hopfield neural network-based fault location in wireless and optical networks for smart city iot amirsh.nll@gmail.com www.amirshnll.ir

1. Hopfield Neural Network-based Fault
Location in Wireless and Optical
Networks for Smart City IoT
Amir Shokri
E-mail : amirsh.nll@gmail.com

2. With the rapid evolution of smart city all over the world, the appealing
services of IoT and big data analytics have prompted the design of more
reliable assurance mechanism for network quality. It has been a crucial issue
of network operation that once multiple links fail simultaneously, the
transmission of real-time services cannot be guaranteed. Therefore, rapid
locating of faults is the premise for network to recover quickly. However,
current faults location methods can’t satisfy the requirement due to the
expansion scale of wireless and optical networks and the growing demands
of customers. In this paper, we propose an efficient multi-link faults location
algorithm based on Hopfield Neural Network (HNN).
Abstract
2

3. We make full use of the information of network topology and the services
transmitted to model the relationship between fault set and alarm set. HNN
is used as an optimization method to analyze the uncertainty of faults and
alarms and to find where the faults most likely occur by constructing a proper
energy function. It has been proved by experiments that this method can
achieve real-time faults location while ensuring positioning accuracy, which
provides a good solution for smart city service assurance.
Abstract
3

4. As a novel cutting edge technology that proffers to connect a mass of digital
devices endowed with several sensing, actuation, and computing capabilities
with the Internet, IoT is playing a significant role in the context of smart city.
Smart city IoT develops fast with the characteristics of explosive growth in
mobile data traffic, huge number of device connections and continuous
emergence of various application scenarios. New type of services such as
telemedicine monitoring, video-based surveillance and intelligent
transportation put higher demands on network infrastructure and carrying
capacity. Designing a high-rate, reliable and resource-efficient network
framework for smart city IoT has attracted ever-increasing attentions and
become a research hotspot .
INTRODUCTION
4

5. A large number of users or providers are deploying their services in wireless
and optical networks, which are characterized by supporting higher data
rates, excellent end-to-end performance, and ubiquitous user coverage with
lower latency, power consumption, and cost. Optical networks have a good
performance for the future services transmission in smart city fronthaul
according to its features of high bandwidth and transparent multi-rate traffic
transmission. Therefore, wireless and optical network has already become a
newly research direction as a promising solution for smart city IoT.
INTRODUCTION
5

6. APPLICATION SCENARIO AND NETWORK MODEL
6

7. PROPOSED ALGORITHM: DESIGN AND IMPLEMENTATION
7

8. Energy Function Construction
8

9. Energy Function Construction
9

10. Energy Function Construction
10

11. Energy Function Construction
11

12. Implementation of CHNN-FL Algorithm
12

13. EXPERIMENTAL SETUP AND RESULT DISCUSSION
13

14. Feasibility analysis under different fault numbers
14

15. Comparison with other fault location algorithms
15

16. Comparison with other fault location algorithms
16

17. We make full use of the information of network topology and the services
transmitted to model the relationship between fault set and alarm set. HNN
is used as an optimization method to analyze the uncertainty of faults and
alarms and to find where the faults most likely occur by constructing a proper
energy function. It has been proved by experiments that this method can
achieve real-time faults location while ensuring positioning accuracy, which
provides a good solution for smart city service assurance.
Abstract
17

18. In this paper, we investigate a CHNN-based multi-link faults location
approach in wireless and optical networks for smart city IoT. Processing
procedure of the directed bipartite graph between suspected faulty links and
alarms is regarded as a combinatorial optimization problem, while the set of
links that are most likely to fail is found out from all the possible solutions of
this problem. Numerical results show that the proposed CHNN-FL algorithm
can achieve rapid locating of multiple links faults while meeting the
requirement of positioning accuracy in large-scale wireless and optical
networks.
CONCLUSION
18

