Improvement of Power Consumption in Wireless Body Area Networks by PCM Sampling ​

doi:10.21203/rs.3.rs-609162/v1

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Improvement of Power Consumption in Wireless Body Area Networks by PCM Sampling

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Due to the aging population and the growing need for hospitals and care for the elderly and limited hospital resources, the need for a mechanism that can handle the limited hospital resources is felt more than ever. Wireless Body Area networks are one of the technologies that monitor people's health remotely, thus avoiding unnecessary hospitalization and thus managing resource constraints. Wireless Body Area Networks (WBANs) face with energy limitations. The battery in WBANs has limited energy. However, it is not possible to replace or charge batteries in many of these networks. Therefore, it is needed to save energy and extend network life. One of the important methods that have been proposed for this purpose is sampling rate management-based methods. In this paper due to the efficiency of the sampling rate management, an efficient sampling rate method has been proposed. For this purpose to minimize unnecessary data transmission, Flat- Top Pulse Amplitude Modulated (PAM) method has been used. Then in order to make the signal from PAM suitable for data transmission, Pulse Code Modulation (PCM) method to digitize the pulses has been used. Finally, the results and outputs show that significant energy savings have been achieved. The results showed that data transmission is reduced by up to 92.7% and energy saving is achieved significantly

Electrical Engineering

Electronic Materials and Devices

Wireless Body Area Networks

Power Consumption

Data Similarity

Pulse Code Modulation

Adaptive Sampling

Data Compression.

Wireless Body Sensor Networks (WBSNs) are tiny sensors that are placed inside or on the patient body. One of the duties of WBSNs is collecting information from the body and sending the captured data to a base station. The captured data are received by health care center. This process reduces healthcare costs by avoiding unnecessary travel to hospital.

According to World Health Organization (WHO), the number of people who are older than 65 in 2010 is estimated 524 million and in 2050 this number will reach to 1.5 billion. Hence, WBSNs can play key role to remote care of patients and the elderly. WBSNs continuously sense vital signs such as the oxygen saturation, the respiration rate, the skin temperature, etc. Hence, they continuously monitor vital signs and if emergency condition occurred the patient travel to hospital. So, this reduces many unnecessary costs such as unnecessary hospitalization and etc.

However, there are several challenges in WBSNs. The most important one is energy consumption. Due to biosensors capture and send huge amount data and this purpose satisfy by much energy. In this purpose and to reduce the energy consumption several solutions have been proposed. In [1]-[2]-[3]-[4], the authors have suggested adaptive data collection and adaptive sensing scheme and compression rate selection. All of them reduce energy consumption but all of sensors are continuously active and consume energy. In [5], authors have suggested distributed sampling rate allocation for data quality maximization, in this approach it is assumed that sensor networks are rechargeable but it’s not applicable in all environments. Others suggested an adaptive method for data reduction; however emergency detection is not being handled [6].

To the best of our knowledge the recently proposed mechanism did not consider the similarity of data. While the similarity of data can be useful in reducing power consumption. The main idea is that if captured data are the same and similar with other data the sampling rate of sending data can be lower rate. Hence, it is desirable to reduce the quantity of sampled data when the captured data is the similar and no changes occurred. In another hand if the captured data be the similar we can use of compression data methods.

WBSNs have limited and low power. Unlike other devices, the batteries of WBSNs can’t easily replace or recharge. So the most important challenge is power consumption and power optimization. WBSNs mainly consume energy when they sense and send data and communicate wirelessly and perform processing on data. In three mentioned activities, sensing and transmitting data consume more energy than others. Therefore, several solutions have been proposed to manage data collection and transmission. In [18]-[19]-[7]-[20]-[21]-[22], authors suggested sending data with adaptive sampling rate. This proposed method reduces power consumption by sending data with appropriate selection method. However, in this method all of sensors continuously are active and consume energy. Others in [30] suggested Local Emergency Detection Approach for Saving Energy in Wireless Body Sensor Networks. In [7] authors combined Emergency detection method with adaptive sampling rate. Therefore, the nodes are no longer active at all time. Others in [23]-[24] suggested data compression and compression rate selection method for reducing volume of transmitting data in network. This method decreases energy consumption. The best optimization is achieved by combining these three methods.

