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Sensors
  • Article
  • Open Access

5 April 2019

A Spectrum-Aware Priority-Based Link Scheduling Algorithm for Cognitive Radio Body Area Networks

and
1
School of Computing and Information Technology, Eastern International University, Hoa Phu, Binh Duong City 75114, Vietnam
2
Department of Computer Engineering, Chosun University, 309 Pilmun-daero, Dong-gu, Gwangju 61452, Korea
*
Author to whom correspondence should be addressed.
This article belongs to the Section Sensor Networks

Abstract

With the development of wireless communication technology, wireless body area networks (WBANs) have become a fundamental support tool in medical applications. In a real hospital scenario, however, the interference between wireless medical devices and WBANs may cause a high packet drop rate and high latency, which is harmful to patients using healthcare services. Nonetheless, cognitive radio is a promising technology for sharing the precious spectrum, which has high efficiency of the wireless resource. Thus, WBANs with cognitive radio capability are also exploited. We propose a spectrum-aware priority-based link scheduling (SPLS) algorithm for cognitive radio body area networks (CRBANs) in a real hospital scenario. In SPLS, three channels are used: DataCh, EDataCh, and CtrlCh for normal data, emergency data, and control messages, respectively. To avoid collision during data transmission, neighboring CRBANs send messages regarding the channel state with CtrlCh before the scheduling. The CRBANs can share DataCh in the time domain for improving the throughput. The SPLS algorithm allows a CRBAN to access idle channels on the licensed and unlicensed spectrum according to the CRBAN traffic. Our simulation results show that the proposed SPLS outperformed the conventional scheme in terms of packet delivery ratio, system throughput, latency, and energy efficiency.

