improving performance in delay/disruption tolerant...
TRANSCRIPT
Improving performance in delay/disruption tolerant networksthrough passive relay points
Saeed Shahbazi • Shanika Karunasekera •
Aaron Harwood
Published online: 30 September 2011
� Springer Science+Business Media, LLC 2011
Abstract In this paper, we study the case of a limited
number of mobile nodes trying to communicate in a large
geographic area, forming a delay/disruption tolerant net-
work (DTN). In such networks the mobile nodes are
disconnected for significantly long periods of time. Tradi-
tional routing protocols proposed for mobile ad hoc net-
works or mesh networks, which assume at least one path
between each source and destination, are ineffective in
DTNs. One approach to improve communication is through
gossip based protocols because these protocols do not rely
on a fixed path. Another approach is to control the move-
ment of the mobile nodes and/or use special mobile nodes
called ferry nodes. Others try to employ a fixed infra-
structure including stationary relay points. One scheme in
stationary relay point approach is to use base stations as
relay points which need their own power supply. In this
paper, we study a passive approach where mobile nodes
deposit/retrieve messages to/ from known stationary loca-
tions in the geographic region. Messages are delivered
from a source by being deposited at one or more locations
that are later visited by the destination. A proposed
implementation of our approach using read/writable pas-
sive Radio Frequency Identification (RFID) tags, one per
point location, is considered in this work. Passive RFID
technology is desirable because it operates wirelessly and
without the need for attached power. Our simulation results
indicate that our approach can achieve competitive mes-
sage delay and delivery rates. We also demonstrate several
techniques for optimizing the stationary relay node place-
ment, namely relay pruning, probability based relay dis-
tribution and a genetic algorithm; the genetic algorithm is
shown to provide the best solutions to this problem.
Keywords Ubiquitous network connectivity �Delay/disruption tolerant networks �Performance evaluation � RFID tags
1 Introduction
The number of connections present in a mobile network at
any one time is an important topological property because
such connections allow communication to take place. We
categorize mobile networks as delay/disruption tolerant
network (DTN) depending on the degree to which con-
nections are available. In DTNs, the number of nodes per
unit area, or the node density, is small and the nodes do not
frequently connect. The network may remain partitioned
into individual nodes for relatively long periods of time.
DTNs arise naturally from applications such as wildlife
tracking [25], vehicle-based disruption-tolerant networks
(VDTN) [12, 31, 38], rural kiosks in developing countries
[47], delay-tolerant mobile sensor network [57], and
environmental monitoring including metropolitan areas
[27] and underwater [1, 40], or from fragility and failures
in the network itself due to disasters, jamming and noise,
and power failure. DTNs are also referred to as sparse
mobile networks, extreme wireless networks, or intermit-
tently connected networks in the literature. In DTNs, if two
nodes are within the broadcast range of each other and the
link between them is up then we say they are connected.
S. Shahbazi (&) � S. Karunasekera � A. Harwood
The University of Melbourne/NICTA, 3.08, 111 Barry St.,
ICT Building, Carlton, VIC 3053, Australia
e-mail: [email protected]
S. Karunasekera
e-mail: [email protected]
A. Harwood
e-mail: [email protected]
123
Wireless Netw (2012) 18:9–31
DOI 10.1007/s11276-011-0384-1
In the literature, the connected link is referred to as a
contact [15].
Perur et al. [44] define sparseness of a network by
measuring connectivity of the network, i.e., the probability
that the network graph forms a single connected compo-
nent, if it is less than 0.95, then it is referred to be as a
sparse network. From another perspective, we consider a
mobile network as a DTN for a given time interval, if the
average number of contacts is less than 5% of all potential
contacts, i.e., all pairs of nodes, over the interval. The
details of calculating this threshold is referred to -1.
As there is no fixed infrastructure in DTNs to deliver the
messages from source nodes to destination nodes, mobile
nodes have to participate in routing and they have to act as
a router similar to mobile ad hoc networks (MANETs).
However, traditional routing protocols proposed for
MANETs [24, 41–43, 48] are ineffective in DTNs as they
typically make an assumption that the underlying network
is connected. A connected network in this context means
that there exists at least one (possibly multi-hop) path
between each pair of nodes and that exists for a long-
enough period of time to allow a packet to traverse it.
Furthermore, these protocols assume that if the path is
disrupted it can be repaired or replaced in a relatively short
period of time. The assumption of connectivity is clearly
ineffective in DTNs because of the lack of instantaneous
end-to-end paths in such networks which prevents estab-
lishing any routes to forward the data packets.
In order to overcome the lack of instantaneous end-
to-end paths in DTNs, a routing protocol can use a
store-and-forward paradigm. Therefore, a new class of
routing protocols, referred to as store-carry-and-forward
[10, 23, 25] has emerged. This class of routing protocols
exploit the mobility of the nodes in the network to forward
the data packets by relaying packets to intermediate nodes.
The intermediate nodes then keep the data and deliver it to
the final destination or to another intermediate node.
Therefore, the data is incrementally distributed throughout
the network, i.e., in the intermediate nodes, leading to
facilitate the data delivery process.
Recently there has been focus on augmenting DTNs
with some low cost, easily deployable fixed relay nodes.
We refer to this emerging class of protocols as stationary
relay point approaches. In these approaches some station-
ary relay nodes are added to the network in order to
improve connectivity [6, 22, 49, 66]. This class of proto-
cols can increase contact opportunities among mobile
nodes, consequently improving DTNs performance. For
example, Banerjee et al. [5] show experimental results
collected from the UMassDieselNet DTN [10] that adding
a fixed relay node, called throwbox, to the network
improves the packet delivery by 37% and reduces the
message delivery latency by at least 10%.
Stationary relay point approaches can be categorized as
active and passive based on the type of relay nodes. If the
relay node can initiate the communication we refer to the
approach as an active relay point; otherwise, we refer it as a
passive one. Further, in active approaches [6, 22, 66],
stationary relay nodes have their own supply of power
while in passive approaches [49], they are powered by
readers, i.e., mobile nodes. Also, in active approaches, the
number of relays is less than the passive approaches while
their broadcasting range is usually bigger.
In this paper, we propose a passive stationary relay point
based protocol to improve the delivery performance of the
DTNs. In contrast to the active approaches proposed in the
literature, in our protocol, the stationary points do not
require their own power supply. Specifically, we propose
an alternative approach where mobile devices deposit/
retrieve messages to/from known point locations in the
geographic region. The point locations act like ‘‘mail-
boxes‘‘. Mobile nodes are assumed to know the position of
the mailboxes. As a mobile node moves around the region,
it checks the mailboxes that it meets. Messages can be
retrieved from a mailbox and copied into another, subse-
quently met mailbox. This mechanism allows all nodes in
the network to help push messages over the geographic
region and thereby expedites message delivery. In fact, in
our protocol nodes never communicate messages directly
to each other, rather they communicate messages only via
these mailboxes which are in known places. This helps
saving energy because the transmission can be off when
mobile nodes are not in the communication range of the
relay nodes, which is a challenging issue in DTNs as the
mobile nodes are battery powered. Banerjee et al. [5] show
that using 802.11 radio to search for contacts in a DTN
devotes 99.5% of the total energy of mobile nodes just to
find other nodes which is leading to have a short network
lifetime. Additionally, the possible communication between
mobile nodes is, by definition of a DTN, infrequent and
therefore its effects are negligible. Although we augment the
network with a passive unconnected infrastructure, mobile
nodes still can make a network on the fly without a priori
connected infrastructure.
A proposed implementation of our approach using pas-
sive read/writable Radio Frequency Identification (RFID)
tags, one per point location, is considered in this work to
evaluate the network performance; hence we refer to our
protocol as a Tag-based Routing (TBR) Protocol. RFID
technology is desirable because it operates wirelessly and
without the need for attached power. This makes its
deployment relatively easy and sustainable. It would be
equally valid to consider the results of our work on a net-
work where the point locations are wireless base stations
(with no connections between the base stations), if such an
infrastructure was feasible for the application. The increased
10 Wireless Netw (2012) 18:9–31
123
range of a wireless base station compared to the range of an
RFID tag would serve to increase the effectiveness of our
protocol in terms of message delivery rate and delay.
The proposed protocol was studied in our previous
works [49, 50]. In this paper we enhance the protocol to
handle more realistic scenarios and we evaluate its per-
formance with various scenarios. Further, we evaluate the
performance of the proposed protocol under the effect of
different mobility models and show how entity mobility
models and group mobility models have different impact
on its performance. Additionally, we present different relay
placement techniques namely relay pruning, probability
based relay distribution, and a genetic algorithm based
optimization and we show their effect on the performance
of the proposed protocol as well as comparing their
effectiveness with the existing placement techniques in the
literature. Our simulation results show that our protocol
outperforms the existing approaches in the literature and
our genetic algorithm based optimization placement tech-
nique is the superior relay placement technique in the lit-
erature. In this paper, we mainly focus on our protocol
performance and the issues related to security, privacy,
fault tolerance, etc. are outside of the scope of the paper.
The main contributions of the paper are as follows:
(i) We propose a novel DTN routing protocol and
evaluate it under a verity of scenarios with one of the
most widely used mobility models.
