International Journal of Computer Networks and Applications

Open Access Open Access  Restricted Access Subscription Access

Link Failure Detection in Multimedia Sensor Networks Using Multi-Tier Clustering Based VGG-CNN Classification Approach


Affiliations
1 Faculty of Electronics, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India
2 Department of ECE, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India
 

The transferring of huge multimedia data over the limited bandwidth environment has many challenges in real time. Wireless Multimedia Sensor Networks (WMSN) is a special type of wireless sensor networks which are used to overcome such bandwidth limitations in order to provide effective transferring of multimedia data. The malicious nodes in WMSN fail the links between the sensor nodes which degrades the efficiency of the entire network. Each node in WMSN may have its own signal transferring capability based on its energy level. If the energy level of the node degrades beyond the threshold level, that node becomes malicious node which is the main reason for the link failure between this node and its surrounding nodes. The data transfer is affected by the link failure nodes which degrades the performance of the entire system. Hence, the detection of link failure is important to improve the performance efficiency of the network system. This paper focuses the link failure detection system using deep learning approach. Hence, the detection of link failure is an important task to improve the performance of the network. This paper proposes an effective methodology for detecting the link failures of clusters in WMSN using deep learning architecture. The nodes in WMSN are grouped in to number of clusters and cluster head is determined using multi tier clustering approach, based on the energy levels and weighting metric approach. Then, the features are computed from each cluster and these features are classified using Visual Geometry Group (VGG) classification approach in order to detect the link failures of the clusters in WMSN. The performance of this developed methodology is analyzed with respect to Packet Delivery Ratio (PDR) and latency.

Keywords

WMSN, Malicious, Link Failure, Deep Learning Architecture, Clusters, VGG.
User
Notifications
Font Size

  • Al-Ariki HDE, Swamy MNS (2017) A survey and analysis of multipath routing protocols in wireless multimedia sensor networks. Wirel Netw 23(6):1823–1835.

  • Wang P, Dai R, Akyildiz IF (2011) A spatial correlation-based image compression framework for wireless multimedia sensor networks. IEEE Trans Multimed 13(2):388–401.

  • AlAmri A, Abdullah M (2018) Cross layer energy location aware routing protocol (XELARP) for wireless multimedia sensor networks WMSNs. Int J Eng Technol 7(4):3346–3353.

  • Sharif A, Potdar V, Chang E (2009) Wireless multimedia sensor network technology: a survey. In: IEEE 15th international conference on industrial informatics, no. May 2014, pp 606–613.

  • Felemban E, Lee C-G, Ekici E (2006) Mmspeed: Multipath multi-speed protocol for qos guarantee of reliability and timeliness in wireless sensor networks. IEEE Trans Mob Comput 5(6):738–754.

  • Reddy VB, Venkataraman S, Negi A (2017) Communication and data trust for wireless sensor networks using D-S theory. IEEE Sens J 17(12):3921–3929.

  • She W, Liu Q, Tian Z, Chen JS, Wang B, Liu W (2019) Blockchain trust model for malicious node detection in wireless sensor networks. IEEE Access 7:38947–38956

  • Razaque, Abdul & Elleithy, Khaled. (2014). Energy-Efficient Boarder Node Medium Access Control Protocol for Wireless Sensor Networks. Sensors (Basel, Switzerland). 14. 5074-117.

  • B. H. Khudayer, M. Anbar, S. M. Hanshi and T. Wan, "Efficient Route Discovery and Link Failure Detection Mechanisms for Source Routing Protocol in Mobile Ad-Hoc Networks," in IEEE Access, vol. 8, pp. 24019-24032, 2020.

  • Qin D, Yang S, Jia S, Zhang Y, Ma J, Ding Q (2017) Research on trust sensing based secure routing mechanism for wireless sensor network. IEEE Access 5:9599–9609.

  • Surabhi Patel, Heman Pathak, “A mathematical framework for link failure time estimation in MANETs,”Engineering Science and Technology, an International Journal,Vol.1, No.1,2021, pp.1-10.

