Multi-objective brown bear optimization algorithm for secure and energy-efficient clustering and routing in IoT-based wireless sensor networks
Shruthi Bennur Mallikarjuna1 and Channakrishna Raju2
Assistant Professor, Department of Computer Science and Engineering,SJCE, JSS Science and Technological University, Mysuru, Karnataka,India1
Professor, Department of Computer Science and Engineering,Sri Siddhartha Institute of Technology, SSAHE, Tumakuru, Karnataka,India2
Professor, Department of Computer Science and Engineering,Sri Siddhartha Institute of Technology, SSAHE, Tumakuru, Karnataka,India2
Corresponding Author : Shruthi Bennur Mallikarjuna
Recieved : 08-September-2024; Revised : 14-July-2025; Accepted : 16-July-2025
Abstract
The internet of things (IoT) refers to a network of interconnected devices, often utilizing wireless communication, which includes sensor nodes (SNs) organized within wireless sensor networks (WSNs) to collect and transmit data. The IoT-WSN approach groups SNs into clusters, with each cluster having a designated cluster head (CH). However, node security and energy efficiency remain significant challenges due to resource constraints, which adversely affect the overall performance and reliability of the network. A multi-objective trust-aware brown bear optimization algorithm (MO-TABBOA) was proposed for cluster head (CH) and route path selection in IoT-WSNs. Node degree, inter-node distance, residual energy, distance between the CH and the base station (BS), and trust are considered as fitness parameters for secure cluster head (SCH) selection. A routing process is then established in MO-TABBOA based on distance and energy factors. Consequently, MO-TABBOA identifies and avoids malicious nodes through a fitness evaluation mechanism that ensures reliable data delivery. For comparative analysis, the existing energy-aware clustering and multi-hop routing protocol with a mobile sink (EACMRP-MS) is utilized to evaluate the effectiveness of the proposed MO-TABBOA against conventional models. The proposed MO-TABBOA achieves superior energy efficiency, consuming only 29.70 mJ of energy to maintain 100 active nodes. This energy consumption is lower than that of the existing EACMRP-MS, thereby demonstrating MO-TABBOA’s superiority over existing approaches.
Keywords
Internet of things (IoT), Wireless sensor networks (WSNs), Cluster head (CH) selection, Trust-aware optimization, Energy efficiency, Routing protocol.
Cite this article
Mallikarjuna SB, Raju C. Multi-objective brown bear optimization algorithm for secure and energy-efficient clustering and routing in IoT-based wireless sensor networks. International Journal of Advanced Technology and Engineering Exploration. 2025;12(128):1056-1074. DOI : 10.19101/IJATEE.2024.111101779
References
[1]
Srivastava A, Paulus R. Elr-c: a multi-objective optimization for joint energy and lifetime aware cluster based routing for WSN assisted IoT. Wireless Personal Communications. 2023; 132(2):979-1006.
[2]
Dogra R, Rani S, Kavita, Shafi J, Kim S, Ijaz MF. ESEERP: enhanced smart energy efficient routing protocol for internet of things in wireless sensor nodes. Sensors. 2022; 22(16):1-15.
[3]
Tumula S, Ramadevi Y, Padmalatha E, Kiran KG, Venu GM, Abualigah L, et al. An opportunistic energy‐efficient dynamic self‐configuration clustering algorithm in WSN‐based IoT networks. International Journal of Communication Systems. 2024; 37(1):1-21.
[4]
Rizwanullah M, Alsolai H, K NM, Aziz AS, Eldesouki MI, Abdelmageed AA. Hybrid muddy soil fish optimization-based energy aware routing in IoT-assisted wireless sensor networks. Sustainability. 2023; 15(10):1-15.
[5]
Sathish KL, Ahmad S, Routray S, Prabu AV, Alharbi A, Alouffi B, et al. Modern energy optimization approach for efficient data communication in IoT‐based wireless sensor networks. Wireless Communications and Mobile Computing. 2022; 2022(1):1-13.
[6]
Yang Y. Adaptive switching and routing protocol design and optimization in internet of things based on probabilistic models. International Journal of Intelligent Networks. 2024; 5:204-11.
[7]
Nagarajan MK, Janakiraman N, Balasubramanian C. A new routing protocol for WSN using limit-based Jaya sail fish optimization-based multi-objective LEACH protocol: an energy-efficient clustering strategy. Wireless Networks. 2022; 28(5):2131-53.
