1Assistant Professor, Department of Computer Science, VET Institute of Arts and Science (co-education) College, Thindal, Erode, Tamilnadu, India
2Associate Professor, Department of Computer Science, Gobi Arts and Science College (Autonomous), Gobichettipalayam, Tamilnadu, India
3Assistant Professor, Department of Computer Science, Gobi Arts and Science College (Autonomous), Gobichettipalayam, Tamilnadu, India
Background: Efficient routing in dense Wireless Sensor Networks (WSNs) is severely affected by packet collisions, repeated retransmissions, and unbalanced energy depletion. To address these shortcomings, the paper proposes a Data Mining-Assisted Collision-Aware Routing System using Multi-Objective Dolphin Optimization (MODO-CAR), in which routing choices are made from acquired knowledge of traffic and congestion.
Methodology: Spatio-temporal data mining is used in the analysis of network telemetry to yield collision probability maps, traffic density indicators, queue congestion levels, and node association strengths. These data-driven attributes are directly passed to the routing optimization algorithm in a bid to prevent collision-prone areas proactively. The routing problem is formulated as the multi-objective optimization, which concurrently reduces the energy consumption, probability of collision, end-to-end delay, and routing overhead. Multi-Objective Dolphin Optimization is a multi-objective adaptive search algorithm that takes advantage of echolocation-based adaptive search and incorporates collision risk penalties based on congestion patterns mined. This search based on knowledge will send candidate paths off of congested links and minimize retransmissions that are not needed at the Medium Access Control layer.
Results: Sufficient simulations have shown that the proposed MODO-CAR framework attains minimal average energy usage of less than 0.139 J per successful data transfer, and it lowers the rate of packet collision as well as routing overhead, by a great margin as compared to the traditional collision-conscious and swarm-based routing schemes.
Conclusion: The findings affirm that incorporation of data mining-based knowledge in the routing optimization exercise based on multi-objectives can enhance energy efficiency, stability in routing and network lifetime significantly and thus the model is appropriate in massive and dense deployment of WSN.
Keywords: Data mining, collision-aware routing, wireless sensor networks, multi-objective optimization, dolphin optimization algorithm, congestion prediction, energy efficiency, packet collision mitigation, spatio-temporal analysis, routing optimization, network lifetime enhancement, telemetry-driven decision making
How to cite this article: Sangeetha S, Srinivasan B, Prabhusundhar P. Data Mining–Assisted Collision-Aware Routing in Wireless Sensor Networks Using Multi-Objective Dolphin Optimization. Int J Drug Deliv Technol. 2026;16(13s): 1009-1022. DOI: 10.25258/ijddt.16.13s.112.
Source of support: Nil.
Conflict of interest: None