performance evaluation of manet protocols in communications … · 2015-08-31 · conditions....

International Journal of Computer Networks and Communications Security VOL. 3, NO. 8, AUGUST 2015, 343–354 Available online at: www.ijcncs.org E-ISSN 2308-9830 (Online) / ISSN 2410-0595 (Print)

Performance Evaluation of MANET Protocols in Communications and Management of Robots in Simulation Modelling

Hanieh Movahedi, Bsc. MSc.

University of Payame Nour, South Center Branch, Ostad Nejatollahi St., Tehran, 1584668611, Iran

E-mail: [email protected]

ABSTRACT

Mobile Ad-hoc Networks (MANET) due to their mobility and their wireless technology has quick access and low cost, which can work efficiently in robotic management. In this paper, three famous MANET protocols have been simulated for four km2 working areas with changes of the same elements in types of robots like speed, pause time, number of nodes, and key parameters that show network optimization rate including PDR, Throughput, and End-To-End Delay by using GloMoSim software. In total, LAR protocol was recognized that has better performance than AODV and DSR, which could be used as one of the optimum protocols.

Keywords: MANET, Glomosim; LAR, AODV, DSR, PDR. 1 INTRODUCTION

Now days, it is almost impossible to imagine our world without robotic systems. It has influenced drastically different aspects of human life and we can see their footprints almost everywhere. Controlling robots or machines is one the most important issues which has been researched and investigated in robotic area. Wireless controlling and robotic communication has great advantages like better maneuverability, lower cost, and faster preparation in different area. Moreover, by using wireless facilities, a network of robots can be acted in performing the given missions as a team and by setting the wireless connection between any of robots to each other, very novel capabilities can be obtained, practically.

The robots are often equipped with low-cost, low power, short-range wireless network interfaces, which only permit direct communication with their close neighbors. Therefore, it is practically impossible for each node to know the entire network topology at any given time. Under these situations the only useful approach to distributed command, control and sensing is to employ an Ad-hoc wireless networking scheme [1], [2]. In the present study, three major Ad-hoc wireless networking protocols have been studied and

compared with each other in a simulation-modeling software. 2 LITERATURE SURVEY

A mobile Ad-hoc network (MANET) is one of the best choices to control robots. MANET is a wireless, highly dynamic self-configuration network of moving nodes and routers. Term “Ad-hoc” indicates “can take diverse forms” [3]. In this system a set of wireless nodes or routers coming together to form a network in which every node acts as a router [4]. In fact, any hardware that could identify radio waves and enter into the network, could be taken into account as a node. Design of Ad-hoc networks has been to set fast communications in military and spatial that information exchange and routing between nodes have been focused based on dynamic routing protocols, Ad-hoc protocols can be classified inside security and searcher protocols, widely [5], [6].

Though mobility has many advantages, it also causes a number of disputes in Ad-hoc networks. For example, as nodes may be mobile, entering and leaving the network, the topology of the network will change constantly [7]. To overcome such complications, routing protocols using several mobility metrics have been suggested [4]. One

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unsettled subject in an Ad - hoc network is to create routing protocol that has almost 100 percent optimal behavior in all unpredicted work conditions. Bunches of Ad-hoc protocols have been introduced and their performance and capabilities were investigated. 2.1 Ad-hoc Protocols

In general Ad-hoc wireless network routing protocols could be divided to proactive, reactive, both proactive and reactive, or hierarchical routing protocols. Proactive protocols, which are famous as table-driven routing, preserves fresh lists of destinations and their routes by intermittently distributing routing tables throughout the network. These protocols requisite each node to maintain one or more tables to store routing information, and their response to changes in network topology by propagating updates throughout the network in order to preserve a consistent network view [8]. In other words, any node has one or numerous routing table (including node identifier and paths that lead to the same node) for all nodes in the network; and based on the type of protocol, a number of these tables are different and it has routed according to the same tables. In these such routings, storing the information of a network breadth, is a serious duty for nodes, when topology is mostly changed that are known as protective protocols [2], [9]–[12].

