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Communications Support for Disaster Recovery. Operations using Hybrid Mobile Ad-Hoc Networks. *. Weiquan Lu2 [email protected]. Winston K. G. ...
32nd IEEE Conference on Local Computer Networks

Communications Support for Disaster Recovery Operations using Hybrid Mobile Ad-Hoc Networks* Weiquan Lu2

Winston K. G. Seah1

Edwin W. C. Peh2

Yu Ge1

[email protected]

[email protected]

[email protected]

[email protected]

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Network Technology Department, Institute for Infocomm Research, A*STAR, Singapore 2 National University of Singapore, Singapore connection allows for large amounts of multimedia data transmissions. The biggest problem with satellite communications is the long propagation delay of the network signal which is typically 270ms for a Geostationary Earth Orbit (GEO) satellite [2]. This introduces long packet delay times to the network, not to mention extra processing delays at the satellite end, which could result in end-to-end delays of more than 400ms that are unacceptable for multimedia data transmission. Real-time multimedia applications usually require the lag times to be less than 150ms to experience transparent interactivity [3]. As this is a physical limitation of the satellite signal, it cannot be solved easily. Moreover, satellite channel band availability is extremely limited, adding to the scarcity of resources during crisis situations.

Abstract—During times of calamity, such as an earthquake or tsunami, rescue and recovery efforts are usually hampered by communications failure as the incumbent communications infrastructure has most likely been damaged or destroyed during the disaster. An ad-hoc communications infrastructure, with support for multimedia traffic such as Voice over IP and videostreaming, must be quickly put in place to support the command, control and communication needs of the rescue and recovery operations. Such applications require relatively fast and robust communications links, and broadband wireless technologies appear to be a viable solution. This paper examines two hierarchical network solutions which allow the delivery of such mission-critical multimedia data between rescue teams and their headquarters over extremely long distances using a combination of wireless network technologies (namely, WiFi, WiMax and GEO Satellite) and multimedia software applications (in particular, Voice-over-IP) to meet the requirements of disaster rescue communication scenarios. The proposed system has been validated experimentally in the field as well as using simulations to demonstrate the scalability of the design.

The latest wireless broadband technology, WiMAX [4][5], provides high-speed transmission of up to 63Mbps for downlink and 28Mbps for uplink [6], with a range of more than 11 km [7] and is ideal for wide area communications, but still cannot match the range of satellite links, hence limiting its effectiveness in long-range strategic command and control scenarios over hundreds of kilometres. Also, as this is a new technology, hardware is not as widely available as WiFi yet, adding to the higher deployment costs. WiFi [8] technology allows high-speed wireless packet-based transmission of data (up to 54Mbps) between nodes, allowing for realtime multimedia data to be transmitted rapidly. The biggest problem with WiFi is its relatively short range (up to 250m in open space and down to 150m with obstructions [9]).

Keywords-ad hoc networks, wireless broadband networks, multimedia communications, disaster recovery.

I. INTRODUCTION Traditionally, walkie-talkies or two-way terrestrial radios have been the medium of choice for rescue scenarios due to their robust analog voice communication for short to medium ranges of several kilometers. Walkie-talkies utilize the Push-toTalk (PTT) service, which allows different parties to broadcast voice information in short bursts to all receivers in the channel, while leaving the channel free in between the bursts. The problem with this system is that typically only analog voice is supported, and the system has difficulty adapting to the delivery of large amounts of multimedia data within short timecritical periods. Also, because it is an analog system, retransmissions of conversations have to be requested manually, severely impairing the efficiency of communication channels during times of unreliable transmissions.

Together, multiple WiFi and WiMAX nodes carried by mobile vehicles and personnel can form mobile ad-hoc networks (MANETs) capable of operating across large distances with minimal need for wired infrastructures. This makes them very suitable for rapid emergency deployment into almost any terrain as they have fewer physical constraints as compared to wired networks. Coupled with satellite links for long-range strategic command and control, a combination of wireless technologies may hold the answer to the needs of disaster recovery operations.