Data sampling rate is one of the effective optimization energy consumption techniques. This technique reduces the energy consumption and extends the lifetime of the network by capturing and sending information with appropriate rate. This technique avoids unnecessary sending data. For this purpose in [7]-[8]–[9] authors proposed some algorithms and methods that optimize power consumption by changing sampling rate. These algorithms operate base on health status and captured data. They proposed local emergency detection algorithms and when emergency conditions occur then sensors send and capture vital signs with a high sampling rate. But when condition is normal, sensors operate with a lower rate. Also if captured data are the same and no significant changes occur then sensors send and collect data with a lower rate.

In this article we have studied a patient's data over a three-hour period. We have worked exclusively on data from four sensors of respiratory and oxygen saturation, heart rate and blood pressure. We have used the sampling rate management method to save energy. In the following, we have used the PCM modulation method for sampling and quantization and have finally achieved good results. The results showed that data transmission is reduced by up to 92.7% and energy saving is achieved significantly.

The rest of this paper is as follows: In Section IV related and previous works that have worked in this field are described in detail. In Section III definitions and benefits and applications of Wireless Body Area Network are briefly described. Section IV discusses the challenge of power and its methods of satisfaction. Section V discusses the importance of data similarity and its role in energy saving. In Section VI, the results of implementing PCM modulation on the data are described in detail and the outputs are analyzed. And finally Section VII briefly describes the conclusions of the proposed method and what can be done to improve it in the future.

In the past years several solutions and discussions proposed about mentioned challenges. Bradai et al. [25] proposed data scheduling and aggregation under WBAN/WLAN healthcare network. They proposed scheduling algorithms to satisfy QoS needs and to overcome the starvation mode of the packets without the highest priority. This proposed method led to reduce latency and increased network capacity. The disadvantage of this method is transmitting data continuously.

In [1] authors proposed adaptive data collection approach for periodic sensor networks. They used adaptive sampling Based on data variance. The result of this method is reducing energy consumption and the disadvantage of it is unnecessary activation of sensors. Makhoul et al. [3] proposed adaptive data collection approach for periodic sensor networks. It is based on the dependence of measurements variance while taking into account the residual energy that varies over time. They used of adaptive data collection technique and this method resulted in a lower transmission rate. The faults of this method are unnecessary activation of sensors and high power consumption of sensors.

Yoon et al. [2] proposed adaptive sensing and compression rate selection scheme. Authors used of selecting the optimum compression rate for captured data in WSNs. In this way, a node periodically selects a compression algorithm and adjusts its sensing rate accordingly. This method led to reducing power consumption in network. However, the sensors are constantly active.

Lee et al. [26] proposed harvesting and energy aware adaptive sampling algorithm for guaranteeing self-sustainability. Authors introduced new methods to extend the lifetime of network. They proposed adaptive sampling algorithm (ASA) and resuscitation adaptive sampling algorithm (RASA) and compensation adaptive sampling algorithm (CASA) and also adaptive sensor management scheme (ASMS) to apply ASA, CASA and RASA according to the energy state of sensors. The results of mentioned method are network self-stability and decreasing power consumption. However, this method is not applicable in most of networks and environments, because of this method assumes the sensors are rechargeable.

Lu et al. [5] proposed distributed sampling rate allocation for data quality maximization in rechargeable sensor networks. Authors introduced a way to calculate and specify distributed sampling rate .In this technique, authors used an algorithm called EAA to determine the amount of energy that each node can access at each interval and another algorithm called RAA to allocate the optimal data sampling rate for each node. The results of this method are increasing data quality and reducing power consumption. However, this method is not applicable in most of networks because of this method assumes the sensors are rechargeable.

Carol et al. [22] proposed self-adaptive data collection and fusion for health monitoring. They proposed some algorithms for sampling. Authors employs an early warning score system to optimize data transmission and estimates in real-time the sensing frequency and a data fusion model on the coordinator level using a decision matrix and fuzzy set theory. The result of their methods is reducing data transfer rate and the disadvantage of their methods is unnecessary activation of sensors.

Marco et al. [4] authors proposed an adaptive sensing scheme. In this content, a set of low-complexity rules auto-regulates the sensing frequency depending on the observed parameter variation. This method led to reducing data overhead and reducing energy consumption. However, in this method there were not considered emergency conditions on the network.

Keally et al. [27] introduced an adaptive approach to determining optimal sampling rate. In this method, authors used of an appropriate interpolation function to determine the optimal sampling rate. The mentioned function for estimating optimal sampling rate considers two factors: the values measured by the pivot node and the patient's risk data.

