A Novel AEB-AODV Based AADITHYA Cross Layer Design Hibernation Algorithm for Energy Optimization in WSN

Patil, Urmila; Kulkarni, A. V.; Menon, Radhika; Venkatesan, M

doi:10.1007/s11277-020-07929-4

Cited by 5 publications

(5 citation statements)

References 21 publications

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“…( / )* X SNR Ps N A  ---------------( 14) where ng > in grid then de is less. where is a low-density grid.…”

Section: 𝐸𝑏/𝑁0mentioning

confidence: 99%

“…It can also update wireless channels on-the-fly, which is helpful for reliable communication. The QCM2R protocol outperforms multi-REBTAM in terms of network life, reliability, latency end throughput In [14] the author proposed a new routing protocol for energy efficiency, a hibernation method, and a lowelectricity hardware layout. The number of sensor nodes that can be part of a WSN's tree cannot always be the same because WSNs need to adapt to constantly changing environments.…”

Section: Introductionmentioning

confidence: 99%

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A cross-layer analytical model for optimization of performance in wireless sensor networks

Kumar¹,

G²

2023

INDJCSE

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Objectives:To develop an analytical model to improve Qos in WSN. Method: Cross-layer protocol architecture is developed to improve energy consumption, delay, and packet delivery in a wireless sensor network. The Routing protocol to Cross layer Communication (RPTC) protocol was used for routing the packets through cross-layer interactions which helped to improve the QoS analytical results for common collector points (CCP) node selection, routing policies, and energy optimization. In the earlier study, a random routing policy was in place which did not take into account any proper mechanisms. Here, we define a policy for routing that takes into consideration Extended TDMA energy conservation techniques by choosing the shortest path with extended coverage. This will eliminate collisions, idle listening times, and entering inactive states until their allocated time slots. The extended TDMA helps reduce packet delays and guarantees reliable communication. The extension allows nodes with the largest one-hop neighbor to select multiple timeslots for themselves, which are scheduled in the MAC layer. We compared our analytical model based on delay, power consumption, and energy consumed in relation to simulation time. Findings: We found that the cross-layer analytical model achieves 20% less delay, power, and 30% less energy consumption when compared to existing cross-layer protocols such as the Hybrid Energy-Efficient Distributed Protocol (HEED), Co-ordinate Test Optimized Protocol (CTP), and Tree Cluster-Based Data Gathering Algorithm (TCBDGA). Novelty: WSNs need to be optimized across several protocol levels in order to achieve good interactions. QoS parameters are explored for this purpose and a cross-layer protocol called RPTC is analyzed using design parameters and resource levels based on the Poisson variate model (PVM). This helps improve network lifetime.

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“…( / )* X SNR Ps N A  ---------------( 14) where ng > in grid then de is less. where is a low-density grid.…”

Section: 𝐸𝑏/𝑁0mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A cross-layer analytical model for optimization of performance in wireless sensor networks

Kumar¹,

G²

2023

INDJCSE

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“…In [13], the authors use a threefold method to improve the lifetime of the IWSNs by adjusting the duty cycle process of sensor nodes, then switch between the active mode and the sleep mode according to the trust value obtained by the nodes. On the other hand, to better improve the service quality of IWSNs, the paper [14] proposes an AODV routing protocol for surplus energy, which realizes the reduction of energy consumption of IWSNs through cross-layer design. Similar to the paper [18], another cross-layer routing method is also proposed.…”

Section: Related Workmentioning

confidence: 99%

Sensor Duty Cycle for Prolonging Network Lifetime Using Quantum Clone Grey Wolf Optimization Algorithm in Industrial Wireless Sensor Networks

Liu

Xiao

et al. 2021

Journal of Sensors

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The application of industrial wireless sensor networks (IWSNs) frequently appears in modern industry, and it is usually to deploy a large quantity of sensor nodes in the monitoring area. This way of deployment improves the robustness of the IWSNs but introduces many redundant nodes, thereby increasing unnecessary overhead. The purpose of this paper is to increase the lifetime of IWSNs without changing the physical facilities and ensuring the coverage of sensors as much as possible. Therefore, we propose a quantum clone grey wolf optimization (QCGWO) algorithm, design a sensor duty cycle model (SDCM) based on real factory conditions, and use the QCGWO to optimize the SDCM. Specifically, QCGWO combines the concept of quantum computing and the clone operation for avoiding the algorithm from falling into a local optimum. Subsequently, we compare the proposed algorithm with the genetic algorithm (GA) and simulated annealing (SA) algorithm. The experimental results suggest that the lifetime of the IWSNs based on QCGWO is longer than that of GA and SA, and the convergence speed of QCGWO is also faster than that of GA and SA. In comparison with the traditional IWSN working mode, our model and algorithm can effectively prolong the lifetime of IWSNs, thus greatly reducing the maintenance cost without replacing sensor nodes in actual industrial production.

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“…In the literature, WSNs use routing protocols with cross-layer interaction to exchange information over wireless sensor nodes to find the path from source to destination. Urmila et al [9] proposed a cross-layer design with fewer intermediate sensor nodes and keep others in the sleep mode to maximize the lifetime of sensor nodes and to manage sensor node energy efficiently.…”

Section: Literature Reviewmentioning

confidence: 99%

“…These cross-layer interactions provide an exchange information to control the dynamic environment and will allow improvement of network performance. [9] In this research, an interaction of the physical layer with the network layer is used with different parameters that result in a new physical-network cross-layer design, the new physicalnetwork cross-layer design is elaborated to implement the proposed WSNs routing protocol that will effect and improve network performance. The remainder of this research will be as follows.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Wireless Sensor Networks Routing Protocols based on Cross Layer Interaction

Jasim¹,

Ismael²,

Solaimanzadeh³

2021

Cihan U Erbil SCI J

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Wireless sensor networks aim to develop a smart city based on sensing environment. The routing protocols of wireless sensor networks is important to transfer the data in smart cities since sensor nodes have limited power and transmission range. The aim of this research is to enhance wireless sensor networks routing protocols based on proposed cross-layer interaction between physical layer and network layer also a proposed routing table information of wireless sensor nodes is developed to consider the transmission power of neighbor’s nodes to determine the next hop. Cross-layer interaction provides a useful information and effective adaptation for WSN routing protocols. As a result, the proposed routing protocol shows an improvement in network performance when number of intermediate nodes are minimized.

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A Novel AEB-AODV Based AADITHYA Cross Layer Design Hibernation Algorithm for Energy Optimization in WSN

Cited by 5 publications

References 21 publications

A cross-layer analytical model for optimization of performance in wireless sensor networks

A cross-layer analytical model for optimization of performance in wireless sensor networks

Sensor Duty Cycle for Prolonging Network Lifetime Using Quantum Clone Grey Wolf Optimization Algorithm in Industrial Wireless Sensor Networks

Enhancing Wireless Sensor Networks Routing Protocols based on Cross Layer Interaction

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