Lee, Woosik
(Research Center, Social Security Information Service)
,
Kim, Heeyoul
(Department of Computer Sciences, Kyonggi University)
,
Hong, Min
(Department of Computer Software Engineering, Soonchunhyang University)
,
Kang, Min-Goo
(Department of IT Contents, Hanshin University)
,
Jeong, Seung Ryul
(Business IT Graduate School, Kookmin University)
,
Kim, Namgi
(Department of Computer Sciences, Kyonggi University)
A wireless body-sensor network (WBSN) refers to a network-configured environment in which sensors are placed on both the inside and outside of the human body. The sensors are much smaller and the energy is more constrained when compared to traditional wireless sensor network (WSN) environments. The ...
A wireless body-sensor network (WBSN) refers to a network-configured environment in which sensors are placed on both the inside and outside of the human body. The sensors are much smaller and the energy is more constrained when compared to traditional wireless sensor network (WSN) environments. The critical nature of the energy-constraint issue in WBSN environments has led to numerous studies on the reduction of energy consumption of WBSN sensors. The transmission-power-control (TPC) technique adjusts the transmission-power level (TPL) of sensors in the WBSN and reduces the energy consumption that occurs during communications. To elaborate, when transmission sensors and reception sensors are placed in various parts of the human body, the transmission sensors regularly send sensor data to the reception sensors. As the reception sensors receive data from the transmission sensors, real-time measurements of the received signal-strength indication (RSSI), which is the value that indicates the channel status, are taken to determine the TPL that suits the current-channel status. This TPL information is then sent back to the transmission sensors. The transmission sensors adjust their current TPL based on the TPL that they receive from the reception sensors. The initial TPC algorithm made linear or binary adjustments using only the information of the current-channel status. However, because various data in the WBSN environment can be utilized to create a more efficient TPC algorithm, many different types of TPC algorithms that combine human movements or fuse TPC with other algorithms have emerged. This paper defines and discusses the design and development process of an efficient TPC algorithm for WBSNs. We will describe the WBSN characteristics, model, and closed-loop mechanism, followed by an examination of recent TPC studies.
A wireless body-sensor network (WBSN) refers to a network-configured environment in which sensors are placed on both the inside and outside of the human body. The sensors are much smaller and the energy is more constrained when compared to traditional wireless sensor network (WSN) environments. The critical nature of the energy-constraint issue in WBSN environments has led to numerous studies on the reduction of energy consumption of WBSN sensors. The transmission-power-control (TPC) technique adjusts the transmission-power level (TPL) of sensors in the WBSN and reduces the energy consumption that occurs during communications. To elaborate, when transmission sensors and reception sensors are placed in various parts of the human body, the transmission sensors regularly send sensor data to the reception sensors. As the reception sensors receive data from the transmission sensors, real-time measurements of the received signal-strength indication (RSSI), which is the value that indicates the channel status, are taken to determine the TPL that suits the current-channel status. This TPL information is then sent back to the transmission sensors. The transmission sensors adjust their current TPL based on the TPL that they receive from the reception sensors. The initial TPC algorithm made linear or binary adjustments using only the information of the current-channel status. However, because various data in the WBSN environment can be utilized to create a more efficient TPC algorithm, many different types of TPC algorithms that combine human movements or fuse TPC with other algorithms have emerged. This paper defines and discusses the design and development process of an efficient TPC algorithm for WBSNs. We will describe the WBSN characteristics, model, and closed-loop mechanism, followed by an examination of recent TPC studies.
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문제 정의
First, the author investigated the ways in which channels change based on a person’s movements in the WBSN environment in order to determine the correlation between channel changes and a person’s movements. The author revealed a strong correlation between channel-path loss and bodily movements. A human-motion recognition algorithm that was configured previously was used to determine if a person was walking or running.
제안 방법
This paper analyzed the characteristics of WBSN environments from various perspectives, including sensor devices, a person’s movements, and sensor placement. Additionally, this paper introduced TPC algorithms that are widely used for the reduction of sensor energy consumption, as well as the closed-loop mechanism that enables the exchange of transmission-power information between the transmitter and receiver sensors through a feedback method.
Additionally, we introduce the model and closed-loop mechanism for the design of a TPC algorithm. Finally, a comparative analysis is performed using the most recent TPC algorithms.
