Energy Optimization in Wireless Body Area Network based on ZigBee

ABSTRACT

Nowadays, the Wireless Body Area Network (WBAN) is popular used for medical applications to support healthcare systems which enable more effective management and early illness detection. Nevertheless, the WBAN is made up of several tiny, light-weighted and battery-operated sensors with the limit of energy. Hence, an energy-effective WBAN system is very important to create the long-term Multi-Patient Monitoring System. In this paper, we have investigated the energy optimization of WBAN based on ZigBee in which adaptive duty cycle operation is obtained by exploiting the BeaOrder(BO) and SupFraOrder(SO) applying Multi-Patient Monitoring System. Thus, the total consumption energy in idle-mode is avoided, as well as the total consumption energy of sensors is also decreased and expanded network lifetime. The experimental results demonstrate that our proposed method can achieve remarkable improvements in saving energy consumption of WBAN while guaranteeing the Quality of Service (QoS) in terms of network performances such as end to end delay and network throughput in long-term Multi-Patient Monitoring System.

CCS Concepts

  • Networks → Wireless access networks;
  • Applied computing → Health care information systems;

Keywords

Energy Consumption Optimization, Wireless Body Area Network, ZigBee, Multi-Patient Monitoring System.

1. INTRODUCTION

Presently, Wireless Body Area Network (WBAN) has been envisioned as emerging field of Wireless Sensor Network (WSN) with increasing applications. The WBAN is made up of several wireless sensors, that can be either on, implanted or around the person and gather the information of human body for later actions [1].

In addition, the WBAN based on ZigBee has been great attention in recent years [2]. Using WBAN based on ZigBee in Healthcare Support Systems bring to a lot of advantages, one of which is that the WBAN based on ZigBee can supply continuous patient monitoring while guaranteeing the important requirements of a Multi-Patient Monitoring System. Moreover, WBAN based on ZigBee will does not affect the health of patient because of using very low transmission power. ZigBee/IEEE 802.15.4 standard introduces a lowcomplex, low-power and low-cost solution for Multi-Patient Monitoring System [3]. The characteristics of ZigBee devices are tiny, light-weighted and low-power which increase the comfort of patient and provide longer Multi-Patient Monitoring System [4]. Therefore, the energy efficiency optimization will be a key problem in implementation of WBAN based on ZigBee applying Multi-Patient Monitoring System.

To deal with this problem, the research community has proposed many studies related to energy efficiency optimization in WBAN based on ZigBee for medical applications [5–7].

In particular, S. Pathak et al. [5] have introduced an energy-effective intra-hospital telemedicine scenario for multi-patient cardiac monitoring using ZigBee network. Performance of the proposed telemedicine scenario was considered at adjusting sensor transmitting power for various MANET based routing protocols (DYMO, LANMAR and AODV) [5].

In [6], an energy-effective fuzzy based adaptive routing protocol for WBAN is introduced. This protocol uses clustering based routing technique that also makes use of direct transmission of data by the sensors, which could be implanted or around the person, to the Coordinator node. By the experimental results, authors demonstrate that the proposed technique improves the stability period and lifetime of the network.

The authors in [7] have contributed comprehensive reviews on remote Multi-Patient Monitoring Systems based on WBAN and IoT concept with focusing on energy efficiency. The main contribution of this paper is to determine and review existing common technologies and protocols using in WBAN for medical applications which specifically focusing at energy saving mechanisms.

Apart from the aforementioned studies, in our work, we have proposed an energy efficiency method in WBAN based on ZigBee applying Multi-Patient Monitoring System [8]. In particular, we utilised the ZigBee standard working in beacon-enabled mode to achieve the adaptive duty cycle. By this way, we have achieved the different duty cycles by adjusting both BO and SO. In addition, in the same duty cycle, by configuring some different (BO, SO) pairs, we also achieve the different influence on consumption energy, network throughput, end to end delay, etc. Therefore, the total consumption energy in idle-mode is avoided, as well as the total consumption energy of sensors is decreased and extended network lifetime while guaranteeing the QoS of network in Multi-Patient Monitoring System [9].

