Chapter 3 Random contention based resource allocation algorithm
3.5 WSN’s transmission schedule
After CPN gets the available bandwidth from the collision-free resource allocation, it is important to assign the available bandwidth to its WSNs. CPN schedules the available transmission slots for the WSNs and sends the transmission schedule to WSNs through the beacon. By considering the priority of WSNs and the emergency case of medical data, the scheduling procedure has two steps: reserving bandwidth for medical data and priority queue. Figure 3-4 shows the basic scheduling concept.
start
BWavb<= BWrequired BWdesired<= BWavb
BWrequired< BWavb< BWdesired
Figure 3‐4 Basic concept of WSN’s transmission schedule
Reserving bandwidth for medical data
During the monitoring of physiological data, the emergency event is unpredictable and sporadic. When the abnormal data is monitored, the WSN is able to give it the priority of transmission over the preceding data in the queue. To make sure that CPN can immediately receive the abnormal data and increase the level of priority, reserving bandwidth for medical data is essential. CPN assigns one transmission slot for each WSN to guarantee each of them has the transmission opportunity in every superframe. The portion of reserving bandwidth is not much in total available bandwidth, but it is necessary for timely response of the emergency event.
CPN Schedule
resource
Available transmission slots
CPN
Reserved Priority Queue
Hybrid scheduling
Priority Queue
Figure 3‐5 The structure of Priority Queue
Priority Queue is the main portion of the intra-WBAN’s bandwidth allocation. The basic concept of Priority Queue is that CPN schedules the WSN’s transmission according to the WSN priority level. Figure 3-5 shows the structure of Priority Queue. The transmission slots are first assigned to the WSN which is critical priority level, and then switched to lower priority level while the higher priority level’s queue is empty. The WSN with higher level of priority would be processed early, and the WSN with lower level of priority may be waiting for processing when the bandwidth resource isn’t enough. The WSN priority level reflects the length of waiting time for transmission schedule. For the same priority WSNs, CPN checks whether the remaining bandwidth is enough or not to plan the transmission schedule. CPN assign the slots according to each WSN’s requirement bandwidth if the remaining bandwidth is enough. If not, CPN assign the remaining bandwidth in proportion to the WSNs’
requirement bandwidth. Hence, the intra-WBAN bandwidth allocation of three priority levels of WSNs can be expressed by the following equation.
classifier
WSN data in critical
high
low
WSN data out
Switching to lower priority queue while the higher priority queue is empty.
(Critical‐>high‐>low)
Therefore, Prioirty Queue provides prioritized bandwidth scheduler for different WSNs.
The WSN priority scheduler is divided the WSNs of WBAN into 3 levels (critical, high, and low). The CPN has the required bandwidth of each level: BWcritical, BWhigh, and BWlow.
Therefore, the total available bandwidth of WBAN can be expressed:
BWavailable= αBWcritical +βBWhigh +γBWlow + BWexcess.
if (BWavailable < BWcritical)
α = BWBW , β= 0, γ=0, BWexcess=0;
else if(BWavailable > BWcritical & BWavailable ‐ BWcritical < BWhigh) α =1, β= BW BW BW , γ=0, BWexcess=0;
else if(BWavailable > BWcritical+ BWhigh & BWavailable ‐ BWcritical ‐ BWhigh< BWlow) α =1, β=1, γ= BW BWBW BW , BWexcess=0;
else
α =1, β=1, γ=1, BWexcess= BWavailable ‐( BWcritical +BWhigh +BWlow);
CHAPTER 4
COMPUTER SIMULATION AND RESULT
In order to verify the effectiveness of the proposed QoS algorithm, we design several simulation scenarios. The QoS should provide the medical data of WBAN reliable transmission which is related to the packet loss probability and the packet transmission delay. In our previous work of probing based network merging, it has shown that there is no packet loss in the mobile WBAN scenario due to the collision avoidance strategy. Therefore, the transmission delay is what we care about here.
Four simulations are set up to see the characteristic of the proposed random contention based resource allocation algorithm.
4.1 Dense WBAN scenario
Simulation Model
The dense WBAN scenario is used to test the inter-WBAN bandwidth allocation algorithm between two priority levels of WBAN groups under different WBAN density. It is assumed that several WBANs are randomly placed in a 3×3 meter square. Each WBAN is formed by a CPN and twelve WSNs. Two WBAN risk levels are in the system. The numbers of each WBAN risk level are increased from one to five which is illustrated in Figure 4-1 to test the limit of system throughput and the available bandwidth of each WBAN risk level. The constant bit rate
(CBR) traffic is applied in each WSN. Table 4-1 lists the detailed simulation settings in the Dense WBAN scenario.
