Presentation outline: Introduction WSN basics Protocols EAR, 2002 CHIRON, 2009 ETR, 2009 REAR, 2011 Proposition of a novel Energy Efficient DGP Conclusion

1. ZUBIN BHUYAN
CSI 11014
STCN Seminar

2. Outline
 Introduction
 WSN basics
 Protocols
 EAR, 2002
 CHIRON, 2009
 ETR, 2009
 REAR, 2011
 Proposition of a novel Energy Efficient DGP
 Conclusion
 Reference
2

3. 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.)
3
ENVIRONMENT
EVENTS

4. 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!!
4

5. 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

6.  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 Routing
6

7. Energy Aware Routing:
Functioning
 Each node is addressable through class-based
addressing, includes
 Location
 Type of the node
 Three phases of the protocol
1. 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 propagation
o paths are chosen probabilistically for data transmission
3. Route maintenance
o Localized flooding to keep paths alive and update route
cost information
7

8. 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
8
† Slide borrowed from Rahul C. Shah, Jan Rabaey, Berkeley Wireless Research Center,
Dept. of EECS University of California, Berkeley
http://bwrc.eecs.berkeley.edu/publications/2002/presentations/WCNC2002/wcnc.ppt

9.  The metric can also include:
 Information about the data buffered for a neighbor
 Regeneration rate of energy at a node
 Correlation of data
initial
remaining
rxtx
E
E
EEC )(
Energy Aware Routing:
Energy Cost
9

10. 1.0
1.0
0.4
0.6
Controller
Sensor0.3
0.7
Each node makes a local
decision
Data Communication Phase:
Energy Aware Routing:
Functioning
10

11. Energy Aware Routing:
Simulation Results
 Energy Usage Comparison
Diffusion Routing Energy Aware Routing
Peak energy usage was ~50 mJ for 1 hour simulation
11

12. 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
12

13. 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

14. 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
14

15. 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.
15

16. 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.
16

17. 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
17

18. 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
18

19. 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
19

20. CHIRON:
Performance comparisons
20

21. CHIRON:
Performance comparisons
21

22. 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

23. ETR: Energy Aware Tree Routing Protocol
 Tree structure used to route data
 Multi-hop route
 Three phases:
 Route setup
 Data Delivery
 Path maintenance
23

24. 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
24

25. ETR:
Phase I
25
Route Setup: Node
selects another node
as its parent node if
it has lowest level
from received route
setup messages.

26. 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
26

27. 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
27

28. ETR:
Performance
28
Average residual energy Network lifeime

29. 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

30. 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
30

31. 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
31

32. REAR:
WSN structure
BS oriented ring-structure
32

33. REAR:
Experimental Results
 Average hop number
decreases as the
transmission radius R
increases
 When 140≤R ≤220
REAR outperforms
greedy algorithm
33

34. REAR:
Experimental Results
 R = 110m
 Area = 20 m2
 Averaging done
over 100 different
network topology
simulation result
 REAR algorithm has
the longest lifetime
34

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

36. 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, Berkeley
http://bwrc.eecs.berkeley.edu/publications/2002/presentations/WCNC2002/wcnc.ppt
36

37. 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
37

38. 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.
38
EWMA weights,
N = 15

39. 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.
39

40. High energy dissipation
zones: Areas of high
activityDip
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
40

41. 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.
41

42. 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!!
42

43. 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
43

44. 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
44

46. 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
46

47. 47
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

48. 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

49. 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
49

50. 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
50

51. 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
51

52. 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
52

53. Directed Diffusion:
Functioning
Destination
Source
Setting up gradients
Destination
Source
Sending data
oEvery node maintains an interest cache
oData message is unicast individually to the relevant neighbour
oRecent data is cached to prevent looping
oReinforcement of one neighbor to draw higher quality
achieved by data driven local rules: observed losses, delay variances
oNegative reinforcement of certain paths: low resource levels, etc
53

54. 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

55. 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
55

56. TEEN : Multi-level hierarchical
clustering
56
Clusters
1st Level Cluster Head
Simple Node
2nd Level Cluster Head
Base Station

57. 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
57

58. 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
58

59. 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
59

60. 60
TEEN: Hierarchical vs. flat
topologies
Jamal N. Al-karaki, Ahmed E. Kamal,” Routing Techniques In
WIRELESS SENSOR NETWORKS: A SURVEY”, IEEE Wireless Communications • December 2004

61. 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

62. 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
62

63. 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
63

64. 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
64

65. 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

66.  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
PEGASIS
66

67.  Greedy Chain Algorithm:
1. Start with node furthest away from BS
2. Add to chain closest neighbor to this node that
has not been visited
3. Repeat until all nodes have been added to chain
4. 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
67

68. PEGASIS:
Illustration
68

69. 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
69

70.  Extension of PEGASIS
 Decrease the delay for the packets during transmission to
the base station
 Simultaneous transmissions of data messages
Hierarchical PEGASIS
70

71.  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 PEGASIS
71

72. 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.
72

73. 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
73