State‐of‐charge estimation to improve decision making by MAC protocols used in WSNs

Quintero, Vanessa; Pérez, A.; Estevez, Claudio; Orchard, Marcos E.

doi:10.1049/el.2018.7666

Cited by 4 publications

(3 citation statements)

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“…For example, the KiBaM model, based on the chemical kinetic process of the battery, has been widely used in UAV, smart phones, and other fields [ 15 , 16 ]. Additionally, some complex algorithms such Bayesian inference [ 17 , 18 ] and Kalman filter [ 19 ] have also been used in SOC estimation. Although these models are more accurate, their computation complexity and measurement difficulty are too high for resource-constrained sensor nodes.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, estimating the state of the battery accurately, especially the state-of-charge (SOC), is essential for maximizing the lifetime of nodes and networks [ 11 , 12 , 13 , 14 ]. Among existing SOC estimation methods, the ones based on electrochemical, analytical, or stochastic models are more accurate [ 15 , 16 , 17 , 18 , 19 ]. However, computation complexity and measurement difficulty of these methods make them unsuitable for sensor nodes.…”

Section: Introductionmentioning

confidence: 99%

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Peukert’s Law-Based State-of-Charge Estimation for Primary Battery Powered Sensor Nodes

Dai

Xia²,

Mao³

et al. 2023

Sensors

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Accurate state-of-charge (SOC) estimation is essential for maximizing the lifetime of battery-powered wireless sensor networks (WSNs). Lightweight estimation methods are widely used in WSNs due to their low measurement and computation requirements. However, accuracy of existing lightweight methods is not high, and their adaptability to different batteries and working conditions is relatively poor. This paper proposes a lightweight SOC estimation method, which applies Peukert’s Law to estimate the effective capacity of the battery and then calculates the SOC by subtracting the cumulative current consumption from the estimated capacity. In order to evaluate the proposed method comprehensively, different primary batteries and working conditions (constant current, constant resistance, and emulated duty-cycle loads) are employed. Experimental results show that the proposed method is superior to existing methods for different batteries and working conditions, which mainly benefits from the ability of Peukert’s Law to better model the rate-capacity effect of the batteries.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Peukert’s Law-Based State-of-Charge Estimation for Primary Battery Powered Sensor Nodes

Dai

Xia²,

Mao³

et al. 2023

Sensors

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“…Such strategies include Y-MAC [58] , SLC [59] , etc. Finally, recent years have also witnessed some MAC protocols which are applied in specific applications or integrated with other energyefficient schemes, e.g., the SLDMAC [60] for the linear-topology WSNs with mobile relays, JRAM [61] and QTSAC [62] which combined routing and medium access control protocols, such as Asym-MAC [63] in which the transmission is triggered by both transmitter and receiver, DTMC [64] using 3-D discrete-time Markov chain model, and the State of Charge based MAC protocol (SoC-MAC) [65] .…”

Section: Classifications Of Eemacmentioning

confidence: 99%

A Survey on Energy-Efficient Strategies in Static Wireless Sensor Networks

Lin

Wang

Min

et al. 2020

ACM Trans. Sen. Netw.

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A comprehensive analysis on the energy-efficient strategy in static Wireless Sensor Networks (WSNs) which are not equipped with any energy harvesting modules was conducted in this paper. Firstly, a novel generic mathematical definition of Energy Efficiency was proposed, which took the acquisition rate of valid data, the total energy consumption, and the network lifetime of WSNs into consideration simultaneously. To the best of our knowledge, it is for the first time that the Energy Efficiency of WSNs is mathematically defined. The energy consumption characteristics of each individual sensor node and the whole network were expounded at length. Accordingly, the concepts concerning the Energy Efficiency, namely the Energy-efficient Means, the Energy-efficient Tier, and the Energy-efficient Perspective, were proposed. Subsequently, the relevant energy-efficient strategies proposed from 2002 to 2019 were tracked and reviewed. Specifically, they were classified into five categories, i.e., the Energy-Efficient Media Access Control (EEMAC) protocol, the Mobile Node Assistance Scheme (MNAS), the Energy-Efficient Clustering Scheme (EECS), the Energy-Efficient Routing Scheme (EERS), and the Compress Sensing based Scheme (CSS) respectively. A detailed elaboration on both the basic principle and the evolution of them was made. Finally, further analysis on the categories was made and the related conclusion was drawn. To be specific, the interdependence among them, the relationships between each of them and the Energy-efficient Means, the Energy-efficient Tier as well as and the Energy-efficient Perspective were analyzed in detail. Besides, the specific applicable scenarios for each of them and the relevant statistics analysis were detailed. The proportion and the number of citations of each category were illustrated by the statistical chart. In addition, the existing opportunities and challenges facing WSNs in the context of the new computing paradigm, and the feasible direction concerning the energy efficiency in the future were pointed out.

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