2.4.2. Performance Analysis of Existing Replication Techniques
Ideally, with respect to sensed data collection with OppNets, message loss would be minimal if only one copy of each message existed in the network at a time. Due to fewer messages in node buffers, more successful message transmissions would be realized per encounter. Results in
Figure 4a show that the throughput achieved when PRoPHET utilizes single-copy replication is comparable with multiple-copy replication. In fact, generating excess copies in an ideal state is likely to result in lower throughput due to buffer overflows. However, real-world implementation scenarios are far from ideal. The wireless communication interfaces on user devices may not always be turned on. Hence, a message may never be delivered if only one copy exists in the network, as its custodian may miss important encounter opportunities. Also, the wireless communication interface on handheld user devices could be shared among different applications, including other OppNet applications such as content dissemination [
64] and location-targeted services [
65]. With single-copy replication, the only existing message copy would thereby compete with these other applications and messages whenever a data transmission opportunity arises. Hence, there is a higher chance of missing a suitable relay node that may never be encountered during the lifetime of the message. Accounting for these factors in the simulation results in significantly less delivery guarantees, as shown in
Figure 4b.
Our results in
Figure 4b support the notion that only one copy of a message may not be sufficient to guarantee its delivery [
66]. The chances that the message may be lost due to buffer overflows or TTL exhaustion is heightened under higher network activity coupled with network characteristics such as high node population and degrees of spatial locality. In this regard, multiple-copy replication is a more preferable strategy. This section provides qualitative and quantitative analysis of the multiple-copy replication techniques overviewed in
Section 2.3 under the network characteristics presented in
Section 2.2. We start by addressing high node population and its impact on message transmission overhead, metadata overhead as well as the impact when coupled with limited bandwidth and high network dynamicity. Next, we investigate the impact of spatial locality on encounter opportunities and on the overhead-throughput trade-off for different sets of messages.
A. Impact of High Node Population
Increased transmission overhead
Considering the versatile nature of portable handheld devices, routing should take minimal toll on available energy. The versatility of portable handheld user devices requires them to perform a lot of other operations besides routing. Considering this, only a limited amount of node resources may be allocated to routing. The overhead incurred by some of the existing message replication techniques tends to rise at noticeable rates with node population. The effects of this may not be significant in networks that consist of a few hundred users and participating endpoints. However, in sensed data collection scenarios, where network coverage increases and extends to thousands of nodes, the number and frequency of encounters are likely to rise. In turn, the number of message transmissions may contribute to significant overhead and energy consumption.
Under high node population, lack of a replication technique may generate excess message copies, hence, high message transmission overhead. Forwarding messages without considering the forwarding ability of nodes (e.g., Epidemic) rapidly exhausts node and network resources. The rate of resource consumption can be reduced by considering the forwarding ability of nodes. In that case, messages are forwarded only when the encountered node is a more suitable relay (i.e., presents a higher forwarding utility, e.g., PRoPHET). However, messages continue to be replicated in this manner unless their TTL is exhausted or dropped due to buffer overflow. Without mechanisms that notify nodes about the delivery status of messages they carry (which are usually unsuitable in OppNets due to increased resource consumption [
67] and delays [
68]), the resulting amount of message copies mostly depends on the forwarding utility of the source node and the total node population. The number of more suitable relay nodes is increased when messages are generated by nodes with relatively low forwarding utilities or when there is a large number of nodes in the network. This results in increased replication such as in
Figure 5 (transmission overhead increases steadily with node population), most of which may be redundant, and eventually lead to higher energy consumption.
Some variable replication techniques also result in excessive transmission overhead under high node population. EpiPRo may generate excess copies of a message before its hop count property reaches the threshold. In Zhang et al.’s [
54] Gossiping and timer threshold proposals for instance, the optimal
and timer threshold values may cause excessive transmission overhead in higher node population. The proposal by Iqbal and Chowdhury [
57] also requires an optimal reference value in order to terminate message replication at the right time, which may still result in excess message copies in highly populated areas. n-Epidemic replicates messages according to
, i.e., the number of nodes in contact. The choice of this parameter is crucial to the performance of the network since extreme values reduce network performance. Higher values of
reduce the probability of transmission while lower values tend towards Epidemic. Hence, there is the risk of excessive replication in high node population, as a large number of nodes may be in contact most of the time–in shopping malls, for instance.
Increased metadata overhead
Most of the variable-quota replication techniques neglect control overhead that results from metadata dissemination [
69], as they frequently transmit metadata in order to update perceived information that may become stale too quickly. Metadata transmission is thus traded for the overhead incurred in data transmissions, which may still consume the limited resources on nodes. Metadata transmissions may also interfere with the process of data transmission, due to limited bandwidth, highly dynamic user movement and intermittent connectivity. The metadata overhead incurred by VACCINE’s anti-packets increases with node population. The delay in propagating them increases as well, since it depends on the rate of node encounters. Anti-packets may therefore fail to fulfil their purpose by the time they are received, as replicas of delivered messages could have been dropped due to TTL exhaustion or buffer overflow. This may result in unnecessary transmission overhead. Further influencing the amount of transmission overhead incurred in the process is the amount of TTL allocated to anti-packets.