Fathy et al. [6] proposed an adaptive method for data reduction in IOT. In this proposed method, authors reduced data sent with using the LMS algorithm and adaptive filters. However, in this method sensors are continuously active and emergency conditions are not included.

Zhou et al. [28] proposed node sleep strategy and energy conservation method based on data compression. They used of representation classification (SRC) algorithm to identify the normal signal and compressed sensing (CS) theory for signal compression sampling, and the compressed signal is sent to the coordinator .This method reduces power consumption by decreasing size of transmitting data and sleeping nodes when signals are normal.

Table 1

Specifications and Disadvantages of State-of-the-art methods
REF	PROPOSED METHOD	RESULTS	DISADVANTAGES
[25]	scheduling and aggregation under WBAN/WLAN	• reducing latency • increasing network capacity	• transmitting data continuously
[1]	adaptive sampling	• reducing energy consumption	• unnecessary activation of sensors
[3]	adaptive data collection	• reducing energy consumption	• unnecessary activation of sensors • high power consumption of sensors
[2]	adaptive sensing and compression rate selection scheme	• reducing power consumption in network	• sensors are constantly active
[26]	adaptive sampling algorithm for guaranteeing self-sustainability	• network self-stability • decreasing power consumption	• not applicable because it assumes that the sensors are rechargeable
[5]	distributed sampling rate allocation for data quality maximization	• increasing data quality • reducing power consumption	• not applicable because it assumes that the sensors are rechargeable
[22]	self-adaptive data collection and fusion	• reducing data transfer rate	• unnecessary activation of sensors
[4]	adaptive sensing scheme	• reducing data overhead • reducing energy consumption	• not considered emergency conditions

Makhoul et al. [29] proposed using DWT lifting scheme for lossless data compression in Wireless Body Sensor Networks. It is based on the Discrete Wavelet Transform using the lifting scheme extended with Lagrange polynomial interpolation. This technique reduces power consumption by decreasing size of data. However, in this technique emergency conditions and sleeping unnecessary nodes did not handle.

Wireless Body Area Networks (WBANs) consist of a set of tiny and low cost and low power and light weight sensors that are located inside of the body or under the skin of human body or maybe located in the clothe of patient. These wearable or implemented sensors communicate with each other wirelessly. Most popular wireless technologies used for Wireless Body Area Networks are WLAN, Wi-Fi, GSM, 3G, 4G, WPAN (Bluetooth, ZigBee) and etc. These sensors also communicate wirelessly with a coordinator near the body as shown in Fig. 1. This coordinator can be smartphone or smart watch of patient. The coordinator receives data from sensors and sends to healthcare center smart systems through an access point, internet, several hops and gateway. Figure 1 shows a simple WBANs architecture.

WBSNs continuously sense and capture vital signs and physiological data such as the oxygen saturation, electrocardiogram (ECG), electroencephalography (EEG), the respiration rate, blood pressure, heart rate, body temperature, etc. WBSNs send captured data to a base station. The base station located on or near the body and even base station can be patient smartphone. Then base station analyze and process on captured data and according to analyzed data and status health decide appropriate decision and send data to healthcare center or doctors smartphones.

WBSNs continuously monitor physiological data and remotely take care of patient and the elderly people. As mentioned before in the future one of most important cancers would be caring the elderly because of Overpopulation of them. Also for some of illnesses like diabetes, this system detects emergency conditions faster so can prevent falling into dangerous situations. On the another hands with these sensors patients and elderly can easily stay at home and do their daily activities comfortably and no more need to stay at hospital .

There are several challenges in Wireless Body Area Networks and the most important one is power consumption. Wireless Body Sensor Networks have low power and limited power and the batteries of these sensors aren’t rechargeable. In other words unlike cell phones and other electronic devices, the batteries of WBSNs are not easily replaceable and rechargeable. Although in wearable Wireless Body Area Networks changing batteries is easier than implemented one. Thus it is essential to optimize power consumption and extend lifetime of these sensors. In other words these wearable or implemented sensors must be able to operate for very long duration of time without charging or replacing batteries. WBSNs capture and send huge amount of data and communicate with others wirelessly, these activities consume energy. Hence, it is essential to manage mentioned activities and avoiding unnecessary sending data. For this purpose researchers do many works and several solutions proposed.