 

Figure 1: Superframe Structure

The rest of this paper is organized as follows. In Section 2, we present the IEEE 802.15.4/ZigBee standard. Then, Section 3 will introduce the energy consumption in WBAN. The envisioned network architecture for the simulation is described in Section 4. Section 5 is dedicated to performance evaluation of our proposed method. Finally, the conclusions are shown in Section 6.

2. OVERVIEW OF IEEE 802.15.4/ZIGBEE STANDARD

IEEE 802.15.4/ZigBee standard is broad in scope and includes a number of characteristics that are too myriad to summarize into this paper. Thus, only the main characteristics relevant to this paper will be introduced in this section.

Interested readers are referred to [10] for more detailed specification. In the beacon-enabled mode of IEEE 802.15.4/ZigBee standard, the WBAN Coordinator periodical sent beaconframes every BeaInterval (BI) to recognize its WBAN, to synchronize sensors that are connected with it, and to characterize the superframe structure (Fig. 1), including an active-period and, an optional inactive-period [11]. The active-period, equal to the SupFraDuration (SD), is partitioned into 16 equally sized time slots, during which data transmission is permitted [11]. Each active-period can be farther partitioned into a Contention Access Period (CAP) and an optional Contention Free Period (CFP) which included Guaranteed Time Slots (GTS) [11]. If CFP is not using, the sensors aspire to interacting within the CAP period must emulate to others by means of a slotted CSMA/CA mechanism [12]. In the case using CFP, GTS should assigned by the WBAN Coordinator in order to achieve lowlatency or specific requirements of data bandwidth. In the optional inactive-period, each sensor may go into a low-power mode (idle-mode) to save more consumption energy [13]. In this mode, there are no changes in data frames.

The structure of the superframe is defined by two param-eters, the BeaOrder(BO) and the SupFraOrder(SO), which
reflect the length of the superframe and its active-period, respectively [14]. The length of the superframe (BI) and the length of its active-period (SD) can be expressed as below:

BI = BaseSupFraDuration × 2BO    (1)

SD = BaseSupFraDuration × 2SO   (2)

Where BaseSupFraDuration = SlotDuration × NumSupFraSlot  and NumSupFraSlot = 16 represents the number of equally spaced slots of the active-portion of the superframe, SlotDuration represents the number of symbols (60 symbols) forming a superframe slot when the SO is equal to zero [12] (1 symbol = 4 bits, assumed the frequency band of 2.4 GHz and bit rate of 250 kbps). Furthermore, BO and SO must be ensured the cons traint which is 0 ≤ SO ≤ BO ≤ 14. The duty cycle which is the ratio of the length of active-period SD over the length of superframe BI, is calculated as below [12]:

Duty cycle = (2)SO−BO × 100% (3)

By this way, the different duty cycle configurations can be obtained by adjusting both BO and SO. For example, by keeping (BO, SO) = (4, 3), we obtain a 50% duty cycle where active-period, inactive-period are 0.123s and beaconinterval is 0.246s while by keeping (BO, SO) = (5, 3), we get a duty cycle of 25% where active-period of 0.123s, inactiveperiod of 0.369s and beacon-interval is 0.492s. In addition, in the same duty cycle, we also achieve the diverse length of active-period and inactive-period. For instance, by keeping (BO, SO) = (2, 1), we also obtain a duty cycle of 50% but having short active-period and inactive-period of 0.03s. Thus, by setting several different (BO, SO) pairs in the same duty cycle, we will also obtain the different influence on consumption energy, network throughput, end to end delay.

3. ENERGY CONSUMPTION IN WBAN

In WBAN, there are two types of data transmission which are (i) intercommunication between physiological sensors and the Base Stations and (ii) transferring the processed data from Base Stations to the Servers [15]. These will causing the constraints about energy in sensors. In addition, the energy consumption in sensors is generated from data sensing, processing and transmitting [16]. Among them, data transmission which is caused by idle-listening, over-emitting, packet collision, control packet over-head and over-hearing as shown in Table 1, is a major reason of energy wastage [17].

