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One of the notable uses of Wireless Sensor Networks (WSN) is target detection and tracking. The primary objectives of a target tracking system are to improve target tracking precision and network longevity. This paper presents a Trustworthy Target Tracking Scheme (3TS) for WSN. The entire network region is divided into several grids of equal size, with each grid functioning as a cluster. All the grids include the same number of nodes. A Cluster Head (CH) node is selected for each grid based on the level of trust. The CH node determines the minimum number of active nodes per grid and regulates node activity. Together with the active nodes, the CH node identifies and tracks the target. In addition, the CH node informs the surrounding clusters that the target may cross. This concept enhances the accuracy of detection. Utilizing task cycle scheduling and a clustering approach, this work significantly increases the network's lifespan. The performance of the suggested work is justified in terms of detection accuracy, energy consumption, and network lifetime. The experimental findings demonstrate the effectiveness of the proposed method.

Keywords

WSN, Target Detection, Target Tracking, Clustering, Task Cycle Scheduling, Energy Efficiency.

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References

Qu, Z., Xu, H., Zhao, X., Tang, H., Wang, J., & Li, B. ‘A fault-tolerant sensor scheduling approach for target tracking in wireless sensor networks’. Alexandria Engineering Journal, Vol. 61, No.12, pp. 13001-13010, 2022.

Qu, Z., & Li, B. ‘An Energy-Efficient Clustering Method for Target Tracking Based on Tracking Anchors in Wireless Sensor Networks’. Sensors, Vol.22, No.15, pp.1-21, 2022.

Kumar, S. V., & Babu, P. S., ‘Energy Aware Generalized Gaussian Distributive Sammon Regularization Analysis for Target Object Tracking in WSN’, Mathematical Statistician and Engineering Applications, Vol.71, No.4, pp. 3438-3462, 2022.

Ahmadi, H., Viani, F., & Bouallegue, R., ‘An accurate prediction method for moving target localization and tracking in wireless sensor networks’, Ad Hoc Networks, Vol.70, pp.14-22, 2018.

Darabkh, K. A., & Alsaraireh, N. R. ‘A yet efficient target tracking algorithm in wireless sensor networks’. In 2018 15th International Multi-Conference on Systems, Signals & Devices (SSD) (pp. 7-11). IEEE, 2018.

Hu, Y.; Niu, Y.; Lam, J.; Shu, Z. ‘An Energy-Efficient Adaptive Overlapping Clustering Method for Dynamic Continuous Monitoring in WSNs’. IEEE Sens. J. Vol.17, pp.834–847, 2017.

Qu, Z.; Xu, H.; Zhao, X.; Tang, H.; Wang, J.; Li, B. ‘An Energy-Efficient Dynamic Clustering Protocol for Event Monitoring in Large-Scale WSN’. IEEE Sens. J., Vol.21, No.20, pp.23614-23625, 2021.

Feng, J.; Zhao, H.W. ‘Dynamic Nodes Collaboration for Target Tracking in Wireless Sensor Networks’. IEEE Sens. J., Vol.21, pp. 21069–21079, 2021.

Ahmad, T.; Abbas, A.M. EEAC: An energy efficient adaptive cluster based target tracking in wireless sensor networks. J. Interdiscip. Math. 2020, 23, 379–392.

Bhagat, D.P. ‘Tracking of Moving Target inWireless Sensor Network with Improved Network Life Time Using PSO’. Wirel. Pers.Commun. vol.2021, pp.1–15, 2021.

Qu, Z.; Xu, H.; Zhao, X.; Tang, H.; Wang, J.; Li, B. ‘A fault-tolerant sensor scheduling approach for target tracking in wireless sensor networks’. Alex. Eng. J., Vol. 61, pp. 13001–13010, 2022.

Fu, C.L.; Zhou, L.; Hu, Z.T.; Jin, Y.; Bai, K.;Wang, C., ‘LEACH-MTC: A Network Energy Optimization Algorithm Constraint as Moving Target Prediction.’ Appl. Sci., Vol.11, No.19, pp. 1-15, 2021.

Zhan, M., Huang, P., Zhu, S., Liu, X., Liao, G., Sheng, J., & Li, S., ‘A modified keystone transform matched filtering method for space-moving target detection’. IEEE Transactions on Geoscience and Remote Sensing, Vol. 60, pp.1-16, 2021.

