DMQR: A Spatial Routing Protocol to Enable VoIP over High-Mobility Wireless Multihop Networks

: A Spatial Routing Protocol to Enable VoIP over High-Mobility Wireless Multihop Networks Amir Aminzadeh Gohari and Volkan Rodoplu Department of Electrical and Computer Engineering, University of California, Santa Barbara Abstract We develop a space-centric routing protocol to enable the delivery of mobile VoIP over high-mobility multihop wireless networks. The novel aspect of this protocol is the attribution of network and MAC layer congestion to space, which provides delay guarantees over much longer durations than can be achieved by node-centric routing protocols. The presented protocol constructs a spatial map of network congestion and utilizes this map to reactively find routes over space (cells) rather than individual nodes. The amount of congestion in each cell is tracked proactively by measurements of experienced local delay and is quickly disseminated among the nodes. Through QualNet simulations, we demonstrate substantial improvements in comparison with and over a realistic terrain with obstacles, for a wide range of node densities and velocities. I. INTRODUCTION Multihop extensions to cellular networks have recently reemerged as an important research topic for coverage expansion and throughput enhancement in the next generation mobile communication systems []. The Delivery of end-to-end QoS, particularly delay guarantees, remains one of the most challenging problems in the area of mobile multihop networks. This is due to the fact that the changes in network topology occur frequently because of high node mobility; therefore, the routes do not remain in place long enough to provide a stable end-to-end QoS delivery []. Furthermore, because each application demands specific QoS requirements, the choice of routing protocol substantially depends on the applications that are to be deployed over such networks. In this paper, we present a scalable QoS routing protocol based on the space-centric view of network congestion. Our proposition is that, for a high mobility scenario, the congestion levels over space show much higher stationarity than the congestion levels of individual erratic nodes, as shown in Fig.. As such, significant improvements can be achieved in the delivery of delay guarantees in comparison with the node-centric ad hoc routing protocols. The presented routing protocol is designed specifically for VoIP and other real-time applications. Mobile VoIP represents a unique application domain for multihop extensions of cellular networks; in a sense that, while it consumes only a fraction of the bandwidth of traditional data applications, it must meet stringent delay constraints, while achieving a high packet delivery ratio (PDR). The main challenges in achieving a high VoIP capacity over such networks are: () the queuing delays incurred at each hop add up to quickly fill the delay deadline, and () the mobility of the nodes poses significant challenges for finding and tracking This work was supported in part by National Science Foundation CAREER Award Grant 643946. Fig.. Congestion levels over space show higher stability. the routes over which VoIP traffic will be transmitted. Hence, if not addressed, putting VoIP specific constraints raise many performance problems in wireless LANs in general [3] and in wireless multihop networks in particular [4]. Traditional QoS routing protocols for VoIP over mobile multihop networks [5], [6] are node-centric in the sense that the routes are conceived of sequences of nodes. Both reactive and proactive routing protocols fall under this node centric approach and have the disadvantage that the mobility of the nodes places constraints on the success of reservation-based schemes; i.e., the measurements made by the nodes may quickly become obsolete. Further, many routing schemes and protocols have been proposed to optimize the performance of ad hoc routing for real-time applications via congestion control, see e.g. [7], [8]. These approaches work well in stationary or low mobility scenarios; however, the accuracy and the performance of these protocols degrades as the nodes move faster. This is because the network and MAC layers congestion is attributed to the nodes, and thus moves with the nodes; therefore, end-to-end QoS guarantees become difficult to maintain. As opposed to this view, we develop Delay Map based QoS Routing () protocol that adopts a spacecentric view of the network congestion, in which the local congestion is attributed not to individual nodes, but rather to space. Particularly, we divide up the space into cells, as shown in Fig.. Whenever a node transmits a packet, the network and MAC layer queuing delays are measured and attributed as a congestion sample to the cell associated to the node. The average taken over many nodes and packets sent in that cell, results in the cell s average congestion level. is a congestion-aware routing protocol which uses these estimations of spatial congestion to discover the delay optimized paths. Furthermore, provides an estimation of the end-