Figure 4‐1 The schematic diagram of increasing WBAN density
Table 4‐1 Simulation setting parameters
Parameter Value
Num of WSN/WBAN 12
Num of WBAN 1~10 (each WBAN risk level : 1~5) Traffic of WSN 64kbps CBR traffic. 512 byte packet size
PHY rate 6Mbps
Framing structure Beacon Period:20 slots/Data Periods:480 slots Slot duration:1 ms/Superframe duration: 0.5 s
Simulation Result
In the dense WBAN scenario, we increase the numbers of WBAN to test the limit of system throughput. When too many WBANs are in the same area, the limited bandwidth resource is not enough for each WBAN’s requirement. The system throughput of the dense WBAN scenario is shown in Figure 4-2. The system throughput is saturated when WBAN numbers are more than five. In Figure 4-3, it shows the available bandwidth of each WBAN can get in the dense WBAN scenario under
different WBAN risk levels. Each WBAN obtains the available bandwidth by random number contention and use the available bandwidth to transmit the physiological data.
Figure 4‐2 System throughput of dense WBAN scenario
In no QoS condition, every WBAN’s available bandwidth degraded when the numbers of WBAN groups are more than five. With the proposed QoS algorithm, high risk WBAN can remain enough desired bandwidth until the numbers of high risk WBAN are five. The low risk WBAN has fewer available bandwidth when the WBAN number excesses the system’s limit. This result shows high risk WBANs can get as much as possible bandwidth they need to have reliable transmission of medical data. The data of low risk WBAN is less urgent than high risk WBAN so that it makes a concession and gets less bandwidth. Thus, the proposed QoS assigns the WBAN risk level according to the user’s health status and provides the important WBAN user the required bandwidth as much as possible to meet the delay requirement.
Figure 4‐5 The delay performance of different priority WSNs
Figure 4-4 shows the WBAN group delay of two risk levels. High risk WBAN group can have short transmission delay even when it is in a dense WBAN system. Figure 4-5 further shows the high risk WBAN whose important WSN data can have timely transmission. Low risk WBAN don’t need timely transmission when the available bandwidth is not enough because its data may storage in the buffer and transmit as soon as the bandwidth is enough.
4.2 Emergency scenario
The emergency event is unexpected and sporadic. To handle this emergency case, the proposed QoS algorithm has the reserving bandwidth mechanism for medical data. Therefore, CPN can immediately increase the WSN’s priority level when it receives the abnormal data from the WSNs and provide the high level WSNs timely transmission.
0
This scenario is used to test the probability of transmission when WSN has emergency case in one superframe, the immediate report probability. The system model is as same as the dense WBAN scenario.
We generate an unexpected emergency event to simulate the case. Figure 4-6 and Figure 4-7 show the result of the immediate report probability.
We can see the fact that the WSNs can have large immediate report probability than without bandwidth reservation mechanism. Hence, the bandwidth reservation for medical data is essential to make CPN aware of the emergency event of the WSNs so that CPN can assign more bandwidth to the WSNs for reliable data transmission.
Figure 4‐6 Immediate report ratio of high risk WBAN
0 0.2 0.4 0.6 0.8 1 1.2
2 4 6 8 10
immediate report ratio
numbers of group
High Risk WBAN
Without Reservation Bandwidth reservation
Figure 4‐7 Immediate report ratio of low risk WBAN
4.3 Heterogeneous WBAN traffics scenario
Simulation Model
Continue with dense WBAN scenario, this scenario is to test the delay performance of each WSN in the WBAN with the characteristic of heterogeneous traffics. The heterogeneous traffics of WSNs are composed of various medical data types. Each data type has its corresponding data rate, numbers of node and the priority assignment.
Table 4-2 lists some common medical data and the setting used in the simulation.
0 0.2 0.4 0.6 0.8 1 1.2
2 4 6 8 10
immediate report ratio
numbers of group
Low Risk WBAN
Without Reservation Bandwidth Reservation
Table 4‐2 The heterogeneous traffic setting
Simulation Result
In the heterogeneous WBAN traffic scenario, we test CPN’s capability of the intra-WBAN bandwidth allocation and the delay performance of two WBAN risk levels. To test the CPN’s capability of intra-WBAN bandwidth allocation, we compare the proposed algorithm with two common scheduling methods, round robin and earliest deadline first. We pick ECG and video among the heterogeneous traffics to represent the different degrees of traffic loading. Figure 4-8 and Figure 4-9 respectively show the delay performance of ECG and video data.
