In this section we compare the performance of the two protocols, Bamboo and Georoy in different conditions. We want to better understand their behavior by means of a comparison using two significant scenarios representing the static backhaul of wireless nodes: grid and random scenarios. Here, a number of SP nodes (i.e., Infostations) are placed within an area of a certain size. The Infostations are static and do not move during the simulations. We vary the number of such stations between 25 and 225. Ns2 v2.26 [37] simulations were run considering a transmission range of 200 m, a carrier sense range of 250 m, an area which size
depends on the number of SP nodes as
and a distance between two SPs in the grid topology equal to 100 m. Routing between the connected Infostations uses AODV-UU [38] but different choices are possible. In the random topology, nodes are thrown randomly in the area. We consider infinite buffer space on the replication nodes. We make such choice because if the buffer size is limited, achievable performance may largely depend on buffer replacement strategies, which is a problem outside the scope of this paper. In the random topology case, for each scenario identified by the number of nodes, we tested 5 different random topologies and for each topology we performed 100 random lookups. Average values and confidence intervals (when applicable) were reported for the following performance metrics being investigated:
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(i)
number of logical hops traveled in the overlay network to perform a lookup for a specific resource,
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(ii)
corresponding number of physical hops traveled in the physical network to perform a lookup for a specific resource as a consequence of the logical path followed,
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lookup delay needed for the lookup to reach the node who stores information about the requested resource. We only consider here correctly completed lookups.
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percentage of lookups correctly completed,
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stretch factor, that is, the ratio between the number of physical hops needed to complete the lookup as a consequence of the logical hops traversed and the number of physical hops going end-to-end according to a shortest path approach.
In the first part of the evaluation, we focus on the impact of the network size on the scalability of the lookup procedure. We then evaluate the impact of the replication technique. Finally, we evaluate the impact of the use of a data mule on the achievable performance in terms of resources download.
7.1. Impact of Network Size in Grid and Random Topologies
In Figure 2 we show the number of logical hops traveled when employing the two algorithms. By comparing the results we observe that, in general, Bamboo results in a smaller number of logical hops as compared to Georoy. This is related to the fact that the amount of overlay routing information used by Bamboo (i.e., leafset and routing table) is higher if compared to Georoy which limits the number of existing logical links to 7. Therefore, Bamboo can more easily identify a requested resource as it has more routing information available. In contrast, the number of physical hops mainly impacts on the lookup performance. This is because this parameter determines the number of forwarding operations a packet needs to undergo in the wireless multihop network to reach the destination (i.e., the node holding the resource). As the network size grows, also the number of physical hops needed to complete a lookup increases (see Figure 3). However, an interesting observation is that for larger topologies, the number of physical hops is in general lower when using Georoy as compared to Bamboo. This is because, due to the overlay addressing scheme in Georoy, the logical and physical topologies are tightly coupled so that the logical path does not differ much from the physical one. In fact, for large network topologies, the ratio between the physical and logical hops is around 2 for Georoy and rises to 5 for Bamboo. Since the formation of the overlay network is independent of the physical location of the nodes in Bamboo, for larger topologies the probability that a peer selects a close logical neighbor located far away in the physical topology is higher. This results in longer routes when topologies are larger. Also, note that the variance for the physical hops is much smaller in Georoy compared to Bamboo. This is again due to the addressing scheme of Bamboo, which randomly selects nodes in the overlay as neighbors, although they might be actually far away in terms of physical distance.
In multihop wireless networks, the more hops a packet is forwarded, the larger the delay and, in general, the higher the packet loss probability. This is because at every intermediate node, the packet needs to compete for medium access and collisions due to, for example, hidden nodes might lead to frequent retransmissions and consequently high packet loss. The impact of an increase in the number of physical hops traveled in case of large topologies can be seen in the average lookup delay comparison shown in Figure 4. Here, we can see that for smaller topologies, Bamboo outperforms Georoy as less physical hops are required. However, due to the efficiency of its addressing scheme, the increase in the number of physical hops is smaller for larger topologies in Georoy, compared to Bamboo. Therefore, Georoy provides better lookup delays with larger topologies. Interestingly, Georoy shows smaller number of physical hops as compared to Bamboo when network size is larger than 144 nodes. However, the lookup delay of Bamboo is smaller as compared to Georoy already at a network size of about 100 nodes. This apparent discrepancy can be explained due to the fact that the random distribution of requests can turn into a different load on the links. There might be situations where the number of physical hops is a bit smaller for one protocol, but the load on the links might be different resulting in an advantage for the other protocol in terms of delay.
Another interesting observation is that the number of successfully completed lookups decreases as network size increases (see Figure 5). By increasing the number of nodes in the network we also increase the amount of messages exchanged (management traffic required to maintain the overlay plus key lookup request/replies) among the nodes and consequently the wireless contention for the medium. Also, when lookup packets traverse more hops, they need to compete more often for medium access and the probability to collide due to, for example, hidden nodes is higher. Interestingly, the number of completed lookup requests is smaller for Bamboo as compared to Georoy, even for small topologies. This can be attributed to the fact that the management traffic of Bamboo is significantly higher. Such high-management traffic leads to more load and contention leading to higher chance that the lookup request cannot be completed correctly [36]. In Bamboo, in this case the lookup request is retransmitted a limited amount of time until the agent gives up and declares the request as not successful.
