A Routing Optimization Based on Cross-Layer Design for Wireless Multimedia Sensor Networks (WMSNs)

Putra, Emansa Hasri; Hidayat, Risanuri; Widyawan,; Mustika, I Wayan

doi:10.3844/jcssp.2017.572.580

Cited by 2 publications

(3 citation statements)

References 19 publications

(19 reference statements)

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“…In this section, our proposed protocol FL‐AODV based on the fuzzy optimization and cross‐layer information exchange is compared with the earlier works, 7,8,10,13–17 existing in literature in terms of throughput and delay metrics. In addition, we compare our improved intelligent version of AODV with the other existing fuzzy‐based implementation of AODV to demonstrate how our proposed solution is more efficient than the previous related solutions 22–25 .…”

Section: Simulation Analysis and Resultsmentioning

confidence: 99%

“…Moreover, a unique routing methodology is designed by adopting the cross‐layer scheme between transport and network layers for achieving energy efficiency, rate adaptation, and lifetime maximization in unsteady network scenarios 13 . Data routing efficiency based on cross‐layer methods in wireless multimedia sensor networks is estimated for enhanced QoS performance 14 . The body of work in Khan and Ajij 15 deployed cross‐layer communication between physical and network layers for improved link quality in AODV routing scheme.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

“…13 Data routing efficiency based on cross-layer methods in wireless multimedia sensor networks is estimated for enhanced QoS performance. 14 The body of work in Khan and Ajij 15 deployed cross-layer communication between physical and network layers for improved link quality in AODV routing scheme. In our previous work, 16 the adaptive transmission power strategy is considered in AODV routing scheme through cross-layer cooperation between the medium access control (MAC) and physical layers.…”

Section: Literature Surveymentioning

confidence: 99%

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Multivariate fuzzy logic‐based ad hoc on‐demand distance vector protocol under the impact of node mobility in wireless ad hoc networks

Mehta

2022

Int J Communication

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Efficient routing of generated packets through the network with minimal overhead in path discovery and subsequent route maintenance is the fundamental objective required in the modern wireless network operation. Recently, the application of autonomic computational learning techniques for design and optimization of routing protocols in ad hoc networks is substantially gaining the research interest. The commonly deployed soft computing methodology of fuzzy inference system is capable of handling uncertain and imprecise networking information related to the frequently changing states of generic mobile technologies. In this paper, we propose a novel fuzzy logic-based ad hoc on-demand distance vector (FL-AODV) routing protocol employing the multivariate cross-layer design architecture to optimize the multiple performance parameters in wireless ad hoc networks. This fuzzy optimization framework essentially applies the header length from data link and physical layers, route timeout from network layer, and node mobility speed from application layer as inputs to the fuzzification interface. Besides, bit rate for application layer and communication range parameter for data link layer are scrutinized as the fuzzy outputs derived from the defuzzifier. The designed adaptive routing protocol is extensively assessed through simulation experimental analysis under the varying effects of node mobility conditions.Various network performance attributes including the reception cache hit, packet delivery ratio, packet errors, ping loss rate, mean throughput, and delay are computed and analyzed for comprehensive comparison between the presented fuzzy-based FL-AODV and classical AODV routing mechanisms.Finally, we compare our fuzzy routing model with previous algorithms to demonstrate its efficacy in terms of key performance metrics of throughput and delay.

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Section: Simulation Analysis and Resultsmentioning

confidence: 99%

Section: Background and Literature Reviewmentioning

confidence: 99%