The second type of protocols is source-initiated on-demand routing. This type of routing generates routes only when desired by the source node. In fact, operation of discovery of route is only being done when a path is requested from a node toward destination node and only information is stored that is required for real paths. When a node needs a route to a destination, it initiates a route detection procedure within the network. This process is finalized once a route is found or all potential route permutations have been tested. Once a route has been recognized, it is maintained by a route maintenance process either until the destination develops inaccessible along every path from the source or until the route is no longer wanted. These paths are preferred since they can decrease induced cost of a network, are known as Reactive protocols [2], [8], [13], [14]. Ad-hoc On-demand Distance Vector routing (AODV) is one of the samples of this type of protocols. In AODV protocol, every intermediate node decides where the routed packet should be forwarded next. AODV uses periodic neighbor detection packets in its routing mechanism. At each node, AODV maintains a routing table [15]. In AODV, Route REQuests (RREQs), Route REPlies (RREPs), and Route ERRors (RERRs) are the message types defined.

AODV builds routes via a route request / route reply query cycle (Figure 1). When a source node requests a route to a destination for which it does not already have a route, it broadcasts a RREQ packet across the network [16], [17]. Nodes receiving this packet update their information for the source node and set up backwards pointers to the source node in the route tables. A receiving RREQ node may send a route reply (RREP) if it is either the destination or if it has a route to the destination with corresponding sequence number greater than or equal to that contained in the RREQ. If this is the case, it unicasts a RREP back to the source. Otherwise, it rebroadcasts the RREQ [17].

Release of RREQ

Inputs of reverse path

Keeping path

Fig. 1. The AODV Route Discovery and Maintenance

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The Dynamic Source Routing (DSR) protocol is another sample which is based on the concept of source routing [18]. Mobile nodes are required to maintain route caches that hold the source routes, of which the mobile is aware. DSR is similar to AODV however; there are a couple of important differences. The greatest notable of these is that the overhead of DSR is potentially larger than that of AODV since each DSR packet must transmit full routing information, whereas in AODV packets need only hold the destination address [8]. The last protocol, which in the present study has been tested, is Location-Aided Routing Protocol (LAR), which belongs to this class as well. LAR is a source routing protocol, as a DSR. Initially it starts flooding in all the directions from the source after expecting the destination the routing is so easy in a network that will be in the direction of the destination [19]–[21]. LAR accepts the accessibility of the global positioning system (GPS) for gaining the geographical position information necessary for routing [22].

Next class is hybrid protocols that have advantages both of proactive and reactive routing features of both protocols. A few hybrid routing protocols have been made, whereby the routing is first started with some proactive routes and then serves the demand from other nodes through reactive flooding [4]. In large Ad-hoc networks, proactive, reactive and hybrid protocols can be used simultaneously [7], [8], [23]–[25]. Zone routing protocol (ZRP) and temporally ordered routing algorithm (TORA) are two typical routing protocols in this class [4].

The last class is hierarchical routing protocols. The network’s capability to provide an acceptable level of service to packets even in the existence of a great number of nodes in the network is known as scalability which is one of the major tribulations in Ad-hoc networking [26]. With this class of protocols, the choice of proactive and of reactive routing based on the hierarchic level of an existing node. The routing is principally established with some proactively prospected routes, and then services the demand from additionally driven nodes through reactive overflowing on the minor levels. The choice for one or the other technique requires proper ascription for respective levels [26], [27]. 3 METHODOLOGY

In the present study, Global Mobile Information System Simulator (GloMoSim) 2.03, which is a network protocol simulator software and has been developed by UCLA was used [28]. GloMoSim has the capacity to simulate networks with up to a

thousand nodes and supports multicast, multi-hop wireless communications using Ad-hoc networking, asymmetric communications using direct satellite broadcasts, and traditional Internet protocols [29], [30]. GloMoSim also has support for the random waypoint mobility model. Moreover, tools are provided for generating different types of data traffic [4]. AODV, DSR and LAR protocol were simulated using GloMoSim. Important parameters of network optimization including Packet Delivery Ratio (PDR), Throughput, End To End Delay, were calculated based on changes of nodes number, movement speed of nodes, pause time of nodes in a 4000 m2 working area. The meaningfulness of outputs difference was investigated by using SPSS 23 software with one way repeated measures analysis of variance (ANOVA) followed by Tukey’s range test as a post hoc test. Differences between different protocols were considered significantly different when the P value was less than 0.05.

