The 11th International Symposium on Communications & Information Technologies (ISCIT 2011)
QoS Routing Algorithm Consuming Minimum Overall Timeslots for Video Streaming over MANET Oh Chan Kwon, Hyung Rai Oh, and Hwangjun Song
GyeongCheol Lee
Dept. of Computer Science and Engineering POSTECH Pohang, Korea {ochanism, raibest, hwangjun}@postech.ac.kr
School of Electrical and Computer Engineering Purdue University West Lafayette, IN 47907, USA
[email protected]
a challenging problem to support multimedia services over mobile ad hoc networks. Several effective algorithms have been proposed to handle QoS problem. QAR (QoS-Aware Routing) [5] incorporates an admission control scheme and a feedback scheme to find the route satisfying the bandwidth requirement. However, it increases the routing overhead because each node periodically sends control packets to gain network state information.
Abstract— In this work, we present an effective QoS routing algorithm based on the IEEE 802.11 multi-rate for video streaming over mobile ad hoc networks. The proposed routing algorithm is designed to minimize the consumed timeslots while guaranteeing the required timeslots at all the pairs of adjacent nodes over the route and the contention neighbors of these nodes to support the route. Furthermore, the proposed routing algorithm can distribute the network loads well over the entire network. This technology is essential because video streaming applications require stringent QoS. Experimental results are presented to show the performance of the proposed routing algorithm.
In this work, we propose an effective QoS routing algorithm with the minimum overall timeslot consumption for video streaming over mobile ad hoc networks. One of the unique features of the proposed routing algorithm selects a route to minimize consumed timeslots over the entire network by considering the number contention neighbors while guaranteeing the required QoS for video streaming service. In addition, the proposed routing algorithm can efficiently achieve load balancing over the entire network.
Keywords-Consumed timeslot; QoS routing algorithm; IEEE 802.11 multi-rate service; Video streaming; MANET
I.
INTRODUCTION
Mobile ad hoc networking technology allows fast and temporary connections among mobile nodes without the need for infrastructure. Ad hoc routing protocols are divided into three groups: table-driven protocols, on-demand protocols, and hybrid protocols. Table-driven protocols are essentially proactive because each node maintains an up-to-date routing table by exchanging periodic routing information. Hence the delay to determine a route is negligible since the route is already known when data packets are forwarded, but many network resources are required to maintain the routing information. In contrast, on-demand protocols are fundamentally reactive because nodes invoke a route discovery process only when a route is needed. Thus, there may be a delay before a route is established. DSR (Dynamic Source Routing) [1], AODV (Ad hoc On-demand Distance Vector) [2], and TORA (Temporally-Ordered Routing Algorithm) [3] are on-demand protocols. Hybrid protocols combine the advantages of tabledriven and on-demand protocols. For example, ZRP (Zone Routing Protocol) [4] proactively maintains topology and link state information within the routing zone and reactively searches for routes beyond the routing zone.
II.
