Optimized Multi Hop Link Strategic Dynamic Routing Orient Data Transmission for Improved Sink Point Streaming in Manet
R.Balamurugan, Ph.D Research Scholar, Department of computer science, VMRFDU Salem.
Dr.M.Prakash, Research Supervisor,
Department of computer science, VMRFDU Salem Abstract
The routing in mobile adhoc network has been analyzed towards the sink point streaming. There exist numerous techniques for the development of routing to support higher sink point streaming. However, they suffer to achieve higher performance in achieving efficient streaming. To improve the performance, an efficient optimized Multi Hop link strategic dynamic routing based data transmission algorithm has been presented in this paper. The method not only considers the traffic parameters but also consider the link strategy. The link strategic model considers the quality of link which is measured according to the traffic, hops status, mobility speed. According to the parameters considered, the method discover the list of routes and for each of them, a multi-hop link transmission support (MHLTS) has been measured. Also the method estimates the multihop streaming support measure (Mss). Using these two values, an MHDTS (multi hop data transmission support) has been measured. According to the MHDTS support measure, the method chooses a single route to perform data transmission. The method introduces higher performance in sink point streaming and improves the throughput performance.
Keywords: Manet, Sink point streaming, link strategy, dynamic routing, LTS, Link quality.
1. Introduction
The mobile adhoc network is the collection of mobile nodes and nonmoving nodes.
The nodes of Manet have no limit for their movement, direction and speed. Also, the nodes of Manet come with limited power which restricts the nodes in performing number of transmission. This also decides the lifetime of the node as well as network. Similarly, the nodes come with a radio device which is capable of transmitting and receiving the data packets. It comes up with limited range which restricts the nodes in communicating with a longer distance node. Also, the mobility of the nodes changes the topology of the entire network at each fraction. There are number of routing protocols available, some of them use hop count based technique which chooses a least hop route to perform data transmission. There are algorithms which uses more energetic route and chooses the route with nodes which has higher energy. In traffic based techniques, the route with least traffic is selected. Similarly, there are number of algorithms available which performs route selection according to specific parameter. But they suffer to achieve higher performance in sink streaming.
The sink point streaming is the procedure of transmitting the data towards sink node where the streaming should be higher. In any network, the streaming is most
important which is achieved by maintaining the data rate. If the route selected has been affected by higher congestion then the latency and packet drop ratio will be higher, which reduces the throughput performance as well. Similarly, if there is higher link failure, then the route discovery should be performed frequently which also affect the throughput performance and increases the latency ratio. If you consider only the traffic parameter in route selection, then the route with least traffic is selected, but reduces the throughput as it would choose a route with higher hop count. Similarly, each parameter should be considered at the selection of data transmission route.
By considering all this, a dynamic routing algorithm which considers the link quality of routes has been presented. The method considers the mobility of hops in measuring the link quality according to the energy and traffic also. By choosing the route based on the link quality in multi hop nature, the route selection performance can be improved which in turn would hike the entire performance.
2. Related Works
Different algorithms have been discussed in literature to improve the performance of sink point streaming in mobile adhoc networks. This section discusses set of methods related to the problem.
In [1], the author presents an optimized multipath routing algorithm with network coding. The method considers the energy parameters of the nodes to utilize the energy in efficient way. It uses the balanced energy and network coding to perform multipath routing in Manet. In [2], the author presents a load balancing algorithm for multipath routing in manet. The method considered the need of load balancing in wireless network and to improve the performance of load balancing to distribute the traffic in global manner, an energy efficient load balancing algorithm which is incorporated with popular AOMDV algorithm to be named ELB-AOMDV.
Similarly, load balancing in multipath routing is presented to work based on the demand in [3]. The method monitors the traffic and according to the demand the load balancing is performed with the routing protocol AOMDV to be named as LBA- AOMDV. To improve the reliability in multipath routing in Manet an efficient algorithm is presented in [4]. The algorithm is designed to support data streaming in heterogeneous networks and performs multi hop routing to improve the quality of service of network.
In [5], a token based routing algorithm is presented which enforces security and mufti hop cooperative routing using clustering algorithm. The path selection is performed using the cluster head identified. The cluster head is elected based on the trust value measured based on different factors like signal strength, traffic, mobility speed and energy depletion. In [6], a mobility based load balancing algorithm for multiple path routing is presented. The method considers the mobility feature of nodes in selection of route to improve the performance of QoS.
To reduce the retransmission frequency a disjoint multipath routing algorithm is presented in [7] which identify the routes which are spatially disjoint in maximum. The MSDM algorithm identifies the routes which are disjoint spatially which is enforced over the AOMDV algorithm. In [8], the author presents a multipath routing algorithm which considers the minimum power management towards the packet routing. The method uses the hops present, distance of transmission, receiving power to identify the routes. The EPAM algorithm works over AODV to produce noticeable results.
In [9], a fitness based AOMDV algorithm is presented for the support of Manet. The method first discovers the routes and based on the energy constraint a optimal path has been selected using the fitness function and named as FF-AOMDV. In [10], a stable routing algorithm for Manet is presented which uses energy parameter and congestion.
