HFBLMS: Hierarchical Fractional Bidirectional Least-Mean-Square prediction method for data reduction in wireless sensor network

Ganjewar, Pramod D.; Barani, S.; Wagh, Sanjeev J.

doi:10.1142/s1793962318500204

Cited by 8 publications

(9 citation statements)

References 19 publications

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“…The autocorrelation function (ACF) is often used to determine the order d of the ARIMA model [ 50 , 51 , 52 ]. After d-order difference transformation, if the ACF value corresponding to the difference series is close to 0, the time series passes the stationarity test.…”

Section: Edge Prediction Algorithm Based On Armamentioning

confidence: 99%

EP-ADTA: Edge Prediction-Based Adaptive Data Transfer Algorithm for Underwater Wireless Sensor Networks (UWSNs)

Wang

Ben

Hua

et al. 2022

Sensors

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The underwater wireless sensor network is an important component of the underwater three-dimensional monitoring system. Due to the high bit error rate, high delay, low bandwidth, limited energy, and high dynamic of underwater networks, it is very difficult to realize efficient and reliable data transmission. Therefore, this paper posits that it is not enough to design the routing algorithm only from the perspective of the transmission environment; the comprehensive design of the data transmission algorithm should also be combined with the application. An edge prediction-based adaptive data transmission algorithm (EP-ADTA) is proposed that can dynamically adapt to the needs of underwater monitoring applications and the changes in the transmission environment. EP-ADTA uses the end–edge–cloud architecture to define the underwater wireless sensor networks. The algorithm uses communication nodes as the agents, realizes the monitoring data prediction and compression according to the edge prediction, dynamically selects the transmission route, and controls the data transmission accuracy based on reinforcement learning. The simulation results show that EP-ADTA can meet the accuracy requirements of underwater monitoring applications, dynamically adapt to the changes in the transmission environment, and ensure efficient and reliable data transmission in underwater wireless sensor networks.

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Section: Edge Prediction Algorithm Based On Armamentioning

confidence: 99%

EP-ADTA: Edge Prediction-Based Adaptive Data Transfer Algorithm for Underwater Wireless Sensor Networks (UWSNs)

Wang

Ben

Hua

et al. 2022

Sensors

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“…Ninisha et al 117 in 2021 proposed a hierarchical fractional quantized kernel least mean square (HFQKLMS) filter to reduce high transmission and energy consumption. HFQKLMS filter is the combination of hierarchical fractional bidirectional LMS (HFBLMS) 118 and modified quantized kernel LMS (M‐QKLMS) 119 and it works with three functioning units. First the SN, here the HFBLMS model is deployed by integrating the HLMS 120 scheme and FC theory.…”

Section: Data Transmission Reduction Techniques At the Bs Stagementioning

confidence: 99%

Data transmission reduction techniques for improving network lifetime in wireless sensor networks: An up‐to‐date survey from 2017 to 2022

Jain

Kumar

Singh

2022

Trans Emerging Tel Tech

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Wireless sensor networks (WSNs) have now become a substantial part of our life as it provides an incredible setup for monitoring environmental conditions such as glaciers melting, earthquakes, climate change, volcanic eruption, agriculture, and other natural calamities. One of the most important challenges in data gathering and examining accurate data is "energy consumption." However, continuously changing environmental conditions, mobility in devices and extreme resource usage, such as battery capacity in WSNs collectively contribute to a high possibility of redundant data transmission thus making it tremendously hard to achieve a satisfactory network lifetime. Additionally, these environmental monitoring applications periodically senses and aggregate data which usually exhibits high temporal and spatial redundancies. As a result, exploiting these redundancies is essential for both resource utilization, as well as accurate data transmission. Since a huge amount of energy is exhausted in transmitting the redundant data, which has become a bottleneck in scaling such applications. In this context, this article provides a thorough examination of existing energy-efficient redundant data transmission reduction techniques, as well as their strengths and drawbacks. The idea of redundant data transmission reduction concept is divided into three stages: the sensor nodes (SN) at the first stage, the cluster heads (CHs) at the second stage, and the base station (BS) at the third stage. Furthermore, this survey focuses on the current issues and challenges, as well as the future directions in reducing redundant data transmission for future investigation.

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“…However, this method not included temporal and spatial correlation of aggregated data to produce routing tree. Ganjewar P D et al (Ganjewar, et al, 2018) modelled Hierarchical Fractional Bidirectional Least-Mean Square (HFBLMS) technique for reducing data in WSN. This scheme was developed through changing Hierarchical Least Mean Square (HLMS) method with Fractional Calculus (FC) and Bidirectional Least Mean Square (BLMS) in weight update procedure.…”

Section: Literature Surveymentioning

confidence: 99%

“…Once optimal path is chosen, then route maintenance is executed in simulated IoT network based on link quality metric. Finally, data aggregation and prediction is processed using HFBLMS (Ganjewar, et al, 2018).…”

Section: Proposed Shuffled Shepherd Squirrel Optimization Algorithm-b...mentioning

confidence: 99%

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Shuffled Shepherd Squirrel Optimization and Fractional LMS Model for In-Network Aggregation in Wireless Sensor Network

Rajesh¹,

Mohan²

2022

International Journal of Business Data Communications and Networking

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The wireless sensor network (WSN) is commonly based on small node collaboration. These nodes are specified by wireless communication, low price, and energy consumption. Moreover, the WSN can be utilized to compute pressure, temperature, as well as monitoring health, military supervision, and so on. A variety of WSN applications need to gather data from sensor nodes based on sink. In this paper, shuffled shepherd squirrel optimization (SSSOA) technique is devised for in-network aggregation in WSN. Here, the path is formulated from source node to destination through routing process, and source node broadcasts a packet concurrently to destination. The WSN is initiated, and the suitable cluster head (CH) is selected from all nodes. Consequently, CH is selected based on the developed shuffled shepherd squirrel optimization (SSSOA) method.

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HFBLMS: Hierarchical Fractional Bidirectional Least-Mean-Square prediction method for data reduction in wireless sensor network

Cited by 8 publications

References 19 publications

EP-ADTA: Edge Prediction-Based Adaptive Data Transfer Algorithm for Underwater Wireless Sensor Networks (UWSNs)

EP-ADTA: Edge Prediction-Based Adaptive Data Transfer Algorithm for Underwater Wireless Sensor Networks (UWSNs)

Data transmission reduction techniques for improving network lifetime in wireless sensor networks: An up‐to‐date survey from 2017 to 2022

Shuffled Shepherd Squirrel Optimization and Fractional LMS Model for In-Network Aggregation in Wireless Sensor Network

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