Buffer Size based Route Discovery Technique and Comparison with AODV

(1)

Buffer Size based Route Discovery Technique and Comparison with AODV

Abstract- In today’s world the amount of information is increasing day by day. The data production rate is increasing exponentially. The existing routing algorithms are finding it difficult to achieve high throughput for the limited capacity of the nodes. In this paper Buffer Size based QOS Algorithm (BSQOS) is proposed which takes into consideration the buffer sizes of the overall route while choosing the best route from source node to destination node. The algorithm is also compared with well known AODV algorithm. Simulation results prove that proposed QOS routing is better than Ad Hoc on Demand Distance Vector (AODV) algorithm with respect to End to End Delay, Number of Hops, Energy Consumption, Number of Alive Nodes, Number of Dead Nodes, Lifetime Ratio, Network Lifetime, Number of Packets Delivered, Number of Packets Dropped and Throughput.

I. INTRODUCTION

The node is a device which has the following characteristics – Battery, Memory and Antenna. Network is simple terms can be treated as the collection of nodes. The network can be classified into 2 categories Non Hierarchical Network and Hierarchical Network. Non Hierarchical network is a network in which all the nodes are spread in the single area. The network can be treated as a infrastructure less. The nodes do not have any controlling agent.

Hierarchical Network is the network in which the nodes will be spread across multiple areas in the network. Each area will have set of nodes. This set is called by various names like cluster head, zone leader, group head or region head. In this kind of Network there are 2 types of communication which are possible one is Inter Cluster and another one is intra cluster communication.

For Inter Cluster Communication the communication happens between the nods within the same group or same cluster or same zone.

For Intra Cluster communication the communication will happen between the nodes in one cluster to a node in a different cluster.

For Intra Cluster communication we require a special node which is one of the node among the set of nodes in the cluster. The special node can be selected based on certain criteria as described below

 The node which is closer to center of the cluster will act like a cluster head or zone leader or group leader or region head

 The node which is having the highest energy will act like a cluster head

 The node is elected by using a random probability measure as the cluster head

The node is selected as the cluster head whose angle of orientation with respect to base station is the lowest.

QOS and AODV algorithms fall under the category of Non- Hierarchical Networks

Fig1: Non – Hierarchical Network

Fig1 shows Non Hierarchical Network. As shown in the fig there are 100 nodes which are deployed in a 100* 100 area. Each node is represented by a unique id known as Node Id.

(2)

II. BACKGROUND

In the paper [1] the authors provides various definitions of network lifetime. First definition defines it as the time at which first dead node into consideration. The second definition defines it as time period for which the region of interest is covered. The third definition defines it as time at which the network satisfies the application requirement.

In the paper [2] real time information is very important for civilian and military applications. The authors divide the network into two tiers to address the network lifetime issue. The nodes known as Aggregate and Forwarding Nodes splits the flows into different paths during transmission. The advantages of the algorithm are - Improvement on the bit rates by making use of multi session flows and Maximizing the network lifetime with the help of forwarding nodes in the network. The disadvantages are Multisession flow requires multiple paths to be active at the same time.

Hence more number of nodes gets involved in routing which decreases the battery level of nodes in the network and Special Nodes known as aggregator and forwarder nodes are chosen randomly instead of any criteria specific information where it increases the possibility of dead node becoming the AFN node

In the paper [3] a smart routing protocol for electricity maintenance. The approach increases the efficiency for utility consumption data computation and then the data is send to aggregation point. The paper describes RPL and Geographical routing algorithm for utility networks. The Geographical routing algorithm is to perform peer to peer communication and then each of the node will have knowledge of other nodes in the network. The nodes will first construct a scan table and the reachable nodes in the scan table are called neighbor nodes. From the neighbor nodes one of node is selected. The link will be selected based on closeness towards the destination. The second algorithm is Routing Protocol for LLN’s (RPL) constructs a graph from each of the neighbor nodes. From each of the node optimized paths are obtained and then best path is used to send the data packets.

III. ALGORITHMS 3.1 Ad Hoc Distance Vector Algorithm (AODV)

AODV performs the route discovery process only when it is absolutely required. AODV unlike the DSDV algorithm does not maintain route from one node to every other node in the network. For each of the destination the AODV algorithm maintains a set of nodes known as Precursor nodes which will be responsible for route maintenance AODV algorithm can be described as per the below flowchart

Fig2: High Level Architecture for AODV

Fig2 shows the high level architecture for AODV algorithm. Source Node, Destination Node and Transmission Range acts as an input, The Multiple routes are discovered using AODV process which is demonstrated in Fig2. For all the routes the distance vector is computer for all the routes across the links. The route with lowest distance will act as a best route. The route distance can be measured by adding the distance of individual links

The detailed process of AODV can be described as follows

(3)

Fig3: AODV Route Discovery Algorithm

Fig3 shows the individual route discovery process for AODV route discovery algorithm. As shown in the fig source node, destination node, transmission range and threshold count acts as an input. First the source node finds its neighbors and then picks a forward node which sends REPLY first and the process is repeated until either destination is reached or threshold count becomes zero. Once the threshold count becomes zero the algorithm will discover routes based on shortest path.

