Proceedings of the Int. Conf. on Computer and Communication Engineering, ICCCE’06 Vol. I, 9-11 May 2006, Kuala Lumpur, Malaysia
QoS Multicast Routing Based on Bandwidth Estimation in Mobile Ad Hoc Networks Mohammed Saghir, Tat-Chee Wan, Rahmat Budiarto Network Research Group, School of Computer Science, Universiti Sains Malaysia, Malaysia Email:
[email protected], {tcwan, rahmat}@cs.usm.my Abstract The need for supporting real time and multimedia applications for users of Mobile Ad hoc Network (MANET) is becoming essential. Mobile ad hoc networks can provide multimedia users with mobility they demand, if efficient QoS multicast strategies were developed. In our previous work (load balancing QoS Multicast Routing - QMR), we assume constant available bandwidth for the link. In this paper, we have extended QMR to make it more effective than the previous work. We propose a cross-layer framework to support QoS multicasting. We have enhanced the IEEE 802.11 MAC layer to estimate the available bandwidth at each node. The results of simulation reflect a good packet delivery ratio associated with lower control overhead and lower packet delivery delay.
1. Introduction In MANETs, users wishing to use multimedia applications such as video conferencing and live movie streaming require efficient QoS multicast strategies. QoS in MANETs is highly dependent upon routing and MAC layer performance. To achieve reliable QoS in MANET requires the cooperation of a QoS MAC protocol, resource reservation scheme, and QoS routing protocol [1]. Quality of service (QoS) routing in MANETs is difficult because the network topology may change constantly. Another challenge with supporting QoS for real-time applications is associated with the design of a decentralized medium access control (MAC) model, therefore best effort distributed MAC controllers are widely used in existing wireless ad hoc networks [2]. In this paper, we propose a cross-layer framework to support admission control using the available bandwidth information. We developed a method to estimate the available bandwidth without extra control overhead. The behavior of load balancing QoS Multicast Routing (QMR) using available bandwidth estimation was compared to the original QMR protocol [3] to determine its impact on QoS performance.
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This paper is structured as follows: Section 2 provides an overview on related work in the area of QoS multicasting in MANET and describes our previous work QMR. A cross-layer QoS Multicast Routing design using available bandwidth estimation (E-QMR) is described in section 3. Performance comparisons between QMR and E-QMR are presented in section 4. Finally, section 5 summarizes the advantages of E-QMR and describes future work.
2. QoS multicast routing Several protocols have been developed for supporting ad hoc multicast routing, i.e. MAODV [4], ODMRP [5], and CAMP [6]. However, these multicast protocols did not address the QoS aspect of ad hoc communication. Only a few protocols support QoS for multicast: examples are Lantern-trees [7] and QAMNet [8]. A lantern-tree [7] topology is used to provide QoS multicast routing. The lantern-tree protocol uses a CDMA-over-TDMA channel model at the MAC layer to share time slots. Available bandwidth in this model is measured in terms of the amount of free slots. In this model, the CDMA is overlaid on the top of the TDMA infrastructure. The lantern-tree takes a long time at startup to find all paths, sharing time slots between all neighbor nodes and find suitable scheduling of free slots. The CDMA/TDMA model assumed to be used where topologies do not change very fast; it is difficult to realize such centralized MAC scheme in a dynamic wireless environment, where the IEEE 802.11 is widely used [9]. The QAMNet [8] approach extends existing ODMRP routing by introducing traffic prioritization, distributed resource probing and admission control mechanisms to provide QoS multicasting. For available bandwidth estimation, it used the same method given in SWAN [2] where the threshold rate for real-time flows is computed and the available bandwidth estimated as the deference between the threshold rate of real-time traffic and the current rate of real-time traffic. It is very difficult to estimate the threshold rate accurately because the threshold rate may change
ISBN 983-43090-0-7
3. Cross-layer design for multicast QoS We believe that stringent performance requirements to support QoS in MANETs can only be met through a cross-layer design. The nature of the wireless channel requires that different layers (especially network and MAC sub-layer) interact in order to provide QoS [10]; in general, the system performance in wireless networks can be enhanced by taking advantage of the available information across different layers [11]. In our proposed cross-layer enhancement to QMR, admission control at the network layer makes a decision to accept or reject the new request depends on the information that comes from the MAC layer.
Copyright © 2006 IEEE. All rights reserved.
Figure 1 gives an overview on the cross-layer framework.