to-end delay that is valid over much longer durations than can be achieved by the node-centric routing protocols in this area. The reminder of this paper is organized as follows: Section II states the network model assumptions of this paper. In Section III, we describe the details of the protocol. In Section IV, the simulation results are presented and we conclude in Section V. II. BASIC IDEA AND ASSUMPTIONS The end-to-end delay of the packets in a wireless multihop network is comprised of the following: () the sum of the network and MAC layer delays of all the relay nodes, () the sum of wireless transmission delays at each hop, which is usually negligible when compared to the other sources of delay. It is assumed that the application layer delay, e.g. encoder/decoder/buffer delay for VoIP, is subtracted from the delay budget in calculating the maximum tolerable networking delay. As mentioned above, the idea is to examine node mobility in the aggregate, by attributing the experienced individual node delays to the patches of space. This means that whenever a node transmits a packet in a cell, it makes measurements of the MAC and network layer delays (called local delay ) and associates those delay values with the cell at which they were measured. We allow the nodes to collect measurements of these local delays and efficiently disseminate them among themselves. This distributed view of spatial congestion is then used to estimate the expected value of end-to-end delay of VoIP calls. Results similar to the ones in [9] help us build a framework to construct, share, and refine the more stable spatial delay measurements, in the joint memory of the nodes. This, in turn, enables delay optimization and provides soft delay guarantees in a high-mobility, high density network. It is shown in [9] that it is possible for the nodes to construct QoS maps of the network via path integration; provided that the node density remains stationary. It is also stated that the QoS maps of the network show higher stability than the QoS measurements based on the set of individual nodes. Equivalently, if () the node density and data traffic pattern along the route remain roughly invariant during a time period (called the coherence time in [9]), and () the average endto-end delay is much smaller than the coherence time, then the end-to-end delay and congestion maps of the network remain roughly invariant. Furthermore, the expected value of end-to-end delay from each location can be approximated by summing over the local delay values along the spatial route. This method of end-to-end delay approximation is called path integration and is the building block of route discovery in our proposed routing protocol. To this end, we constrain ourselves to a mobile network, with a fixed gateway/access point, which serves as the destination for the VoIP traffic in the network. This assumption is made to simplify the constructions of delay maps. However, the scope of can be extended to ad hoc communication provided that the location of the destination is known by the sender, similar to the underlying assumption of geographic routing protocols. We partition the network deployment region D into a set of M cells, C ij, as in Fig.. Our investigations Fig.. Location & Map Info Beacon Tx Tx Location Info Congestion Map Dissemination Neighbor Lookup Table Update Congestion Map Consolidation Timer Expired Location & Map Info Beacon Rx Initial State Packet Tx Packet in Buffer No Connectivity No Node Found Neighbor Discovery The high level state diagram of protocol Spatial Path Selection Neighbor Lookup Generate the Network Graph Run Dijkstra Algorithm suggest that a cell diameter of half the transmission range will provide an acceptable delay estimation accuracy while keeping the complexity of the protocol limited. The spatial end-to-end delay between cell C ij and the gateway is defined as the delay that a packet (generated by a node within C ij ) experiences to reach the gateway. The delay map of the network is defined as the set of expected values of these spatial end-to-end delays for the cells in D. Similarly, we define the local delay of a given cell C ij, as the expected value of the time that a packet spends in that cell until it is successfully transmitted to a node outside that cell. If all the nodes use omnidirectional antennas, the local delay of cell C ij is proportional to the congestion level of that cell (including both MAC and network layer effects). The congestion map (or local delay map) of a network is the set of congestion values of the cells in D. Note that unlike the end-to-end delay map, the congestion map can be locally measured, constructed, and shared by the nodes in the network. III. ROUTING PROTOCOL Delay Map estimation QoS Routing () is a hybrid routing protocol that constructs and disseminates the congestion map of the network, as well as a neighbor lookup table, in a proactive fashion. However, it does not maintain an end-to-end route database for the possible source/destination pairs. discovers a delay-optimized geographic path (a chain of cells) on demand, while choosing the individual hops proactively. To accomplish its mission, builds a distributed view of the congestion map via the dissemination of locally measured MAC and network layer delays. This can be exploited to () find a delay-optimized route upon request via path integration, () give a soft end-to-end delay guarantee while avoiding the congested areas, and (3) perform resource planning in a delay-sensitive, high-density, and high-mobility network. The high level state diagram of the protocol is illustrated in Fig. and consists of the following building blocks: () spatial path selection, () neighbor discovery, and (3) congestion map construction and dissemination. In, whenever a node has a data packet to send, it uses its local copy of the congestion map to discover the best chain of cells over which the sum of the local delays is minimum. The path integration method guarantees that if the average node density and the traffic patterns remain stationary, the sum of local delays estimates the average end-to-end delay. Spatial path selection is followed by a proactive neighbor

