an acc approach and turned support vector machine for enhancing quality of service and efficient fault diagnosis respectively in iot enabled wsn application
S.Gowsalya,Lavina Balraj
Published in International Journal of Advanced Research in Computer Science Engineering and Information Technology
ISSN: 2321-3337 Impact Factor:1.521 Volume:6 Issue:3 Year: 05 March,2022 Pages:1668-1676
Abstract
The advancement of the Internet of Things (IoT) technologies will play a significant role in the growth of smart cities and industrial applications. Network sustainability is considered as a significant characteristic for IoT based applications. Wireless Sensor Network (WSN) is one of the emerging technologies utilized for sensing and data transferring processes in IoT-based applications offers such network sustainability where WSN is acted as the subnets in the IoT model. However, heterogeneous faults like hardware, software, and time-based faults are the major determinants that affect the network stability of IoT based WSN (IWSN) model and also the multi- objectives like coverage, connectivity and energy consumption are required to improve the quality of service in IoT based WSN (IWSN) model. In this paper, the Adaptive Coverage and Connectivity (ACC) scheme is proposed to attain the efficient IWSN model. It employs two underlying methodologies in which the first method provides the optimal coverage to all target objects and its mathematical model guarantees the coverage rate. The second method deals with connectivity and energy consumption of the network. The experimental results manifest that, unlike existing schemes, the proposed ACC scheme can sustain the network for a prolonged time. And at the same time, a novel Energy-Efficient Heterogeneous Fault Management scheme has been proposed to manage these heterogeneous faults in IWSN. Efficient heterogeneous fault detection in the proposed scheme can be achieved by using three novel diagnosis algorithms. The new Tuned Support Vector Machine classifier facilitates to classify the heterogeneous faults where the tuning parameters of the proposed classifier will be optimized through Hierarchy based Grasshopper Optimization Algorithm. Finally, the performance results evident that the diagnosis accuracy of the proposed scheme acquires 99% and the false alarm rate sustains below 1.5% during a higher fault probability rate. The diagnosis accuracy rate is enhanced up to 17% as compared with existing techniques.
Kewords
Fault diagnosis, Heterogeneous Faults, Internet of Things, Network Stability, Wireless Sensor Network, Multi-objective, Quality of service, ACC.
Reference
1. Ghasempour A (2019) Internet of things in smart grid: architecture, applications, services, key technologies, and challenges. Inventions 4:22 2. Ullah A, Said G, Sher M, Ning H (2020) Fog-assisted secure healthcare data aggregation scheme in IoT-enabled WSN. Peer Peer Netw Appl 13:163–174 3. Behera TM, Mohapatra SK, Samal UC, Khan MS, Daneshmand M, Gandomi AH (2019) Residual energy based cluster-head selection in WSNs for IoT application. IEEE Internet Things J 6:5132–5139 4. Carreno R, Aguilar V, Pacheco D, Acevedo M (2019) An IoT expert system Shell in block-chain technology with ELM as infer- ence engine. Int J Inf Technol Decis Mak 18:87–104 5. He Y, Han G, Wang H (2019) A sector-based random routing scheme for protecting the source location privacy in WSNs for the internet of things. Futur Gener Comput Syst 96:438–448 6. Zakariayi S, Babaie S (2019) DEHCIC: a distributed energy-aware hexagon based clustering algorithm to improve coverage in wire- less sensor networks. Peer Peer Netw Appl 12(4):689–704 7. Romero E, Blesa J, Araujo A (2019) An adaptive energy aware strategy based on game theory to add privacy in the physical layer for cognitive WSNs. Ad Hoc Netw 92:101800 8. Wu J, Chen Z, Wu J (2020) An energy efficient data transmission approach for low-duty-cycle wireless sensor networks. Peer Peer Netw Appl 13:255–268 9. Prasanth A, Pavalarajan S (2020) Implementation of efficient intra- and inter-zone routing for extending network consistency in wire- less sensor networks. J Circuit Syst Comp 29:1–25 10. Sharma G, Rajesh A, Babu L, Mohan E (2019) Three-dimensional localization in anisotropic wireless sensor networks using fuzzy logic system. Ad Hoc Sens Wirel Netw 45:29–57 11. Senouci MR, Mellouk A (2019) A robust uncertainty-aware clus- ter-based deployment approach for WSNs: coverage, connectivity, and lifespan. J Netw Comput Appl 146:102414 12. Prasanth A, Pavalarajan S (2019) Zone-based sink mobility in wire- less sensor networks. Sens Rev 39:874–880 13. Farhat A, Guyeux C, Haddad M, Hakem M (2020) Energy- efficiency and coverage quality management for reliable diagnos- tics in wireless sensor networks. Int J Sens Netw 32:127–138 14. Kavidha V, Ananthakumaran S (2019) Novel energy-efficient se- cure routing protocol for wireless sensor networks with Mobile sink. Peer Peer Netw Appl 12:881–892 15. Boukerche A, Sun P (2018) Connectivity and coverage based pro- tocols for wireless sensor networks. Ad Hoc Netw 80:54–69 16. Zygowski C, Jaekel A (2020) Optimal path planning strategies for monitoring coverage holes in wireless sensor networks. Ad Hoc Netw 96:101990 17. Chakraborty S, Goyala NK, Mahapatrac S, Sohb S (2020) A Monte-Carlo Markov chain approach for coverage-area reliability of mobile wireless sensor networks with multistate nodes. Reliab Eng Syst Saf 193:106662 18. Binh H, Hanh N, Quan L, Nghia N, Dey N (2020) Metaheuristics for maximization of obstacles constrained area coverage in hetero- geneous wireless sensor networks. Appl Soft Comput 86:105939 19. Hajjej F, Hamdi M, Ejbali R, Zaied M (2020) A distributed cover- age hole recovery approach based on reinforcement learning for wireless sensor networks. Ad Hoc Netw 101:102082 20. Kabakulak B (2019) Sensor and sink placement, scheduling and routing algorithms for connected coverage of wireless sensor net- works. Ad Hoc Netw 86:83–102 21. Nguyen P, Hanh N, Khuong N (2019) Node placement for connect- ed target coverage in wireless sensor networks with dynamic sinks. Pervasive Mob Comput 59:101070 22. Etancelin J, Fabbri A, Guinand F, Rosalie M (2019) DACYCLEM: a decentralized algorithm for maximizing coverage and lifetime in a Mobile wireless sensor network. Ad Hoc Netw 87:174–187 23. Elhoseny M, Tharwat A, Yuan X, Hassanien A (2018) Optimizing K-coverage of mobile WSNs. Expert Syst Appl 92:142–153 24. Xu Y, Ding O, Qu R, Li K (2018) Hybrid multi-objective evolu- tionary algorithms based on decomposition for wireless sensor net- work coverage optimization. Appl Soft Comput 68:268–282 25. Dahiya S, Singh PK (2018) Optimized Mobile sink based grid coverage-aware sensor deployment and link quality based routing in wireless sensor networks. Int J Electron Commun 89:191–196 26. Mostafaei H, Montieri A, Persico V, Pescape A (2017) A sleep scheduling approach based on learning automata for WSN partial coverage. J Netw Comput Appl 80:67–78 27. Guptaa SK, Kuilab P, Jana PK (2016) Genetic algorithm approach for k-coverage and m-connected node placement in target based wireless sensor networks. Comput Electr Eng 56:544–556 28. Torkestani JA (2013) An adaptive energy-efficient area coverage algorithm for wireless sensor networks. Ad Hoc Netw 11:1655– 1666 29. Cardei M, Wu J (2006) Energy-efficient coverage problems in wire- less adhoc sensor networks. Comput Commun 29:413–420 30. Mostafaei H (2015) Stochastic barrier coverage in wireless sensor networks based on distributed learning automata. Comput Commun 55:51–61 31. Kumar CS, Lai T (2010) Local barrier coverage in wireless sensor networks. IEEE Trans Mob Comput 9:491–504 32. Yardibi T, Karasan E (2010) A distributed activity scheduling al- gorithm for wireless sensor networks with partial coverage. Wirel Netw 16:213–225 33. Yetgin H, Cheung KK, El-Hajjar M, Hanzo L (2017) A survey of network lifetime maximization techniques in wireless sensor net- works. IEEE Commun Surv Tutor 19:828–854 34. Sun G, Liu Y, Li H, Wang A, Liang S (2018) A novel connectivity and coverage algorithm based on shortest path for wireless sensor networks. Comput Electr Eng 71:1025–1039 35. Samie M, Dragffy G, Tyrrell AM (2013) Novel bio-inspired ap- proach for fault-tolerant VLSI systems. IEEE Trans Very Large Scale Integr (VLSI) Syst 21:1878–1891 36. P.Nayak, G.K.Swetha, S.Gupta, K.Madhavi, Routing in Wireless Sensor Networks Using Machine Learning Techniques: Challenges and Opportunities, Measurement, 170 (2021) 108974:1-18. 