Adaptive algorithms for sensor activation in renewable energy based sensor systems

Mereddy, Sandeep Reddy; Jaggi, Neeraj; Pendse, Ravi

doi:10.1016/j.adhoc.2011.04.008

Cited by 10 publications

(11 citation statements)

References 16 publications

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“…The determinant condition of H-AIMD is if the Harvestor gets new data or not, which is more simple than the threshold comparison of the AIMD used for sensor activation process. Considering many studies that have verified that when 1 = 1, 2 = 2, the performance is the best [24,30], we use the same parameter setting in this study. Moreover, since the Harvestor could easily get the acquired values (getNewData, SIprev) as inputs, this algorithm is extremely easily realized in practice.…”

Section: H-aimd and H-miadmentioning

confidence: 99%

“…The modified algorithm H-AIMD, in which "H" represents the Harvestor, is introduced for SI computation. Similarly, we also describe the H-MIAD in this section and will perform the experiments on them in Section 4, due to the huge performance difference between the algorithm AIMD and MIAD, found in [24].…”

Section: H-aimd and H-miadmentioning

confidence: 99%

“…We borrow insights from AIMD and use it in the provision process of sensor data instead of it original application in network congestion [29] and sensor recharge [20,24,26] studies. The modifie algorithm H-AIMD, in which "H" represents the Harvestor, is introduced for SI computation covariance matrix of measurement noise, is the transmission matrix to change from the state space to measurement space, is the measurement vector:…”

Section: H-aimd and H-miadmentioning

confidence: 99%

“…We borrow insights from AIMD and use it in the provision process of sensor data instead original application in network congestion [29] and sensor recharge [20,24,26] studies. The mo t n`1 .…”

Section: H-aimd and H-miadmentioning

confidence: 99%

“…However, we could not get the global parameters in most hen, the local algorithm AIMD [20] was introduced to the sensor recharge sensor node n to determine the status and parameters of sensor nodes dynamically, including activation, , and sleeping time intervals. borrow insights from AIMD and use it in the provision process of sensor data instead of its application in network congestion [29] and sensor recharge [20,24,26] studies. The modified m H-AIMD, in which "H" represents the Harvestor, is introduced for SI computation.…”

Section: Static Policymentioning

confidence: 99%

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Pull-Based Modeling and Algorithms for Real-Time Provision of High-Frequency Sensor Data from Sensor Observation Services

Fan

et al. 2016

IJGI

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Abstract:The widely used pull-based method for high-frequency sensor data acquisition from Sensor Observation Services (SOS) is not efficient in real-time applications; therefore, further attention must be paid to real-time mechanisms in the provision process if sensor webs are to achieve their full potential. To address this problem, we created a data provision problem model, and compare the recursive algorithm Kalman Filter (KF) and our two proposed self-adaptive linear algorithms Harvestor Additive Increase and Multiplicative Decrease (H-AIMD) and Harvestor Multiplicative Increase and Additive Decrease (H-MIAD) with the commonly used Static Policy, which requests data with an unchanged time interval. We also developed a comprehensive performance evaluation method that considers the real-time capacity and resource waste to compare the performance of the four data provision algorithms. Experiments with real sensor data show that the Static Policy needs accurate priori parameters, Kalman Filter is most suitable for the data provision of sensors with long-term stable time intervals, and H-AIMD is the steadiest with better efficiency and less delayed number of data while with a higher resource waste than the others for data streams with much fluctuations in time intervals. The proposed model and algorithms are useful as a basic reference for real-time applications by pull-based stream data acquisition.

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Section: H-aimd and H-miadmentioning

confidence: 99%

Section: H-aimd and H-miadmentioning

confidence: 99%

Section: H-aimd and H-miadmentioning

confidence: 99%

Section: H-aimd and H-miadmentioning

confidence: 99%

Section: Static Policymentioning

confidence: 99%

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Pull-Based Modeling and Algorithms for Real-Time Provision of High-Frequency Sensor Data from Sensor Observation Services

Fan

et al. 2016

IJGI

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A review on solar forecasting and power management approaches for energy‐harvesting wireless sensor networks

Kakkar

2020

Int J Communication

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Summary For rechargeable wireless sensor nodes, effective power management is of prime importance because of the stochastic behaviour of the environmental resources. A key issue in integrating solar resources with wireless sensor networks (WSNs) is the need of precise irradiance measurements and power to resource modelling. WSNs are employed in an adhoc manner comprises of numerous sensing nodes and organised as a network for the sake of checking and balancing the environmental factors. Each node has sensing, computation, communication, and locomotion capabilities but operates with limited battery life. Energy harvesting is a way of powering these WSNs by harvesting energy from the environment. By considering harvested energy as an energy source, certain considerations are different from that of battery‐operated networks. Nondeterministic energy availability with respect to time is the reason behind these differences, which put a limit on the maximum rate at which energy can be used. Thus, reliable knowledge of solar radiation is essential for informed design, deployment planning, and optimal management of energy in rechargeable WSNs. Further, power management is essential in self‐powerssed networks to efficiently utilize the available energy. In this paper, a detailed survey on different solar forecasting techniques has been presented for precise energy estimates. A detailed study on energy efficient power management techniques is also proposed to address the feasibility of energy‐harvesting approach in WSNs.

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Software Defined Sensing

Zeng

Pan

et al. 2019

Software Defined Systems

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Adaptive algorithms for sensor activation in renewable energy based sensor systems

Cited by 10 publications

References 16 publications

Pull-Based Modeling and Algorithms for Real-Time Provision of High-Frequency Sensor Data from Sensor Observation Services

Pull-Based Modeling and Algorithms for Real-Time Provision of High-Frequency Sensor Data from Sensor Observation Services

A review on solar forecasting and power management approaches for energy‐harvesting wireless sensor networks

Software Defined Sensing

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