We assume that the transmission power and carrier sensing threshold are the same at every node. A modulation scheme is automatically selected based on the received signal strength by RBAR (Receiver-Based Auto-Rate) [6]. When a route is configured by many short-distance hops, the received signal strength at each node becomes stronger because the distances are short. Consequently, intermediate nodes over the route can select a modulation scheme supporting higher data rates, and thus the total amount of consumed timeslots (a timeslot is the minimum time interval allocated to a node) over the entire network may be reduced. A. Problem Description The proposed routing algorithm is designed to minimize the consumed timeslots over the entire network while satisfying the required timeslots at all the pairs of adjacent nodes over the route and their contention neighbors to support the route. Actually, the proposed routing algorithm uses the ratio of the consumed timeslots to the total timeslots as a routing metric instead of the hop count, delay, or transmission power. It is necessary to estimate how many timeslots will be available at each node during a fixed monitoring interval ( Tp ) in order to build up an efficient route, which may incur a large amount of
Recently, researchers have investigated QoS (Quality of Service) routing algorithms for various multimedia services including video streaming over mobile ad hoc networks. It is well known that multimedia services require stringent QoS for smooth display-out and much larger network resources compared with traditional data services, and that these demands place a serious burden over mobile ad hoc networks. Thus, it is
978-1-4577-1295-1/11/$26.00©2011IEEE
PROPOSED ROUTING ALGORITHM
256
where CT (n) R is the set of nodes over the route R that are
control packet transmission. To reduce the routing overhead, instead of using control packets, we monitor the wireless channel periodically and detect busy time interval in which the received carrier signal strength is larger than the carrier sensing threshold. The busy timeslot ratio of the mth interval at node n is measured by
tsmbusy n smbusy n Tc Tp ,
in the carrier sensing range of a node n , nipkt is the required number of transmitting packets during Tp for the ith flow, and
ts pkt (n) is the amount of timeslots for a packet transmission at node n . The physical meaning of Eq. 4 can be easily understood in the following a simple example. As shown in Figure 1, route R is {S, 1, 2, 3, 4, 5, D}. If n is the node 3, CT (n) consists of {2, 3, 4, 6, 7}, and CT ( n) R is {2, 3, 4}. When Tp is set to 1 second, we can calculate the required
(1)
where smbusy n is the number of busy timeslots during the mth monitoring interval at node n , and Tc is the carrier sensing interval which is set to a value much smaller than Tp . Based on this measured information, we use the Kalman filter [7],[8] to estimate the available timeslot ratio for the next interval. This filter is a linear dynamic system that effectively removes nonstationary white Gaussian noise from the measured data. The estimated available timeslot ratio during the mth monitoring interval at node n is calculated by avail
timeslot ratio at node 3 to support route R by 3 nipkt ts pkt (3) .
avail
ts m (n) A ts m 1 (n) avail
K m ((1 tsmbusy (n)) H A ts m 1 (n)),
(2)
Figure 1. Simple example to calculate tsireq n (dotted circle denotes carrier sensing range of node 3).
avail
where ts m ( n) , A , K m , and H are the estimate of the system state, the state transition matrix, the Kalman gain, and
The cost function of Eq. 3 can be simply expressed by
avail
the observation model, respectively. ts m ( n) represents the estimated available timeslot ratio at time m when the observations up to and including time m are given. A is chosen to predict the system state at time m given the estimated system state at time m 1 . K m is determined by considering the stochastic nature of the process and the measurement dynamics. H indicates the relationship between the measurement sample ( 1 tsmbusy (n) ) and the system state. We can formulate the problem as follows:
nkroute R
nkroute R nCT nkroute
subject to tsireq n ts
avail m
nkroute
ts n
nkroute R D
nkroute R
CT nkroute ts nkroute
CT nkroute ts pkt (nkroute )
iF route nk
nipkt , Tp
(5)
where D is the destination node, CT nkroute is the number of nodes in CT nkroute , and Fnroute is a set of flows at node nkroute . k
In IEEE 802.11 multi-rate service [9], ts pkt nkroute is roughly calculated by 8 LDATA _ MAC ts pkt nkroute link route 1542 106 , tr nk
(3)
(6)
where LDATA _ MAC is the data length at the MAC layer, and
tr link nkroute is the transmission rate over the wireless link at
n
for n CT nkroute , nkroute R, and m,
node nkroute . In fact, the constraint of Eq. 3 takes into account the timeslot constraint and works as admission control at all the pairs of adjacent nodes over the route and their contention neighbors.
where nkroute is the kth node over the route R , CT nkroute is the set of contention neighbors of nkroute including the node
itself, ts n is the consumed timeslot ratio at node n , and
B.
