Cross Layering in Wireless Multi-hop Networks

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discussion, http://www.eas.asu.edu/junshan/ICC . [2] A. J. Goldsmith, S. B. Wicker, ... [11] R. J. Punnoose, P. V. Nikitin and D. D. Sancil,. “Efficient Simulation of ...
Cross Layering in Wireless Multi-hop Networks G. Karbaschi*, A. Fladenmuller* *Université Pierre et Marie Curie, LIP6-CNRS, 8 rue du Capitaine Scott 75015 Paris, France. Emails: [email protected] , [email protected]

Abstract Recently in an effort to improve the performance of Wireless Multi-hop Networks in the face of random variations of physical channel and network status, there has been a trend towards employing interactions between different layers in the protocol stack. Using this approach called cross layering, resource allocation and decisions will be done with a more global view of the network and can reduce instabilities of wireless network. In this paper we propose a new approach in cross layering which exploits information of MAC layer passed to the routing agent which tries to choose the best quality path with least congestion towards the final destination. We show that by using the proposed method better performance in terms of delay and throughput is achievable.

Keywords: Cross Layering, Multi-hop Wireless Network, IEEE802.11 MAC Layer, Network Layer. Introduction Recently the major challenge for researchers in multihop wireless network is to achieve a network with a good performance while linking this new technology to the rest of the network. Current wireless network protocol design is largely based on layered stack protocol in which each layer is designed and operated independently. This paradigm has greatly simplified network design and led to robust scalable protocols in the internet. However, using this traditional layered architecture in the face of some fundamental properties of wireless medium such as variability of the link characteristics, it is difficult to guarantee performance of the network to transmit real-time or even critical data. To overcome the network performance problem and reduce instabilities of wireless networks, Cross Layer design has been proposed which lets protocols that belong to different layers share in network status

information, while still maintaining the layers’ separation at the design level [1], [2], [3]. It has been shown that by exploiting the lower layer channel information for making decision at higher layers (as shown in figure 1), growth of performance in both the network throughput and delay has substantially increased. The main reason for this improvement is that employing this approach, the resource allocation and decisions made at the higher layers will be done with a more global view of the network and can cause a much better performance for the network. Network MAC Information

MAC PHY

Rate Adaptation Information Channel condition Estimation (ex. SNR)

Wireless Channel Fig. 1 Illustration of cross layer architecture in network stack

Researches show that Network layer has the major role in improving the performance of network. Because at this layer by choosing different paths to the destination we can control network behavior through defining proper routing metric and avoid using insufficient links. It is worth considering that depending on the information passed to routing agent, the routing algorithm could exhibit different properties. In this paper, we introduce a cross layer approach based on interaction between MAC layer and Network layer. The main goal of this method is to optimize consumption of network bandwidth which causes to reduce its throughput and delay.

The rest of the paper is organized as follows: In section 1 we describe a brief survey of current research. Section 2 describes the proposed approach and in section 3 we evaluate this method and show that it can improve the performance in terms of delay and throughput.

1. Related Work There has been significant research directed toward optimization and improving the performance of multihop wireless networks. Example include energyconstrained and delay routing, application adaptation, adaptive techniques at the link layer, etc. In all of these researches some isolated components of the overall network design have been targeted and important independencies between them have been ignored. After introduction of wireless networks optimization through Cross Layering, most of the effort in this area converged to use this approach and mainly introduced a design based on interactions between the three higher layers. Some researchers try to employ information obtained from Physical layer at the lower layers to appraise the interference and PER (Packet Error Rate) of wireless links. G. Holland, et. al. in [5] have proposed a Rate Adaptive MAC. The main idea is to send data at higher rate when the channel quality is good. In other words, selecting the rate at the MAC layer will be done to give the optimum throughput for the given channel conditions. Some others use information of link quality passed from physical layer for choosing a neighbour which has the best quality link to forward the data packet towards the destination [6]. One downside of obtaining directly information of estimation of channel from physical layer is that the measured SNR at this layer depends on the employed coding technique and its parameters. Because decoding the received information may improve its quality and repair some occurred errors, the measured value may be an imprecise estimation. In addition, at higher layers there is more knowledge about the status of the network. For example, at MAC layer we not only can evaluate the quality of links but also we know about the congestion status of different links and therefore network can be aware to keep the data flow from passing through a congested area. There are not many researches that use information of MAC layer to optimize the behaviour of network. [7] introduces a way to measure the MAC802.11 delay at each node. This delay is defined as the interval from when the RTS packet for reservation the channel is sent to when the data packet is received. The authors claim that it is a measure of the contention experienced by a node and therefore if used as the routing metric the employed routing algorithm can be interference aware. We argue in this paper that in wireless network the consumption of available bandwidth in sending data is