19. Amir Shokri
Amirsh.nll@gmail.com
www.amirshnll.ir
Thanks!

20. 1. Y. Mehmood, F. Ahmad, and et al, “Internet-of-Things-Based Smart Cities:
Recent Advances and Challenges” IEEE Communications Magazine, 2017,
55(9).
2. F. B. Saghezchi, G. Mantas, and et al, “Towards a secure network
architecture for smart grids in 5G era” IWCMC, 2017.
3. F. Marzouk, J. Rodriguez, and et al, “Analysis and Enhancement of Platoon
Management in Smart City” IWCMC, 2018.
4. H. Yang, W. Bai, and et al, “Bandwidth Compression Protection against
Collapse in Fog-based Wireless and Optical Networks,” IEEE Access, vol. 6,
pp. 54760-54768, 2018.
5. K. M. Addali, and M. Kadoch, “Matching Game-Based User Association
Approach for LTE-A Mobile Relay Systems” IEEE Canadian Conference on
Electrical & Computer Engineering (CCECE), pp. 1-6, 2018.
REFERENCES

21. 6. H. Yang, A. Yu, and et al, “Multi-dimensional resources allocation based on
reconfigurable radio-wavelength selective switch in cloud radio over fiber
networks,” Optics Express, vol. 26, no. 26, pp. 3471934733, 2018.
7. H. Yang, J. Zhang, and et al, “Experimental demonstration of
multidimensional resources integration for service provisioning in cloud
radio over fiber network” Scientific reports, 2016, 6, 30678.
8. K.X. Wang, X. S. Qiu, and et al, “Fault Tolerance Oriented Sensors Relay
Monitoring Mechanism for Overhead Transmission Line in Smart Grid” IEEE
Sensors Journal, 2015, 15(3), 1982-1991.
9. H. Yang, J. Zhang, and et al, “C-RoFN: multi-stratum resources
optimization for cloud-based radio over optical fiber networks” IEEE
Communications Magazine, 2016, 54(8), pp.118-125.
REFERENCES

22. 10. M. A. M. Sayed, R.K. Liu, and et al, “A Novel Scrambler Design for
Enhancing Secrecy Transmission Based On Polar Code” IEEE
Communications Letters, 2017, 21(8), 1679-1682.
11. H. Yang, J. Zhang, and et al, “SUDOI: Software Defined Networking for
Ubiquitous Data Center Optical Interconnection” IEEE Communications
Magazine, 2016, 54(2), pp 86-95.
12. S. Shahin, and et al. “Machine-Learning-Based Soft-Failure Detection and
Identification in Optical Networks” Optical Fiber Communication
Conference, San Diego Convention Center, USA, 2018, pp M3A.5.
13. H. Yang, J. Zhang, and et al, “CSO: Cross Stratum Optimization for Optical
as a Service,” IEEE Communications Magazine, vol. 53, no. 8, pp. 130-139, 2015.
REFERENCES

23. 14. G. Amaral, J. Garcia, and et al, “In-service location of multiple fiber faults in
WDM/SCM-PONs with low-frequency stepwise sweep and l1 regularization”
Optical Fiber Communication Conference, Optical Society of America, 2017,
pp. Tu2K-3.
15. S. Zhang, W. Ji, and et al, “Precise failure location and protection
mechanism in long-reach passive optical network” Journal of Lightwave
Technology, 2016, 34(22), pp.5175-5182.
16. D. Rafique, L. Velasco, “Machine learning for network automation:
overview, architecture, and applications [Invited Tutorial]” Journal of Optical
Communications and Networking, 2018, 10(10), pp. D126D143.
17. A. Vela, M. Ruiz, and et al, “BER degradation detection and failure
identification in elastic optical networks” Journal of Lightwave Technology,
2017, 35(21), pp.4595-4604.
REFERENCES

24. 18. J. Yang, L. Wang, and et al, “A novel memristive Hopfield neural network
with application in associative memory” Neurocomputing, 2017, 227, pp.142-
148.
19. W. Gao, N. Tang, X. Mu, “A distribution network reconfiguration algorithm
based on Hopfield neural network” ICNC. 2008, pp.9-13.
20. V. Patel, S. Chakrabarti, and et al, “A hopfield neural network based
reconfiguration algorithm for power distribution systems” IEEE Region 10
Symposium (TENSYMP), 2017, pp.1-5.
21. N. Chen, B. Rong, and et al, “Self-Organizing Scheme Based on NFV and
SDN Architecture for Future Heterogeneous Networks” Mobile Networks
and Applications, 2015, 20(4), pp. 466–472.
22. Ma B., Deng H., Xie X. Z., and et al, “An optimized vertical handoff
algorithm based on Markov process in vehicle heterogeneous network”
China Communications, 2015, 12(4), 106-116.
REFERENCES