(ii) We propose different relay placement techniques to
optimize the performance of our protocol given a
mobility model. Accordingly, we evaluate them with
different mobility models including entity and group
mobility models to identify the best placement
technique.
(iii) We present a comprehensive comparison between
our proposed protocol and well-known routing
protocols in DTNs namely Epidemic Routing [56],
Message Ferrying [65], and Throwbox [66].
The remainder of the paper is organized as follows:
Section 2 reviews the background and related works, Sect.
3 presents a detailed description of the proposed protocol,
Sect. 4 defines different node mobility models used in this
paper, Sect. 5 shows different techniques for distributing
relays in the region, Sect. 6 provides simulation results for
our protocol, Sect. 7 compares our work to representatives
of both existing reactive and proactive approaches in Store-
Carry-Forward paradigms, and Sect. 8 concludes the paper.
2 Background
A DTN routing protocol should be able to accommodate
disconnections in the network without significant impact
on message delivery performance. A pragmatic approach to
overcome partitions in DTNs is by using longer transmis-
sion ranges and thereby maintaining persistent network
connectivity [46]. Increased sparseness of the network then
leads to increased power requirements, which is a clear
shortcoming of this approach. Furthermore, using long
radio range in some applications may not be possible or
desirable.
Recently there has been focus on developing routing
protocols for DTNs. Al Hanbali et al. [2] classify these
protocols based on the degree of knowledge of the mobile
nodes about their future contacts with other mobile nodes.
We categorize DTN routing into two major categories:
Store-Carry-Forward paradigms and Stationary Relay
Points approaches based on possibility of using fixed relay
points.
2.1 Store-carry-forward
The class of Store-Carry-Forward paradigms exploits the
mobility of nodes to buffer data packets during network
partitions and forward them when connections become
available. They are divided into two categories: reactive
and proactive schemes. Reactive routing protocols [52, 53,
56] use mobility of the participating nodes to buffer and
deliver messages across network partitions. While, proac-
tive routing protocols [9, 18, 30, 59, 60, 65]) control the
mobility of some nodes to accommodate disconnections.
More details are provided in the related work section.
2.1.1 Reactive schemes
Reactive approaches use mobility of the participating
nodes to buffer and deliver messages across network par-
titions without forcing any mobile node to change its tra-
jectory. Vahdat et al. [56] propose a routing protocol for
partially-connected ad hoc networks called Epidemic
Routing. In this protocol, every node exchanges all the data
packets stored in its buffer while encountering with other
mobile nodes until meeting the destination(s). Spyropoulos
et al. [52] tried to improve the performance of flooding-
based routing schemes such as Epidemic Routing by
bounding the number of data packet replicas that happens
by spraying a limited number of identical messages by
source node to the network using distinct relays and wait
until one of those relays meets the destination to perform a
direct transmission. They enhanced their approach in [53].
Also, Tournoux et al. [55] propose a measurement-oriented
variant of the spray-and-wait algorithm called DA-SW
(density-aware spray-and-wait) that can tune the number of
message replicas in a dynamic manner. Gao et al. [17]
improve Epidemic Routing scheme by reducing the data
forwarding cost. They employ a multi-cast scheme to select
Wireless Netw (2012) 18:9–31 11
123
the relay nodes considering the forwarding probabilities to
multiple destinations simultaneously.
Some reactive approaches use historic information about
node contacts, spatial information, etc. to calculate the
delivery expectation of next hops indicating their proba-
bility of being able to successfully deliver a data packet to
select the message carriers. These approaches are including
PROPHET [32], NECTAR [14], pattern-based Mobyspace
Routing [28], location-based Routing [29], and context-
based Forwarding [37]. Burgess et al. [10] tried to improve
the performance of the network by deleting useless data
packets and scheduling packets for transmission to other
peers. More recently, Balasubramanian et al. [4] study
routing in DTNs as a resource allocation problem and
propose a protocol to enhance the performance of a specific
routing metric by using some heuristics. Yuan et al. [61]
propose the Predict and Relay scheme which predicts the
future contacts of specified nodes at a specified time. Then,
source/intermediate nodes select a proper neighbor as the
next hop to forward their messages using their estimations
about the future contacts of their neighbors and the
destinations.
Other approaches try to make a network structure sim-
ilar to social networks in order to find the nodes as message
carriers with the highest centrality, i.e., the structural
importance of the node, which typically have a stronger
capability of connecting other network members together.
The representatives of these approaches are SimBet [13]
and BubbleRap [21]. Hossmann et al. [17] further evaluate
SimBet and BubbleRap performance under real mobility
traces.
2.1.2 Proactive schemes
Proactive approaches control the mobility of some nodes to
accommodate disconnections. Goldenberg et al. [18] use
mobility control to reach optimality by moving relay nodes
to their optimal positions. Li and Rus [30] propose the
possibility of changing hosts trajectories to send messages
in disconnected ad hoc wireless networks. Using motion
information of destination node, they try to utilize coop-
eration of the intermediate nodes to deliver messages by
asking them to modify their trajectory while getting the
messages.
Zhao et al. [65] use a set of nodes, called ferries,
responsible for carrying data for all nodes in the network
(it is called Message Ferrying (MF)). Ferries act as a
moving communication infrastructure for the network. Wu
et al. [59] propose a logarithmic store-carry-forward
scheme through a hierarchical structure of trajectory for
ferries that controls the number of relays which ends up
with reducing average delay which was very high in MF
and they also utilize some new technical issues like
on-demand ferry solicitation, dynamic trajectory planning
of ferries, rendezvous point placement, and adaptive ferry
migration and load balancing to enhance the network per-
formance. Tariq et al. [9] extend the MF approach by
forcing ferries to follow fixed routes; therefore, they sim-
plified designing complex routes where the ferry can con-
tact the nodes with certainty which needs an on-line
collaboration between ferry and the nodes in MF.
Jeonghwa et al. [60] extends MF approach by proposing a
mechanism to replace ferry nodes. Since the network
operation relies on the ferries to provide connectivity in the
whole network, it can be a single point of failure. Also, to
keep a balance among all mobile nodes (with limited
resources) in the network it may be desirable to rotate this
role with others after a fixed duration. A summary of some
existing routing approaches for DTNs can be found in [63].
2.2 Stationary relay points
In Stationary Relay Points approaches such as [6, 22, 66],
by adding some stationary relay points, the connectivity of
the network is increased and the performance of the net-
work is improved as a result. This class of intermittently
connected MANET routing protocols works best in certain
circumstances such as when we cannot expect a ferry to
move on in an inhospitable terrain, or through the obstacles
due to a disaster, etc. where missed contact opportunities
may decrease network performance.
Based on relay point type, we can divide the class of
stationary relay points into two categories: active and
passive. In active approaches [6, 22, 66], stationary relay
points can initiate the communication and have their own
supply of power while in passive approaches [49], sta-
tionary relay points are unable to initiate any communi-
cations and they are powered by readers, i.e., mobile nodes.
Further, in active approaches, the number of relay nodes is
less than the passive approaches while their broadcasting
range is usually bigger.
One of the active approaches proposed in the literature
is the use of Base Stations. This approach tries to increase
the network connectivity based on a fixed infrastructure
using base stations. Although Ibrahim et al. [22] introduce
some powerful platforms to meet the requirements of base
stations in DTNs, the possibility of making such an infra-
structure may not be feasible or desirable.
Zhao et al. [66] proposes an active approach for the case
of Stationary Relay Point. Relay nodes are called
‘‘throwboxes’’ and they are considered to be powered,
wireless base stations that cannot interact with each other.
However, they assume that relay nodes can initiate com-
munication. Furthermore, in their work, a mixed integer
programming solution is proposed for the placement of
throwboxes, which discretizes space and is NP hard.
12 Wireless Netw (2012) 18:9–31
123
Therefore, a greedy heuristic is used to place throwboxes.
The throwbox work is further analyzed in [22].
Banerjee et al. [6] use different types of infrastructure to
enhance mobile networks, namely untethered relays, base
stations, and a mesh. In the case of base stations and a
mesh, they propagate packets through an in-network proxy
with storage which relaxes contemporaneous connections
between mobile nodes; however, there is a trade-off
between enhancing the network and the cost of the infra-
structure. Use of untethered relays is similar to [66].
Our proposed protocol is very similar to the work done
by Zhao et al. [66]; however, on the contrary to the
throwboxes passive relay nodes in our work do not initiate
communication and they are merely storages. Additionally,
in their work relay nodes need their own supply of power
while passive relay nodes in our approach can be powered
by readers and consequently they can have a much longer
lifetime in the network. Also, our approach could be more
suitable for radio silent applications where relay nodes
should not originate radio signals and should have a short
radio range which makes using passive relay nodes, e.g.,
passive RFID tags, more economical. Furthermore, we
have proposed continuous space placement techniques and
in particular we provide a genetic algorithm approach for
placement optimization which outperforms their greedy
placement technique. Also, the number of throwboxes
considered is relatively small compared to the number of
relays we consider.
3 Proposed protocol
In this section, we first present the motivation of our work
and then we introduce our protocol and also the evaluation
metrics. Since the protocol was initially considered for
passive RFID tags in prior work, we refer to our protocol as
a Tag-based Routing or TBR Protocol.
