A Tuned classification approach for efficient heterogeneous fault diagnosis in IoT-enabled WSN applications

Abstract The advancement of the Internet of Things (IoT) technologies will play a significant role in the growth of smart cities and industrial applications. Wireless Sensor Network (WSN) is one of the emerging technology utilized for sensing and data transferring processes in IoT-based applications. 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. In this paper, 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.

[1]  Ajanta Das,et al.  Distributed Fault Tolerant Architecture for Wireless Sensor Network , 2017, Informatica.

[2]  Mahua Bhattacharya,et al.  Memetic Algorithm-Based Data Gathering Scheme for IoT-Enabled Wireless Sensor Networks , 2020, IEEE Sensors Journal.

[3]  M. W. Guo,et al.  Improved Grasshopper Algorithm Based on Gravity Search Operator and Pigeon Colony Landmark Operator , 2020, IEEE Access.

[4]  Mahmoud Ammar,et al.  Journal of Information Security and Applications , 2022 .

[5]  Padmalaya Nayak,et al.  Routing in wireless sensor networks using machine learning techniques: Challenges and opportunities , 2021, Measurement.

[6]  Hongyang Chen,et al.  Energy-Efficient Relay-Selection-Based Dynamic Routing Algorithm for IoT-Oriented Software-Defined WSNs , 2020, IEEE Internet of Things Journal.

[7]  Lorenzo Chiari,et al.  Comparison of Standard Clinical and Instrumented Physical Performance Tests in Discriminating Functional Status of High-Functioning People Aged 61–70 Years Old , 2019, Sensors.

[8]  Nadeem Javaid,et al.  Fault Detection in Wireless Sensor Networks through the Random Forest Classifier , 2019, Sensors.

[9]  Pabitra Mohan Khilar,et al.  Neural network based automated detection of link failures in wireless sensor networks and extension to a study on the detection of disjoint nodes , 2019, J. Ambient Intell. Humaniz. Comput..

[10]  Ruchuan Wang,et al.  Distributed Soft Fault Detection for Interval Type-2 Fuzzy-Model-Based Stochastic Systems With Wireless Sensor Networks , 2018, IEEE Transactions on Industrial Informatics.

[11]  Pabitra Mohan Khilar,et al.  Heterogeneous fault diagnosis for wireless sensor networks , 2018, Ad Hoc Networks.

[12]  S. Jayachitra,et al.  A novel multi-objective optimization strategy for enhancing quality of service in IoT-enabled WSN applications , 2020, Peer-to-Peer Networking and Applications.

[13]  A Prasanth,et al.  Zone-based sink mobility in wireless sensor networks , 2019 .

[14]  Mehdi Hosseinzadeh,et al.  Fault management frameworks in wireless sensor networks: A survey , 2020, Comput. Commun..

[15]  Abdul Hanan Abdullah,et al.  Virtualization in Wireless Sensor Networks: Fault Tolerant Embedding for Internet of Things , 2018, IEEE Internet of Things Journal.

[16]  Ting Liu,et al.  Recent advances in convolutional neural networks , 2015, Pattern Recognit..

[17]  Subbu Neduncheliyan,et al.  Genetic algorithm based fault tolerant clustering in wireless sensor network , 2017, IET Commun..

[18]  Papia Ray,et al.  Support vector machine based fault classification and location of a long transmission line , 2016 .

[19]  Salah Zidi,et al.  Fault Detection in Wireless Sensor Networks Through SVM Classifier , 2018, IEEE Sensors Journal.

[20]  Jun Wang,et al.  Distributed Fault Detection for Wireless Sensor Networks Based on Support Vector Regression , 2018, Wirel. Commun. Mob. Comput..

[21]  A. Prasanth,et al.  Certain Investigations on Energy-Efficient Fault Detection and Recovery Management in Underwater Wireless Sensor Networks , 2020, J. Circuits Syst. Comput..

[22]  Prasenjit Chanak,et al.  Congestion Free Routing Mechanism for IoT-Enabled Wireless Sensor Networks for Smart Healthcare Applications , 2020, IEEE Transactions on Consumer Electronics.

[23]  Andrew Lewis,et al.  Grasshopper Optimisation Algorithm: Theory and application , 2017, Adv. Eng. Softw..

[24]  Noureddine Moussa,et al.  A cluster‐based fault‐tolerant routing protocol for wireless sensor networks , 2019, Int. J. Commun. Syst..

[25]  Anand Nayyar,et al.  NLFFT: A Novel Fault Tolerance Model Using Artificial Intelligence to Improve Performance in Wireless Sensor Networks , 2020, IEEE Access.

[26]  Lei Shu,et al.  BP neural network based continuous objects distribution detection in WSNs , 2016, Wirel. Networks.

[27]  Zhiyang Li,et al.  DynaPro: Dynamic Wireless Sensor Network Data Protection Algorithm in IoT via Differential Privacy , 2019, IEEE Access.

[28]  Hamid Reza Shaker,et al.  A probabilistic sequence classification approach for early fault prediction in distribution grids using long short-term memory neural networks , 2020 .

[29]  C. R. Tripathy,et al.  An ANFIS estimator based data aggregation scheme for fault tolerant Wireless Sensor Networks , 2018, J. King Saud Univ. Comput. Inf. Sci..

[30]  Xiaomei Wang,et al.  A self-adaptive and fault-tolerant routing algorithm for wireless sensor networks in microgrids , 2019, Future Gener. Comput. Syst..

[31]  A. Prasanth,et al.  Particle Swarm Optimization Algorithm Based Zone Head Selection In Wireless Sensor Networks , 2019 .

[32]  Riaz Ahmed Shaikh,et al.  An analysis of fault detection strategies in wireless sensor networks , 2017, J. Netw. Comput. Appl..

[33]  Pabitra Mohan Khilar,et al.  Fault diagnosis in wireless sensor network using negative selection algorithm and support vector machine , 2020, Comput. Intell..

[34]  Qian Lu,et al.  Wireless Sensor Network Fault Sensor Recognition Algorithm Based on MM* Diagnostic Model , 2020, IEEE Access.

[35]  Mohammad Masdari,et al.  Towards Coverage-Aware Fuzzy Logic-Based Faulty Node Detection in Heterogeneous Wireless Sensor Networks , 2019, Wireless Personal Communications.

[36]  K. Karunanithy,et al.  Energy efficient cluster and travelling salesman problem based data collection using WSNs for Intelligent water irrigation and fertigation , 2020 .

[37]  S. Pavalarajan,et al.  Implementation of Efficient Intra- and Inter-Zone Routing for Extending Network Consistency in Wireless Sensor Networks , 2019, J. Circuits Syst. Comput..

[38]  Wenqing Cheng,et al.  On the Tradeoff between Performance and Programmability for Software Defined WiFi Networks , 2018, Wirel. Commun. Mob. Comput..

[39]  Pabitra Mohan Khilar,et al.  Composite Fault Diagnosis in Wireless Sensor Networks Using Neural Networks , 2017, Wirel. Pers. Commun..