In addition to two-way terrestrial radio technologies, satellite technology has been used for longer range communications of hundreds of kilometers. The advantage of satellite communications over radio communications is that satellite can support digital packet-based data transmitted at broadband speeds (typically up to 4.1 Mbps for return link and 12 Mbps for forward link [1]). Furthermore, the broadband

In our study, we have selected as the network layer protocol, the Optimized Link State Routing (OLSR) protocol [10], to exploit its use of multipoint relays (MPRs) which reduces the number of forwarding nodes and thus, minimizes the control traffic overhead. Moreover, OLSR has been shown to increase the network’s robustness for voice traffic in rescue networks [11].

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This work is funded in part by the French programme ICT-Asia.

0742-1303/07 $25.00 © 2007 IEEE DOI 10.1109/LCN.2007.97

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Evidently, the different technologies have their merits and limitations. Hence, it is important to study how to integrate these different technologies to synergize their advantages while allowing their combined strengths to make up for their individual limitations. Our work aims to accomplish this and this paper examines two hierarchical, hybrid ad-hoc network architectures for use in disaster rescue operations. Section 2 provides an overview of related work, and the motivation and requirements of the proposed system, while Section 3 briefly describes the E-model that has been standardized by ITU-T for objectively determining voice quality in telecommunications. Section 4 introduces the two-tier WiFi/Satellite network and discusses the performance evaluation of that architecture. Section 5 then discusses the multi-tier WiFi/WiMax/Satellite network and related performance evaluations. We then determine the VoIP capacity of the multi-tier network in Section 6, and conclude our presentation in Section 7. II.

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The implications of these requirements are: • •

RELATED WORK AND MOTIVATION

In this section, we present a few related efforts on VoIP applications over MANETs using OLSR and also over satellite link communications. In addition, we also introduce work on the application of the E-model on VoIP systems.



A multicast routing scheme has been proposed for voice group communication in MANETs [11]. The solution was implemented and validated through experiments; they obtained delays between 100 and 300 ms for a three to four hop MANET which meets the requirements for PTT voice service.

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A method to calculate the portion of the mouth-to-ear delay budget that can be allocated to satellite access networks while maintaining traditional PSTN quality calls is proposed in [12]. Using a satellite-PC-to-satellite-PC VoIP scenario to conduct the tests, the authors concluded that for a given codec, echo loss value and packet loss ratio, the satellite delay budget decreases as the efficiency obtained on the network increases. Also, the higher the echo loss value and/or the lower the packet loss ratio, the larger the mouth-to-ear delay budget.

The system should be a wireless system for rapid deployment. Each team member should be self-contained with the ability to communicate with all other members despite single-point failures. Hence, a multi-hop mesh network should be used for short-ranged communications. Communications should be divided into intra-team and inter-team communications. Headquarters should be able to monitor all inter-team communications to ensure the strategic and tactical fit of the operation. Hence, a multicast is required. Voice codecs must be able to deliver sufficient quality for reliable and comprehensible communications. Routing protocols must be robust and have up-to-date information of the state of the entire network. III.

E-MODEL

The E-Model [13][14][3] is a computational model designed by ITU-T that uses the combined effects of several transmission parameters to predict the subjective quality of a telephone call. A transmission rating factor R is calculated by combining all the transmission parameters relevant for a connection, which can then be used to predict subjective user reactions. The factor R is made up of the approximate sum of the impairments caused by these transmission parameters. The mathematical representation of R is shown in the following formula:

In [2], the E-model (cf: section III) is used to calculate mouth-to-ear delay and distortion bounds when traditional PSTN quality is desired in packetized voice calls. It concluded that echo control is recommended due to the small tolerable mouth-to-ear delay budget for compressed voice. Moreover, the margin between the intrinsic quality of a codec and the bound for traditional quality can either be used for allowing a mouth-to-ear delay of above 150 ms some packet loss.

R = R0 – Is – Id – Ie + A The basic signal-to-noise (SNR) ratio R0 takes into account the effects of background noise and circuit noise. The simultaneous impairment factor Is is the sum of all impairments which may occur more or less simultaneously with the voice transmission. The delay impairment factor Id consists of all impairments due to delay of voice signals such as mouth-to-ear delay [3]. The equipment impairment factor Ie encompasses the distortion impairment caused by the use of special equipment [14]. The advantage factor A measures the amount of decrease in the factor R that a user is willing to tolerate when using a given technology over traditional wired telephony. The value of R lies in the range 0 to 100, where R = 0 represents an extremely bad quality and R = 100 represents very high quality.