discovery scheme that selects the next hop along the path, as shown in the Fig.. Furthermore, borrows the route maintenance and queuing schemes from other routing protocols with small modifications. First, since does not have an end-to-end route discovery as a set of individual nodes, the route maintenance is performed by invalidating the inactive nodes in the neighbor database. This ensures the availability of active neighbors for future data transmissions. Second, the geographic path to the destination is updated in real-time during the course of a session. Third, the data packets are preemptively dropped from the routing queue after about 5 ms at each hop, because of the VoIP delay limitations on data delivery. A. Spatial Path Selection The spatial path selection algorithm constructs a graph in which the vertices represent the cells, and the edges connect each vertex (cell) to its neighbor cells. The edges of the network graph are weighted by the local delay (congestion) map values, or infinity (or a large number), if the local delay for a cell is undeterminable. This helps the protocol to route around the obstacle in D. There is also a piece of information fed back from the neighbor discovery algorithm that overwrites the weights of those edges that do not contain a neighbor node inside their connecting cells. This feedback, which is only available for one hop, enhances the path selection algorithm substantially because it adds the real-time connectivity information to the long-run average of delay measurements. runs Dijkstra s algorithm on demand, to obtain the best set of cells, over which the sum of the local delays, i.e. the estimated end-to-end delay, is minimum. Furthermore, the algorithm gives an estimate of the expected value of the endto-end delay if this spatial route is taken. B. Neighbor Discovery The output of the path selection algorithm points to the next cell along the optimal route. So, the protocol has to find an individual relay node as the recipient of the packet in that cell. On the other hand, the methods which do not require neighbor discovery and broadcast the data packets to multiple nodes, such as opportunistic routing and limited flooding, are not suitable for VoIP traffic. This is due to the fact that the packets will end up in multiple copies at each hop limiting the available bandwidth for voice transmission. The challenge of fast neighbor discovery comes from the high-mobility, highdensity nature of the network. Note that here, a node is used as the next hop while it remains in the desired region (cell). This is in contrast with the node centric protocols that use the same node until it moves out of the transmission range. We employ a proactive neighbor discovery algorithm that is similar to the one in the TDR protocol []. This choice is made because in a high-mobility network, neighbors change frequently, and VoIP packets cannot meet the end-to-end delay constraint if a neighbor is found reactively. To achieve this, The neighbor cells are considered the adjacent cells, plus further cells that host a potential next hop within the transmission range. every node maintains a neighbor database (lookup table). Each element of this lookup table holds the following information: () the neighbor node ID, () the mobility information of the neighbor, i.e., the location and velocity, (3) the lifetime of that element, and (4) a validity flag. The lifetime of an element is defined as the duration that the neighbor node remains within its current associated cell. The validity flag is used for route maintenance purposes as explained before. To update the neighbor lookup tables, nodes are required to periodically broadcast HELLO packets that carry their location and mobility information (refer to Fig. ). Upon the reception of a HELLO packet, the receiver updates the neighbor database by calculating the lifetime of that piece of information. The transmission frequency of the HELLO packets plays an important role in the performance of the protocol. The shorter the time period between the updates, the more recent the neighbor lookup database of a node is, and the more likely that a node can find a neighbor located in the desired zone, albeit at the cost of increasing the control overhead. Once the nodes build their neighbor lookup database, the routing protocol chooses the neighbor with the longer lifetime to maintain continuous traffic flow, and to decrease the delay jitter. Reducing the route flaps at each hop decreases the endto-end delay fluctuations as well. C. Congestion Map Construction and Dissemination Another novel aspect of is the construction and dissemination of the congestion map, using the local measurements by different nodes, at different times and locations. It is required to construct and maintain the congestion map in the joint memory of the nodes in a time period (spreading period) shorter than the coherence time of the congestion map. To do so, every node measures the network and MAC layer delays of actual data packets and attribute them to the cell it is located at. The local delay measurements of the data packets build an enormous distributed data set, that usually contains bursts of measurements by multiple nodes, each of which becomes obsolete at some point. The challenge is to find a minimum amount of dissemination overhead such that the nodes reach a consensus on the approximation of the congestion map, with an acceptable error. We use the following algorithm that combines the local measurements and the received information into a time-stamped moving average at every node. To compute the congestion map, a simple moving average (SMA) within the coherence time window is used. In this algorithm, for each cell, every node keeps () the moving average estimation of the local delays, () the number of measurements (samples) over which the average is taken, (3) the window start time and end time. Whenever a new measurement is made, the node updates its local version of the congestion map including the above variables. Furthermore, as the time passes, some of the measurements become obsolete and the algorithm should remove them from the averaging process. Even though we cannot remove the expired samples from the moving average one by one, we can normalize the weight of the moving average with respect to the portion of the window that has not expired. This method works