QoS Routing Algorithm Consuming Minimum Overall Timeslots In this section, we describe the proposed routing algorithm in detail. It is divided into the route discovery mechanism including the route request/selection process and the route reply process, and the route maintenance mechanism when a link failure occurs over the route.
ts n is the required timeslot ratio for the ith flow at node n to support the route R , which is calculated by req i
tsireq n CT (n) R nipkt ts pkt (n) Tp ,
nCT
Problem Formulation: Determine the route R (a set of nodes) between the source node and the destination node for the ith flow to minimize ts n ,
(4)
257
LDATA _ MAC
ts
avail rreq
n route k
tr n | CT n | n RC n link
nipkt
LC
route k
neighbor
based route selection can be employed with a huge number of flooding packets since information of all possible routes is needed. To reduce the control overhead and the computational complexity, each intermediate node calculates the cost function of the interim route Rˆ between the source node and itself by setting Lneighbor nkroute to | CT ( nkroute ) Rˆ | and increasing
route k
route k
neighbor
route k
Figure 2. Additional information in RREQ packet.
1) Route Discovery Mechanism: It is assumed that each node includes 2-hop neighbors in its carrier sensing range and maintains a contention neighbor table including a 1-hop neighbor list and a 2-hop neighbor list. A hello packet is used to recognize contention neighbors. Each hello packet includes the addresses of the 1-hop contention neighbors. When a node receives a hello packet, the transmitting node is inserted into the 1-hop neighbor list and removed from the 2-hop neighbor list if listed there. If the 1-hop neighbors of the transmitting node are not included in either the 1-hop or the 2-hop neighbor list, they are added to the 2-hop neighbor list. By repeating these processes, each node can identify its contention neighbors. Based on the obtained contention neighbor information, the proposed routing algorithm is performed.
RC neighbor (nm ) |n
route ) Rˆ m CT ( nk
by 1. Then, a corresponding node
broadcasts an RREQ packet for only the interim route Rˆ with the minimum cost. When admission control is enabled, a corresponding node checks whether the timeslot constraint is satisfied or not. If a corresponding node cannot guarantee the required timeslot ratio to support the interim route, then the node simply drops the RREQ packet. When the RREQ packets finally arrive at the destination node, the destination node selects the route with the minimum cost. With admission control, the destination node verifies the timeslot constraint for the nodes along the route. If the constraint is not satisfied, the corresponding route is excluded from the route selection process at the destination node. Step 2) Route Reply Process: The destination node sends an RREP packet for the selected route via the reverse path. Only nodes over the selected route and their 1-hop contention neighbors broadcast an RREP packet with TTL=1 to their neighbors. Figure 3 shows a flow chart of the proposed routing algorithm.
The proposed routing algorithm follows the source routing during the route construction and uses a routing table during the data transmission. Whereas the existing DSR inserts the addresses of nodes into the RREQ packet, the proposed routing algorithm adds more information to the RREQ packet as shown
in Figure 2, where ts rreq nkroute , LC neighbor nkroute , and avail
RC
neighbor
n route k
Packet receive
are the estimated available timeslot ratio
Routing control packet?
when the corresponding RREQ packet is created, the numbers of previous contention neighbors, and the next contention neighbors of node nkroute along the route, respectively. Thus, the control packet size of the proposed routing algorithm increases slightly. For the data representation, LDATA _ MAC and nipkt
avail rreq
n , LC route k
neighbor
link
route k
route k
route k
neighbor
Routing table lookup
Reject Msg RREQ
Destination node?
tr n , CT n , n , and RC n
require one byte whereas
ts
Yes RREQ, RREP, Reject msg?
No
RREP
are variable size since one byte element is added every hop and are linearly proportional to the number of the encountered nodes until the RREQ packet arrives at the destination node. Using this information, each node calculates the cost function (Eq. 5) and checks the timeslot constraint in two stages to reduce the computational complexity: (1) only the nodes that receive the RREQ packet examine the timeslot constraint during the route request/selection process and (2) the contention neighbors of the intermediate nodes over the selected route check the timeslot constraint during the route reply process. Due to the maintenance of the contention neighbor table, the proposed routing algorithm may require some changes to be extended to large scale networks.