In this paper, we propose a solution for VoIP service in MANETs, extending the work on wireless ad hoc networks [11], by interconnecting multiple WiFi-based MANETs via a satellite link, and proposing a multi-tier hybrid ad hoc network involving WiFi, WiMax and GEO satellites. This scenario models a typical real-life deployment of an emergency network in times of disasters. In addition to the timeliness and reliability needs of the communications, the user requirements include: •

Rescue team members which are mobile and travelling at up to 5km/hour must be able to communicate with one another at all times within the same team. Each mobile rescue team should be able to cover a wide disaster area of more than 40,000 square meters. Rescue team leaders should be able to communicate with one another across distances of more than 5km to ensure that rescue teams can be deployed far apart to cover multiple disaster zones simultaneously. Rescue teams should be in contact with their headquarters located several hundreds of kilometres away for command and control. All inter-team communications should be relayed to the headquarters for monitoring and planning purposes.

Fully ad-hoc and mobile infrastructure with rapid deployment capabilities and redundancy to prevent cascading failure of the network.

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IV.

TWO-TIER WIFI/SATELLITE NETWORK

A. Network Architecture We first consider the case of multiple rescue teams, communicating among themselves as well as with the headquarters which is located hundreds of kilometers away. The team members communicate among themselves using a multihop MANET running OLSR; communication between teams and with the headquarters is done via the satellite link. The two-tier network is depicted in Figure 1. This approach is validated using an actual field deployment and measurements, and also, using simulations to determine appropriate parameter values for subsequent performance studies. In the field tests, to minimize the need of protocol translation and gateway complexity, the multiple MANETs (of the rescue teams) are connected by a virtual private network such that all the MANETs’ nodes appear to be in the same logical MANET.

Figure 1 Two-tier Hierarchical Network

B. Testbed Implementation A prototype two-tier network was implemented and tested in a rural area on the island of Phuket, Thailand. The mobile nodes are Windows XP laptops using IEEE 802.11g Wireless LAN cards. The nodes were mounted on the top of elephants, which served as the primary mode of transport for the rescue personnel. The reason why elephants were used is because elephants are extremely rugged all-terrain “vehicles” which can carry large amounts of equipment and supplies, and are still being used in Thailand. For measurement purposes, we used Iperf and Ping as the packet generators instead of a real voice application. This allows us to measure the true network delay without any application delay. It also gives us the flexibility to change the packet sizes and data rates.

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Figure 2 Testbed Scenario A

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C. Testbed architecture The testbed experimentation is divided into two scenarios: intra-team communications between nodes within the same MANET (referred to as Testbed Scenario A or Scenario A) and inter-team communications between nodes in different MANETs (referred to as Testbed Scenario B or Scenario B).

Gateway 2 Gateway 1

For Testbed Scenario A, the network architecture is shown in Figure 2. The deployment area of Scenario A is 200m by 200m in a jungle environment, and the laptops represent mobile wireless nodes moving randomly at speeds of less than five meters per second. All nodes are transmitting at 100mW in the same subnet, using the OLSR protocol in ad hoc mode. Readings were taken from Receiver 1 communicating with Receiver 2. One of the nodes is connected to a satellite transmitter using a wired interface, and will serve as the rescue team’s gateway between the Headquarters and other rescue teams. With this scenario, we are able to measure delay and connectivity for 1-hop, 2-hops and 3-hops scenarios. Testbed Scenario B (Figure 3) was designed with the aim of studying the performance of inter-team communication via the satellite link. Receiver 1 in Rescue Team 1 is located 8 km away from Rescue Team 2, where Receiver 2 is situated. Communications between Receiver 1 and Receiver 2 are relayed via the local gateways, to the Headquarters, which was 862 km away from both rescue sites.

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Figure 3 Testbed Scenario B

All inter-team communications was done via the satellite Shinsat iPStar which is modelled according to [1], while the intra-team communications were modelled as per Scenario A. For the satellite link in the simulation model, we assumed the satellite channel is 512 kbps, with a one-way propagation delay of 270ms which is that of a geostationary satellite link [2]. D. Network traffic parameters Since we are emulating voice traffic for our current study, with the potential of transmitting large data files, e.g. for video footage of the disaster area, we need a range of data rates; thus we used data traffic rates of 30kbps, 50kbps, 100kbps, 200kbps, 300kbps, 500kbps, 1Mbps and 2Mbps, and each traffic flow lasted for 60 seconds.

