & APTEEN

Overhead & complexity of forming clusters in multiple levels and implementing threshold-based functions

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TEEN: Hierarchical vs. flat topologies

Jamal N. Al-karaki, Ahmed E. Kamal,” Routing Techniques InWIRELESS SENSOR NETWORKS: A SURVEY”, IEEE Wireless Communications • December 2004

M.J. Handy, M. Haas, D. Timmermann, “Low Energy Adaptive Clustering Hierarchy with Deterministic Cluster-Head Selection”, Fourth IEEE Conference on Mobile and Wireless Communications Networks, Stockholm, September 2002

LEACH: Low Energy Adaptive Clustering Hierarchy

LEACH:Phases

Cluster-based approach The LEACH network has two phases: the

set-up phase and the steady-state

The Set-Up Phase Where cluster-heads are chosen

The Steady-State The cluster-head is maintained Nodes transmit to cluster-head

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LEACH:The Cluster-Head

The LEACH Network is made up of nodes, some of which are called cluster-heads The job of the cluster-head is to collect data from their

surrounding nodes and pass it on to the base station LEACH is dynamic because the job of cluster-head

rotates Cluster-heads can be chosen stochastically

If n < T(n), then that node becomes a cluster-head

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LEACH:An Example

While neither of these diagrams is the optimum scenario, the second is better because the cluster-heads are spaced out and the network is more properly sectioned

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S. Lindsey, C.S.Raghavendra, “PEGASIS: Power Efficient Gathering in Sensor Information Systems”, Proceedings of IEEE ICC 2001, pp. 1125-1130, June 2001

Power-Efficient GAthering for Sensor Information Systems

An enhancement over the LEACH Minimize distance nodes must transmit Minimize number of leaders that

transmit to BS Minimize broadcasting overhead Distribute work more equally among

all nodes increase the lifetime of each node by

using collaborative techniques

PEGASIS66

Greedy Chain Algorithm:1. Start with node furthest away from BS2. Add to chain closest neighbor to this node that

has not been visited3. Repeat until all nodes have been added to chain4. Constructed before 1st round of communication

and then reconstructed when nodes die Data fusion at each node (except end nodes)

Only one message is passed at every node Delay calculation: N units for an N-node

network Sequential transmission is assumed

Node i (mod N) is the leader in round i

PEGASIS:Greedy Chain Algorithm

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PEGASIS:Illustration

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PEGASIS:

Drawbacks: Assumes that each sensor node is able to

communicate with the BS directly Assumes that all sensor nodes have the same

level of energy and are likely to die at the same time

The single leader can become a bottleneck. Excessive data delay

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Extension of PEGASIS Decrease the delay for the packets during transmission

to the base station Simultaneous transmissions of data messages

Hierarchical PEGASIS70

Another extension of PEGASIS The sensing area, centered at the BS, is

circularized into several concentric cluster levels. For each cluster level a node chain is constructed Farthest to nearest multi-hop and leader-by-

leader data propagation

(S. M. Jung, Y. J. Han, and T. M. Chung, “The Concentric Clustering Scheme for Efficient Energy Consumption in the PEGASIS,” Proceedings of the 9th International Conference on Advanced Communication Technology, Vol. 1, pp. 260-265, 2007)

Enhanced PEGASIS71

REAR:Algorithm

Assumptions:

1. All sensor nodes are static and homogeneous after deployment.

2. The communication links are symmetric.3. Each sensor node has several power levels

which they can adjust.4. Each sensor node can know the distance to its

neighbors and to the BS.5. There is no obstacle between nodes.

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References

[1] Ming Liu, Yuan Zheng, Jiannong Cao, Guihai Chen, Lijun Chen,Haigang Gong, “An Energy-Aware Protocol for Data Gathering Applications in Wireless Sensor Networks”, IEEE Communications Society subject matter experts for publication in the ICC 2007 proceedings

[2] Jones, Sivalingam, Agrawal, and Chen survey article in ACM WINET, July 2001;[3] Lindsey, Sivalingam, and Raghavendra book chapter in Wiley Handbook of

Mobile Computing, Ivan Stojmenovic, Editor, 2002.[4] C. Intanagonwiwat, R. Govindan and D. Estrin, “Directed Diffusion: A

scalable and robust communication paradigm for sensor networks”, IEEE/ACM Mobicom, 2000

[5] A. Manjeshwar , D. P. Agarwal, “TEEN: a Routing Protocol for Enhanced Efficiency in Wireless Sensor Networks,” 1st Int’l. Wksp. on Parallel and Distrib. Comp. Issues in WirelessNetworks and Mobile Comp., 2001

[6] M.J. Handy, M. Haas, D. Timmermann, “Low Energy Adaptive Clustering Hierarchy with Deterministic Cluster-Head Selection”, Fourth IEEE Conference on Mobile and Wireless Communications Networks, Stockholm, September 2002

[7] S. Lindsey, C.S.Raghavendra, “PEGASIS: Power Efficient Gathering in Sensor Information Systems”, Proceedings of IEEE ICC 2001, pp. 1125-1130, June 2001

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