4 RESULTS

Difference of testing protocols was shown in figures 1 to 9. As it is shown in Fig. 2 to 4, for End to End Delay factor, DSR and AODV protocol have non-significant differences in pause time and node numbers (p>0.05). Both of these protocols are significantly better than LAR protocol on this matter (p<0.05). Interestingly, by increasing node numbers from 150 and above, LAR showed similar outcomes like AODV and DSR protocols. Increasing pause time also could improve End to End delay of LAR protocol. Although AODV has shown almost similar outcomes in max speed factor with DSR protocol, it showed a better result at speed 20 m/s (p<0.05). By increasing max speed in this simulation test, End to End Delay factor has increased drastically for LAR protocol.

As it is illustrated in Fig. 5 to 7, for PDR factor, both LAR and AODV protocol had a significantly higher PDR than DSR protocol based on node numbers, pause time and max speed (p<0.05). AODV showed a similar pattern to LAR protocol with slightly lower lever than LAR. Although increasing speed has a positive effect on PDR factor of the DSR protocol, still it was far behind LAR and AODV protocols in this regard (p<0.05).

Similar to PDR factor, as it is shown in Fig. 8 to 10, for throughput factor, both LAR and AODV protocol had a significantly higher throughput than DSR protocol based on node numbers, pause time and max speed (p<0.05). AODV presented similar pattern to LAR protocol with slightly lower lever than LAR for both pause time and node number

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factors. Also based on the present study, increasing speed has a minor positive effect on throughput factor of DSR protocol.

Fig. 2. Comparison of different protocols in End to End Delay based on node numbers

Fig. 3. Comparison of different protocols in End to End Delay based on pause time

Fig. 4. Comparison of different protocols in End to End Delay based on max speed

Fig. 5. Comparison of different protocols in PDR based on node numbers

Fig. 6. Comparison of different protocols in PDR based on pause time

Fig. 7. Comparison of different protocols in PDR based on max speed

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Fig. 8. Comparison of different protocols in throughput based on node numbers

Fig. 9. Comparison of different protocols in throughput based on pause time

Fig. 10. Comparison of different protocols in throughput based on max speed

As Table 1 shows, End to End Delay rate in

sending data in LAR protocol was significantly more than both AODV and DSR protocols based on the pause time factor (p<0.05). Although in first glimpse, End to End delay of LAR protocol was significantly higher than other tested protocols, LAR has shown better results in both PDR and throughput significantly (p<0.05).

As displayed in Table 2, End to End Delay has not shown a meaningful difference among three protocols. Though in first view, values of AODV and DSR were less than LAR, due to high standard deviation this difference has become meaningless.

As Table 3 illustrates, the effect of changes in node’s speed on optimization elements of the network did not show a meaningful difference which was due to high fluctuations in tested elements (p>0.05). However, a meaningful difference was not seen, like two previous tests, LAR protocol has a better situation in Throughput and PDR, practically (p<0.05).

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Table 1: Comparison of mean and standard deviation of different protocols based on key elements of pause time changes on the network nodes

Test Mean±SD

AODV DSR LAR

End To End Delay 0.028±0.001a 0.018±0.003 a 0.610±0.337 b

Throughput 10367.375±86.293 a 8780.875±162.526 b 10711.375±63.990 c

PDR 0.775±0.015 a 0.504±0.027 b 0.834±0.011 c abc Values with the different superscripts are significantly different at p<0.05 based on one way ANOVA, Tukey-B

post hoc test.