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We measured the end-to-end delays of the packets, as well as the packet loss rates and jitter rates since these will be used for the R value in our E-model calculations. We also measured the throughput and Ping ICMP packet roundtrip times to monitor the connectivity and state of the network, which will be used to validate the simulation model and show that the connectivity and state of the network modelled by the simulations reflects the real-life field tests, with an accuracy of within 10%.

Instead, we focus on the results for Testbed Scenario B, as shown in Figure 4 and Figure 5. In Figure 4, we can see that the simulation values are similar to the experimental results at 30kbps, 50kbps and 100kbps. However, at higher data traffic rates of between 100kbps and 200kbps, the experimental results start to trail the simulation results by up to 63 kbps. In Figure 5, the simulation values for roundtrip time at 30kbps, 50kbps and 100kbps are similar to the experimental results, with the experimental results being slightly higher than the simulation results and this could be due to additional delays imposed by the operating systems of the nodes and the satellite which the simulation has not accounted for. However, the difference between the experimental results and the simulation results at the 200kbps range are large, with the experimental results being up to 300ms higher than the simulation results. The trends, as seen in Figure 4 and Figure 5, could be due to additional load or processing taking place on the satellite which we had no control over, and we intend to investigate the cause of this phenomenon in further studies.

E. Experimental Results and Simulation Model Validation From the measurements done during the field experiments, we constructed the simulation model that will be used for further performance studies. It is important for the simulations to model the real system as closely as possible. Our simulations are implemented using QualNet [15]. Signal attenuation and fading due to environmental conditions were factored into the construction of the simulation model. For the simulation model, we used the same parameters as the real-life experiment, with the only difference being that we adjusted the transmission power of the nodes to match the signal power received by the nodes in the real-life field test to mimic the environmental conditions. We validated our simulation model with our real-life experimental results, and have found the two to be in reasonable agreement within our intended accuracy of 10%. This allows us to use our simulation model to extensively study Voice-over-IP (VoIP) performance of our hybrid ad hoc network in other scenarios. The aim of Testbed Scenario A is to provide performance results that we can use to fine tune and validate our simulation model. Hence, we shall only refer to Testbed A results, where appropriate, for comparison purposes.

F. VoIP Performance Study based on E-Model Using our simulation model, we generated values required for calculation of the E-Model’s R values as stated in [13] for the following VoIP codecs G.711, G.711 with PLC (Packet Loss Concealment), G.723.1 (6.3Kbps) and G.729A (with advantage factor, A) in the two experimental testbed scenarios for a single VoIP call. First, the relevant values such as end-toend delay and packet loss are generated. After which, the results are used along with the recommended values stated in [14] to generate the corresponding R values. Based on Testbed Scenarios A and B, we implemented the VoIP tests using our models for the selected codecs. Each VoIP call lasted for more than five minutes, which models a normal phone call [16].

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According to [12], traditional PSTN calls have an R rating of at least 70. Hence, we have adopted R = 70 as the limit for traditional “toll quality” of a voice call. From the results (as shown in TABLE I. ), we can clearly see that only the G.711 codec produces an acceptable result in Scenario A, and when we add Packet Loss Concealment (PLC), the quality improves by 20%, but in Scenario B, none of the codecs produce acceptable results. Hence, for a network like Scenario B, we propose the use of Push-to-talk (PTT) VoIP as a possible solution. PTT has been shown to have a delay bound of up to 500ms and a packet loss ratio (PLR) of 3% [11], which our test results show is well suited for use in the testbed scenarios. However, further work needs to be done to test our recommendation in relation to the capacity of the network using PTT instead of real-time VoIP.

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Figure 4 Throughput achieved in Testbed Scenario B

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As shown above, the WiFi/Satellite architecture is adequate for intra-team real-time VoIP communications, but is unable to meet the requirements for inter-team communications. To address this problem, we introduce WiMAX into the inter-team communications architecture. This section discusses the design of the network architecture, and the process of validation using experimental field tests and simulations.

