Multi-hop Communication for the Next Generation (xG) Wireless Network
- 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
- 7. 7 Cognitive Radio Network (cont) S D D D 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)
- 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.)
- 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,
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- 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
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- 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
- 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
- 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!!!!