1. Introduction

In the recent years, the development of wireless communication technology has had a great impact on modern medical applications, especially, for healthcare monitoring [,]. A medical wireless body area network (MWBAN) comprises multiple wireless biosensor nodes and a coordinator node. The wireless biosensor nodes collect biomedical data such as blood pressure, heartbeat, electrocardiography data, electroencephalogram data, and body temperature which are then transmitted to a coordinator node []. An example of an e-health system is shown in Figure 1, in which the collected vital signals at the coordinator node are transmitted to a controller before they are forwarded to the medical server, emergency server or doctor [,]. Moreover, the application of MWBANs is also gradually increasing in the advanced healthcare facilities in the hospital []. In particular, its medical applications may include patient monitoring or telemedicine.
Figure 1. Example of an e-health system.
The traffic priority for various services is summarized in Table 1 according to the IEEE standard 802.15.6 []. The IEEE standard 802.15.6 introduced the medium access control (MAC) and physical (PHY) layers of a wireless body area network (WBAN) using unlicensed band ISM and ultra- wideband []. However, in crowded places such as hospitals, interference amongst WBANs or between WBANs and other devices using the same radio frequency at the same time can occur. Such interference may result in an unexpected situation that is harmful to the patients. Therefore, the wireless communication systems at a hospital should have low interference in order to ensure the continuity of the signal for e-health systems.
Table 1. WBAN priority for various services [].
Recently, cognitive radio (CR) has become a paradigm for the efficient reuse of spectrum resources in terms of the opportunistic access of the licensed (primary users) part of the spectrum by unlicensed users (secondary users). Therefore, CR may be a promising solution for the MAC and PHY layers to mitigate the interference with medical WBAN. More specifically, a CR-based-approach has been modeled for WBAN in a hospital environment, which can operate on either an unlicensed or a licensed band []. In addition, CR technology has been introduced to reduce the interference in medical environments []. The CR technique has been applied to medical WBANs, which aims to improve spectrum usage and mitigate interference in healthcare applications [].
Taking motivation from the aforementioned works, we propose a link scheduling algorithm for multiple CR body area networks (CRBANs) in the hospital scenario. We assume that each CRBAN collects vital signals which belong to either telemedicine or hospital applications as in []. We consider the applications of CRBAN services shown in Table 1 according to the IEEE 802.15.6 standard []. The CRBAN with the highest traffic priority or telemedicine applications can be regarded as primary user (PU). On the other hand, the CRBAN with the healthcare monitoring applications or low traffic priority plays the role of secondary user (SU) when the unused licensed spectrum is opportunistically utilized. The interference can be categorized into two types: interference at the medical devices caused by the intra-CRBAN or inter-CRBAN transmission and interference at the CRBAN caused by a neighboring CRBAN transmission or medical devices. However, we only consider the latter case, in which the transmission from a CRBAN is interfered by nearby CRBANs in this paper. The link scheduling algorithm is proposed for channel access by various types of services with different priorities. The CRBAN with the highest traffic priority will occupy the emergency-traffic data channel (EDataCh), while the others will occupy the normal-traffic data channel (DataCh). The control packets of the CRBANs will be transmitted on the control channel (CtrlCh).
The objective of our work is to reduce the interference or maximize the concurrent transmission with a given bandwidth while assuring minimum interference to the medical devices. The link scheduling algorithm has been considered as the most effective algorithm for multiple nodes to access the medium with low interference. The transmission of different WBANs who stay in the same area will be scheduled in time domain. For example, the scheduling scheme allows the WBANs to transmit at different timeslots according to the traffic priority in the IEEE 802.15.6 QoS constraints []. In CRBANs, a link scheduling algorithm should allow each CRBAN to access the data channel for transmission while considering the primary users’ (PUs’) activities and medical devices in the hospital. The control channel is used for broadcasting the exchange message amongst the CRBAN, which includes the CRBAN traffic priority. However, multiple CRBANs can reuse a channel through schedule transmission in the time domain. Therefore, the channel utilization increases despite the higher latency in the case of low-priority CRBANs. In the overlay paradigm of cognitive radio, the SUs know the PU’s transmission parameters such as channel gain and transmitted data sequence, and then SUs can start their transmission with the PUs []. We also implement overlay CR, in which the CRBANs have knowledge of the transmission schedule of medical devices. As a result, the scheduling algorithm adapts quickly to the medium and ensures its coexistence with medical devices. The simulation results show that the packet delivery ratio drops while the PUs’ activities increase with an acceptable delay. The contribution of the paper could be summarized as follows:
  • The usage of cognitive radio has been applied to WBANs in the healthcare applications. The emergency data channel is used only for emergency data of CRBANs while the normal data will be transmitted by using the normal data channel.
  • The CRBANs with high traffic priority could play the role of a primary user in the unlicensed channel, which aims to access the spectrum earlier than other CRBANs. As a consequence, the latency of the data with high traffic priority is guaranteed to be less than a threshold value.
  • The neighboring CRBANs can share or reuse the spectrum to increase the network throughput because the idle spectrum is limited.
The rest of this paper is organized as follows: in the following section, some related works are reviewed while focusing on CR technology adapted to WBANs for various applications. In Section 3, the system model, including the network model and channel sensing, is introduced. In Section 4, the proposed SPLS algorithm is presented in detail, and the superframe structure, formulation, inter-CRBAN transmission, and intra-CRBAN transmission are discussed with the link scheduling algorithm. In Section 5, the performance of the proposed SPLS algorithm is evaluated via a computer simulation and compared with the conventional scheme. Finally, the paper is concluded in Section 6.