Fig4: Shortest Path Algorithm Fig4 shows the shortest path algorithm

 Source Node, Destination Node & Transmission Range acts as an input.

 The neighbor nodes are computed w.r.t Source Node.

 If the neighbor nodes has the destination node then stop the process.

 If the Destination node is not present then jump to Step5

 Compute the distance of each of the neighbor w.r.t destination

 Find the node which corresponds to minimum distance.

 Repeat the process until destination is reached.

(4)

3.2 QOS based Route Discovery

QOS route discovery also can be divided into 3 phases namely Multiple Route Discovery, Individual Route Discovery and finally Best Route Selection. The number of routes discovered during multiple route discovery phases will be equal to number of nodes which are present in the transmission range. Individual route discovery process introduces a new change in the control packet process as compared to AODV. it maintains list of already traversed nodes and reduces amount of back and forth propagation as compared to AODV.

For the best route selection the buffer sizes of the routes are found out. If N are the number of routes then N/2 best routes are chosen in order to deliver the packets in the network which has lowest overall buffer sizes.

The individual route discovery of QOS algorithm can be described as follows 1. Source node, destination node and transmission range will acts as input 2. The neighbor nodes are computed w.r.t Source Node.

3. If the neighbor nodes has the destination node then stop the process 4. Find the new neighbors by removing the trace neighbor

5. Pick a neighbor which REPLY first

6. Repeat the Steps from 1 to6 until either TC=0 or destination is reached.

7. If TC=0 make use of Shortest path algorithm,

The individual route discovery of QOS is summarized in the following fig

Fig5: QOS Individual Route Discovery

3.3 QOS Data Packet Delivery Mechanism

The QOS Data Packet Delivery mechanism makes use of Single Buffer Policy Threshold. In single buffer policy threshold value will be called as Qmin .

The packets are generally divided into Low Priority Packets (LP) and High Priority Packets (HP). The buffer size is the summation of HP and LP for the node. The threshold is defined as

Q_min= min( BS1, BS2,..., BSn)

(5)

Where, BS_i=buffer size of i^th node

n=total number of nodes

The policy will be executed as follows

 First the buffer size of the node is found. If the buffer size is greater than Qmin then only HP packets are sent to destination

 If the Buffer Size is less than or equal to Qmin then both high priority and low priority packets are send towards the destination

IV. RESULTS 4.1 Simulation Set Up

Parameter Name Value

Number of Nodes 100 Transmission Range (m) 40 m Energy for amplification 10 mJ Energy for transmission 20 mJ Attenuation Factor 0.7

TTL 4

Number of Iterations 25 Initial Battery Energy 9999 mJ

Fig7: Node Topology

Fig7 shows the Node Topology which has 100 nodes spread across an area of 100*100m

Fig8: Initial Battery Energy for Nodes Fig8 shows the initial battery energy of 999 mJ for 100 nodes

(6)

Fig9: Time Takes v/s Route Nos

Fig9 shows that there are 90 possible routes found out by the AODV algorithm and the time taken by those routes

Fig10: Best Route No and Time Fig10 shows that best route no is 25 and the corresponding time taken

Fig11: Best Route using AODV

Fig 11 shows the best route discovered using AODV algorithm between source node 9 and destination node 36.

Fig12: Residual Energy Levels after routing

(7)

Fig 12 shows the residual energy levels after routing. The nodes which participate in routing will have their energy level decreased where as the nodes which do not participate in routing will have the same energy levels.

4.2 QOS Algorithm Results

Fig13: Node Topology

Fig13 shows the Node Topology which has 100 nodes spread across an area of 100*100m

Fig 14: Distribution of LP Packets

Fig 14 shows the node ids versus the number of LP packets residing in the nodes memory.

Fig 15: Distribution of HP Packets Fig18: Number of Routes v/s Buffer Sizes Fig 15 shows the node ids versus the number of HP packets residing in the nodes memory.