Locating each sensor in suitable position in the body and also utilizing of appropriate topology for the network can lead to optimize power consumption, for this purpose authors in [10] explained in detail. Another technique for optimization power consumption and reducing energy consumption is using efficient routing in MAC layer. In [11], authors showed use of appropriate routing protocol may cause reduce power consumption. Also selecting and employing the best wireless technology for sensors in the body can lead to reducing power consumption. On the other hand, using the best power supply and batteries with appropriate power supply technology may lead to energy optimization.

In the past several methods suggested for optimization power consumption in application layer, such as collecting and sending data with appropriate sampling rate. Authors in [8]-[9] proposed this technique and showed how changing sampling rate can reduce power consumption. Also in [12]-[13]-[14]-[15] authors suggested using compression methods to decrease amount and volume of data that transmits in network, therefore the mentioned methods reduce power consumption in network. On the another hand, authors in [7] proposed a method for local emergency detection and when emergency conditions occur, WBSNs transmit data with maximum rate and when conditions and status health of patient are normal, sensors operate with a lower sampling rate. Furthermore, authors in [16] showed that, in order to optimize energy consumption, it is also possible to reduce the sampling rate below the Nyquist rate while still achieving an acceptable quality reconstruction. Others suggested sleeping unnecessary nodes would be lead to reduce power consumption. For example if the patient has diabetes, it’s not necessary all sensors continuously be active, sensors like EEG sensors and other irrelevant sensors can go to sleep mode [17].

Much works have been done on Wireless Body Area Networks energy optimization so far. Among the methods proposed for energy optimization we can mention the methods like collecting and transmitting data at a consistent sampling rate and data compression in various ways, even identifying emergency situations locally and sleeping unnecessary nodes and also a combination of these methods together. However, none of these approaches point to the importance of the data similarity and have not even been used data similarity to optimize energy consumption.

Data are often divided into two types: continuous and discrete. Data similarity is usually denoted by a number between 0 and 1. If the number is zero, the data are completely different, and if the number is one, the numbers are completely equal. There are two methods for measuring data similarity. One is using Euclidean formulas as (1). The distance between vectors x and y is defined as follows:

$$\text{d}\left(x,y\right)=\sqrt{{({x}{1}-{y}{1})}^{2}+{({x}{2}-{y}{2})}^{2}+\dots +{({x}{n}-{y}{n})}^{2}}$$

In other words, Euclidean distance is the square root of the sum of squared differences between corresponding elements of the two vectors. Note that the formula treats the values of x and y seriously: no adjustment is made for differences in scale. Euclidean distance is only appropriate for data measured on the same scale. Second is using cosine metric as (2). The distance between vectors x and y is defined as follows:

$$sim\left(x,y\right)=\frac{x.y}{‖x‖‖y‖}$$

Data similarity is important because if the data collected are the same or similar, no needs to be sent and processed, and only one instance of them can represent all the same data. On the other hand, as long as the data are similar and no significant change has occurred it’s not necessary to send data and process on it for saving energy. As soon as the data collected is significantly different from the previous data, we send data and process on data. So using data similarity can save a lot of energy and reduce many unnecessary sending and processing on data.

On the other hand, the similarity of data is important because we can compress similar data easily and reduce its volume to require less energy to send it. Also, if captured data are similar, we can send them at a much smaller sampling rate. Also, if the data is the same and the value of data does not show us the emergency conditions, we can skip sending that data. In other words, as long as the data does not show a change, it can be neglected to send and process the data in order to save energy. And as soon as the data is noticeably changed, we start sending. As mentioned, if captured data are similar, the methods mentioned above and other methods can greatly save energy.

In this paper we want to examine and measure the similarity of the data. And specify the amount of data similarity and where the data changes. Then use this data similarity to optimize energy. In this paper we studied data on a patient over a 3-hour period and compared the data. To do this, we have examined only the data of the four sensors respiration rate and oxygen saturation and systolic BP and heart rate of the National Early Warning Score (NEWS) system.

In this section, we examine data on a patient over a 3-hour period. We used the MATLAB environment to do this and simulate. To this end, we have selected and evaluated only four of sensors from the patient's body sensors in MIMIC dataset [34]. Respiration Rate and Oxygen Saturation and Systolic BP and Heart Rate sensors are our four selected sensors. And we used the PAM method for sampling the data to minimize unnecessary data transmission. But the result of PAM is not useful for data transmission because it only converts the original waveform into a string of pulses, and these pulses can have any domain (meaning they are still analog). So in order to digitize them we used PCM modulation.

