Jamming Detection Technique Using Improved Exponentially Weighted Moving Average (IEWMA) Algorithm for WSN


  • S. G. Hymlin Rose Department of Electronics and Communication Engineering, R.M.D Engineering College, Kavaraipettai, Chennai 601 206, Tamil Nadu, India
  • Selvaraj Janani Department of Electronics and Communication Engineering, Periyar Maniammai Institute of Science & Technology (Deemed to be University), Vallam, Thanjavur 613 403, Tamil Nadu, India


Jamming attack, Statistical Process Control, Improved Exponentially Weighted Moving Average, Break Advent Time


WSN is emerging field with various application areas including health monitoring, military operations, agriculture, and environmental monitoring, have recently come to light as extremely promising solutions. Given the potential widespread use of Wireless Sensor Networks (WSNs), there is a growing concern about the security vulnerabilities faced by WSN sensor nodes, particularly due to their deployment in resource-constrained and hostile environments. One prevalent and disruptive form of attack is the DoS attack, with the jamming attack being a subcategory. The jamming attack is in which radio frequency signals are emitted to interfere with and disrupt the normal functioning of sensor nodes in the WSN, leading to service denial. To tackle this concern, a suggested approach is presented, utilizing a sequential method rooted in the Statistical Process Control (SPC) method for the identification of jamming attacks. For more exact detection of variations sin the strength of jamming attacks, an Improved Exponentially Weighted Moving Average (IEWMA) technique is suggested. This is achieved by analyzing a parameter called the packet Break Advent Time (BAT), which is derived from packets received from sensor nodes. Through simulation experiments, outcomes demonstrate that proposed methodology can accurately and efficiently detect jamming attacks in the sensor network, with minimal overhead, reduced energy consumption, and high precision.


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Misra, S.; Singh, R.; Mohan, S.V. Information warfare-worthy jamming attack detection mechanism for wireless sensor networks using a fuzzy inference system. Sensors 2010, 10, 3444–3479.

Strasser, M.; Danev, B.; ˇCapkun, S. Detection of reactive jamming in sensor networks. ACM Trans. Sens. Netw. (TOSN) 2010, 7, 16.

Spuhler, M.; Giustiniano, D.; Lenders, V.; Wilhelm, M.; Schmitt, J.B. Detection of reactive jamming in DSSS-based wireless communications. IEEE Trans. Wirel. Commun. 2014, 13, 1593–1603.

Guan,Y.;Ge,X.DistributedSecureEstimationoverWirelessSensorNetworksAgainstRandomMultichannel Jamming Attacks. IEEE Access 2017, 5, 10858–10870.

Cordero, C.V.; Lisser, A. Jamming Attacks Reliable Prevention in a Clustered Wireless Sensor Network. Wirel. Pers. Commun. 2015, 85, 925–936.

Mpitziopoulos, A.; Gavalas, D. An effective defensive node against jamming attacks in sensor networks. Secur. Commun. Netw. 2009, 2, 145–163.

Alnifie, G.; Simon, R. A multi-channel defense against jamming attacks in wireless sensor networks. In Proceedings of the 3rd ACM Workshop on QoS and Security for Wireless and Mobile Networks, Chania, Crete Island, Greece, 22 October 2007; pp. 95–104.

Del-Valle-Soto, C.; Mex-Perera, C.; Monroy, R.; Nolazco-Flores, J.A. MPH-M, AODV-M and DSR-M Performance Evaluation under Jamming Attacks. Sensors 2017, 17, 1573.

Çakirog˘lu, M.; Özcerit, A.T. Design and evaluation of a query-based jamming detection algorithm for wireless sensor networks. Turk. J. Electr. Eng. Comput. Sci. 2011, 19, 1–19.

Bhavathankar, P.; Mondal, A.; Misra, S. Topology control in the presence of jammers for wireless sensor networks. Int. J. Commun. Syst. 2017.