Routing table update Broadcast RREP packet with TTL=1
No Destination node? Yes
Yes
Calculate cost of routes and verify the timeslot constraint
Broadcast RREP packet with TTL=1 for the route with minimum cost
No Calculate cost of interim routes
route k
Send the received packet to next node
Minimum cost with satisfying the timeslot constraint? Yes RREQ packet update
No
RREQ packet drop
Broadcast RREQ packet to neighbors
Figure 3. Flow chart of the proposed routing algorithm.
2) Route Maintenance Mechanism: The proposed routing algorithm uses AODV’s route maintenance mechanism to send an RERR (Route ERRor) packet to a source node: it uses periodic hello packets to detect link failures. The intermediate nodes take no action if they fail to receive a hello packet from inactive neighbors that are not in their routes. However, if the intermediate nodes fail to receive hello packets from active neighbors, they send an RERR packet to the source node. If the source node still needs a new route after receiving an RERR packet, it can reinitiate the route discovery mechanism.
Step 1) Route Request/Selection Process: When a node broadcasts an RREQ packet, adjacent nodes estimate the possible link data rate based on the received signal strength and insert the additional information into the RREQ packet received from the previous node. To obtain the best solution to the above constrained optimization problem, a full search-
258
III.
A. Performance Comparison with Respect to General Routing Performance Metrics The proposed routing algorithm is compared with DSR [1], AODV [2], TORA [3], and QAR [5]. For a fair comparison, RBAR functionality is added to the existing routing algorithms. The delivery ratio (the ratio of data packets delivered to the destination nodes to those generated by the source nodes), endto-end delay, routing overhead ratio (the amount of timeslots for routing control packets divided by the amount of timeslots for data packets delivered to the destination nodes), and number of collisions are used as performance measures. The results are summarized in Figure 4.
SIMULATION RESULTS
During the experiment, we use an NS-2 simulator [10] with a two-ray ground model for the wireless channel. It is assumed that the transmission power is the same at every node. The transmission rate over the wireless link is determined using RBAR [6] based on the Orinoco data sheet [11]. In a semi-open environment, 50 m, 70 m, 90 m, and 110 m are the distances for supporting data rates of 11 Mbps, 5.5 Mbps, 2 Mbps, and 1 Mbps, respectively. The carrier sensing threshold is set to the signal strength observed at 230 m away. Thirty nodes are randomly distributed using the random-way point model in 400 m × 400 m area that is moving at [0, 5m/s]. The number of nodes is set empirically considering the transmission range at a node and the network topology size. The detailed simulation parameters are summarized in Table I. TABLE I.
SIMULATION PARAMETERS.
Parameter
Value
The total number of nodes Network topology size
30 400 m × 400 m Random way point model with speed [0, 5 m/s] 512 bytes 3, 5, 7, 9, 11 500 seconds 10 packets/sec. 0.0127 watt 1.559e-11 watt
Mobility model Packet size Number of flows Simulation time Average packet transmission rate Transmission power Carrier sensing threshold
1
4 AODV DSR QAR TORA Proposed Alg. w/o AC Proposed Alg. with AC
3.5 3 Delay (sec)
Delivery Ratio
0.8
0.6 AODV DSR QAR TORA Proposed Alg. w/o AC Proposed Alg. with AC
0.4
0.2
3
5 7 9 The number of flows
2.5 2 1.5 1 0.5
11
3
(a) 2.1
Routing Overhead Ratio
0.3 0.25
The number of collisions
AODV DSR QAR TORA Proposed Alg. w/o AC Proposed Alg. with AC
0.35
0.2 0.15 0.1 0.05 3
5 7 9 The number of flows
5 7 9 The number of flows
11
(b)
0.4
0
The proposed routing algorithm with AC (admission control) shows the better performance in all aspects except for routing overhead since it guarantees the amount of required timeslots at all the pairs of adjacent nodes over the route and their contention neighbors to support the route. Thus, it reduces the network load over the entire network and the number of collisions relatively decreases compared to the existing routing algorithms as shown in Figure 4 (d). However, the proposed routing algorithm without AC degrades the QoS compared to the proposed routing algorithm with AC because it accepts new route requests even when the timeslot constraint shown in Eq. 3 is not satisfied. Thus, it may sometimes generate excessive traffic load over the network and degrade the QoS of the existing routes. QAR has slightly lower performance than the proposed routing algorithm with AC since it takes into account only the available bandwidth of a new route without considering QoS requirements of the existing flows. Thus, QAR may violate the QoS of the existing flows when accepting a new flow. DSR, AODV, and TORA show lower performance because they do not consider the QoS requirement as shown in Figure 4(a), 4(b), and 4(d). In terms of routing overhead, the proposed routing algorithm with AC shows better performance than the existing routing algorithms except for DSR as shown in Figure 4(c). DSR has the smallest routing overhead because it uses source routing instead of relying on the routing table at each intermediate node. Actually, the proposed routing algorithm requires larger RREQ packets to hold the additional information and more hello packets compared to the existing routing algorithms. However, it is apparently observed that the proposed routing algorithm has a smaller routing overhead since it can significantly reduce the number of collisions by providing more stable routes whereas AODV, TORA, and QAR require a large number of control packets as a result of the frequent collisions.