QoS request received
Intermediate node checks its available BW at Network
MAC layer
Bandwidth estimation
dynamically depending on traffic pattern [2]. The value of threshold rate should be chosen in a sensible way: Choosing a value that is too high results in a poor performance of real-time flows, and choosing a value that is too low results in the denial of real-time flows for which the available resource would have sufficed. In our previous work [3], we propose a QoS Multicast Routing protocol (QMR) with a flexible hybrid scheme for QoS multicast routing. QMR is a mesh-based protocol which is established on-demand to connect group members and provides QoS paths for multicast groups. We define Forward Nodes (FNs) as a subset of the network topology that provides at least one path from each source to each destination in the multicast group. The QMR protocol integrates bandwidth reservation function into a multicast routing protocol with the assumption that available bandwidth is constant and equal to the raw channel bandwidth. QMR contains mechanisms that provide hybrid fix-reservation and shared-reservation bandwidth to guarantee QoS multicast routing. It uses FNs to apply QoS multicast routing from source(s) to a group of destinations and support load balancing. In QMR we use admission control to prevent intermediate node from being overloaded and reject requests of new sources if there is no available bandwidth; when an intermediate node receives a QoS route request and has enough available bandwidth, it accepts this request and allocates bandwidth for it. When the intermediate node receives a reply for the request, it changes the status of the reserved bandwidth from Allocated to Reserved. The reserved bandwidth stays in reserved status until the forward node is reset. If the intermediate node receives a QoS route request and there is no available bandwidth, it rejects this request. To make a correct decision for accepting or rejecting the new request, the network layer should interact with the MAC layer to estimate available bandwidth.
Figure 1: Overview on the cross-layer QoS framework Estimating available bandwidth using the IEEE 802.11 MAC in MANETs is still a challenging problem, because the bandwidth is shared among neighboring hosts. In addition, accurate estimation of a node's bandwidth utilization is difficult in a multi-hop packet radio networks. When we estimate the available bandwidth, we must take into account the activities of the neighbors of nodes since the wireless medium of a node is shared among neighboring nodes. We estimate the available bandwidth based on the channel status of the radio and compute the idle periods of the shared wireless media. By using this method we consider the activities of neighbors of node; where any send or receive from other nodes will affect the channel status. In this method, for estimating the available bandwidth, each node can listen to the channel to determine the channel status and computes the idle duration for a period of time t; in our approach t = 1 s. The IEEE 802.11 MAC utilizes both a physical carrier sense and a virtual carrier sense. Since multicast transmission does not use virtual carrier sense (RTS/CTS), we rely on physical carrier sense to determine the idle and busy state of the channel to determine channel activity. In this case the IEEE 802.11 wireless radio has two states: 1- Busy state (transmitting, receiving and carrier sensing channel). 2- Idle state. Each node will constantly monitor when the channel state changes; it starts counting when channel state changes from busy state (transmitting, receiving and carrier sensing channel) to idle state and stops counting when channel state changes from idle state to busy state. The Idle Time (Ti) is composed of several idle periods during an observation interval t; the node adds
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all the idle periods to compute the total idle time. We calculate the idle ratio (*) for each period of time t as:
*
7i t
(1)
The available bandwidth BWavail:
BWavail
* u BW
(2)
where BW is the raw channel bandwidth (2Mbps for standard IEEE 802.11 radio). After the node finishes computing the available bandwidth during a period of time t at the MAC layer, it sends the information of the available bandwidth to the Network layer and starts computing available bandwidth during the next period of time t. The work in [12] compared passive listening method with the active hello messages method and concluded that passive methods are straightforward and relatively accurate with no control overhead. However passive method does not consider the impact of mobility. They proposed an active approach using hello messages that account for mobility but has the disadvantage of very high control overheads; this control overhead increases with the number of nodes. In our case, limiting overheads is a higher priority, so the passive listening method is used to estimate available bandwidth. The QMR protocol address the impact of mobility by updating forward nodes (FNs) periodically by freeing the allocated BW for old paths and allocating it for new paths. However, there might be an interval where FNs in the old path might not be aware that the amount of allocated bandwidth was changed since we use 5 second FN update intervals. During this time, QoS requirements of other ongoing flows that use the same or nearby FNs are respected and protected [13]. This is better than using extra overhead to free the allocated bandwidths. This proposed version of bandwidth estimation is what is found in E-QMR.
