Stability Aware Routing in Mobile Ad-Hoc Networks using multiple Route

Previous work on routing in MANETs has resulted in numerous routing protocols that aim at satisfying constraints such as minimum hop or low energy. Existing routing protocols often fail to discover stable routes between source and sink when route availability is transient, i.e., due to mobile devices switching their network cards into low-power sleep modes whenever no communication is taking place. In this thesis, we introduce a new approach stability aware source routing protocol that is capable of predicting the stability (i.e., expiration time) of multiple routes. Proposed protocol selects the route that minimizes hop count while staying available for the expected duration of packet transmission. The stability aware routing (SAR) resolve the problem of SADSR protocol indicate a significant increase in route discovery success rate with comparable route establishment and maintenance overheads.


INTRODUCTION
A Mobile Ad-Hoc Network (MANET) is a collection of mobile nodes (such as laptops, PDAs) forming as arbitrary networks without the support of any fixed infrastructure such as base station or access point.
In MANET, each node functions as a router and forwards packets for other peer nodes. There is no fixed topology due to the node mobility, which results in interference, multipath propagation and path loss.
Mobile nodes are constraints to battery power, computation capacity, bandwidth, and wireless channel leading to number of challenges while design routing procedures. Determining viable routing paths and delivering messages in a decentralized environment where network topology fluctuates has always attracted the attention of researchers to design new and new mechanism to solve these problems. While the shortest path (based on a given cost function) from a source to a destination in a static network is usually the optimal route, this idea is not easily extended to MANET.
MANET Routing protocols are classified into three categories: tabledriven, on-demand and hybrid routing protocols. The table-driven routing protocols (DSDV, FSR, CGSR, WRP, GSR) determine the path to destination before it is needed. On-demand routing protocol (AODV, DSR, TORA, ABR) determines the route to destination when required. Hybrid routing protocols used both approaches.
In a mobile ad hoc network, nodes are often powered by batteries. The power level of a battery is finite and limits the lifetime of a node. Every message sent and every computation performed drains the battery. One solution for power conservation in mobile ad hoc network is power awareness routing. This means that routing decisions made by the routing protocol should be based on the power-status of the nodes.
Nodes with low batteries will be less preferably for forwarding packets than nodes with full batteries, thus increasing the life of the nodes. A routing protocol should try to minimize control traffic, such as periodic update messages to improve the lifetime of the nodes and network.
paper3 focuses on routing in MANETs with transient route availabilities, i.e., route establishment takes into consideration the expiration time and therefore the stability of a potential route. This new approach is based on the prediction of future sleep times of mobile nodes (i.e., the times when mobile nodes' DPM techniques will turn off their network cards). The goal of this approach is to introduce DPM-awareness in routing decisions and thereby to increase the number of successful packet transmissions. the concept of stability awareness can be added to any routing protocol. A variety of stability predictors can be used, including hints given by applications and/or the DPM mechanism.

Network Sleep Time
In

PROBLEM FORMULATION
DPM supports energy conservation by making mobile nodes put their wireless network cards to sleep when no data communication is taking place. A consequence of this technique is that mobile nodes will be unreachable for large periods of time. Therefore, we need to know the accurate network `up' and `down' times (DPM schedule) in order to introduce DPM awareness in routing decisions. Currently the protocol predicts the DPM schedule for mobile nodes from the queue contents of the packet scheduler and the network device timeout value. Toward that end, the protocol computes the minimum time to transmit all packets currently residing in the packet scheduling queue and adds the devicespecific timeout value, each data packet's transmission time is calculated as follows[4]: T data = T rts + T cts + T ack + T difs + 3T sifs + (P + Q) BW channel Where T data -transmission time of each data packet We define the route uptime factor (RUF) as a metric which indicates the earliest up time when the link between any pair of adjacent nodes on a route is going to be interrupted due to one (or both) of the nodes being put to sleep. Now we derive RUF as follows: If we assume nodes as vertices and the links between the nodes as edges connecting them, then let G(V,E) be the graph representing the network topology where V is the set of vertices and E is the set of edges. Let Where t i up is the earliest up time for node Vi. We define the link uptime vector or Lij for the route Rij as Where t i uptime is the uptime of the link Ei, i + 1 connecting nodes Vi and Vi+1 and is defined by average(t i up , t i+1 up ), since uptime of a link is determined by how long the link will be alive before breaking down due to one of its end nodes going to sleep and thus essentially is expressed by the minimum of the Lij along the route since it will indicate how long the route will be alive before breaking down due to the break in any of its constituent links: given the next earliest time to sleep ti off for each node ∈V, in the graph G(V,E), accumulate the set of all possible routes Ωij between nodes Vi and Vj with the corresponding route uptime factors RUF ij for each Rij ∈ Ωij and find the min-hop route Rij from the set of all stable routes Ωij . If there are more than one routes with the same min-hop length, then the one with the minimum route uptime factor value is selected. since it has the highest predicted lifetime. The route uptime factor contains the all link uptime value between two node which is satisfied the transmission threshold value. The each node stores the link uptime vector which contains the all stable link between two nodes from source to destination.