11
x 10
5
AODV DSR QAR TORA Proposed Alg. w/o AC Proposed Alg. with AC
1.8 1.5
B. Entire Netowork Load Comparison For the entire network load comparison, we assume that 30 nodes are moving according to the random waypoint model with 7 flows. All the source nodes transmit 10 data packets per second. As a performance measure, we use the average consumed timeslots during Tp (1 second) over the entire network. Table II shows the statistics of network load over the entire network. It is apparently observed that the proposed routing algorithm with AC selects the route to decrease the total amount of consumed timeslots over the entire network by reducing a number of the overlapped links among routes and thus distributing the traffic compared to the existing routing
1.2 0.9 0.6 0.3 3
5 7 9 The number of flows
11
(c) (d) Figure 4. Performance comparison with existing routing algorithms: (a) delivery ratio, (b) end-to-end delay, (c) routing overhead ratio, and (d) number of collisions.
259
algorithms. The proposed routing algorithm with AC shows better performance than other existing routing algorithms since it is designed to minimize the consumed timeslots over the entire network by considering the number of contention neighbors while guaranteeing the required timeslots. Since QAR does not consider the consumed timeslots of the contention neighbors when creating a new route, it consumes more timeslots than the proposed routing algorithm with AC. TORA requires many timeslots due to a large number of control packets as shown in Figure 4(c). AODV and DSR show better performance than TORA since they consider the hop count during the route discovery process. However, if the shortest routes are located in a dense region, a new video streaming flow may dramatically increase the network load. In contrast, the proposed routing algorithm selects the route that consumes the least amount of timeslots over the entire network and minimizes the impact of the existing data flows. TABLE II.
balances the network load in the distributive way over the entire network. 42 AODV DSR QAR TORA Proposed Alg. w/o admission control Proposed Alg. with admission control
PSNR (dB)
39
36
33
30
27 0
50
STATISTICS OF NETWORK LOAD OVER THE ENTIRE NETWORK. Measure
Algorithm
Average total consumed timeslots (msec)
Confidence interval with less than 5% error (msec)
685.43 740.68 705.12 789.56 868.42 918.31
(675.97, 694.88) (730.42, 750.93) (693.34, 716.89) (779.40, 799.71) (854.62, 882.21) (903.60, 933.01)
Proposed Alg. with AC Proposed Alg. w/o AC QAR AODV DSR TORA
150 # of frame
200
250
300
Figure 5. PSNR plot comparison.