Cui, H., Li, L., Liu, X., Su, X., & Chen, F. ‘Infrared Small Target Detection Based on Weighted Three-Layer Window Local Contrast’. IEEE Geoscience and Remote Sensing Letters, Vol.19, pp.1-5, 2021.

Wang, X., Wang, L., Wu, H., Wang, J., Sun, K., Lin, A., & Wang, Q. ‘A Double Dictionary-Based Nonlinear Representation Model for Hyperspectral Subpixel Target Detection’. IEEE Transactions on Geoscience and Remote Sensing, vol.60, pp.1-16, 2022.

Du, X., Shang, H., Wang, L., & Sun, Y., ‘Low-Altitude Target Detection Method Based on Distributed Sensor Networks’. IEEE Access, Vol.10, pp.56458-56468, 2022.

Lin, B., Yang, X., Wang, J., Wang, Y., Wang, K., & Zhang, X., ‘A Robust Space Target Detection Algorithm Based on Target Characteristics’. IEEE Geoscience and Remote Sensing Letters, Vol. 19, pp. 1-5, 2021.

Xu, X., Hou, Q., Wu, C., & Fan, Z., ‘Improved GSO Algorithms and Their Applications in Multi-Target Detection and Tracking Field’. IEEE Access, Vol. 8, pp. 119609-119623, 2020.

Xu, X., Yuan, Z., & Wang, Y., ‘Multi-target tracking and detection based on hybrid filter algorithm’. IEEE Access, Vol.8, pp.209528-209536, 2020.

Feng, Q., Huang, J., & Yang, Z., ‘Jointly optimized target detection and tracking using compressive samples’. IEEE Access, Vol. 7, pp.73675-73684, 2019. How to cite this article:

Li, J., Wang, J., & Liu, W., ‘Moving target detection and tracking algorithm based on context information’. IEEE Access, Vol.7, pp. 70966-70974, 2019.

Delay Aware Clustered Service Discovery Scheme Based on Trust for Mobile Ad Hoc Networks (MANET)

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Authors

Prabu B. ¹, G. Jagatheeshkumar ¹

Affiliations
1 PG & Research Department of Computer Science, Karuppannan Mariappan College, Muthur, Tamil Nadu, IN

Source

International Journal of Computer Networks and Applications, Vol 10, No 5 (2023), Pagination: 806-815

Abstract

Service discovery is one of the most difficult aspects of MANETs. The primary concern is the assignment of the optimal service to the service requester. This work intends to address this issue by proposing a clustered trustworthy service discovery scheme. The Cluster Head (CH) node selection and recycling, 𝑺𝑬𝑹𝑽_𝑨𝑫, request, response and service ranking are the crucial phases of this work. The CH node is chosen by considering the trust parameters like mobility, energy and number of neighbors. The selected CH node calculates the level of trust for each of its member nodes by employing trust criteria such as energy consumption, packet forwarding ratio, and node behavior. The node responsible for requesting services delivers the 𝑺𝑬𝑹𝑽_𝑹𝒆𝒒 packet to the CH node, which thereafter searches its local memory for the corresponding service. Finally, the matching services are evaluated based on the distance of the service, the level of trust and the workload of the service provider. As significant metrics are considered for recommending service, the service requester is assured with reliable and faster service provisioning, which is proven by the experimental results.

Keywords

MANET, Service Discovery, Service Provision, Service Ranking, Trust, Energy Efficiency.

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References

N. Veeraiah and B. Tirumala Krishna, “Trust-aware fuzzyclus-fuzzy NB: Intrusion detection scheme based on fuzzy clustering and Bayesian rule”, Wireless Netw., vol. 25, pp. 4021–4035, Jan. 2019, doi: 10.1007/s11276-018-01933-0.

N. Veeraiah, ‘‘A comparative analysis of energy efficient multipath routing in MANET,’’ J. Adv. Res. Dyn. Control Syst., vol. 12, no. 3, pp. 261–267, Mar. 2020.

Quy, V. K., Nam, V. H., Linh, D. M., Ban, N. T., & Han, N. D. (2021). A survey of QoS-aware routing protocols for the MANET-WSN convergence scenarios in IoT networks. Wireless Personal Communications, 120(1), 49-62.

