This document discusses multi-hop communication techniques for next generation wireless networks. It covers challenges with wireless networks like limited bandwidth and power. It then describes cognitive radio networks and how they can help address bandwidth issues. Multi-hop networking is presented as a solution for power limitations. Relay selection and power allocation algorithms are summarized as ways to improve multi-hop networks. Applications of these techniques to underwater wireless sensor networks and wireless body area networks are also outlined.

1. Multi-hop Communication for the Next
Generation (xG) Wireless Network
M Shamim Kaiser
Visiting Research Fellow,i-Lab
​​Anglia Ruskin IT Research Institute
Anglia Ruskin University, Chelmsford
Problems and Perspectives

2. 2
Presentation Outline
• Why Wireless?
• Challenges of Wireless Network
• Cognitive Radio Network
• Multi-Hop Networking
• Relay Selection Algorithm
• Power Allocation Algorithm
• UWSN
• Anomaly in UWSN
• WBAN

3. 3
Why wireless?
Increased mobility & collaboration
Roam without losing your connection
Improved responsiveness
Connect to the information when
needed
Better access to information for all
Connect hard-to-reach areas
Easier network expansion
Add users quickly
Enhanced guest access
Give secure network access to
customers and business partners
Figure: Wireline connections
between communication devices

4. 4
Challenges of Wireless Network

5. 5
Challenges of Wireless Network (cont.)
• Limited bandwidth (Can be solved by CRN).
• The transmit power is limited, cannot raise beyond a
certain level (in dB) (Multi-hop is the only option) .
• Capacity is limited (Improve SNR ane BW).
• Received power decreases with the increase of
frequency, and distance

6. 6
Cognitive Radio Network

7. 7
Cognitive Radio Network (cont)
S
D
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D
D
DR
f1 f2 f3 f4 f5 f6

8. 8
Multi-Hop Networking
Mobile ad hoc networks (MANETS)
• No AP, a group of mobile nodes communicates
• Communication between nodes is performed by direct
connection or through multiple hop relays.
• Mobile ad hoc networks have several practical
applications including battlefield communication,
emergency first response, and public safety systems.

9. 9
Multi-Hop Networking (cont.)
Multi-hop cellular networks
• Cellular systems conventionally employ single hops between
mobile units and the base station.
• As cellular systems evolve from voice centric to data centric
communication, edge-of-cell throughput is becoming a
significant concern.
• This problem is accentuated in systems with higher carrier
frequencies (more path loss) and larger bandwidth (larger
noise power).
• A promising solution to the problem of improving coverage and
throughput is the use of relays.
• Several different relay technologies are under intensive
investigation including fixed relays (connected to power
source) and mobile relays (energy constrain)

10. 10
Relay Selection Algorithm

11. 11
Relay Selection Algorithm (cont.)
Relay Selection parameters
1. Signal to Noise Ratio

12. 12
Relay Selection Algorithm (cont.)
Relay Selection parameters
2. Link delay

13. 13
Relay Selection Algorithm (cont.)
Relay Selection parameters
3. Energy Saving

14. 14
Relay Selection Algorithm (cont.)
Relay Selection Criterion
*M S Kaiser et al, Neuro-Fuzzy based Joint Relay-Selection and Resource-Allocation for Cooperative Networks ECTI
Transactions on Electrical Engineering, Electronics, and Communications, 9(2), 187-196, 2008
*M S Kaiser et al, Neuro-Fuzzy Relay selection for Cognitive Cooperative Network, Journal of Communications,
Inderscience (Article in the press)

15. 15
Relay Selection Algorithm (cont.)

16. 16
Relay Selection Algorithm (cont.)

17. 17
Relay Selection Algorithm (cont.)

18. 18
Power Allocation Algorithm

19. 19
Power Allocation Algorithm (cont.)

20. 20
Power Allocation Algorithm (cont.)

21. 21
Power Allocation Algorithm (cont.)

22. 22
Power Allocation Algorithm (cont.)