Fig16: Distribution of Buffer Sizes

(8)

Fig16 shows the distribution of Buffer Sizes across the 100 nodes in the network. The buffer size is combination of LP and HP packets in the network. For example if one can inspect fig 14, fig 15 and fig16. Node1 has 9800 LP packets, 7800 HP Packets and 17600 is the buffer size which is sum of HP Packets and LP Packets.

Fig17: Residual Energy for Nodes

Fig17 shows that all nodes have been initialized with same energy of 9999mJ during the network formation

Fig18: Number of Routes v/s Buffer Sizes

Fig18 shows that there are 16 possible routes and the buffer sizes for all the possible routes are also shown.

Fig19: Best Routes v/s Buffer Sizes

Fig19 shows the best routes and there corresponding buffer sizes. QOS routing chooses N/2=16/2 =8 best possible routes. Route5, Route6, Route7, Route10, Route11, Route14, Route15 and Route16 are the 8 best possible routes because of low buffer sizes as compared to other routes in the network.

Fig20: Best Route 16

Fig 20 shows the best route i.e route number 16 discovered between source node 58 and destination node 33

(9)

Fig21: Best Route 6

Fig22: Best Route 7

Fig23: Best Route 5

(10)

Fig28: Residual Energy

Fig 28 shows the remaining energy for the nodes in the network after route discovery is completed. There are 16 routes possible but among them 8 routes are the best

(11)

Fig29: Low Priority Packets

Fig 29 shows set of Low Priority Packets. As shown is the fig there are set of LP packets delivered and are lesser as compared initial set of LP packets.

Fig30: High Priority Packets

Fig 30 shows set of High Priority Packets. As shown is the fig there are set of HP packets delivered and are lesser as compared initial set of HP packets.

Fig31: Buffer Size Packets

Fig 31 shows set of Buffer Size Packets. As shown is the fig there are set of Buffer Size Packets delivered and are lesser as compared initial set of Buffer Size packets.

V. COMPARISION

Fig32: Number of Hops

Fig 32 shows Number of Hops. As shown is the fig the number of hops of is more for AODV as compared to QOS.

(12)

Fig32: Number of Hops

Fig 32 shows comparison of Number of Hops. As shown is the fig the number of hops of is more for AODV as compared to QOS.

Fig33: End to End Delay

Fig 33 shows comparison of End to End Delay between QOS and AODV. As shown in the fig End to End Delay of QOS is lesser than AODV.

Fig34: Comparison of Energy Consumption

Fig 34 shows comparison of Energy consumption between QOS and AODV. As shown in the fig Energy consumption of QOS is lesser than AODV.

Fig35: Comparison of Number of Dead Nodes

Fig 35 shows comparison of Number of Dead Nodes between QOS and AODV. As shown in the fig Number of Dead Nodes of QOS is lesser than AODV.

(13)

Fig36: Comparison of Number of Alive Nodes

Fig 36 shows comparison of Number of Alive Nodes between QOS and AODV. As shown in the fig Number of Alive Nodes of QOS is lesser than AODV.

Fig37: Comparison of Routing Overhead

Fig 37 shows comparison of Routing Overhead between QOS and AODV. As shown in the fig Routing Overhead of QOS is lesser than AODV.

Fig38: Comparison of Lifetime Ratio

Fig 38 shows comparison of Lifetime Ratio between QOS and AODV. As shown in the fig Lifetime of QOS is better than AODV.

Fig39: Comparison of Residual Energy

Fig 39 shows comparison of Residual Energy between QOS and AODV. As shown in the fig Residual Energy of QOS is higher than AODV.

(14)

VI. CONCLUSION

In this paper we introduce QOS routing algorithm and in depth routing process. The QOS routing algorithm is also compared with AODV. Simulation Results shows the route discovery process for both AODV and QOS algorithms.

Comparison between QOS and AODV for various parameters also show that the with respect to 8 different parameters that QOS is better than AODV.

VII. REFERENCES

[1] A novel destination-based routing protocol (DBRP) in DTNs”, Iranmanesh, S. ; Sch. of Electr., Comput. & Telecommun. Eng., Univ. of Wollongong, Wollongong, NSW, Australia ; Raad, R. ; Kwan-Wu Chin, Communications and Information Technologies (ISCIT), 2012 International Symposium on

[2] “Comparative Performance Analysis of DSDV, AODV and DSR Routing Protocols in MANET Using NS2”, Tuteja, A; M.M.Univ., Mullana, India Gujral, R. ; Thalia, S. Advances in Computer Engineering (ACE), 2010 International Conference on

