Performance Analysis of Burst Traffic Awareness Based Mobile Sink Routing Technique for Wireless Sensor Networks

Yıl 2022, Cilt: 35 Sayı: 2, 506 - 522, 01.06.2022

Sercan Yalçın , Ebubekir Erdem

https://doi.org/10.35378/gujs.863598

Cited By: 1

Öz

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.

Anahtar Kelimeler

Wireless sensor network, Mobile routing, Mobile sinks, Burst traffic

Destekleyen Kurum

Scientific and Technological Research Council of Turkey (TUBITAK)

Proje Numarası

120E379

Teşekkür

This study is supported by the Scientific and Technological Research Council of Turkey (TUBITAK) with project number 120E379.

Kaynakça

• [1] Rawat, P., Singh, K. D., Chaouchi, H., Bonnin, J. M., “Wireless sensor networks: a survey on recent developments and potential synergies”, The Journal of Supercomputing, 68(1): 1-48, (2014).
• [2] Mehrabi, A., Kim, K., “Maximizing data collection throughput on a path in energy harvesting sensor networks using a mobile sink”, IEEE Transactions on Mobile Computing, 15(3): 690-704, (2015).
• [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).
• [5] Shi, J., Wei, X., Zhu, W., “An efficient algorithm for energy management in wireless sensor networks via employing multiple mobile sinks”, International Journal of Distributed Sensor Networks, 12(1): 1-9, (2016).
• [6] Khan, M. I., Gansterer, W. N., Haring, G., “Static vs. mobile sink: The influence of basic parameters on energy efficiency in wireless sensor networks”, Computer communications, 36(9): 965-978, (2013).
• [7] Di Francesco, M., Das, S. K., Anastasi, G., “Data collection in wireless sensor networks with mobile elements: A survey”, ACM Transactions on Sensor Networks (TOSN), 8(1): 1-34, (2011).
• [8] Yarinezhad, R., Hashemi, S. N., “Solving the load balanced clustering and routing problems in WSNs with an fpt-Approximation algorithm and a grid structure”, Pervasive and Mobile Computing, 58: 101033, (2019).
• [9] Mohemed, R. E., Saleh, A. I., Abdelrazzak, M., Smara, A. S., “Energy-efficient routing protocols for solving energy hole problem in wireless sensor networks”, Computer Networks, 114: 51-66, (2017).
• [10] Kim, B. S., Park, H., Kim, K. H., Godfrey, D., Kim, K. I., “A survey on real-time communications in wireless sensor networks”, Wireless Communications and Mobile Computing, 2017: 1-13, (2017).
• [11] Sabor, N., Sasaki, S., Abo-Zahhad, M., Ahmed, S. M., “A comprehensive survey on hierarchical-based routing protocols for mobile wireless sensor networks: review, taxonomy, and future directions”, Wireless Communications and Mobile Computing, 2017: 1-23, (2017).
• [12] Toor, A. S., Jain, A. K., “Energy Aware Cluster Based Multi-hop Energy Efficient Routing Protocol using Multiple Mobile Nodes (MEACBM) in Wireless Sensor Networks”, International Journal of Electronics and Communications (AEÜ), 102: 42-53, (2019).
• [13] Darabkh, K. A., Odetallah, S. M., Alqudah, Z., Khalifeh, A. F., Shurman, M. M., “Energy-Aware and Density-Based Clustering and Relaying Protocol (EA-DB-CRP) for gathering data in wireless sensor networks”, Applied Soft Computing, 80: 154-166, (2019).
• [14] Zhu, C., Shu, L., Hara, T., Wang, L., Nishio, S., Yang, L. T., “A survey on communication and data management issues in mobile sensor networks”, Wireless Communications and Mobile Computing, 14(1): 19-36, (2014).
• [15] Yu, S., Zhang, B., Li, C., Mouftah, H. T., “Routing protocols for wireless sensor networks with mobile sinks: A survey”, IEEE Communications Magazine, 52(7): 150-157, (2014).
• [16] Afsar, M. M., Tayarani-N, M. H., “Clustering in sensor networks: A literature survey”, Journal of Network and Computer Applications, 46: 198-226, (2014).
• [17] Zhang, L., Wan, C., “Dynamic Path Planning Design for Mobile Sink with Burst Traffic in a Region of WSN”, Wireless Communications and Mobile Computing, 2019: 1-8, (2019).
• [18] Naghibi, M., Barati, H., “EGRPM: Energy efficient geographic routing protocol based on mobile sink in wireless sensor networks”, Sustainable Computing: Informatics and Systems, 25: 1-10, (2020).
• [19] Yalçın, S., Erdem, E., “Bacteria Interactive Cost and Balanced-Compromised Approach to Clustering and Transmission Boundary-Range Cognitive Routing in Mobile Heterogeneous Wireless Sensor Networks”, Sensors 2019, 19(4): 1-30, (2019).
• [20] Ahmad, A., Rathore, M. M., Paul, A., Chen, B. W., “Data transmission scheme using mobile sink in static wireless sensor network”, Journal of Sensors, 2015: 1-8, (2015).
• [21] Alsaafin, A., Khedr, A. M., Al Aghbari, Z., “Distributed trajectory design for data gathering using mobile sink in wireless sensor networks”, AEU-International Journal of Electronics and Communications, 96: 1-12, (2018).
• [22] Wang, J., Cao, J., Ji, S., Park, J. H., “Energy-efficient cluster-based dynamic routes adjustment approach for wireless sensor networks with mobile sinks”, The Journal of Supercomputing, 73(7): 3277-3290, (2017).
• [23] Alhasanat, A. I., Matrouk, K. D., Alasha'ary, H. A., Al-Qadi, Z. A., “Connectivity-based data gathering with path-constrained mobile sink in wireless sensor networks”, Wireless Sensor Network, 6(6): 118-128, (2014).
• [24] Salarian, H., Chin, K. W., Naghdy, F., “An energy-efficient mobile-sink path selection strategy for wireless sensor networks”, IEEE Transactions on vehicular technology, 63(5): 2407-2419, (2013).
• [25] Zhu, C., Wu, S., Han, G., Shu, L., Wu, H., “A tree-cluster-based data-gathering algorithm for industrial WSNs with a mobile sink”, IEEE Access, 3: 381-396, (2015).
• [26] Tang, J., Yang, W., Zhu, L., Wang, D., Feng, X., “An adaptive clustering approach based on minimum travel route planning for wireless sensor networks with a mobile sink”, Sensors, 17(5): 1-19, (2017).
• [27] Dash, D., “Approximation algorithm for data gathering from mobile sensors”, Pervasive and Mobile Computing, 46: 34-48, (2018).
• [28] Vancin, S., Erdem, E., “Threshold Balanced Sampled DEEC Model for Heterogeneous Wireless Sensor Network”, Wireless Communications and Mobile Computing 2018: 1–12, (2018).
• [29] Agamy, A.F., Mohammed, A.M., “Performance Modeling of WSN with Bursty Delivery Mode”, Computer Science of Cornell University, 68: 1-12, (2017).
• [30] Braekers, K., Ramaekers, K., Nieuwenhuyse, I. V., “The vehicle routing problem: State of the art classification and review”, Computers and Industrial Engineering, 99: 300–313, (2016).