Efficient Location Services Using Hierarchical Topology of Mobile Ad Hoc Networks

The thesis work was carried out in two phases. Firstly, we have designed the Cluster-Based Object Location Algorithm (CBOLA) for finding the object location in a cluster based network topology. Secondly, to take the advantages of clustering architecture of Mobile Ad hoc Networks, we have especially designed Object Location Clusters Algorithm (OLCA) for efficient Object Location management in MANET. Our comparison of simulation analysis shows that the CBOLA is more efficient and accurate in location information services and more robust than GLS and HRLS. In addition, we believe that our comparative study with the various location services schemes was facilitated accurate results in ad hoc networks. we are portioned our network area by designing a new clustering algorithm called OLCA (Object Location Clusters Algorithm) for efficient object location in Mobile Ad hoc Networks. Simulation results and comparative analysis shows the performance and effectiveness of CBOLA and OLCA.

• Location; Clusters; Objects; Nodes; MANET

1. Introduction

Location information has recently been applied to MANET [1]. There are three types of Location Services are available with the Mobile Ad hoc Networks. Such are Proactive, Reactive and Hybrid as explained at Figure 1.

In order to provide end-to-end communication throughout the network, mobile nodes must cooperate in handling network topology functions. It is very challenging issue in order to maintain the location information of the mobile hosts due to absence of centralized/dedicated servers in Mobile ad-hoc networks. Therefore location management becomes an important issue.

2. Literature Review

Routing protocols [2-6] are studied as a part of our research and an important subject. A variety of location based routing protocols [7-12] are exist and these protocols are of good scalability, and less overhead. These protocols usually assume that its part of the algorithm for obtaining location’s information with the help of a global position system (GPS) [13]. Though these protocols seem to offer better location services, but they increase the routing delay time due to updation of location services as part of its algorithm. So, it is necessary to design an efficient algorithm for better location updates and searches. Clustering for efficient location services in MANET by utilizing the benefits of clusters for achieving the better throughput and performance.

After study of literature review, we have exclusively proposed a cluster based location services protocol called Cluster Based Object Location Services (CBOLS) for efficient location updates and searches.

3. CBOL Algorithm

1)   Initialize LOCN packet;¹

2)   Initialize LACK packet;

3)   Initialize LREQ packet;

4)   Initialize LREP packet;

5)   int Location Registration() {

6)   broadcast LOCN to its neighbors;

7)   Acknowledge LACK who receive LOCN;

8)   for (LOCN=0; LOCNits neighbors; LOCN++) do;

9)   if (LOCN received = true) do;

10)   send LACK;

11)   return LACK;

12)   else

13)   repeat step 8; }

14)   call Location Query();

15)   int Location Query(){

16)   do

17)   broadcast LREQ to its destination node via its Clusterheads;

18)   record its field in Location_table before sending LREQ;

19)   if (CH received LREQ = true) do

20)   record its LREQ field in its Location_table;

21)   if (check LREQ field seen previously = true) do

22)   discard LREQ packet;

23)   else if (LREQ field & CH Location_table field =       true) do {

24)   send LREP packet;

25)   return LREP;

26)   Terminate(); }

27)   else if (LREQ field & CH Location_table field ! = true) do

28)   send LREQ packet to its nearest CH via Gateway;

29)   check if (two Gate exists = true) do

30)   Find the shortest path using CH routing table entries;

31)   Send LREQ packet to its nearest Gateway having highest weight;

32)   if (GW received LREQ = true) do

33)   record its LREQ field in its Location_table;

34)   if (check LREQ field seen previously = true) do

35)   discard LREQ packet;

36)   else if (LREQ field & GW Location_table field = true) do {

37)   send LREP packet;

38)   return LREP;

39)   Terminate(); }

40)   else if (LREQ field & GW Location_table field ! = true) do

41)   send LREQ to its nearest CH;

42)   if (CH received LREQ = true) do

43)   record its LREQ field in Location_table;

44)   if (check LREQ field seen previously = true) do

45)   discard LREQ packet;

46)   else if (LREQ field & CH Location_table field = true) do {

47)   send LREP packet;

48)   return LREP;

59)   Terminate(); }

50)   else

51)   repeat step 23 - 49; }

52)   void Terminate();{

53)   Exit Application(); }

4. Simulation

For simulation purposes, we have used GloMoSim (Global Mobile Information System Simulator-Zeng et al., [14] 1998). GloMoSim is a discrete event parallel environment for large wireless and wireline communication networks. GloMoSim uses a parallel discrete-event simulation capability provided by PARSEC (PARallel Simulation Environment for Complex systems) (Bagrodia [15], 1998). PARSEC is a C-based discrete-event simulation language developed by the Parallel computing laboratory at UCLA, for sequential and parallel execution of discrete-event simulation models. It can also be used as a parallel programming language. GloMoSim is developed at UCLA (California, USA) and is the second most popular wireless network simulator.

5. Performance Analysis of CBOLS

In this simulation work we have compared the metrics with Grid Location Services (GLS) and Home Region Location Services (HRLS) with our proposed ClusterBased Object Location Services (CBOLS). Our simulation result shows the average location registration cost at 550 nodes at the mobility rate at 10 m/s compared with GLS and HRLS.

The below Figure 2 depicts that our CBOLS (ClusterBased Object Location Services) shows the minimum registration cost when compared with GLS and HRLS. Even though the GLS and HRLS results are similar, the CBOLS show better result as the cost of a node to register in the network is very low. Each node in the network gets registered with each cluster while the cluster is being formed.

Figure 3 shows the average location update cost of all the nodes in the network. In CBOLS, we see, the location update cost grows much more slowly than GLS and HRLS so we can define CBOLS update cost as O (v log N) vs O (v). When the number of nodes (N) of the network density (order O) grows, the number of location update cost (v log N) also grows along with the varied network density.

References

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2. D. B. Johnson and D. A. Maltz, “Dynamic Source Routing in Ad Hoc Wireless Networks,” SpringerLink, 26 August, 2007, pp. 153-181.

3. C. E. Perkins and E. M. Royer, “Ad-hoc On-Demand Distance Vector Routing,” Workshop on Mobile Computing Systems and Applications, 1999, p. 90.

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13. Garmin, “GPS Beginner’s Guide,” July 2008.

14. J. Nuevo, “Comprehensible GloMoSim Tutorial,” 2004. INRS—Universite du Quebec Nuevo@inrstelecom. Uquebec.ca

15. R. A. Meyer and R. Bagrodia, “PARSEC User Manual for PARSEC,” 1999. http://pcl.cs.ucla.edu/