[8]
Awan KM, Sherazi HH, Ali A, Iqbal R, Khan ZA, Mukherjee M. Energy‐aware cluster‐based routing optimization for WSNs in the livestock industry. Transactions on Emerging Telecommunications Technologies. 2022; 33(3):e3816.
[9]
Roberts MK, Thangavel J, Aldawsari H. An improved dual-phased meta-heuristic optimization-based framework for energy efficient cluster-based routing in wireless sensor networks. Alexandria Engineering Journal. 2024; 101:306-17.
[10]
Thangavelu A, Rajendran P. Energy-efficient secure routing for a sustainable heterogeneous IoT network management. Sustainability. 2024; 16(11):1-20.
[11]
Suresh SS, Prabhu V, Parthasarathy V, Senthilkumar G, Gundu V. Intelligent data routing strategy based on federated deep reinforcement learning for IOT-enabled wireless sensor networks. Measurement: Sensors. 2024; 31:1-9.
[12]
Panahi U, Bayılmış C. Enabling secure data transmission for wireless sensor networks based IoT applications. Ain Shams Engineering Journal. 2023; 14(2):1-11.
[13]
Aravind K. Optimized fuzzy logic based energy-efficient geographical data routing in internet of things. IEEE Access. 2024; 12:18913-30.
[14]
Pedditi RB, Debasis K. Energy efficient routing protocol for an IoT-based WSN system to detect forest fires. Applied Sciences. 2023; 13(5):1-22.
[15]
Bhargava D, Prasanalakshmi B, Vaiyapuri T, Alsulami H, Serbaya SH, Rahmani AW. Cuckoo‐ANN based novel energy‐efficient optimization technique for IoT sensor node modelling. Wireless Communications and Mobile Computing. 2022; 2022(1):1-9.
[16]
Dogra R, Rani S, Gianini G. REERP: a region-based energy-efficient routing protocol for IoT wireless sensor networks. Energies. 2023; 16(17):1-16.
[17]
Reddy CS, Narsimha G. Secure optimized routing and data transmission in wireless sensor networks with elliptic curve cryptography. International Journal of Intelligent Engineering & Systems. 2022; 15(4):279-91.
[18]
Dogra R, Rani S, Babbar H, Krah D. Energy‐efficient routing protocol for next‐generation application in the internet of things and wireless sensor networks. Wireless Communications and Mobile Computing. 2022; 2022(1):1-10.
[19]
Arya G, Bagwari A, Chauhan DS. Performance analysis of deep learning-based routing protocol for an efficient data transmission in 5G WSN communication. IEEE Access. 2022; 10:9340-56.
[20]
Yankanaik KC, Munivenkatappa SB. Dual step hybrid routing protocol for network lifetime enhancement in WSN-IoT environment. International Journal of Reconfigurable and Embedded Systems. 2024; 13(2):323-31.
[21]
Singh R, Singh R, Kaur P. Clustering and securing IoT wireless sensor network. International Journal of Computer Science and Mobile Computing. 2022; 11(4):49-60.
[22]
Ramalingam R, Karunanidy D, Balakrishnan A, Rashid M, Dumka A, Afifi A, et al. Ogwo-ch: hybrid opposition-based learning with gray wolf optimization based clustering technique in wireless sensor networks. Electronics. 2022; 11(16):1-23.
[23]
Nathiya N, Rajan C, Geetha K. An energy-efficient cluster routing for internet of things-enabled wireless sensor network using mapdiminution-based training-discovering optimization algorithm. Sādhanā. 2023; 49(1):12.
[24]
Jesi PM, Antony ADV, Rajagopal R, Femila L. Cluster head selection using multi-dilation convolutional neural network optimized with BCMO for IoT networks. IETE Journal of Research. 2024; 70(8):6702-10.
[25]
Kumar L, Kumar P. BITA-based secure and energy-efficient multi-hop routing in IoT-WSN. Cybernetics and Systems. 2023; 54(6):809-35.
[26]
Naveen G, Prathap PJ. Network energy optimization and intelligent routing in WSN applicable for IoT using self‐adaptive coyote optimization algorithm. International Journal of Communication Systems. 2023; 36(9):e5464.
[27]
Rekha, Garg R. K-LionER: meta-heuristic approach for energy efficient cluster based routing for WSN-assisted IoT networks. Cluster Computing. 2024; 27(4):4207-21.
