A distributed, multi‐hop, adaptive, tree‐based energy‐balanced routing approach

Arora, Vishal; Sharma, Vishal; Sachdeva, Monika

doi:10.1002/dac.3949

Cited by 19 publications

(12 citation statements)

References 25 publications

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“…Experimental Parameters. In order to verify the feasibility and effectiveness of the proposed MHRA-PT algorithm, it is compared with three algorithms IMHT-LEACH [14], DMA-TEB [15], and CEED [19] in terms of cluster head energy consumption, network energy balance, and network node survival time through MATLAB simulation platform. Assume that N sensor nodes are randomly deployed in a monitoring area with an area of S M , and base station is located at the center of the monitoring area (Figure 8).…”

Section: Simulation Results and Analysismentioning

confidence: 99%

“…However, this algorithm fastens the structure of WSNs and may reduce the diversity of cluster head alternative paths. Arora et al proposed a distributed multihop routing approach (DMATEB) to tackle this problem [15]. The DMATEB algorithm marks the cluster head that is closer to the base station than the current cluster head as neighbor cluster heads and then selects the cluster head with the largest remaining energy in the neighbor cluster heads as the next hop, which effectively reduces the network energy consumption.…”

Section: Related Workmentioning

confidence: 99%

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Tree‐Based Multihop Routing Method for Energy Efficiency of Wireless Sensor Networks

et al. 2021

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In wireless sensor networks (WSNs), due to the limited energy of sensor nodes, how to design efficient hierarchical routing algorithms to balance network resources and extend network life is an important problem to be solved. Aiming at the problems such as random selection of cluster head, redundancy of working node, and construction of cluster head transmission path, which affect network energy consumption, this paper proposes a multihop routing algorithm based on path tree (MHRA-PT) to optimize the network energy. Firstly, some nodes are those close to the base station and have large remaining energy which are selected to construct a cluster head set. Then, after clustering, each cluster is divided into different regions, and in each region, nodes with residual energy greater than the average residual energy of the cluster are selected as a working node. Finally, the cluster heads are sorted according to their distance from base station, and the next hop node is selected for each cluster head in turn until a path tree rooted at base station is formed completely, leading to data transmission from working node to base station. Simulation results show that the proposed algorithm can effectively reduce network energy consumption, balance network resources, and prolong network life cycle.

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Section: Simulation Results and Analysismentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Tree‐Based Multihop Routing Method for Energy Efficiency of Wireless Sensor Networks

et al. 2021

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“…However, a single packet loss at any level of the tree results in information loss for the whole subtree. 43,44 A grid-based data aggregation scheme (GBDAS) divides the experimental area into various cells, where each cell has a sensor node, probably the one with the maximum residual energy capable of fusing neighbor nodes data in addition to its own. 45 A Kalman filter-based approach was used to detect and rectify outliers, ie, noisy data, generated by a malfunctioning sensor node.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Similarly, a tree‐based data aggregation approach was presented by Kuo et al, to minimize the cost of data transmission. However, a single packet loss at any level of the tree results in information loss for the whole subtree . A grid‐based data aggregation scheme (GBDAS) divides the experimental area into various cells, where each cell has a sensor node, probably the one with the maximum residual energy capable of fusing neighbor nodes data in addition to its own .…”

Section: Literature Reviewmentioning

confidence: 99%

PFARS: Enhancing throughput and lifetime of heterogeneous WSNs through power‐aware fusion, aggregation, and routing scheme

Khan

Zakarya

Tan

et al. 2019

Int J Communication

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Summary Heterogeneous wireless sensor networks (WSNs) consist of resource‐starving nodes that face a challenging task of handling various issues such as data redundancy, data fusion, congestion control, and energy efficiency. In these networks, data fusion algorithms process the raw data generated by a sensor node in an energy‐efficient manner to reduce redundancy, improve accuracy, and enhance the network lifetime. In literature, these issues are addressed individually, and most of the proposed solutions are either application‐specific or too complex that make their implementation unrealistic, specifically, in a resource‐constrained environment. In this paper, we propose a novel node‐level data fusion algorithm for heterogeneous WSNs to detect noisy data and replace them with highly refined data. To minimize the amount of transmitted data, a hybrid data aggregation algorithm is proposed that performs in‐network processing while preserving the reliability of gathered data. This combination of data fusion and data aggregation algorithms effectively handle the aforementioned issues by ensuring an efficient utilization of the available resources. Apart from fusion and aggregation, a biased traffic distribution algorithm is introduced that considerably increases the overall lifetime of heterogeneous WSNs. The proposed algorithm performs the tedious task of traffic distribution according to the network's statistics, ie, the residual energy of neighboring nodes and their importance from a network's connectivity perspective. All our proposed algorithms were tested on a real‐time dataset obtained through our deployed heterogeneous WSN in an orange orchard and also on publicly available benchmark datasets. Experimental results verify that our proposed algorithms outperform the existing approaches in terms of various performance metrics such as throughput, lifetime, data accuracy, computational time, and delay.

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“…The numerous hierarchical routing protocols like TEEN, APTEEN, and PEGASIS are developed and extended from conventional strategies [1]. Further, an improved Distributed, Multi-hop, Adaptive, Tree-based Energy-Balanced (DMATEB) routing scheme is demonstrated in which a relay node is selected in view of minimum distance and high energy from a current sensing node [2]. Further, the parent node is chosen among the selected relay nodes on the basis of high residual energy and less power consumption with due consideration of its associated child nodes.…”

Section: Introductionmentioning

confidence: 99%

Diverse Congestion Control Schemes for Wireless Sensor Networks

al.¹

2021

TURCOMAT

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Wireless Sensor Networks (WSNs) comprised of battery operated sensor nodes that collect data from their neighbor nodes and transmit the aggregated information to the sink node or the Base Station (BS). This may result in congestion near the BS and leads to a bottleneck situation in the network. In this paper, an extensive study of earlier reported diverse congestion techniques explicitly diverse Algorithm based - and Layer based-congestion techniques is carried out. Accordingly, a recommendation is drawn based upon their performance comparison. Furthermore, a demonstration is carried out for contemporary earlier reported strategies such as Pro-AODV, CC-AODV, EDAPR, ED-AODV and PCC-AODV by evaluating delay, packet delivery ratio (PDR) and packet loss ratio (PLR). Accordingly, a recommended congestion strategy is suggested depending upon the comparison of the demonstrated schemes.

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A distributed, multi‐hop, adaptive, tree‐based energy‐balanced routing approach

Cited by 19 publications

References 25 publications

Tree‐Based Multihop Routing Method for Energy Efficiency of Wireless Sensor Networks

Tree‐Based Multihop Routing Method for Energy Efficiency of Wireless Sensor Networks

PFARS: Enhancing throughput and lifetime of heterogeneous WSNs through power‐aware fusion, aggregation, and routing scheme

Diverse Congestion Control Schemes for Wireless Sensor Networks

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