Opportunistic Routing Based on Partial CSI in MIMO Random Ad-hoc Networks

Richter, Yiftach; Bergel, Itsik

doi:10.21203/rs.3.rs-72282/v1

Cited by 1 publication

(16 citation statements)

References 33 publications

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“…Because of transmission process, the energy level of sensor nodes gets reduced. Consequently, the energy efficiency of the SRTRBC method using 100 sensor nodes is 96.1%, while the energy efficiency of the NoC Architecture [1] and Opportunistic Routing [2] is observed as 88.5% and 90.2%, respectively. Likewise, different energy efficiency results are obtained by applying different number of sensor nodes.…”

Section: Simulation Settings and Results Discussionmentioning

confidence: 99%

“…e proposed and existing method performance is determined with help of ten runs. e performance analysis of the proposed SRTRBC method and existing methods namely network-on-chip (NoC) architecture [1] and opportunistic routing [2] are explained with five different metrics, namely energy efficiency, data delivery rate, data loss rate, throughput, and latency.…”

Section: Simulation Settings and Results Discussionmentioning

confidence: 99%

“…Table 2 describes the energy efficiency performance results with respect to number of sensor nodes. Analyze the residual energy by using softmax regression ( 4) if (SRA < 0.5), then (5) SN i is energy-efficient node ( 6) else (7) SN i is nonenergy efficient node (8) end if (9) end for (10) Create the route paths between source and destination node (11) sn sends Req R to sink (12) sn send Rep R to source (13) Construct the multiple routes 'r 1 , r 2 and r 3 ' ( 14) for each constructed route path (15) Construct 'n' weak learners (16) Measure the Tanimoto similarity correlation (17) Classifies route path into two different classes (18) Combines all weak learners y i (19) Assign similar weights to the weak classifier ( 20 As shown in Table 2, the three different methods of energy efficiency are SRTRBC method, NoC Architecture [1] and Opportunistic Routing [2]. Among these three different methods, the SRTRBC method outperforms well through increasing the energy efficiency during the data transmission.…”

Section: Simulation Settings and Results Discussionmentioning

confidence: 99%

“…e y-axis denotes the energy efficiency of the sensor nodes. In graphical representation, the blue colour bar denotes the energy efficiency of NoC Architecture [1] and the red colour bar represents the energy efficiency of Opportunistic Routing [2]. e green colour bar symbolizes the energy efficiency of SRTRBC method.…”

Section: Simulation Settings and Results Discussionmentioning

confidence: 99%

“…However, energy efficiency was not improved by networkon-chip (NoC) architecture. An opportunistic routing was carried out in [2] for MIMO in wireless ad-hoc networks (WANETs). But, the latency was not reduced by opportunistic routing.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A Machine Learning-Based Intelligence Approach for Multiple-Input/Multiple-Output Routing in Wireless Sensor Networks

Sridhar¹,

Rao²,

Kumar

et al. 2022

Mathematical Problems in Engineering

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Computational intelligence methods play an important role for supporting smart networks operations, optimization, and management. In wireless sensor networks (WSNs), increasing the number of nodes has a need for transferring large volume of data to remote nodes without any loss. These large amounts of data transmission might lead to exceeding the capacity of WSNs, which results in congestion, latency, and packet loss. Congestion in WSNs not only results in information loss but also burns a significant amount of energy. To tackle this issue, a practical computational intelligence approach for optimizing data transmission while decreasing latency is necessary. In this article, a Softmax-Regressed-Tanimoto-Reweight-Boost-Classification- (SRTRBC-) based machine learning technique is proposed for effective routing in WSNs. It can route packets around busy locations by selecting nodes with higher energy and lower load. The proposed SRTRBC technique is composed of two steps: route path construction and congestion-aware MIMO routing. Prior to constructing the route path, the residual energy of the node is determined. After that, the residual energy level is analyzed using softmax regression to determine whether or not the node is energy efficient. The energy-efficient nodes are located, and numerous paths between the source and sink nodes are established using route request and route reply. Following that, the SRTRBC technique is used for congestion-aware routing based on buffer space and bandwidth capability. The path that requires the least buffer space and has the highest bandwidth capacity is picked as the optimal route path among multiple paths. Finally, congestion-aware data transmission is used to minimize latency and data loss along the route path. The simulation considers a variety of performance metrics, including energy consumption, data delivery rate, data loss rate, throughput, and delay, in relation to the amount of data packets and sensor nodes.

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Section: Simulation Settings and Results Discussionmentioning

confidence: 99%

Section: Simulation Settings and Results Discussionmentioning

confidence: 99%