Multi-Collector Control for Workload Balancing in Wireless Sensor and Actuator Networks
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The data gathering delay and the network lifetime are important indicators to measure the service quality of wireless sensor and actuator networks (WSANs). This study proposes a dynamically cluster head (CH) selection strategy and automatic scheduling scheme of collectors for prolonging the network lifetime and shorting data gathering delay in WSAN. First the monitoring region is equally divided into several subregions and each subregion dynamically selects a sensor node as CH. These can balance the energy consumption of sensor node thereby prolonging the network lifetime. Then a task allocation method based on genetic algorithm is proposed to uniformly assign tasks to actuators. Finally the trajectory of each actuator is optimized by ant colony optimization algorithm. Simulations are conducted to evaluate the effectiveness of the proposed method and the results show that the method performs better to extend network lifetime while also reducing data delay.
Abstract
The data gathering delay and the network lifetime are important indicators to measure the service quality of wireless sensor and actuator networks (WSANs). This study proposes a dynamically cluster head (CH) selection strategy and automatic scheduling scheme of collectors for prolonging the network lifetime and shorting data gathering delay in WSAN. First the monitoring region is equally divided into several subregions and each subregion dynamically selects a sensor node as CH. These can balance the energy consumption of sensor node thereby prolonging the network lifetime. Then a task allocation method based on genetic algorithm is proposed to uniformly assign tasks to actuators. Finally the trajectory of each actuator is optimized by ant colony optimization algorithm. Simulations are conducted to evaluate the effectiveness of the proposed method and the results show that the method performs better to extend network lifetime while also reducing data delay.
참고문헌 (9)
- N. Primeau, "A Review of Computational Intelligence Techniques in Wireless Sensor and Actuator Networks", IEEE Communications Surveys Tutorials, 20(4), pp.2822~2854 (2018)
- David Jea, "Multiple Controlled Mobile Elements(data mules)for data Collection in Sensor Networks", International Conference on Distributed Computing in Sensor Systems, pp.244~257 (2005)
- J. Faigl, "Autonomous data Collection Using a Self-organizing map", IEEE Transactions on Neural Networks and Learning Systems, 29(5), pp.1703~1715 (2018)
- Abdul Waheed Khan, "VGDRA : a Virtual Grid-based Dynamic Routes Adjustment Scheme for Mobile Sink-based Wireless Sensor Networks", IEEE Sensors Journal, 15(1), pp.526~534 (2014)
- M. Abo-Zahhad, "Mobile Sink-based Adaptive Immune Energy-efficient Clustering Protocol for Improving the Lifetime and Stability Period of Wireless Sensor Networks", IEEE Sensors Journal,, 15(8), pp.4576~4586 (2015)
- Olayinka Ogundile, "A Survey on an Energy-Efficient and Energy-Balanced Routing Protocol for Wireless Sensor Networks", Sensors, 17(5), pp.1084~ (2017), MDPI AG[DOI]
- M. Zhao, "Mobile data Gathering with load Balanced Clustering and Dual data Uploading in Wireless Sensor Networks", IEEE Transactions on Mobile Computing, 14, pp.770~785 (2015)
- S. N. Sivanandam, "Genetic Algorithm Optimization Problems", pp.165~209 (2008), Springer Berlin Heidelberg
- Thomas Stutzle, "ACO Algorithms for the Traveling Salesman Problem", Evolutionary algorithms in engineering and computer science, 4, pp.163~183 (1999)