4. Performance evaluations We have conducted experiments using GLOMOSIM [14] to evaluate the effectiveness of the proposed cross-layer approach. The main concern of these experiments is to evaluate the E-QMR’s efficiency for supporting QoS multicast compare with the original QMR approach. This simulation was run using a MANET with 50-100 nodes moving over a rectangular 1000 m × 1000 m area for over 600 seconds of simulation time. The multicast traffic sources in our simulation are constant bit rate (CBR) traffic. Each traffic source originates 512-byte data packets. Nodes in our simulation move according to the Random Waypoint mobility model provided by GLOMOSIM [14]. The range of mobility speed is 0-20
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m/s and pause time is equal to 30 s. In order to observe the behavior of the sub cross-layer framework, we considered a scenario with 3 multicast sources and 15 multicast destinations in all experiments (assuming that all destinations were interested to receive from all sources and sources use same bandwidth requirements). Each node has the same transmission range of 250 m and raw data rate of 2Mb/s. The minimum bandwidth requirements are 0.1, 0.2, and 0.4 Mb/s. The IEEE 802.11 MAC is enhanced to estimate the available bandwidth using equation (2). The efficiency of the proposed cross-layer framework is evaluated through the following performance metrics: x Average delivery ratio: The average of the ratio between the number of data packets received and the number of data packets that should have been received at each destination. x Control overhead: Number of transmitted control packet (request, reply, acknowledgment) per data packet delivered. Control packets are counted at each hop. x Average latency: the average end-to-end delivery latency is computed by subtracting packet generation time at the source node from the packet arrival time at each destination.
4.1. Packet delivery ratio (PDR) vs. mobility The performance of PDR vs. mobility is given in Figures 2 and 3. As a result of using bandwidth estimation, admission control prevents FNs from being overloaded and provides load balancing which results in good PDR for E-QMR although it is less than the PDR for QMR. Without consequent the available bandwidth, FNs may become overloaded by forwarding extra control and data packets. This increases control overhead and data packet delay as we can see in Sections 4.2 and 4.3. In Figure 3, when bandwidth requirement is 0.4 Mb/s, PDR for E-QMR and QMR are relatively similar although E-QMR is superior to QMR in control overhead and data packet delay. 4.1.1. The effect of different population sizes. Each value in Figure 2 is obtained by assuming that the bandwidth requirement is 0.2 Mb/s, number of mobile hosts is 50, 75, and 100; and mobility is 0-20 m/s. Figure 2 demonstrates the PDR vs. mobility for different mobile host population sizes. The values of PDR for E-QMR are (90.24%, 93.3%, 94%) and (98.32%, 95%, 95.9%) for QMR when mobility is 0 m/s (static). When the mobility is 20 m/s, the values of PDR for E-QMR are (82.3%, 84.4%, 85%) and (85%, 88%, 89%) for QMR. When the number of mobile host
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increases, the PDR increases because the chances increase for data packet to be forwarded instead of being dropped. PDR is still quite good with high mobility; this is because some forward nodes have enough residual bandwidth to forward data packets for other sources. FNs are updated periodically when new nodes participate in the network and establish new reservations even though older nodes are not longer available as FNs. 105 50 E-QMR 50 QMR 75 E-QMR 75 QMR 100 E-QMR 100 QMR
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95 90 85 80 75 70 65 60 0
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Figure 2: Performance of PDR vs. mobility for different population size. 4.1.2. The effect requirements.
of
different
bandwidth
do not have available bandwidth to forward data packet with high bandwidth requirements. The PDR decreases when mobility increases, because FNs lost their bandwidths as a result of mobility and interference between neighbors.