a one of the major criteria in determining the best path to the destination. As we base our mechanism on infrastructure of IEEE802.11, a study of the protocol shows that the retransmission mechanism of lost packets not only wastes the bandwidth but also may cause interference with other current traffic in the network. In this paper we measure information based on the number of retransmission at the MAC layer representing an estimation of consumed bandwidth and then pass it to the Network layer as a routing metric. One of the advantages of the proposed approach in spite of most of the current researches ([8]) is that it doesn’t rely on the broadcast of extra probe packets to estimate the channel.

2. The Proposed Cross Layer approach This section describes the proposed cross layer approach. As argued above, designing an optimized routing algorithm can greatly improve the network performance. Therefore, we introduce a new cross layer metric for the routing algorithm which is passed from MAC layer. Using this new metric, the routing algorithm intends to choose the shortest path with the highest quality due to poor quality and congestion of wireless links. In this approach it is supposed that the best quality links require the least number of retransmission of lost packets. Therefore, the network layer employing this metric can prevent to waste the bandwidth in retransmission mechanism of IEEE 802.11b. We briefly describe the new metric and the way to find the route based on it.

2.1. Description the Proposed Metric The proposed metric is based on estimating the channel quality and congestion status of adjacent links of a node due to number of retransmitted frames. In IEEE802.11 the retransmission of an unicast packet is detected if an ACK or a CTS is not received and this can be due to either the dropping of a frame or network congestion. If the mobile node employs DCF (distributed coordination function) for medium access as illustrated in figure 2, data transmission is preceded by the RTS/CTS phase to reserve the channel and avoid the hidden/exposed node problem. Therefore, before actual data transmission between NodeA and NodeB, RTS and CTS frames are exchanged. It is clear that the retransmission of RTS/CTS can occur during this process due to poor link-quality or high contention level among different resources for getting the channel.

RTSFailureCount i or Retransmission of RTS/CTS Timeout

These metrics are normalized values for success in sending successfully the frames of Data and RTS. It is clear that a large number of retransmission for RTS causes a small value for FTE_CAB(i) which we interpret that there is a congestion on the link of (A-B) and with the same argument, a small value for FTE_QAB(i) leads the link (A-B) as a low-quality link. Obviously, if the number of retransmission reaches to a predefined threshold, the sender gives up transmitting the packet and its related FTE is set to zero.

ACKFailureCount i or Retransmission of DATA/ACK

Node A RTS

CTS

DATA

ACK

Node B Fig. 2 Illustration of the retransmission mechanism of IEEE802.11b with DCF

The same process is then repeated for sending Data frame, i.e. it is retransmitted until receiving an ACK packet. Due to the fact that the packet error rate is linked to the packet size and BER (Bit Error Rate) of the link, the larger the packet is, the more susceptible it will be to the existing error of channel. This relation is shown in the following equation:

PER = 1 − (1 − BER ) L

(1)

Where L is the Length of the packet. We can conclude that it is more likely that Data frames will be affected by BER than small sized RTS and CTS frames. Therefore, for assessing a link quality for transmitting Data packets which usually have larger length than control packets, RTS/CTS retransmission could under-estimate the error rate of the channel. For this reason, we extrapolate the link quality information from number of Data retransmission, and the congestion status information from number of RTS retransmission which are called ACKFailuerCount and RTSFailureCount.

2.2. Measuring the Proposed Metric As argued in the previous section, the metric contained two knowledge of the link: the LinkQuality for transmitting Data packets and Congestion status of the link. The construction process of the metric is as follows: • In each transmission we measure a success rate called Frame Transmission Efficiency (FTE) in both cases of sending Data and RTS from ACKFailuerCount and RTSFailureCount. • Suppose that in sending ith packet from NodeA to NodeB these success rates are respectively FTE_QAB(i) and FTE_CAB(i). We calculate these values as follows:

• After each packet transmission we keep an EWMA (Exponentially Weighted Moving Average) value of the metrics, like below:

FTE_QAB(i) =αi FTE_QAB(i) +(1−αi )FTE_QAB(i −1) (4)

FTE_CAB(i) =δi FTE_CAB(i) +(1−δi )FTE_CAB(i −1)

(5)

In which FTE_QAB(i) and FTE_CAB(i) are the moving average of the metrics at the time of transmission of the ith packet. The coefficients αi and δi are the weights we give to the current measured value. Determining the right values for αi and δi depends on the speed of channel variations. If it changes quickly to reflect this fluctuation, higher values should be assigned to the current measurement. Instead, it may cause instability in path selection which is not a suitable solution specially in wireless networks. Because more instability in selecting routes causes more overhead to update routing tables. Moreover, it may cause a longer convergence time in routing tables in finding the best path.