A. Azim et al., “Efficient Jammed Area Mapping in Wireless Sensor Networks,” IEEE Embedded Sys. Lett., 2014, vol. 6, no. 4, pp. 93–96.

P. Ganeshkumar, K. P. Vijayakumar, M. Anandaraj (2016)., “A novel jammer detection framework for cluster-based wireless sensor networks” EURASIP Journal on Wireless Communications and Networking, pp.1-25 , https://doi.org/10.1186/s13638-016-0528-1

Sang Quang Nguyen, Hyung Yun Kong (2015), “Combining Binary Jamming and Network Coding to Improve Outage Performance in Two-Way Relaying Networks under Physical Layer Security”, International Journal on Wireless Pers Communication, Volume 85, Issue 4, pp 2431–2446.

Michael Riecker, Sebastian Biedermann, Rachid El Bansarkhani, Matthias Hollick (2014), “Lightweight energy consumption-based intrusion detection system for wireless sensor networks” International Journal of Information Security, Volume 14, Issue 2, pp 155–167.

Domenico Giustiniano, Vincent Lenders, Jens B.Schmitt (2013), “Detection of Reactive Jamming in DSSS-based Wireless Networks”, International conference on Security and privacy in wireless and mobile networks, Pp 43-48.

Michael Spuhler, Domenico Giustiniano, Vincent Lenders(2014), “Detection of Reactive Jamming in DSSS-based Wireless Communications”, IEEE Transactions On Wireless Communications, Vol. 13, No. 3,pp.1593-1604.

Yuzhe Li, Ling Shi, Peng Cheng, Jiming Chen, and Daniel E. Quevedo,(2015)” Jamming Attacks on Remote State Estimation in Cyber-Physical Systems: A Game-Theoretic Approach” , IEEE Transactions On Automatic Control, Vol. 60, No. 10, pp. 2831-2836.

Haijun Zhang,Hong Xing,Julian Cheng (2016) , “Secure Resource Allocation for OFDMA Two-Way Relay Wireless Sensor Networks Without and With Cooperative Jamming” IEEE Transactions on Industrial Informatics, Volume: 12, Issue: 5, , PP. 1714 – 1725.

Xingkun Xu ,Kunlun Gao, Xiaokun Zheng ,Ting Zhao (2012), “A zero-sum game theoretic framework for jamming detection and avoidance in Wireless Sensor Networks” International Conference on Computer Science and Information Processing (CSIP) pp.265-270.

Donggang Liu , Joshua Raymer ,Andy Fox (2013), “Efficient and timely jamming detection in wireless sensor networks”, IEEE 9th International Conference on Mobile Adhoc and Sensor Systems (MASS), pp.335-343

Ahmad YusriDak , Noor Elaiza Abdul Khalid , SaadiahYahya (2013) “A novel framework for jamming detection and classification in wireless networks” International Conference on Computing and Networking Technology (ICCNT),pp.240-246.

Ying Xuan, Yilin Shen, Nam P. Nguyen , My T. Thai (2012) ”A Trigger Identification Service for Defending Reactive Jammers in WSN” IEEE Transactions on Mobile Computing, Volume: 11, Issue: 5, pp. 793 - 806 .

Aasha Nandhini, Kishore Rajendiran, Radha Sankararajan (2014) ”A Novel Frequency Hopping Spread Spectrum Technique using Random Pattern Table for WSN” , International Journal of Ad Hoc & Sensor Wireless Networks,pp. 255-275.

Xiaofeng Liu, Liusheng Huang, Hongli Xu, Wenbo Shi, Dashan Wang (2011)”An Energy-Efficient k-connected Scheme for Wireless Sensor Networks” International Journal of Ad Hoc & Sensor Wireless Networks ,pp 1-21.

Amirthasaravanan Arivunambi and Arjun Paramarthalingam(2022),” Intelligent slime mold algorithm for proficient jamming attack detection in wireless sensor network, Global Transitions Proceedings(3)pp.386-391.