[28]
Subramani N, Perumal SK, Kallimani JS, Ulaganathan S, Bhargava S, Meckanizi S. Controlling energy aware clustering and multihop routing protocol for IoT assisted wireless sensor networks. Concurrency and Computation: Practice and Experience. 2022; 34(21):e7106.
[29]
Lakshmanna K, Subramani N, Alotaibi Y, Alghamdi S, Khalafand OI, Nanda AK. Improved metaheuristic-driven energy-aware cluster-based routing scheme for IoT-assisted wireless sensor networks. Sustainability. 2022; 14(13):1-19.
[30]
Pravin RA, Murugan K, Thiripurasundari C, Christodoss PR, Puviarasi R, Lathif SI. Stochastic cluster head selection model for energy balancing in IoT enabled heterogeneous WSN. Measurement: Sensors. 2024; 35:1-6.
[31]
Srivastava A, Paulus R. Coverage hole aware optimal cluster-based routing for wireless sensor network assisted IoT using hybrid deep recurrent neural network. Concurrency and Computation: Practice and Experience. 2023; 35(6):1-11.
[32]
Cherappa V, Thangarajan T, Meenakshi SSS, Hajjej F, Munusamy AK, et al. Energy-efficient clustering and routing using ASFO and a cross-layer-based expedient routing protocol for wireless sensor networks. Sensors. 2023; 23(5):1-15.
[33]
Abbad L, Nacer A, Abbad H, Brahim MT, Zioui N. A weighted markov-clustering routing protocol for optimizing energy use in wireless sensor networks. Egyptian Informatics Journal. 2022; 23(3):483-97.
[34]
Shah IK, Maity T, Dohare YS, Tyagi D, Rathore D, Yadav DS. ICIC: a dual mode intra-cluster and inter-cluster energy minimization approach for multihop WSN. IEEE Access. 2022; 10:70581-94.
[35]
Ramezanzadeh F, Shokrzadeh H. Efficient routing method for IoT networks using bee colony and hierarchical chain clustering algorithm. E-Prime-Advances in Electrical Engineering, Electronics and Energy. 2024; 7:1-12.
[36]
Asha A, Srivastava AK, Doohan NV, Sharma D, Bist AS, Neware R, et al. An optimized DEEC approach for efficient packet transmission in sensor based IoTs network. Microprocessors and Microsystems. 2023; 96:104714.
[37]
Gorikapudi S, Kondaveeti HK. A novel clustering model via optimized fuzzy C‐means algorithm and sandpiper optimization with cycle crossover process in IoT. Concurrency and Computation: Practice and Experience. 2023; 35(23):e7776.
[38]
Sharma A, Kansal A. Enhanced CH selection and energy efficient routing algorithm for WSN. Microsystem Technologies. 2024: 735-47.
[39]
Qaffas AA. Applying an improved squirrel search algorithm (ISSA) for clustering and low-energy routing in wireless sensor networks (WSNs). Mobile Networks and Applications. 2023:1-6.
[40]
Pandey S, Dubey K, Dubey R, Kumar S. EEDCS: energy efficient data collection schemes for IoT enabled wireless sensor network. Wireless Personal Communications. 2023; 129(2):1297-313.
[41]
Verma V, Jha VK. Secure and energy-aware data transmission for IoT-WSNs with the help of cluster-based secure optimal routing. Wireless Personal Communications. 2024; 134(3):1665-86.
[42]
Khan MA, Shavkatovich SN, Nagpal B, Kumar A, Haq MA, Tharini VJ, et al. Optimizing hybrid metaheuristic algorithm with cluster head to improve performance metrics on the IoT. Theoretical Computer Science. 2022; 927:87-97.
[43]
Malisetti N, Pamula VK. Energy efficient cluster based routing for wireless sensor networks using moth levy adopted artificial electric field algorithm and customized grey wolf optimization algorithm. Microprocessors and Microsystems. 2022; 93:104593.
[44]
Sahayaraj JM, Gunasekaran K, Verma SK, Dhurgadevi M. Energy efficient clustering and sink mobility protocol using improved dingo and boosted beluga whale optimization algorithm for extending network lifetime in WSNs. Sustainable Computing: Informatics and Systems. 2024; 43:101008.
[45]
Sorour SE, Albarrak KM, Abohany AA, Abd EAA. Credit card fraud detection using the brown bear optimization algorithm. Alexandria Engineering Journal. 2024; 104:171-92.