In this research, to avoid fluctuation in choosing paths and preventing oscillation of the network in the case of transient condition of wireless links we give lower weight to the current metric and higher to the previous average. For example, in this paper we have set arbitrarily α and δ to 10%. For more adaptation to some particular cases of wireless links such as fading, in the case of loosing a packet we increase this percentage to 30% to reflect this loss. • For each link which is used for forwarding the packets to the final destination, two moving-averaged metrics are calculated during the network lifetime and a combined metric of both is assigned to each link. For the link (A-B), the assigned value (FTEAB) is made like bellow:

FTEAB = FTE_ QAB × FTE_ CAB

(6)

• For a multihop path from NodeA to the

ACKFailureCount (i ) (2) FTE _ Q AB (i ) = 1 − ACKFailureCount (i ) + 1

destination NodeD, FTEAD is made by multiplying

RTSFailuer Count (i ) FTE _ C AB (i ) = 1 − RTSFailure Count (i ) + 1

between pairs of nodes along the path towards the destination from NodeA to NodeD. Therefore, each

(3)

FTEXY along the path when (X-Y) is each link

node knows about the FTE of multi-hop paths towards the destination and will decide to choose the path with the maximum efficiency which is the least congested route with the best quality.

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• We have chosen a table driven routing algorithm like DSDV [9] to evaluate the proposed metric. To avoid consuming additional bandwidth we piggy-back our measured metric into the table-update DSDV messages that already exist. Thus, each node keeps the FTEs related to its adjacent links and at the time of broadcasting routing tables sends them to its neighbors.

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3. Simulation and Experimental Results Fig. 3 a 30 node topology randomly placed in a 600×600 m area

3.1. Simulation Model

In this simulation a simple model of small scale Ricean fading channel has been used [11] as the AWGN (Additive White Gaussian Noise) model is an idealistic channel condition where no signal fading occurs. We assume that the channel condition during the transmission of one packet is not changed which is a realistic assumption in a flat fading channel. As the high value for Ricean K factor would make the channel similar to AWGN and low values lead to a channel with high variation it has been set to 20 which is a typical value in most of the current simulation. The In order to obtain realistic results we have modeled the bit error rate (BER) as described in 802.11b for WIFI [12]. We consider multiple 30-node random topologies with the radio range of 110m which one of them is described in figure 3. As it can be seen, the most distant nodes have been considered as the sender and receiver for transferring CBR (Constant Bit Rate) packets at the rate of 100Kbps. We run the application flow in the network after initialization of routing tables.

3.2. Results We have measured the average end-to-end latency. To do this, for each received packet at the final destination the latency is measured and at the end of the simulation time these values are averaged. We can see the result in figure 4 in which we have compared the end-to-end delay for both the metric.

We achieve less than half end-to-end delay in comparison to minimum hop. This shows that with the new metric we can reduce the end-to-end transmission time and prevent to wasting it in retransmission process. 0.45 DSDV with the proposed metric DSDV with minimum hop

0.4 0.35 Average End-end Latency(s)

We use ns2[10] to evaluate the performance of the proposed method. We compare the performance a wireless multi-hop network with DSDV as the routing algorithm in two cases of using minimum hop metric and the proposed metric.

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Fig. 4 Average End-to-End delay vs. Simulation time.

In Figure 5 we show the averaged ratio of delay by the proposed metric to minimum hop and its variation in more than 10 random topologies. It can be seen that for all the topologies, the delay caused by minimum hop is between two and three times larger than when we used our metric. The measurement of the network throughput is shown in figure 6. We define throughput as the ratio of the received data packets to the sent data packets.

Ratio of Delay by minimum hop/ Delay by the proposed metric

3.3. Analysis the results

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The results in the previous section can be an evidence to show that routing algorithm which employs the proposed metric intends to choose a path which has higher throughput and lower end-to-end delay in comparison to the path selected by traditional metric of minimum hop.

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Fig. 5 The averaged ratio of delay caused by minimum hop to the proposed metric in 10 random topologies

The result shows that we outperform about 15% to the minimum-hop metric.