Table 2: Comparison of mean and standard deviation of different protocols based on the variation of the number of nodes on the main elements of the network

Test Mean±SD

AODV DSR LAR

End To End Delay 0.029±0.002 0.0154±0.001 0.670±0.982

Throughput 10982.250±526.177 a 8827.7500±199.902 b 11219.000±416.006 a

PDR 0.880±0.090 a 0.5116±0.034 b 0.921±0.071 a ab Values with the different superscripts are significantly different at p<0.05 based on one way ANOVA, Tukey-B post hoc test. Table 3: Comparison of mean and standard deviation of different protocols based on key elements of speed changes on the network nodes

Test Mean±SD

AODV DSR LAR

End To End delay 0.025±0.0127 0.062±0.097 1.407±1.419

Throughput 10831.375±591.868 9447.625±1499.284 11199.625±480.466

PDR 0.855±0.101 0.6177±0.2567 0.918±0.082

5 DISCUSSION

The performance of a protocol includes many factors. One of these factors is End-to-End delay, which is the average time taken by a data packet to arrive in the destination. It also includes the delay caused by route discovery process and the queue in data packet transmission as well as containing all possible delays affected during route discovery latency, propagation and transfer times, and retransmission delays at the MAC. Only well delivered data packets to destinations would be counted. Therefore, higher end-to-end delay indicates weakness of performance of the protocol due to network congestion. In the present study, DSR protocol showed significantly better performance than both AODV and LAR protocols, which was in line with other studies [20], [31], [32]. Number of nodes in this issue is an important factor and likewise previous studies [7], [32], the present study showed that by increasing the node numbers, Both AODV and LAR had similar results

like DSR. Although few studies have shown lower End to End delay of AODV and LAR than DSR [31], [33]–[35], this difference might be due to number of analysis nodes in different simulations and overall, it also shows that increasing node numbers lead to decreasing End to End delay.

Another performance factor is PDR. The PDR displays how effective a protocol performs delivering packets from source to destination. It measures the loss rate as seen by transport protocols, and as such, it characterizes both the correctness and efficacy of Ad-hoc routing protocols [7]. A high packet delivery ratio is desired in any network. The higher in the value gives us better results. Although both LAR and AODV had shown better PDR than DSR, LAR had slightly non-significantly better outcome. The result of the present study was supported with some of previous studies [34], [36]–[38].

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Throughput is given as the actual amount of data, which is being transmitted in a given time. Therefore, high throughput gives better performance for protocol. Scalability and throughput are two very important factors for mobile ad-hoc network protocols, as it determines if a protocol will function or fail when the number of mobile users increases and also shows the performance of the Ad-hoc network [32]. The present study showed not only significantly higher throughput of both LAR and AODV protocol than DSR, which was similar to previous studies [34], [37], [39]–[41], but also the constant level which is a desirable factor. Even increasing node numbers did not have significantly different positive effect of the DSR protocol. Similar to PDR, LAR had slightly non-significantly better outcome than AODV. Overall, LAR protocol could be a suitable for wireless robotic systems.

Surely, increasing movement speed of the robot or in other words, more softness in movement of robot in response to commands in different conditions needs higher speed of the robot processor unit, which improve efficiency of the network and help the performance of protocols. Even in this case, by increasing the speed of robot movement consequently network topology will change faster, which might cause errors in robot movement management. Moreover, possible breaking connection links due to robots breakdown by a crash with objects will increase End to End Delay in sending data, decrease Throughput, decrease PDR, and hence increase network scoria and decrease network efficiency. To prevent such crises choosing correct network protocols will be very effective in the management of them. Even though, on simulation basis, it was concluded that the LAR and in some degree AODV routing protocols give best performance compare to DSR routing protocol, unfortunately, no alone solution fits all criteria of an ideal protocol. Studies on both massive number nodes as well as little number nodes with different protocols and new algorithm, which can decrease errors and improve the simulation test, are recommended.

