The MSc defense ceremony was held on 6-7-2017 in Mansoura University, Faculty of Engineering. This presentation is shared to help MSc students in Faculty of Engineering prepare their thesis presentation and ease their tension before their presentation time

1. An Energy-Efficient Technique to
Cover Routing Holes Using
Directional Smart Antennas
Supervisors
Prof. Ahmed S. Samra
Electronics and
communications
engineering
Mansoura University, Egypt
Prof. Maher Abdelrazzak
Mohamed
Electronics and
communications engineering
Mansoura University,
Mansoura Egypt
Assoc. prof. Ahmed I. Saleh
Computers and systems
engineering
Mansoura University, Egypt
Submitted by
Reem Essam Mohamed kamal
Faculty of Engineering, Electronics and Communication Department
Mansoura University, Egypt
Mansoura University- Egypt
2017
1

2. Scientific Papers
• Published
R. E. Mohemed, A. I. Saleh, M. Abdelrazzak, and A.
S. Samra, “Energy-Efficient Routing Protocols for
Solving Energy Hole Problem In Wireless Sensor
Networks,” Comput. Networks, Elsevier 2016.
• Under review
R. E. Mohemed, A. I. Saleh, M. Abdelrazzak, and A.
S. Samra, “Survey on Wireless Sensor Network
Applications and Energy Efficient Routing
Protocols,” Comput. Networks, Elsevier, Jan 2017.
2

3. Presentation Outline
• Thesis Aim and Objectives
• WSN Definition, Applications And Challenges
• Related Work
• Problem Definition
• Thesis Contribution
• Conclusion And Future Work
3

4. Aim
Solve Routing Hole
Problem in Wireless
Sensor Networks
4

5. Thesis Objectives
• Cover the most recent
applications of WSN
• Study the most effective
design issues in WSN routing
protocols
• Analyze energy-hole problem
and its consequences
5

6. Thesis Objectives
• Introduce two adaptive
energy-efficient routing
protocols for WSNs, which
6
solves routing hole problem
maximizes network lifetime,
and preserves network
stability period

7. WSN Definition,
Applications and
Challenges
7

8. Overview on WSN
8

9. 9

10. Applications of WSN
10

11. 11

12. 12

13. Routing Hole
13

14. Causes of Routing Hole
14
Causes of Routing Hole
Energy
Hole
Black
Hole

15. Related work
15

16. Year Protocol description weakness
2014
IBLEACH
[1]
It elongates the round to contain a set of
frames, so that the setup operation is
performed every set of frames.
No process to calculate the number of
rounds/frame value which may result in
data loss if CHs deplete their energy
within the round.
2014 SETA [2]
designed to fulfill confidentiality,
integrity, adaptive aggregation, and
privacy issues while minimizing
communication overhead.
It concerns with data security more than
lifetime maximization
2015
REAC-IN
[3]
CHs are selected based on weight. that is
determined according to the residual
energy of each sensor and the regional
average energy of all sensors in each
cluster. It solves the problem of node
isolation.
Incomplete data transmission may occur
on isolated nodes. Periodic topology
reformation leading to high energy
overhead.
2016 NEECP [4]
Cluster is formed, then intra cluster and
inter cluster chains are formed
Energy overhead in cluster head
selection, intra cluster and inter cluster
chain formation
2016
UCCGRA
[5]
combine the unequal clustering using
vote -based measure and the connected
graph theories. CHs are elected and non-
CH uses a fitness function to select the
most suitable CH to join.
The optimal parameters for energy
minimization are not covered.
The periodic voting and connected graph
formation introduce very high
complexity and energy overhead during
the setup phase. 16

17. Problem of Recent Energy
Efficient Routing Protocols
The need for continuous topology
reformation to achieve high network
lifetime made the researchers ignore the
impact of
High energy overhead due to continuous
network setup
on limiting network lifetime
Maximization
17
details

18. The Proposed
Energy Efficient
Routing Protocols
18

19. General Characteristics of The
Proposed Protocols
• Designed for Static networks with determined
node placement
• Trigger based
• fully distributed
• Designed for solving the premature end of
network lifetime.
• Reconnects any multi-hop single path network
topology with single setup phase.
• Operate in tree fashion
19

20. On Hole Children
Reconnection (OHCR)
Protocol Overview
• Has local nature
• Independent on initial network
topology
20

21. 𝑠 𝑖 . 𝐷 𝑏: :The distance between the parent node
and its furthest child
𝑠(𝑖). 𝐸 𝑏: The 𝑂𝐶𝐻𝑅 Breakdown
energy of parent nodes 𝒫s
OHCR protocol flowchart
21

