Mobile Ad-Hoc Networks: Applications 446 (a) DSDV (b) DSR (c) AODV (d) AOMDV (e) OLSR Fig. 11. Throughput measurement in the random topology The Effect of Packet Losses and Delay on TCP Traffic over Wireless Ad Hoc Networks 447 5.3.3 Throughput measurement The TCP variants over DSDV achieve a higher throughput by a factor of almost 1.5 on average compared to others as shown in Fig. 11(a). The better stability of throughput for the TCP variants could be encountered in proactive routing protocols DSDV and OLSR (Fig. 11(e)). When the number of nodes increases, the possibility of congestion and the contention at the MAC layer increase in the network. However, when the routing layer protocols receive the collision reports from the link layer, they re-discover routes by sending the broadcast messages throughout the network. Therefore, in Fig. 11(c), AODV suffers a lower throughput if compared to others. Another thing is that DSR suffers the instability throughput for all TCP variants because when the node density and the number of connections increase, the stale route problem of DSR comes active and makes the performance worse (Fig. 11(b)). 6. Conclusion In this chapter, we analyze the performance of TCP variants across ad hoc routing protocols in static and mobile ad hoc environments. The performance of TCP variants vary depending on the routing protocols, their core mechanisms and background changes, such as the node mobility, node speed, pause time and number of tcp connections and network topologies. In the chain topology, all of the TCP variants achieve a significantly lower delay over AODV routing protocol in both environments. Moreover, AODV provides a higher throughput for all TCP variants, especially for Vegas in both environments. One interesting thing is that AODV always achieves a lower delay, it suffers a higher delay than others in the grid topology. In the grid topology, although TCP variants have the lowest delay over DSDV in both environments, in the random topology, TCP variants incur a lower packet losses over DSR and OLSR, and encounter a lower delay over DSDV. On the other hand, DSDV and OLSR provide the highest data transfer rate (i.e. throughput) for all TCP variants in random topology. Among all TCP variants, Vegas is the best transport protocol and performs better than others in most situations. 7. Acknowledgement This work is supported in part by University of Malaya Research Grand (UMRG) under grant RG024/09ICT. 8. References BonnMotion: a Mobility Scenario Generation and Analysis Tool (2009). Available from: http://net.cs.unibonn.de/fileadmin/ag/martini/projekte/BonnMotion/src/Bonn Motion_Docu.pdf. Ahuja, A.; Agarwal, S.; Singh, J. P. & Shorey, R. (2000). 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Wireless Networks, Vol. 8, No. 2/3, (March-May 2002) page number (153-167), 1002-0038. Xu, S. & Saadawi, T. (2000). Performance Evaluation of TCP Algorithms in Multi-hop Wireless Packet Networks, Wireless Communications and Mobile Computing, Vol. 2, No. 1, (December 2001) page number (85-100), 1530-8669. Part 5 Other Topics 1. Introduction Even though the interest in ad hoc wireless networks has begun in the early 1970s, several technological difficulties, particularly those related to implementation, have postponed advances in this field until the 1990s, when important issues were investigated and solved, including medium access control, routing, energy consumption, among others. These advances have allowed for actual implementation and commercial deployment of wireless communication systems based on the ad hoc concept, including wireless sensor networks, Internet access in rural areas, etc. Despite the formidable advances in this field observed in the last two decades, one key problem remains open and is still subject to intense research effort: that of modeling and measuring the capacity of ad hoc networks (Andrews et al., 2008). The intrinsic characteristics of ad hoc networks, particularly the lack of a central coordination entity and its consequences, added to the peculiarities of the wireless communication channel, make the estimation of capacity of ad hoc networks a challenging task. Despite the mentioned difficulties, researchers have proposed a myriad of metrics for characterizing the capacity of ad hoc networks under different conditions and emphasizing different aspects of the network, as described throughout this chapter. One of the first key results in this field was achieved by Kleinrock and Silvester (Kleinrock & Silvester, 1978) in