3. System Models

3.1. Network Model

We consider network deployment in the hospital with two main e-health applications, telemedicine and hospital information systems, as in [,]. The telemedicine application involves providing real-time healthcare service delivery to distant users, such that the sensor nodes send the vital signal to the coordinator, and the coordinator transmits those signals to the gateway or controller []. The hospital information system collects the data of patients at the hospital. Because the telemedicine application has a higher priority than the hospital information application, users of telemedicine application are considered as PUs and user of hospital information application is considered as SUs. We assume that PUs will turn on within a specific duration of time according to the needs of patients. In addition, on taking into consideration the traffic priority in the WBAN, we also consider the WBAN with the highest traffic priority in Table 1 as the PU. The notations of the network model and scheduling problem are shown in Table 2.
Table 2. Notations for spectrum-aware priority-based link scheduling algorithm.
In order to evaluate the SPLS algorithm, we assume that the three-tier CRBAN is set up as in Figure 1. The particular network scenario of our proposed work in Figure 2 is similar to the hospital scenario in [,], which is divided into nine similar rooms, where each room has an area of 9 m2. The network consists of a controller and multiple CRBANs with the LS and NLS medical devices. Each CRBAN or SU comprises several sensors on the human body, which is allowed to move freely through the area. In each room, the locations of the NLS and LS medical devices are fixed, and the CRBANs are uniformly distributed in the area. The movement of CR clients is modeled as a random mobility model as in [].
Figure 2. Deployment in the hospital environment.
Taking into consideration the priority of various healthcare services, we define the priority level of CRBANs as follows. The CRBANs can be PUs or SUs depending on the healthcare services as listed in Table 1. In the case of a licensed channel, the CRBANs are SUs with transmissions scheduled according to the CRBAN services. In the case of the unlicensed channel, the nodes for the CRBAN service with the highest priority in Table 1 are regarded as PUs, and the nodes for other CRBAN services are regarded as SUs.
The CRBANs use three kinds of channels called CtrlCh channel, DataCh channel, and EDataCh channel. The CtrlCh is used for transmission of control signals between the coordinators and sensor nodes or between the coordinators and the controller. For example, the CtrlCh is used for the broadcast packet and the beacon packet of the coordinator. The EDataCh is only used for transmission of services with the highest priority shown in Table 1, while the DataCh is used for transmission of normal services. In this system model, CRBANs use the control channel to exchange messages with each other, and the beacon signal of the CRBAN is also transmitted on the control channel when the coordinator senses the idle control channel. The sensor nodes wait for the beacon packet on the control channel and then switch to the data channel for data transmission according to the information in the beacon signal. The CRBAN accesses the control channel by using carrier-sense multiple-access/collision-avoidance (CSMA/CA) protocol. The overlay spectrum is used as an interference mitigation model where the CRBANs use the spectrum that is not occupied by the PUs. The medical devices work on either licensed or unlicensed channels.
The spectrum is divided as follows: the licensed band is specified for wireless medical telemetry service in the spectrum bands 608–614 MHz, 1395–1400 MHz, and 1427–1432 MHz and medical implant communications service in the spectrum band 402–405 MHz; the unlicensed band is the ISM band at 2.4 GHz [,]. In [], as per the IEEE standard 802.15.6, the narrowband band 2400–2483.5 MHz is divided into 79 channels of 1-MHz bandwidth. However, we only consider 20 unlicensed channels at 2.4 GHz for simplicity. In the unlicensed band, the frequency 2483 MHz is selected for CtrlCh because it is not overlapped with the IEEE 802.11 channels, and the frequency 2475 MHz is selected for EDataCh, while DataCh is selected as any idle channel in the ISM band (18 channels). The licensed band is used for the medical devices or PUs, CRBANs sense the vacant channel or the spectrum hole.
We consider two types of transmission: the transmission between the sensor nodes and the coordinator as an intra-CRBAN transmission and the transmission from the coordinator to the controller or the transmission from the coordinator of one CRBAN to the coordinator of other CRBANs as an inter-CRBAN transmission.
In the 2.4–2.5-GHz band, the channel model for intra-CRBAN follows a power law model as per the IEEE standard 802.15.6 as in [], and the path loss is calculated by:
P L ( d ) = a log d + b + N P L ,
where d is the distance between the transmitter and receiver in mm, a (6.60) and b (36.1) are parameters of the model, and NPL is a normally distributed variable with standard deviation σN of 3.80.
The inter-CRBAN channel model is considered as a distance-dependent path loss model, the path loss exponent is less than two, and the fading follows a gamma distribution. The mean and variance values follow a power law with respect to the distance between CRBANs, in which the rate of increase of path loss depends on the increase of the distance between two CRBANs [,]. In [,], the measurement of inter-CRBAN channel model is modeled as the classical distance-dependent path loss model as follows:
P g a i n = a r d B + b ,
where Pgain is either the mean or variance of the path gain, rdB = 20log10r, where r is the distance between two CRBANs, and a and b are the fitting parameters at 2.45 GHz, respectively. The mean of a and b is −0.05 and −0.19, respectively, and the variance of a and b is −0.19 and −52.8, respectively.
The path loss between the CRBAN and controller is considered as an indoor path loss model. The transmit power of the medical devices is similar to that in [,], and thus the total path loss of a CRBAN in the hospital environment is calculated as follows:
P L t o t a l = 37.7 + 3.3 log d + 16.2 n ,
where d is the distance from the medical devices to the CRBAN, and n is the number of floors (or walls) the radio signal has to traverse.
The upper bound on the transmit power for NLS and LS devices are defined in [,] as follows:
P N L S ( n ) = ( D N L S ( n ) E N L S ( n ) 7 ) 2
P L S ( m ) = ( D L S ( m ) E L S ( m ) 23 ) 2
where DNLS(n) and DLS(m) are the distances from the CRBAN to the NLS and LS devices, respectively. ENLS(n) and ELS(m) are the electromagnetic interference immunity levels for the NLS and LS medical devices n and m, respectively, in Figure 2.
Because the NLS and LS devices operate in the same vicinity of CRBANs, the interference probability was defined if the CRBAN causes interference with the medical devices by violating the transmit power constraints PNLS and PLS. As in [,], the transmit power in the data channel allows a successful transmission from the CRBANs to the controller, which is defined as:
P t x d a t a = min { min n ( P N L S ( n ) ) , min m ( P L S ( m ) ) ) } ,
where PNLS and PLS are derived from (4) and (5), respectively.
In [], in the area with a high electromagnetic interference (EMI) level (mainly due to a large number of life-supporting medical devices), the CRBAN cannot reach the controller with the minimum required signal. In our overlay algorithm, the CRBANs change the operating channel such that they can transmit with a high power while causing no interference with medical devices.

