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A bonded channel in cognitive wireless body area network based on IEEE 802.15.6 and internet of things
Computer Communications 150 (2020) 131–143
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A bonded channel in cognitive wireless body area network based on IEEE 802.15.6 and internet of things Fahim Niaz a , Muhammad Khalid b ,∗, Zahid Ullah a , Nauman Aslam b , Mohsin Raza b , M.K. Priyan c a
Center of Excellence in IT, Institute of Management Sciences, Peshawar, Pakistan Department of Computer & Information Sciences, Northumbria University, Newcastle Upon Tyne, UK c Middlesex University, London, UK b
ARTICLE
INFO
Keywords: Cognitive body area network Wireless body area network Channel allocation WSN Interference management and channel model
ABSTRACT Despite the recent developments in communication technologies for wireless body area networks (WBAN), the reliability of packet transmission, especially for emergency and critical data transfer remains a significant challenge. This may be that most of the existing techniques in WBAN use single-channel for data transmission with no intelligence. The cognitive bonded channel which provides high data rate for emergency and the demanding situation. Existing techniques in WBAN rely on the use of the single-channel for data transmission and do not exploit the use of the bonded channel, which can improve the WBAN capacity of off-body communication. In our research, we propose a traffic load aware bonded channel algorithm (TLA-BCA), which exploits channel bonding to improve the performance of off-body communication between the WBAN sink nodes and gateway. Our proposed algorithm, i.e., TLA-BCA utilizes high-quality channels for channel bonding to maximize the capacity of off-body communication in WBANs. TLA-BCA demonstrates significant performance improvement over Traffic Priority Based Channel Assignment Technique (TP-CAT) and Static Channel Assignment (SCA) in terms of average throughput, average end-to-end delay with comparable energy performance.
1. Introduction WBAN consists of low power sensor nodes where nodes are deployed on or inside the human body to the monitoring of various physiological parameters. It provides the daily activity of the patient and its health condition. WBAN is significantly used in medical applications and health care. In the medical online monitoring environment, WBAN provides flexibility and low cost to both patient and monitoring professionals. Both healthcare and surveillance become more modified with recent technological advances in [1,2]. Advances in microelectronics and communications technology are leading to more and more personal health monitoring and advanced healthcare products with a wide range of products that already available in our society. Various sensor applications and systems are develop with a wide range of features for heartbeats or temperature, proper insulin level, Electrocardiogram (ECG) and for even wireless pacemakers. The introduction of advanced telecommunications technologies into the healthcare environment and the use of wireless communication solutions for healthcare products have led to increased user-friendliness and accessibility for users and health service providers. In the medical field, WBAN plays an important role to monitor patient health situations for early diagnoses. These sensors sense human
body activity and send it to the cluster head or coordinator node. The cluster head is a high power node that collects information from neighboring nodes and sends it to the base station or doctor [3]. Numbers of sensor nodes that can be implanted in the human body, each sensor performs its own functions such as fear detection, heartbeat, blood pressure, etc. In WBAN, some events need high data rate transmission, like in an emergency situation high data rate is required to send the patient health information to the monitoring unit [4]. In wireless communication, cognitive radio (CR) is a transceiver which senses a frequency spectrum and is capable to concatenate free adjacent channel for high data rate. In CR, the PU is the primary user to transmit data and has the highest priority to use the channel. If PU is not using the channel and channel is free then it is allocated to the SU. Fig. 1 shows coexisting WBANs, in which multiple WBANs communicate with medical staff for regular health monitoring. These WBANs consists of low power sensor nodes which have a low data rate. They do not use CB and always rely on a single channel. Due to multiple adjacent WBANs and nearby IoT devices, the co-existence interference affects reliability and overall performance of the system. However, numerous challenges are present in WBANs and their reliability is effected by wireless sensor nodes with limited resources.
∗ Corresponding author. E-mail address: [email protected] (M. Khalid).
https://doi.org/10.1016/j.comcom.2019.11.016 Received 8 October 2019; Received in revised form 29 October 2019; Accepted 11 November 2019 Available online 15 November 2019 0140-3664/© 2019 Elsevier B.V. All rights reserved.
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Computer Communications 150 (2020) 131–143
Fig. 1. Coexisting WBANs.
channel model and algorithms are presented in Section 3. The performance evaluation and simulation results are discussed in Section 4 and finally, the conclusion is presented in Section 5. In the medical online monitoring environment, WBAN provides flexibility and low cost to both patient and monitoring professionals.
CR is smart and an intelligent transceiver which can intelligently sense frequency spectrum at regular intervals to monitor user and free channels. CR control unit consists of basic function blocks for channel sensing’s such as fusion center, scheduling center, and free channel list. Each function block performs its own functions. The fusion center receives information about the channel usage of different technologies. The scheduling center schedules the order of transmission according to the environment and adjusts the priority. CR performance depends on the correct and efficient use of channel information. Existing techniques in WBAN focus only on interference management. They rely on the use of the single-channel for data transmission and do not exploit the use of bonded channel which can achieve high data rate and therefore can improve WBAN capacity. We have suggested that WBAN networks with high-frequency cognitive abilities (CRWBANs) can help solve these problems. In addition, channel connectivity can also meet the bandwidth requirements of sensor nodes. In fact, in the channel connection, a set of non-overlapping contiguous channels is interconnected to form a single broadband-connected channel. This results in high overall bandwidth, higher packet transfer rates, and better bandwidth requirements [5]. Note that channel connection [6] and channel aggregation [7] are two different concepts. In cluster clustering, cluster channels need not be continuous, as in the case of channel coupling. Channel aggregation requires increased complexity and cost due to extended load management, planning, and balancing. In this paper, we aim to propose a novel capacity improvement and interference management technique that uses low interference channels to achieve a high data rate according to the user data requirements. In particular, we propose a cognitive bonded channel with minimum interference in coexisting wireless body network and satisfy the bandwidth requirements of sensor nodes in different situations. Our interference and capacity improvement technique called traffic load aware bonded channel algorithm (TLA-BCA) uses a bonded channel in order to fulfill the data rate requirements of the users depending on the traffic load. Simulation results show that TLA-BCA achieves better results on existing technique I-e TP-CAT and SCA under different scenarios in terms of average throughput, average end-to-end delay, and energy consumption. The rest of this research paper is organized as under: the related work to WBANs is described in Section 2. The system model for WBAN,
2. Related works We introduce CB techniques that have been applied to different types of networks, as well as their discussion and analysis. In literature, channel bonding (CB) has been extensively studied for various wireless networks. However, Wireless technologies in different situations will play an important role in the field of professional health care, Environmental sensing, Air Pollution Monitoring, and industrial monitoring, etc. 2.1. Channel bonding in CR based WSN In CR based WSN, major benefits from CR networks are minimizing and mitigate interference, avoiding collisions, and maximizing the available bandwidth performance in the network. Cognitive radio sensor networks (CRSNs) are WSN-aware networks, which can easily handle mobility issues in both static and mobile sensor nodes. In the WSN family, wireless multimedia sensor (WMS) node is a new addition to this technology. These WMSs need high bandwidth while transmitting data to the servers or gateways, in this situations CB is the right option to achieve high bandwidth. In [8], a novel PR aware channel bonding (PRACB) algorithm for CRSNs is presented. Using PRACB, CB is used for high data transmission in a multimedia environment. Before channel bonding PRACB, first, check the availability of PU in the network. If PU present than CB cannot be possible. 2.2. Channel bonding in CR sensor networks (CRSNs) To solve the problem of channel allocation of static resources to native radio frequencies, CR networks are considered for a reasonable solution [9]. With the DSA (Dynamic Spectrum Allocation) method, CRN spectral holes exist in licensed and unauthorized zones. A CB has been made, once these free holes have been identified and high bandwidth meets their requirements. Since it takes RP activity into 132
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Computer Communications 150 (2020) 131–143
account, it should be noted that the capacity of CB depends on the type of RP activity [10]. These Low RP channels are suitable for CB because there is a low risk of interference. A zone protection concept is used to decrease the likelihood of interference of adjacent channels in the network. In frequency band, the size of the protected bands can be optimized by using the method that is considered in the [11], where the spread spectrum protocol can adjust the bandwidth size according to dynamic traffic conditions in the system. CB is the most attractive and effective method to enhance the network bandwidth and to minimize end to end delays [12], but their real advantage cannot be achieved due to cognitive radio network (CRN) constraints. The static nature of CRN neglects the sensor nodes to get the maximum advantages by using DSA. Channel Bonding in CRSNs.