23. 23
Power Allocation Algorithm (cont.)
Flow chart of the resource allocation algorithm for the RT service users.

24. 24
Power Allocation Algorithm (cont.)
Flow chart of the resource allocation algorithm for the NRT service users.

25. 25
Power Allocation Algorithm (cont.)

26. 26
Power Allocation Algorithm (cont.)

27. 27
Power Allocation Algorithm (cont.)
*M S Kaiser and Kasi M Ahmed, Radio Resource Allocation for Heterogeneous Services in Relay Enhanced OFDMA
System, J of Communications, 5(6), 2010,

28. 28
Network Coded CRN

29. 29

30. 30

31. 31
M S Kaiser, S A Mamun and K M Ahmed, Interference Temperature Constraint-based Radio Resource Allocation for the
Network Coded Cognitive Cooperative Network (NCCCN), Journal of Computer Application, Volume 67 - No. 7, April 2013

32. 32

33. 33
Wireless Sensor Network

34. 34
Wireless Sensor Network
• Lower power nodes
• Multi-hop routing
• Energy-efficiency
• Efficient MAC
• Self Configuration
• Hybrid Network
• Collaborative Processing
• Processing Power
A handful of network sensor 'dots'
Video sensorsUltra sound sensors

35. 35
Wireless Sensor Network
Applications: Patient monitoring system

36. 36
Wireless Sensor Network
Seismic event monitoring
Wearable devices
Smart home

37. 37
Wireless Sensor Network
Energy Harvesting
http://www.digitaleng.news/

38. 38
Wireless Sensor Network
Model used in Fire detection/weather monitoring system for Sundarbar Forest
Fire detection/weather monitoring

39. 39
Wireless Sensor Network

40. 40
Under water wireless Sensor Network
Energy-efficiency and reliability in MAC and routing
protocols for underwater wireless sensor network

41. 41
Under water wireless Sensor Network

42. 42
Under water wireless Sensor Network

43. 43
Under water wireless Sensor Network

44. 44
Under water wireless Sensor Network
Nusrat Z. Zenia et al, An Energy Efficient and Reliable Cluster-based Adaptive MAC protocol for UWSN, IEEE ICEEICT 2015
Nusrat Z. Zenia et al, Energy-efficiency and reliability in MAC and routing protocols for underwater wireless sensor network,
Journal of Network and Computer Applications 71 (2016) 72–85

45. 45
WSN Attack detection
• Node behaviour (Good/Faulty/Attacker)
based on Dempster–Shafer theory
• It is a generalization of the Bayesian
theory of subjective probability.
• Belief functions base degrees of
belief (or confidence, or trust) for
one question on the probabilities for
a related question.
• It combines multiple evidences to
identify the malicious attacks.
• Finding: It can detect faulty nodes
and attack nodes. But the accuracy
is more than 70%
M. R. Ahmed, M. Aseeri, M. S. Kaiser, N. Z. Zenia and Z. I. Chowdhury, "A novel algorithm for malicious attack detection in
UWSN," ICEEICT), 2015, Dhaka, 2015, pp. 1-6. doi: 10.1109/ICEEICT.2015.7307516
A novel algorithm for malicious attack detection in UWSN

46. 46
WSN Attack detection

47. 47
WBAN
An Energy Efficient Cluster based Forwarding Scheme for WBAN using Nano-scale EM Communication

48. 48
WBAN (cont.)

49. 49
WBAN (cont.)

50. 50
WBAN (cont.)
Phase 1: NC will allocate variable-length
transmission time slot for each of the layers
Phase 2: NC will divide total allocated time
for a particular layer among its clusters
based on data the cluster needs to transmit.
Phase 3: Based on the assigned time slots
of each node transmit

51. 51
WBAN (cont.)

52. 52
WBAN (cont.)

53. 53
WBAN (cont.)
Energy Model of Nanosensor

54. 54
WBAN (cont.)

55. Thank you very much!!!
Any suggestions!!!!