Metadata dissemination may also increase the number of failed message transmissions. Besides the issue of limited bandwidth, encounter duration is shared between metadata and data transmission [
69]. The metadata overhead incurred by some replication techniques increases in higher node population. For example, the performance of Shin et al.’s [
55] replication technique depends on a mechanism for deleting delivered messages. This requires nodes to exchange metadata in the form of lists containing IDs of delivered messages and expiration times. Another example is Lo et al.’s [
49] proposal, which requires information about each encountered node in order to maintain and update a neighbour table. The neighbour table contains node ID, buffer occupancy, a list of messages in the buffer, a list of neighbouring nodes, and a list of time stamps indicating encounter start and end times. Miao et al. [
58] require encountered nodes to update their community table, community graph and gateway graph. The information exchanged includes node ID with the corresponding community ID and node ID of gateway nodes for communities. In high node population, frequently transmitting such metadata (alongside existing ones such as summary vectors) may require more time [
70] to successfully exchange messages. This contributes to increased contention due to the short-lived nature of node encounters in OppNets, and may eventually result to throughput degradation [
71].
Figure 6 shows that the implementation of summary vectors alone reduces PRoPHET’s throughput by about 14%.
Poor adaptability to increased network dynamicity
Increased network dynamicity (i.e., more rapid and significant changes in network conditions such as topology, node density and encounter rates) in higher node population may also prevent some replication techniques from performing as expected, especially in the absence of global knowledge. Shin et al. [
55] focus on scenarios in which the number of nodes in the network is known (e.g., battlefield scenarios). However, this is not applicable to OppNets for sensed data collection in which the number of nodes increases without prior notification. You et al.’s [
59] dynamic replication is based on the average hop count of existing routes to the destination. Due to the numerous portable handheld devices and highly dynamic network topology, it is difficult to determine the total number of routes between a node pair in real-world implementation. In the case of n-Epidemic, the choice of
may need to adapt to different network conditions in order to be suitable for implementation in realistic scenarios. For instance, high encounter frequencies during rush hours may cause messages generated within the period to be over-replicated, while messages generated before or afterwards may be under-replicated, cf.
Figure 7 (two peaks occur every 24 h which correspond to periods of increased encounter frequencies such as during rush hours). Similarly, Gossiping and the time threshold approach require more adaptive parameters, i.e.,
and the time threshold, respectively. Iqbal and Chowdhury [
57] and Miao et al. [
58] also require an adaptive reference value and TTL threshold, respectively. Due to increased network dynamicity in higher node population, values that may be optimal under one network condition may not be suitable under another. Hence, distributed mechanisms that can enable them to adapt to different network conditions are required.
B. Impact of High Degrees of Spatial Locality
Uneven distribution of encounter opportunities
As observed in
Section 2.2, spatial locality plays an important role in the encounter opportunities experienced between different sets of nodes. Users often visit only few places and mostly move within a local region, thereby reducing the likelihood of seeing people on a regular basis the farther away their homes are located. In terms of OppNets (depending on the geographic location of source and destination nodes), the delivery of some messages through encounter-based utilities may be less likely than others. Hence, in the design of OppNet routing solutions for collecting sensed data, entirely neglecting spatial locality inherent to user movement may lead to poor throughput.
From
Section 2.3, we observe that existing fixed-quota replication techniques overlook the impact of spatial locality on network performance. First, in the spray phase, some techniques tend to allocate the remaining quota according to a ratio determined from encounter-based or social-based node relationships. For example: SAS, a function of the forwarding utilities of the encountered nodes; Huang et al. [
51], connection strength with the destination; ISW, encounter duration with the destination; degree centrality [
48]; average waiting time between successive encounters [
49]; probability of encountering the destination in the near future [
50]; frequency of encounters [
51]; or one derived from two or more encounter-based features—frequency of encounters and encounters with the destination [
52], for instance. Second, most of them utilize only encounter-based properties in the second phase of replication after the spraying quota is exhausted. For example, SnF utilizes single-copy replication, entirely relying on the encounter-based forwarding utility for the number of transmissions.