3.1 Motivation
There are approaches that try to reinforce connectivity on
demand in DTNs by utilizing additional communication
resources in the network. Examples of these resources
include satellites1, base-stations [6], unmanned aerial
vehicles [65], etc. Satellite communication needs a direct
view of receiver/ sender to the satellite and is expensive as
it requires all user stations such as handsets, portables,
mobile stations, etc. to be equipped with specific opera-
tional units to allow the communication to take place [34].
Further, satellite availability might be poor in noisy
environments, e.g., when the operator is very close to
large machineries. Blind spots is a well-known problem
when using base-stations for communication due to the
possible obstacles in the network [39]. Additionally,
deploying base-stations might be difficult in areas which
are difficult to reach and needs time/cost to take place. In
addition, base-stations usually need to have a wired con-
nection to the backhaul [39]. We consider a hypothetical
scenario shown in Fig. 1 in which miners try to commu-
nicate to improve mining productivity making a DTN
together. Satellite communication fails in underground as
there is no direct line of sight. Using base-stations in
underground like mines/tunnels suffers from the short
communication range, deploy-ability, and cost effective-
ness since UHF/VHF technology has a very short com-
munication range in underground [62] and there could be
blind spots due to the lack of line of sight transmission
path of a wireless signal because of the existence of
obstacles underground (e.g., curvy tunnels or branched
mines). Moreover, since mines might be extending, e.g., in
gold mines to explore new source of golds, setting up new
base-stations is time-consuming. For the above mentioned
reasons and in order to arrive at a competitive protocol, we
have considered the introduction of low cost and easily
deployable fixed infrastructure. Miners can communicate
to each other through passive relay devices, e.g., RFID
tags, as well as communicating to the base station through
the vehicles/others visiting the relays hit by the miners in
the past.
3.2 Protocol description
We consider a static distribution of Nrelay stationary relay
nodes (relays) over the region where Nnode mobile nodes
(nodes) will move. The region is defined by its extents,
(Xmin, Xmax) and (Ymin, Ymax). Each relay contains a mes-
sage buffer of size Brelay messages (each message has unit
size), and each node contains a message buffer of size Bnode
messages. A node can interact with a relay if it is within
distance r from the relay. Later in this section we show
how a node can interact with a relay.
The notation is summarized in Table 1. Units are always
meters for distance, seconds for time, speed in meters/
second, unless otherwise noted.
Messages are only transmitted between nodes and relays
and vice versa, never between nodes and other nodes.
Communication is only initiated by nodes since the relays
are passive. To consider node-relay interactions we define
relay connection and relay disconnection events in time.
When a node moves from a distance[r to within a distance
of any relay at time t1 then we say the node has connected
with the relay at time t1. When the node moves, say at time
1 Disruption Tolerant Networking. [Online]. Available: http://www.
darpa.mil/ato/solicit/DTN/.
Wireless Netw (2012) 18:9–31 13
123
t2, outside distance r of a relay that it is connected to, then
we say the node has disconnected from the relay at time t2.
In this way, given the paths for all nodes, we can consider
the set of distinct connection and disconnection events in
time. To be complete, we also consider message arrival as
an event in time. Nodes move obliviously to each other and
to any other aspects of the system that makes the imple-
mentation of our protocol simpler and more scalable as no
mobile node needs to know extra information about other
mobile nodes.
Our protocol is now defined by what happens at each of
the three events, also depicted in Fig. 2.
Relay Connection: the connecting node examines the
relay’s buffer to see if any of the messages are destined to
itself, if so then the message is said to have reached its
destination at the time of the connection. The node then
merges its buffer with the relay’s buffer according to
Algorithm 1.
Relay Disconnection: the disconnecting node examines
its state to see if it is still in the merge operation with the
relay, if so then it terminates the merge operation.
Message Arrival: the node puts the newly arrived mes-
sage into its buffer, the oldest message, i.e., the one with
the earliest arrival time, is discarded if the buffer is already
full. Merging does not happen at this event (even though
the node may be within range of a relay).
Figure 2 provides a brief example of the movement of
two nodes through a regular spaced field of relays. Node
A deposits a message on relays 2,3 and 1. Assuming node
B passes through relay 1 before node A, but passes through
relay 3 after node A, then node B retrieves the message
from A at relay 3 and deposits the message in relay 4.
To describe node-relay interaction we define a merge
operation between a node message buffer and a relay
message buffer. Algorithm 1 shows the merge operation.
According to Algorithm 1, the mobile node acquires some
meta-data regarding the current messages in the relay’s
buffer. Based on the messages in its buffer it decides to
read/write messages one by one until it is disconnected
from the RN or all messages are replicated. Therefore, the
merge operation virtually combines both buffers into a
single buffer with messages ordered according to their
arrival times in the system. Then, the node and relay both
keep as many of the latest messages, i.e., the ones with
larger arrival times, as can fit in their respective buffer.
Older messages are discarded. In general, it is not possible
for a node to know at first face whether a message has been
delivered to a destination or not (apart from the destination
node itself), so delivered messages will continue to prop-
agate until they are pushed out by newer messages. It is
possible to increase the delivery performance by using
more intelligent buffer policies, for example, Ma and
Jamalipour [33] propose a fuzzy logic-based delivery
framework called FLDF to facilitate the ranking of mes-
sages based on their delivery preference in the future to
increase the message delivery rate.
Interactions are considered to be collision free; however,
there could be two possible collisions. First, relays could be
placed in such a way that a node falls into the communi-
cation range of them simultaneously. In order to avoid this
kind of collision, nodes can label the relays and send a
request to communicate with the relay with the lowest ID
first and then with the second lowest ID relay, and so on.
Labeling the relays is possible because the mobile nodes
know the positions of all the relays. However, in most
cases relays are placed in such a way that this kind of
Fig. 1 Mining scenario as an
application of our protocol
Table 1 Notation in the paper
Nrelay Number of relays
Nnode Number of nodes
X/Ymin/max Extents of the geographic region (m)
d Regular spaced distance between relays (m)
Brelay Relay message buffer size (messages)
Bnode Node message buffer size (messages)
r Relay range (m)
t Time (s)
q Message delivery rate (msgs/s)
Lavg Average message delay (s)
E Network efficiency
14 Wireless Netw (2012) 18:9–31
123
collision is highly unlikely to be occurred. Second, two or
more mobile nodes may try to communicate with the same
relay simultaneously. This kind of interference/collision
should not be ignored as possibly two mobile nodes can
communicate with a relay node respectively but with
interference/collision. To handle this kind of collision we
need collision detection and recovery/avoidance techniques
in wireless networks [58] which is out of the scope of this
paper. However, since two nodes by the definition of a
DTN is highly unlikely to access a relay simultaneously,
the impact of this kind of collision on the performance of
the protocol is negligible.
3.3 Evaluation metrics
We evaluate our proposed protocol using four metrics
defined below.
3.3.1 Message delivery rate
The message delivery rate is defined as the ratio of the
number of successfully delivered messages to total number
of messages, i.e., identically generated messages by all
source nodes:
q ¼ Mdelivered
Mtotal:
A low q indicates that the buffer sizes are not large
enough to handle the rate of messages in comparison to
average delay experienced by a message to get from the
source node to the destination node.
3.3.2 Message delay
Since a typical destination node may receive multiple
copies of a message, we define message delay, Li, for
message i, as the time between when a message is gener-
ated to the first time the message is received by the desti-
nation. The average message delay is then:
Lavg ¼1
Mdelivered
XMdelivered
i¼1
Li:
3.3.3 Network efficiency/inefficiency
We define network efficiency given as E = q/Lavg. The
goal is to maximize q and minimize Lavg simultaneously.
Conceptually, network inefficiency could be defined as the
fraction of 1 over E. In this paper, we focus on average
message delay and delivery rate. We leave other metrics
such as power consumption, transmission number, inter-
action failures, etc. to future work.
connection event
tagdisconnection event
mobile node movement
mobile node
r
Ymax
Ymin
XmaxXmin
A
B
d
d
1
23
4
(a)
(b)
Fig. 2 Aspects of the tag based routing model. a Movement of a node
through the circular range of a relay. b Example of two mobile nodes,
A and B, moving through a field of relays spaced at regular intervals
of distance d. In the example, node A reaches relay 2 and 3, but not
relay 1, before node B. Node A deposits a message on these relays,
node B retrieves the message from relay 3 and deposits it on relay 4
Algorithm 1 Merge operation (MN,RN)
MN and RN stand for mobile MN and relay MN respectively
S1 list of message IDs in MN’s buffer
S2 list of message IDs in RN’s buffer
S ½S1S2�Sort(S, arrival time)
counter 1
for all i 2 S do
if i 62 S1 and len(S1) [ counter then
Replicate msg i from RN to MN
if MN’s buffer overflow then
Evict oldest message from MN
end if
end if
if i 62 S2 and len(S2) [ counter then
Replicate msg i from MN to RN
if RN’s buffer overflow then
Evict oldest message from RN
end if
end if
if IsDisconnected(MN,RN) then
break;
end if
counter counter þ 1
end for
Wireless Netw (2012) 18:9–31 15
123
4 Mobility models
Mobility is a natural phenomenon in DTNs. Studying the
mobility models in DTNs is important because it has a
direct impact on the performance of network protocols
including our proposed protocol. Therefore, in this section
we review some widely used mobility models which are
later used to evaluate the performance of the proposed
protocol.
