Resource reservation

 Allocation permitted

After the above procedure, resource manager permits to reserve resources to the vehicular sensors/devices, and decide the QoS parameter settings including QCI, GBR and MBR based on QoS requirements.

5.4.5 Resource reservation

Finally, the resource manager sends the resource allocation results back to the LTE P-GW. Once the dedicated bearer establishment procedure starts, P-GW will inform UEs (vehicles) how resources are allocated based on the previous results.

Chapter 6

Performance Evaluation

6.1 Experimental setup

We implement a system level simulation for our resource management algorithm for multimedia traffic report systems (QoS-MTRS). The environment we built is under the open source network simulator, NS3 [10]. The simulation parameters show in Table 5 below. We simulated 50 eNodeBs, 100 UEs (vehicles) and 200 sensors distributed in a map with 24/54/100 location blocks. The map and the movement trace file of UEs are generated via VanetMobiSim [11].

Table 5 Simulation parameter settings [12][13].

Parameter Value

Mobility model IDM_LC (by VanetMobiSim) RandomWayPoint

Simulated areas 0.3 x 0.2 / 0.4 x 0.5 / 1 x 1 km²

Topology Urban/suburban

Simulation time 5 / 10 / 30 s

Table 6, Table 7 and Table 8 are the importance degrees we adopted in the simulations. Since we regard importance degrees as given values, the numerical values of them are generated randomly.

Table 7 Importance degrees of vehicles.

Vehicle Type 𝒅_𝒊^𝒗

Police/ Ambulance/ Fire truck 3

Public transportation 2

Household vehicle 1

Table 6 Importance degrees of sensors/devices.

Data Type Data Format Sensor /

6.2 Evaluation results of QoS-MTRS

For performance evaluation, we compare the proposed QoS-MTRS with BQA (bandwidth and QoS-aware scheduler) [5] and two static methods: FCFS (first-come, first-served) and Greedy (greedy, based on traffic data value) scheduler. The performances we concerned are the appearance probability of received traffic data types, overall system throughput, end-to-end delay, duplication ratio (Dup), coverage (Cov) and overall value of received traffic data (S). The above performance metrics have been defined in Chapter 4.

Table 8 Importance degrees of location blocks.

Location

Figure 5 shows the appearance probability distribution of the received sensor/device types per location block by using our proposed QoS-MTRS and other scheduling mechanisms. Let n represents the number of received sensor/device types in a certain block. n = 0 means there is no traffic data reported by any sensor/device.

In our proposed method, the probability of n = 0 is 80% lower than that of any other method on average, and the probability of n > 1 is 65%, 58% and 48% higher than BQA [5], Greedy and FCFS, respectively, representing that the proposed QoS-MTRS has more chances to receive traffic data in various types.

Figure 5 Comparison of appearance probability distributions of the received data types per location block.

Figure 6 presents the comparison of duplication ratio, . The duplicated data may result in resource wasting because we allocate more resources but only received few kinds of data. Hence, low is preferred. Simulation result shows that our proposed method can decrease 50% of the duplicated data on average.

Figure 6 Comparison of duplication ratio.

The overall traffic data value, , (excluded duplicated data) is shown in Figure 7.

It represents the overall traffic data value we received, which means the worth of the received traffic data. Simulation result shows that our proposed QoS-MTRS can get the most valuable traffic data even when duplicated data are discarded. In terms of , the proposed QoS-MTRS is 27%, 18% and 54% higher than BQA [5], Greedy and FCFS, respectively.

Figure 7 Comparison of overall traffic data value.

Figure 8 shows the cumulative distribution function of end-to-end delay. Here, end-to-end means from UE to the traffic report application server in the Internet. We measured the end-to-end delay from the simulated trace file produced by NS3.

Simulation result shows that there are 90% of the end-to-end delay under 0.016 seconds in the proposed QoS-MTRS and BQA [5], while for Greedy and FCFS, there are just 63% of the end-to-end delay under 0.016 seconds.

Figure 8 Comparison of End-to-end delay.

Figure 9 presents the system throughput. The throughput was derived from the simulated packet trace files produced by NS3, and was further processed by the Wireshark software. The stable growth of our proposed method indicates that the resources are allocated properly with channel quality checking. Simulation result indicates that the throughput of the proposed QoS-MTRS is 27%, 27% and 83%

higher than that of BQA [5], Greedy and FCFS, respectively.

Figure 9 Comparison of system throughput.

Figure 10 shows the coverage ( ) comparison among different methods. Our proposed QoS-MTRS always achieves at least 87% coverage even when the number of vehicles is only 20, while BQA [5], Greedy and FCFS just reach 30%, 58% and 64% coverage, respectively.

Figure 10 Comparison of coverage.

Chapter 7

Conclusion and Future Work

7.1 Concluding remarks

In this paper, we have presented a QoS-aware resource management mechanism for multimedia traffic report systems (QoS-MTRS) over LTE. The main idea of the proposed mechanism is that we consider both information importance and channel quality to improve the diversity, completeness and overall traffic data value under certain resource (e.g., radio) limitations. We have conducted a system-level simulation that includes LTE network environment to evaluate our design. Simulation results show that by adopting the proposed QoS-MTRS, the probability of receiving more than one kind of traffic data in a location is 65%, 58% and 48% higher than BQA [5], Greedy and FCFS, respectively; the duplication ratio can be decreased by 50% on average. The overall traffic data value of QoS-MTRS is 27%, 18% and 54% higher than that of BQA [5], Greedy and FCFS, respectively. In addition, QoS-MTRS always achieves at least 87% coverage even when the number of vehicles is as low as 20.

7.2 Future work

In the proposed QoS-MTRS, the importance degrees of vehicle, sensor/device types and locations are given values, and we just focus on the end-to-end resources management in LTE networks. For our future works, we will derive importance degrees from statistical data and integrate the QoS-MTRS with M2M architecture to support resource management between LTE networks and sensor networks, and further port the QoS-MTRS to LTE-A environments. Moreover, the resource

management problem may be modeled as an optimization problem for mathematical analysis.

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