Derivative based prediction with look ahead

Bartoň, Tomáŝ; Musílek, Petr

doi:10.1109/ijcnn.2016.7727461

Cited by 6 publications

(3 citation statements)

References 13 publications

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“…It is hard to apply in low-performance sensor nodes due to high complexity. Raza et al [24] and Barton et al [25] introduced an less complex adaptive filter for short-term linear behavior. It is improper for disasters having non-linear behavior.…”

Section: ) Mobility-basedmentioning

confidence: 99%

Most Efficient Sensor Network Protocol for a Permanent Natural Disaster Monitoring System

Lee,

Kim

2021

Preprint

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To minimize enormous havoc from disasters, permanent environment monitoring is necessarily required. Thus we propose a novel energy management protocol for energy harvesting wireless sensor networks (EH-WSNs), named the adaptive sensor node management protocol (ASMP). The proposed protocol makes system components to systematically control their performance to conserve the energy. Through this protocol, sensor nodes autonomously activate an additional energy conservation algorithm. ASMP embeds three sampling algorithms. For the optimized environment sampling, we proposed the adaptive sampling algorithm for monitoring (ASA-m). ASA-m estimates the expected time period to occur meaningful change. The meaningful change refers to the distance between two target data for the monitoring QoS. Therefore, ASA-m merely gathers the data the system demands. The continuous adaptive sampling algorithm (CASA) solves the problem to be continuously decreasing energy despite of ASA-m. When the monitored environment shows a linear trend property, the sensor node in CASA rests a sampling process, and the server generates predicted data at the estimated time slot. For guaranteeing the self-sustainability, ASMP uses the recoverable adaptive sampling algorithm (RASA). RASA makes consumed energy smaller than harvested energy by utilizing the predicted data. RASA recharges the energy of the sensor node. Through this method, ASMP achieves both energy conservation and service quality.

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Section: ) Mobility-basedmentioning

confidence: 99%

Most Efficient Sensor Network Protocol for a Permanent Natural Disaster Monitoring System

Lee,

Kim

2021

Preprint

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“…In the forecasting‐based framework proposed in [11], two extensions of the original DBP algorithm are presented to construct

bold-italicC

. The first, called delayed DBP, accumulates some elements of

bold-italicM

then uses them to compute the slope of a linear approximator.…”

Section: Related Workmentioning

confidence: 99%

“…On the upside, it can achieve very low transmission ratios. Actually, when tested on real‐world datasets, it reportedly achieved transmission ratios lower than those achieved by three predecessors: [6, 10, 11]. Using the controllable delay feature of the HLM framework, if one sets

K = 2

, the lowest possible, one obtains a version suitable for real‐time applications.…”

Section: Related Workmentioning

confidence: 99%

Lightweight energy‐efficient framework for sensor real‐time communications

2019

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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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Derivative based prediction with look ahead

Cited by 6 publications

References 13 publications

Most Efficient Sensor Network Protocol for a Permanent Natural Disaster Monitoring System

Most Efficient Sensor Network Protocol for a Permanent Natural Disaster Monitoring System

Lightweight energy‐efficient framework for sensor real‐time communications

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

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