There have been many mobility models proposed so far
in the literature. Camp et al. [11] classified mobility
models for ad hoc network research into two categories:
entity mobility models and group mobility models. In
entity mobility models, the movement of one node is
independent to all other nodes, whereas in group mobility
models, a set of mobile nodes move in a group. The widely
used representatives of entity mobility models [7, 8] are
Random Waypoint Model (RWP) and Random Walk Model
(RWM), while Reference Point Group Mobility (RPGM)
Model [20] is a well-studied representative of group
mobility model. Figure 3(a, b) show the traveling pattern
of a single mobile node using RWP and RWM models
respectively. Figure 3(d) shows the traveling pattern of five
mobile node as a group using RPGM. Although, some of
the above mobility models are artificial, they are widely
used in DTNs [56, 65] to evaluate the performance of
routing protocols. Therefore, in this paper we use them to
evaluate the performance of our protocol and also to
compare our protocol with some of the state-of-the-art
protocols which are using similar mobility models. How-
ever, there is other work that uses traced-based simulation.
For example, Banerjee et al. [6] use traces from a bus route
model to evaluate the effectiveness of their work.
Additionally, we propose a restricted version of the
RWP model where every mobile nodes can move only in a
restricted area. Figure 3(c) shows the traveling pattern of 4
typical mobile nodes where the area is divided to 4 sub-
areas with 40% overlap in each sub-area; however, we can
divide the area based on the application to different sec-
tions. According to Fig. 3(c), in two sub-areas there is only
one node while in other sub-areas there have two and no
nodes respectively.
5 Relay placement techniques
Placement of relays plays an important role in our protocol
as its performance is dependent to their positions. Relay
node placement is already studied in VDTN [16]; however,
in this section, we propose different relay placement
strategies.
5.1 Uniform grid
In this scheme, relays are placed on a regularly spaced grid
with known distance d between two neighbor relays, such
that the relay coordinates are (Xmin ? i d, Ymin ? j d) for
i ¼ 0; 1; 2; . . . and j ¼ 0; 1; 2; . . .. This leads to
Nrelay ¼
Xmax � Xmin
d
� �þ 1
! Ymax � Ymin
d
� �þ 1
!:
0 200 400 600 800 10000
200
400
600
800
1000
Y P
ositi
on (
m)
X Position (m)
200 400 600 800 10000
200
400
600
800
1000
Y P
ositi
on (
m)
X Position (m)
0 200 400 600 800 10000
200
400
600
800
1000
Y P
ositi
on (
m)
X Position (m)
0 200 400 600 800 10000
200
400
600
800
1000
Y P
ositi
on (
m)
X Position (m)
(a) (b)
(d)(c)
Fig. 3 Node mobility models.
a Traveling Pattern of a MN
using theRandom Waypoint
Model (50 steps). b Traveling
Pattern of a MN using
theRandom Walk Model (50
steps). c Traveling Pattern of 4
MNs using theRestricted
Random Waypoint Model (50
steps). d Traveling Pattern of 3
MNs in a group using theRPGM
(50 steps)
16 Wireless Netw (2012) 18:9–31
123
Although this placement technique is not optimal, for
simplicity we mostly consider uniform grid placement as a
baseline to evaluate the proposed protocol.
5.2 Relay pruning
This is a simple pruning scheme where a certain percentage
of relays are removed based on the number of messages
delivered by the relays when placed in a regular grid.
Relays responsible for delivering least number of messages
are removed until the specified percentage of relays are
retained. The remaining relays will still remain at the ori-
ginal grid locations.
5.3 Probability based relay distribution
By analysing the number of messages delivered by dif-
ferent relays, when placed in a regular array, relays can be
redistributed based on the probability distribution of the
messages carried by the relays (higher relay densities in
areas where the probability of a message being carried is
high, and lower relay densities in areas where the proba-
bility of a message being carried is low). We have imple-
mented this scheme using an efficient re-sampling scheme
given in Table 3.2 of [45], for repeating points, followed by
jittering the points by adding Gaussian noise.
5.4 Genetic algorithm based optimization
To compare the previous heuristics to a more general
heuristic, we have implemented a basic genetic algorithm
(GA). The purpose of using a GA is to explore a more
general approach to finding the optimal placement, thereby
to see if there exist better solutions than our deterministic
approaches. We did not intend to undertake an extensive
study of GA solutions, but rather we intended to compare
previous heuristics. GAs have been used before to find the
optimal location of base-stations (transmitters) in order to
satisfactorily cover subscribers [19, 35, 54]. There are
many other non-linear optimization techniques, e.g., sim-
ulated annealing, neural networks, etc., that could be
explored but we leave them to future research. For these
reasons, we have referred the details of our GA approach to
Appendix -1. Our discussion assumes a general knowl-
edge of GA terminology that can be found here [36].
We implemented a genetic algorithm, using MATLAB
Genetic Algorithm and Direct Search Toolbox, to search
for the locations of Nrelay relays in the region with the
following objective function: minimizeh
Lavg
q
i.
6 Simulation results
6.1 TBR implementations
In our simulation results the proposed protocol is consid-
ered for passive RFID tags, however, it would be equally
valid to consider the results of our work on a network
where the point locations are other type of wireless devices
with a capability of storing messages, if such an infra-
structure was feasible for the application. A comparison
between different implementation of Stationary Relay
Point approach is shown in Table 2. The network designer
can choose between them based on the application.
6.1.1 RFID MANET implementation
Radio-frequency identification (RFID) is an automatic
identification method, which is based on remote data
storage/ retrieval using devices called RFID tags or tran-
sponders. An RFID tag is a module which is used for
identification purposes using radio-waves. In this work we
have considered its use for storing/retrieving messages in
DTNs. Some commercially available tags in the industry
Table 2 Stationary relay points approaches
Implement. Type Cost Power resource Radio range Device size Commun. Initial.
WiMAX Base Stationa � $24000 Stand-alone/high power 500 m–4 km [.005 m3 Capable
Active RFID tag b [ $20 Stand-alone/medium power Up to 100 m � 3�5m3 Capable
Throwbox $100� $300 Stand-alone Up to 250 m From 1.12-4 m3 Capable
Passive RFID tag $:07� $:20 Powered by reader Up to 35 m 10-8 to 4.8-4 m3 Unable
a http://www.mobilesociety.typepad.com/mobile_life/2007/03/wimax_base_stat.htmlb http://www.gaorfid.comc Zhao et al. [66] introduce Intel Stargate as a device which can meet throwbox requirements. Stargate specification is available at http://www.
willow.co.uk/Stargate_Datasheet.pdfd http://www.rfidjournal.com/faq and http://www.omni-id.com/products/RFID_tags-ultra.ph. Mojix, http://www.mojix.com/products, proposes a
solution to increase the range of reading from a tag up to 200m using their eNodes which is promising reaching longer radio ranges in future
Wireless Netw (2012) 18:9–31 17
123
can be read from a distance of several meters away and
beyond the line of sight of the reader; e.g. Mojix2 has
recently developed a way to dramatically increase the
range of passive RFID tags up to 200m, opening up many
new applications for low-cost tags. Some tags are currently
able to store kilobits of information; e.g. Fujitsu has
recently reported the development of a 64 KByte High-
Capacity FRAM RFID Tag3. RFID technology is rapidly
improving and in the near future we expect to see more
powerful RFID tags, which can be set-up more efficiently,
with a wider range and higher memory capacity.
Mobile nodes must be equipped with RFID readers.
Recently, there has been significant attention in developing
mobile RFID reader devices with Wi-Fi Network support
and GPS4. This makes it possible for the applications
where the mobile station cannot approach the RFID tags by
itself, by using a smaller unit equipped with the mobile
reader that can do the task of reading/writing from/to the
mobile station from/to the tags.
6.2 Methodology
We implemented an event driven simulation of TBR in
MATLAB. We assumed that we have an ideal wireless link
layer that causes no collision with other transmissions
during any transmission session. This simplification does
not significantly effect our results because in a typical
DTN, the density of mobile nodes in a specific area is
usually very low (as discussed earlier in Sect. 3) Similarly,
we assume communication from a tag to a mobile node and
vice versa is instantaneous. In a real implementation, this
assumption may not hold for some kinds of applications
where MNs move relatively rapidly or cannot pause for the
time taken to operate on an RN. However, there has been
focus on designing low delay passive RFID reader systems
which can accommodate this issue [3, 64]. Additionally, in
Algorithm 1 we showed how we can prioritize the mes-
sages based on their arrival time, to be exchanged between
RNs and MNs for the time that they are being connected,
using meta-data related to the messages. We leave the issue
of read/write delay to future work.
Our simulation starts at time t = 0 with all tag buffers
and node buffers empty. Nodes are placed randomly and
messages arrive with exponentially distributed random
inter-arrival times, at a global mean rate of k, such that
each node generates messages at a mean rate k/Nnode. Each
message has a single destination node picked uniformly at
random from the nodes (not including the source node).
The simulation is run for a fixed period of time, tmax.
