An Auction-based Cluster Head Selection Approach for Real Wireless Sensor Networks
محورهای موضوعی : Majlesi Journal of Telecommunication Devices
Sepideh Adabi
1
(Islamic Azad University, North Tehran Branch)
Nazanin Hamzejunushi
2
(Islamic Azad University, North Tehran Branch)
Sahar Adabi
3
(Islamic Azad University, North Tehran Branch)
کلید واژه: Auction, Wireless Sensor Network, Energy Management, Mobile Gateway, Cluster Head,
چکیده مقاله :
In this paper, a hierarchical routing approach based on network clustering and using mobile sinks is proposed in WSN. The first, second, and third levels of hierarchy are composed of sensors, cluster heads (CHs) and mobile sinks (gateways), respectively. The most important challenges in the second level of hierarchy are: 1) election of the most suitable node as CH, and 2) reduction of communication overhead of CH election algorithm. Mobile gateway uses different data transfer technologies (e.g. SMS, WiFi, and 3G) and each communication technology has different characteristics in terms of cost, energy consumption pattern, etc. However, the characteristics of available mobile gateway(s) are ignored in designing CH election algorithm in previous studies. Designing CH election algorithm without considering the characteristics of gateways may lead to problems such as increasing data transfer costs and network fragmentation. Thus, unlike previous studies, a new fitness function is designed with respect to local fitness value of a sensor and fitness value of its available mobile sink(s). In addition, an auction-based method is adopted to control communication overhead of CH election algorithm. The performance of the proposed approach in name DACMS is evaluated in OPNET 14.5 simulation platform. The simulation results show that DACMS outperforms MECA.
[1] Ghosh, N., Baberjee, I., and Simon Sherratt, R. “On-demand Fuzzy Clustering and Ant-colony Optimization based Mobile Data Collection in Wireless Sensor Network”. (2019), Wireless Networks, vol. 25, pp. 1829-1845.
[2] Stephan, Th., Al-Turjman, F., Suresh Joseph, K., Balusamy, B., and Srivastava, S. “Artificial Intelligence Inspired Energy and Spectrum aware Cluster based Routing Protocol for Cognitive Radio Sensor Networks”. (2020), Journals of parallel and Distributed Computing, vol. 142, pp.90-105.
[3] Adabi, S., Abdolkarimi, M., Sharifi, A. “A New Multi-Objective Distributed Fuzzy Clustering Algorithm for Wireless Sensor Networks with Mobile Gateways”. (2018), AEU-International Journal of Electronics and Communications, vol. 89, pp. 92-104.
[4] Khodashahi, M.H., Tashtarian, F., Yaghmaee Moghaddam, M.H., and Tolou Honary, M. “Optimal Location for Mobile Sink in Wireless Sensor Networks”. (2010), 2010 Wireless Communications and Networking Conference, Sydney, NSW, pp.1-6.
[5] Fanian, F., and Kuchaki Rafsanjani, M. “A New Fuzzy Multi-hop Clustering Protocol with Automatic Rule Tuning for Wireless Sensor Networks”. (2020), Applied Soft Computing Journal, vol. 89, pp. 106-115.
[6] Singh Mehra, P., Najmud Doja, M., and Alam, B. “Fuzzy-based Enhanced Cluster Head Selection (FBECS) for WSN”. (2020), Journal of King Saud University- Science, vol. 32, no. 1, pp. 390-401.
[7] Ghosal, A., Halder, S., Das, S.K. “Distributed On-Demand Clustering Algorithm for Lifetime Optimization in Wireless Sensor Networks”. (2020), Journal of Parallel and Distributed Computing, vol. 141, pp. 129-142.
[8] Parvin Renold, P., and Balaji Ganesh, A. “Energy Efficient Secure Data Collection with Path- constrained Mobile Sink in Duty-cycled Unattended Wireless Sensor Network”. (2019), Pervasive and Mobile Computing, vol. 55, pp. 1-12.
[9] Gharaei, N., Abu bakar, K., Zaiton Mohd Hashim, S., and Hosseingholi Pourasl, A. “Inter- and Intra-Cluster Movement of Mobile Sink Algorithms for Cluster-based Networks to Enhance the Network Lifetime”. (2019), Ad Hoc Networks, vol. 85, pp. 60-70.
[10] Wang, J., Yin, Y., Zhang, J., Lee, S., and Sherratt, R.S. “Mobility based Energy Efficient and Multi-Sink Algorithms for Consumer Nome networks”. (2013), IEEE Transactions on Consumer Electronics, vol. 59, no. 1, pp. 77-84.
[11] Gao, S., Zhang, H., and Das, S.K. “Efficient Data Collection in Wireless Sensor Networks with Path-Constrained Mobile Sinks”. (2011), IEEE Transaction on Mobile Computing, vol. 10, no.5,pp.592-608.
[12] Marta, M., and Cardei, M. “Improved Sensor Network Lifetime with Multiple Mobile Sinks”. (2009), Pervasive and Mobile Computing, vol. 5, pp. 542-555.
[13] Elshrkawey, M., Elsherif, S.M., Elsayed Wahed, M. “An Enhancement Approach for Reducing the Energy Consumption in Wireless Sensor Networks”. (2018), Journal of King Saud University-Computer and Information Sciences. vol. 30, no. 2, pp. 259-267.
[14] Alazab, M., Lakshmanna, K., Reddy G., T., Pham, Q., Kumar R.M., P. “Multi-oblective Cluster Head Selection Using Fitness Averaged Rider Optimization Algorithm for IoT Networks in Smart Cities”, (2021), Sustain. Energy Technol. Assess. vol. 43, 100973.
[15] E lmonser, M., Ben Chikha, H., Attia, R. “Mobile Routing Algorithm with Dynamic Clustering for Energy Large-scale Wireless Sensor Networks”, (2020), IET Wireless Sensor Systems. vol. 10, no. 5, pp. 208-213.
[16] Han, G., Chao, J., Zhang, Ch., Shu, L.,Li, Q. “The Impacts of Mobility Models on DV-hop based Localization in Mobile Wireless Sensor Networks”, (2014), Journal of Network and Computer Applications, vol. 42, pp. 70-79.
[17] Mersini, P., Sakkopoulos, E., Sourla, E., and Tsakalidis, A. “Health Internet of Things: Metrics and methods for efficient data transfer”. (2013), Simulation Modeling Practice and Theory, vol. 34, pp. 186-199.
[18] Kim, B., Cho, Y., and Hong, J. “AWNIS: Energy-Efficient Adaptive Wireless Network Interface Selection for Industrial Mobile Devices”. (2014), IEEE Transactions on Industrial Informatics, pp. 714-729.
[19] Dai, Sh., Chen, Ch., Tang, Ch., and Qiao, Sh. “Light-Weight Target Tracking in Dense Wireless Sensor Networks”, (2009), Fifth International Conference on Mobile Ad-hoc and Sensor Networks, pp. 480-487.