2.5. Summary In the literature, different techniques for CB in different networks have multiple advantages and limitations. But none of them used a bonded channel technique in WBAN, which is a more efficient technique for interference mitigation and high data rate transmission. CB has multiple advantages it can be used for high bandwidth, decrease latency and increase the performance of the overall system. Therefore, a robust CB algorithm is required to achieve these performance metrics. The CB is used to improve system performance in terms of network capacity, power consumption, transmission range, and so on. 3. TLA-BCA system model
2.3. Channel bonding in WSN This section shows the WBAN system model and the communication layer for the proposed scheme. A healthcare nursing home for the patient is considered as a scenario for this application. The actual physical size of the area is varied, but to remove complexity of the system, we consider room ten (10) meters wide and ten (10) meters long. The area is logically divided into small units, called ‘‘zones’’. Each zone has its own gateway, which acts as a base station and provides radio coverage in the range of 5 × 5 m2 . Each zone has a fixed gateway and fixed power consumption and each individual patient correspond to each node in the zone. The hospital patient may have numerous body sensors that are fixed or place inside or outside the body. All body sensors send their sensed data to a central unit placed at the middle level of body called WBAN sink. The sink node immediately sends all the collected information to the nearest gateway as shown in Fig. 2. In this paper, we will consider the IEEE 802.15.6 standard specially designed for WBAN [26]. WBAN standard is basically divided into three physical layer technologies explicitly called narrowband (NB), ultrawideband (UWB) and human body communication (HBC). In WBAN each physical layer has its own design requirements. The physical NB level of 2.4–2483 GHz band called Industrial and Medical (ISM) is used for this application. The possible range of data rates in WBAN with IEEE 802.15.6 complaint is very large (121.4 kbps–971 kbps [27]), but determining the system capacity under demanding and saturated situation, we consider a data rate between 220 kbps to 970 kbps. Future, we assume that data rate is the traffic load of the sink node that can be used for off-body communication.
In WSN networks CB concatenating free channels that are observed by SU for the transmissions. In [13] the use of CB technology has been reported to achieve a high data rate transmission. It provides high data rates with a minimum end-to-end delay. The interference of another user can be avoided by using variable-width distributions. Since primary radio (PR) traffic does not have high priority for wireless sensor networks (WSNs), all nodes have the same priority to sense and allocate the channels for transmission. The dynamic allocation of channels in wireless local area networks (WLANs) as a function of high traffic load is presented in [14] and can be applied to different WSN network scenarios. If low power is required for low data rate, a narrow band frequency can be applied and if there is a need for high power and high data rate, then CB can be used to bond the channels dynamically. In [15] variable frequency distributions are presented, in which CB can be done based on free channels and user traffic load. The dynamic allocation of channels as a function of high traffic load transmission is presented in [16,17] and can be applied to WSN networks taking into account the impact on energy consumption. If low power is required for a low data rate, a narrow range frequency can be allocated for communication, and if high power and high data rate are required, CB can be used to allocate a dynamic channel. Even though CB addressed high load problems of WSN but due to the lack of cognitive abilities, demand cannot be completely solved [18]. In a network all nodes can only use the same channel using CSMA protocol to switch. In that case, frequency loss is happened due to non-use of spectral holes of frequency band
3.1. Network topology for WBAN
2.4. Channel bonding in mobile networks
WBAN considers both topologies with a single hop and multi-hop star topology, with a sink node in the middle of the body or placed on the body center. There are two types of transmissions: (A) transmissions from the body sensor to the sink and (B) from sink node to gateway device. In star topology, body sensor nodes are connected to the sink node and send sensitive data in a bi-directional way to the gateway through multiple directions [28]. In this proposed network model, the two-way star topology consists of the body sensors are being transported by the patient via the central node in the middle of the body to the gateway. We considered that the data sent from the sink node to gateway device is the sensed data of body sensors. As shown in Fig. 3, the two-hop network topology between the sink node and gateway. Nevertheless, every sink node in the network is individually connected to the gateway through a single-hop star topology, where number of sink nodes communicates with the gateway. Since in research paper, we aim to optimize the frequency spectrum and increase the overall throughput of the sink node. The CSMA protocol is used for medium access [29,30]. A time slot is available for each sink node to communicate with gateway. Thus, further for diagnostic and data processing, the data of all sink nodes are gathered into superframes for transmission between the sink nodes to the backbone i.e. gateway.
CB has been applied to mobile networks, opening up a new challenge in next-generation mobile technology. In licensed and unlicensed frequency bands, mobile networks can implement their operational frequency [19–23]. Using these frequency bands, the mobile networks dynamically contact the spectral zones and perform like a CR device. Mobile devices are usually self-governing of power issues, therefor these devices continue to consume significant bandwidth at the rate of great energy consumption. The new exciting next-generation application mobile telephony networks have been made possible by CB approach that is accomplished to provide a high data rate. Since mobile devices are rechargeable, energy consumption for providing high data rate is not a big deal, but it is still necessary to develop battery-friendly protocols for CB system. [24] Introduced a CB model in which mobile devices identify several communication channels in the frequency bands and use these channels when it is needed. The mobile devices broadcast channel information to the base station so that in CB the base station sets the operational frequency. Nevertheless, the orthogonality of channel can be lost by using the CB scheme, so that attackers can exploit [25]. Attackers can make harmful interference that impedes PR traffic and channel quality. Therefore, safe CB systems are required to address these weaknesses. 133
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Fig. 2. Wireless body area network.
Fig. 3. Network topology.
channel model. The below mathematical model for path loss provides realistic channel properties [32]. ( ) ( ) 𝑑 𝑝𝑎𝑡ℎ𝑙𝑜𝑠𝑠 (𝑑) = 𝑃 𝐿 𝑑𝑜 + 10𝑛 log10 +𝑆 𝑑0
3.2. Channel model for WBAN The standard document of IEEE 802.15.6 presents the WBAN channel model [31]. In Fig. 4 shows the (CM1-CM4) channel model for WBAN. This document deals simply off-body communication, between the central node and the gateway. Channel 4 Model (CM4) in the IEEE standard document is used between them. In WBAN, many factors influence and degrade the quality of the signal, for example, shadows, reflections, diffraction, noise, etc. Shadowing is the key factor that causes the degradation of the signal due to the body environment; even the movements and positions of the body can cause shadows. Therefore, at any time, the node is in the line of sight (LOS) with the gateway or in the non-visible line of sight (NLOS). This creates additional difficulties in creating an exact mathematical model for CM4. The IEEE standard document presents path loss measures for the
where 𝑑0 is the reference distance 50 mm, PL(𝑑𝑜 ) is the PL in dB at 𝑑𝑜 , n is the PL exponent and S is the random scattering term. 3.3. Channel allocation The base station or gateway is responsible for spectrum-related jobs along with the routing and data processing. The sensor node senses information and sends this information nearest gateway node. The sink node or gateway is also answerable to assign free channels to the sensor node for communication. Let there are channels C = {C1, C2, Cn}, PU 134
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Computer Communications 150 (2020) 131–143
Fig. 4. Channel model.
= {PU1, PU2, . . . PUn} as the PU and S = {SU1, SU2, . . . .. SU} is SU in cognitive radio. Suppose N. channels are used by PU, at given time t. Therefore, the other M-N channels are free at that time t. Secondary users can use these free channels. There are S SUs competing for these channels. Now, the secondary user base station first recognizes the channels based on the cognitive cycle and then uses these channels based on the unmanaged set of solutions. Assuming that the secondary user SUi ∈ S wants to use the Ci ∈ C channel, the SU station first considers the SINR, the probability, and the time in that channel. We propose the Traffic Load Aware Bonded Channel Assignment algorithm for WBAN, which can bond free good quality channels for high data rate transmission between sink node and gateway. CR module allocates multiple channels for CB using control channel.
on a portable stage is presented in [35]. The presenter believes that by using four body sensors, the detailed order of physiological parameters can be measured and monitor such as ECG, HR variability (HRV), BT, BP, GSR, RR and SpO2 in real-time: ECG, GSR, and BT. In this paper, we assumed that multiple sensors like ECG, BP, and temperature are sent their collected information to the sink node called IEEE 802.15.6. Further we assumed that sink node has received huge traffic from these sensor and sink node forward this traffic as a data rate between 220 kbps to 970 kbps. Different data rates are possible for each sensor, for example, an ECG sensing data rate is based on the sampling rate and sense data. According to different sampling rates are 100 Hz, 250 Hz, and 500 Hz.
3.4. Sensors application and references
Table 1 shows the detail data rates required for some known sensors used in medical and healthcare monitoring [36].