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Fig. 6 Average Throughput vs. Simulation time

The averaged ratio of throughputs by the two metrics in more random topologies (figure 7) shows that by choosing the paths with minimum number of retransmission we have less packet loss and so more packets are transferred healthily. Ratio of Throughput by the Proposed metric/Minimum hop

In addition, the reduced transmission latency achieved by this new metric is very valuable for some real-time applications. The proposed metric is also able to prevent choosing a path which passes through a congested area. This advantage causes still less end-to-end delay by reducing the waiting time at the queue of a congested node. This fact can be better evaluated as the future work in a congested network with multiple sources and multiple destinations. Another advantage of the proposed metric is that because the quality of the links is measured during the normal operation of routing in sending data packets, it is able to estimate the real consumed bandwidth of each link.

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Due to the fact that the selected path by our metric is not necessarily the shortest path and also because the capacity of process for all the nodes is identical, we can conclude that the proposed metric by selecting the paths with the least number of retransmission is able to prevent wasting the bandwidth resource which is an important and rare resource in wireless networks.

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Another advantage is that in the case of a deep fading or breaking down a wireless link or even turning off a mobile node, the metric related to the link or the node drops down and we can detect the breakdown in the path and so automatically another link or node will be chosen. Therefore, it can preserve the survivability of the network. It should be noted that this approach needs a certain convergence time to find the optimal path. Therefore, when channel variation is too high or if the nodes move with high speed, the algorithm can not follow the changes. It can be concluded that as infer in [8] all the methods based on measuring and estimating the link-quality are not suitable to be employed in a very fast and dynamic ad hoc network.

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Fig. 7 The averaged ratio of throughput caused by the proposed metric to minimum hop

We suppose that employing cross layer methods at the network layer has the great impact on the overall performance of the network. Therefore, in this paper we introduced a cross layer approach through defining a new metric for routing algorithm based on the information obtained from MAC layer. Route selection based on this cross layer metric intends to choose paths with minimum BER due to the number

of data retransmission. In addition it has the potential of load balancing through conducting the flow from least congested area.

[5] G. Holland, et. al., “A Rate-Adaptive Protocol for

The results show that this method can greatly improve the network performance in terms of average throughput and end-to-end latency.

[6] L. Iannone, et. al., “Cross-Layer Routing in

It is worth while to mention that cross layer optimization is often difficult to implement due to its dependency to quality of the measurement and also choosing a good metric in each layer. As it is inferred in [4] a careless cross layer design may have unintended results such as instability. This may be our future work to reduce it and still remained as an issue that needs to be resolved.

Multi-Hop Wireless Networks”, MOBICOM 2001, 236-251.

ACM

Wireless Mesh Networks”, 1st International Symposium in Wireless Communication Systems, September 2004.

[7] W. H. Yuen, H. Lee, T. D. Andersen, “A Simple and Effective Cross Layer Networking System for Mobile Ad Hoc Networks”, Proceeding IEEE PIMRC 2002.

[8] D. D. Couto, D. Aguayo, J. Bicket and R. Morris, “High-throughput path metric for multi-hop wireless routing “, MOBICOM 03.

[9] C. E. Perkins and P. Bhagwat, “Highly dynamic

References [1] J. Zhang, “ICC panel on Defining Cross-layer Design in Wireless Networking”, ICC2003 Panel discussion, http://www.eas.asu.edu/junshan/ICC .

[2] A. J. Goldsmith, S. B. Wicker, “Design Challenges for Energy-Constrained Ad Hoc Wireless Networks”, IEEE Wireless Communications, Vol. 9 No. 4, August 2002.

[3] M. Conti, G. Maselli, G. Turi, S. Giordano, “Cross-Layering in mobile ad hoc network design”, IEEE Computer journal, Vol. 37, Issue 2, pp. 48-51, Feb 2004 .

[4] V. Kawadia, P. R. Kumar, “A Cautionary Perspective on Cross-Layer Design”, IEEE Wireless Communication, February 2005.

destination-sequenced distance-vector routing (dsdv) for mobile computers”, In Proc. ACM SIGCOMM Conference, pages pp. 234–244, August 1994.

[10] Network Simulator Notes and Documenation, UCB/LBNL, http://www.isi.edu/nsnam/ns/.

[11] R. J. Punnoose, P. V. Nikitin and D. D. Sancil, “Efficient Simulation of Ricean Fading within a Packet Simulator”, Poceeding 52nd IEEE Vehicular Technology Conference, PP. 764-767, 2000 Boston.

[12] J. Proakis. Digital Communications. McGrawHill, Third Edition, 1995.

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