Implementation of Fuzzy Priority Scheduler
for MANET and Performance Analysis with
Reactive Protocols
Prof.S.P. Setti Vijay Kumar D V Nagendra Prasad G S M Narasimha Raju K
CS&SE Dept., AU College of Engineering, Andhra University, India
ABSTRACT
In this paper, we analyze the performance of a fuzzy based Priority Scheduler in wireless Adhoc networks, with real time traffic. An Adhoc network is composed of mobile nodes connected by wireless links without any preexisting infrastructure. The network’s dynamic topology poses challenging problems for QoS routing. Hence a scheduling discipline can be used to transmit the packets before its expiry, which improves the QoS of the network. The performance of the scheduler is studied using QUALNET5.0 and is evaluated in terms of the quantitative metrics such as Packet delivery ratio, through put and average end-to-end delay. The results are verified for the reactive routing protocols such as Dynamic Source Routing protocol (DSR) and Adhoc on demand distance vector routing protocol (AODV) and they prove that inclusion of the scheduler improves the Packet delivery ratio, through put and reduces the end-to-end delay.
Keywords: MATLAB fuzzy tool, Fuzzy Priority Scheduler, QUALNET5.0, AODV, DSR, MANETs.
1. Introduction
Mobile Adhoc network [1] is an autonomous system of mobile nodes connected dynamically in an arbitrary manner by wireless links. Since the mobile nodes in the network dynamically establish routing among themselves to form their own network, the adhoc network is also called infrastructureless network. All nodes of these networks behave not only as hosts but also as routers, forwarding packets to other mobile nodes in the network that may not be within direct wireless transmission of each other.
Adhoc networks are characterized by multihop wireless connectivity, frequently changing network topology and the need for efficient adaptive routing protocols. By the very nature of Adhoc networks mobile nodes wander around, changing their network location and link status on a regular basis. New nodes may unexpectedly join the network and existing nodes may leave or turned off. The routing algorithms develop a route minimizing the time required to converge and bandwidth overhead at the same time enabling proper routing. Once the route is established, a scheduler schedules the packets on packet-by-packet basis. The simplest possible scheduling discipline is first in first out (FIFO). The disadvantage of this technique is that it cannot differentiate among connections. Hence the choice of scheduling algorithm to determine which queued packet to process next will have a significant effect on overall end-to-end performance. The effects of scheduling algorithms on two different protocols are studied: DSR and AODV [2],[3],[4],[5]. DSR is an on demand, nongeographic routing protocol and AODV is on demand variation of distance vector protocol. DSR has the advantages of source routing and the unique feature of AODV is route expiry. Both deal with best effort traffic only. In DSR as route is part of the packet itself, routing loops, either short or long lived, cannot be formed as they can be immediately detected and eliminated. This property opens up the protocol to a variety of useful optimizations. AODV uses destination sequence numbers to determine the freshness of routing information.
Scheduling algorithms determine which packet is served next among the packets in the queues [5] . The scheduler used in DSR and AODV is a priority scheduler, which gives priority to control packets over data packets and serves data packets in FIFO order reduces the average delay compared to no priority scheduler. Along with the three inputs,
ARTICLES
IN
viz. expiry time of packet, queue length and data rate, we found that packet delivery ratio improves, minimizing the delay because it is possible to schedule the packets at the verge of expiry.
The rest of this paper is organized as follows. Section 2 describes the salient features of the DSR and AODV protocols. Section 3 details the scheduling algorithms studied. Section 4 describes the Fuzzy scheduler. In section 5, we explain the methodology and metrics used and the simulation results of the proposed scheduler. Section 6 details our conclusions.
2. PROTOCOL DESCRIPTION
Reactive routing techniques, also called on-demand routing. Routes are only discovered when they are actually needed. When a source node needs to send data packets to some destination, it checks its route table to determine whether it has a route. If no route exists, it performs a route discovery procedure to find a path to the destination. Hence, route discovery becomes on-demand. The route discovery typically consists of the network-wide flooding of a request message. Once a route has been established, it is maintained by some form of route maintenance procedure until the destination becomes inaccessible or until the route is no longer desired. Reactive routing protocol includes Dynamic Source Routing (DSR) protocol, Ad hoc On-demand Distance Vector (AODV) protocol.
2.1 DYNAMIC SOURCE ROUTING PROTOCOL (DSR)
DSR is an on-demand, source routing protocol. Transmitting nodes discover the route to their destination nodes on demand. This route is included in the data packets as the route header. The DSR protocol consists of two phases –
Route Discovery and Route Maintenance. When a source node A wants to send a packet to a destination node B, it
first checks if it already has a route to B stored in its cache. If the route is not stored, a Route Request (RREQ) packet is broadcast with the address of node A in the route record. An intermediate receiving node checks if its route cache has a route to the destination node. In that case, it appends the route in the route record and sends back a Route
Reply (RREP) packet by using the reverse route (assuming symmetrical links). If the intermediate receiving node
does not know the route, it appends its own address to the route record and broadcasts another RREQ packet. Using the route cache helps conserve network resources by limiting the number of route discovery packets.