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Figure 5 Round Trip Time achieved in Testbed Scenario B

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TABLE I. VOIP PERFORMANCE FOR 2-TIER NETWORK Codec G.711 G.723.1 G.729

Codec G.711 G.723.1 G.729

Codec G.711 G.711 + PLC G.723.1 G.729

station in Point-to-Multipoint (PMP) mode, and communicate with each other over the WiMax backbone via the base station. Both the base station and subscriber stations are mobile and typically move at speeds less than 5 km/h depending on the mode of transportation, but the base station is assumed to be stationary after it has been deployed at its designated location during the rescue mission. Each rescue team is about 8km apart. The mobile base station is never more than 5km away from either rescue team. The router gateway is used to connect the WiFi subnet to the WiMax backbone. For simplicity, we assume that the WiMax backbone operate at a single data rate, without Adaptive Modulation and dynamic resource allocation.

End-to-end delay (ms) Scenario B Scenario A 2.854 276.85 0.939 273.14 0.939 272.99 (a) End-to-end delay Packet Loss Ratio (PLR) (%) Scenario A Scenario B 1.026 1.113 0.974 1.076 0.959 1.039 (b) Packet Loss

The design of WiMax enables the uplink (UL) and downlink (DL) data rates to be varied according to different ratios – a common UL/DL ratio was 1/3 [6]. This is used to increase the efficiency of the network by preventing the network from being overloaded by uplink traffic. This means that the data-rate bottleneck would be at the uplink. As we have earlier established that the best link quality could be achieved by using the bandwidth of 5MHz and, since the speeds at which the nodes will be traveling at are within the 3-10 km/hr range specified in the WiMAX Forum, our architecture will function as specified. The Single-In-Multi-Out (SIMO) antenna mode gives an uplink data rate of 1.8Mbps while the Multi-InMulti-Out (MIMO) antenna mode’s uplink data-rate is 2Mbps. We chose to use MIMO mode as this would provide us with a higher transmission rate at the same bandwidth. This decision is based on the fact that our testbed’s equipment can use the MIMO antenna mode, and it is reasonable to expect that hardware specifications can be defined as requirements prior to the execution of the rescue mission. Furthermore, we can establish a lower bound in terms of performance, since we are using the lowest possible bandwidth, and hence lowest possible data-rate. This also means that we can establish the most reliable link quality that can be offered by WiMax within a 5km signal range. Using these results, we can say that if realtime VoIP can be supported even with these base-line parameters, this suggests that with the use of wider bandwidths, should environmental conditions permit, our architecture can achieve even better performance. In short, we are focusing on the worst-case scenario.

E-Model R values Scenario A Scenario B 70.5 39.5 85.1 55 37.3 15.4 63.2 33.6 (c) E-Model R values

A. Network Architecture The proposed multi-tier network has three layers (Figure 6.) In each team’s MANET, the gateway is equipped with a WiMax subscriber unit instead of a satellite interface/dish. An ad hoc WiMax-based wireless network to support inter-team communications is deployed in the field using a mobile base station unit to link up all the subscriber units on the gateways in every team. The mobile WiMax base station is then connected via satellite link to the headquarters. The communications between the rescue teams on the ground now goes through WiMax network instead of satellite link. This is aimed to cut down the end-to-end delay of the inter-team communications.

B. Experimental Results and Simulation Model Validation As in the previous case, we set up a small experimental testbed to measure and study various parameters and metrics. We implemented and tested the WiMax and WiFi part where most of the traffic for this scenario will be traversing. Considering that a typical disaster area is an extremely harsh environment, we therefore chose the narrowest WiMax bandwidth possible (i.e. 5MHz) to ensure that the links are robust. At this bandwidth, the maximum transmission rate achievable is 2Mbps. Our field tests provide the real-life performance of the WiMax/WiFi coupling, and together with the field test results for the satellite segment, we have concrete data to construct the simulation model for a full-scale study of the performance. The simulation model was developed using the model for the two-tier model studied earlier, extended with the addition of the WiMax component.