3.2. Channel Sensing

The coordinator performs channel sensing on entire licensed channels and unlicensed channels. The coordinator records the signal to interference plus noise ratio (SINR) of each channel in order to determine whether there is any transmission of PUs on a channel. The SINR observed at the coordinator of CBi in channel Ck is defined as:
γ i ( C k ) = P r I k + N 0 ,
where Pr is the received signal, Ik is the interference power in channel Ck, and N0 is the additive white Gaussian noise.
In a practical scenario, the value of SINR can be estimated at the PHY layer of the receiver as in [] using the RSSI (received signal strength indicator) as follows:
S I N R ( t ) = 10 ( R S S I ( t ) η 0 C 30 ) / 10 ,
where η0 is the thermal noise, the constant C is the measurement offset that is empirically measured in [] using Chipcon CC2420 on the Telos motes (C = 45 dB), and the value of 30 is the conversion of dBm to dB.
As in [,,], channel Ck is idle if there is no PU activity on the licensed channel or no transmission on the unlicensed channel. In such a case, the result of channel sensing at the coordinator of CBi on channel Ck is denoted as:
S t a t e i ( C k , t ) = I d l e i f γ i ( C k ) < γ t h ,
Otherwise:
S t a t e i ( C k , t ) = B u s y i f γ i ( C k ) > γ t h ,
We assume that the PU user-activity models are similar to those in [,,]. The PU’s user-activity model has two states, which are Idle and Busy, as shown in Figure 3. The values p and q are the probability that an idle channel becomes busy and the probability that a busy channel becomes idle, respectively. The durations of the busy and idle times of the PU are defined as Tbusy(k) and Tidle(k), respectively. The arrival of the PU is independent of CRBANs’ activities, and the transition follows a Poisson process in which the lengths of both periods are exponentially distributed with rate λ and mean value E = 1/λ. If q > qthres or (1 – p) > pthres, then the coordinator estimates an idle channel Ck at CRBAN CBi is obtained as:
Δ T i , k = t = t 0 t Δ T t ( C k ) ,
where ΔTt(Ck) = 1 indicates that Ck is idle during one superframe. The coordinator evaluates the possible time that the duration ΔTk for which the CBi can occupy Ck is longer than a threshold duration Tthres, which is equal to the length of a superframe.
Figure 3. PU activity model.
The list of idle channels for the data transmission that is observed by the coordinator is denoted as:
C I i ( t ) = { C k | S t a t u s ( C k , t ) = I d l e , Δ T k T t h r e s , C k C L C U }
The coordinator broadcasts the list CIi(t) on CtrlCh to the network to discover the nearby CRBANs. The neighboring discovery and link scheduling steps are explained in the next section.

