Wireless Sensor Networks (WSNs) consist of a large number of spatially distributed wireless sensor nodes that monitor and collect environmental data, subsequently transmitting it to a central base station for processing and analysis. Efficient and energy-aware data collection remains a critical challenge in WSNs due to the limited power resources of individual sensor nodes. One widely adopted strategy involves the use of a sink node to aggregate data from across the network. Recently, mobile sink approaches have gained significant attention for their potential to reduce energy consumption and improve data collection efficiency. In this thesis, a novel routing strategy is proposed that leverages the Artificial Bee Colony (ABC) algorithm for managing the trajectory and data aggregation process of a mobile sink. The ABC algorithm, inspired by the foraging behavior of honeybees, is applied to dynamically optimize the path of the mobile sink, minimizing communication overhead and balancing energy usage across the network. The proposed approach is designed to reduce the latency in data acquisition and prolong the overall lifetime of the network. Comprehensive simulation experiments were conducted to evaluate the performance of the proposed method against conventional techniques. The results demonstrate that the ABC-based routing method significantly reduces energy consumption and minimizes data reading delays. Consequently, it contributes to enhanced quality of service (QoS) and greater sustainability of the wireless sensor network infrastructure.
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Poyamehr,G. , Fazeli,M. and Moghaddasi,S. (2022). Optimizing Mobile Sink Movement Using Bee Algorithm in Wireless Sensor Networks. Transactions on Machine Intelligence, 5(3), 160-173. doi: 10.47176/TMI.2022.160
MLA
Poyamehr,G. , , Fazeli,M. , and Moghaddasi,S. . "Optimizing Mobile Sink Movement Using Bee Algorithm in Wireless Sensor Networks", Transactions on Machine Intelligence, 5, 3, 2022, 160-173. doi: 10.47176/TMI.2022.160
HARVARD
Poyamehr G., Fazeli M., Moghaddasi S. (2022). 'Optimizing Mobile Sink Movement Using Bee Algorithm in Wireless Sensor Networks', Transactions on Machine Intelligence, 5(3), pp. 160-173. doi: 10.47176/TMI.2022.160
CHICAGO
G. Poyamehr, M. Fazeli and S. Moghaddasi, "Optimizing Mobile Sink Movement Using Bee Algorithm in Wireless Sensor Networks," Transactions on Machine Intelligence, 5 3 (2022): 160-173, doi: 10.47176/TMI.2022.160
VANCOUVER
Poyamehr G., Fazeli M., Moghaddasi S. Optimizing Mobile Sink Movement Using Bee Algorithm in Wireless Sensor Networks. Trans. Mach. Intell., 2022; 5(3): 160-173. doi: 10.47176/TMI.2022.160