As seen in all figures, existence of fluctuations is among the main points in any diagram, if changes in number, speed, and pause time of nodes were investigated as ascending and fragments, diagrams would have turned into like–curve instead of broken lines. However, changes of the node were supposed as fiftyfold ascending trajectory and pause time and speed as tenfold ascending route. Since one of goals to display fluctuation–like behaviors of protocols is by making the same working conditions. It is necessary to be

noteworthy, what is important in measuring protocols in this research, is doing it in a completely random way by making the same chance (the same working conditions) for them as general average of existence of much Throughput, much PDR and less End To End Delay. In order to choose protocols for controlling robots, the chief key is the type of robot as well as its maneuverability. Based on these items a suitable protocol would be considered among protocols.

6 CONCLUSION AND FUTURE WORKS

This paper assessed the performance of AODV, DSR, and LAR routing protocols for MANET in a simulation model using GloMoSim simulator. Based on the present study, both LAR and AODV routing protocol recognized with higher scores than DSR for both PDR and throughput and could act more efficient than DSR in MANET or robotic industries. Making new protocols based on LAR protocol with less End To End delay could be a great help in robotic industries.

As it is almost impossible to find a protocol, which could act perfectly in all situations, it is suggested to create a software for robot’s main chip, which not only supports all the present Ad-hoc protocols, but also can choose smartly the best one based on its situation. This software should analyze the number of nodes, node speed, pause time, working area, the active nodes, then based on this information should choose the best protocol automatically. In addition, each node should have an ID, which could be managed and controlled by the network manager. Any correct and strong signals should be recognized automatically from each node and weak nodes should be eliminated either manually or automatically and be replaced by the next node neighbor.

In algorithm of this software that has been programmed for any chip of Ad–hoc network in any robot, it receives all the information, which affects network performance, which have been discussed previously and run a suitable protocol from its program functions by using conditional loops. For network unit management, especially in times when there is a probability of crashing the robots like war and convulsive environments which decreases the number of robots and consequently the number of active nodes, network manager could give the number of nodes to the software and software calls a suitable protocol from its conditional functions as online, by taking ID of any robot and its situation.

To provide this software, firstly all cases of robot structure would be simulated and investigated from

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speeds of zero to maximum 100 m/s, pause time of robot from zero to maximum 3600 second, work area from 100 m2 to maximum 100 km2, work time of robot from 10 to maximum 600-1440 min with changes in the number of nodes ascending trajectory from 2 nodes to maximum 1000 nodes for both uniform and random topology.

For convulsive environments like full–dangerous war environments and assistive environments that time is very valuable, priority of measuring the superiority of protocols should be noticed based on decreasing End To End Delay in term of random topology, but for other robots like explorative,

industrial and entertaining that time is not a vital factor, the priority of protocols should be based on increasing PDR and Throughput as per uniform topology.

As this software act smartly and supports all the known Ad-hoc protocols, and choose them wisely, security and efficiency of the network increases and might be near to the optimum and golden goal. work breakdown structure (WBS), Object linking and embedding for process control (OPC), and flowchart of suggested software are shown in Fig. 11 to 13.

Fig. 11. WBS of Creating Proposed Software

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Fig. 12. OPC of Creating Proposed Software

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Fig. 13. Proposed Software Flowchart

7 ACKNOWLEDGMENTS

The author wishes to thank Dr. Mageda Sharafeddine from American University of Beirut, Dr. Ariyo Movahedi from Putra University Malaysia, Dr. Sasan Mohammadi, and Dr. Alireza Shams Shafiq for their kind guidance. 8 REFERENCES

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AUTHOR PROFILES:

Hanieh Movahedi obtained her Bachelor degree in Computer Science from Islamic Azad University, Damavand, Iran in 2009 and Master degree in Mechatronics Engineering from Islamic Azad University at Kashan, Iran in 2013 with

distinction. Her main research interest in computer field are wireless networking protocol and robotic controls. Currently, she is doing her PhD in Biomaterial in Medical Engineering at IAU and is a lecturer at Payame Noor University, Iran.

performance evaluation of manet protocols in communications … · 2015-08-31 · conditions....

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