Energy and load balancing scheme based path tracing for WSN networks

Abstract

Wireless sensor networks (WSN) groups specialized transducers that provide sensing services to Internet of Things (IoT) devices with limited energy and storage resources. Since replacement or recharging of batteries in sensor nodes is nearly impossible, power consumption becomes one among the crucial design issues in WSN. Most sensor networks employ dynamic routing protocols in order that the routing topology are often dynamically optimized with environmental changes. The routing behaviours are often quite complex with increasing network scale and environmental dynamics. Knowledge on the routing path of each packet is certainly a great help in understanding the complex routing behaviours, allowing effective performance diagnosis and efficient network management. We propose PAT, a universal SensorNet path tracing approach. PAT includes an intelligent path encoding scheme that allows efficient decoding at the base station side. To make PAT more scalable, we propose techniques to accurately estimate the degree information by exploiting timing information, allowing more compact path encoding. Moreover, we employ subpath concatenation to infer excessively long paths with a high re covery probability. We propose an analytical model to quantify the advantages of PAT with varying network scale, network density, routing dynamics and packet delivery performance. Simulation analysis shows the modified version performs better than the existing protocol by enhancing the throughput, endto-end delay, and residual energy .

Keywords

Wireless sensor networks; Internet of things; Recharging of batteries; SensorNet

References

1. Zhang H, Shen H. Balancing Energy Consumption To Maximize Network Lifetime In Data-Gathering Sensor Networks. IEEE Trans. Parall el and Distributed Systems 2009;20(10):1526-39.
2. AlShawi IS, Yan L, Pan W, Luo B. Lifetime Enhancement In W ireless Sensor Networks Using Fuzzy Approach And A - Star Algorithm. IEEE Sensors J 2012;12:3010-3018.
3. Cassandras C, Wang T, Pourazarm S. Optimal Routing And Energy Allocation For Lifetime Maximization Of Wireless Sensor Networks With Nonideal Batteries. IEEE Tr ans. Control of Network Systems 2014;1(1):86-98.
4. Habibi, J, Aghdam, AG, Ghrayeb A. A Framework For Evaluating The Best Achievable Performance By Distributed Lifetime-Efficient Routing Schem es In Wireless Sensor Networks. IEEE Transactions on Wireless C ommunications 2015;14:3231-46.
5. Cheng P, Qi Y, Xin K, Chen J, Xie L. Energy-Efficient Data Forwarding For State Estimation In Multi -Hop Wireless Sensor Networks . IEEE Trans. Autom. Control 2016;61:1322-27.
6. Kurt S, Yildiz HU, Yigit M, Tavli B, Gungor VC. Packet Size Optimization In Wireless Sensor Networ ks For Smart Grid Applications, IEEE Trans Ind Electron. 2017;64:2392-1.
7. Liu T, Gu T, Jin N, Zhu Y. A Mixed Transmission Strategy To Achieve Energy Balancing In Wireless Sensor Networks,” IEEE Trans. Wireless Commun. 2017;16:2111-22.
8. Nayak P, Vathasavai B. E nergy Efficient Clustering Algorithm For Multi -Hop Wireless Sensor N etwork Using Type -2 Fuzzy Logic. IEEE Sensors J 2017;17:4492-99.
9. Pandey OJ, Mahajan A, Hegde RM. Joint Localization and Data Gathering Over Small World WsnWith Optimal Data Mule Allocation. IEEE Transactions on Vehicular Technology 2018;67:6518-32.
10. Qianao D , Rongbo Z, Hao L, Maode M. An Overview of Machine Learning -Based Energy-Efficient Routing Algorith ms in Wireless Sensor Networks. Electronics 2021;10(13):1539.