5. Performance Evaluation

In this section, we evaluate the performance of the proposed algorithm using the MATLAB simulator. We consider the network area as in Section 3.1, which is similar to the network in []. We also compare our work with the RTS/CTS protocol in []. In [], a CRBAN sends its RTS message to the central controller. Then, the central controller allows the CRBAN to transmit data in the data channel with the required transmit power. We select the dual CR WBAN for our comparison work because the hospital application is used in [], and the traffic priority is considered by using an emergency data channel. In [], WBANs are applied the cognitive radio, and sensor nodes transmit vital signals to the controller by using the RTS/CTS protocol.

5.1. Simulation Environment

The network area is shown in Figure 9 and is divided into nine similar rooms, and each room has an area of 9 m2. In the simulation scenario, each room has one LS device and one NLS device. The ENLS and ELS of each room, which are the EMI immunity levels for the NLS device n and LS device m, are shown in Figure 2. The transmission range from the CRC to the CRBANs is 10 m to cover the whole network area. The transmission range of each WBAN is 2 m, in which the sensor nodes transmit and receive signals to/from the coordinator. In this simulation, we consider the performance on an unlicensed channel at 2.4 GHz, which is divided into 10 channels with a 1-MHz bandwidth for simplicity (2400–2483.5 MHz). We study four performance metrics: packet delivery ratio, packet delay, throughput, and energy consumption. To consider the impact of the PUs’ activities at each superframe, we vary the number of PUs in each area, in which the probability that the PUs are on is 0.5 (pthres = 0.5 and qthres = 0.5 as in Section 3.1). We assume that the probability of misdetection is 0.01. For simplicity, we only implement sensors that generate a normal data packet with various packet generation rates. We also vary the number of CRBANs in each area. However, the NLS and LS devices are on during the simulation time. The interference is considered to occur between CRBANs and medical devices or between the PUs and SUs. In the former case, the interference occurs when the transmit power is higher than the acceptable level or when the status of the medical devices is incorrectly reported at the CRC. In the latter case, the interference occurs when the SUs and PUs transmit at the same time. The simulation parameters are listed in Table 3.
Figure 9. Network deployment.
Table 3. Simulation parameters.

5.2. Simulation Results and Discussion

5.2.1. Packet Delivery Ratio

The packet delivery ratio (PDR) is considered as the ratio of the number of successfully received packets to the number of sent packets at the CRBAN. The PDR result is shown in Figure 10. The PDR depends on the packet generation rate and number of CRBANs in the network. However, the PDR decreases slightly in the scenario with a high number of PUs while increasing the number of CRBANs and number of PUs, as shown in Figure 10. If the packet generation rate increases, the PDRs of both algorithms decrease as shown in Figure 10. In the multiple CRBANs network, the coordinator of the CRBAN starts its neighbor discovery and exchanges information with the specific neighbors in order to share the same channel in the presence of the PU. Therefore, the CRBAN transmission is scheduled into an idle channel, which ensures the successful transmission. However, when the packet generation rate at the sensor nodes increases, the sensor can transmit packets within the scheduled superframe in the time domain. Therefore, some packets at the sensor nodes may be dropped because of collision.
Figure 10. Packet delivery ratio: (a) varying the number of CRBANs; (b) varying packet generation rate.

5.2.2. Packet Delay

The packet delay is considered as the time from which a packet is received at the CRC to the generated time at the CRBAN. The packet delay is shown in Figure 11. As with the PDR, the packet delay also depends on the packet arrival rate and number of PUs. In Figure 11, the latency per packet of the SPLS is lower than that of the RTS/CTS. In the SPLS, the sensor nodes transmit the data packet according to the assigned schedule in an idle channel. However, in the RTS/CTS algorithm, the coordinator allows the sensor nodes to send the data packet when the coordinator senses an idle channel. However, the packet delay of the SPLS is increased when the number of PUs and CRBANs is increased. Figure 11 shows the delay when the number of CRBANs is increased while the packet generation rate is 2 packets/s. In Figure 11, the delay of the SPLS is high when the packet generation rate increases in the case of 72 CRBANs in the network.
Figure 11. Delay per packet: (a) Varying the number of CRBANs; (b) Varying packet generation rate.

