ENHANCING THE PERFORMANCE OF MANET THROUGH MAC LAYER DESIGN

I J I T E ISSN: 2229-7367 3(1-2), 2012, pp. 19-24 ENHANCING THE PERFORMANCE OF MANET THROUGH MAC LAYER DESIGN 1 R. MANIKANDAN, 2 K. ARULMANI AND 3 K. SELVAKUMAR Department of Computer Science and Engineering, Annamalai University, Chidambaram E-mails: 1 rmkmanikandan@yahoo.co.uk; 2 arulmanirogith@gmail.com; 3 kskaucse@yahoo.co.in Abstract: In this paper, the performance of the ALOHA and CSMA MAC protocols are analyzed in spatially distributed wireless networks. The main system objective is correct reception of packets, and thus the analysis is performed in terms of outage probability. In our network model, packets belonging to specific transmitters arrive randomly in space and time according to a 3-D Poisson point process, and are then transmitted to their intended destinations using a fully-distributed MAC protocol. A packet transmission is considered successful if the received SINR is above a predefined threshold for the duration of the packet. Accurate bounds on the outage probabilities are derived as a function of the transmitter density, the number of back offs and retransmissions, and in the case of CSMA, also the sensing threshold. The analytical expressions are validated with simulation results. For continuous-time transmission, CSMA with receiver sensing (which involves adding a feedback channel to the conventional CSMA protocol) is shown to yield the best performance. The sensing threshold of CSMA is optimized. It is shown that introducing sensing for lower densities (i.e., in sparse networks) is not beneficial, while for higher densities (i.e., in dense networks), using an optimized sensing threshold provides significant gain. Keywords: Ad hoc networks, MAC protocols, outage probability, Poisson point process. 1. INTRODUCTION In multi hop wireless networks, it is important to efficiently utilize the network resources and provide fairness to competing data flows. These objectives require the cooperation of different network layers. The transport layer needs to inject the right amount of traffic into the network based on the congestion level, and the MAC layer needs to serve the traffic efficiently to achieve high throughput. Through a utility optimization framework, this problem can be naturally decomposed into congestion control at the transport layer and scheduling at the MAC layer. It turns out that MAC-layer scheduling is the bottleneck of the problem. In particular, it is not easy to achieve the maximal throughput through distributed scheduling, which in turn prevents full utilization of the wireless network. Scheduling is challenging since the conflicting relationships between different links can be complicated.in order to derive precise results, we focus exclusively on single-hop communication, as in [1], [2], [3]. All multiuser interference is treated as noise, and our model uses the SINR to evaluate the performance (in terms of OP) of the communication system. On the other hand, a number of lowcomplexity but suboptimal scheduling algorithms have been proposed in the literature. By using a distributed greedy protocol similar to IEEE 802.11, shows that only a fraction of the throughput region can be achieved. The fraction depends on the network topolog y and interference relationships. The algorithm is related to Maximal Scheduling, which chooses a maximal schedule among the nonempty queues in each slot. Different from Maximal Scheduling, the Longest- Queue-First (LQF) algorithm takes into account the queue lengths of the nonempty queues. It shows good throughput performance in simulations. In fact, LQF is proven to be throughput-optimal if the network topology satisfies a local pooling condition or if the network is small. In general topologies, however, LQF is not throughput-optimal, and the achievable fraction of the capacity region can be

20 R. Manikandan, K. Arulmani and K. Selvakumar characterized as in. Reference studied the impact of such imperfect scheduling on utility maximization in wireless networks. A number of researchers have analyzed slotted ALOHA using a Poisson model for TX locations, considering transmission capacity and success probability of the network [2], [4], [5]. Ferrari and Tonguz [6] have analyzed the transport capacity of slotted ALOHA and CSMA, showing that for low transmission densities the performance of slotted ALOHA is almost twice that of CSMA. We adopt the concept of guard zones in our analysis, with the difference that instead of incorporating into the protocol a guard zone within which no TX are permitted, we consider actual MAC protocols that employ virtual guard zones in order to make the backoff decision and evaluate the OP. Other analytical models m ay also be used for performance evaluation [18], [9]. In [10], the throughput of the CSMA protocol is evaluated in a multi-hop ad hoc network. 2. EXISTING WORK The main obstacles in extending the work to general resource allocation problems for multi hop wireless networks. The optimal scheduling component at the MAC layer is very complex, and thus needs simpler (potentially imperfect) distributed solutions. The drawback of this scheme is that it relies on the collection of information from the environment over a period of time, which entails high complexity and is not able to handle fast variations of the interference decomposed into congestion control at the transport layer and scheduling at the MAC layer. 3.2. Cross Layer Optimization The following cross-layer control algorithm is decoupled into separate algorithms for ûow control at the clients, power aware uplink/ downlink transmission scheduling, and routing in the mesh router nodes. The mesh clients are power constrained mobile nodes with relatively little knowledge of the overall network topology. The mesh routers are stationary wireless nodes with higher transmission rates and more capabilities. We develop a notion of instantaneous capacity regions, and construct algorithms for multi-hop routing and transmission scheduling that achieve network stability and fairness with respect to these regions. 3.3. Outage Probability of Aloha In ALOHA packets are transmitted to their intended RXs immediately upon their arrival, regardless of the channel conditions. Here the concept of guard zone[4] is used to analyze the outage probability. First, define s to be the distance between a randomly selected RX on the plane, RX0, and its closest interfering TX that causes the SINR to fall just below the threshold. By manipulation of the SINR expression, S is derived to be: S = (R - / / ) -1/ (1) 3. MODULE DESCRIPTIONS The modules which are implemented in this paper are given below. 3.1. Interference Data Model In multi hop wireless networks, it is important to efficiently utilize the network resources and provide fairness to competing data flows. These objectives require the cooperation of different network layers. The transport layer needs to inject the right amount of traffic into the network based on the congestion level, and the MAC layer needs to serve the traffic efficiently to achieve high throughput. Through a utility optimization framework, this problem can be naturally Figure 1: Guard Zone Fig. 1. When at least one interferer TX 1 falls within a distance s away from RX 0, i.e., inside B(RX 0, s), it causes for RX 0.

