Performance Analysis of Burst Traffic Awareness Based Mobile Sink Routing Technique for Wireless Sensor Networks
Year 2022,
, 506 - 522, 01.06.2022
Sercan Yalçın
,
Ebubekir Erdem
Abstract
In wireless sensor networks (WSNs), it is vital to adopt a suitable mobile routing algorithm between sensor nodes and mobile sinks (MSs) for data gathering efficiently. In WSNs, random mobility of the MSs increases the mobile path length in the network when data traffic bursts. Therefore, the focus of this study is to overcome burst traffic in an energy-efficient way using the MSs in the network. In this study, a new burst traffic awareness adaptive mobile routing scheme based on heterogeneous WSNs has been developed. The network area is divided into two cluster groups in the proposed scheme, each with a certain number of clusters. In the network, a MS of each cluster group acts. The MSs gather all data in a single-hop attitude as soon as they arrive at the clusters. In this way, the energy load is distributed evenly among the network. Once a burst data is detected in the routing model, a MS updates its trajectory to the cluster head (CH) where the burst occurs. The performance results validate that the proposed methodology outperforms recent studies based on the network lifetime, average energy consumption, and average mobile path length. Also, the effect of the burst traffic situations on network efficiency is analyzed with simulation.
Supporting Institution
Scientific and Technological Research Council of Turkey (TUBITAK)
Thanks
This study is supported by the Scientific and Technological Research Council of Turkey (TUBITAK) with project number 120E379.
References
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Year 2022,
, 506 - 522, 01.06.2022
Sercan Yalçın
,
Ebubekir Erdem
References
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- [3] Vancin, S., Erdem, E., “Implementation of the vehicle recognition systems using wireless magnetic sensors”, Sadhana Springer, Indian Academy of Sciences, 42 (6): 841-854, (2017).
- [4] Khan, R. A., Pathan, A. S. K., “The state-of-the-art wireless body area sensor networks: A survey”, International Journal of Distributed Sensor Networks, 14(4): 1-23, (2018).
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