22. Operational Conditions for
OHCR
The breakdown packet BD pkt and join request
jReq packet sizes are limited to 10% of the
control packet size to limit
– The delay till the reconnection is maintained
– The energy consumed in this process
– Collision occurrence
22
1
2
3

23. OHCR Algorithm
Inputs: 𝒊, , 𝒔 𝒊 . 𝑪, 𝒔(𝒊). 𝑬, 𝒕_𝒍𝒆𝒗, 𝒔 𝒊 . 𝒍𝒆𝒗, t_lev
Outputs: reconnect the disconnected children
// for any parent node
1. While 𝑠(𝑖). 𝐶 ≠ ∅ do
2. find 𝑠(𝑖). 𝐷 𝑏 and calculate 𝑠 𝑖 . 𝐸 𝑏
// when the parent is dead
3. If 𝑠 𝑖 . 𝐸 = 𝑠 𝑖 . 𝐸 𝑏
4. Multicast BD pkt to 𝑠(𝑖). 𝐶 nodes within 𝑠(𝑖). 𝐷 𝑏
𝑠 𝑖 . 𝑡𝑦𝑝𝑒 is “dead”
5. CR(𝑠 𝑖 . 𝐶, 𝑡_𝑙𝑒𝑣)
23

24. Children Reconnection
Algorithm
CR(s(i).C,𝒕_𝒍𝒆𝒗)
1. For each s(𝒊𝒊) ∈ 𝒔 𝒊 . 𝑪 do
// acknowledgment flag is reset for every orphan child
2. 𝑠(𝑖𝑖). 𝑎𝑐𝑘 = 0
3. While 𝒔(𝒊𝒊). 𝒂𝒄𝒌 = 𝟎 do
4. multicast a jReq to 𝑗∀ 𝑠 𝑗 . 𝑙𝑒𝑣 = 𝑠 𝑖𝑖 . 𝑙𝑒𝑣 − 1 within 𝑑 𝑜
5. Wait 𝑡_𝑙𝑒𝑣
6. If ack received
7. 𝑠(𝑖𝑖). 𝑎𝑐𝑘 = 1
8. Join the parent that replies first
9. Else
10. Decrement 𝑠(𝑖𝑖). 𝑙𝑒𝑣
11. If 𝒔(𝒊𝒊). 𝒍𝒆𝒗 = 𝟏
12. 𝑠 𝑖𝑖 . 𝑎𝑐𝑘 = 1
13. Join BS
24

25. Characteristics of OHCR
• It suits all multi-hop single path networks
where network management is performed in a
distributed fashion.
• It doesn’t need BS or GPS to manage network
operations or locate the lost nodes during the
network lifetime.
25
1
2

26. On Hole Alert (OHA)
Protocol
• Has global nature.
• Primarily dependent on the initial network topology
• Redistributes routing load among the remaining relay nodes
• It doesn’t add delay
26

27. OHA Protocol Flowchart
27

28. Inputs:𝑵𝒊𝒏𝒊𝒕, 𝒔(𝒊). 𝑬, 𝑬 𝒃, 𝑴, 𝒔(𝒊). 𝒑𝒂𝒓𝒆𝒏𝒕, 𝒔 𝒊 . 𝑪, TF, t_lev
Outputs:𝑵,𝑺, network reconnection
// user input
1. Set TF
2. For each 𝑖 ∈ 𝑁 do
3. Calculate 𝑬 𝒃
// new dead node
4. If 𝑠 𝑖 . 𝐸 ≤ 𝐸 𝑏 do
5. 𝑠 𝑖 is dead
6. 𝑁 = 𝑁 − 1
// Root hole
7. If 𝑠(𝑖). 𝑝𝑎𝑟𝑒𝑛𝑡 = 𝑠(𝑁𝑖𝑛𝑖𝑡 + 1 ) & 𝑠 𝑖 . 𝐶 ≠ ∅ do
8. 𝑠 𝑖 . 𝑑𝑐 = ’𝑅’
9. Root-hole advertisement in BD pkt broadcast
10. If TF={0} do
11. Use topology formation algorithm for topology re-setup of the 𝑁 nodes
12. Else
Use the topology formation algorithm specified by the network administrator for topology re-setup of the 𝑁 nodes
// Parent hole
13. Else if 𝑠(𝑖). 𝑝𝑎𝑟𝑒𝑛𝑡 ≠ 𝑠(𝑁𝑖𝑛𝑖𝑡 +1) & 𝑠 𝑖 . 𝐶 ≠ ∅ do
14. 𝑠 𝑖 . 𝑑𝑐 = ’𝑃’
15. Parent-hole advertisement in BD pkt broadcast
16. RC(S(i).C, t_lev)
// Pendent hole
17. Else if 𝑠 𝑖 . 𝐶 = ∅ do
18. 𝑠(𝑖). 𝑑𝑐 = ’𝑝’
19. Pendent hole advertisement in BD pkt broadcast
28