Unfortunately, since the chances of determining more suitable relay nodes through encounter-based or social-based forwarding utilities alone reduce under higher degrees of spatial locality, allocating message quota accordingly may not be effective. Hence, messages may make little progress towards the destination despite the quota allocated to relay nodes during the spray phase. In the second phase, less encounter opportunities between nodes from different local regions reduces the chances of finding relay nodes with better encounter-based forwarding utilities. While a transitive property (i.e., a property that infers the forwarding utility of a node for a destination it has never encountered from neighbouring nodes that encounter the destination, e.g., PRoPHET and SnF) is able to perceive encounter-based knowledge over multiple hops, the resulting forwarding utilities are fine-grained. For messages traversing multiple regions, the quality of more suitable relay nodes becomes less, i.e., the difference between the sender’s and receiver’s forwarding utility becomes less (cf.
Figure 8). Consequently, such messages are subjected to more number of transmissions.
Some variable-quota replication techniques may also suffer from the obliviousness of spatial locality. For instance, You et al.’s [
59] proposal generates more replicas when relay nodes have a low chance of delivering the message. Without considering the properties of spatial locality, this replication may result to excessive transmission overhead in high degrees of spatial locality, since most neighbouring nodes may have low chances of delivering messages to distant destinations through encounter-based or social-based forwarding utilities alone. Similarly, the proposal by Huang et al. [
51] may result in excessive replication, since more message copies are generated when the connection strength between the source and the destination is low.
Knowledge about
, the total number of nodes in the network, is required to compute: UMVUE’s total number of message replicas existing in the network; and
, the replication quota for fixed-quota replication techniques. UMVUE estimates
by counting unique IDs of encountered nodes. Unfortunately, this may be too costly for resource constrained nodes as it requires large storage and frequent lookup operations. Also, it may take long for this method to converge, especially in networks comprising of disjointed communities. Furthermore, UMVUE relies on the assumption that nodes are assigned IDs of the same pattern. In Smart City scenarios however, node IDs may not be of the same pattern, considering that they may be from different communities, regions or sub-networks. Spyropoulos et al. [
72] show that in an independent and identically distributed (IID) uniform mobility, the value of
for a required expected delay can be determined as a function of only
. The authors propose a method of determining
through inter-contact time statistics, that is, if nodes are assumed to perform independent random walks. This method converges faster than ID-counting, since the only requirement, which is sufficient inter-contact time samples, may come from nearby nodes. However, nodes do not show location preference under IID uniform mobility and these estimations have not been investigated under a more realistic mobility model such as WDM. WDM increases reality by introducing spatial locality in node movement, which allows nodes to reveal different levels of location preference.
Higher chances of message loss
With different number of message copies and varying amounts of progress made in the network, the order in which they are transmitted and dropped needs to conform to their priority. Although many strategies for determining these orders exist, desirable results are realized only when they are in accordance with the message replication technique. Hence, some message replication techniques (e.g., Lo et al. [
49] and Shin et al. [
55]) are complemented with buffer management policies, which often consist of rules for queuing messages, dropping messages or both. In order to create room in node buffers, most replication techniques utilize the first-in-first-out (FIFO) dropping policy in which the message that was received first is dropped first, while others drop messages with higher hop count (e.g., Lo et al. [
49]) or less remaining TTL (e.g., Shin et al. [
55]). The idea is that there is a higher probability of having more copies of such messages in the network and dropping them is unlikely to impact significantly on their delivery. However, in Smart City scenarios where user movement exhibits high degrees of spatial locality, dropping such messages may have significant impact on throughput. Since fewer nodes travel across multiple local districts [
62] (i.e., regions), related messages are likely to be delivered through more number of hops and may need to stay longer in node buffers (cf.
Figure 9). Hence, with the existing dropping policies, messages traversing longer distances tend to be dropped before arriving their destination.
Sub-optimal message copies
The results in
Figure 10 indicate that a fixed quota may cause some messages to be under-replicated (i.e., generating less copies than required) and others over-replicated (i.e., generating more copies than necessary) under high degrees of spatial locality. Depending on the location of source and destination nodes, some messages may require more quota to guarantee delivery, while others may require less [
28]. For instance, less copies may be required to guarantee the delivery of messages destined to nodes that are located nearby, while messages to nodes father away may require more copies—since they are more likely to be dropped. Hence, to maximize throughput with minimal transmission overhead, different messages may require different quota. The lack of flexibility in fixed-quota replication may cause unnecessary transmission overhead or reduced throughput, and either case degrades network performance. As shown in
Figure 10, messages traversing more regions have less chances of delivery with SnF. Only 26% of messages generated to destinations 3 regions away are delivered, as compared with 74% for messages whose source and destination nodes are located within the same region.
In order to achieve optimal performance, the replication quota needs to be carefully selected, and this may be challenging and almost impractical without global knowledge of network parameters. So far, there is yet to be a suitable means of varying these parameters according to the requirements of different messages under high degrees of spatial locality. With these replication techniques, the choice that better guarantees the delivery of messages traversing multiple local regions may cause other messages to be replicated in excess. The additional transmission overhead may lead to frequent buffer overflows and eventually reduce achievable throughput.