Sensing data rate = Sampling rate ∗ sense data
A healthcare application system provides a monitoring solution serves a specific purpose or function. These solutions are usually hidden in the application running on the various devices or sensors that are being used or in an application distributed within the network. Therefore, the specification of an application is necessary for the correct modeling of use cases and scenarios. The vital basic parameters like heart rate, low or high body temperature (BT), blood pressure (BP), etc. are regularly checked and monitor by the health care professional for a better overview of the patient’s health. In literature different healthcare monitoring systems are presented. But however, most applications measure one or two basic healthcare parameters. In [31,33], the healthcare application measured and monitored only ECG and HR, and only body temperature was monitored in [34]. The use of individual application for each vital parameter is not practical and it can happen problems for the patient, especially in continuous, demanding and long-term healthcare monitoring. The idea of various parameter monitoring application
4. Traffic load-aware bonded channel algorithm (TLA-BCA) In this section, we presented the functionality and working of proposed algorithms. The TLA-BCA is the combination of two algorithms I-e channel sensing and channel bonding. The algorithms were developed to bond free good quality channels in the spectrum. Algo.1 first recognizes the total number of used and free channels. The channel request can be dynamically defined or changed by the application. Depending on the parameters and the threshold provided, Algo.1 Create a list of usable channels that meet the CR node requirements. It checked all the channels in the network. Suppose if there are a total of 20 channels, it will check all the occupied and free channels. Algo.1 looks for the usable good channel list, if the usable channel list is not full, then the CR module initiates a sequential channel detection until a usable channel list is full. If the CR module finds a usable channel list, 135
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Table 1 Sensors parameters. Parameter names
Heart rate
Temperature
Activity recognition
Active FFD sensors
Sensed data amount Sensing interval Data rate Reporting interval
4 bytes 200 ms 160 bits/s <2 s
4 bytes 200 ms 64 bits/s <2 s
25 bytes 100 ms 2 kbit/s <1 s
8 bytes 100 Hz 6.4 kbit/s <1 s
Table 2 Abbreviation. CL. UL Ci Crate 𝐵𝑜𝑛𝑑𝑒𝑑 𝑟𝑎𝑡𝑒 𝑆𝐶 𝐵𝐶 𝑁𝑜𝑑𝑒𝑟𝑎𝑡𝑒
The time interval that a channel is being utilized by PU/SU is called the utilization factor of the ith channel and can be written as Channel List (All the channels in the network) Usable List (Usable free channel list for CB) ith Channel (single channel in the channel list) Channel Data Rate (channel capacity) Bounded Channel Data Rate (high data rate for CB) Single Channel Bounded Channel WBAN Sink Node Data Rate
𝑢𝑖 =
Values
Total simulation time Total region Zone area Total nodes per gateway The maximum distance between nodes and gateway Sink node energy Body sensor energy Node throughput Frame size TX power Rx power Idle power Channel capacity Traffic type SNR threshold
400 s 100 m2 25 m2 10 3.54 m 5 J 0.8 J 220 kbps–970 kbps 2040 bits 414 μW 393 μW 267 μW 250 kbps CBR 37 dB
(2)
( ) where ∅ is the threshold to take the decision of sensing function ∅ 𝐶𝐿𝑖 that declare channel to be in ON state. ( ) 𝑈𝐿𝑣 = 𝐶𝐿𝑖 ∶ ∅ 𝐶𝐿𝑖 ≤ ∅ (4) After sensing the spectrum and find the usable list that is free can be used for CB. To calculate the SNR value of each channel to identify good quality channels. ) ( 𝑃𝑠𝑖𝑔𝑛𝑎𝑙 𝑆𝑁𝑅𝑑𝑏 = 10𝑙𝑜𝑔10 (5) 𝑃𝑛𝑜𝑖𝑠𝑒 P is the power of each signal and noise. We assume that distance from the gateway is the power of the signal if a node nearest to gateway it SNR value is high from the node channel which is far away. The functionality of the channel bonding algorithm is step by step; each step has a strong relation to the next step. In step1: we assume that both single and bonded channel lists are empty. Step 2: in this step when communication starts between node and gateway and each node generate a high data rate from channel capacity, then TLA-BCA attempt for channel bonding. It will bond the channels until the node data rate is equal to the number of channels capacity. Step 3: if a node generates a minimum data rate from channel capacity then TLA-BCA will work as a single-channel assignment and it will allocate only a single channel for data transmission. In singlechannel assignment (SCA), TLA-BCA will allocate the first channel which has highest SNR value. To compare the performance of the proposed TLA-BCA, we compare it with existing schemes I-e TP-CAT [37] and SCA [38].
then the second step is performed to find SNR value of each channel in the usable channels list. The highest SNR value channels have priority to be allocated first. The TLA-BCA algorithm is based on real-time traffic load. Both Algo.1 and Algo.2 are working on gateway device, the gateway is responsible for channel allocation. It provides high data rate transmission to the sensor node that wants to communicate with the gateway. When the selected usable channel list satisfies the condition for channel allocation and the nodes’ data rate is greater than channel capacity than TLA-BLA attempt for channel bonding. If the data rate of a node is less than channel capacity, it will use the Single channel for transmission. If the number of the channel is busy and not free for channel bonding, then TLA-BLA starts communication using a single channel and when will channel become free it attempts for channel bonding (see Table 2).
5. Simulation results This section presents the simulation results of our proposed algorithms. In order to evaluate the performance of our proposed protocol, we simulated our protocol using MATLAB. The simulation graphs are generated using gun plot. At the start of this simulation, there are N = 12 WBAN nodes are deployed, i.e., the gateway and the nodes are located in a region of 10 × 10 m2 . Each zone area of 5 × 5 m2 , equal number of nodes are deployed. Mobility is considered into our account, nodes in all Zone can move freely and randomly (see Table 3).
The channel sensing algorithm is based on the scanning of the frequency band. The input for this algorithm is in channel list, channel capacity and we have to ensure a reliable usable list for transmission of data. After ensuring the usable channel, we have calculated the SNR of each channel by using standard SNR formula. Channel Sensing (CS) activity gives information about the presence/absence of PU. We demonstrated CS activity as a continuous alternating Markov Renewal Process (MRP) alternating ON/OFF that is widely used in [10]. ON represent that the channel is currently busy while OFF state represents that the channel is idle and free. The time duration for which a channel ‘‘i’’ is in OFF/ON state is denoted as 𝐶 𝑖 𝑂𝑁 and 𝐶 𝑖 𝑂𝐹 𝐹 respectively. The duration which a channel takes to complete one consecutive ON/OFF is called renewal period and denoted by 𝐶 𝑖 (𝑡) = 𝐶 𝑖 𝑂𝑁 + 𝐶 𝑖 𝑂𝐹 𝐹 = 1
𝐸[𝐶 𝑖 𝑂𝑁 ] 𝑖 𝑂𝑁 ] + 𝐸[𝐶 𝑂𝐹 𝐹 ]
where 𝐸[𝐶 𝑖 𝑂𝑁 ] and 𝐸[𝐶 𝑖 𝑂𝐹 𝐹 ] are the data rate parameters. The set of that channel which is busy and occupied is denoted by 𝑈𝐿0 . Let CL is the total number of channel and CL = 𝐶𝐿𝑖 where 𝑖 = 1, 2. . . . . . m. So ( ) 𝑈𝐿0 = 𝐶𝐿𝑖 ∶ ∅ 𝐶𝐿𝑖 ≥ ∅ (3)
Table 3 Simulation parameters. Simulation parameters
𝐸[𝐶 𝑖
5.1. Network throughput Network throughput: A throughput is the total number of packets that are successfully received at the receiver end. WBAN has important and critical health information of the patient, so that a protocol is required that send this information with a minimum drop packet and achieve maximum data rate. Network Performance is calculated by
(1) 136
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Table 4 Throughput with respect to nodes. S. NO
Protocols names
1 2 3
SCA TP-CAT TLA-BCA
Average throughput in kbps with respect to the number of nodes Nodes
0
1
2
3
4
5
6
7
8
9
10
11
12
0 0 0
0.2 0.2 0.2
0.28 0.28 0.28
0.36 0.30 0.42
0.40 0.52 0.76
0.79 0.92 1.10
0.70 1.20 1.40
0.98 1.39 1.66
1.23 1.42 1.81
1.59 1.59 2.03
1.70 1.84 2.20
1.89 2.08 2.59
2.07 2.38 2.93
Table 5 Throughput with respect to the data rate. S. NO Protocols names
Average throughput in kbps with respect to the data rate Data rate
1 2 3
SCA TP-CAT TLA-BCA
0
220
300
400
500
600
700
800
900
970
0 0 0
0.3 0.3 0.3
0.37 0.37 0.42
0.4 0.45 0.61
0.42 0.53 0.80
0.46 0.79 1.1
0.71 0.99 1.4
0.91 1.29 1.69
1.5 1.67 1.90
1.6 1.9 2.20
summing the total Performance of all nodes involved in data transfers and by dividing by the total number of nodes in the network.
better results from the existing protocols. At the beginning of the simulation, the throughput of all protocols almost remains the same but later on, the differences occur in average throughput. It can also observed in Table 4 that throughput of the proposed scheme gradually increasing as compared to existing schemes with respect of number of nodes (see Table 6). To evaluate the network under demanding an emergency condition we have used higher data rate transmission. The range of possible data rate is 220 kbps – 970 kbps. Figs. 6 and 7 evaluate the impact of
𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑁𝑒𝑡𝑤𝑜𝑟𝑘 𝑇 ℎ𝑟𝑜𝑢𝑔ℎ𝑝𝑢𝑡 ∑ Network Throughput of active Sinks = ∑ Number of all Nodes Fig. 5 shows the simulation result of the proposed scheme and existing schemes in terms of average throughput in kbps. Using multiple comparisons it is cleared that the proposed TLA-BCA protocol achieves 137
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Fig. 5. Average network throughput vs. nodes.