C. Video Quality Comparison To compare video quality, H.264 (JM 15.1) is adopted as the video codec. We use a QCIF (Quarter Common Intermediate Format)-sized test video sequence (Foreman). The video sequence is encoded at 15 frames per second. Each video sequence is simply encoded by the maximum average bit rate without incurring buffer underflow at the receiver when the initial latency is 5 seconds. The PSNR curves are given in Figure 5. The proposed routing algorithm improves the average PSNR by 1 ~ 4 dB compared to the existing routing algorithms. Figure 6 shows subjective video quality comparisons. It is apparently observed that subjective video quality of the proposed routing algorithm with AC is better than that of the existing routing algorithms. The perceived video quality will improve if an advanced video coding scheme and adaptive rate control algorithm are integrated together with the proposed routing algorithm. IV.
100
(a)
(b)
(c)
(d)
(e)
(f)
Figure 6. Subjective video quality: (a) Proposed routing algorithm with admission control, (b) Proposed routing algorithm without admission control, (c) QAR, (d) AODV, (e) DSR, and (f) TORA.
ACKNOWLEDGMENTS This research was supported by the KCC (Korea Communications Commission), Korea, under the R&D program supervised by the KCA (Korea Communications Agency) (KCA-2011-09913-05006) and the MKE (The Ministry of Knowledge Economy), Korea, under the HNRC (Home Network Research Center) - ITRC (Information Technology Research Center) support program supervised by the NIPA (National IT Industry Promotion Agency) (NIPA2011-C1090-1111-0010).
CONCLUSION
We have presented a QoS routing algorithm minimizing the overall timeslot consumption for video streaming over mobile ad hoc networks. The proposed routing algorithm minimizes the amount of consumed timeslots over the entire network while satisfying the timeslot constraint at a pair of adjacent nodes over the route and their contention neighbors. Experimental results have shown that the proposed routing algorithm can provide seamless video streaming of high quality compared to the existing routing algorithms. Furthermore, it is observed that the proposed routing algorithm efficiently
REFERENCES [1]
[2]
[3]
260
D. B. Johnson, D. A. Maltz, and J. Broch, “DSR: the dynamic source routing protocol for multi-hop wireless Ad Hoc networks,” Ad Hoc networking (edited by C. E. Perkins), pp. 139~172, Addison-Wesley, 2001. C. Perkins, E. Belding-Royer, and S. Das, “Ad hoc on-demand distance vector (AODV) routing,” IETF RFC 3561, http://www.ietf.org/rfc/rfc35 61.txt. V. D. Park and M. S. Corson, “A highly adaptive distributed routing algorithm for mobile wireless networks,” IEEE INFOCOM, pp. 1405~1413, April 1997.
[4]
[5]
[6]
[7]
M. R. Pearlman, and Z. J. Hass, “Determining the Optimal Configuration for the Zone Routing Protocol,” IEEE Journal on Selected Area in Communications, Vol. 17, No. 8, pp. 1395~1414, 1999. L. Chen and W. B. heinzelman, “QoS-aware routing based on bandwidth estimation for mobile ad hoc networks,” IEEE Journal on Selected Area in Communications, Vol. 23, No. 3, pp. 561~572, 2005. G. Holland, N. Vaidya, and P. Bahl, “A Rate-Adaptive MAC Protocol for Multi-Hop Wireless Networks,” ACM MOBICOM 2001, pp. 236~251, August 2001. G. Bianchi and I. Tinnirello, “Kalman Filter Estimation of the Number of Competing Terminals in an IEEE 802.11 Network,” IEEE INFOCOM, pp. 844~852, April 2003.
261
[8]
S. Ekelin, M. Nilsson, E. Hartikainen, A. Johnsson, J.-E. Mångs, B.Melander, and M. Björkman, “Real-time measurement of end-to-end available bandwidth using Kalman filtering,” In Proc. 10th IEEE/IFIP Network Operations and Management Symposium, 2006. [9] M. M. Carvalho, J. J. Garcia-Luna-Aceves, “Delay analysis IEEE 802.11 in single-hop networks,” IEEE International Conference on Network Protocols, November 2003. [10] NS-2 [Online]. Available: http://www.isi.edu/nsnam/ns/index.html. [11] Datasheet for ORiNOCO 11b PC Card [Online]. Available: http://www.proxim.com/.
