Dynamic Node Lifetime Estimation for Wireless Sensor Networks

Rukpakavong, Wilawan; Guan, Lin; Phillips, Iain

doi:10.1109/jsen.2013.2295303

Cited by 33 publications

(16 citation statements)

References 17 publications

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“…A medication adherence monitoring system can be battery-powered, for example, in the case of sensor networks and mobile device based systems, such as smartwatches, mobile phones, and tablets. This poses a challenge as the battery has limited energy budget [147]. From a system point of view, it is anticipated that a sufficient amount of electric current is being fed to the system to ensure its functionality.…”

Section: Energy Consumption and Lifetimementioning

confidence: 99%

A Review of Medication Adherence Monitoring Technologies

Aldeer

Javanmard

Martin

2018

ASI

108

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Medication non-adherence is a prevalent, complex problem. Failure to follow medication schedules may lead to major health complications, including death. Proper medication adherence is thus required in order to gain the greatest possible drug benefit during a patient's treatment. Interventions have been proven to improve medication adherence if deviations are detected. This review focuses on recent advances in the field of technology-based medication adherence approaches and pays particular attention to their technical monitoring aspects. The taxonomy space of this review spans multiple techniques including sensor systems, proximity sensing, vision systems, and combinations of these. As each technique has unique advantages and limitations, this work describes their trade-offs in accuracy, energy consumption, acceptability and user's comfort, and user authentication.

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Section: Energy Consumption and Lifetimementioning

confidence: 99%

A Review of Medication Adherence Monitoring Technologies

Aldeer

Javanmard

Martin

2018

ASI

108

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“…These areas include healthcare (monitoring patients and related medical management), providing emergency services in cases of disasters (e.g., earthquakes), and traffic control [37,38]. WSNs differ from traditional networks because the former consists of many small nodes called sensors, but these sensors have many limitations [39].…”

Section: Wsnsmentioning

confidence: 99%

Variants of the Low-Energy Adaptive Clustering Hierarchy Protocol: Survey, Issues and Challenges

et al. 2018

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Abstract:A wireless sensor network (WSN) is a modern technology in radio communication. A WSN comprises a number of sensors that are randomly spread in a specific area for sensing and monitoring physical attributes that are difficult to monitor by humans, such as temperature, humidity, and pressure. Many problems, including data routing, power consumption, clustering, and selecting cluster heads (CHs), may occur due to the nature of WSNs. Various protocols have been conducted to resolve these issues. One of the important hierarchical protocols that are used to reduce power consumption in WSNs is low-energy adaptive clustering hierarchy (LEACH). This paper presents a comprehensive study of clustering protocols for WSNs that are relevant to LEACH. This paper is the first to emphasis on cluster formation and CHs selection methods and their strengths and weaknesses. A new taxonomy is presented to discuss LEACH variants on the basis of different classes, and the current survey is compared with other existing surveys. A complete comparison of the location, energy, complexity, reliability, multi-hop path, and load balancing characteristics of LEACH variants is conducted. Future research guidelines for CHs selection and cluster formation in WSNs are also discussed.

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“…The results obtained when using using Peukert’s Law [7,45,46,47] were also included for comparative purposes. This is a simpler analytical battery model, which can capture part of the non-linear properties of the batteries, where the battery lifetime ( L ) can be evaluated using:

L = \frac{a}{I^{b}},

I being the discharge current and a and b experimentally-obtained constants.…”

Section: Figure A1mentioning

confidence: 99%

A Temperature-Dependent Battery Model for Wireless Sensor Networks

Rodrigues

Montez

Moraes

et al. 2017

Sensors

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Energy consumption is a major issue in Wireless Sensor Networks (WSNs), as nodes are powered by chemical batteries with an upper bounded lifetime. Estimating the lifetime of batteries is a difficult task, as it depends on several factors, such as operating temperatures and discharge rates. Analytical battery models can be used for estimating both the battery lifetime and the voltage behavior over time. Still, available models usually do not consider the impact of operating temperatures on the battery behavior. The target of this work is to extend the widely-used Kinetic Battery Model (KiBaM) to include the effect of temperature on the battery behavior. The proposed Temperature-Dependent KiBaM (T-KiBaM) is able to handle operating temperatures, providing better estimates for the battery lifetime and voltage behavior. The performed experimental validation shows that T-KiBaM achieves an average accuracy error smaller than 0.33%, when estimating the lifetime of Ni-MH batteries for different temperature conditions. In addition, T-KiBaM significantly improves the original KiBaM voltage model. The proposed model can be easily adapted to handle other battery technologies, enabling the consideration of different WSN deployments.

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Dynamic Node Lifetime Estimation for Wireless Sensor Networks

Cited by 33 publications

References 17 publications

A Review of Medication Adherence Monitoring Technologies

A Review of Medication Adherence Monitoring Technologies

Variants of the Low-Energy Adaptive Clustering Hierarchy Protocol: Survey, Issues and Challenges

A Temperature-Dependent Battery Model for Wireless Sensor Networks

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