Round robin has small delay in ECG traffic but large delay in video traffic because it fairly assigns the bandwidth to each WSN and doesn’t consider the heterogeneous traffic problem. Thus, round robin causes some bandwidth wastage on low traffic loading data and hardly satisfies high traffic loading data’s requirement. Earliest deadline first assign the transmission slots to WSN whose packet is closest to its deadline. Hence, the low traffic loading data gets less bandwidth and has large delay when the WBAN group’s available bandwidth is not enough. The proposed method’s delay performance shows it can be the best choice among these
Data Type Data rate The number in
three scheduling methods because we consider the heterogeneous traffic issue and WSN data priority.
Figure 4‐8 ECG traffic’s delay performance
0 500 1000 1500 2000 2500 3000
10 14 18 22 26 30 34 38
delay(ms)
numbers of group
Traffic : ECG(3.2kbps )
Priority Round Robin Early Deadline First
Figure 4‐9 Video traffic’s delay performance
The delay performance of WBAN’s heterogeneous traffic by using the RACOON algorithm is showed in Figure 4-10 and Figure 4-11. The delay performance in high risk WBAN can have real time transmission and also the important WSN data in low risk WBAN. The assistant data (audio and video) in low risk WBAN is less important so it has large delay performance.
0 5000 10000 15000 20000 25000
10 14 18 22 26 30 34 38
delay(ms)
numbers of group
Traffic : Video(64kbps)
Priority Round Robin Early Deadline First
Figure 4‐10 The heterogeneous traffic’s delay performance in high risk WBAN
4.4 Mobile WBAN scenario
Simulation Model
The mobile WBAN scenario is used to test the delay performance when many WBAN users randomly walk in a specific size of area. Each WBAN is formed by a CPN and twelve WSNs. We increase the WBAN users from 4 to 16 to increase the density of mobile WBANs. The area size is 20×20 meter square and the probing channel coverage range is 6 meter from the CPN. Table 4-3 lists the parameter setting about mobile WBAN scenario.
Table 4‐3 Mobile WBAN’s parameter setting
Parameter Value
Num of WSN/WBAN 12
Num of WBAN 4~16
Traffic of WSN 64kbps CBR traffic. 512 byte packet size Walking velocity 4m/s
Area Size 400 m2
Probing channel coverage rage 6m
Simulation Result
In the walking simulation process, we collect the delay time of each WSN and analysis the statistical characteristic including the average and
the standard deviation. Figure 4-12 ~ Figure 4-14 show the delay statistic of NoQoS, high risk WBAN with proposed QoS, and low risk WBAN with proposed QoS. The delay average and standard deviation of NoQoS increase dramatically when the WBAN users increase to 14 in the specific area. The delay distribution shows the instable and dynamic features of random walking. Therefore, the delay time varies with the unpredictable WBAN walking and has no guarantee for the important medical data. On the other hand, the delay average and standard deviation of high risk WBAN is small even when the WBAN number is large. This means the important WBAN users can have relatively stable and reliable transmission during the walking scenario. The critical data in low risk WBAN is also guaranteed the smooth-going transmission of delay time.
The less important data which has large delay standard deviation is the trade-off to other important data.
Figure 4‐12 Delay’s statistical characteristic without QoS
Figure 4‐14 Delay’s statistical characteristic of low risk WBAN
2 4 6 8 10 12 14 16 18
0 2000 4000 6000 8000 10000 12000
numbers of group
Delay(ms)
Low WBAN Priority
Critical High Low
Low Risk WBAN
CHAPTER 5 CONCLUSION
In this thesis, we propose the random contention based resource allocation method (RACCON) for mobile WBAN to provide reliable transmission for the critical vital signals. At the beginning, we analyze the features of two basic MAC protocols and the QoS issues with mobile WBAN, and introduce the probing based network merging protocol which shows its advantage in power consumption in mobile WBAN scenario. Then, we design a two-level priority structure according to different critical levels of WBAN users and physiological signals. Finally, the CPN of WBAN manages the bandwidth allocation between multiple WBANs and the transmission schedule of its WSNs for differentiated services when the available bandwidth is not enough for each WBAN.