4.2. Control overhead (OH) vs. mobility Control OH vs. mobility results are given in Figures 4 and 5. To study the performance of control OH, two kinds of effects are analyzed. We use the passive listening method to estimate available bandwidth which does not introduce additional signaling packets compared to the hello messages method [12]. MANETs are very sensitive to the control OH as its bandwidth is very limited. The results for control OH in Figures 4 and 5 show that control OH for E-QMR is significantly lower than QMR, this is because FNs estimate the available bandwidth and drop any Requests and Replies for other flows; this avoids wasting bandwidth by forwarding Requests and Replies for flows that can not be admitted. 4.2.1. The effect of different population sizes. 0.7
100 O v erH ea d p er P a ck e t D e live ry
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Figure 3: Performance of PDR vs. mobility for different bandwidth requirement. Each value in Figure 3 is obtained by assuming that the bandwidth requirement is 0.1, 0.2, and 0.4 Mb/s, mobility is 0-20 m/s and number of mobile hosts is 75. Figure 3 demonstrates the PDR vs. mobility for different bandwidth requirements. When mobility hosts is 0 m/s (static), the values of PDR for E-QMR are (98.7%, 93.3%, 73%) and (99.7%, 95%, 73.8%) for QMR. When mobility is 20 m/s, the values of PDR for E-QMR are (95%, 84.5%, 65%) and (99%, 88%, 64%) for QMR. The results show that PDR decreases when bandwidth requirements increases, this is because FNs
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Figure 4: Performance of control OH vs. mobility for different population sizes. Each value in Figure 4 is obtained by assuming that the bandwidth requirement is 0.2 Mb/s, number of mobile hosts is 50, 75, and 100; and mobility is 0-20 m/s. Figure 4 shows the Control OH vs. mobility for different population sizes. When topology of MANET is static (mobility is 0 m/s), the Control OH for EQMR are (0.38, 0.41, 0.42) and (0.52, 0.453, 0.451) for QMR. When topology of MANET is dynamic (mobility is 20 m/s), the Control OH for E-QMR are (0.47, 0.452, 0.44) and (0.536, 0.462, 0.495) for QMR. Generally, the control OH increases slowly when mobility increases, because there is no extra control OH to update FNs or extra signaling to estimate the
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4.2.2. The effect of different bandwidth requirements. Each value in Figure 5 is obtained by assuming that the bandwidth requirements is 0.1, 0.2, and 0.4 Mb/s, mobility is 0-20 m/s and number of mobile hosts is 75. Figure 5 shows the Control OH vs. mobility for different bandwidth requirements. The Control OH for E-QMR are (0.45, 0.41, 0.34) and (0.55, 0.453, 0.356) for QMR with no mobility. When mobility is 20 m/s, the Control OH for E-QMR are (0.48, 0.45, 0.39) and (0.595, 0.462, 0.417) for QMR. We can see that Control OH decreases as bandwidth requirements increase because admission control prevents FNs from relaying extra Requests and Replies packets. Similarly we find E-QMR perform better than QMR in Control OH.
m/s, the AL for E-QMR are (0.065 s, 0.063 s, 0.062 s) and (0.078 s, 0.076 s, 0.077 s) for QMR. The AL decreases when number of mobile host increases because data packet can arrive at destination through shorter paths. When mobility increases, some FNs lost their bandwidth as a result of interference between neighbors; therefore, packets may be congested and wait longer in the queues. From the results E-QMR has lower AL compared with QMR. 0.09 0.085 0.08 0.075 0.07 Average latency (s)
available bandwidth. In addition, E-QMR has lower Control OH compare with QMR.
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Figure 5: Performance of control OH vs. mobility for different bandwidth requirements.
4.3. Average latency (AL) vs. mobility If we don't estimate the actual available bandwidth, FNs will accept extra QoS requests and will be overloaded; therefore, the data that finally reach the destination have to wait in packet queues for a considerably long time, which results in a significantly increased delay [12]. The performance of AL vs. mobility is given in Figures 6 and 7; the results show that the AL values for E-QMR is significantly lower than QMR because FNs in E-QMR were not overloaded.
Figure 6: Performance of AL vs. mobility for different population sizes. 4.3.2. The effect of different bandwidth requirements. Each value in Figure 7 is obtained by assuming that the bandwidth requirement is 0.1, 0.2, and 0.4 Mb/s, mobility is 0-20 m/s and number of mobile host is 75. Figure 7 shows the AL vs. mobility for different bandwidth requirements. When mobility is 0 m/s, the AL for E-QMR are (0.041 s, 0.040 s, 0.027 s) and (0.041 s, 0.05 s, 0.034 s) for QMR. When mobility is 20 m/s, the AL for E-QMR are (0.065 s, 0.063 s, 0.052 s) and (0.069 s, 0.076 s, 0.059 s) for QMR. The AL decreases when the bandwidth requirement increases because some data packets are consequently does not contribute to the AL calculations. 0.09 0.085 0.08 0.075 0.07 Average Latency (s)
O verHead per Packet Delivery
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0.065 0.06 0.055 0.05 0.045
4.3.1. The effect of different population sizes. Each value in Figure 6 is obtained by assuming that the bandwidth requirement is 0.2 Mb/s, number of mobile hosts is 50, 75, and 100, and mobility is 0-20 m/s. Figure 6 shows the AL vs. mobility for different population sizes. When mobility is 0 m/s, the AL for E-QMR are (0.056 s, 0.047 s, 0.041 s) and (0.059 s, 0.05 s, 0.04 s) for QMR. When mobility of host is 20
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0.04 0.035 0.03 0.025 0
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Figure 7: Performance of AL vs. mobility for different bandwidth requirements.