Enhancing the Performance of Manet through Mac Layer Design 21 Through Eq. (1), sens corresponds to ssens, req to sreq etc. The guard zone B(RX0, s) is a circle of radius s around RX0, as illustrated in Fig. 1. One situation that would cause RX0 to go into outage is if the accumulation of powers from all the interfering nodes outside B(RX0, s) results in the SINR at RX0 falling below the threshold. Another situation is if at least one active TX, other than RX0 s own TX, TX0, falls inside B(RX0, s) at any time during the packet transmission. Considering only the latter event yields a lower bound to the OP. It has previously been shown that this lower bound is in fact fairly tight around the actual OP [5], and hence, we only focus on this bound in our analysis. Given the probability of a packet being retransmitted in ALOHA is Prt, the density of packets on the plane at each time instant is aloha(p rt ) = (1 + P rt + P 2 rt + +PN rt ) = (1-p rt N+1 /1 p rt ) (2) Applying the concept of guard zones in our ad hoc network with spatial node density of aloha(p rt ), we shall now derive the OP of slotted and unslotted ALOHA in the following. (A) Slotted Aloha In a slotted system, the time line is divided into slots of fixed duration T, and TXs can only start their transmissions at the beginning of the next time slot after each packet has been formed. In this there is no partial overlap of packets. In slotted system we consider the locations of packet arrivals in each slot, which follow a homogeneous 2-D ppp. (B) Unslotted Aloha In unslotted ALOHA, communication is continuous in time, the packets are transmitted as soon as they are formed. Unslotted protocols are particularly of interest in system that has no synchronization abilities. We expect the outage probability of unslotted ALOHA to exceed that of the slotted case, due to the partial overlap of transmissions. 3.4. CSMA (Carrier Sense Multiple Access) We introduce an adaptive carrier sense multiple access (CSMA) scheduling algorithm that can achieve the maximal throughput distributive. Some of the major advantages of the algorithm are that it applies to a very general interference model and that it is simple, distributed, and asynchronous. The algorithm is combined with congestion control to achieve the optimal utility and fairness of competing flows. It is inspired by CSMA, but may be applied to more general resource sharing problems (i.e., not limited to wireless networks). We show that if packet collisions are ig nored (as in s ome of the mentioned references), the algorithm can achieve maximal throughput. The algorithm may not be directly comparable to those throughput-optimal algorithms we have mentioned since it utilizes the carrier-sensing capability. 3.5. Congestion Control Now, we combine congestion control with the CSMA scheduling algorithm to achieve fairness among competing flows as well as the maximal throughput. Here, the input rates are distributed adjusted by the source of each flow. In multi hop wireless networks, designing distributed scheduling algorithms to achieve the maximal throughput is a challenging problem because of the complex interference constraints among different links. Traditional maximal-weight scheduling ( MWS), although throughputoptimal, is difficult to implement in distributed networks. It is well known that maximal-weight scheduling (MWS) is throughput-optimal. That is, that scheduling can support any incoming rates within the capacity region. A simple way to reduce the delay is by using joint CSMA scheduling and congestion control. 3.6. Performance Analysis Extensive simulation results of the proposed MAC protocol along with ALOHA and CSMA. In the simulations, the source nodes always have data packets to send and the following performance metrics are evaluated. End-to-end delay Packet delivery ratio Throughput Flow rate comparison Performance of SINR outage probability