In the following section, we study the effect of different
node mobility models and different relay placement strat-
egies for our protocol. Furthermore, we evaluate our pro-
tocol when using many small RNs and when using a super
RN which is placed in the middle of the area.
6.3 TBR evaluation
In this section we evaluate the effectiveness of TBR based
on RWP mobility model and regular grid tag placement to
show the effect of node speed, node buffer size, tag buffer
size, tag transmission range, and tag spacing on the per-
formance of our protocol. Later, we show how we can
significantly improve the performance of our protocol
using a better tag placement technique. In addition, we
show the required power consumption of nodes in terms of
the required transmission number per second versus some
of the latter parameters such as node speed and tag spacing.
Finally, we present the distribution of message delivery
delay using the proposed protocol.
6.3.1 Control variables
We use the following settings unless otherwise noted:
Xmax - Xmin = 1, 000, Ymax - Ymin = 1, 000, d = 50 ()Nrelay = 441), the average speed of mobile nodes is 5
(Smin = 2.5, Smax = 7.5) with no pause time in the Random
Way Point model, Bnode = 1, 000, Brelay = 100, and
r = 5. In most cases we have provided all simulations for
Nnode = 10 with 0.5 message/second per node (k = 5).
Tags are placed on a regularly spaced grid. All messages
have equal size.
6.3.2 TBR effectiveness
Figures 4 and 5 show the effect of Node Speed, Tag Buffer
Size, Tag Transmission Range, and Tag Spacing on the
delivery performance of the proposed protocol. According
to the latter figures, in most cases the message delivery rate
of our protocol is higher when there are less mobile nodes
in the network while the message latency is higher. The
reason is that more mobile nodes in the network leads to
more generated messages in the network and the proba-
bility that mobile nodes overwrite the tags is higher as a
result; therefore, messages have a lower probability to stay
in the tag’s buffer for a longer time.
Figure 5(a) shows that increased node speed signifi-
cantly reduces the message delay. As the node speed
increases the node meets more tags per message, however
the node may also meet the same tags more than once and
2 http://www.mojix.com/products.3 http://www.fujitsu.com/global/news/pr/archives/month/2008/20080
109-01.html.4 http://www.rfid.net/product-listing/reviews/176-csl-cs101-handheld-
reader.
18 Wireless Netw (2012) 18:9–31
123
so higher speeds does not continue to decrease message
delay with the same rate. Figure 4(a) shows the corre-
sponding delivery rates. Similarly, meeting tags sooner
greatly increases the delivery rate because messages have a
smaller chance of being dropped if they exist in more
buffers. Figures 4(b) and 5(b) show that increased tags
transmission range also significantly increases the message
delivery rate and reduces the message delay; however, the
speed up in delivery performance is not as high as the
effect of increased average node speed. In general, with a
0 10 20 300
0.2
0.4
0.6
0.8
1
Average Node Speed (m/s)
Mes
sage
Del
iver
y R
ate
0 10 20 300
0.2
0.4
0.6
0.8
1
Tag Transmission Range
Mes
sage
Del
iver
y R
ate
N=5
N=10
N=20
N=50
0 500 10000
0.2
0.4
0.6
0.8
1
Tag Buffer Size
Mes
sage
Del
iver
y R
ate
N=5
N=10
N=20
N=50
0 100 200 300 400 5000
0.2
0.4
0.6
0.8
1
Tag Spacing (m)
Mes
sage
Del
iver
y R
ate
N=5
N=10
N=20
N=50
(a)
(d)
(b)
(c)
N=5
N=10
N=20
N=50
Fig. 4 Message delivery rate
versus various parameters:
a Average node speed, b Tag
transmission range, c Tag buffer
size, d Tag spacing
0 10 20 300
500
1000
1500
Average Node Speed (m/s)
Ave
rage
Mes
sage
Del
ay N=5
N=10
N=20
N=50
0 10 20 300
500
1000
1500
Tag Transmission Range
Ave
rage
Mes
sage
Del
ay
N=5
N=10
N=20
N=50
0 500 10000
500
1000
1500
Tag Buffer Size
Ave
rage
Mes
sage
Del
ay
N=5
N=10
N=20
N=50
0 100 200 300 400 5000
500
1000
1500
Tag Spacing (m)
Ave
rage
Mes
sage
Del
ay
N=5
N=10
N=20
N=50
(a)
(d)
(b)
(c)
Fig. 5 Average message delay
versus various parameters:
a Average node speed, b Tag
transmission range, c Tag buffer
size, d Tag spacing
Wireless Netw (2012) 18:9–31 19
123
larger tag transmission range, there is a larger chance that a
mobile node meets more tags per unit time which results in
meeting more tags per message and meeting tags sooner.
Figures 4(c) shows that an increasing tag buffer size
increases the message delivery rate much higher than the
latter parameters. As the tag buffer size increases, the
mobile nodes can replicate more messages, spread over
more tags, and replicated messages can exist in more
buffers for a longer period of time; therefore, messages
could be delivered to the destination nodes with a higher
chance instead of being dropped at an early stage. How-
ever, according to Fig. 5(b) increased tag buffer size can
lead to an increase in the average message delay. There are
two factors which affect the average message delay: (1) an
increased tag buffer size allows the messages to stay in the
buffers for a longer time and those messages can be
delivered in a longer period of time rather than being
dropped when the buffer size is smaller, which thereby
increases the average message latency; (2) an increased tag
buffer size reminds that more buffers can hold the same
messages which leads to a decrease in the average message
delay. According to Fig. 5(b) (1) has a greater impact when
the delivery rate is low while (2) has a greater impact when
the message delivery rate is approaching 1.
Figure 5(d) show the delivery performance of the pro-
posed protocol versus tag spacing, i.e., the total number of
tags in the regular array, as given in Sect. 3. Significant
variation in delay does not occur until the number of tags is
quite small, i.e., tag spacing is quite big, and the tags are
near the perimeter of the region. Message delivery rate is
significantly affected by the tag spacing and the number of
nodes. A smaller number of tags leads to a significantly
decreased rate. For large numbers of nodes, there is a
greater total arrival rate of messages and so buffers are
exhausted more quickly.
6.3.3 Transmissions per second
Figure 6 shows the average transmissions per second that a
mobile device uses as a result of TBR, for various tag
spacing and speeds. The number of transmissions is
directly proportional to the power requirements. Slower
moving nodes and fewer tags both lead to fewer
transmissions.
6.3.4 Power consumption versus number of relays
Passive nature of relay nodes in our protocol requires
communication cost from the mobile nodes. Assuming that
RFID readers on average need equal amount of energy for
each interaction with relay nodes, e.g., Klair et al. [26]
present a table showing RFID readers power consumption,
Equation shows the relation between the number of relays
and the required power consumption for reading/writing
the relays in the network, �E.
�E ¼ Nnode
XNrelay
i¼1
Pi½H�Er=w; ð1Þ
where Pi[H] is the the probability of hitting the relay i by a
node per second and Er/w is the average required energy for
each interaction between nodes and relays. Er/w depends on
the storage space of the passive relay nodes and the traffic
load. In priori work [50] we showed how to calculate the
average hit probability of a relay node by a mobile node using
a conceptual mobility model. Further, in [51] we showed
how we can extend this conceptual mobility model to RWP.
In order to see the average number of relay hits by
mobile nodes per second, we ran an experiment where
Xmax - Xmin = 1, 000, Ymax - Ymin = 1, 000, the average
speed of mobile nodes is 5 (Smin = 2.5, Smax = 7.5) with
no pause time in the Random Way Point model, r = 5, and
Nnode = 10 while we change the number of relay nodes
placed on a regular grid. Figure 7 shows the corresponding
results. According to Fig. 7 by placing 2,500 relays on a
grid, on average 1.26 of nodes would hit the relays per
second. If we place 400 relays on average every 5 s one
mobile node would meet a relay and if we assume
Er/w = 180 m watt for the case of Skye Module M1-Mini
introduced in [26] then we need 36mwatt/sec for the read/
write operations in the system.
6.3.5 Distribution of message delay
Figure 8 shows the cumulative distribution function of
message delay, for delivered messages, versus various
parameters. Additionally, we have fit these distributions to
the Generalized Extreme Value distribution using MAT-
LAB dfittool:
0 50 100 150 200 250 300 350 400 450 50010 -3
10 -2
10-1
100
101
Tag Spacing
Mes
sage
Tra
nsm
issi
ons
per
seco
nd
(
for
each
nod
e)
S=50S=40S=30S=20S=10
Fig. 6 Transmissions per second required by a mobile device
20 Wireless Netw (2012) 18:9–31
123
f ðx; l; r; kÞ ¼ 1
r1þ k
x� lr
� ��1k�1
e� 1þkx�lrð Þ�1=k
;
for 1þ kx�lr [ 0.
The Generalized Extreme Value distribution was the
only distribution that consistently fit the data over various
ranges of parameters that we tested. Figure 9 shows an
example message delay probability density function fit to
the distribution. Further analytical work is required to
substantiate this relationship.