37. M. Ammar, G. Russello, B. Crispo, Internet of Things: A survey on the security of IoT frameworks, Journal of Information Security and Applications, 38 (2018) 8-27. 38. S.li, Z.liu, Z.huang, DynaPro: Dynamic Wireless Sensor Network Data Protection Algorithm in IoT via Differential Privacy, IEEE Special Section on Data Mining for Internet of Things, IEEE Access, 7 (2019) 167754 – 167765. 39. A.Prasanth, S.Pavalarajan, Zone-based sink mobility in wireless sensor networks, Sensor Review, 39 (2019) 874-880. 40. G.Kaur, P.Chanak, M.Bhattacharya, Memetic Algorithm-Based Data Gathering Scheme for IoT-Enabled Wireless Sensor Networks, IEEE Sensors Journal, 20 (2020) 11725- 11734. 41. Z.Ding, H.Chen, N.Ansari, Energy-Efficient Relay-Selection-Based Dynamic Routing Algorithm for IoT-Oriented Software-Defined WSNs, IEEE Internet Of Things Journal, 7 (2020) 9050-9065. 42. IoT enabled WSN Applications, Peer-to-Peer Networking and Applications, 13 (2020) 1905–1920. 43. P.Chanak, I.Banerjee, Congestion Free Routing Mechanism for IoT-Enabled Wireless Sensor Networks for Smart Healthcare Applications, IEEE Transactions On Consumer Electronics, 66 (2020) 223-232. 44. A.Prasanth, S.Pavalarajan, Implementation of Efficient Intra-and Inter-Zone Routing for Extending Network Consistency in Wireless Sensor Network, Journal of Circuits, Systems and Computers, 29 (2020) 1-25. 45. K.Karunanithy, B.Velusamy, Energy efficient cluster and travelling salesman problem based data collection using WSNs for Intelligent water irrigation and fertigation, Measurement, 161, (2020) 107835:1-21. 46. A.Prasanth, S.Pavalarajan Particle Swarm Optimization Algorithm Based Zone Head Selection in Wireless Sensor Networks, International Journal of Scientific & Technology Research, 8 (2019) 1594-1597. 47. M.R.Skydt, M.Bang, H.R.Shaker, A probabilistic sequence classification approach for early fault prediction in distribution grids using long short-term memory neural networks, Measurement, 170 (2021) 108691:1-17. 48. E.Moridi, M.Haghparast, M.Hosseinzadeh, S.J.Jassbi, Fault management frameworks in wireless sensor networks: A survey, Computer Communications 155 (2020) 205–226 49. S.Zidi, T.Moulahi, B.Alaya, Fault Detection in Wireless Sensor Networks through SVM Classifier, IEEE Sensors Journal, 18 (2018) 340- 347. 50. S.Mitra, A.Das, Distributed Fault Tolerant Architecture for Wireless Sensor Network, 51. Informatica, 41 (2017) 47–58. 52. T.Muhammed, R.A.Shaikh, An analysis of fault detection strategies in wireless sensor networks, Journal of Network and Computer Applications, 78 (2017) 267–287. 53. S.Mohapatra, P.M.Khilar, Fault diagnosis in wireless sensor network using negative selection algorithm and support vector machine, Computational Intelligence, 36 (2020) 1374–1393. 54. V.K.Menaria, S. C. Jain, N.Raju , R.Kumari, NLFFT: A Novel Fault Tolerance Model Using Artificial Intelligence to Improve Performance in Wireless Sensor Networks, IEEE Access, 8 (2020) 149231-149254. 55. M.Masdari, S.Ozdemir, Towards coverage-aware fuzzy logic-based faulty node detection in heterogeneous wireless sensor networks, Wireless Personal Communication, 111 (2020) 581-610. 56. W.Gui, Q.Lu, M.Su, F.Pan, Wireless Sensor Network Fault Sensor Recognition Algorithm Based on MM* Diagnostic Model, IEEE Access, 8 (2020) 127084-127093. 57. Y.Gao, F.Xiao, J.Liu, Distributed Soft Fault Detection for Interval Type-2 Fuzzy-Model- Based Stochastic Systems with Wireless Sensor Networks, IEEE Transactions On Industrial Informatics, 15 (2019) 334-347.