29. Performance
analysis
• Algorithm complexity
• Breakdown energy
• Network overhead
• Estimation of stability period and network
lifetime
29

30. Algorithm Complexity
• OHCR complexity is upper bounded by
for each node in the network,
for each parent and
for the whole network
• OHA complexity is upper bounded by the topology
formation algorithm used in re-setup operation.
• Both protocols OHCR and OHA are lower bounded by
children reconnection (CR) algorithm Ω(1).
30
1
2
3
(1)𝒪 𝐿
(2)𝒪 𝐶𝐿
(3)𝒪 𝑃𝐶𝐿

31. Breakdown Energy
s i . Eb−OHCR= ቐ
lc Eelec + εfs s i . Db
2
lc Eelec + εmp s i . Db
4
s i . Dm < do
otherwise
(4)
While the breakdown energy of OHA is dependent on the
dimensions of the ROI; thus, it is constant for all nodes, as given in
equation (6)
𝐸 𝑏−𝑂𝐻𝐴= ቐ
𝑙 𝑐 𝐸𝑒𝑙𝑒𝑐 + 𝜀𝑓𝑠 𝑀2
𝑙 𝑐 𝐸𝑒𝑙𝑒𝑐 + 𝜀 𝑚𝑝 𝑀4
𝑀 < 𝑑 𝑜
otherwise
(5)
In multi-hop networks, the distance between the parent and its child
hardly reach the diameter of the ROI; thus, max 𝑠 𝑖 . 𝐷 𝑏 < 𝑀
31

32. Network Overhead
Lemma 1 For homogeneous networks with
constant network flows and BS far from the ROI,
the probability of energy depletion of any non-
relay node in the ROI before any relay RN node is
exactly zero.
𝒪𝐸 = ෍
𝑟=1
𝑟 𝒩
𝐸𝒩(𝑟)
𝐸𝒢(𝑟)
(6)
32
Lemma 2 OHA adds higher energy overhead 𝒪𝐸
than OHCR to homogeneous networks in case of
constant network flows and far BS from the ROI

33. Estimation of Network
Lifetime OHCR
𝐸 𝑜| 𝑟𝑒𝑙𝑎𝑦 =
𝑙 𝑐(∆𝐸𝑒𝑙𝑒𝑐+𝜀 𝑚𝑝 𝑠 𝑖 . 𝐷 𝑏
4
)
+𝑟 𝒩| 𝑚𝑖𝑛 𝑙 ∆𝐸𝑒𝑙𝑒𝑐 + 𝜀 𝑚𝑝 𝑑 𝑡𝑜𝐵𝑆
4
+ ∆𝐸 𝐷𝐴
𝑙 𝑐(∆𝐸𝑒𝑙𝑒𝑐+𝜀𝑓𝑠 𝑠 𝑖 . 𝐷 𝑏
2
)
+𝑟 𝒩| 𝑚𝑖𝑛 𝑙 ∆𝐸𝑒𝑙𝑒𝑐 + 𝜀 𝑚𝑝 𝑑 𝑡𝑜𝐵𝑆
4
+ ∆𝐸 𝐷𝐴
𝑙 𝑐(∆𝐸𝑒𝑙𝑒𝑐+𝜀 𝑚𝑝 𝑠 𝑖 . 𝐷 𝑏
4
)
+𝑟 𝒩| 𝑚𝑖𝑛 𝑙 ∆𝐸𝑒𝑙𝑒𝑐 + 𝜀𝑓𝑠 𝑑 𝑡𝑜𝐵𝑆
2
+ ∆𝐸 𝐷𝐴
𝑙 𝑐(∆𝐸𝑒𝑙𝑒𝑐+𝜀𝑓𝑠 𝑠 𝑖 . 𝐷 𝑏
2
)
+𝑟 𝒩| 𝑚𝑖𝑛 𝑙 ∆𝐸𝑒𝑙𝑒𝑐 + 𝜀𝑓𝑠 𝑑 𝑡𝑜𝐵𝑆
2
+ ∆𝐸 𝐷𝐴
𝑠 𝑖 . 𝐷 𝑏, 𝑑 𝑡𝑜𝐵𝑆
≥ 𝑑 𝑜
𝑠 𝑖 . 𝐷 𝑏 < 𝑑 𝑜
≤ 𝑑 𝑡𝑜𝐵𝑆
𝑑 𝑡𝑜𝐵𝑆 < 𝑑 𝑜
≤ 𝑠 𝑖 . 𝐷 𝑏
else
(7)
33