6.4 Effect of tag placement and mobility models
on TBR
In this section, we study the effect of different tag place-
ment strategies on the performance of TBR. In this study,
we also consider the effect of mobility models mentioned
in Sect. 4 on TBR effectiveness. We compare our place-
ment strategies with the ThrowBox algorithm proposed in
[66]. In ThrowBox, base stations are greedily placed one
by one so as on a grid of possible places in such a way to
maximize the network throughput. In other words, given
the traffic model and the mobility model of mobile nodes,
all possible positions for a base station are considered.
After placement of one base station, the algorithm con-
tinues to the next base station excluding the previously
taken positions. Since our optimization to find the optimal
position, is based on E, in the case of ThrowBox we used
the same objective function for optimization, which is
different to the original objective function, i.e., increasing
the delivery rate, that they used in their work.
The simulation methodology is as described in Sect. 2.
We also used the following configuration to evaluate the
efficiency of each placement technique: the average speed
0 500 1000 1500 2000 25000
0.5
1
1.5
Ave
rage
Hit
Num
ber/
sec
Relay Number
Fig. 7 The average relay hit number per second versus the number of
relays placed on a regular grid
0 1000 2000 30000
0.5
1
Cum
ulat
ive
prob
abili
ty
Message latency (sec)
d=25d=50d=100d=200d=250d=500
0 200 400 6000
0.5
1
Cum
ulat
ive
prob
abili
ty
Message latency (sec)
0 100 200 300 4000
0.5
1
Cum
ulat
ive
prob
abili
ty
Message latency (sec)
0 100 200 300 4000
0.5
1
Cum
ulat
ive
prob
abili
ty
Message latency (sec)
BS=50BS=100BS=200BS=500BS=1000BS=2000
r=5r=10r=20r=30r=40r=50
S=10S=20S=30S=40S=50S=60
(a)
(c) (d)
(b)Fig. 8 CDF of message delay
versus various parameters.
a CDF of message latency
fordifferent tag spacing, b CDF
of message latency fordifferent
tag ranges, c CDF of message
latency fordifferent mean node
speeds, d CDF of message
latency fordifferent tag/node
buffer sizes
0 20 40 60 80 100 120 140 160 1800
0.005
0.01
0.015
0.02
Message Latency (sec)
Den
sity
Fig. 9 PDF of message delay fit to a generalized extreme value
distribution. In this example, the distribution parameters are k =
-0.104493, r = 24.1557 and l = 39.6301
Wireless Netw (2012) 18:9–31 21
123
of MNs is 20 (Smin = 10, Smax = 30) and there are 16 RNs
in the network () Nrelay = 16). We show using a relay
placement optimization technique we can still achieve
competitive message delivery performance even using low
number of RNs, i.e., 16. Other settings are as Sect. 3.
Simulation time is 5,000 s and each result is based on 10
different runs to reduce the existing variation in the sim-
ulation. This configuration is chosen based on the results of
previous experiments reported in Sect. 3. In RPGM, there
are 3 different groups moving together. One group includes
4 mobile nodes while the others include 3 mobile nodes. In
the Random Walk model, a constant time interval t = 10 s
is chosen to choose the new movement. In addition, in
Restricted RWP model, the area is partitioned to 4 equal
sized sub-areas which have an overlap equal to 40% of the
area length and the mobile nodes are distributed uniformly
in these sub-areas.
Furthermore, to evaluate the effect of tag placement
techniques on TBR, two scenarios are studied. In scenario
1, TBR performance is measured based on the same
instances of traffic and mobility model which are used in
each relay placement strategy. The results are shown in
Table 3. According to Table 3, the GA approach exhibits
the best performance among other approaches and the
ThrowBox approach has the best second performance. The
reason that the GA outperforms the ThrowBox approach is
that the GA approach searches the area for a set of place-
ment solutions than searching for a single tag position at a
time as in ThrowBox. Placing the relays on a regular grid
and the relay pruning approaches have the worst perfor-
mance as they place the relays on a grid with no respect to
the mobility models of the nodes. Accordingly, they do not
place many relays in strategic locations. Although the relay
pruning approach trims some of the relays responsible for
delivering the least number of messages, its performance is
very poor to make it a good alternative. The probability
based distribution approach performs much better than the
regular grid placement and the relay pruning; however, its
performance is much worse than the GA approach.
In scenario 2, new instances of traffic and mobility
models are used in relay placement techniques. This sce-
nario is more general to be used as it is independent to the
traffic model. The results are shown in Table 4. According
to Table 4, the GA approach is the best in ranking among
other placement techniques except for the Random Walk
mobility model where ThrowBox competes with the GA.
For the same reason mentioned above, the GA approach
can learn spatial characteristics of the relays in a more
effective way than other approaches for all mobility models
except the Random Walk which results in having a better
performance than other approaches. More investigation is
required to find out why the GA approach is unable to
effectively learn the Random Walk characteristics. One
hypothesis to describe this situation can be related to the
limited iteration number of learning process used in the
GA. As the Random Walk has the highest random behavior
Table 3 Comparison of tag placement techniques (same traffic/mobility instance)
RWP Restricted RWP RWalk RPGM
q Lavg Ea Nrb q Lavg Ea Nr
b q Lavg Ea Nrb q Lavg Ea Nr
b
Reg. Grid .122 192.5 6.34 16 .102 214.2 4.76 16 .149 293.8 5.07 16 .123 172.4 7.13 16
Relay Prun. .113 168.3 6.71 6 .091 182.3 4.99 7 .151 293.2 5.15 16 .114 168.7 6.76 9
Prob. based .286 153.9 18.6 16 .305 118.7 25.7 16 .183 205.5 8.9 16 .256 146.7 17.5 16
GA .424 100.1 42.4 16 .491 71.7 68.5 16 .308 149.9 20.6 16 .402 99.0 40.6 16
Throwbox .396 114.7 34.5 16 .493 84.9 58.1 16 .313 157.3 19.9 16 .398 103.8 38.3 16
a times 10-4
b Nrelay
Table 4 Comparison of tag placement techniques (different traffic/mobility instance)
RWP Restricted RWP RWalk RPGM
q Lavg Ea Nrb q Lavg Ea Nr
b q Lavg Ea Nrb q Lavg Ea Nr
b
Reg. Grid .121 197.1 6.14 16 .099 218.4 4.53 16 .147 296.7 4.95 16 .125 183.3 6.82 16
Relay Prun. .111 172.3 6.44 6 .093 192.6 4.83 7 .142 297.6 4.77 14 .109 181.2 6.02 9
Prob. based .267 155.4 17.2 16 .29 119.2 24.3 16 .169 215.9 7.83 16 .241 152.31 15.8 16
GA .349 115.7 30.1 16 .445 82.1 54.2 16 .213 193.3 11 16 .346 116.5 29.7 16
Throwbox .348 120.6 28.9 16 .449 84.7 53 16 .222 196.4 11.3 16 .328 118.8 27.6 16
22 Wireless Netw (2012) 18:9–31
123
among the other mobility models, by adding a random
traffic model the problem of learning spatial characteristics
of the relays becomes more complicated and needs more
observations. The analytical modeling of the placement
techniques is out of the scope of this paper and can be
found in our priori work [51].
6.4.1 Discussion
We initialize all the relay placement techniques by utiliz-
ing uniform grid placement and later each relay placement
technique optimizes the location of relays by replacing
their positions based on the given mobility model. In other
words, our relay placement optimization techniques learn
the spatial characteristics of the mobile nodes through a
learning phase and later they place the relays on strategic
locations. We ran another experiment using probability
based optimization relay placement technique where
the relay nodes initially are placed in random locations.
We used the following setting: Ntag = 441, Nnode = 10,
X/Ymin/max = 1, 000 m, Bnode = 4 9 Brelay, r = 5, Smin/max =
[2.5, 7.5], and k = 0.5. Figure 10 shows the corresponding
results. According to Fig. 10 the performance of the net-
work in both cases are very close in terms of message
delivery rate and delay.
The relay placement techniques proposed in this paper is
based on simulation results. However, the connections
between a mobility model and the relay placement strate-
gies are presented in our priori work [51], where we pro-
posed a generic analytical model in order to evaluate the
performance of relay placement strategies in DTNs.
6.5 Buffer management policy
In a typical DTN, the mobile nodes are disconnected for a
relatively large period of time to any other mobile/relay
nodes. During this time, due to their limited buffer size
they have to evict some messages to accommodate new
messages. Additionally, when a mobile node forwards/
reads some messages to/from a relay node, both mobile and
relay nodes may have to evict some other messages from
their buffer to give space to the incoming messages.
Therefore, a buffer management policy is quite demanding
to define which messages should be dropped, if the buffer
is full, when a new message has to be accommodated.
One possible way to enhance our proposed protocol is
by deleting the delivered messages if they exist in any other
buffers. In this case, destination nodes delete the copies of
previously delivered messages from encountered relay
nodes. Figure 11 shows that using this simple technique,
the performance of TBR in terms of message delivery rate
is increased while the latency is almost at the same level.
We also evaluated TBR in this paper based on the first in
first out (FIFO) queuing Policy. In FIFO queuing Policy the
message that first entered the queue is the first message to
be evicted from the queue. This policy is easy to be
implemented but it can be inefficient as it does not consider
other useful information about the probability of a message
reaching the destination. We leave improving buffer
management policies to future work. For example, another
queuing policy can be evicting the most forwarded mes-
sages first (MOFO) in which nodes/relays keep track of the
number of times each message has been forwarded and
based on this information they evict those messages that
have been forwarded the largest number of times. Conse-
quently, nodes/relays can provide a higher chance of for-
warding to the messages that have been forwarded fewer
times.