Fig. 6. Average network throughput vs. data rate.
the TLA-BCA algorithm with the existing technique I-e TP-CAT and SCA in terms of data rate and time. We observed that increasing the number of sink node with increasing data rate give better results when channel bonding is taken out by TLA-BCA algorithm. Tables 4 and 5 also indicate that increasing data rate and simulation time can improve the performance of the proposed scheme. As compared to the TLA-BC algorithm, there was little or no improvement of existing schemes I-e TP-CAT and SCA which only operates on a single channel. The reason for the improvement in TLA-BCA is that each sink node has received a huge amount of data from the sensing neighbor nodes and it sends this huge traffic to the gateway by using CB technique that gives high data rate transmission. The improvement proves that Increasing number of nodes and data rates in the network provide better Performance in terms of average network throughput.
Table 6 Throughput with respect to time.
5.2. Energy consumption
5.3. End to end delay
Energy Consumption is the total amount of energy or power is used in the simulation. It means that how much energy is used to deliver a packet from source to destination. Figs. 8, 9 and 10 show the experimental results of the proposed TLA-BCA scheme that achieve
The delay measurement can be divided into simple delay measurements (E2E) including average E2E delay values or the judgment as to whether the application delay limits are below or above a limit line special. The E2E delay is defined that a packet takes its time
S. NO
Protocols names
1 2 3
SCA TP-CAT TLA-BCA
Average throughput in kbps with respect to time Time
0
10
20
30
40
50
60
0 0 0
0.16 0.16 0.16
0.34 0.51 0.68
0.54 0.90 1.1
0.67 1.49 1.73
1.24 1.98 2.10
1.60 2.42 2.61
better performance from the existing SCA and TP-CAT. It can also determine from Tables 7, 8, and 9 that increasing the number of nodes, data rate and simulation time increases the performance of proposed TLA-BCA.
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Fig. 7. Average network throughput vs. time.
Fig. 8. Average energy consumption vs. nodes.
Table 7 Energy consumption with respect to nodes. S. NO
Protocols names
1 2 3
SCA TP-CAT TLA-BCA
Average energy consumption in joules (5000 mA) with respect to the number of nodes Nodes
0
1
2
3
4
5
6
7
8
9
10
11
12
0 0 0
200 200 200
400 400 400
520 470 480
705 590 550
875 798 680
1040 960 765
1306 1201 900
1500 1485 1030
1695 1650 1195
1833 1790 1390
1950 1910 1510
2175 2100 1735
Table 8 Energy consumption with respect to the data rate. S. NO
Protocols names
1 2 3
SCA TP-CAT TLA-BCA
Average energy consumption in joules (5000 mA) with respect to the data rate Data rate
0
220
300
400
500
600
700
800
900
970
0 0 0
400 400 400
510 480 480
686 670 640
892 830 800
1010 970 890
1308 1183 1000
1505 1403 1199
1745 1650 1439
1980 1902 1620
139
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Fig. 9. Average energy consumption vs. data rate.
Fig. 10. Average energy consumption vs. time.
Table 9 Energy consumption with respect to time. S. NO
Protocols names
1 2 3
SCA TP-CAT TLA-BCA
Average energy consumption in joules (5000 mA) with respect to time Time
0
10
20
30
40
50
60
0 0 0
378 378 378
672 610 563
835 820 784
1010 1144 1065
1611 1455 1399
1944 1811 1695
to reach the destination. For the proposed scheme, the E2E delay is calculated by using to measure the simulation time between the creation of a packet and the receipt of the destination application from the packet. The average E2E delay is the average delay during which all E2E delays accumulate and are divided by the number of packets received. Therefore, the delay measurement applies only to packets successfully received. Propagation delay has a significant impact on the delay of E2E implementation. In the simulation model used. The distance is calculated based on the location of the nodes (determined by the parameters according to the specific reference applications). The
effect of propagation delay is negligible, as small distances between knots (placement on a body surface network) are used. 𝐸2𝐸 𝐷𝑒𝑙𝑎𝑦 = (𝑃 𝑎𝑐𝑘𝑒𝑡 𝑅𝑒𝑐𝑒𝑝𝑡𝑖𝑜𝑛 𝑇 𝑖𝑚𝑒 − 𝑃 𝑎𝑐𝑘𝑒𝑡 𝐶𝑟𝑒𝑎𝑡𝑖𝑜𝑛 𝑇 𝑖𝑚𝑒) ∑ (𝐸𝑛𝑑 𝑡𝑜 𝐸𝑛𝑑 𝐷𝑒𝑙𝑎𝑦) 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝐸2𝐸 𝐷𝑒𝑙𝑎𝑦 = ∑ (𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑅𝑒𝑐𝑒𝑖𝑣𝑒𝑑 𝑃 𝑎𝑐𝑘𝑒𝑡𝑠) Figs. 11 and 12 shows the E2E delay of the proposed technique and existing techniques. E2E delay remains constant for some time then there is a sudden increase in the delay this is because of the CB approach which provides multiple good channels for high data rate 140
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Fig. 11. Average end to end delay vs. data rate.
Fig. 12. Average end–end delay vs. nodes.
Table 10 End to End delay with respect to the data rate. S. NO
Protocols names
1 2 3
SCA TP-CAT TLA-BCA
Average End to End delay in seconds with respect to the data rate Data rate
0
90
180
270
360
450
0 0 0
0.325 0.325 0.325
0.790 0.790 0.590
1.050 1.431 0.843
1.788 1.291 1.021
2.3723 3.105 2.709 2.969 1.432 1.676
540
630
720
810
900
970
3.323 3.322 1.990
3.88 3.464 2.611
4.29 3.692 2.991
4.44 4.210 3.165
5.593 3.165 3.509
6. Conclusion
transmission. The delay in the existing protocol is due to the high data transmission on a single channel, they only rely on one channel for data
A novel Traffic Load Aware Channel Bonded Algorithm (TLA-BCA) is presented in this paper. Using this scheme high-quality free channels are used to achieve high throughput between the sink node and the destination. Off body communication between the sink node and
transmission. Tables 10 and 11 indicate that increasing the number of nodes and data rates in the existing schemes can cause more E2E delay as compared to the proposed scheme. 141
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Table 11 End to End delay with respect to nodes. S. NO
Protocols names
1 2 3
SCA TP-CAT TLA-BCA
Average End to End delay in seconds with respect to the number of nodes Nodes
0
1
2
3
4
5
6
7
8
9
10
12
0 0 0
0.139 0.139 0.139
0.498 0.498 0.453
0.812 0.787 0.687
0.943 0.898 0.877
1.520 1.298 1.111
1.912 1.754 1.309
2.222 1.998 1.578
2.356 2.159 1.799
2.591 2.279 1.986
2.701 2.492 2.098
2.796 2.585 2.151
gateway required higher throughput communication. Existing off body standards does not achieve higher throughput. Recently, the use of a bonded channel has acquired significant attraction to achieve a high data rate. In the bonded channel, SU can concatenate adjacent channels to achieve a high data rate. In our research, we aim to propose a novel interference management scheme based on a bonded channel. Our novel interference management approach based on a bonded channel using cognitive radio can concatenate low interference adjacent channel by using the concept PU and SU. SU concatenates channel with the highest SNR value to form a bonded channel for high data rate transmission. Through multiple experiments, the proposed TLABCA achieves better results in terms of network throughput, End to End delay and energy consumption from the existing techniques I-e TP-CAT and SCA. The compared experimental result proves that the proposed Schemes outperforms from TP-CAT and SCA.
