EPKF: Energy Efficient Communication Schemes Based on Kalman Filter for IoT

Huang, Yanqiu; Yu, Wanli; Ding, Enjie; García-Ortiz, Alberto

doi:10.1109/jiot.2019.2900853

Cited by 20 publications

(14 citation statements)

References 26 publications

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“…Consider a connected multi-agent network 1 with K nodes scattered in a geographical space. Two nodes are neighbors if they can exchange information with each other, i.e.…”

Section: Preliminaries a Network Modelmentioning

confidence: 99%

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Energy-Efficient Diffusion Kalman Filtering for Multiagent Networks in IoT

Khalili¹,

Vahidpour²,

Rastegarnia³

et al. 2022

IEEE Internet Things J.

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Increasing the energy efficiency of an Internet of Things (IoT) system is a major challenge for its successful implementation. To reduce the computation and storage burden and enhance the efficiency of traditional IoT, an energy-efficient diffusion-based algorithm for state estimation in multi-agent networks is proposed in this paper. In the proposed algorithm (referred to as reduced-link diffusion Kalman filter (RL-diffKF)) the nodes (agents) can communicate only with a fraction of their neighbors and each node runs a local Kalman filter to estimate the state of a linear dynamic system. This algorithm results in a significant reduction in communication cost during both adaptation and aggregation processes albeit at the expense of possible degradation in the network performance. To justify the stability and convergence of the RL-diffKF algorithm, an indepth analysis of the performance is reported. We also consider the problem of optimal selection of combination weights and use the idea of minimum variance estimation to analytically derive the adaptive combiners. The theoretical findings are verified through numerical simulations.

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“…Consider a connected multi-agent network 1 with K nodes scattered in a geographical space. Two nodes are neighbors if they can exchange information with each other, i.e.…”

Section: Preliminaries a Network Modelmentioning

confidence: 99%

“…The IoT refers to a system of interrelated physical devices and their virtual implementations that are able to receive and diffuse data over an Internet-like network [1]. Such a framework is based on sensing, communication, networking, and information processing technologies.…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Diffusion Kalman Filtering for Multiagent Networks in IoT

Khalili¹,

Vahidpour²,

Rastegarnia³

et al. 2022

IEEE Internet Things J.

View full text Add to dashboard Cite

show abstract

“…Reference [11] presents a mobile energy harvesting and data collection platform designed to provide a deeper understanding of energy harvesting dynamics. Two extensions of predictable Kalman filter methods are proposed in [12] to reduce the unnecessary transmission of end devices to the internet. Assuming that the amount of harvested energy during a given period for a day is close to that in the previous days, the exponential weighted moving average (EWMA) algorithm [13] was proposed for solar energy, by performing prediction using an exponentially weighted average of past harvested values.…”

Section: A Motivations and Related Workmentioning

confidence: 99%

Kalman Filtering Based Adaptive Transfer in Energy Harvesting IoT Networks

Yao

Muqing

2020

IEEE Access

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In this paper, we investigate an energy prediction algorithm based on Kalman filtering in energy harvesting IoT networks. The IoT nodes harvest renewable energy from nature and powered by green energy only. Owing to the space-time instability and non-uniformity of renewable energy, the IoT nodes may have insufficient energy supply. An unresolved challenge is accurately predicting the available renewable energy, and developing low complexity solutions that incorporate a lossless transfer guarantee. With this in mind, we propose the energy prediction algorithm based on Kalman filtering to bridge the gap between lossless transfer and unstable renewable energy. The energy prediction is performed at the access point in order to dynamically adjust the number of bits to be sent, and the data loss due to receiver energy depletion will be improved. In addition, real solar and wind energy profiles are exploited by simulations. The simulation results show that the proposed energy prediction algorithm can improve transmission efficiency in terms of the rate of drop bits and the number of time slots needed to transmit a given payload, and reduce the wastes of harvested renewable energy. INDEX TERMS Energy harvesting, IoT networks, Kalman filtering, energy prediction.

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“…In recent years, convolutional neural networks (CNNs) have been making great progress and success in image classification and description [1]. As the computation of CNNs involves a large number of parameters, they cannot be directly deployed on the emerging Internet of Things (IoT) end devices, which typically have limited resources [2] such as mobile phones, wireless sensor nodes, etc. Lightweight networks have gained great attention, since they require less computation than traditional CNNs (e.g., VggNet [3], ResNet [4], etc).…”

Section: Introductionmentioning

confidence: 99%

Efficient Attention Mechanism for Dynamic Convolution in Lightweight Neural Network

et al. 2021

Self Cite

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Light-weight convolutional neural networks (CNNs) suffer limited feature representation capabilities due to low computational budgets, resulting in degradation in performance. To make CNNs more efficient, dynamic neural networks (DyNet) have been proposed to increase the complexity of the model by using the Squeeze-and-Excitation (SE) module to adaptively obtain the importance of each convolution kernel through the attention mechanism. However, the attention mechanism in the SE network (SENet) selects all channel information for calculations, which brings essential challenges: (a) interference caused by the internal redundant information; and (b) increasing number of network calculations. To address the above problems, this work proposes a dynamic convolutional network (termed as EAM-DyNet) to reduce the number of channels in feature maps by extracting only the useful spatial information. EAM-DyNet first uses the random channel reduction and channel grouping reduction methods to remove the redundancy in the information. As the downsampling of information can lead to the loss of useful information, it then applies an adaptive average pooling method to maintain the information integrity. Extensive experimental results on the baseline demonstrate that EAM-DyNet outperformed the existing approaches, thus it can achieve higher accuracy of the network test and less network parameters.

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EPKF: Energy Efficient Communication Schemes Based on Kalman Filter for IoT

Cited by 20 publications

References 26 publications

Energy-Efficient Diffusion Kalman Filtering for Multiagent Networks in IoT

Energy-Efficient Diffusion Kalman Filtering for Multiagent Networks in IoT

Kalman Filtering Based Adaptive Transfer in Energy Harvesting IoT Networks

Efficient Attention Mechanism for Dynamic Convolution in Lightweight Neural Network

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