Figure 6 Multi-tier WiFi/WiMax/Satellite Network

While the satellite and WiFi layers remain unchanged from the previous two-tier network architecture, we have added an intermediate WiMax layer to enhance the communications between rescue teams on the ground. This layer consists of a single WiMAX mobile base station connected to the satellite transceiver via a wired interface, and multiple WiMax subscriber stations, each connected to a router gateway via a wired interface. The subscriber stations connect to the base

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with PLC produces a result which is 20% better than G.711, and this can be considered high call quality, similar to the result for Scenario A in the two-tier network. We also note that while G.729A falls below but close to the limit of R = 70, we will demonstrate a technique to justify the use of the G.729A codec in the next section to allow for more simultaneous calls, thus enhancing the call capacity of the system.

We first determine the end-to-end delay budget which is a critical factor in determining whether the proposed network can support VoIP communications. The total delay budget of the system is defined as the combined delay from all the system’s components, such as, propagation, application, packetization and queuing delays. To model this delay, we compared our simulation results to the Ping round-trip times obtained in the field experiments. We also ensured that the simulation modeled packet loss exactly as the field test results. To obtain the delay budget that best models our field-test results, we ran the simulations of a representative network (cf: Figure 6) with a range of delays as shown in Figure 7. We found that the delay of 4ms in the simulations best matched the roundtrip times of the Ping packets in the field experiments. Similarly, we measured PLRs of 1% for field experiments and validated these with the simulation model.

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We define the maximum call capacity as the highest number of simultaneous calls that the network can support before performance degrades below acceptable levels. Using R = 70 as the benchmark, and as long as there is a single call within a test case that falls below R = 70, we regard all the calls within that test case unacceptable as we require all calls to be of acceptable and reliable quality. A call is defined as two streams of VoIP packets between between a pair of communicating nodes, flowing in opposite directions. This definition assumes that we are using VoIP codecs that use a constant bit rate, similar to pure G.711 with PLC and G.729A. The quality of a voice call can be based on the scale shown in Figure 8 which classifies the G.711 with PLC as falling within the high quality range. G.729A’s R value falls into the low quality range based on our criteria and disqualifies it as a potential candidate. Hence, we introduce the advantage factor A for the calculation of the R value for G.729A which measures the user’s tolerance for the decrease in R when using a given technology over traditional wired telephony. This means that although G.729A produces a low R value score, it might still be tolerable due to other factors. A set of provisional examples for A is shown in Figure 9 [13]. Our scenarios fit within the criteria for “Access to hard-to-reach locations” as disaster areas can be considered relatively inaccessible with most of the communications and vital infrastructure destroyed. By adding the A value of 20, the G.729A’s R value becomes 82.8, which is above the threshold value of 70. We can thus justify the use of G.729A as a low quality alternative to G.711 with PLC.

Figure 7 Measured Ping RTTs using 4, 5 and 10 ms delay settings

C. VoIP Performance Study based on E-Model Using our validated simulation model, we then generated the PLR and Packet End-to-End Delay required for the calculation of the E-model R values for the codecs G.711 (29,900 pkts transmitted), G.711 with PLC, G.723.1 (6.3 kbps, 7,860 pkts transmitted) and G.729A (14,950 pkts transmitted). As before, with each (normal phone) call lasting for five minutes, the simulation results are as shown in TABLE II. TABLE II.

Codec G.711 G.723.1 G.729

VOIP CAPACITY FOR MULTI-TIER NETWORK

VOIP PERFORMANCE OF MULTI-TIER NETWORK

End to End Delay (ms) 8.53979 ms 7.103192 ms 7.076944 ms

Packet Loss (%) 1% 1% 1%

Figure 8 Speech quality classes [12]

(a) End-to-end Delay and Packet Loss

Codec G.711 G.711 + PLC G.723.1 G.729A

E-Model R value R= 70.6 R= 84.8 R= 36.4 R= 62.8

Figure 9 Provisional examples of the advantage factor A [13]

A. Capacity for Simultaneous High-Quality Calls From Figure 10, it is clear that beyond four simultaneous calls, the R value falls below the threshold of 70 and voice quality drops below acceptable levels, thus giving us a maximum call capacity of four simultaneous high-quality calls. To substantiate this, we need to study the system performance in terms of throughput, packet loss and end-to-end delay.