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Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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Computer Communications journal homepage: www.elsevier.com/locate/comcom
A bonded channel in cognitive wireless body area network based on IEEE 802.15.6 and internet of things Fahim Niaz a , Muhammad Khalid b ,∗, Zahid Ullah a , Nauman Aslam b , Mohsin Raza b , M.K. Priyan c a
Center of Excellence in IT, Institute of Management Sciences, Peshawar, Pakistan Department of Computer & Information Sciences, Northumbria University, Newcastle Upon Tyne, UK c Middlesex University, London, UK b
ARTICLE
INFO
Keywords: Cognitive body area network Wireless body area network Channel allocation WSN Interference management and channel model
ABSTRACT Despite the recent developments in communication technologies for wireless body area networks (WBAN), the reliability of packet transmission, especially for emergency and critical data transfer remains a significant challenge. This may be that most of the existing techniques in WBAN use single-channel for data transmission with no intelligence. The cognitive bonded channel which provides high data rate for emergency and the demanding situation. Existing techniques in WBAN rely on the use of the single-channel for data transmission and do not exploit the use of the bonded channel, which can improve the WBAN capacity of off-body communication. In our research, we propose a traffic load aware bonded channel algorithm (TLA-BCA), which exploits channel bonding to improve the performance of off-body communication between the WBAN sink nodes and gateway. Our proposed algorithm, i.e., TLA-BCA utilizes high-quality channels for channel bonding to maximize the capacity of off-body communication in WBANs. TLA-BCA demonstrates significant performance improvement over Traffic Priority Based Channel Assignment Technique (TP-CAT) and Static Channel Assignment (SCA) in terms of average throughput, average end-to-end delay with comparable energy performance.
1. Introduction WBAN consists of low power sensor nodes where nodes are deployed on or inside the human body to the monitoring of various physiological parameters. It provides the daily activity of the patient and its health condition. WBAN is significantly used in medical applications and health care. In the medical online monitoring environment, WBAN provides flexibility and low cost to both patient and monitoring professionals. Both healthcare and surveillance become more modified with recent technological advances in [1,2]. Advances in microelectronics and communications technology are leading to more and more personal health monitoring and advanced healthcare products with a wide range of products that already available in our society. Various sensor applications and systems are develop with a wide range of features for heartbeats or temperature, proper insulin level, Electrocardiogram (ECG) and for even wireless pacemakers. The introduction of advanced telecommunications technologies into the healthcare environment and the use of wireless communication solutions for healthcare products have led to increased user-friendliness and accessibility for users and health service providers. In the medical field, WBAN plays an important role to monitor patient health situations for early diagnoses. These sensors sense human
body activity and send it to the cluster head or coordinator node. The cluster head is a high power node that collects information from neighboring nodes and sends it to the base station or doctor [3]. Numbers of sensor nodes that can be implanted in the human body, each sensor performs its own functions such as fear detection, heartbeat, blood pressure, etc. In WBAN, some events need high data rate transmission, like in an emergency situation high data rate is required to send the patient health information to the monitoring unit [4]. In wireless communication, cognitive radio (CR) is a transceiver which senses a frequency spectrum and is capable to concatenate free adjacent channel for high data rate. In CR, the PU is the primary user to transmit data and has the highest priority to use the channel. If PU is not using the channel and channel is free then it is allocated to the SU. Fig. 1 shows coexisting WBANs, in which multiple WBANs communicate with medical staff for regular health monitoring. These WBANs consists of low power sensor nodes which have a low data rate. They do not use CB and always rely on a single channel. Due to multiple adjacent WBANs and nearby IoT devices, the co-existence interference affects reliability and overall performance of the system. However, numerous challenges are present in WBANs and their reliability is effected by wireless sensor nodes with limited resources.
∗ Corresponding author. E-mail address: [email protected] (M. Khalid).
https://doi.org/10.1016/j.comcom.2019.11.016 Received 8 October 2019; Received in revised form 29 October 2019; Accepted 11 November 2019 Available online 15 November 2019 0140-3664/© 2019 Elsevier B.V. All rights reserved.
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Fig. 1. Coexisting WBANs.
channel model and algorithms are presented in Section 3. The performance evaluation and simulation results are discussed in Section 4 and finally, the conclusion is presented in Section 5. In the medical online monitoring environment, WBAN provides flexibility and low cost to both patient and monitoring professionals.
CR is smart and an intelligent transceiver which can intelligently sense frequency spectrum at regular intervals to monitor user and free channels. CR control unit consists of basic function blocks for channel sensing’s such as fusion center, scheduling center, and free channel list. Each function block performs its own functions. The fusion center receives information about the channel usage of different technologies. The scheduling center schedules the order of transmission according to the environment and adjusts the priority. CR performance depends on the correct and efficient use of channel information. Existing techniques in WBAN focus only on interference management. They rely on the use of the single-channel for data transmission and do not exploit the use of bonded channel which can achieve high data rate and therefore can improve WBAN capacity. We have suggested that WBAN networks with high-frequency cognitive abilities (CRWBANs) can help solve these problems. In addition, channel connectivity can also meet the bandwidth requirements of sensor nodes. In fact, in the channel connection, a set of non-overlapping contiguous channels is interconnected to form a single broadband-connected channel. This results in high overall bandwidth, higher packet transfer rates, and better bandwidth requirements [5]. Note that channel connection [6] and channel aggregation [7] are two different concepts. In cluster clustering, cluster channels need not be continuous, as in the case of channel coupling. Channel aggregation requires increased complexity and cost due to extended load management, planning, and balancing. In this paper, we aim to propose a novel capacity improvement and interference management technique that uses low interference channels to achieve a high data rate according to the user data requirements. In particular, we propose a cognitive bonded channel with minimum interference in coexisting wireless body network and satisfy the bandwidth requirements of sensor nodes in different situations. Our interference and capacity improvement technique called traffic load aware bonded channel algorithm (TLA-BCA) uses a bonded channel in order to fulfill the data rate requirements of the users depending on the traffic load. Simulation results show that TLA-BCA achieves better results on existing technique I-e TP-CAT and SCA under different scenarios in terms of average throughput, average end-to-end delay, and energy consumption. The rest of this research paper is organized as under: the related work to WBANs is described in Section 2. The system model for WBAN,
2. Related works We introduce CB techniques that have been applied to different types of networks, as well as their discussion and analysis. In literature, channel bonding (CB) has been extensively studied for various wireless networks. However, Wireless technologies in different situations will play an important role in the field of professional health care, Environmental sensing, Air Pollution Monitoring, and industrial monitoring, etc. 2.1. Channel bonding in CR based WSN In CR based WSN, major benefits from CR networks are minimizing and mitigate interference, avoiding collisions, and maximizing the available bandwidth performance in the network. Cognitive radio sensor networks (CRSNs) are WSN-aware networks, which can easily handle mobility issues in both static and mobile sensor nodes. In the WSN family, wireless multimedia sensor (WMS) node is a new addition to this technology. These WMSs need high bandwidth while transmitting data to the servers or gateways, in this situations CB is the right option to achieve high bandwidth. In [8], a novel PR aware channel bonding (PRACB) algorithm for CRSNs is presented. Using PRACB, CB is used for high data transmission in a multimedia environment. Before channel bonding PRACB, first, check the availability of PU in the network. If PU present than CB cannot be possible. 2.2. Channel bonding in CR sensor networks (CRSNs) To solve the problem of channel allocation of static resources to native radio frequencies, CR networks are considered for a reasonable solution [9]. With the DSA (Dynamic Spectrum Allocation) method, CRN spectral holes exist in licensed and unauthorized zones. A CB has been made, once these free holes have been identified and high bandwidth meets their requirements. Since it takes RP activity into 132
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account, it should be noted that the capacity of CB depends on the type of RP activity [10]. These Low RP channels are suitable for CB because there is a low risk of interference. A zone protection concept is used to decrease the likelihood of interference of adjacent channels in the network. In frequency band, the size of the protected bands can be optimized by using the method that is considered in the [11], where the spread spectrum protocol can adjust the bandwidth size according to dynamic traffic conditions in the system. CB is the most attractive and effective method to enhance the network bandwidth and to minimize end to end delays [12], but their real advantage cannot be achieved due to cognitive radio network (CRN) constraints. The static nature of CRN neglects the sensor nodes to get the maximum advantages by using DSA. Channel Bonding in CRSNs.