Respiration rate is actually the number of breaths per minute. Breathing may increase during exercise or during illness or abnormal conditions. Average resting respiratory rates by age are:

Birth to 6 weeks: 30–40 breaths per minute
6 months: 25–40 breaths per minute
3 years: 20–30 breaths per minute
6 years: 18–25 breaths per minute
10 years: 17–23 breaths per minute
Adults: 12–18 breaths per minute
Elderly ≥ 65 years old: 12–28 breaths per minute
Elderly ≥ 80 years old: 10–30 breaths per minute

As you can see in the Fig. 2, the PCM signal is quantized due to the similarity of the data in the data signal and has fewer pulses. If you look more closely at the signals in Fig. 2, you will notice that more than 10,000 different but somewhat similar data have been mapped to less than 50 pulses. So the fewer pulses sent results in less energy consumption.

The oxygen saturation level measures the amount of hemoglobin in red blood cells that bind to oxygen molecules. These oxygen molecules reach the blood through the lungs while breathing. Its normal value is 95–97%. If this rate is less than 90% in patients, the situation becomes urgent. But for the amount of oxygen saturated in the blood the lower chart in the Fig. 3 obtained after applying the PCM. As you can see in Fig. 3 similar data of Oxygen Saturation have been mapped to the same pulse in PCM. As you can see in Fig. 3, due to the high similarity of the oxygen saturation data, the PCM signal is mapped to less than 10 pulses. This saves a lot of energy.

Blood pressure is the amount of pressure that the blood enters the vessel wall. The blood pressure measurement unit is mm Hg. Blood pressure is usually expressed in terms of the systolic pressure (maximum during one heartbeat) over diastolic pressure (minimum in between two heartbeats). Normal systolic blood pressure is between 100 and 140. And the blood pressure data you see in Fig. 4 above are all mapped to the same pulse in PCM because of the similarity of the data. And that only sends a single pulse, which saves a lot of energy. But as you can see in Fig. 4, the accuracy has been greatly reduced.

Heart rate indicates the number of heart beats per minute.

In infants (1 to 30 days): 140 to 160 beat per minute
Infants (1 to 3 years old): 120 to 140 beat per minute
Child (3 to 12 years old): 100 to 120 beat per minute
Adults: 60 to 100 beat per minute

As you can see in Fig. 5, the PCM signal, like the data signal, has gone up and down, but the data has been mapped to less than 20 pulses because of their similarity. This has resulted in energy savings and data sent.

This paper proposes an efficient method to reduce power consumption in WBANs. As shown in results using the PCM method, the data signal is mapped to a much smaller number of pulses, which consumed much less power to transmit data and thus conserved battery life very well. As shown in the results, we achieved our goal of managing the sampling rate to save energy. But the important point is that with this method, the accuracy of downsizing and sending data is accompanied by some error. Delta modulation may be used in the future to reduce this error or, in other words, to increase the accuracy of sending information.

Author Contributions

Mohammad Javad Khani and Zahra Shirmohammadi designed the experiments. Mohammad Javad Khani and Zahra Shirmohammadi collected the data. Mohammad Javad Khani and Zahra Shirmohammadi interpreted the results and wrote the manuscript.

Funding : No funding

Availability of data and material Code availability : The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Acknowledgment

This work is completely self-supporting, thereby no any financial agency's role is available.

Conflict of Interest

The authors declare no potential conflict of interest regarding the publication of this work. In addition, the ethical issues including plagiarism, informed consent, misconduct, data fabrication and, or falsification, double publication and, or submission, and redundancy have been completely witnessed by the authors.

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Improvement of Power Consumption in Wireless Body Area Networks by PCM Sampling

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I. Introduction

Ii. Related Work

Iii. Wireless Body Area Network

Iv. Challenge: Power Consumption

V. Importance Of Data Similarity

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VII. Conclusion

Viii. Declarations

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Improvement of Power Consumption in Wireless Body Area Networks by PCM Sampling ​

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I. Introduction

Ii. Related Work

Iii. Wireless Body Area Network

Iv. Challenge: Power Consumption

V. Importance Of Data Similarity

Vi. Experimental Results

VII. Conclusion

Viii. Declarations

Ix. References

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Improvement of Power Consumption in Wireless Body Area Networks by PCM Sampling