P Bhavathankar, S Chatterjee, S Misra, “Link-quality aware path selection in the presence of proactive jamming in fallible wireless sensor networks”, IEEE Trans. Commun. 66 (4) (2018) 1689–1704 .

Nashab Alikh and Amir Rajabzadeh,” Using a lightweight security mechanism to detect and localize jamming attack in wireless sensor networks”, Optik(2022),pp.1-14.

L. Pang, X. Chen, Z. Xue, R. Khatoun, A novel range-free jammer localization solution in wireless network by using PSO Algorithm, Commun. Comput. Inf. Sci. vol. 728 (2017) 198–211

G. Pan, et al., On secrecy performance of MISO SWIPT systems with TAS and imperfect CSI, IEEE Trans. Commun. vol. 64 (9) (2016) 3831–3843.

I. Sudha , Mohammed Ahmed Mustafa , R. Suguna ,Sathishkumar Karupusamy , Veeraswamy Ammisetty , Shavkatov Navruzbek Shavkatovich , M. Ramalingam , Pratik Kanani ,” Pulse jamming attack detection using swarm intelligence in wireless sensor networks”, Optik,272(2023),pp.1-13.

Feriel Cherifi , Mawloud Omar , Tinhinane Chenache , Sylia Radji ," Efficient and lightweight protocol for anti-jamming communications in wireless body area networks", Computers and Electrical Engineering(2022),pp.1-11.

Xiao L, Lu X, Xu T, Wan X, Ji W, Zhang Y. Reinforcement learning-based mobile offloading for edge computing against jamming and interference. IEEE Trans Commun 2020;68(10).

Muhammad Adil , Mohammed Amin Almaiah , Alhuseen Omar Alsayed and Omar Almomani ,” An Anonymous Channel Categorization Scheme of Edge Nodes to Detect Jamming Attacks in Wireless Sensor Networks”,Sensors(2020), 20, 2311,pp.1-19.

Cortés-Leal, A.; Del-Valle-Soto, C.; Cardenas, C.; Valdivia, L.J.; Del Puerto-Flores, J.A. Performance Metric Analysis for a Jamming Detection Mechanism under Collaborative and Cooperative Schemes in Industrial Wireless Sensor Networks. Sensors 2022, 22, 178. https://doi.org/10.3390/s22010178

Jennifer S. Raj, Joy Iong‐Zong Chen, Ivan Kotuliak, Khaled Kamel, Caps Net‐based computing in cognitive communications, International Journal of Communication Systems, 10.1002/dac.5066, 35, 2, (2021).

Hymlin Rose S G, Jayasree T,” Detection of jamming at-tack using the timestamp for WSN” Journal of Adhoc Net-works” Elsevier,2019.

Dhanwanth, B. ., Saravanakumar, R. ., Tamilselvi, T. ., & Revathi, K. . (2023). A Smart Remote Monitoring System for Prenatal Care in Rural Areas. International Journal on Recent and Innovation Trends in Computing and Communication, 11(3), 30–36. https://doi.org/10.17762/ijritcc.v11i3.6196

Juan Lopez, Machine Learning-based Recommender Systems for E-commerce , Machine Learning Applications Conference Proceedings, Vol 2 2022.

Sherje, N. P., Agrawal, S. A., Umbarkar, A. M., Dharme, A. M., & Dhabliya, D. (2021). Experimental evaluation of mechatronics based cushioning performance in hydraulic cylinder. Materials Today: Proceedings, doi:10.1016/j.matpr.2020.12.1021




How to Cite

Rose, S. G. H. ., & Janani, S. . (2023). Jamming Detection Technique Using Improved Exponentially Weighted Moving Average (IEWMA) Algorithm for WSN. International Journal of Intelligent Systems and Applications in Engineering, 11(11s), 151–164. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3442



Research Article