2.5. Summary In the literature, different techniques for CB in different networks have multiple advantages and limitations. But none of them used a bonded channel technique in WBAN, which is a more efficient technique for interference mitigation and high data rate transmission. CB has multiple advantages it can be used for high bandwidth, decrease latency and increase the performance of the overall system. Therefore, a robust CB algorithm is required to achieve these performance metrics. The CB is used to improve system performance in terms of network capacity, power consumption, transmission range, and so on. 3. TLA-BCA system model
2.3. Channel bonding in WSN This section shows the WBAN system model and the communication layer for the proposed scheme. A healthcare nursing home for the patient is considered as a scenario for this application. The actual physical size of the area is varied, but to remove complexity of the system, we consider room ten (10) meters wide and ten (10) meters long. The area is logically divided into small units, called ‘‘zones’’. Each zone has its own gateway, which acts as a base station and provides radio coverage in the range of 5 × 5 m2 . Each zone has a fixed gateway and fixed power consumption and each individual patient correspond to each node in the zone. The hospital patient may have numerous body sensors that are fixed or place inside or outside the body. All body sensors send their sensed data to a central unit placed at the middle level of body called WBAN sink. The sink node immediately sends all the collected information to the nearest gateway as shown in Fig. 2. In this paper, we will consider the IEEE 802.15.6 standard specially designed for WBAN [26]. WBAN standard is basically divided into three physical layer technologies explicitly called narrowband (NB), ultrawideband (UWB) and human body communication (HBC). In WBAN each physical layer has its own design requirements. The physical NB level of 2.4–2483 GHz band called Industrial and Medical (ISM) is used for this application. The possible range of data rates in WBAN with IEEE 802.15.6 complaint is very large (121.4 kbps–971 kbps [27]), but determining the system capacity under demanding and saturated situation, we consider a data rate between 220 kbps to 970 kbps. Future, we assume that data rate is the traffic load of the sink node that can be used for off-body communication.
In WSN networks CB concatenating free channels that are observed by SU for the transmissions. In [13] the use of CB technology has been reported to achieve a high data rate transmission. It provides high data rates with a minimum end-to-end delay. The interference of another user can be avoided by using variable-width distributions. Since primary radio (PR) traffic does not have high priority for wireless sensor networks (WSNs), all nodes have the same priority to sense and allocate the channels for transmission. The dynamic allocation of channels in wireless local area networks (WLANs) as a function of high traffic load is presented in [14] and can be applied to different WSN network scenarios. If low power is required for low data rate, a narrow band frequency can be applied and if there is a need for high power and high data rate, then CB can be used to bond the channels dynamically. In [15] variable frequency distributions are presented, in which CB can be done based on free channels and user traffic load. The dynamic allocation of channels as a function of high traffic load transmission is presented in [16,17] and can be applied to WSN networks taking into account the impact on energy consumption. If low power is required for a low data rate, a narrow range frequency can be allocated for communication, and if high power and high data rate are required, CB can be used to allocate a dynamic channel. Even though CB addressed high load problems of WSN but due to the lack of cognitive abilities, demand cannot be completely solved [18]. In a network all nodes can only use the same channel using CSMA protocol to switch. In that case, frequency loss is happened due to non-use of spectral holes of frequency band
3.1. Network topology for WBAN
2.4. Channel bonding in mobile networks
WBAN considers both topologies with a single hop and multi-hop star topology, with a sink node in the middle of the body or placed on the body center. There are two types of transmissions: (A) transmissions from the body sensor to the sink and (B) from sink node to gateway device. In star topology, body sensor nodes are connected to the sink node and send sensitive data in a bi-directional way to the gateway through multiple directions [28]. In this proposed network model, the two-way star topology consists of the body sensors are being transported by the patient via the central node in the middle of the body to the gateway. We considered that the data sent from the sink node to gateway device is the sensed data of body sensors. As shown in Fig. 3, the two-hop network topology between the sink node and gateway. Nevertheless, every sink node in the network is individually connected to the gateway through a single-hop star topology, where number of sink nodes communicates with the gateway. Since in research paper, we aim to optimize the frequency spectrum and increase the overall throughput of the sink node. The CSMA protocol is used for medium access [29,30]. A time slot is available for each sink node to communicate with gateway. Thus, further for diagnostic and data processing, the data of all sink nodes are gathered into superframes for transmission between the sink nodes to the backbone i.e. gateway.
CB has been applied to mobile networks, opening up a new challenge in next-generation mobile technology. In licensed and unlicensed frequency bands, mobile networks can implement their operational frequency [19–23]. Using these frequency bands, the mobile networks dynamically contact the spectral zones and perform like a CR device. Mobile devices are usually self-governing of power issues, therefor these devices continue to consume significant bandwidth at the rate of great energy consumption. The new exciting next-generation application mobile telephony networks have been made possible by CB approach that is accomplished to provide a high data rate. Since mobile devices are rechargeable, energy consumption for providing high data rate is not a big deal, but it is still necessary to develop battery-friendly protocols for CB system. [24] Introduced a CB model in which mobile devices identify several communication channels in the frequency bands and use these channels when it is needed. The mobile devices broadcast channel information to the base station so that in CB the base station sets the operational frequency. Nevertheless, the orthogonality of channel can be lost by using the CB scheme, so that attackers can exploit [25]. Attackers can make harmful interference that impedes PR traffic and channel quality. Therefore, safe CB systems are required to address these weaknesses. 133
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Fig. 2. Wireless body area network.
Fig. 3. Network topology.
channel model. The below mathematical model for path loss provides realistic channel properties [32]. ( ) ( ) 𝑑 𝑝𝑎𝑡ℎ𝑙𝑜𝑠𝑠 (𝑑) = 𝑃 𝐿 𝑑𝑜 + 10𝑛 log10 +𝑆 𝑑0
3.2. Channel model for WBAN The standard document of IEEE 802.15.6 presents the WBAN channel model [31]. In Fig. 4 shows the (CM1-CM4) channel model for WBAN. This document deals simply off-body communication, between the central node and the gateway. Channel 4 Model (CM4) in the IEEE standard document is used between them. In WBAN, many factors influence and degrade the quality of the signal, for example, shadows, reflections, diffraction, noise, etc. Shadowing is the key factor that causes the degradation of the signal due to the body environment; even the movements and positions of the body can cause shadows. Therefore, at any time, the node is in the line of sight (LOS) with the gateway or in the non-visible line of sight (NLOS). This creates additional difficulties in creating an exact mathematical model for CM4. The IEEE standard document presents path loss measures for the
where 𝑑0 is the reference distance 50 mm, PL(𝑑𝑜 ) is the PL in dB at 𝑑𝑜 , n is the PL exponent and S is the random scattering term. 3.3. Channel allocation The base station or gateway is responsible for spectrum-related jobs along with the routing and data processing. The sensor node senses information and sends this information nearest gateway node. The sink node or gateway is also answerable to assign free channels to the sensor node for communication. Let there are channels C = {C1, C2, Cn}, PU 134
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Fig. 4. Channel model.
= {PU1, PU2, . . . PUn} as the PU and S = {SU1, SU2, . . . .. SU} is SU in cognitive radio. Suppose N. channels are used by PU, at given time t. Therefore, the other M-N channels are free at that time t. Secondary users can use these free channels. There are S SUs competing for these channels. Now, the secondary user base station first recognizes the channels based on the cognitive cycle and then uses these channels based on the unmanaged set of solutions. Assuming that the secondary user SUi ∈ S wants to use the Ci ∈ C channel, the SU station first considers the SINR, the probability, and the time in that channel. We propose the Traffic Load Aware Bonded Channel Assignment algorithm for WBAN, which can bond free good quality channels for high data rate transmission between sink node and gateway. CR module allocates multiple channels for CB using control channel.
on a portable stage is presented in [35]. The presenter believes that by using four body sensors, the detailed order of physiological parameters can be measured and monitor such as ECG, HR variability (HRV), BT, BP, GSR, RR and SpO2 in real-time: ECG, GSR, and BT. In this paper, we assumed that multiple sensors like ECG, BP, and temperature are sent their collected information to the sink node called IEEE 802.15.6. Further we assumed that sink node has received huge traffic from these sensor and sink node forward this traffic as a data rate between 220 kbps to 970 kbps. Different data rates are possible for each sensor, for example, an ECG sensing data rate is based on the sampling rate and sense data. According to different sampling rates are 100 Hz, 250 Hz, and 500 Hz.
3.4. Sensors application and references
Table 1 shows the detail data rates required for some known sensors used in medical and healthcare monitoring [36].
