Predicting the energy consumption in software defined wireless sensor networks: a probabilistic Markov model approach

Rahimifar, Atefeh; Kavian, Yousef S.; Kaabi, Hooman; Soroosh, Mohammad

doi:10.1007/s12652-020-02599-3

Cited by 25 publications

(9 citation statements)

References 33 publications

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“…There are approaches where they model and analyze energy consumption [12] and try to predict such consumption [13] based on Markov process tools. They consider consumption in various states and during transitions, not taking into account the MAC or incorporating it very basically into the model.…”

Section: Of 20mentioning

confidence: 99%

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Energy Consumption Modeling for Heterogeneous IoT-WSN Devices: Entire Modes and Operation Cycles Considerations

Portillo,

Martinez-Bauset,

Pla

et al. 2024

Preprint

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Wireless Sensor Network (WSN) and the sensing devices are considered part of the core components of the Internet of Things (IoT). Hence, the modeling of IoT-WSNs is fundamental to having a better understanding, management, and deployment of this kind of technology. A fundamental issue in WSN is energy consumption due to the inherent limitations of this resource in the sensor devices. On the other hand, several issues arise in heterogeneous scenarios due to the coexistence of different sensor devices. Therefore, the modeling process becomes challenging and must consider the different transmission and operation cycles and modes that the transmission protocol implements to assure successful functioning in terms of transmission, synchronization, and energy savings. A duty-cycled MAC WSN protocol (PSA-MAC) has been modeled based on a pair of two-dimensional Discrete-Time Markov Chain (2D-DTMC), whose solution in terms of stationary probability distribution is used by expressions that have been derived to evaluate the energy consumption of sensor devices that could be used for WSN-based IoT applications. Analytical results of average energy consumption are obtained, where the synchronization, data, and sleep periods of a full transmission cycle are considered in a heterogeneous scenario with different node classes and priority assignments. Also, the normal and awake operation cycles are considered to assure synchronization and energy savings. Moreover, the transmission schemes of SPT (transmission of single packets) and APT (transmission of packets in aggregation) are also considered in the model. These results are validated through discrete event-based simulations, and the obtained results are accurate.

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Section: Of 20mentioning

confidence: 99%

“…Note that the stationary probability distribution used is π 1 (i, k), so the average size of the frame, f 1,k , is determined by the expression (11). The probability of failure in transmission P f 1 ,k and P f 1 ,k (expressions (10) and (13), respectively), are calculated considering W 1 .…”

Section: Energy Consumption In Cycles Of Type 2cmentioning

confidence: 99%

Energy Consumption Modeling for Heterogeneous IoT-WSN Devices: Entire Modes and Operation Cycles Considerations

Portillo,

Martinez-Bauset,

Pla

et al. 2024

Preprint

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“…A reduced energy consumption and control overhead can be achieved by using a model that predicts the energy consumption of each sensor node. Rahimifar et al [158] proposed a Markov-based model to predict the future energy consumption of sensor nodes. The controller predicts the individual energy consumption of sensor nodes; thus, sensor nodes avoid reporting energy levels to the controller.…”

Section: Energy Efficiencymentioning

confidence: 99%

A Survey on Machine Learning Software-Defined Wireless Sensor Networks (ML-SDWSNs): Current Status and Major Challenges

et al. 2022

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“…A reduced energy consumption and control overhead can be achieved by using a model that predict the energy consumption of each sensor node. Rahimifar et al [143] proposed a Markov-based model to predict future energy consumption of sensor nodes. The controller predicts individual energy consumption of sensor nodes; thus, sensor nodes avoid reporting energy levels to the controller.…”

Section: Energy Efficiencymentioning

confidence: 99%

A Survey on Software-Defined Wireless Sensor Networks: Current status, machine learning approaches and major challenges

Lasso¹,

Marchegiani²,

Jurado³

et al. 2021

Preprint

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This paper is aimed to present a comprehensive survey of relevant research over the period 2012-2021 of Software-Defined Wireless Sensor Network (SDWSN) proposals and Machine Learning (ML) techniques to perform network management, policy enforcement, and network configuration functions. This survey provides helpful information and insights to the scientific and industrial communities, and professional organisations interested in SDWSNs, mainly the current state-of-art, machine learning techniques, and open issues.

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Predicting the energy consumption in software defined wireless sensor networks: a probabilistic Markov model approach

Cited by 25 publications

References 33 publications

Energy Consumption Modeling for Heterogeneous IoT-WSN Devices: Entire Modes and Operation Cycles Considerations

Energy Consumption Modeling for Heterogeneous IoT-WSN Devices: Entire Modes and Operation Cycles Considerations

A Survey on Machine Learning Software-Defined Wireless Sensor Networks (ML-SDWSNs): Current Status and Major Challenges

A Survey on Software-Defined Wireless Sensor Networks: Current status, machine learning approaches and major challenges

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