The method measure the stability of nodes and energy compatibility. The link stability has been measured based on which a stable route has been selected for data transmission.
In [11], the author presents a Least Common Multiple based Routing (LCMR) towards the management of load. The method first identifies the paths and measures the latency on the routes. Based on the latency measured, the load distribution is performed.
All the methods discussed above suffer to achieve higher performance in sink point streaming and introduces higher latency.
3. Optimized Multi Hop Link Strategic Dynamic Routing
The proposed optimized multi hop link strategic routing algorithm performs route discovery based on the broadcasting mechanism. For the discovered routes, the method estimates link transmission support (LTS) for different hops. Based on the link quality, the method estimates the Multi hop link transmission support (MLTS) for each route.
Similarly, for each route the method estimates the multi hop streaming support (MSS).
Using these two measures, the method estimates the Sink Point Streaming Support (SPSS) for different route. Finally, a single route has been selected to perform data transmission.
The detailed approach is discussed in this section.
OLDR Route Discovery:
The method generates a OLDR-RREQ message with source and destination id.
Generated OLDR-RREQ message has been broadcasted in the network and the neighbors receives the message and check for the presence of route to reach the destination node. If it founds a route, then a OLDR-RREP message has been generated and sent to the source.
Otherwise, the same has been forwarded to the neighbors to get the reply from other nodes. The reply contains the information related to any node which includes energy, speed, traffic and location. The source node receives the list of reply and extracts the routes with strategies to update the route table.
MLTS Estimation:
The multi hop link transmission support is the measure which represent quality of link to perform efficient data transmission. The hops of the route may be moving in different direction and with different mobility speed. To perform efficient data transmission, the hop of the route should be more stable so that the performance can be improved. The stability of the route has been measured in two ways one by multi hop link quality (MHLQ) and Route Link Quality (RLQ) measures. The MLHQ measure represent the quality of link at specific number of hops, because at least for certain number of hops the route should be stable and it would reduce the retransmission frequency. Similarly, the RLQ (route link quality) represent the suitability of route in performing efficient transmission. Using these two values, the MLTS measure has been estimated.
The list of hops in a route is identified asRhl = The multi hop constant Mhc is measured asMhc =
The Multi Hop Link Quality MHLQ = //Nmth-
Neighbor Mobility threshold
The Route Link Quality is measured as RLQ = . // Here Th-mobility threshold
Finally the MLTS value is measured as MLTS = MHLQ×RLQ MSS Estimation:
The multi hop streaming support measure represent the strength of route in providing service to achieve higher streaming support. In order to achieve higher streaming support, the nodes of routes should have less mobility, less traffic at each hop
and the energy of all the intermediate nodes should be higher. By considering all this, the method estimates the multi hop streaming support measure. The performance of sink point streaming is improved only when the route has higher multi hop streaming support value. It has been measured as follows.
First, the mobility support sink point streaming is measured as follows:
Mssps =
Second, traffic support sink point streaming is measured as follows:
Tssps=
Finally, energy support sink point steaming is measured as follows:
Essps =
Using all these, the method estimates multi hop streaming support using all the above measured values.
MSS = MSSPS×TSSPS×ESSPS Multi-hop Link Strategic Dynamic Routing:
The multi hop link strategic dynamic routing algorithm first discovers the routes between any source and destination. For each route identified, the method estimates the multihop link transmission support (MLTS) measure. Also, for each route the method estimates the multihop streaming support measure. Using these two measures, the method estimates, multi hop data transmission support (MHDTS) measure. Based on the value of MHDTS, a single route has been selected to perform data transmission
.
4. Results and Discussion
The proposed dynamic multi constraint routing algorithm has been implemented and evaluated for its efficiency under different parameters. The proposed algorithm has been implemented using network simulator NS2. The results obtained has been presented in this section and compared with the results of other methods The simulation is performed with 200 nodes in the area of 1000 meters and the simulation time is 50 seconds.
Figure 1: Packet Delivery Ratio at Varying number of nodes
The packet delivery ratio produced by different methods at different number of nodes present in the network has been measured and presented in Figure 1. The proposed OLDR algorithm has achieved higher PDR than other approaches.
Figure 2: Performance on routing overhead
The routing overhead produced by different methods at varying number of nodes has been measured and presented in Figure 2. The proposed DMR algorithm has produced less overhead than others.
Figure 3. Performance on end to end delay
The latency introduced by different methods have been measured and presented in Figure 3. The proposed OLDR algorithm has produced less latency than other algorithms.
Figure 4. Performance on throughput
The performance on throughput produced by different methods have obtained and compared. The OLDR approach produces maximum throughput performance compare to other methods.
4
.Conclusion
In this paper an optimized link strategy based dynamic routing algorithm is presented. As like AODV algorithm, the method performs route discovery to identify the routes between the source and destination. But differs by collecting different information of hop nodes like energy, traffic, mobility and so on. Based on
these information’s, the method estimates multi hop link transmission support (MLTS) and multi hop steaming support (MSS) for reach routes identified. Using these measures, the method computes the data streaming support (DSS) for each route. Based on the value of DSS, a single route has been selected to perform streaming. The proposed OLDR algorithm has produced higher throughput performance and reduces the latency.
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