energy efficient data gathering protocol in wsn
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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
2
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
21
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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