late 1970’s, when they investigated the relationship between capacity and transmission radius in a network of packet radios operating under ALOHA protocol. Takagi and Kleinrock further investigated this relationship in (Takagi & Kleinrock, 1984). Both works were based on the metric so called expected forward progress, defined in such way to capture the tradeoff relating the one-hop throughput and the average one-hop length. In fact, decreasing the one-hop length has conflicting effects on throughput: it may increase throughput due to the resulting link quality improvement, but it may also decrease throughput, due to a larger traffic and a higher contention level caused by the consequent larger number of hops between source and destination. Subbarao and Hughes (Subbarao & Hughes, 2000) improved the model previously proposed, by including the effects of the transmission system, and introduced the concept of information efficiency, defined as the product of the expected forward progress and the spectral efficiency of the transmission system. Nardelli and Cardieri extended the concept of information efficiency by taking into account the effects of channel reuse and multi-hop transmissions, leading to a new metric, named aggregate multi-hop information efficiency (Nardelli & Cardieri, 2008a; Nardelli et al., Paulo Cardieri 1 and Pedro Henrique Juliano Nardelli 2 1 University of Campinas 2 University of Oulu 1 Brazil 2 Finland A Survey on the Characterization of the Capacity of Ad Hoc Wireless Networks 20 2 Theor y and Applications of Ad Hoc Networks 2009). Based on a similar concept as that of information efficiency, Weber et al. introduced the metric transmission capacity (Weber et al., 2005), which is related to the optimum density of concurrent transmissions that guarantees that outage constraints are met. Simply stated, transmission capacity is the area spectral efficiency of successful transmissions resulted from the optimal contention density. The capacity metrics cited above, to be described in Section 2, have in common their statistical basis, resulted from the statistical nature of several mechanisms related to wireless communications, such as the interaction among nodes sharing a given channel and the propagation effects. Following a deterministic approach to characterizing capacity of ad hoc networks and focusing on the behavior of capacity scaling laws, Gupta and Kumar introduced the concept of transport capacity (Gupta & Kumar, 2000), which relates transmission rate and source-destination distance. Gupta and Kumar formulated the transport capacity from the perspective of the requirements for successful transmission, which were described according to two interference models: the Protocol Interference Model, which is geometric-based, and the Physical Interference Model, based on signal-to-interference ratio requirements. Gupta and Kumar investigated the behavior of the network capacity when the number of nodes grows (i.e., asymptotic capacity), to show that the per-node throughput decreases as O (1/ √ n ), where n is the number of nodes in the network. This approach was followed by several authors to investigate the asymptotic capacity of wireless ad hoc networks in a variety of scenarios, such as different transmission constraints (Xie & Kumar, 2004; 2006), and with directional antennas (Sagduyu & Ephremides, 2004). Grossglauser and Tse presented an important extension of the work of Gupta and Kumar by considering the effects of mobility on the capacity (Grossglauser & Tse, 2002). They showed that, in a network with mobile nodes operating under a 2-hop relaying transmission scheme, the per-node throughput capacity may remain constant as the number of nodes in the network increases, at the cost of unbounded packet transmission delay. This important result motivated other researchers to further investigate the tradeoff between capacity and delay in mobile wireless networks (El Gamal et al., 2006), (Herdtner & Chong, 2005), (Neely & Modiano, 2005). In Section 3 we will discuss the main results on network capacity evaluation from the perspective of scaling laws. The brief review presented above is an evidence of the complexity of the problem of characterizing capacity of ad hoc networks, leading to a number of different metrics, with different focuses and perspectives. While this large number of metrics is also an evidence of the importance of this field, it may also mislead researchers looking for appropriate models and metrics for a particular application or scenario. This chapter therefore aims at providing readers with an overview of capacity metrics for wireless ad hoc networks, emphasizing the rationale behind the metrics. 