5 5 3 35 4.4. Average End-to-End Packet Delay.9 Packet Delivery Ratio.45.4 Average End-to-End Packet Delay.95 Packet Delivery Ratio.8.35 Average Delay (s).8.6 PDR.7.6.5.4 Average Delay (s).3.5..5 PDR.9.85.8.4.3.75... Number of the Nodes. 5 5 3 35 4 Number of the Nodes.5 5 5 5 3.7.65 5 5 5 3 (c) (d) Fig. 3. Performance comparison of, and protocols. Effect of node density on the end-to-end delay, v = m/s. Effect of node density on PDR, v = m/s. (c) Effect of node velocity on the end-to-end delay, N =. (d) Effect of node velocity on PDR, N =. without error if the samples are distributed uniformly, while it introduces some error for the bursty measurements. Finally, to make the local measurements available to the network, the nodes broadcast their local copies of the congestion map along with the periodic HELLO packet broadcasts. Every node consolidates the received map information with its own using the time stamps and the weights mimicking the error and avoiding the measurement duplicates. The simulation results in Section IV study the trade-offs of the congestion map construction and dissemination performance and overhead. IV. SIMULATION RESULTS We perform a detailed evaluation of our protocol using the QualNet [] simulator. The simulations are set up to examine the performance of a multihop network running protocol to carry VoIP traffic. The simulation results are compared to and Location Aided Routing () [] protocols. is chosen because, similar to our framework, it uses the location of the nodes and performs well when the destination is stationary or moves slowly. In all of the simulations, deployment region D is a km km square with the gateway located at its center. D is divided into a by bounded lattice, made up of 4 square cells. The number of mobile nodes N, and their mobility model is specified in each subsection. is implemented on top of 8.a MAC protocol with a raw channel bandwidth of Mb/s, and 5 m transmission range. This is chosen because the capacity of an 8.a network carrying VoIP traffic is considerably better than that of an 8.b, even when the channel rates are chosen close to each other ( Mb/s vs. Mb/s) [3]. The coherence time of the network is assumed to be one minute and the HELLO packets are broadcasted every 5 ms, unless otherwise stated. Finally, the application traffic in our simulations consists of voice calls and background data traffic. The VoIP agents use ITU G.7 codec with ms audio payload, i.e., 6 bytes of payload, at a constant rate of 64 Kb/s. A. Delay and Packet Delivery Ratio The experiment in this section demonstrates the effectiveness of the protocol in a high-density, high-mobility network at low traffic. In this scenario, VoIP traffic is generated by four stationary nodes that are placed at equal distance (55 m) around the gateway. To better study the effect of mobility and node density on the routing performance, these four nodes are located at least 4 hops away from the gateway. The rest of the nodes move according to the Random Waypoint (RWP) mobility model with a constant speed of v and a pause time of seconds, within a flat deployment region without any obstacles. 96 of the remaining nodes transmit background data traffic to the gateway at the rate of Kb/s. The following results show the average of four VoIP calls, each of which lasts for 3 minutes, over multiple simulation runs. Fig. 3 and 3 study the effect of node density on the routing protocol performance, while the speed of the nodes is set to m/s. As shown in Fig. 3, the end-to-end delay of shows a minimum sensitivity to the density of the nodes compared with the delay of and. To explain this, remember that unlike, both and form the routes as a chain of individual nodes. Therefore, when a route break occurs because of the node mobility, these protocols have to rediscover a new route; which results in large buffering delays. On the other hand, the packet delivery ratio (PDR) of tends to under-perform and at the sparse set-ups (Fig. 3). This phenomenon appears because tries to find a neighbor node at a desired set of cells, rather than looking up any possible route to the destination. The effect of node velocity on the protocol performance is studied in Fig. 3(c) and 3(d), where the total number of the nodes is set to N =. As expected, Fig. 3(c) implies that is more robust to the node mobility than and. This, again, comes from the advantage of discovering the delay optimized route over cells rather than individual nodes. In order to keep the neighbor database updated at the higher velocities, the nodes have to either send HELLO packets more often or face a linear performance degradation. Since the frequency of HELLO packets is set to fixed intervals of 5 ms, shows approximately linear performance degradation, in terms of PDR, as shown in Fig. 3(d). Furthermore, even though provides a better packet delivery ratio, it comes with the cost of delay increase due to longer packet buffering. Average end-to-end delay and packet loss are the general network performance metrics; yet, it is required to address the quality of VoIP calls using mean opinion score (MOS), which is derived from the average R-score of the voice traffic, and presented in Fig. 4 and 4. Here, even though the network is under-utilized, and cannot achieve acceptable call quality, whereas provides good VoIP call quality.