The random contention based resource allocation for mobile WBAN is designed to solve the unique QoS issues that WBAN application has. First, we use the imbalanced CPN/WSNs architecture to mitigate the processing loading from resource limited WSNs to resourceful CPN and control the transmission schedule of the heterogeneous traffic types. Second, the interference of dynamic network due to WBAN’s mobile feature is avoided by using probing based network merging protocol. Third, the packet critically and unpredictable traffic can be taken care by the two-level priority structure and cooperation of inter-WBAN and intra-WBAN bandwidth allocation. This
makes the important WBAN users have sufficient bandwidth for detailed diagnosis of the assistant signals and the critical packet can have timely data transmission. The performance evaluation shows the random contention based resource allocation algorithm provides the reliability of vital signal’s transmission no matter in the dense WBAN scenario or in the random walk scenario. Hence, the mobile WBAN can have low power consumption and reliable data transmission including the packet loss and delay.
REFERENCE
[1] Stefan Drude, Member, IEEE, "Requirements and Application Scenarios for Body Area Networks," Mobile and Wireless Communications Summit, 2007.
16th IST
[2] Mark A. Hanson, Harry C. Powell Jr., Adam T. Barth, Kyle Ringgenberg, Benton H. Calhoun, James H. Aylor, and John Lach, "Body Area Sensor Networks: Challenges and Opportunities," IEEE Computer, Vol. 42, No. 1, pages 58-65, January 2009
[3] Benoît Latré, Ingrid Moerman, Piet Demeester, "A Survey on Wireless Body Area Networks", Wireless Networks
[4] Katrin Bilstrup, "A Preliminary Study of Wireless Body Area Networks", Technical Report IDE0854, August 2008
[5] IEEE 802.15.1, "Wireless medium access control and physical layer specifications for wireless personal area networks, "2002, http://www.ieee802.org/15/pub/TG1.html
[6] IEEE 802.15.4, " Wireless medium access control and physical layer specifications for low-rate wireless personal area networks," 2003, http://www.ieee802.org/15/pub/TG4.html
[7] Sana Ullah Member IAENG, Pervez Khan, and Kyung Sup Kwak, " On The Development of Low-power MAC Protocol for WBANs", Proceedings of the International MultiConference of Engineers and Computer Scientists 2009 Vol I, IMECS 2009, March 18-20, 2009, Hong Kong
[8] Wikipedia, "ISM band," http://en.wikipedia.org/wiki/ISM_band
[9] Sana ULLAH, Pervez KHAN, Niamat ULLAH, Shahnaz SALEEM, Henry HIGGINS, Kyung Sup KWAK "A Review of Wireless Body Area Network for Medical Applications" International Journal of Communications, Network and System Sciences. Vol. 2, no. 8, pp. 797. Nov. 2009
[10] Federal Communications Commission, “Medical Device Radio communications Service,”http://wireless.fcc.gov/services/index.htm?job=service_home&id=medi cal_implant
[11] Federal Communications Commission, ”Wireless medical telemetry”, http://wireless.fcc.gov/services/index.htm?job=service_home&id=wireless_medi cal_telemetry
[12] Wikipedia,” Wireless Medical Telemetry Service,”
http://en.wikipedia.org/wiki/Wireless_Medical_Telemetry_Service
[13] Wikipedia.”Ultra-Wideband”, http://en.wikipedia.org/wiki/Ultra-wideband
[14] Huimin She, Zhonghai Lu, Axel Jantsch, Li-Rong Zheng, Dian Zhou, "A Network-based System Architecture for Remote Medical Applications," Life and Medical Science – medical information systems, Asia Pacific Advanced Network 2007, 27-31 August 2007, Xi’an, People’s Republic of China
[15] Artificial Retina Project,” Overview of the Artificial Retina Project”, http://artificialretina.energy.gov/about.shtml
[16] Jamil. Y. Khan and Mehmet R. Yuce, " Wireless Body Area Network (WBAN) for Medical Applications", New Developments in Biomedical Engineering
[17] M.A. Ameem, Ahsanun Nessa, Kyung Sup Kwak,"QoS issues with focus on Wireless Body Area Networks," Third 2008 International Conference on Convergence and Hybrid Information Technology
[18] ShinhHeng Chen, MeiLing Liu, ChingYao Huang, "Probing based network merging for low power mobile wireless body area network," The 4th International Symposium on Medical Information and Communication Technology, 22-25 March, 2010, Taipei