30-10-2012, 05:01 PM
Multicast Multi-path Power Efficient Routing in Mobile ADHOC networks
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Introduction
Multicast traffic over the Internet is growing steadily with increasing number of demanding applications including Internet broadcasting, video conferences, data stream applications and web-content distributions [1]. Many of these applications require certain rate guarantees, and demand that the network be utilized more efficiently than with current approaches to satisfy the rate requirements. Traffic mapping (load balancing) is one particular method to carry out traffic engineering, which deals with the problem of assigning the traffic load onto pre-established paths to meet certain requirements. Our focus is to scrutinize the effects of load balancing the multicast traffic in an intra domain network.
Multipath Multicasting
The proposed scheme is multicast video in multiple paths over wireless networks. It consists of two parts. The first part is to split the video into multiple parts and transmit each part in a different path. In the latter part, employ multicast method to transmit the video packets to all the nodes. In this scheme, we assume that the network is lightly loaded, i.e., mobility and poor channel condition rather than congestion are major reasons for packet drop. Begin by showing the feasibility of multiple path multicasts, and then move on to describe ways to forward packets through multiple paths. The proposed method has three basic steps, discovery of the shortest route, maintenance of the Route and Data Transmission.
Route Discovery
The first criterion in wireless medium is to discover the available routes and establish them before transmitting. To understand this better let us look at the example below. The below architecture consists of 11 nodes in which two being source and destination others will be used for data transmission. The selection of path for data transmission is done based on the availability of the nodes in the region using the ad-hoc on demand distance vector routing algorithm. By using the Ad hoc on Demand Distance Vector routing protocol, the routes are created on demand, i.e. only when a route is needed for which there is no “fresh” record in the routing table. In order to facilitate determination of the freshness of routing information, AODV maintains the time since when an entry has been last utilized. A routing table entry is “expired” after a certain predetermined threshold of time. Consider all the nodes to be in the position. Now the shortest path is to be determined by implementing the Ad hoc on Demand Distance Vector routing protocol in the wireless simulation environment for periodically sending the messages to the neighbors and the shortest path.
Multipath Multicasting Using Power Algorithm
Since a MANET may consist of nodes which are not able to be re-charged in an expected time period, energy conservation is crucial to maintaining the life-time of such a node. In networks consisting of these nodes, where it is impossible to replenish the nodes’ power, techniques for energy-efficient routing as well as efficient data dissemination between nodes is crucial.
An energy-efficient mechanism for unipath routing in sensor networks called directed diffusion has been proposed. Directed diffusion is an on-demand routing approach. In directed diffusion, a (sensing) node which has data to send periodically broadcasts it. When nodes receive data, they send a reinforcement message to a pre-selected neighbor which indicates that it desires to receive more data from this selected neighbor. As these reinforcement messages are propagated back to the source, an implicit data path is set up; each intermediate node sets up state that forwards similar data towards the previous hop.
Proposed Power aware Algorithm
The proposed algorithm and the parameters considered for conducting this experiment extend the power-cost efficient algorithm to implement timing constraints. The results of the power-cost aware algorithm show that it performs better when the network/graph is dense. In a large network, a node will have a large number of neighbors. The computation time for calculating the minimum power-cost among the nodes’ neighbors is quadratic or exponential (depending on the algorithm used, power+cost or power*cost). In order to reduce this computational time we introduce a threshold value for the remaining battery power of the nodes.
While selecting a route, nodes with battery power greater than the threshold will only be considered. It would then go on to compute the minimum power-cost route. However, if none of the nodes meet the threshold, the threshold is reduced by half. This will continue until a node meeting the threshold is found or the threshold reduces to a minimum specified value. This would imply that the network is broken and the packet cannot be delivered. An appropriate error message is then given.
Conclusion
The proposed power aware multicast identifies the characteristics of the proposed routing algorithm. It evaluates its performance under various network conditions. Each plot presented illustrates the average of 10 independent runs that are initiated with different random seeds. For the optimization algorithm, the link cost function is selected, and defined. In all simulations, the period of link state measurements is selected as one second. As a consequence, source nodes can update their rates at best approximately every two seconds since it require two measurements for estimating the gradient vector according to the modified power algorithm. For simplicity set the rate of redundancy due to source coding, to zero.