5 5 3 35 4 MOS 4.5 4 3.5 3.5 MOS, Random Drop Assumption (v = m/s) MOS 4.5 4 3.5 3 MOS, Random Drop Assumption ( nodes) Total Routing Overhead (pkt/sec) 45 4 35 3 5 5 5 Average Routing Overhead (N=) Total Routing Overhead (pkt/sec) 3 5 5 5 Average Routing Overhead (v=m/s).5 Number of the Nodes.5 5 5 5 3 Fig. 4. Average MOS of four stationary VoIP calls routed by, and protocols. Effect of node density, v = m/s. Effect of node velocity, N =. End-to-End Delay (Milliseconds) 8 7 6 5 4 3 Dissemination Performance 5ms HELLO Interval 5ms HELLO Interval 3 4 5 6 7 8 Time (s) End-to-End Delay (Milliseconds) 8 7 6 5 4 3 Dissemination Performance ms HELLO Interval 5ms HELLO Interval 3 4 5 6 7 8 Time (s) Fig. 5. The effect of spreading period on the routing performance. 5 ms, 5 ms, and ms, 5 ms dissemination broadcast interval. B. Route Adaptation, Dissemination Performance To analyze the performance of route maintenance and the congestion map updates, we set up a simple experiment with 35 nodes that are uniformly distributed over two independent, non-overlapping paths. The target sender is placed such that one of the possible routes to the gateway is slightly better than the other. To minimize the effect of node mobility and neighbor discovery, all nodes are assumed to be static. The target node initiates a VoIP session to the gateway at t = s, and selects the least-delay route, based on the current value of the congestion map. We start multiple sources of high rate data traffic on the main path at t = 5s to make its resulting delay considerably higher than that of the alternative route. Proactive maintenance of the congestion map helps to find out about the route degradation and switch the traffic to the alternative route which is now better. Fig. 5 shows the moving averages of the end-to-end delay of the target voice traffic for different values of control message transmission range. Fig. 5 illustrates the end-to-end delay for the target call, when the HELLO packets are being sent every 5 ms or 5 ms. In this scenario, the target node receives the updates about the congestion on the main path and switches the data to the alternative route soon enough with a short overshoot for 5 ms HELLO intervals. As the rate of control packets decreases to and 5 ms intervals in Fig. 5, it takes more and more time for the target node to update its view of the network. Therefore, endto-end delay irregularities and overshoots occur until the node reaches stability or it becomes unstable if the spreading period becomes longer than the coherence time of the network. 3 4 5 5 5 5 3 35 4 45 5 Number of Nodes Fig. 6. Overhead Comparison Effect of node velocity. Effect of node density. C. Overhead Comparison In this subsection, we investigate the control overhead of in comparison to and under various motion speeds and node densities. Generally speaking, the routing overhead of reactive routing protocols is directly related to amount of data traffic in the network; whereas proactive methods have minimum sensitivity to the amount of generated data in the network. Therefore to establish a fair comparison, we fixed the number of nodes that are initiating data traffic to 5 nodes and set the total offered traffic of these nodes to.4 Mb/s. Fig. 6 shows the effect of node velocity on the routing overhead for a network with N = nodes. As expected, the routing overhead of and protocols is increasing proportional to the nodes velocity, because of frequent route breaks at higher speeds. On the hand, shows more robustness to node mobility in terms overhead due to its space-centric nature. The effect of node density on the routing overhead is considered in Fig. 6, where v = m/s. We can see that as the node density increases, the control packet overhead of shows an exponential increase until the network becomes saturated, while the control overhead of protocol increases linearly. This makes a scalable choice for high-mobility, dense networks. D. VoIP Performance Evaluation over a Realistic Terrain Finally, we present the