Improving the performance of opportunistic routing using min-max range and optimum energy level for relay node selection in wireless sensor networks

Abstract

Opportunistic routing is an emerging routing technology that was proposed to overcome the drawback of unreliable transmission, especially in Wireless Sensor Networks (WSNs). Over the years, many forwarder methods were proposed to improve the performance in opportunistic routing. However, based on existing works, the findings have shown that there is still room for improvement in this domain, especially in the aspects of latency, network lifetime, and packet delivery ratio. In this work, a new relay node selection method was proposed. The proposed method used the minimum or maximum range and optimum energy level to select the best relay node to forward packets to improve the performance in opportunistic routing. OMNeT++ and MiXiM framework were used to simulate and evaluate the proposed method. The simulation settings were adopted based on the benchmark scheme. The evaluation results showed that our proposed method outperforms in the aspect of latency, network lifetime, and packet delivery ratio as compared to the benchmark scheme.

Hybrid state analysis with improved firefly optimized linear congestion models of WSNs for DDOS & CRA attacks

“…With network participation of all the nodes for data, transmission needs a large amount of power that can resolve by node optimization. Basha & Shankar, 2021 , Yee et al, 2020 ; Sarkar & Murugan, 2019 , Yee et al, 2020 .…”

“…As part of this article, we introduce Lucid, a practical and lightweight deep learning DDoS detection system that uses the characteristics of convolutional neural networks (CNNs) to categorize traffic flows as either malicious or benign. The four significant contributions are now: (1) a creative framework of a CNN to pinpoint DDoS traffic with limited computational overhead; (2) a dataset-agnostic data preparation method for producing traffic predictions for web security attacks; (3) a stimulation evaluation to illustrate Lucid’s DDoS identification; and (4) an accurate understanding of the alternative model on a resource-constrained computing system ( Yee et al., 2020 ). Lucid can match the current state-of-the-art detection accuracy using the most recent datasets while exhibiting a 40x decrease in computing time compared to the state-of-the-art accuracy rate.…”

An Efficient Opportunistic Routing Protocol with Low Latency for Farm Wireless Sensor Networks

“…Adjustable and non-adjustable transmission energy models were introduced to calculate the routing cost and selected the optimal candidate sets. Many studies select candidate sets by simultaneously measuring the impact of network energy and transmission distance [18][19][20]. Bangotra et al adopted Bayesian machine learning to model the candidate nodes selection process as a supervised multiclass non-linear separable problem [18].…”

“…Bangotra et al adopted Bayesian machine learning to model the candidate nodes selection process as a supervised multiclass non-linear separable problem [18]. Yee et al further proposed the energy threshold, combined with the maximum and minimum communication range to select the relay node with higher residual energy at different distances [19]. Additionally, an energy-saving opportunity routing protocol based on area density, relative distance, and residual energy was studied to balance energy consumption [21][22][23].…”

Performance Modeling Analysis of D-MSMR-CARQ with Relay Selection in Wireless Sensor Networks

“…CC technology forwards the data packet from the source node through the relay node; thus, the communication link of users can be strengthened and CARQ technology can simultaneously reduce the packet error rate and improve the system throughput. erefore, Zhou et al [23] proposed a multisource single-relay CARQ protocol, but the single relay cannot meet the signal coverage required by current users [24,25]. Li et al [26] studied the single-source multirelay CARQ protocol and analyzed its throughput and delay performance by establishing the DTMC model.…”