6.6 Possibility of using a single base station to cover
the area
In this section, we study the possibility of using a single
super tag placed in the middle of the area instead of using
many small tags distributed over the area. Instead of using
many small tags, one can use a few super tags to achieve
similar delivery performance; however, there is a trade-off
between the power usage and using longer radio range. In
this section, we evaluate the the delivery performance of a
super tag, i.e., a base-station, placed in the middle of area
versus of its broadcasting range and later we show the
effectiveness of using the base-station in comparison to
0 50 1000
0.2
0.4
0.6
0.8
1
Tag Buffer Size
Mes
sage
Del
iver
y R
ate
0 50 1000
200
400
600
800
1000
1200
Tag Buffer Size
Mes
sage
Del
ay
Reg. Grid Based
Random Based
Reg. Grid Based
Random Based
(a) (b)Fig. 10 By initializing the
position of the relay nodes with
different techniques, i.e., on a
regular grid and randomly
chosen locations, there is no
significant changes in the
performance of the network
using probability based
optimization placement
technique. a Message delivery
rate versus tag buffer size,
b Message delay versus tag
buffer size
Wireless Netw (2012) 18:9–31 23
123
using multiple small tags introduced in this paper. The
results show that using the latter alternative we achieve
better performance in terms of power usage.
In order to evaluate the performance of using a single
base-station placed in the middle of the region we ran an
experiment. In the experiment we chose same configuration
as scenario 1 in Sect. 4 except Nrelay = 1 and Brelay =
1,600. Our objective function is to maximize q and mini-
mize Lavg simultaneously. Conceptually, network ineffi-
ciency could be defined vice versa given as Lavg/q.
The goal is then to minimize network inefficiency.
Figure 12(a) shows that an increased broadcast range of the
corresponding base station improves the performance of
the network in terms of increasing message delivery rate
and decreasing message delay together. As an example, for
the RWP mobility model, when the broadcast range of the
base station approaches 55m, the performance is almost the
same as the GA in Table 3. The covered area by tags is
equal to 942.48m2 (16� 34p52) but the covered area by the
base station is 7127.49m2 (34p552); therefore, the required
area to be covered by the base station is 7.5625 times
larger. If we assume that to cover each unit of area we need
the same amount of energy, then to achieve the same
delivery performance using a base-station we need 750%
0 50 1000.05
0.1
0.15
0.2
0.25
Tag Buffer Size
Del
iver
y R
ate
Optimized bf.
Regular bf.
0 50 10050
60
70
80
Tag Buffer Size
Del
ay
Optimized bf.
Regular bf.
(a) (b)
Fig. 11 By deleting the copies of previously delivered messages the
performance of TBR is increased. We used the following setting:
Ntag = 36, Nnode = 10, X/Ymin/max = 1, 000m, d = 200, Bnode = 1000,
Brelay = 100, r = 50, Smin/max = [20, 30], k = 0.5, and the mobility
model used is random waypoint. Relays are placed on a regular grid.
a Delivery rate versus tag buffer size, b Delay versus tag buffer size
(a)
(c) (d)
(b)Fig. 12 Single base station
results and application.
a Network inefficiency
versusbase station broadcast
range, b Packet delivery rate
versusbase station broadcast
range, c Average end- to-end
delay versusbase station
broadcast range, d Inhospitable
terrain inside the area
thatdoesn’t allow deployability
of any basestation in the middle
24 Wireless Netw (2012) 18:9–31
123
more power than using multiple tags. In addition,
Fig. 12(b, c) show the delivery rate and average end-to-end
delay versus base station range respectively.
In some applications, such as military, relying on a
single base station may end up with a point of vulnerability
which may be attacked and, if destroyed, terminate the
network operation. Additionally, in some scenarios such as
ring like areas, it is not possible to place a base station in
the middle of the area which is unreachable. Alternatively,
we can place the tags all over the rings (Fig. 12(d)).
7 Comparisons to existing work
7.1 Comparison to epidemic routing
In prior work, Vahdat et al. [56] presented a routing pro-
tocol called Epidemic Routing (ER). In ER, every host acts
as a carrier to distribute application messages and when-
ever hosts meet each other they exchange all the messages
together; therefore, after a while all the messages will
spread over the network. In this section we will show how
we can improve ER performance reported in [56] using
TBR considering that we have involved a number of sta-
tionary RFID tags in the network.
Figure 13(b) shows the CDF of message delay for var-
ious transmission ranges in TBR. To have a fair compari-
son to ER, we use the same parameters as following: the
area size is 1,500 9 300, the average speed of mobile
nodes is 10 (Smin = 0, Smax = 20). The message rate is 1
(k = 1) and d = 50 () Nrelays = 217). The buffer size for
all the tags and mobile nodes is 1,000 messages.
Most of the messages are delivered by time 100, when
r C 25. In ER, this phenomenon happens only when the
transmission range, i.e., the wireless range between nodes,
is more than 100. Furthermore, according to Table 5(a), ER
results show that it is not scalable due to its weak perfor-
mance for small transmission ranges, where e.g. it takes
44,829.7 s to deliver a message, on average. ER has a better
performance when we have a high density network, i.e., 50
mobile nodes with transmission range of higher than 100 in
an area 1,500 9 300 m2.
If we scale the size of network from 1,500 9 300 to a
larger size or reduce the number of participating nodes,
using even a larger transmission range, TBR will outper-
form the ER approach. In addition, the message delivery
rate of TBR will be higher in very sparse networks.
We have also investigated the performance of TBR and
ER by using different MN/RN buffer sizes. According to
Table 5(b), when the transmission range is 50 m, TBR has
a higher delivery performance than ER. This case is more
significant when we have a limited buffer size.
In ER, they also presented the CDF for bounded
resource consumption, i.e., bounded buffer sizes. In this
case, the amount of buffer space in the nodes is limited.
Assuming all the nodes and tags in TBR have the same
buffer space and all the parameters are the same as earlier
with a transmission range of 50 for all the tags, the results
for message delivery rate are plotted in Fig. 13(a).
7.2 Comparison to message ferrying
In prior work, Zhao et al. [65] presented a Message Fer-
rying (MF) approach for data delivery in sparse MANETs.
They described two approaches called NIMF and FIMF.
We chose the NIMF (Node-Initiated MF) to be compared
with our work based on their results reported in [65]. We
ran our simulation based on the same parameters reported
in [65] as following: the area is 5, 000 9 5, 000, there is 1
ferry with average speed of 15m/s, Smin = 0, Smax =
5, k = 1.25, d = 50 ()Nrelays = 10,201), Nnode = 40 and
r = 20. Note that from a comparison point of view the
transmission range of the tags is much less than the
transmission range of the mobile nodes in NIMF.
Figure 13(c, d) show the effect of node/tag buffer size
on average message delay and message delivery rate for
different approaches including TBR, NIMF, and ER. The
delivery rate in TBR is significantly better than other
Table 5 TBR versus ER
Range ER TBR ER TBR
r q q Lavg Lavg
(a) Different transmission range
250a 100 100 0.2 7.8
100 100 100 12.8 15.5
50 100 100 153.0 42.7
25 100 100 618.9 90.6
10 89.9 100 44829.7 248.9
Buffer
size
ER TBR ER TBR
q q Lavg Lavg
(b) Different buffer size
2,000 100 100 147.3 40.6
1,000 100 100 148.7 41.6
500 100 100 149.2 43.8
200 99.6 100 152.0 46
100 95.2 99.5 157.5 41.9
50 79.7 94.1 148.2 41.4
20 50.2 73.1 129.5 35.2
10 29.3 53.2 98.9 34.3
a This network is not sparse by our definition (average number of
connections is more than 20% and our definition requires less than
5%)
Wireless Netw (2012) 18:9–31 25
123
approaches, because of the added buffer capacity of the
tags, which makes it suitable for bounded resource, lossless
applications. The message delay in TBR seems to be high
for small buffer size but we believe that this higher delay is
due to having more messages delivered because there are
some messages in the network which are delivered after a
long time and in NIMF they are dropped. This phenomenon
is intensified in ER which has a low delivery late
(according to Fig. 13(c), by increasing buffer size in ER/
NIMF, the delay becomes higher which confirms our
belief). In addition, although TBR has a greater delivery
rate with buffer size of 800, the message delay is less than
MF.
Furthermore, our comparisons are based on a regular
array of tags. Using either probability based or genetic
algorithm based tag placement would further increase the
performance of TBR.