Kurian, S., & Ramasamy, L. (2021). Securing Service Discovery from Denial of Service Attack in Mobile Ad Hoc Network (MANET). International Journal of Computer Networks and Applications, 8(5), 619-633.

Islam, N. (2019, December). A secure service discovery scheme for mobile ad hoc network using artificial deep neural network. In 2019 International Conference on Frontiers of Information Technology (FIT) (pp. 133-1335). IEEE.

Veeresh, P., Praveen Sam, R., & Shoba Bindhu, C. (2018). Energy Constraint Service Discovery and Composition in Mobile Ad Hoc Networks. In Proceedings of the Second International Conference on Computational Intelligence and Informatics (pp. 175-187). Springer, Singapore.

Jayapal, C., Jayavel, S., & Sumathi, V. P. (2018). Enhanced service discovery protocol for MANET by effective cache management. Wireless Personal Communications, 103(2), 1517-1533.

Ramalingam, R., Muniyan, R., Dumka, A., Singh, D. P., Mohamed, H. G., Singh, R., ... & Noya, I. D. (2022). Routing Protocol for MANET Based on QoS-Aware Service Composition with Dynamic Secured Broker Selection. Electronics, 11(17), 2637.

J. Manoranjini, A. Chandrasekar and S. Jothi, "Improved QoS and avoidance of black hole attacks in MANET using trust detection framework", Automatika, vol.60, no. 3, pp. 274-284, 2019. [Online]. https://doi.org/10.1080/00051144.2019.1576965.

Setijadi, E., Purnama, I. K. E., & Purnomo, M. H. (2019, September). Analysis of reactive routing protocols in MANET based on quality of service. In 2019 International Seminar on Application for Technology of Information and Communication (iSemantic) (pp. 342-345). IEEE.

R. Popli, V. Juneja, K. Garg, and D. V. Gupta, “Fuzzy based trust evaluation models for enhancing security in MANETs,” International Journal of Engineering and Advanced Technology (IJEAT), vol. 8, no. 5, pp. 506–510, 2019.

H. Jiang, Y. Sun, R. Sun, and H. Xu, “Fuzzy-logic-based energy optimized routing for wireless sensor networks,” International Journal of Distributed Sensor Networks, vol. 9, no. 8, 2013.

S. K. Gupta, P. Kuila, and P. K. Jana, “Genetic algorithm approach for k-coverage and m-connected node placement in target based wireless sensor networks,” Computers and Electrical Engineering, vol. 56, pp. 544–556, 2016.

S. Amri, F. Khelifi, A. Bradai, A. Rachedi, M. L. Kaddachi, and M. Atri, “A new fuzzy logic based node localization mechanism for wireless sensor networks,” Future Generation Computer Systems, vol. 93, pp. 799–813, 2019.

Kumanan, T., & Duraiswamy, K. (2010, February). Reinforcement learning for quality of service in mobile ad hoc network (MANET). In Proceedings of the 12th international conference on Networking, VLSI and signal processing (pp. 250-257).

S. Chettibi and S. Chikhi, “Routing in mobile ad-hoc networks as a reinforcement learning task,” in Networked Digital Technologies. NDT 2011. Communications in Computer and Information Science, S. Fong, Ed., vol. 136, Springer, Berlin, Heidelberg, 2011.

F. Dong, O. Li, and M. Tong, “Intelligent routing control for MANET based on reinforcement learning,” MATEC Web of Conferences, vol. 232, article 04002, 2018.

A. Ghaffari, “Real-time routing algorithm for mobile ad hoc networks using reinforcement learning and heuristic algorithms,” Wireless Networks, vol. 23, no. 3, pp. 703–714, 2017.

M. Maleki, V. Hakami, and M. Dehghan, “A model-based reinforcement learning algorithm for routing in energy harvesting mobile ad-hoc networks,” Wireless Personal Communications, vol. 95, no. 3, pp. 3119–3139, 2017.

G. Jinarajadasa, L. Rupasinghe, and I. Murray, “A reinforcement learning approach to enhance the trust-level of MANETs,” in 2018 National Information Technology Conference (NITC), pp. 3119–3138, Colombo, Sri Lanka, October 2018.

Juneja, K., & Singh, Y. (2021). Trust-adaptive Fuzzy-Statistical Protocol for Optimizing the Communication in Attacked Network. IETE Journal of Research, 1-18.

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