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4.4. Comparison protocol
with
Lantern-tree-based
At this time we cannot do a quantitative comparison with other protocols, e.g. Lantern-tree [7] because insufficient details we given for us to re-implement and simulate the protocol in GLOMOSIM. Qualitatively, E-QMR and Lantern-tree-based protocols have relatively the same effect for PDR given different mobility speeds and bandwidth requirements. However, Control OH in Lantern-tree-based protocol increases dramatically for different mobility speed; whereas it increases slowly for E-QMR. The AL remains relatively constant in both E-QMR and Lantern-tree-based protocols for different mobility speeds.
5. Conclusions and future work In this paper we have extended our initial work QMR [3] to support QoS multicasting by use of available bandwidth estimation at the MAC layer. We have proposed a cross-layer framework to support QoS multicasting and estimate available bandwidth using the passive listening method. Passive Listening method is an efficient way to estimate available bandwidth with no extra control OH. The results for our proposed cross-layer E-QMR protocol show a good PDR with low control OH and low AL comparing with QMR; these results from controlling the load at FNs through the use of more accurate distributed admission controls. The simulation that we have done shows that there are small sizes of residual bandwidth that can be used for best effort data traffic. Therefore in future work we will add best effort traffic to exploit the residual bandwidth efficiently. Our final goal is to design a cross-layer framework from the application layer to the MAC layer to support QoS multicasting.
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Lecture Notes in Computer Science, Ed. K. Cho, P. Jacquet, Springer-Verlag, Vol. 3837, 2005, pp. 83 – 97. [4] E. Royer and C. Perkins, "Multicast Ad-hoc OnDemand Distance Vector (MAODV) Routing", draftietfmanet-maodv-00.txt, 2000. [5] Y. Yi, S. Lee, W. Su, and M. Gerla, "On-Demand Multicast Routing Protocol (ODMRP) for Ad-hoc Networks", draft-yi-manet-odmrp-00.txt, 2003. [6] J. Garcia-Luna-Aceves and E. Madruga. "The Core Assisted Mesh Protocol", IEEE Journal on Selected Areas in Communications, vol. 17, no. 8, 1999. [7] Y. Chen and Y. Ko, “A Lantern-Tree Based QoS on Demand Multicast Protocol for A wireless Ad hoc Networks”, IEICE Transaction on Communications Vol.E87-B., 2004, 717-726. [8] H. Tebbe, and A. Kassler, “QAMNet: Providing Quality of Service to Ad-hoc Multicast Enabled Networks”, 1st International Symposium on Wireless Pervasive Computing (ISWPC), Thailand, 2006. [9] K. Xu, K. Tang, R. Bagrodia, M. Gerla, and M. Bereschinsky, "Adaptive Bandwidth Management and QoS Provisioning in Large Scale Ad hoc Networks", Proceedings of MILCOM, Boston, MA, 2003. [10] S. Sivavakeesar and G. Pavlou, "Quality of Service Aware MAC Based on IEEE 802.11 for Multihop AdHoc Networks", In Proc. of IEEE Wireless and Communications and Networking Conference, USA, 2004, pp. 1482-1487. [11] H. Jiang, W. Zhuang, and X. Shen, "Cross-layer design for resource allocation in 3G wireless Networks and beyond", Communications Magazine, IEEE Vol. 43, 2005, pp. 120 – 126. [12] L. Chen and W. Heinzelman, "QoS-aware Routing Based on Bandwidth Estimation for Mobile Ad hoc Networks", IEEE Journal on Selected Areas of Communication, Special Issue on Wireless Ad hoc Networks, Vol. 23, 2005. [13] R. Renesse, M. Ghassemian, and V. Friderikos, A. Aghvami, “Adaptive Admission Control for Ad Hoc and Sensor Networks Providing Quality of Service”, Technical Report, Center for Telecommunications Research, King’s College London, UK, 2005. [14] http://pcl.cs.ucla.edu/projects/glomosim.
[3] M. Saghir, T. C. Wan, and R. Budiarto, "Load Balancing QoS Multicast Routing Protocol in Mobile Ad Hoc Networks”, AINTEC, Bangkok, Thailand,
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