In this section, we evaluate the performance of our proposed redirection schemes. We imple-mented the relay redirection framework in QualNet simulator [10]. We place network nodes by uniform random deployment in a 750m × 750m 2D terrain. Each simulation run is simu-lated for 1 hour duration and each of the results presented in this section is averaged over thirty simulation runs.
We simulate the ad hoc network under IEEE 802.11b environment with a channel data rate of 2 Mbps and use 802.11 MAC protocol for wireless transmission through a free space path loss model. The maximum transmission power of each node was set to 6.633 dBm which corresponds to a transmission range of approximately 250 meters under a packet reception threshold of -91 dBm. We set the power consumption model in a similar way represented in [1]
to simulate a realistic implementation of the network interface. There are three categories of end-to-end traffic flows in the network:
1. Four high-rate 120kbps UDP request traffic with 1 KB packet size.
2. Four medium-rate 40kbps UDP request traffic with 1 KB packet size.
3. Four low-rate 0.8kbps UDP request traffic with 100 byte packet size.
For each request from the source node, the destination node responses one reply packet with 512 byte packet size. All traffic flows are random source-destination pairs with random start
times and durations. Besides, through the simulation, all static nodes in the network broadcast a Beacon message once every Beacon Interval. The beacon is used to exchange location in-formation and preserve connectivity among neighbor nodes. These inin-formation can help static nodes to estimate the distances between neighbors at a specific time and to appropriately adjust their transmit power for communication. We let Beacon Interval be equal to the length of the service phase.
We compare our proposed protocol (denoted by ’MR-MRD’) against the deployment frame-work with minimum total energy (denoted by ’Min-Total’) proposed in [11] and use AODV as the underlying routing protocol in the simulation. The energy reduction in both MR-MRD and Min-Total can be divided into two parts. The first part is from the controllable power scheme which minimizes the total energy consumption by reduce the transmission power according to the distance between the communication pairs; the second part is due to the traffic redirection scheme by controlled mobile nodes. To quantify the part of total reduction by power control, we apply the controllable power scheme to AODV (denoted by ’Aodv-Pc’) and without the assistance of any relay nodes as a comparison protocol. In our simulation results, we use the network environment, which is only composed of non-relay nodes and functions AODV (de-noted by ’Aodv-Pure’), as a basis and design two metrics to evaluate the performance among different protocols as follows.
1. The total transmission energy saving: We measure the energy saving in the network as follows. Let E be the total transmission energy in Aodv-Pure and EP denote the total energy consumed in transmission when the protocol P is applied. Then the energy saving in transmission is computed as (E − EP)/E.
2. Normalized throughput: We measure the throughput of the traffic flows in a protocol and normalize the value by the throughput in Aodv-Pure.
In the first experiment, we observe the performance when varying number of mobile relay nodes. We fix the number of static nodes and the length of the service phase to 25 nodes and 15 seconds respectively. Fig. 5.1 (a) shows that power control scheme (i.e. Aodv-Pc) reduce about 24 percents of total transmission energy consumption. Our redirection scheme, which can improve the saving to about 28 to 40 percents, is much better than Min-Total. This result shows that redirecting in route level has more opportunities than in link level, and thus has better performance gain. However, the increasing number of additional relay nodes results in the increasing channel contention when a node start to transmit packets and decreases the channel utilization. Fig. 5.1 (b) shows that the network throughput decreases due to the assistance of additional relay nodes rather than power control in transmissions. Besides, when a route entry in the route table is used to transmit a data packet, its lifetime will be extended. The route-level redirection of flows in MR-MRD let some intermediate nodes be omitted in the new path and has less chance to update their entries. Thus, these entries expire quickly during the redirection. Note that route entries will always be updated in link-level redirection since data packets are only forward a single hop. This phenomenon results in more data packets dropped due to expired routes in MR-MRD than in Min-Total after relay nodes leave, and explains the performance gap between them.
Figure 5.1: Results with varying number of relay nodes in static networks. (a) Performance Improvement. (b) Throughput.
In the next experiment, we deploy 4 relay nodes and keep the length of the service phase in 15 seconds. We vary the network density by deploying different number of static nodes and compare the performance. In a sparse network, longer distance between nodes and their neighbors result in more energy consumed on some nodes in data transmissions, and thus the flow traffics relaying by additional relay nodes can significantly leverage the overhead of these static nodes, as can be seen in Fig. 5.2 (a). The networks with variety of density do not affect
the occurrences of redirection since the hop distances of the routing paths selected by AODV remain stable through these networks, and result in a stable trend of throughput which is shown in Fig. 5.2 (b).
Figure 5.2: Results with varying network density in static networks. (a) Performance Improve-ment. (b) Throughput.
To observe the effect of different periods of service length, we deploy 25 static nodes and 4 mobile relay nodes, and vary the length of the service phase. Fig. 5.3 (a) shows that the traffic pattern of the network environment may be suitable for a particular setting of the service phase.
Appropriate setting of the service phase can lead to a better traffic redirection; that is, mobile nodes will not stay in the neighborhood of flows with short durations, and wait for service even if the flows become inactive. Fewer redirections occur mitigate the control message overheads in the network, increase the channel utilization and thus increase the throughput of the network.
Specifically, static nodes in Min-Total need to initiatively detect active flows and inform relay nodes about these information; therefore, Min-Total has more benefits from reduced control message overheads than MR-MRD in terms of throughput. Fig. 5.3 (b) shows the results for throughput.
Figure 5.3: Results with varying service length in static networks. (a) Performance Improve-ment. (b) Throughput.
Chapter 6 Conclusion
Energy-conserving is a critical issue in wireless ad hoc networks. We consider controlled mo-bility as a solution that some controllable resource-rich nodes can act as relay nodes to help network lessen the total energy costs. In this paper, we defined the mobile relay deployment problem that aims to minimize the energy consumption at static nodes with the assistance of relay nodes, and proposed a novel distributed protocol that utilizes underlying ad hoc protocol information to optimally shorten the routing paths of existing flows. The simulation results indi-cate that our protocol results in significant total energy reductions with comparable throughput under different network environments.
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