22 R. Manikandan, K. Arulmani and K. Selvakumar Performance of SINR outage probability ratio Finally, MAC can significantly improve the performance of throughput, packet delivery ratio, end-to-end delay, performance of SINR outage probability and flow rate comparison compared to ALOHA and CSMA. 4. SIMULATION RESULTS Figure 3: Packet Delivery Ratio Figure 2: End-to-end Delay Fig. 2 explains End-to-end delays with respect to time. The time it takes for a packet to reach the destination is end-to-end delay. It includes all possible delays in the source and each intermediate host, caused by routing discovery, queuing at the interface queue, transmission at the MAC layers, etc. only successfully delivered packets are counted. Fig. 3 explains Packet delivery ratios with respect to time. The ratios of packet delivered to the destinations to the packet send out by the sources are shown in the figure. Fig. 4 explains Throughput of MAC with respect to time. The amount of packets moved successfully from one place to another in a given time period are shown in the figure. Fig. 5 explains Performance of SINR with respect to outage probability. The outage probability of unslotted ALOHA and CSMA as a function of normalized sensing threshold is shown. The outage probability of ALOHA Figure 4: Figure 5: Throughput of MAC Performance of SINR

Enhancing the Performance of Manet through Mac Layer Design 23 remains almost constant but in CSMA it varies. The outage probability of CSMA is lower. So CSMA has better performance of SINR value when compared to ALOHA. 5. CONCLUSION In this paper the performance of ALOHA and CSMA protocols in terms of outage probability with respect to packet delivery ratio, end-to-end delay, throughput, performance of SINR outage probability and flow rate comparison are presented. By finding those values maximal throughput can be achieved and the delay can be reduced. The outage probability of ALOHA remains almost constant but in CSMA it varies. The outage probability of CSMA is lower. So CSMA has better performance of SINR value when compared to ALOHA. Other possible extensions are to apply adaptive rate and power control to improve the performance of CSMA in wireless ad hoc networks. References Figure 6: Performance of SINR Threshold Fig. 6 explains Performance of SINR threshold with respect to outage probability ratio. Ratio of the outage probability of unslotted ALOHA and CSMA over that of slotted ALOHA as a function of SINR threshold. Figure 7: Flow Rate Comparison Fig. 7 explains Flow rate comparison of ALOHA and CSMA. Comparison of the flow rate between ALOHA and CSMA is shown in this graph. [1] M. Kaynia and N. Jindal, Performance of ALOHA and CSMA in Spatially Distributed Wireless Networks, in Proc. IEEE International Conf. on Communications (ICC), 1108 1112, Beijing, China, May 2008. [2] M. Haenggi, Outage, Local Throughput, and Capacity of Random Wireless Networks, IEEE Trans. Wireless Commun., 8, 4350 4359, 2009. [3] N. Jindal, J. Andrews, and S. Weber, Optimizing the SINR Operating Point of Spatial Networks, in Proc. Workshop on Info. Theory and its Applications, San Diego, CA, Jan. 2007. [4] A. Hasan and J. G. Andrews, The Guard Zone in Wireless Ad hoc Networks, IEEE Trans. Wireless Commun., 6(3), 897 906, 2005. [5] S. P. Weber, X. Yang, J. G. Andrews, and G. de Veciana, Transmission Capacity of Wireless Ad hoc Networks with Outage Constraints, IEEE Trans. Inf. Theory, 51(12), 4091 4102, 2005. [6] G. Ferrari and O. Tonguz, MAC Protocols and Transport Capacity in Ad hoc Wireless Networks: Aloha versus PR-CSMA, in Proc. IEEE Military Communications Conf., Boston, USA, 2, 1311 1318, 2003. [7] R. Vaze, Throughput-delay-reliability tradeoff with ARQ in Wireless Ad hoc Networks, available on http:/ /arxiv.org/abs/1004.4432, Apr. 2010. [8] X. Wang and K. Kar, Throughput Modeling and Fairness Issues in CSMA/CA based Ad-hoc Networks, in Proc. INFOCOM, Miami, FL, Mar. 2005. [9] M. Garetto, T. Salonidis, and E. Knightly, Modeling Per-flow throughput and Capturing Starvation in CSMA Multi-hop Wireless Networks, in Proc. INFOCOM, Barcelona, Spain, Apr. 2006. [10] P. Muhlethaler and A. Najid, Throughput Optimization in Multihop CSMA Mobile Ad hoc Networks, in Proc. European Wireless Conf., 2004.

24 R. Manikandan, K. Arulmani and K. Selvakumar [11] B. J. B. Fonseca, A Distributed Procedure for Carrier Sensing Threshold Adaptation in CSMA-based Mobile ad hoc networks, in Proc. Vehicular Technology Conf. (VTC), pp. 66 70, Baltimore, MD, Oct. 2007. [12] M. Kaynia, G. E. Qien, and N. Jindal, Impact of Fading on the Performance of ALOHA and CSMA, in Proc. IEEE International Workshop on Signal Processing Advances for Wireless Communications (SPAWC), 394 398, 2009.