  • Dsouza MB, Manjaiah DH. Improving the QoS of Multipath Routing in MANET by Considering Reliable Node and Stable Link. In Sustainable Communication Networks and Application 2021 (pp. 535-546). Springer, Singapore.

  • S. Patel, H. Pathak, “A regression-based technique for link failure time prediction in MANET”, Int J High Perform Comput Networking, 16 (2– 3) (2020), pp. 95-101.

  • Chiwariro, R., .N, T. Quality of service aware routing protocols in wireless multimedia sensor networks: survey. Int. j. inf. tecnol. (2020).

  • R. Jain, I. Kashyap, “An QoS aware link defined OLSR (LD-OLSR) routing protocol for MANETs”, Wireless Pers Commun, 108 (3) (2019), pp. 1745-1758.

  • Genta A, Lobiyal DK, Abawajy JH (2019) Energy Efficient Multipath Routing Algorithm for Wireless Multimedia Sensor Network. Sensors 19(17):1–21.

  • Gutiérrez, S.; Ponce, H. An Intelligent Failure Detection on a Wireless Sensor Network for Indoor Climate Conditions. Sensors 2019, 19, 854.

  • Acharya BM, Nayak AK (2018) Hierarchical multi path routing protocol for wireless multimedia sensor networks. Int J Intell Eng Syst 11(1):239–247.

  • Bhanu KN, Bhaskar Reddy T (2017) Multi-agent based context aware multipath routing in wireless multimedia sensor networks. IOSR J Comput Eng 19(4):64–73.

  • Razaque, Abdul & Elleithy, Khaled. (2014). Energy-Efficient Boarder Node Medium Access Control Protocol for Wireless Sensor Networks. Sensors (Basel, Switzerland). 14. 5074-117.

  • G. Han, J. Jiang, M. Guizani and J. J. P. C. Rodrigues, "Green Routing Protocols for Wireless Multimedia Sensor Networks," in IEEE Wireless Communications, vol. 23, no. 6, pp. 140-146, December 2016.


Abstract Views: 304

PDF Views: 1




  • Link Failure Detection in Multimedia Sensor Networks Using Multi-Tier Clustering Based VGG-CNN Classification Approach

Abstract Views: 304  |  PDF Views: 1

Authors

S. Arockia Jayadhas
Faculty of Electronics, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India
S. Emalda Roslin
Department of ECE, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India

Abstract


The transferring of huge multimedia data over the limited bandwidth environment has many challenges in real time. Wireless Multimedia Sensor Networks (WMSN) is a special type of wireless sensor networks which are used to overcome such bandwidth limitations in order to provide effective transferring of multimedia data. The malicious nodes in WMSN fail the links between the sensor nodes which degrades the efficiency of the entire network. Each node in WMSN may have its own signal transferring capability based on its energy level. If the energy level of the node degrades beyond the threshold level, that node becomes malicious node which is the main reason for the link failure between this node and its surrounding nodes. The data transfer is affected by the link failure nodes which degrades the performance of the entire system. Hence, the detection of link failure is important to improve the performance efficiency of the network system. This paper focuses the link failure detection system using deep learning approach. Hence, the detection of link failure is an important task to improve the performance of the network. This paper proposes an effective methodology for detecting the link failures of clusters in WMSN using deep learning architecture. The nodes in WMSN are grouped in to number of clusters and cluster head is determined using multi tier clustering approach, based on the energy levels and weighting metric approach. Then, the features are computed from each cluster and these features are classified using Visual Geometry Group (VGG) classification approach in order to detect the link failures of the clusters in WMSN. The performance of this developed methodology is analyzed with respect to Packet Delivery Ratio (PDR) and latency.

Keywords


WMSN, Malicious, Link Failure, Deep Learning Architecture, Clusters, VGG.

References





DOI: https://doi.org/10.22247/ijcna%2F2021%2F210719