The stretch factor presented in Figure 6 shows that both protocols can satisfy lookup requests with a limited increase in the number of hops traversed when compared to the shortest path approach. As we have seen in Figure 3, Georoy needs fewer hops to forward a lookup request to the destination when the network is composed of 144 nodes or more. Consequently, the stretch factor of Georoy is smaller compared to Bamboo at large network sizes.
When considering the random topologies, similar conclusions can be drawn. However observe that, in the random case, nodes are not distributed on the vertices of a grid, so physical proximity can help to reduce the number of physical hops and, thus, decrease delay significantly as evident in Figures 8 and 9. In fact when performing a lookup operation, one can move in any direction to a neighbor node which is not constrained to be located on a grid vertex. In addition, due to the random nature of the node location, we could observe more clustering of nodes as compared to a grid scenario. Therefore, as nodes are more close to each other in most of the area, less physical hops are required, thus implying less delay to complete the lookup operation. Clearly, due to the randomness in node location, there is more variability in the number of physical hops and delay. The logical hops instead do not vary much as compared to the grid scenario (see Figure 7).
7.2. Impact of Number of Replication for Grid Topologies
Besides the impact of network size in grid and random topologies, another important point that we address is to determine the benefit of using a replication mechanisms in opportunistic scenarios. We start by looking at the impact of having different number of replicas as a way to speed up the resource lookup process. In our experiments we considered that both in Georoy and Bamboo each resource was replicated at 3, 5, or 7 different nodes. We assume a random waypoint mobility of the LP node providing the resource and consequently the replicas of the resource are randomly distributed in Georoy and are assigned to random nodes in the leafset in Bamboo, independently of the LP movement. In Figures 10 and 11 we observe that, upon increasing the number of copies of a resource, both the number of logical and physical hops slightly decrease. As expected this is because, when increasing the number of replicas, the probability of finding the resources closer raises as well. As a result, when using more replicas, the delay to complete a resource lookup can be reduced as evident looking at Figure 12. Also, consider that in Bamboo no significant variations in the number of logical hops as a consequence of a change in the number of resource replicas are met. The reason for this behavior is to be searched in the replication mechanism which in Bamboo disseminates replicas randomly at nodes in the leafset which are thus very close in the logical space but could not give meaningful help in speeding up the lookup procedure. Also observe that in Georoy it is sufficient to use a controlled number of replicas (i.e., higher than or equal to 5) to achieve quite stable performance.
7.3. Impact of Data Mule Mobility
Finally, we wanted to test how the two protocols behave in case of a disconnected scenario where an isolated node wants to perform a lookup but can only execute it during the limited time spent by a data mule, who travels around the network area, in its coverage range. In particular in this case we estimated the delay for a resource retrieval. We assumed a mobile data mule moving with a velocity variable in (4 Km/h (pedestrian case), 10 Km/h (vehicular case 1) and 25 Km/h (vehicular case 2)).
An isolated node, in the best case, will have the data mule in its coverage area for a time equal to
where
is the transmission range and
is the data mule velocity. We assumed a retrieval for a file of size 2 MB with links of capacity equal to 1 Mbs. We considered a variable number of retransmissions on each link in
. Accordingly in Figures 13 and 14, we show:
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the maximum delay taken for performing the lookup and retrieving the file in case of 1 retransmission on each link (delay 1 retr),
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the maximum delay taken for performing the lookup and retrieving the file in case of 3 retransmissions on each link (delay 3 retr),
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the maximum available time for lookup and retrieval depending on the data mule velocity (max delay).
Comparing the two plots we observe that for both Georoy and Bamboo the resources can be retrieved during the limited proximity time if the data mule moves around 4 Km/h. Instead, when the velocity of the mule is higher (10 or 25 Km/h), the percentage of retrieved resources during the contact time decreases and the delivery will be delayed of an amount equal to the intercontact time, that is, the time passed since previous exit until next entry of the mule into the coverage area of the isolated node. Supposing to employ a random way-point model for the data mule movements, the CDF of intercontact time [39] is shown in Figures 15 and 16 by varying the number of super peers and the mule's velocity, respectively.
Looking at the curves related to the velocity of mule around 10 Km/h, we observe that Georoy can complete the retrieval of a resource during the proximity time when the number of SPs is lower than or equal to 49; in Bamboo, instead, the delivery can be satisfied when the number of SPs is lower than 81.
Finally, in Figure 17 we show the percentage of downloads completed by an isolated node during the transit period of the data mule, when the latter moves at 10 Km/h, by varying the number of copies for each resource. As we observed, upon increasing the number of replicas, the retrieval procedure can be speed up: without replication, Georoy is able to efficiently exploit the limited proximity time for exchanging data until a maximum number of SPs equal to 49; exploiting the random dissemination of the resources, instead, this threshold can be increased until 81 SPs employing 7 copies for each resource.
Conclusions of this analysis are the following.
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Bamboo performs better than Georoy in small to medium size topologies both grid or random. This is due to the more complete view of the overlay given by the larger overlay routing information, which also requires higher management traffic. When network size increases, Georoy overcomes Bamboo in performance due to the location aware addressing scheme.
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Random topologies lead to a reduction in the number of hops and, thus, in the delay with respect to more regular cases like grid topology. This is mainly due to the clustering of nodes, which reduces the number of required physical hops.
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Bamboo in general exhibits a lower number of lookups completed successfully due to its high overhead.
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In opportunistic scenarios, where a data mule travels around and helps to connect remote nodes to infostations, when the data mule does not move too fast both protocols can allow lookup and delivery during the limited proximity time although Bamboo is more convenient also for slightly higher velocities. Performance improves when the download volume reduces or the data mule moves slower.