simulation results of a more realistic regime where the D resembles a typical urban area that contains a few buildings and obstacles. In this case, it is not clear a priori if the routing protocol can achieve the required end-to-end delay and PDR, to get the acceptable MOS quality for the voice calls. The simulated terrain is shown in Fig. 7. Each node, out of N = mobile nodes, exploits a modified Random Waypoint Mobility (RWP) model as follows: () the destination of the node is not chosen inside the buildings, () the node trajectory does not go through the building areas, and (3) as before, each node moves with a constant speed of m/s with the pause time of. For every simulation run, there are simultaneous mobile VoIP calls generated by randomly chosen nodes. The VoIP sessions are minutes long each with a constant bit rate of 64 Kb/s, as explained above. The simulation is run ten times, choosing different call initiating nodes and mobility realizations in every run. In Fig. 7, we present the average end-to-end delay map of the network described above. This figure shows that the VoIP delay requirements are satisfied for the calls originating from

Gateway End to End Delay Map Per Cell Delay Map (Congestion Map) Packet Delivery Ratio (PDR) Contour Graph.9 Delay (s).35.3.5..5..5 8 6 4 8 6 4 Delay (s).6.5.4.3.. 8 6 4 8 6 4 9 8 7 6 5 4 3 3 4 5 6 7 8 9.8.7.6.5.4.3.. (c) (d) Fig. 7. Delay performance of a realistic network. Network topology. End-to-end delay map. (c) Congestion (local delay) map. (d) Delivery ratio contour map of the network 9 8 7 6 5 4 3 MOS Quality Regions 3 4 5 6 7 8 9.5.5.5 9 8 7 6 5 4 3 MOS Quality Regions 3 4 5 6 7 8 9 Fig. 8. The MOS performance of the network in Fig. 7 carrying VoIP calls simultaneously. Quality regions for the network with, and routing protocol. 4 3.5 3.5.5.5 V. CONCLUSIONS In this paper, we developed, a scalable congestionaware routing protocol for delay-sensitive, high-mobility wireless multihop networks. The novelty of comes from the fact that it searches for delay optimal routes, looking at the node mobility in aggregate. Specifically, constructs, distributes and maintains a congestion map of the network by attributing the measured local delays to the patches of space. The congestion map of the network is then used to discover a suitable route and to guarantee the expected value of the end-to-end delay. The simulation results show that is able to achieve good VoIP call quality in a high-mobility, high-density multihop wireless network. most of the cells within D. However, there are a few cells that show an average end-to-end delay of more than ms. This figure implies that the cells adjacent to the obstacles or the cells that have a single, long path to the gateway, face a higher end-to-end delay. This is because of the higher congestion at the single routing option as illustrated in the congestion map graph of Fig. 7(c). A noticeable behavior of the network in Fig. 7(c), is that the congestion of the cells located around 5 m (the transmission range) from the gateway is high. This phenomenon is a result of higher MAC-layer congestion since eventually, all the packets that are generated in the network have to go through this region. Next, the contour graph of the packet delivery ratio in Fig. 7(d) implies that more than 9 95% of the packets generated at the network are received correctly. While there are a few cells for which more than 5% of the packets are dropped, mostly due to lack of connectivity. Finally, it is essential to demonstrate the average MOS of the mobile VoIP calls being made throughout the simulations. Since, call quality outperforms quality even with a few calls (Fig. 4), we compare the quality score of with only. Fig. 8 and 8 illustrate the call quality regions of and protocols over D, respectively. The main point here is that, as expected, cannot provide acceptable quality for voice transmission for the locations that are at more than hop away from the gateway. 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