First, the author investigated the ways in which channels change based on a person’s movements in the WBSN environment in order to determine the correlation between channel changes and a person’s movements.
The author of [27] proposed a system that could not be resolved using existing TPC algorithms in a dynamic WBSN environment. The author analyzed an acceleration value in real time and extracted its features. They then determined if the status was moving or static based on the features.
The channel status was predicted using the system proposed in [21]. The authors measured the energy by using a sensor that utilizes the CC2420-transmission chip. They measured the energy-consumption values from both the transmission messages and control messages.
These findings were used to propose an equation-based TPC algorithm. The proposed TPC algorithm finds an efficient TPL by obtaining an average RSSI value based on the slope of linear equation, dividing the difference between the average and current RSSI values by the slope in real time, and then adding the current power value to obtain a new TPL.
They conducted an experiment by placing sensors that transmit data periodically on a wall and putting the receivers on a person’s wrists. The proposed method collected data from the sensors in real time to find the location of the current cycle. If the cycle value was greater than an “a” value that was set in advance, the algorithm calculated the average cycle and a prediction value for the person’s movement.
They conducted an experiment by placing sensors that transmit data periodically on a wall and putting the receivers on a person’s wrists.
This paper analyzed the characteristics of WBSN environments from various perspectives, including sensor devices, a person’s movements, and sensor placement.
The main types of TPC algorithms that were discussed are the linear, binary, and dynamic types. This paper introduced the basic foundational TPC algorithms, as well as various recent TPC algorithms that were derived from the basic algorithms, such as the Hybrid, ACC-based, Human-motion, and Relay-aided algorithms.
Through the analysis of various recent TPC algorithms, this paper gives the insight that the paradigm of TPC algorithms in the near future will be changed from feedback mechanism to self-control mechanism based on big-data and artificial intelligence. Moreover, the general analysis of WBSN environments in this paper will become a significant reference for the formulation of algorithms that extend the lifespan of sensors in the future.
이론/모형
If the status was moving, then the acceleration-based TPC algorithm was used. Otherwise, the RSSI/LQI-based TPC algorithm was used. Upon the discovery of a strong correlation between the acceleration value and the RSSI value through various tests, the author controlled the current TPL based on the number of values that exceeded the acceleration threshold.
The TPC algorithm was then applied. RSSI and TPL values were measured in real time and a new TPL was obtained by using the M-TPC algorithm. The new value was then shared with nearby sensors to facilitate communications that used the minimum adequate transmission power.
The proposed algorithm showed that the received-power strength in the standing and running states differed based on the linear-equation type when the sensor was placed on the chest, stomach, or arm in an indoor or an outdoor environment. These findings were used to propose an equation-based TPC algorithm. The proposed TPC algorithm finds an efficient TPL by obtaining an average RSSI value based on the slope of linear equation, dividing the difference between the average and current RSSI values by the slope in real time, and then adding the current power value to obtain a new TPL.
성능/효과
Additionally, the data-loss rate was derived from the channel status based on the LQI value that represents link quality. Tests that controlled TPL by considering both the RSSI and LQI values demonstrated that this technique is superior to using RSSI alone.
In [23], an algorithm that controls the TPL by creating an equation with the channel-analysis value of a dynamic WBSN environment was proposed. The proposed algorithm showed that the received-power strength in the standing and running states differed based on the linear-equation type when the sensor was placed on the chest, stomach, or arm in an indoor or an outdoor environment. These findings were used to propose an equation-based TPC algorithm.
To this end, the author proposed a prediction algorithm using covariance, mean, and likelihood functions to express transmission-power prediction through a formula. The proposed algorithm was tested using an Android phone and it displayed outstanding performance.
The bars in the graph represent the PDR values and the dots represent the RSSI values. When an analysis was performed from an RSSI/PDR perspective, both the RSSI and PDR values were improved when the TPL value was increased for both the CC1000 and CC2420 radio modules. This is because the transmission range was extended and the output power was increased at a higher TPL.
후속연구
Next, the received signal-strength indicator (RSSI) and packet-delivery ratio (PDR) based on a person’s movements will be examined using the CC1000 and CC2420, which are the main radio modules used in WBSN environments. Finally, an RSSI-distribution analysis will be performed based on the locations of sensors that are placed in the human body.
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