Therefore, this major energy wastage in WBAN data communication should be reduced to improve the performance and extend network lifetime.

The wearable human body sensors can be placed on patient’s body to continuously monitor physiological signals of the patient such as body temperature, blood pressure, respiratory rate etc., and transmitted to the doctors or nurses over a wireless network [3]. Because sensors are operated in battery mode, it cannot be recharged or replaced without employing a serious medical procedure. Therefore, it assumed that at least ten to fifteen years for battery lifetime of medical sensors [15]. In addition, if the battery drained, the overall medical systems and patients will be influenced because sensors are no longer functioning. To solve this problem, it is crucial to design an energy-effective system apart from cost-effective, reliability and flexibility in WBAN. This will also support to realize effective a longterm Multi-Patient Monitoring System. Thus, energy ef- ficiency optimization problem should be considered when designing any framework or application of WBAN.

Factor Explanation
Idle-listening The node remains in reception mode listening to receive possible traffic that is not sent and picking up packets destined to other nodes [18].
Over-emitting A message is transmitted when the destination node is not ready.
Packet collision Increased consumption energy due to collisions and network latency due to the retransmissions when the received packets are corrupted [18].
Control packet over-head A minimal number of control packets should be used to make a data transmission.
Over-hearing Node receives packets that are destined to other nodes.

Table 1: The major energy waste sources in WBAN

4. PROPOSED WBAN MODEL BASED ON ZIGBEE IN MULTI-PATIENT MONITORING SYSTEM

The WBAN based on ZigBee used for Multi-Patient Monitoring System are basically comprised a Multi-Patient Monitor Center, some Patient Sensor Nodes and several Patient Base Station which acts as a Sink ( WBAN Coordinator) [19], as shown in Fig. 2.

In Multi-Patient Monitoring System, Patient Sensor Nodes will collect the physiological signals of human body and transmit them to the Patient Base Station through WBAN based on ZigBee. Next, the Patient Base Station will accumulate the data from all Patient Sensor Nodes that are connected to it through wireless ZigBee links. Then, the Patient Base Station transfers the processed data to the Multi-Patient Monitor Center. Finally, the Multi-Patient Monitor Center will analyse and generate the alarm signal to the doctor/nurse or the patient.

5. PERFORMANCE EVALUATION

In our work, we have used the OPNET-ZB simulation model which was developed on OPNET Modeler 14.5 [20, 21], by IPP-HURRAY Research Group in Polytechnic Institute of Porto, Portugal [13].

5.1 Simulation Setting

As aforementioned proposed WBAN Model based on ZigBee, the architecture of a WBAN consists of one Patient Base Station (WBAN Coordinator) and 6 Patient Sensor Nodes. All of 6 Patient Sensor Nodes are directly communicating with the Patient Base Station node. The main objec-tive of this paper is to find out the solution to minimize the consumption energy of WBAN based on ZigBee while guaranteeing the QoS of network performances such as network throughput and average end to end delay in Multi-Patient Monitoring System.

 

Figure 2: WBAN Model based on ZigBee in Multi-Patient Monitoring System

The parameters being monitored in this proposed scenario is duty cycle which is being varied with adjusting in macSupFraOrder(SO) and BeaOrder(BO) [8]. The finding out optimum duty cycle is being executed by adjusting load by taking varied in arrival data rate. Furthermore, we also investigate the influence on the consumption energy, network throughput, average end to end delay when adjusting both BO, SO for a specific duty cycle. In the same duty cycle, by changing some different (BO, SO) pairs, we also achieved the different influence on consumption energy, end to end delay, network throughput. We will minimize the consumption energy by studying the best setting of (BO, SO) while guaranteeing the QoS of network in Multi-Patient Monitoring System. We consider the simulation setting with some important parameters [4] given in Table 2.

