Performances Evaluation of ABE-MM
Evaluation Method
To evaluate the effectiveness of the proposed mobility management approach in bandwidth measurement, we conducted a comparative experiment using Network Simulator 2 (NS2.34) and the IEEE 802.11 implementation provided with the simulator. The scheme used was (CSMA/CA) without (RTS/CTS) Mechanism. Four constant bit rate (CBR) flows (flow1, flow2, flow3) are generated with 1000 bytes of data packets size. A free space propagation model is adopted in our experiment, with radio propagation range length for each node was 250 meters (d) and channel capacity was 2Mbits/sec.
Our simulation models a network of 4 sources and 4 destinations (for each flow) in 12 mobile nodes that move to a common and constant velocity equal to 12m/s. the movement of nodes is random following the model of Manhattan [11] without buildings (without obstacle. to ensure an free space environment) on an area of 1000mX1000m, where the length of a street is 100m where nodes move through. At each arrival in a corner, a node can stay where it is, or continue its movement in the same direction or change it. For this simulation we used the prediction model that is explained in the previous section where nodes predict with accuracy when the move away each from other is on a straight line (other prediction models more efficient like in [8] can be used). Efficiency of our approach is evaluated through a comparison between ABE and ABE-MM following criteria:
• The throughput obtained along the simulation by each flow sent by source nodes.
• Average loss ratio: The average of the ratio between the number of data packets sent but not received by sources and the total number of data packets.
• Data packet delivery: The total number of data packets delivery to each destination along simulation.
Simulation Results
Throughput
Following the simulation logs (movement of nodes, routing flow and control packets) we note the following:
Figure 6 (a) shows the throughput of the four flows along simulation time when the ABE is enabled for paths reservation. In the absence of a subsequent monitoring of the bandwidth evolution depending on the mobility, the flow 1 continues to consume bandwidth while the path is in failure, penalizing other flows (2) and (3), given the slow process of detecting the link failure. We also note that the absence of mobility criterion in ABE formula, has allowed flow (4) a reservation path which contains a link being missing, that caused still delaying the admission of the flow2.
Figure 6 (b) shows the throughput of the four flows when the paths reservation is activated with ABE-MM. we observe that the consumption of bandwidth on the network is much more optimal compared to ABE. Through the equitable utilization of this resource (flow1 is stopped because of the mobility that has reduced the available bandwidth with which it was admitted, allowing a non-belated admission of flows 2 and 3). Also, eliminating unemployment times in the network caused by a flow 4 with ABE technique (the interconnection zone of flow 2 and 4 paths for [26 ... 45] seconds).
Average Loss Ratio
The Average loss ratio along simulation is shown in diagram of figure 7. We observe that the absence of the mobility criterion in ABE has caused a high loss rate which reaches up to 27% for flow 1, particularly in the [14...18] seconds. And the admission of flow (4) has generated also a significant ratio loss. With ABE-MM, we see a very interesting results where losses caused by the mobility have been avoided by stopping the flow 1 at right time and a non-admission of the flow(4) because of the link that was going to disappear has been taken into consideration in measurements of available bandwidth through the mobility criterion "M".
Fig. 6. Throughput of each flow using ABE, ABE-MM
Data Packet Delivery
The total data packet delivery to each destination is shown in diagram of figure 8. We observe that almost the same number of data packets was delivered of flow (1) by ABE-MM and ABE, which confirms the rate of loss due to mobility in the previous diagram. The late admission of the two flows (2) and (3) with ABE has resulted in a very poor numbers of deliveries compared to ABE-MM. But we also note that the delivery of the flow (4) with the ABE technique is very little but it exists, on the other hand, with ABE-MM no data packets of flow (4) are delivered to destination.
Fig. 7. Average loss ratio diagram using ABE, ABE-MM
Fig. 8. Data packets delivery diagram using ABE, ABE_MM
Conclusion
In this paper, we present the importance of the taking into consideration the mobility phenomenon in available bandwidth measurement, especially during the path reservations. Our solution is based on the distances changing between neighboring nodes which are linking together. ABE_MM was the result of the extension of the ABE technique by our approach.
The results obtained from a comparison between ABE and ABE_MM are satisfactory in terms of the consumption optimality of bandwidth in the network. We have noticed an improvement of flow circulation where the density of traffic has increased over the network while decreasing of loss rates.
Despite good results, ABE_MM presents some problems in some kind of dynamicity topology where each node must have an ability to analyze its mobile environment to the admission flow; this will be the subject of our next work.
