EMA-PRBDS: efficient multi-attribute packet rank based data scheduling in wireless sensor networks for real-time monitoring systems

Mahendran, N.; Shankar, S.; Mekala, T.

doi:10.1080/00207217.2019.1692244

Cited by 10 publications

(9 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By using the Network Simulator version-2 (NS2) tool, the protocol methodology is implemented and evaluated. 26 The LSAPSP network fundamentally used parameter descriptions enclosed in Table 1. The energy consumption of the sensor nodes is derived from above equation.…”

Section: Resultsmentioning

confidence: 99%

“…Based on the packet interval as a data generation gap considered from 0.1 to 0.5 second, total simulation varied from 100 to 200 seconds and the number of sensors deployed from 75 to 175 in this test bed. By using the Network Simulator version‐2 (NS2) tool, the protocol methodology is implemented and evaluated . The LSAPSP network fundamentally used parameter descriptions enclosed in Table .…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

LSAPSP: Load distribution‐based slot allocation and path establishment using optimized substance particle selection in sensor networks

Natarajan¹,

Subapriya

Thangavel³

2020

Int J Communication

Self Cite

View full text Add to dashboard Cite

Summary Sensor node energy conservation is the primary design parameters in wireless sensor networks (WSNs). Energy efficiency in sensor networks directly prolongs the network lifetime. In the process of route discovery, each node cooperates to forward the data to the base station using multi‐hop routing. But, the nodes nearer to the base station are loaded more than the other nodes that lead to network portioning, packet loss and delay as a result nodes may completely loss its energy during the routing process. To rectify these issues, path establishment considers optimized substance particle selection, load distribution, and an efficient slot allocation scheme for data transmission between the sensor nodes in this paper. The selection of forwarders and conscious multi‐hop path is selected based on the route cost value that is derived directly by taking energy, node degree and distance as crucial metrics. Load distribution based slot allocation method ensures the balance of data traffic and residual energy of the node in areal‐time environment. The proposed LSAPSP simulation results show that our algorithm not only can balance the real‐time environment load and increase the network lifetime but also meet the needs of packet loss and delay.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

LSAPSP: Load distribution‐based slot allocation and path establishment using optimized substance particle selection in sensor networks

Natarajan¹,

Subapriya

Thangavel³

2020

Int J Communication

Self Cite

View full text Add to dashboard Cite

show abstract

“…For reliable transmission, the consumption of energy relies on the energy required in running amplifier energy consumption

e_{A}

as well as an embedded circuit in transmitting and receiving signals a

e_{E}

. Therefore, 33 …”

Section: Proposed Methodologymentioning

confidence: 99%

Energy‐efficient model using optimal route discovery based on adaptive spider monkey optimization model

Justus

Muthukrishnan

Jothi

2022

Int J Communication

View full text Add to dashboard Cite

Summary During the past few years, wireless sensor networks (WSNs) have created a substantial interest because of their capabilities namely, self‐healing and self‐organizing under restricted network resources. Numerous WSN‐based routing algorithms are implemented to perform well, but the energy consumption of every data packet and information loss is more. Due to the power restriction in the sensor nodes, energy consumption is regarded as the major issue in networks that need to be minimized to improve the network lifetime. Hence, to conquer these consequences, this paper aims to propose an adaptive spider monkey optimization (ASMO)‐based effective optimal routing path from various sensor nodes with enhanced network lifetime. Here an ASMO is employed in selecting an optimal routing path. In addition to this, the proposed ASMO algorithm is capable of selecting energy‐aware routes by energy consumption. It also chooses the routes with minimum end‐to‐end delay using the bare least hop count criteria, thus enhancing the network life span. The results of the proposed method are compared with other methods such as DMEERP, EGSMRP, EEIABC, and RACO. The results reveal that the proposed approach consumed less amount of energy; hence, network lifetime is more when compared with other approaches.

show abstract

“…Mahendran, N et al [35] has proposed a rank-based real-time monitoring data scheduling technique named Packet Rank Based Data Scheduling (PRBDS). The proposed algorithm schedules the incoming data packets based on ranking done concerning packet deadline, size, and priority.…”

Section: Rank-based Data Schedulingmentioning

confidence: 99%

Energy Efficient Congestion Control using Rank-based Ant-Colony Optimization and Random Forest Regression (RAC-RFR) in Wireless Sensor Network

N.S,

P.S

2024

Preprint

View full text Add to dashboard Cite

Currently, plenty of sensor devices have come to use which communicate with each other using Wireless Sensor Networks (WSN). The increase in the number of sensor devices increases the rate of congestion and traffic, as numerous sensed data try to reach the destination node, than the channel capacity. As a result, there is a loss of packets, degradation in throughput, and an increase in energy consumption, thereby degrading the efficiency and robustness of WSN. To address the above-mentioned issues, the paper has proposed a traffic-oriented and resource-oriented congestion control mechanism namely Rank-based Ant-Colony Optimization and Random Forest Regression (RAC-RFR). The Rank-based Ant-Colony Optimization is used for detecting multiple congestion-free paths based on a ranking system that ranks based on the length of the path. The Random Forest Regression is Machine learning-based optimal pathfinder, which chooses the optimal congestion-free path among the paths found by RAC based on the packet loss rate and path rank. From the experimental results conducted the proposed approach showed enhanced performance in terms of throughput, delay, packet loss, queue size, congestion level, and energy consumption against existing congestion control methodologies.

show abstract

EMA-PRBDS: efficient multi-attribute packet rank based data scheduling in wireless sensor networks for real-time monitoring systems

Cited by 10 publications

References 19 publications

LSAPSP: Load distribution‐based slot allocation and path establishment using optimized substance particle selection in sensor networks

LSAPSP: Load distribution‐based slot allocation and path establishment using optimized substance particle selection in sensor networks

Energy‐efficient model using optimal route discovery based on adaptive spider monkey optimization model

Energy Efficient Congestion Control using Rank-based Ant-Colony Optimization and Random Forest Regression (RAC-RFR) in Wireless Sensor Network

Contact Info

Product

Resources

About