5.2.3. Network Throughput

The network throughput is considered as the number of successful packets in the simulation time, which is measured in Kbits/s. The network throughput is shown in Figure 12, wherein the number of CRBANs and the packet generation rate are varied. In Figure 12, the throughput of each CRBAN decreases slightly because the network density increases with the decrease in the PDR, as shown in Figure 10. However, the throughput of each CRBAN increases with the packet generation rate in the scenario of 72 CRBANs. The SPLS realizes a superior performance than the RTS/CTS. In Figure 12, the throughput of the CRBANs increases with the increase in the packet generation rate, and the sensor nodes can successfully transmit more packets according to the schedule in the time domain. However, the network throughput depends on the number of PUs in the network, and the network throughput in the case of 4 PUs per area is lower than that in case of 1 PU per area.
Figure 12. Throughput of CRBAN: (a) varying the number of CRBANs; (b) varying packet generation rate.

5.2.4. Energy Consumption

The energy consumption is considered to be the energy required for transmitting and receiving data packets and negotiation packets, the energy for sensing channels, and the energy for switching channels. We assume that the energy consumed during the transmission and receiving of packets is similar to that in []. The energy consumption per bit is calculated as the ratio of the total energy consumed during the transmission over the number of successful received packets in bits []. The total energy consumption for transmitting and receiving data packets increases with the number of CRBANs and the packet generation rate. The energy consumption is shown in Figure 13, which shows that the SPLS consumes less energy than the RTS/CTS algorithm. The energy consumption of both the algorithms is increased as shown in Figure 13. The presence of PUs results in a high energy consumption of the CRBAN when varying the network density. In contrast, the energy consumption is increases gradually in Figure 13 according to the packet generation rate. In Figure 13, the energy consumption per bit at the packet generation rate of 1 and 2 packets per second is similar. This is because, as the packet generation rate increases from 1 to 2 packets per second, the total energy consumed during transmission is increased but the number of successful received packets is also increased as shown in the Figure 10. As a result, the energy consumption per bit is not increased in this case. When the packet generation rate increases up to 3 and 4 packets per second, PDR gradually decreases as shown in Figure 10, resulting in the increased energy consumption per bit in Figure 13. In the SPLS, the energy is consumed in the sensing duration only one time per superframe before negotiating with the neighbors. The CBRANs negotiate with the neighbors before creating a cluster of two CRBANs sharing the same idle channel. As a consequence, the CRBANs do not sense the channel as in the case of the RTS/CTS.
Figure 13. Energy consumption per bit: (a) Varying the number of CRBANs; (b) Varying packet generation rate.

6. Conclusions

In this paper, the spectrum-aware link scheduling algorithm for CRBANs in the multi-channel environment has been proposed. In order to guarantee that CRBANs can be adapted to a high-interference scenario with an acceptable performance, the use of an SPLS algorithm is required in CRBANs. The proposed SPLS algorithm allows a CRBAN to switch to an idle channel with low latency. The traffic of the CRBANs is also taken into consideration by using a separate channel for emergency data in order to improve the network reliability. The coordinator performs the channel sensing and negotiation with the neighbors in order to reduce the energy consumption. In addition, the CRBANs negotiate for the schedule, which results in reduced collisions owing to the use of the control channel. The scheduling algorithm allows two neighboring CRBANs to share the same idle channel, which increases the probability of successful transmission in the presence of the PU’s activity. The simulation results show that the proposed SPLS realizes a superior network performance with lower energy consumption as compared to the conventional scheme. In future works, we intend to improve the present work in terms of energy efficiency and high reliability.

Author Contributions

The individual contributions of authors are as follows: T.T.T.L. developed and simulated the protocol of SPLS by using Matlab; S.M. directed the research and contributed to the refinement of the protocol and the interpretation of the simulation results. The paper was drafted by T.T.T.L. and subsequently revised and approved by S.M.

Funding

This research was supported in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2016R1D1A1A09918974).

Acknowledgments

The authors wish to thank the editor and anonymous referees for their helpful comments in improving the quality of this paper. Correspondence should be addressed to SangmanMoh (smmoh@chosun.ac.kr).

Conflicts of Interest

The authors declare no conflict of interest.

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