2. Statistical-based capacity metrics The inherent random nature of ad hoc networks suggests a statistical approach to quantify capacity of such networks. Specifically, a statistical approach is very useful for the design of practical communication systems, when a set of quality requirements is imposed by the user application in mind. In this section we will discuss some statistical-based capacity metrics found in the literature, namely expected forward progress, information efficiency, transmission capacity and aggregate multi-hop information efficiency metrics. The specificities of each metric will be discussed and their application scenario will be pointed out. 454 Mobile Ad-Hoc Networks: Applications A Survey on the Characterization of the Capacity of Ad Hoc Wireless Networks 3 2.1 Expected forward progress As already mentioned, the work done by Kleinrock and Silvester (Kleinrock & Silvester, 1978) in the late 1970’s was one of the first attempts to model capacity of ad hoc wireless networks (Kleinrock & Silvester, 1978). They proposed the metric expected forward progress (EFP), measured in meters and defined as the product of the distance traveled by a packet toward its destination and the probability that such packet is successfully received. Formally, EFP = d × (1 − P out ), (1) where d is the transmitter-receiver separation distance and P out is the outage probability, i.e., the probability that the bit error rate (or other related metric) is higher than a given threshold. In (Kleinrock & Silvester, 1978) the authors introduced the idea of modeling network as a collection of nodes following a spatial point process, allowing for the use of tools and properties of Stochastic Geometry (Baddeley, 2007), making possible to derive analytical formulation relating several network parameters, such node density, propagation channel parameters, number of hops, packet error probability, etc. In fact, a plethora of analysis was performed based on the metric EFP (e.g. (Sousa & Silvester, 1990), (Sousa, 1990), (Zorzi & Pupolin, 1995)). 2.2 Information efficiency Subbarao and Hughes (Subbarao & Hughes, 2000) extended the work done by Silvester and Kleinrock by including in the model the spectral efficiency of the transmission system, resulting in a new metric, named information efficiency (IE), which is formally defined as the product of EFP and the spectral efficiency η of the link connecting transmitter and receiver nodes, or IE = η ×d × (1 − P out ). (2) Roughly speaking, IE quantifies how efficiently the information bits can travel towards its destination. In order to understand the tradeoff captured by the information efficiency, let us consider a transmission system in which modulation and error-correcting coding techniques should be selected to optimize the IE of the network. If a modulation technique with large cardinality is used, then the spectral efficiency of the system increases, at expenses of a higher minimum required signal-to-interference plus noise ratio (SINR) to achieve a given packet error probability. This higher required SINR clearly increases the outage probability P out . Error correcting coding also plays an important role in this tradeoff, as it can reduce the minimum required SINR, at the expenses of a higher bandwidth, reducing therefore the spectral efficiency of the transmissions. These tradeoffs are captured by the information efficiency metric, allowing for a joint system design involving modulation, coding, transmission range, among other parameters. Following this approach, the performance of different transmission schemes was investigated, such as, discrete sequence spread spectrum (Subbarao & Hughes, 2000), frequency hopping (Liang & Stark, 2000), direct sequence mobile networks (Chandra & Hughes, 2003), direct sequence code-division multiple access with channel-adaptive routing (Souryal et al., 2005) and coded MIMO frequency hopping CDMA (Sui & Zeidler, 2009). It should be noted that, from the perspective of the whole network, the information efficiency of a link does not tell us much about how efficiently the channel is being reused throughout the network area. We will return to this point when discussing the next two metrics. 455 A Survey on The Characterization of the Capacity of Ad Hoc Wireless Networks [...]