[3] “Destination-Sequenced Distance-Vector (DSDV), i.e. a proactive routing protocol”, 7-9 March 2009, Mahdipour, E. ; Sci. & Res. Branch, Islamic Azad Univ. (IAU), Tehran, Iran ; Rahmani, A.M. ; Aminian, E. , International Conference on Future Networks, 2009

[4] “AODV routing protocol implementation design”,Chakeres, I.D. ; Dept. of Electr. & Comput. Eng., California Univ., Santa Barbara, CA, USA ; Belding-Royer, E.M., Distributed Computing Systems Workshops, 2004. Proceedings. 24th International Conference on

[5] A Low-Cost Flooding Algorithm for Wireless Sensor Networks, Ou Liang ; Centre for Telecommun. & Inf. Eng., Monash Univ.,Melbourne,Vic.; Şekercioğlu,Y.A. ; Mani, Nallasamy, Wireless Communications and Networking Conference, 2007.WCNC 2007.

IEEE

[6] G. Han et al., ``Cross-layer optimized routing in wireless sensor networks with duty-cycle and energy harvesting,'' Wireless Commun.

Mobile Comput., vol. 15, no. 16, pp. 1957_1981, 2015.

[7] M. Zhao, Y. Yang, and C. Wang, ``Mobile data gathering with load balanced clustering and dual data uploading in wireless sensor networks,'' IEEE Trans. Mobile Comput., vol. 14, no. 4, pp. 770_785, Apr. 2015.

[8] L. Ji, Y. Yang, and W. Wang, ``Mobility assisted data gathering with solar irradiance awareness in heterogeneous energy replenishable wireless sensor networks,'' Comput. Commun., vol. 69, pp. 88_97,Sep. 2015.

[9] M. Dong et al., ``Mobile agent-based energy-aware and user-centric data collection in wireless sensor networks,'' Comput. Netw., vol. 74, pp. 58_70, Dec. 2014.

[10] S. Guo, C. Wang, and Y. Yang, ``Joint mobile data gathering and energy provisioning in wireless rechargeable sensor networks,'' IEEE Trans. Mobile Comput., vol. 13, no. 12, pp. 2836_2852, Dec. 2014.

[11] W. B. Heinzelman, A. P. Chandrakasan, and H. Balakrishnan, ``An application-speci_c protocol architecture for wireless microsensor networks,'' IEEE Trans. Wireless Commun., vol. 1, no. 4, pp. 660_670, Oct. 2002.

[12] G. Iyer, P. Agrawal, E. Monnerie, and R. S. Cardozo, “Performance analysis of wireless mesh routing protocols for smart utility networks,”

in Proc. IEEE Int. Conf. Smart Grid Commun., Oct. 2011, pp. 114–119.

[13] F. Xue and P. R. Kumar, “The number of neighbors needed for connectivity of wireless networks,” Wireless Netw., vol. 10, no. 2, pp. 169–

181, Mar. 2004.

[14] O. Cayirpunar, E. Kadioglu-Urtis, and B. Tavli, ``Optimal base station mobility patterns for wireless sensor network lifetime maximization,'' IEEE Sensors J., vol. 15, no. 11, pp. 6592_6603, Nov. 2015.

[15] F. Tashtarian, M. H. Y. Moghaddam, K. Sohraby, and S. Effati, ``On maximizing the lifetime of wireless sensor networks in event-driven applications with mobile sinks,'' IEEE Trans. Veh. Technol., vol. 64, no. 7pp. 3177_3189, Jul. 2015.

[16] M. Ma, Y. Yang, and M. Zhao, ``Tour planning for mobile data-gathering mechanisms in wireless sensor networks,'' IEEE Trans. Veh.

Technol.,vol. 62, no. 4, pp. 1472_1482, May 2013.

[17] L. E. J. Brouwer, “Über abbildung von mannigfaltigkeiten,” Math. Ann., vol. 71, no. 4, p. 598, 1912.

[18] Y.-C. Wang, ``A two-phase dispatch heuristic to schedule the movement of multi-attribute mobile sensors in a hybrid wireless sensor network,'' IEEE Trans. Mobile Comput., vol. 13, no. 4, pp. 709_722,Apr. 2014.

[19] Y.-C. Wang, ``Ef_cient dispatch of multi-capability mobile sensors in hybrid wireless sensor networks,'' in Proc. IEEE VTS Asia Paci_cWireless Commun. Symp. (APWCS), 2012, pp. 1_5.

[20] G. K. Shwetha, S. Behera, and J. Mungara, ``Energy-balanced dispatch of mobile sensors in hybrid wireless sensor network with obstacles,'' IOSR J. Comput. Eng., vol. 2, no. 1, pp. 47_51, 2012.