8 Conclusion
In this paper, we studied the applicability of using point
locations as ‘‘mailboxes‘‘ for storage/retrieval of messages
to facilitate ubiquitous network connectivity. A practical
implementation of our work could use low cost, tiny,
unattached-power RFID tags. Our approach is an alterna-
tive, for achieving competitively low message delay and
high message delivery rates, to existing methods that rely
on altering/controlling some of the mobile nodes’ move-
ments. We designed and analyzed a new protocol called
TBR (Tag-based Routing) in which mobile nodes com-
municate to each other only via passive relay nodes such as
passive RFID tags. Our results show that TBR is effective
at expanding the connectivity of DTNs, over a broad range
of parameter values. Our TBR protocol can provide lower
message delay and higher message delivery rates than
existing methods including NIMF and ER. We also showed
heuristics methods of placing tags over the geographic
region, including relay pruning, a probabilistic distribution
based on relay utility and a genetic algorithm approach that
attempt to minimize the message delay/rate ratio. The
genetic algorithm was shown to be a superior tag place-
ment technique. In our future work we will study TBR for
intelligent buffer policy management to increase its
effectiveness, more advanced relay placement techniques
and develop a rigorous analytical framework.
DTN definition
The performance of the traditional routing protocols for
mobile ad hoc networks (MANETs) or mesh networks [24,
42, 43, 48] in terms of packet delivery rate is significantly
decreased at a point when the network is getting sparse. To
find that point, we ran an experiment shown in the Fig. 14.
In Fig. 14 we show a number of well known MANET
routing protocols (e.g. AODV [42], DSDV [43], DSR [24],
and ADIAN [48]) compared to random gossiping in terms
of the fraction of delivered packets versus average per-
centage of available contacts. To do this, we used NS2. We
0 100 200 300 4000
20
40
60
80
100
Message Latency
Mes
sage
Del
iver
y R
ate
0 500 10000
20
40
60
80
100
Message Latency
Mes
sage
Del
iver
y R
ate
200 400 600 8000
2000
4000
6000
Tag/Node Buffer Size
Mes
sage
del
ay (
sec)
200 400 600 8000
20
40
60
80
100
Tag/Node Buffer Size
Mes
sage
Del
iver
y R
ate
BS=10
BS=20
BS=50
BS=100
BS=200
BS=500
BS=1000
BS=2000
r=10
r=25
r=50
r=100
r=250
TBRP
NIMF
ER
TBRP
NIMF
ER
(a)
(c) (d)
(b)Fig. 13 TBR’s CDF for
message delivery rate versus
message delay, (a) and (b). Note
that these CDFs cumulate to the
percentage of delivered
messages. Comparisons to
existing work are shown in
(c) and (d). a CDF for message
delivery rateas a function of
available buffer space for r = 50,
b CDF for message delivery
rateas a function of tage range,
r, c The effect of tag buffer
sizeon the average message
delivery time, d The effect of
tag buffer sizeon the message
delivery rate
26 Wireless Netw (2012) 18:9–31
123
set a fixed number of mobile nodes, i.e., 20, while
increasing the area within which they could move, thus
increasing sparseness. The data traffic used in the simula-
tion is CBR with a rate of 8 kbps. Mobile nodes move
according to the RWP mobility model with a pause time of
0 s and maximum allowed speed of 3 m/s. The simulation
time is 1,000 s and radio range of the nodes is 250 m. In the
Random Gossip protocol, each node picks a connected
node at random and forwards the packet. The maximum
number of possible contacts between each pair of nodes
can be calculated as follows:
Max Contact No: ¼ Nnode Nnode � 1ð Þ=2:
According to Fig. 14, the other protocols converge to the
performance of the Random Gossip protocol when the total
contacts are about 5% of all 190 possible contacts between
each pair of nodes.
GA placement details
In our GA placement approach, the genome is represented
as a sequence of relay coordinates:
x1; y1; x2; y2; . . .; xn; yn½ �
where xi; yi 2 0::1½ � (0. . .1 is a normalized coordinate).
A single population was used of 120 genomes, with each
genome initialized by placing relays uniformly at random
in the region. The number of elite genomes was set to 10
and the search was run for 100 iterations. We use Genetic
Algorithm Solver Toolbox provided by Matlab for our
simulation. Through some preliminary experiments we
determined some GA parameters values that improved GA
performance which is presented in Table 6. Other param-
eters are set as the default value in the latter toolbox. We
did not do an exhaustive search over the entire parameter
space. We leave this to future work. The mutation and
crossover functions were customized for our problem. We
hypothesized that the structure/topology of the geographic
relationships between relays is important in terms of per-
formance. In order to allow the GA to learn about spatial
characteristics of relay placement, we identified regions of
relays using a breadth first tree construction based on the
Delaunay graph defined by the genome. Initially, we sim-
ply chose relays at random, rather than using a breath first
tree approach and the resulting GA performance was
comparably poor.
We use the Delaunay triangulation operator in Matlab to
generate edges between spatially close nodes, creating a
mesh. The choice of the Delaunay triangulation is arbitrary
and unimportant; there are many different ways to generate
these edges. We then select a node at random in the mesh and
construct a tree using a breadth search search from that node,
with a target number of nodes to be included in the tree.
Figure 15(a, b), show two genomes, each consisting of
50 relays. The Delaunay graph of the relays is shown using
light lines. A random breadth first tree is constructed by
choosing a relay at random and forming a breadth first tree
that consists of the required number of relays. An example
set of relays in such a tree is shown using solid dots for
each genome.
The child shown in Fig. 15(c) is constructed from the
selected trees in genome 1 and genome 2. In practice, for
our crossover function, the number of trees and the number
0 10% 20% 30% 40% 50%0
10
20
30
40
50
60
70
80
90
100
Contact_Fraction Percentage
Pac
ket D
eliv
ery
Rat
io
ADIANDSRAODVDSDVRndGossip
Fig. 14 Packet delivery rate versus fraction of available contacts to
all potential contacts between each pair of nodes
Table 6 GA parameter settings
Parameter name Description Parameter value
Population size number of individuals 120
Elite count Number of best individuals that survive to next generation without any change 10
Crossover fraction The fraction of genes swapped between individuals 0.8
Mutation probability The probability that how often will be parts of chromosome mutated 0.05
Generations Maximum number of generations allowed 100
Migration interval The number of generations between the migration of the fittest individuals to other sub-populations 5
Migration fraction Fraction of those individuals scoring the best that will migrate 0.2
Wireless Netw (2012) 18:9–31 27
123
of relays in each tree selected from genome 1 is random-
ized, i.e., we choose a small number of random trees from
genome 1. The resulting number of relays (and number of
trees) selected from genome 2 is constrained so that the
total number of relays in the child equals Nrelay. If genome
1 and 2 have identical selected relays (as in the case when
they have common ancestors) then one of the identical
relays is replaced with a point selected at random in the
region.
As an example representing crossover operator, assume
the following two genomes:
g1 ¼ x1;1; y1;1; x1;2; y1;2; :::; x1;n; y1;n
� �;
g2 ¼ x2;1; y2;1; x2;2; y2;2; :::; x2;n; y2;n
� �:
The crossover function then takes a subset of points from
g1, and the remaining points from g2. Therefore, the result
of crossover operator would be a new genome as follows:
g3 ¼ g1 � g2 ¼ x3;1; y3;1; x3;2; y3;2; . . .; x3;n; y3;n
� �
Our mutation operation similarly selects a random
breadth first tree (in practice consisting of up to 5% of
the relays). For mutation, the selected relays are replaced
with relays chosen at random in the region. Figure 15(d) is
a mutation of genome 2. Note that a ‘‘hole‘‘ appears where
the selected relays were, since the new relays are less likely
to appear in the selected region (for a small number of
selected relays and hence a small selected region).
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Author Biographies
Saeed Shahbazi received the
BEng degree in computer soft-
ware engineering from Iran
University of Science and Tech-
nology in 2002 and the M.Eng.
degree in Artificial Intelligence
in Sharif University of Technol-
ogy in 2005. He recieved his
Ph.D. degree in computer sce-
ince and software engineering at
the University of Melbourne in
Aug. 2011. He is currently a
research engineer in National
ICT of Australia. Previously, he
was with Iran Telecommunica-
tion Research Center and Nokia. His research interests include routing
in mobile ad hoc networks, cross-layer network protocol design for
30 Wireless Netw (2012) 18:9–31
123
wireless networks, distributed systems, distributed artificial intelli-
gence, and fuzzy systems.
Shanika Karunasekera received
the B.Sc. (Honours) degree in
electronics and telecommunica-
tions engineering from the Uni-
versity of Moratuwa, Sri Lanka,
in 1990 and the Ph.D. degree in
electrical engineering from the
University of Cambridge, UK, in
1995. From 1995 to 2002, she
was a Software Engineer and a
Distinguished Member of Tech-
nical Staff at Lucent Technolo-
gies, Bell Labs Innovations,
USA. Since January 2003, she
has been a Senior Lecturer at the
Department of Computer Science and Software Engineering, Univer-
sity of Melbourne. Her current research interests are distributed
computing, software engineering and peer-to-peer computing. Dr.
Karunasekera is a member of the ACM.
Aaron Harwood received the
BIT, B.Eng. (M.E.), and Ph.D.
degrees from Griffith University,
Brisbane, Australia, in 1998 and
2002, respectively. He is cur-
rently a senior lecturer in the
Department of Computer Sci-
ence and Software Engineering,
University of Melbourne, Mel-
bourne. He is a founding member
of the Peer-to-Peer Networks and
Applications research group. His
research interests are in the per-
formance of large-scale, decen-
tralized, autonomous systems.
He has been a member of the ACM, the IEEE, and the IEEE Computer
Society since 2003.
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123