2.2 AD-HOC ON DEMAND DISTANCE VECTOR ROUTING PROTOCOL (AODV)
AODV is an on demand distance vector protocol and it is and on demand variation of the distance vector protocols AODV uses destination sequence numbers to determine the freshness of routing information. In AODV, flooded requests are used to create routes, with the destination responding to first such request, much as in DSR. However AODV maintains routes in distributed fashion, as routing table entries, on all intermediate nodes on the route. Nodes forwarding queries remember the earlier hop taken by the query packet. This hop is used to forward the reply packet back to the source. AODV advocates use of early quenching of request packets, i.e., any node having a route to the destination can reply to a request. AODV also uses a technique called route expiry, where a routing table expires after a predetermined period, after which fresh route discovery must be initiated.
3. SCHEDULING ALGORITHMS
For improving the performance of the mobile ad-hoc networks, a scheduler can be used. Scheduling algorithms determine which packet is served next among the packets in queues. The scheduler is positioned between the routing agent and above the MAC layer. In general, control queues have higher priority than data queues. And among data queues, the proposed scheduler is experimented.
The drop tail policy is used as queue management algorithm in all scheduling algorithms. No-priority scheduling services both control and data packets in FIFO order. This scheduling algorithm contrasts with the effect of giving high priority to control packets. In priority scheduling, control and data packets are maintained in separate queues in FIFO order and it gives high priority to control packets. Currently, only this scheme is used in mobile ad-hoc networks [1], [6]. Considering the suitability of the different types of scheduling methods for MANET, several scheduling schemes were studied in literature.
ARTICLES
IN
Due to the distributed nature of the ad-hoc networks, nodes may not be able to determine the next packet to be transmitted [7]. Consequently, the nodes actually possessing the highest priority packet is unaware that this is the case, nor are other nodes with lowest priority packets aware that they should defer access. In multihop Ad-hoc networks, in which packets are forwarded across multiple broadcast regions, it becomes increasingly challenging to satisfy a flow's end to end QoS target.
The proposed scheduler based on fuzzy logic to find the priority of the packets, which has to be scheduled next. The application of fuzzy logic to find the priority index of the packet is found to be suitable in improving the overall performance of MANET. This led to the design of a fuzzy based priority scheduler.
4. THE FUZZY SCHEDULER
Fuzzy logic implements human experiences and preferences via membership functions and fuzzy rules. The fuzzy scheduler proposed [8], [9], [10] here, calculates the priority index of each packet. The fuzzy scheduler uses three input variables and one output variable. The three input variables to be fuzzified are the expiry time and data rate of the packet and Queue length of the nodes to which the packet is associated with. The inputs are fuzzified, implicated, aggregated and defuzzified to get the crisp value of the output.
The linguistic variables associated with the input variables are low (L), medium (M), and high (H). For the output variable, priority index, five linguistic variables are used. They are very low (VL), low (L), medium (M), high (H) and very high (VH).
The table 1 shows the fuzzy conditional rules for the fuzzy scheduler. The three input variables have 27 combinations (3*3*3) and the corresponding output is shown in the tabulation. The rule base is split into three tables and the first table gives out the rule base for Expiry time low and nine combinations of the other two input variables. The Table gives out the rule base for Expiry time medium and the third for expiry time high. To illustrate one rule in the first table, the first rule can be interpreted as, if (Expiry time is low) and (Date rate is low) and (Queue length is low), then priority index is low. Since in this rule, Data rate and Queue length are low and packets are associated with low delay, the priority index is set to be low. In the Table, for medium expiry time when data rate and queue length both are high, the priority index is set to be medium as seen from the last column of the table. Similarly the other rules are framed.
The output priority index, if very low, indicates that packets are attached with a very high priority and should be immediately scheduled. Similarly, if the priority index is very high, it indicates that packets are attached with least priority and will be scheduled only after all high priority packets are scheduled.
D/Q L M H
Expiry time (low)
L L L VL M VL VL VL H L VL VL Expiry time (medium)
L M M L M M M L H M M M Expiry time (high)
L VH VH H M H M M H H H M
Table 1: Fuzzy Rule Base
ARTICLES
IN
5. PERFORMANCE EVALUATION
The simulation for evaluating the fuzzy scheduler was implemented within the QualNet library. The simulation package QualNet version 5.0 is used to analyze and evaluate the performance of the proposed fuzzy scheduler. The QualNet version 5.0 [11], [12], a software that provides scalable simulations of Wireless Networks. In this simulation, we consider a network of 20, 40, 60, 80, 120 and 150 nodes (one source and one destination) placed in a dimension of 1000m x 1000m area. Each simulation is run for 600 seconds of simulation time. Multiple simulations run with different seed values were conducted for each scenario and collected data was averaged over those runs. Table 2 lists the simulation parameters.