(b) E-Model R Values

Adopting R = 70 as the limit for traditional “toll quality” of a voice call, we can clearly see that the G.711 codec and the G.711 codec with PLC produce acceptable results for the multi-tier network architecture. Thus, we can now use G.711 and G.711 with PLC for the inter-team communications. G.711

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The call quality is directly related to the throughput for each call in the network, because only complete packets are used by the VoIP application to reproduce a voice conversation. Hence, if the throughput for a single call drops below the required codec bandwidth, namely, 64kbps for G.711 with PLC, and this occurs when there are more than four simultaneous calls in progress, then that call is no longer of acceptable quality. This observation is supported by studying the PLR of the system.

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calls as compared to the G.711 codec, which requires 64 kbps. However, if we extrapolate from the results of the high quality call test, the channel should theoretically be able to support eight times more low quality calls. The reason why this theoretical capacity is not met can be deduced from the packet loss rate (Figure 15).

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As shown Figure 12, when there are more than four simultaneous calls, the packet loss starts to increase, due to the congestion in the network. (This suggests that the system could potentially support more calls with greater channel bandwidth.) However, it has been shown that the G.711 with PLC codec can tolerate packet loss of 10% [12], implying that the unacceptable R value for five simultaneous calls must be a consequence of other factors. Indeed, the average end-to-end delay of each packet surges to extremely high levels when there are more than four simultaneous calls in progress, as shown in Figure 13. With the 400ms delay bound for G.711, the end-toend delays of more than 400 ms incurred when the number of calls goes beyond four accounts for the unacceptable R values.

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B. Capacity for Simultaneous Low-Quality Calls We now consider the case of using the G.729A codec, with the advantage factor included in the R value, and show that 12 simultaneous calls at lower quality can be supported. As shown in Figure 14, the R value drops abruptly below the threshold value of 70 when the number of simultaneous calls exceeds 12. As in the high quality call scenario, we examine the different performance metrics to support the findings of the E-model. For throughput, since the G.729A codec requires only 8kbps, the 2Mbps channel that we have assumed can support more

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Figure 15 Packet loss using low quality calls

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proposed network architectures and determine the networks’ capacity in terms of the number of simultaneous VoIP connections that they can support.

While the packet loss is 42% lower for low quality calls than high quality calls, taking into account the difference in the rate of increase of packet loss after the maximum call capacity threshold has been exceeded, the packet loss tolerance for the G.729A codec (3.4%) [17] is also 66% lower than that of the G.711 with PLC codec (10%). This suggests that the reason why the low-quality calls are unable to reach the theoretical capacity of 32 calls is because of their low tolerance for packet loss. The inability to achieve the theoretical capacity can also be attributed to increased congestion arising from the higher number of simultaneous calls, and this is supported by examining the end to-end delay performance results, as shown in Figure 16. The end-to-end delay experienced in the low quality calls scenario is 77% higher compared to high quality calls scenario. This is characteristic of a network experiencing higher packet congestion [18].

ACKNOWLEDGEMENT The authors wish to acknowledge Apinun Tunpan and his team from the Internet Education and Research Laboratory, Asian Institute of Technology, Thailand, for their invaluable assistance in the field tests in Phuket, Thailand. REFERENCES [1]

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[7]

Figure 16 End-to-end delay using low quality calls

C. Summary of findings Our proposed multi-tier network architecture can support four simultaneous high quality calls per 2Mbps channel, or three times as many low quality calls, peaking at 12 simultaneous low quality calls. Since this is the achievable performance using a single WiMAX base station, it would be logical that more calls could be supported using multiple WiMAX base stations with higher bandwidths. This is a scalability issue that is part of our ongoing work.

[8] [9]

[10] [11]

VII. CONCLUSIONS

[12]

In the event of a disaster when bulk of the incumbent communications infrastructure has been damaged or destroyed, it is important to quickly put in place an ad hoc communications system that is able to support the rescue and recovery operations. While Push-to-Talk communications has been the traditional mode of communications, the advanced applications and multimedia services used today by emergency services require high bandwidth wireless communications technology.

[13] [14] [15] [16]

[17]

We study two hybrid mobile ad hoc network architectures for supporting rescue operations in disaster rescue and recovery operations. Field tests have been conducted to validate their ability to support VoIP and data services and the measurements taken in the field experiments are used to build simulation models for further performance studies. We use the E-model from ITU-T to determine the VoIP quality provided by the two

[18]

770

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