PROPOSED WORK
4.1 The propose protocol will follow the following steps to identify the stable path:

Route discovery phase in stability aware routing
When a source node needs to send a data packet to a target node, it first searches its routing cache for any entry using the target node address as the key. An entry in the routing cache contains a list of stable routes to the target node. If a routing cache entry is found, then the source node picks a route . If no entry is found for the target node, then the source node initiates a route discovery for the target. The proposed protocol adds four new entry types to the RREQ packet format of standard DSR .Link Uptime Vector (t i uptime ; i ∈ (1, … … , N − 1)) for the route, 1. Partial route Rij   to find the stable route for transmitting the data packet to the destination node 5. The RREQ packets contain the information of partial route and link uptime vector for each route. The link uptime vector store the value of each link uptime value which is the average of uptime value of both nodes. In this figure each node has an uptime value. In this example the threshold value is 2.the node 4 receives the two RREQ packets from node 3 and 2.Node 4 discard the RREQ of node 3 because the uptime value of node 3 is less then to the uptime value of node 2 .

Route reply phase in SAR
When the RREQ reaches the target. the route is predicted to be stable, the target node sends an RREP packet back to the source along the reverse path recorded in the RREQ. In this proposed protocol the routing  The RUF of first route{1,2,4} is 4.5 and RUF of route{1,2,3,6} is 3.5.

EXPERIMENTAL RESULTS
In order to validate the proposed protocol and show its efficiency we present simulations using MATLAB. MATLAB is a very popular network simulation tool. MATLAB is an interactive software package which was developed to perform numerical calculations on vectors and matrices. Initially, it was simply a Matrix Laboratory. However, today it is much more powerful:

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It can do quite sophisticated graphics in two and three dimensions.
 It contains a high-level programming language (a \baby C") which makes it quite easy to code complicated algorithms involving vectors and matrices.
 It can numerically solve nonlinear initial-value ordinary differential equations.
 It can numerically solve nonlinear boundary-value ordinary differential equations.
 It contains a wide variety of toolboxes which allow it to perform a wide range of applications from science and engineering.
Since users can write their own toolboxes, the breadth of applications is quite amazing.
Mathematics is the basic building block of science and engineering, and MATLAB makes it easy to handle many of the computations involved.
You should not think of MATLAB as another complication programming language, but as a powerful calculator that gives you fingertip access to exploring interesting problems in science, engineering, and mathematics. And this access is available by using only a small number of commands and function because MATLAB's basic data element is a matrix (or an array).

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The simulated network area is 25pixelX25pixel with 10 to 25 nodes. The node transmission range is 5 meter

Results
We evaluate the performance of SAR in terms metric as follows:

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Best optimal path will be selected from the received paths. SAR has been compared to shortest path routing which does not consider powersaving but optimizes routing for shortest delay and the proposed protocol provides a significant increase in successful packet transmissions with comparable route establishment and maintenance overheads. SAR solves the problem of SADSR Routing problems and reduces the packet overhead on each node and reduces the traffic of packet transmission. SAR is based on the prediction of future sleep times of mobile nodes (i.e., the times when mobile nodes' DPM techniques will turn off their network cards).
 SAR is aggressive in sense that it proactively discards any RREQ which is predicts to be non-stable and thus might lead to a scenario where the source node fails to find any stable path to the sink node

FUTURE WORK
 Future works would study how SAR performs with respect to other protocol. The concept of stability awareness can be added to any routing protocol. A variety of stability predictors can be used, including hints given by applications and/or the DPM mechanism.
 SAR combines the two metrics stability and minimum threshold power. Future work would also study the combination of other metrics such as real-time or minimum-energy communication and how to obtain a balance of these multiple constraints to make the new protocol yield good performance.

No. of route per source node
No. of route per source node