Random node sampling approach for energy efficient data gathering in wireless sensor networks

Kumar, Ajay; Amarlingam, M; Rajalakshmi, P.

doi:10.1109/tenconspring.2017.8070094

Cited by 4 publications

(5 citation statements)

References 10 publications

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“…Similarly,

V_{rx}

and

I_{rx}

are the voltage and current energy consumed by the radio when receiving

N_{rx}

bits during

t_{rx}

period of time, whereas

V_{lx}

and

I_{lx}

are the voltage and current energy consumed by the radio when it is in idle state. The energy consumed by radio communication is the highest among the sensor modules [7, 8], especially when the sensor is in transmission state as in our case, where the transceiver energy consumption depends on the number of bits to transmit to the coordinator as per (4).…”

Section: Application To Bsns: Experiments and Resultsmentioning

confidence: 99%

“…In BSNs, sensor energy is consumed by three main modules: the sensing module responsible for sensing physiological data, the microcontroller unit (MCU) in charge of performing of data processing such as data sampling and the radio communication module responsible of the wireless communication between the sensor and coordinator [7, 8, 63, 64]. Thus, the total energy consumed by a sensor is expressed as:

E_{normalt} = E_{sensing} + E_{MCU} + E_{radio}

where

E_{sensing} = V_{normals} I_{normals} t_{normals}

E_{MCU} = V_{MCU a} I_{MCU a} t_{MCU a} + V_{MCU i} I_{MCU i} t_{MCU i}

E_{radio} = V_{tx} I_{tx} N_{tx} t_{tx} + V_{rx} I_{rx} N_{rx} t_{rx} + V_{lx} I_{lx} t_{lx}

E_{normals}

is the energy consumed from sensing;

V_{normals}

and

I_{normals}

are the voltage and current energy consumed from sensing data during the time

t_{normals}

.…”

Section: Application To Bsns: Experiments and Resultsmentioning

confidence: 99%

“…In BSNs, sensor energy is consumed by three main modules: the sensing module responsible for sensing physiological data, the microcontroller unit (MCU) in charge of performing of data processing such as data sampling and the radio communication module responsible of the wireless communication between the sensor and coordinator [7,8,63,64]. Thus, the total energy consumed by a sensor is expressed as:…”

Section: Energy Modelmentioning

confidence: 99%

“…The corresponding energy consumption originates from sensing, processing, and communicating data via the radio transceiver. Radio communication accounts for the major part of the energy consumption, and it mostly depends on the number of bits that is sent within the network [7, 8]. Therefore, reducing the size of the transmitted data is very important to increase energy efficiency and prolong the sensors’ lifetime.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Toward fast and accurate emergency cases detection in BSNs

Boudargham

Sibai

Abdo

et al. 2020

IET wirel. sens. syst.

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In body sensor networks (BSNs), medical sensors capture physiological data from the human body and send them to the coordinator who act as a gateway to health care. The main aim of BSNs is to save peoples' lives. Therefore, fast and correct detection of emergencies while maintaining low-energy consumption of sensors is essential requirement of BSNs. In this study, the authors propose a new adaptive data sampling approach, where the sampling ratio is adapted based on the sensed data variation. The idea is to use the modified version of the cumulative sum (CUSUM) algorithm (modified CUSUM) that they previously proposed for wireless sensor networks to monitor the data variability, and adapt the sampling rate accordingly. Modified CUSUM is then applied to the adaptively sampled data to detect anomalies, and the correlation property between physiological parameters will be used to identify emergency cases from false alarms. Several experiments are performed and compared to evaluate the efficiency of their approach, and different parameters are considered.

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“…Similarly,

V_{rx}

and

I_{rx}

are the voltage and current energy consumed by the radio when receiving

N_{rx}

bits during

t_{rx}

period of time, whereas

V_{lx}

and

I_{lx}

Section: Application To Bsns: Experiments and Resultsmentioning

confidence: 99%

E_{normalt} = E_{sensing} + E_{MCU} + E_{radio}

where

E_{sensing} = V_{normals} I_{normals} t_{normals}

E_{MCU} = V_{MCU a} I_{MCU a} t_{MCU a} + V_{MCU i} I_{MCU i} t_{MCU i}

E_{radio} = V_{tx} I_{tx} N_{tx} t_{tx} + V_{rx} I_{rx} N_{rx} t_{rx} + V_{lx} I_{lx} t_{lx}

E_{normals}

is the energy consumed from sensing;

V_{normals}

and

I_{normals}

are the voltage and current energy consumed from sensing data during the time

t_{normals}

.…”

Section: Application To Bsns: Experiments and Resultsmentioning

confidence: 99%

“…In BSNs, sensor energy is consumed by three main modules: the sensing module responsible for sensing physiological data, the microcontroller unit (MCU) in charge of performing of data processing such as data sampling and the radio communication module responsible of the wireless communication between the sensor and coordinator [7,8,63,64]. Thus, the total energy consumed by a sensor is expressed as:…”

Section: Energy Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Toward fast and accurate emergency cases detection in BSNs

Boudargham

Sibai

Abdo

et al. 2020

IET wirel. sens. syst.

View full text Add to dashboard Cite

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“…Detail comparison of connection specifications of some of the network types are; [96][97][98][99][100][101][102][103][104][105][106][192][193][194][195] Detail comparison based on the communication specifications of some of the network types are; [96][97][98][99][100][101][102][103][104][105][106][192][193][194][195];…”

Section: Discussionmentioning

confidence: 99%

Analysis of factors affecting IoT-based smart hospital design

2020

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Currently, rapidly developing digital technological innovations affect and change the integrated information management processes of all sectors. The high efficiency of these innovations has inevitably pushed the health sector into a digital transformation process to optimize the technologies and methodologies used to optimize healthcare management systems. In this transformation, the Internet of Things (IoT) technology plays an important role, which enables many devices to connect and work together. IoT allows systems to work together using sensors, connection methods, internet protocols, databases, cloud computing, and analytic as infrastructure. In this respect, it is necessary to establish the necessary technical infrastructure and a suitable environment for the development of smart hospitals. This study points out the optimization factors, challenges, available technologies, and opportunities, as well as the system architecture that come about by employing IoT technology in smart hospital environments. In order to do that, the required technical infrastructure is divided into five layers and the system infrastructure, constraints, and methods needed in each layer are specified, which also includes the smart hospital’s dimensions and extent of intelligent computing and real-time big data analytic. As a result of the study, the deficiencies that may arise in each layer for the smart hospital design model and the factors that should be taken into account to eliminate them are explained. It is expected to provide a road map to managers, system developers, and researchers interested in optimization of the design of the smart hospital system.

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