Learned Lessons from the Experimental Evaluation

In this section, we will discuss some of the insights than can be taken from the experimental evaluation of our ZS, ZP, ZS/ZP/ZPR schemes. Our main goal is to study the benefits of implementing our schemes versus their implementation overhead. Also, we would like to determine the environments that are most fitting for our schemes, in terms of network size, hotspot types and sizes, etc.

Before discussing the learned lessons out of our experimental evaluation, we summarize our results in the following points:

1. Our schemes are best suited for decomposing single hotspots while introducing a tolerable energy consumption overhead on the sensor nodes across the network.

2. For single hotspots, QoD improvements are around 50% for single storage hotspots (using MHZS), 25% for single query hotspots, and 75% for single mixed hotspots while energy consumption overheads are 5%, 7%, and 12% respectively.

3. For multiple hotspots, QoD improvements are around 20% for single storage hotspots (using SHZS), 20% for single query hotspots, and 50% to 70% for single mixed hotspots while energy consumption overheads are 2%, 5%, and 12% respectively.

4. For large-scale hotspots (especially those larger than 80%) and moving hotspots, the per-formance improvements of our schemes, in terms of QoD, become limited. Additionally, the energy consumption overhead becomes relatively large (above 15%). This mainly

results from the inefficiency of the local hotspot decomposition strategy followed by our schemes against complex hotspot settings.

In general, experiments have shown that the schemes achieve good results in terms of improving the QoD of DIM for the different types of hotspots. The QoD improvements of our schemes were varying depending on the hotspot type. In general, our schemes perform better when facing single hotspots rather than when facing multiple or moving hotspots. This result can be explained by the fact that our schemes do not introduce any major changes to the underlying DCS index structure. As our schemes do not implement any global load balancing scheme that coordinates the hotspot load balancing across the network, their ability to deal with hotspots arising in different locations is limited. Additionally, the local hotspot decomposition might introduce additional hotspots in the network if the dissipation of the data of the different hotspots leads them to a single new location which becomes the new hotspot area.

The network size was another important factor in determining the efficiency of our schemes. For most of the hotspot types, our schemes did not add much benefit for small networks (less than 150 nodes). The effect of hotspots is not considered relatively high on these networks in terms of the negative effects of hotspots on QoD. Therefore, the addition of our schemes did not help DIM perform any better. The effect of our schemes became to appear for sensor networks of medium sizes (150 to 350 nodes). This effect became very obvious for larger networks.

Concerning hotspot sizes, the performance improvements of our schemes were very lim-ited for small hotspot sizes (less than 40%). This is due to the fact that the negative effects of such hotspots on the DIM performance are already limited. Additionally, the relatively short durations of our experiments helped in further reducing the negative effects of small hotspots. Therefore, it was hard to achieve a tangible performance improvement for these small hotspot sizes out of implementing our schemes. Our schemes started to score better improvements when hotspot sizes became larger, starting from 50% to 80%. As the nega-tive effects of these hotspots are very obvious on the DIM performance, the addition of our schemes improved the DIM ability to deal with hotspots. Decomposing hotspots of larger sizes became a problem for our schemes. This is due to the fact that the amount of events

falling in these hotspots became too large for our local schemes to compete with. We be-lieve this is mainly due to the fact that our schemes do not introduce load balancing to the underlying index structure. This definitely limits their gains against large hotspots.

The energy consumption overhead is also another important factor to take into account when deciding to implement any of our schemes. As our results show, the overhead is low for uniform loads. This is mainly due to piggy-backing the information needed by the DMC and PC on the regular messages sent within the network. This helps our schemes in introducing a small overhead throughout the network operation. For single hotspots, the overhead was low to moderate for most of the cases. It mainly comes from the continuous migration of readings in the network. Note that the zone sharing and partitioning processes continuously take place in order to increase the DIM ability to deal with hotspots. This overhead is higher for multiple hotspots regardless whether they are static or dynamic due to the increase in the energy consumed by the different network nodes in data migration.

One of the limitations of our experimental evaluation is that we did not consider simul-taneous updates and queries. This limited our ability to determine the effect of such a case on the correctness of query results for the ZPR scheme. However, as we discussed earlier, our experimental evaluation scenario and our hotspot formation technique was fitting for the ZP scheme rather than for the ZPR scheme. We believe that, in real-world scenarios, the application of the ZP scheme would be more frequent than that of the ZPR scheme. We plan for validating this assumption when experimenting our schemes on sensor network testbeds in the near future.

In general, we believe that our schemes are fitting medium-sized networks that are ex-pected to decompose single hotspots of medium sizes. Due to the local hotspot decomposition technique of our schemes, they are not suited to deal with large and consistent hotspots nor with multiple hotspots (both static and dynamic). Our local schemes are well fitting for search/discovery sensor network applications, where limited hotspots may exist and solving them is required not to consumer a lot of energy from the sensor network. Chapter5presents our KDDCS scheme whose global load balancing technique is perfectly fitting to deal with these hotspot settings.

4.4 SUMMARY

In this chapter, we presented a set of local schemes to detect and decompose different types of hotspots imposed on DCS sensor networks, including storage, query, and mixed hotspots.

Our schemes use the concept of data migration to decompose hotspots without changing the underlying index structure of the DIM scheme. Our experimental results show that our schemes achieve good QoD and load balancing achievements, especially against single hotspots, while adding a tolerable energy consumption overhead on the different sensor nodes. The performance improvements of our schemes become limited in the cases of large-scale single hotspots and multiple hotspots (both static and dynamic). An important benefit of our local schemes is their low messaging overhead compared to the DIM scheme. Another advantage of our schemes is their ability to be applied in sensor networks with different storage capacities. The next chapter will present our KDDCS scheme to improve the sensor network performance against against these cases.