34. Estimation of Stability Period
OHCR
𝐸 𝑜| 𝓅 =
𝑙 𝑐 𝐸𝑒𝑙𝑒𝑐 + 𝜀 𝑚𝑝 𝑑 𝑛𝑛
4
+𝑟 𝒩 𝑙 𝐸𝑒𝑙𝑒𝑐 + 𝜀 𝑚𝑝 𝑑 𝑛𝑛
4
𝑙 𝑐 𝐸𝑒𝑙𝑒𝑐 + 𝜀𝑓𝑠 𝑑 𝑛𝑛
2
+𝑟 𝒩 𝑙 𝐸𝑒𝑙𝑒𝑐 + 𝜀𝑓𝑠 𝑑 𝑛𝑛
2
𝑑 𝑛𝑛 ≥ 𝑑 𝑜
else
(8)
34

35. Estimation of Network
Lifetime OHA
Eo|RN =
lc(∆Eelec+εmp M4
)
+r 𝒩|minl ∆Eelec + εmpdtoBS
4
+ ∆EDA
lc(∆Eelec+εfs M2
)
+r 𝒩|minl ∆Eelec + εmpdtoBS
4
+ ∆EDA
lc(∆Eelec+εmp M4)
+r 𝒩|minl ∆Eelec + εfsdtoBS
2
+ ∆EDA
lc(∆Eelec+εfs M2)
+r 𝒩|minl ∆Eelec + εfsdtoBS
2
+ ∆EDA
M, dtoBS
≥ do
M < do
≤ dtoBS
dtoBS < do
≤ M
else
(9)
35

36. Estimation of Stability Period
OHA
Eo| 𝓅 =
lc 2Eelec + εmpdnn
4
+ εmp M4
+r 𝒩 l Eelec + εmpdnn
4
lc 2Eelec + εmpdnn
2
+ εmp M4
+r 𝒩 l Eelec + εmpdnn
2
lc 2Eelec + εfsdnn
2
+ εfs M2
+r 𝒩 l Eelec + εfsdnn
2
dnn, M ≥ do
dnn < do ≤ M
else
(10)
36

37. Experimental
Results
37

38. Radio Transmission Model
• Using the energy model in [6]
• Energy consumed in data transmission
• Energy consumed in data reception
𝐸 𝑇𝑥 𝑙, 𝑑 = ൝
𝑙𝐸𝑒𝑙𝑒𝑐 + 𝑙𝜀𝑓𝑠 𝑑2
𝑙𝐸𝑒𝑙𝑒𝑐 + 𝑙𝜀 𝑚𝑝 𝑑4
𝑑 < 𝑑 𝑜
𝑒𝑙𝑠𝑒 (11)
𝐸 𝑅𝑥 𝑙, 𝑑 = 𝐸 𝑅𝑥−𝑒𝑙𝑒𝑐 𝑙 = 𝑙 𝐸𝑒𝑙𝑒𝑐 (12)
38

39. Type Parameter symbol Value
Homogeneous
network
The number of nodes in the ROI 𝑁 100
Initial energy of sensor node 𝐸 𝑜 0.5
Node distribution - random
BS location - (50, 200)
Application
Minimum distance from ROI to BS 𝑑 𝑡𝑜 𝐵𝑆 100
Length of maximum dimension of ROI M 100
Data packet size in bits 𝑙 𝑠 800 𝐵𝑦𝑡𝑒𝑠
Control packet size in bits 𝑙 𝑐 50 𝐵𝑦𝑡𝑒𝑠
Transmitter/Receiver Electronics 𝐸𝑒𝑙𝑒𝑐 50nJ/bit
Energy consumed in data aggregation 𝐸 𝐷𝐴
5nJ
bit
/signal
Radio model
Multi-path propagation loss 𝜀 𝑚𝑝 0.0013
𝑝𝑗
𝑏𝑖𝑡
/𝑚4
Free space propagation loss 𝜀𝑓𝑠 10
𝑝𝑗
𝑏𝑖𝑡
/𝑚2
Threshold distance of wireless
propagation energy model 𝑑 𝑜
𝜀𝑓𝑠
𝜖 𝑚𝑝
Antenna Model - Omni-directional
Protocol setup
Tree formation TF 0
Waiting time for reconnection in OHCR 𝑡_𝑙𝑒𝑣 0.8 𝑚𝑠
Number of children in DCT c 3 39

