Energy Optimised Security against Wormhole Attack in IoT-Based Wireless Sensor Networks

被引:15
作者
Shahid, Hafsa [1 ]
Ashraf, Humaira [1 ]
Javed, Hafsa [1 ]
Humayun, Mamoona [2 ]
Jhanjhi, Nz [3 ]
AlZain, Mohammed A. [4 ]
机构
[1] Int Islamic Univ Islamabad, Dept Comp Sci & Software Engn, Islamabad, Pakistan
[2] Jouf Univ, Coll Comp & Informat Sci, Dept Informat Syst, Al Jouf, Saudi Arabia
[3] Taylors Univ, Sch Comp Sci & Engn SCE, Subang Jaya, Selangor, Malaysia
[4] Taif Univ, Coll Comp & Informat Technol, Dept Informat Technol, At Taif 21944, Saudi Arabia
来源
CMC-COMPUTERS MATERIALS & CONTINUA | 2021年 / 68卷 / 02期
关键词
IoT; Internet of Things; energy; wormhole; WSN; wireless sensor networks; LOCALIZATION;
D O I
10.32604/cmc.2021.015259
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
An IoT-based wireless sensor network (WSN) comprises many small sensors to collect the data and share it with the central repositories. These sensors are battery-driven and resource-restrained devices that consume most of the energy in sensing or collecting the data and transmitting it. During data sharing, security is an important concern in such networks as they are prone to many threats, of which the deadliest is the wormhole attack. These attacks are launched without acquiring the vital information of the network and they highly compromise the communication, security, and performance of the network. In the IoT-based network environment, its mitigation becomes more challenging because of the low resource availability in the sensing devices. We have performed an extensive literature study of the existing techniques against the wormhole attack and categorised them according to their methodology. The analysis of literature has motivated our research. In this paper, we developed the ESWI technique for detecting the wormhole attack while improving the performance and security. This algorithm has been designed to be simple and less complicated to avoid the overheads and the drainage of energy in its operation. The simulation results of our technique show competitive results for the detection rate and packet delivery ratio. It also gives an increased throughput, a decreased end-to-end delay, and a much-reduced consumption of energy.
引用
收藏
页码:1966 / 1980
页数:15
相关论文
共 39 条
[1]   An attack resistant key predistribution scheme for wireless sensor networks [J].
Ahlawat, Priyanka ;
Dave, Mayank .
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES, 2021, 33 (03) :268-280
[2]   Energy Preserving Secure Measure Against Wormhole Attack in Wireless Sensor Networks [J].
Aliady, Wateen A. ;
Al-Ahmadi, Saad A. .
IEEE ACCESS, 2019, 7 :84132-84141
[3]   Detection and Prevention of Wormhole Attack in Wireless Sensor Network using AOMDV protocol [J].
Amish, Parmar ;
Vaghela, V. B. .
PROCEEDINGS OF INTERNATIONAL CONFERENCE ON COMMUNICATION, COMPUTING AND VIRTUALIZATION (ICCCV) 2016, 2016, 79 :700-707
[4]  
[Anonymous], 2015, International Journal of Computer Applications, DOI DOI 10.5120/21565-4589
[5]   Reliability Improvement of Multi-Path Routing for Wireless Sensor Networks and Its Application to Wormhole Attack Avoidance [J].
Arai, Masayuki .
IEEE 12TH INT CONF UBIQUITOUS INTELLIGENCE & COMP/IEEE 12TH INT CONF ADV & TRUSTED COMP/IEEE 15TH INT CONF SCALABLE COMP & COMMUN/IEEE INT CONF CLOUD & BIG DATA COMP/IEEE INT CONF INTERNET PEOPLE AND ASSOCIATED SYMPOSIA/WORKSHOPS, 2015, :533-536
[6]   EDAK: An Efficient Dynamic Authentication and Key Management Mechanism for heterogeneous WSNs [J].
Athmani, Samir ;
Bilami, Azeddine ;
Boubiche, Djallel Eddine .
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2019, 92 :789-799
[7]  
Bhagat S., 2017, P 2016 INT C ICT BUS, P1
[8]   Security of the Internet of Things: Vulnerabilities, Attacks, and Countermeasures [J].
Butun, Ismail ;
Osterberg, Patrik ;
Song, Houbing .
IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 2020, 22 (01) :616-644
[9]  
Dutta Nishigandha, 2019, Advanced Computing and Communication Technologies. Proceedings of the 11th ICACCT 2018. Advances in Intelligent Systems and Computing (AISC 702), P147, DOI 10.1007/978-981-13-0680-8_14
[10]   DFUNet: Convolutional Neural Networks for Diabetic Foot Ulcer Classification [J].
Goyal, Manu ;
Reeves, Neil D. ;
Davison, Adrian K. ;
Rajbhandari, Satyan ;
Spragg, Jennifer ;
Yap, Moi Hoon .
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE, 2020, 4 (05) :728-739