...4 456 Theory and ApplicationsNetworks: Applications Mobile Ad- Hoc of Ad Hoc Networks 2.3 Transmission capacity Weber et al proposed in (Weber et al., 2005) the transmission capacity (TmC) metric of single-hop ad hoc networks TmC is defined as the product of the density of successful links and their communication rates,... 2 receiving nodes within a circle of radius R = ( P/Pth )1/α , and the throughput capacity per bits/sec per node is upper bounded as cW ( N − 2) λ(n) ≤ (28) n log n 16 468 Theory and ApplicationsNetworks: Applications Mobile Ad- Hoc of Ad Hoc Networks Phase 1 Source Phase 2 Source Relay Destination Destination (a) (b) Fig 8 The 2-hop relaying transmission scheme adopted by Tse and Grossglauser: (a)... ApplicationsNetworks: Applications Mobile Ad- Hoc of Ad Hoc Networks 5 References Andrews, J., Shakkottai, S., Heath, R., Jindal, N., Haenggi, M., Berry, R., Guo, D., Neely, M., Weber, S., Jafar, S & Yener, A (2008) Rethinking information theory for mobile ad hoc networks, IEEE Communications Magazine 46(12): 94–101 Baddeley, A (2007) Spatial point processes and their applications, Stochastic Geometry, Springer,... between Xi and XR(i) is 2 2sn , and the distances between receiver XR(i) and interferers of the k-th tier are larger than kMsn − 2sn The aggregate interference power 14 466 Theory and ApplicationsNetworks: Applications Mobile Ad- Hoc of Ad Hoc Networks can therefore be upper bounded as Pk | Xk − X R (i ) | α k ∈N ,k =i ∑ ≤ ≤ ∞ P ∑ 8k (kMsn − 2sn )α k =1 ∞ 8P k α ∑ ( k − 2/M )α ( Msn ) k=1 k It can be shown... Xi − X R ( i ) | η σ2 + ∑k∈N ,k =i | X −Pk |η k X R (i ) ≥ β, (7) 8 460 Theory and ApplicationsNetworks: Applications Mobile Ad- Hoc of Ad Hoc Networks Network area Δ |X - X | R(k) 2 k XR(i) Xi Fig 3 Arbitrary network under the Protocol Interference model: successful links correspond to disjoint disks where σ2 is the additive noise power The threshold β depends on transmission parameters, such as modulation... d−α j 10 462 Theory and ApplicationsNetworks: Applications Mobile Ad- Hoc of Ad Hoc Networks Noting that√ T-R separation distance di is smaller than the diameter of the network area, the i.e., di ≤ 2/ π, then α di ≤ ( β + 1) Pi π α/2 ∑ j∈T 4 β N+ ≤ Pj ( β + 1) Pi π α/2 ∑ j∈T 4 β (13) Pj α Now, summing the quantities di of all active links, we get ∑ diα ≤ i ∈T ( β + 1) β 4 π α/2 (14) Next, we use the... scheduling on ad hoc networks, IEEE Trans on Information Theory 53(11): 4127– 4149 Weber, S., Andrews, J., Yang, X & de Veciana, G (2007) Transmission capacity of wireless ad hoc networks with successive interference cancellation, IEEE Transactions on Information Theory 53(8): 2799–2 814 Weber, S., Yang, X., Andrews, J & de Veciana, G (2005) Transmission capacity of wireless 20 472 Theory and ApplicationsNetworks:... Transactions on Information Theory 53(8): 2799–2 814 Weber, S., Yang, X., Andrews, J & de Veciana, G (2005) Transmission capacity of wireless 20 472 Theory and ApplicationsNetworks: Applications Mobile Ad- Hoc of Ad Hoc Networks ad hoc networks with outage constraints, IEEE Transactions on Information Theory 51(12): 4091–4102 Xie, L & Kumar, P (2004) A network information theory for wireless communication:... capacity in wireless networks, IEEE Transactions on Information Theory 52(6): 2313–2328 Xue, F & Kumar, P R (2006) Scaling laws for ad hoc wireless networks: An information theoretic approach, Foundations and Trends in Networking 1(2): 127–248 Yi, S., Pei, Y., Kalyanaraman, S & Azimi-Sadjadi, B (2007) How is the capacity of ad hoc networks improved with directional antennas?, Wireless Networks 13(5): 635–648... efficiency in wireless ad hoc networks with outage constraints, IEEE International Workshop on Signal Processing Advances in Wireless Communications Nardelli, P., de Abreu, G & Cardieri, P (2009) Multi-hop aggregate information efficiency in wireless ad hoc networks, Communications, 2009 ICC ’09 IEEE International Conference A Survey on the Characterization the Capacity of of Hoc Wireless Networks A Survey . be pointed out. 454 Mobile Ad- Hoc Networks: Applications A Survey on the Characterization of the Capacity of Ad Hoc Wireless Networks 3 2.1 Expected forward progress As already mentioned, the. Improving performance of TCP over mobile wireless networks, Wireless Networks, Vol. 15, No. 3, (April 2009) page number (331-340), 1002-0038. Mobile Ad- Hoc Networks: Applications 450 Stevens,. over Mobile Ad Hoc Networks, Wireless Networks, Vol. 8, No. 2/3 (March 2002) page number (275-288), 1002-0038. The Effect of Packet Losses and Delay on TCP Traffic over Wireless Ad Hoc Networks