40. Network Setup
Protocol Description Parameter
NEECP The data aggregation version was
used
NEECPWA
UCCGRA 𝑅 𝑚𝑎𝑥 the maximum competition
radius.
c is a constant coefficient between 0
and 1
𝑅 𝑚𝑎𝑥 = 𝑑 𝑜
𝑐 = 0.3
LEACH The optimal probability of cluster
head selection was used
𝑝=𝑝 𝑜𝑝𝑡
OHCR and OHA are examined on both Degree
Constrained Tree (DCT) and Shortest Path Tree
(SPT) 40

41. Performance Metrics
• Energy overhead 𝓞𝐄
• Average network energy 𝐄 𝐚𝐯𝐠
• Percentage of dead nodes
• Network lifetime in terms of number of rounds r 𝒩 ;the
maximum lifetime of all the sensors in the network; it may
end due to energy depletion or inability to reach the BS; such
that,
𝒪𝐸 = ෍
𝑟=1
𝑟 𝒩
𝐸𝒩(𝑟)
𝐸𝒢(𝑟)
(6)
𝐸 𝑎𝑣𝑔 =
σ𝑖=1
𝑁
𝐸𝑖
𝑁
(13)
41
1
2
3
4

42. Energy Overhead
42

43. Average Network Energy
43

44. The Percentage of Dead Nodes
Through Network Lifetime
44

45. Network Lifetime Vs. Number
of Nodes in The ROI
45

46. Using Directional Antennas
in SPT
46

47. Directional Transmission
Parameters
ValueParameter
Omni-directionalAntenna model for control packets
directionalAntenna model for data transmission
Switched beamDirectional antenna model
450Beam width for data transmission
D802.15.4 MACMAC layer for directional transmission
15 dBiMain lobe gain
omnidirectionalAntenna model for data reception
47

48. Energy Overhead
48

49. Average Network Energy
49

50. The Percentage of Dead Nodes
Through Network Lifetime
50

51. Network Lifetime Vs. Distance
Between the ROI and the BS
51

52. Conclusions
and
Future Work
52

53. • OHCR Adds significant delay due to the time
taken in each reconnection trial. However, this
time can be adjusted by network
administrator.
• OHCR and OHA are characterized by high
adaptability to application requirements.
• Applying OHCR or OHA to any network
topology doesn’t affect its stability period.
53

54. • The simulation results proved that the
proposed protocols outperform the recent
ones in terms of network lifetime, node loss
rate, and network overhead.
• OHCR and OHA are better applied on trees
with as limited constraints as possible to
provide the best results
• Using OHCR or OHA, the network lifetime of
any single setup phase tree can be extended
about 5 to 3 times, respectively. 54

55. Future Work
At the end of this work, we are looking forward
to do the following
– Studying the scheduling problem of the
implemented routing protocols to find the
tradeoff between network lifetime and schedule
length.
– Studying other disconnection reasons; e.g.
physical damage
– Studying the behavior of the proposed algorithms
on heterogeneous networks.
55

56. List of References
[1] A. Salim, W. Osamy, and A. M. Khedr, “IBLEACH : intra-balanced
LEACH protocol for wireless sensor networks,” Wirel. Networks, no.
20, pp. 1515–1525, 2014.
[2] S. Sicari, L. A. Grieco, A. Rizzardi, G. Boggia, and A. Coen-
porisini, “SETA : A SEcure sharing of Tasks in clustered wireless
sensor networks,” in 9th IEEE International Conference on Wireless
and Mobile Computing, Networking and Communications 2013,
WiMob 2013: 239-246, 2014, no. i.
[3] J. S. Leu, T. H. Chiang, M. C. Yu, and K. W. Su, “Energy efficient
clustering scheme for prolonging the lifetime of wireless sensor
network with isolated nodes,” IEEE Commun. Lett., vol. 19, no. 2, pp.
259–262, 2015.
56

57. List of References (cont)
[4] S. Singh, S. Chand, R. Kumar, A. Malik, and B. Kumar, “NEECP :
Novel energy-efficient clustering protocol for prolonging lifetime of
WSNs,” IET Wirel. Sens. Syst., pp. 1–7, 2016.
[5] H. Xia, R. Z. Jia, and Y. Z. Pan, “Energy-Efficient Routing
Algorithm Based on Unequal Clustering and Connected Graph in
Wireless Sensor Networks,” Int. J. Wirel. Inf. Networks, vol. 23, no. 2,
pp. 141–150, 2016.
[6] W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan,
“Energy-efficient communication protocol for wireless microsensor
networks,” Proc. 33rd Annu. Hawaii Int. Conf. Syst. Sci., vol. 0, no. c,
pp. 3005–3014, 2000.
57

58. 58