Figure 3 – uploaded by Pulak Chowdhury

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Figure 5 Traffic profile: average load on active routers. Fig. 4. Traffic profile: ratio of active routers.

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Fig. 2. Residual capacity as link weights.

Fig. 3. Hypothetical WOBAN deployment in Davis.

Fig. 7. Power savings versus extra wireless power.

Fig. 6. Power savings in energy-aware WOBAN.

POWER SAVINGS VERSUS LOW WATERMARK and path delay are similar in regular and power-save mode, and in energy-efficient routing mode, average path length and path delay increase slightly. However, our results show that, at 10% low watermark, the impact is very high for both power-save mode and energy-efficient routing mode [(average path length, path delay) —(3.02, 0.385 s) (regular), (4.05, 0.626 s) (power- save), and (4.6, 0.858 s) (energy-efficient routing)]. Therefore, we need to be careful in selecting the low watermark. From our results, we can see that selecting the low watermark at 5% gives us better scope for energy savings as well as less impact on net- work performance. These results also show another interesting factor in energy savings. If the service level agreement (SLA)

Fig. 8. Energy-aware WOBAN performance. (a) Average path length, (b) average path delay, and (c) ONU utilization.

Fig. 9. Performance of MILP and heuristics: average path length. method, so only ONUs not carrying any traffic will be shut down since the MILP does the same. The heuristic method closely follows the MILP results in energy savings. The reason is, similar to MILP, the heuristic method also tries to minimize energy consumption by routing traffic through the already-used paths. As the MILP model tries to maximize ONU energy savings, average path length obtained by MILP is higher (since there is no limit on average path length in the MILP) than no-power-saving mode (where all ONUs are on) with shortest-path routing (see Fig. 9). We can reduce this path length by setting H in (14) and (15) to a reasonable value. The heuristic method provides slightly higher average path length compared to MILP. Hence, we can deploy these heuristics to obtain near-optimal performance in an energy-aware WOBAN.