Parameters Values
Network Scale 100 m * 100 m
ZigBee Frequency Band 2.4 GHz
Channel Bit Rate 250 kbps
Simulation Time 20 s
Arrival Data Rate 5 kbps, 15 kbps, 120 kbps
BeaOrder(BO) 3, 4, 5, 6, 10
SupFraOrder(SO) 0, 1, 3, 4, 8
Energy Model MICAz
Initial Energy 2 AA Batteries (1.5V, 1600 mAh)

Table 2: Simualtion Parameters

5.2 Results and Analyses

Firstly, we will study the effect on system performance when taking into account some different duty cycle configurations which can be achieved by adjusting both BO and SO. Fig. 3 depicts the consumption energy of overall network in different duty cycles. It is clearly seen that when the duty cycle decreases, the consumption energy also decreases. The reason is that, when the duty cycle decreases, the inactive-period will be extended and then each sensor node may go into a low-power mode (sleep-mode) to save more consumption energy.

Fig. 4 depicts the effect on average end to end delay in different duty cycle. When the duty cycle decreases, then the average end to end delay will be increased. Because when duty cycle decreases then the inactive-period will be expanded. That denotes the end to end delay will be grown for packet coming in the inactive-period.

 

Figure 3: Energy Consumption in Different Duty Cycles

 

Figure 4: End To End Delay in Different Duty Cycles

Besides that, the network throughput with 50% and 100% duty cycle are quite high while 25% duty cycle is very low (see Fig. 5). Because the network throughput will be increased when the active-period expands (duty cycle increases).

Secondly, we will take into account the scenario of keeping the same BO to study about the influence of increasing SO. As SO escalates, SD, essentially the superframe expands, the active-period extends. Because the active-period implies the more energy consumed tasks of packet receiving and transmitting, thus the consumption energy will be increased (see Fig. 6). Larger superframes let sensor nodes to remain in active-mode longer, allowing more opportunity for communications, so the network throughput will be increased and average end to end delay will be decreased (see Fig. 7 and Fig. 8).

 

Figure 5: Network Throughput in Different Duty Cycles

 

Figure 6: Energy Consumption (increasing SO)

Considering the same duty cycle scenario, average end to end delay will be raised exponentially when increasing (BO, SO) pair (see Fig. 9). Because, when we keep the same duty cycle, increasing (BO, SO) pair will generate a new superframe configuration which extends the inactive-period. Therefore, the average end to end delay will be raised for packet coming in the inactive-period. In other words, when decreasing (BO, SO) pair, the average end to end delay also be decreased but in such as case the number of beaconpackets changed would be increased the total consumption energy and network throughput will also be increased (see Fig. 10 and Fig. 11).

 

Figure 7: End To End Delay (increasing SO)

 

Figure 8: Network Throughput(increasing SO)

Figure 9: End To End Delay (increasing (BO, SO))

Figure 10: Energy Consumption (increasing (BO, SO))

Figure 11: Network Throughput (increasing (BO, SO))

6. CONCLUSIONS

It is clearly seen that the wireless body area network is an emerging technology and play an important role in develop-ing Multi-Patient Monitoring System. Hence, the requirement of more researches about WBAN is essential to real-ize the Multi-Patient Monitoring System, which will bring to a lot of advantages in caring the health of patients. In this paper, we have investigated the energy optimization in the WBAN based on ZigBee in which adaptive duty cycle operation is obtained by exploiting the BeaOrder(BO) and SupFraOrder(SO) applying Multi-Patient Monitoring System. Thus, overall energy consumed in sleep-mode is avoided, as well as the overall energy consumed by sensors is decreased and prolonged network lifetime. Through simulation, it has been demonstrated that our proposed method was able to save efficient consumption energy of WBAN based on ZigBee while guaranteeing the QoS such as end to end delay and network throughput in Multi-Patient Monitoring System.

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De-Thu Huynh
School of Computer Science
and Technology
Huazhong University of
Science and Technology
Wuhan, China
huynhdethu@gmail.com

 

Tran Quang Nhat
Faculty of Information
Technology
Ho Chi Minh City University of
Transport
Ho Chi Minh, Vietnam
nhat.tran@ut.edu.vn

 

Dong Doan Van
School of Information
Engineering
Wuhan University of
Technology
Wuhan, China
dongdoan2211@yahoo.com

 

Anh Tuấn

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