Sensing data rate = Sampling rate ∗ sense data
A healthcare application system provides a monitoring solution serves a specific purpose or function. These solutions are usually hidden in the application running on the various devices or sensors that are being used or in an application distributed within the network. Therefore, the specification of an application is necessary for the correct modeling of use cases and scenarios. The vital basic parameters like heart rate, low or high body temperature (BT), blood pressure (BP), etc. are regularly checked and monitor by the health care professional for a better overview of the patient’s health. In literature different healthcare monitoring systems are presented. But however, most applications measure one or two basic healthcare parameters. In [31,33], the healthcare application measured and monitored only ECG and HR, and only body temperature was monitored in [34]. The use of individual application for each vital parameter is not practical and it can happen problems for the patient, especially in continuous, demanding and long-term healthcare monitoring. The idea of various parameter monitoring application
4. Traffic load-aware bonded channel algorithm (TLA-BCA) In this section, we presented the functionality and working of proposed algorithms. The TLA-BCA is the combination of two algorithms I-e channel sensing and channel bonding. The algorithms were developed to bond free good quality channels in the spectrum. Algo.1 first recognizes the total number of used and free channels. The channel request can be dynamically defined or changed by the application. Depending on the parameters and the threshold provided, Algo.1 Create a list of usable channels that meet the CR node requirements. It checked all the channels in the network. Suppose if there are a total of 20 channels, it will check all the occupied and free channels. Algo.1 looks for the usable good channel list, if the usable channel list is not full, then the CR module initiates a sequential channel detection until a usable channel list is full. If the CR module finds a usable channel list, 135
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Table 1 Sensors parameters. Parameter names
Heart rate
Temperature
Activity recognition
Active FFD sensors
Sensed data amount Sensing interval Data rate Reporting interval
4 bytes 200 ms 160 bits/s <2 s
4 bytes 200 ms 64 bits/s <2 s
25 bytes 100 ms 2 kbit/s <1 s
8 bytes 100 Hz 6.4 kbit/s <1 s
Table 2 Abbreviation. CL. UL Ci Crate 𝐵𝑜𝑛𝑑𝑒𝑑 𝑟𝑎𝑡𝑒 𝑆𝐶 𝐵𝐶 𝑁𝑜𝑑𝑒𝑟𝑎𝑡𝑒
The time interval that a channel is being utilized by PU/SU is called the utilization factor of the ith channel and can be written as Channel List (All the channels in the network) Usable List (Usable free channel list for CB) ith Channel (single channel in the channel list) Channel Data Rate (channel capacity) Bounded Channel Data Rate (high data rate for CB) Single Channel Bounded Channel WBAN Sink Node Data Rate
𝑢𝑖 =
Values
Total simulation time Total region Zone area Total nodes per gateway The maximum distance between nodes and gateway Sink node energy Body sensor energy Node throughput Frame size TX power Rx power Idle power Channel capacity Traffic type SNR threshold
400 s 100 m2 25 m2 10 3.54 m 5 J 0.8 J 220 kbps–970 kbps 2040 bits 414 μW 393 μW 267 μW 250 kbps CBR 37 dB
(2)
( ) where ∅ is the threshold to take the decision of sensing function ∅ 𝐶𝐿𝑖 that declare channel to be in ON state. ( ) 𝑈𝐿𝑣 = 𝐶𝐿𝑖 ∶ ∅ 𝐶𝐿𝑖 ≤ ∅ (4) After sensing the spectrum and find the usable list that is free can be used for CB. To calculate the SNR value of each channel to identify good quality channels. ) ( 𝑃𝑠𝑖𝑔𝑛𝑎𝑙 𝑆𝑁𝑅𝑑𝑏 = 10𝑙𝑜𝑔10 (5) 𝑃𝑛𝑜𝑖𝑠𝑒 P is the power of each signal and noise. We assume that distance from the gateway is the power of the signal if a node nearest to gateway it SNR value is high from the node channel which is far away. The functionality of the channel bonding algorithm is step by step; each step has a strong relation to the next step. In step1: we assume that both single and bonded channel lists are empty. Step 2: in this step when communication starts between node and gateway and each node generate a high data rate from channel capacity, then TLA-BCA attempt for channel bonding. It will bond the channels until the node data rate is equal to the number of channels capacity. Step 3: if a node generates a minimum data rate from channel capacity then TLA-BCA will work as a single-channel assignment and it will allocate only a single channel for data transmission. In singlechannel assignment (SCA), TLA-BCA will allocate the first channel which has highest SNR value. To compare the performance of the proposed TLA-BCA, we compare it with existing schemes I-e TP-CAT [37] and SCA [38].
then the second step is performed to find SNR value of each channel in the usable channels list. The highest SNR value channels have priority to be allocated first. The TLA-BCA algorithm is based on real-time traffic load. Both Algo.1 and Algo.2 are working on gateway device, the gateway is responsible for channel allocation. It provides high data rate transmission to the sensor node that wants to communicate with the gateway. When the selected usable channel list satisfies the condition for channel allocation and the nodes’ data rate is greater than channel capacity than TLA-BLA attempt for channel bonding. If the data rate of a node is less than channel capacity, it will use the Single channel for transmission. If the number of the channel is busy and not free for channel bonding, then TLA-BLA starts communication using a single channel and when will channel become free it attempts for channel bonding (see Table 2).
5. Simulation results This section presents the simulation results of our proposed algorithms. In order to evaluate the performance of our proposed protocol, we simulated our protocol using MATLAB. The simulation graphs are generated using gun plot. At the start of this simulation, there are N = 12 WBAN nodes are deployed, i.e., the gateway and the nodes are located in a region of 10 × 10 m2 . Each zone area of 5 × 5 m2 , equal number of nodes are deployed. Mobility is considered into our account, nodes in all Zone can move freely and randomly (see Table 3).
The channel sensing algorithm is based on the scanning of the frequency band. The input for this algorithm is in channel list, channel capacity and we have to ensure a reliable usable list for transmission of data. After ensuring the usable channel, we have calculated the SNR of each channel by using standard SNR formula. Channel Sensing (CS) activity gives information about the presence/absence of PU. We demonstrated CS activity as a continuous alternating Markov Renewal Process (MRP) alternating ON/OFF that is widely used in [10]. ON represent that the channel is currently busy while OFF state represents that the channel is idle and free. The time duration for which a channel ‘‘i’’ is in OFF/ON state is denoted as 𝐶 𝑖 𝑂𝑁 and 𝐶 𝑖 𝑂𝐹 𝐹 respectively. The duration which a channel takes to complete one consecutive ON/OFF is called renewal period and denoted by 𝐶 𝑖 (𝑡) = 𝐶 𝑖 𝑂𝑁 + 𝐶 𝑖 𝑂𝐹 𝐹 = 1
𝐸[𝐶 𝑖 𝑂𝑁 ] 𝑖 𝑂𝑁 ] + 𝐸[𝐶 𝑂𝐹 𝐹 ]
where 𝐸[𝐶 𝑖 𝑂𝑁 ] and 𝐸[𝐶 𝑖 𝑂𝐹 𝐹 ] are the data rate parameters. The set of that channel which is busy and occupied is denoted by 𝑈𝐿0 . Let CL is the total number of channel and CL = 𝐶𝐿𝑖 where 𝑖 = 1, 2. . . . . . m. So ( ) 𝑈𝐿0 = 𝐶𝐿𝑖 ∶ ∅ 𝐶𝐿𝑖 ≥ ∅ (3)
Table 3 Simulation parameters. Simulation parameters
𝐸[𝐶 𝑖
5.1. Network throughput Network throughput: A throughput is the total number of packets that are successfully received at the receiver end. WBAN has important and critical health information of the patient, so that a protocol is required that send this information with a minimum drop packet and achieve maximum data rate. Network Performance is calculated by
(1) 136
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Table 4 Throughput with respect to nodes. S. NO
Protocols names
1 2 3
SCA TP-CAT TLA-BCA
Average throughput in kbps with respect to the number of nodes Nodes
0
1
2
3
4
5
6
7
8
9
10
11
12
0 0 0
0.2 0.2 0.2
0.28 0.28 0.28
0.36 0.30 0.42
0.40 0.52 0.76
0.79 0.92 1.10
0.70 1.20 1.40
0.98 1.39 1.66
1.23 1.42 1.81
1.59 1.59 2.03
1.70 1.84 2.20
1.89 2.08 2.59
2.07 2.38 2.93
Table 5 Throughput with respect to the data rate. S. NO Protocols names
Average throughput in kbps with respect to the data rate Data rate
1 2 3
SCA TP-CAT TLA-BCA
0
220
300
400
500
600
700
800
900
970
0 0 0
0.3 0.3 0.3
0.37 0.37 0.42
0.4 0.45 0.61
0.42 0.53 0.80
0.46 0.79 1.1
0.71 0.99 1.4
0.91 1.29 1.69
1.5 1.67 1.90
1.6 1.9 2.20
summing the total Performance of all nodes involved in data transfers and by dividing by the total number of nodes in the network.
better results from the existing protocols. At the beginning of the simulation, the throughput of all protocols almost remains the same but later on, the differences occur in average throughput. It can also observed in Table 4 that throughput of the proposed scheme gradually increasing as compared to existing schemes with respect of number of nodes (see Table 6). To evaluate the network under demanding an emergency condition we have used higher data rate transmission. The range of possible data rate is 220 kbps – 970 kbps. Figs. 6 and 7 evaluate the impact of
𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑁𝑒𝑡𝑤𝑜𝑟𝑘 𝑇 ℎ𝑟𝑜𝑢𝑔ℎ𝑝𝑢𝑡 ∑ Network Throughput of active Sinks = ∑ Number of all Nodes Fig. 5 shows the simulation result of the proposed scheme and existing schemes in terms of average throughput in kbps. Using multiple comparisons it is cleared that the proposed TLA-BCA protocol achieves 137
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Fig. 5. Average network throughput vs. nodes.
