Comparative Study of Proactive and Reactive Routing Protocols in Wireless Grids

Das, Debashreet; Tripathy, C. R.; Kabat, Manas Ranjan

doi:10.1109/cesys.2018.8723914

Cited by 1 publication

(1 citation statement)

References 5 publications

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“…Examples DSDV [7], OLSR [8], STAR [9], WRP [10] AODV [8], DSR [9] ZRP [11], TORA [12] In Table 1, ZRP is the most popular hybrid routing protocol in WMN by implementing zone-based routing protocols and utilizing the benefit of both reactive and proactive protocol mechanisms [13]. ZRP as a routing protocol is considered one of the potential methods to implement in the next-generation wireless network to maintain the network's link stability.…”

Section: More Complexmentioning

confidence: 99%

Determining Optimal Zone Radius of Zone Routing Protocol Based on Deep Recurrent Neural Networks in the Next Generation Wireless Backhaul Networks

Sirait¹,

Jusoh²,

Dimyati³

et al. 2022

Int. J. Adv. Sci. Eng. Inf. Technol.

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Next-generation wireless networks are becoming more popular and rely on reliable backhaul networks to work properly. Wireless backhaul networks also adopt various innovative technologies to improve capacity and provide more flexible deployments to meet networks' high-quality requirements. One of the essential innovations to maintain the wireless backhaul performance is combining the existing routing protocol technology and the deep learning concept. The concept of deep learning is gaining traction as a powerful way to add intelligence to wireless networks with complex topologies and radio environments. This is because conventional routing protocols do not learn from their previous experiences with various network anomalies. This paper proposed a predictive model of zone radius value using the deep recurrent neural network variant, namely the long short-term memory recurrent neural network (LSTM-RNN) algorithm. Determination of zone radius value conducted by measuring the whole of nodes routing zone using various network performance as input parameters such as Routing Overhead, Energy Consumption, Throughput, and User Usage. Performance measurements such as mean square error (MSE), error distribution histogram, training state, regression, correlation, and time series response are gauged and compared for static and mobile node environments. Results showed that the proposed algorithm can accurately predict zone radius for both environments. However, the accuracy of the proposed algorithm is higher when implemented in a static node environment.

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