5.1 Performance Metrics
The following metrics are used to evaluate the effect of fuzzy scheduler. The metrics were derived from one suggested by the MANET working group for routing protocol evaluation.
Simulation Environment
Area 1000m x 1000m Simulation Time 600 Sec
Nodes 20, 40, 60, 80, 120, 150 Nodes Placement Random
Path loss Model Two Ray
Mobility Model Random Way Point Pause Time 30 Sec
Maximum Speed 10mps
Traffic CBR Packet Size 512 bytes MAC layer 802.11
Table 2: Simulation parameters
Average end-to-end delay: End-to-end delay indicates how long it took for a packet to travel from the source to the application layer of the destination
.
Packet Delivery Ratio: Packet delivery ratio is the ratio of the number of data packets actually delivered to the destination to the number of data packets supposed to be received. This number presents the effectiveness of the protocol.
Throughput: This is measured in bytes per sec, which also serve as the performance measure for the scheduler.
Figure 1: DSR Average End-to-End Delay
ARTICLES
IN
Figure 2: AODV Average End-to-End Delay
Figure 3: DSR Packet Delivery Ratio
Figure 4: AODV Packet Delivery Ratio
Figure 5: DSR Throughput
ARTICLES
IN
Figure 6: AODV Throughput
6. CONCLUSION
In this paper, we have analyzed the performance of the fuzzy-based priority scheduler for data traffic and evaluated the effect of inclusion of this scheduler with reactive routing protocols DSR and AODV. We have addressed a fuzzy-based priority scheduler for data packets, which improves the quality-of-service parameters in mobile ad hoc networks. The fuzzy scheduler attaches a priority index to each packet in the queue of the node. Unlike the normal sorting procedure for scheduling packet, a crisp priority index is calculated based on the inputs such as queue length, data rate, and expiry time of packets, which are derived from the network. The membership functions and rule bases of the fuzzy scheduler are carefully designed and the output is verified using Matlab7.0 fuzzy logic toolbox with FIS editor. Then the inputs are identified in the library of Qualnet5.0 and the fuzzy scheduler is attached. This work can be also extended with inclusion of this fuzzy scheduler with different underlying multicast routing protocols with different mobility models.
References
[1] Charles. E.Perkins, “ Introduction to Adhoc networking”, Addison Wesley, Dec 2001.
[2] C.E.Perkins and E.M.Royer, “ Adhoc on demand distance vector routing ” in Proc IEEE WMCSA’99, New Orleans, LA, Feb 1999 pp 90 – 100.
[3] J.Broch. D.B.Johnson and D.a. Maltz, “ The dynamic Source routing protocol for mobile adhoc networks” Internet draft, draft-ietf-manet-dsr-00.txt, Mar 1998.
[4] E.M.Royer and C.K. Toh,,” A review of current protocols for adhoc mobile wireless networks”, IEEE Pers. Commn., Vol 6, no.2, pp 46-55, April 1999.
[5] S.R.Das, C.E.Perkins and E.M.Royer, “ Performance comparison of two on demand routing protocols for adhoc networks”. in Proc. IEEE INFOCOM’2000, Tel Aviv, Israel, Mar 2000, pp 3-12.
[6] Byung-Gon Chun and Mary Baker,” Evaluation of Packet scheduling algorithms in mobile adhoc networks ” , in Mobile Computing and communication review, Vol. 6, No.3, pp 36 – 49.
[7] V.Kanodia, C.Li, A.Sabharwal, B.Sadeghi and E.Knightly,“Distributed Priority Scheduling and Medium access in Adhoc networks”, in ACM wireless networks, Vol 8, Nov. 2002.
[8] Prof.S.P.Setti, Vijay Kumar D V and Nagendra Prasad G S M, “Application of Fuzzy Priority Scheduler for supporting QoS in MANET for
DSR Protocl”, International Journal of Computational Intelligence and Information Security , Vol. 1, No.5 , July 2010.
[9] C. Gomathy and S. Shanmugavel, “Supporting Qos in manet by a fuzzy priority scheduler and performance analysis with multicast routing
protocols” in EURASIP journal on wireless communications and networking, 2005:3, 426-436.
[10] C. Gomathy and S. Shanmugavel, “Performance evaluation of a novel Fuzzy based Priority Scheduler formobile AdHoc networks and its
effect onMAC protocols,” in Proc. 12th International Conference on Advanced Computing & Communication (ADCOM ’04), Ahmedabad,
Gujarat, India, December 2004, at IEEE Gujarat section.
[11] QualNet Network Simulator; Available: http://www.scalable-networks.com.
[12] QualNet documentation, “QualNet 5.0 Model Library: Advanced Wireless”; Available: http://www.scalable-networks.com/products/ qualnet/download.php#docs.