Fig. 6. Average network throughput vs. data rate.
the TLA-BCA algorithm with the existing technique I-e TP-CAT and SCA in terms of data rate and time. We observed that increasing the number of sink node with increasing data rate give better results when channel bonding is taken out by TLA-BCA algorithm. Tables 4 and 5 also indicate that increasing data rate and simulation time can improve the performance of the proposed scheme. As compared to the TLA-BC algorithm, there was little or no improvement of existing schemes I-e TP-CAT and SCA which only operates on a single channel. The reason for the improvement in TLA-BCA is that each sink node has received a huge amount of data from the sensing neighbor nodes and it sends this huge traffic to the gateway by using CB technique that gives high data rate transmission. The improvement proves that Increasing number of nodes and data rates in the network provide better Performance in terms of average network throughput.
Table 6 Throughput with respect to time.
5.2. Energy consumption
5.3. End to end delay
Energy Consumption is the total amount of energy or power is used in the simulation. It means that how much energy is used to deliver a packet from source to destination. Figs. 8, 9 and 10 show the experimental results of the proposed TLA-BCA scheme that achieve
The delay measurement can be divided into simple delay measurements (E2E) including average E2E delay values or the judgment as to whether the application delay limits are below or above a limit line special. The E2E delay is defined that a packet takes its time
S. NO
Protocols names
1 2 3
SCA TP-CAT TLA-BCA
Average throughput in kbps with respect to time Time
0
10
20
30
40
50
60
0 0 0
0.16 0.16 0.16
0.34 0.51 0.68
0.54 0.90 1.1
0.67 1.49 1.73
1.24 1.98 2.10
1.60 2.42 2.61
better performance from the existing SCA and TP-CAT. It can also determine from Tables 7, 8, and 9 that increasing the number of nodes, data rate and simulation time increases the performance of proposed TLA-BCA.
138
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Fig. 7. Average network throughput vs. time.
Fig. 8. Average energy consumption vs. nodes.
Table 7 Energy consumption with respect to nodes. S. NO
Protocols names
1 2 3
SCA TP-CAT TLA-BCA
Average energy consumption in joules (5000 mA) with respect to the number of nodes Nodes
0
1
2
3
4
5
6
7
8
9
10
11
12
0 0 0
200 200 200
400 400 400
520 470 480
705 590 550
875 798 680
1040 960 765
1306 1201 900
1500 1485 1030
1695 1650 1195
1833 1790 1390
1950 1910 1510
2175 2100 1735
Table 8 Energy consumption with respect to the data rate. S. NO
Protocols names
1 2 3
SCA TP-CAT TLA-BCA
Average energy consumption in joules (5000 mA) with respect to the data rate Data rate
0
220
300
400
500
600
700
800
900
970
0 0 0
400 400 400
510 480 480
686 670 640
892 830 800
1010 970 890
1308 1183 1000
1505 1403 1199
1745 1650 1439
1980 1902 1620
139
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Fig. 9. Average energy consumption vs. data rate.
Fig. 10. Average energy consumption vs. time.
Table 9 Energy consumption with respect to time. S. NO
Protocols names
1 2 3
SCA TP-CAT TLA-BCA
Average energy consumption in joules (5000 mA) with respect to time Time
0
10
20
30
40
50
60
0 0 0
378 378 378
672 610 563
835 820 784
1010 1144 1065
1611 1455 1399
1944 1811 1695
to reach the destination. For the proposed scheme, the E2E delay is calculated by using to measure the simulation time between the creation of a packet and the receipt of the destination application from the packet. The average E2E delay is the average delay during which all E2E delays accumulate and are divided by the number of packets received. Therefore, the delay measurement applies only to packets successfully received. Propagation delay has a significant impact on the delay of E2E implementation. In the simulation model used. The distance is calculated based on the location of the nodes (determined by the parameters according to the specific reference applications). The
effect of propagation delay is negligible, as small distances between knots (placement on a body surface network) are used. 𝐸2𝐸 𝐷𝑒𝑙𝑎𝑦 = (𝑃 𝑎𝑐𝑘𝑒𝑡 𝑅𝑒𝑐𝑒𝑝𝑡𝑖𝑜𝑛 𝑇 𝑖𝑚𝑒 − 𝑃 𝑎𝑐𝑘𝑒𝑡 𝐶𝑟𝑒𝑎𝑡𝑖𝑜𝑛 𝑇 𝑖𝑚𝑒) ∑ (𝐸𝑛𝑑 𝑡𝑜 𝐸𝑛𝑑 𝐷𝑒𝑙𝑎𝑦) 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝐸2𝐸 𝐷𝑒𝑙𝑎𝑦 = ∑ (𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑅𝑒𝑐𝑒𝑖𝑣𝑒𝑑 𝑃 𝑎𝑐𝑘𝑒𝑡𝑠) Figs. 11 and 12 shows the E2E delay of the proposed technique and existing techniques. E2E delay remains constant for some time then there is a sudden increase in the delay this is because of the CB approach which provides multiple good channels for high data rate 140
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Fig. 11. Average end to end delay vs. data rate.
Fig. 12. Average end–end delay vs. nodes.
Table 10 End to End delay with respect to the data rate. S. NO
Protocols names
1 2 3
SCA TP-CAT TLA-BCA
Average End to End delay in seconds with respect to the data rate Data rate
0
90
180
270
360
450
0 0 0
0.325 0.325 0.325
0.790 0.790 0.590
1.050 1.431 0.843
1.788 1.291 1.021
2.3723 3.105 2.709 2.969 1.432 1.676
540
630
720
810
900
970
3.323 3.322 1.990
3.88 3.464 2.611
4.29 3.692 2.991
4.44 4.210 3.165
5.593 3.165 3.509
6. Conclusion
transmission. The delay in the existing protocol is due to the high data transmission on a single channel, they only rely on one channel for data
A novel Traffic Load Aware Channel Bonded Algorithm (TLA-BCA) is presented in this paper. Using this scheme high-quality free channels are used to achieve high throughput between the sink node and the destination. Off body communication between the sink node and
transmission. Tables 10 and 11 indicate that increasing the number of nodes and data rates in the existing schemes can cause more E2E delay as compared to the proposed scheme. 141
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Table 11 End to End delay with respect to nodes. S. NO
Protocols names
1 2 3
SCA TP-CAT TLA-BCA
Average End to End delay in seconds with respect to the number of nodes Nodes
0
1
2
3
4
5
6
7
8
9
10
12
0 0 0
0.139 0.139 0.139
0.498 0.498 0.453
0.812 0.787 0.687
0.943 0.898 0.877
1.520 1.298 1.111
1.912 1.754 1.309
2.222 1.998 1.578
2.356 2.159 1.799
2.591 2.279 1.986
2.701 2.492 2.098
2.796 2.585 2.151
gateway required higher throughput communication. Existing off body standards does not achieve higher throughput. Recently, the use of a bonded channel has acquired significant attraction to achieve a high data rate. In the bonded channel, SU can concatenate adjacent channels to achieve a high data rate. In our research, we aim to propose a novel interference management scheme based on a bonded channel. Our novel interference management approach based on a bonded channel using cognitive radio can concatenate low interference adjacent channel by using the concept PU and SU. SU concatenates channel with the highest SNR value to form a bonded channel for high data rate transmission. Through multiple experiments, the proposed TLABCA achieves better results in terms of network throughput, End to End delay and energy consumption from the existing techniques I-e TP-CAT and SCA. The compared experimental result proves that the proposed Schemes outperforms from TP-CAT and SCA.
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Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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