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Journal of Information & Computational Science 12:2 (2015) 787–796
Available at http://www.joics.com
January 20, 2015
A Practical Routing Protocol Based on WiFi for
Resources-constrained Opportunistic Networks ?
Ding Liu ∗, Xiaoming Wang, Junling Lu, Yufei Gao
School of Computer Science, Shaanxi Normal University, Xi’an 710062, China
Abstract
Opportunistic networks based on WiFi technology can be widely deployed in special area which wired
network can not cover or the infrastructure of mobile telephone network is paralysed in consequence of
severe earthquakes, floods, or other natural calamities. Two most important issues should be considered
to guarantee message delivery in opportunistic networks: routing protocols and efficient buffer management
strategy. Considering above two issues, we propose an opportunistic networks routing protocol based on
WiFi (ONRWF) which aims to make up for the lack of bandwidth and buffer space. ONRWF contains
delivery prediction which can decide next hop routing by comparing the prediction of future meeting time
of all nodes. In the strategy of buffer management, scheduling policy aims to improve message delivery
of high priority, and the proposed drop policy focuses on improving the efficiency of buffer space. Finally,
a testbed is also set up to validate the ONRWF in real life. Compared with the traditional WiFi and
SVSS (our previous work), experimental results demonstrate significant performance benefits in terms
of message delivery for opportunistic networks based on WiFi.
Keywords: Opportunistic Networks; WiFi; Resources-constrained; Buffer Management
1
Introduction
Delay Tolerant Networks (DTNs) are a class of emerging networks where disconnections may
occur frequently due to node mobility, power outages and communication uncertainty. As one
type of Delay Tolerant Networks, opportunistic networks do not need lay out communication
infrastructure, and use node mobility to set up delay-tolerant routines for communication [1, 2].
In recent years, with the rapid development of mobile communication, network communications
are gradually moving towards the trajectory of the mobile internet. Some of the mobile devices
(such as smart phone, PDA, etc.) have become increasingly diverse. They not only have embedded operating system, Bluetooth communications, GPS positioning, WiFi communications
and video cameras feature, but also have strong data calculation and processing capabilities.
?
Project supported by the National Nature Science Foundation of China (No. 60970054, No. 60773224)
and the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry
(No. 61173094).
∗
Corresponding author.
Email address: [email protected] (Ding Liu).
1548–7741 / Copyright © 2015 Binary Information Press
DOI: 10.12733/jics20105259
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Current mobile phones with WiFi enabled are becoming increasingly popular. It is especially
noteworthy that WiFi (IEEE 802.11-based wireless local area network) is taking the leading role
for the wireless communication due to its greater coverage, free-of-charge and higher transmission
rates. In opportunistic networks, routing and buffer management are challenging issues [3, 4, 5].
The conventional routing protocols are not applicable for opportunistic networks based on WiFi
technology (known as WiFi peer-to-peer).
Opportunistic networks usually composed of human beings equipped with smart phones, can be
applied to many scenarios. In these scenarios, self-organizing opportunistic networks are located
in special areas where wired or wireless network can not cover caused by natural disasters. China,
a multi-earthquake shock country, lies in the earthquake belt along the west of the Pacific Ocean.
Many earthquakes happen in China, which would have caused the deaths of many people. How
to use more advanced technology to reduce human loss is a serious problem. Imagine a major
earthquake happens in a mountainous area of western China. The earthquake devastates most
infrastructures of mobile telephone networks, leading to the collapse of mobile telephone networks
services for a long time. In this critical scenario, the rescuers should move to the disaster area
quickly, and immediately return information to the headquarters. The rescuers equipped with
smart phones can construct an opportunistic networks based on WiFi. At the beginning of the
earthquake, all the rescuers would try to send the information of the disaster situation to the
headquarters as early as possible. Also, each mobile node may be both a sender and receiver. All
of these will result in a large number of data flows that may overburden the limited bandwidth
and buffer space of mobile nodes.
To cope with such a disaster scenario, we need to design a WiFi-based routing protocol for
the resources-constrained opportunistic networks. Usually information sharing in wireless mobile
environment is achieved by the method of information diffusion. SVSS was introduced in our
previous work [8] in order to make up for the lack of broken-point continuingly-transferring file
transfer mechanisms to support for third party. The SVSS can increase the packet forwarding
of network, balance the energy consumption of mobile phones, and get more data transmission
practical in mobile opportunistic network. But in a disaster scenario, disaster information needs
to be timely convergence. Moreover, rescuers are often facing harsh natural conditions, and mobile nodes they equipped maybe receive and forward large amount of data in a very short time.
Therefore, we propose a WiFi-based routing protocol for resource-constrained opportunistic networks, which aims to maximize the message delivery probability in the case of limited bandwidth
and buffer space. The main contributions of this paper are summarized as follows:
• We propose the formula to calculate the prediction of the future meeting between two nodes
as the next hop routing decision based on WiFi for opportunistic networks, in which the
average prediction of probability value is used to maximize the delivery probability.
• In order to improve the efficient of resource-constrained node’s buffer space, we propose
the buffer management strategy which contains scheduling policy and drop policy. The
proposed scheduling policy aims to improve message delivery of high priority, while the
proposed drop policy focuses on improving the efficiency of buffer space.
• We implement our protocol in Android testbed based on WiFi and obtain significant performance in term of message forwarding in opportunistic networks.
The remainder of the paper is organized as follows: In Section 2, we introduce the system
model. The proposed delivery prediction of all nodes is described in Section 3. Section 4 presents
D. Liu et al. / Journal of Information & Computational Science 12:2 (2015) 787–796
789
the buffer management strategy which contains scheduling policy and drop policy, and the testbed
and performance evaluation are discussed in Section 5. Finally, we conclude the paper in Section 6.
2
System Model
An opportunistic network can be represented as G(V, E, D), where V = {v1 , v2 , · · · , vm |m ∈ N }
is a finite set of mobile nodes, vm ∈ V denotes any node in the network, and the number of node
m = |V |; E = {(vi , vj )|vi , vj ∈ V } is a finite set of links between nodes, vj is in the transmission
range of vi , and vice versa, one link (vi , vj ) can be simply represented as eij ; D = {dij |i, j ∈ m}
is a set of delivery probability between nodes, dij represents the delivery probability when a
message moves from node vi to node vj , among in the set D, any link between two nodes, eij ∈ V ,
corresponds to a delivery rate dij ∈ D.
We assume that the transmission range of each node is R. Given any two nodes vi and vj , let (vix ,
viy ) and (vjx , vjy ) represent coordinate position of two nodes, respectively. They can communicate
with each other at time t only if their geographical locations are within the transmission range.
So, the Euclidean Distance between vi and vj is dij (t) at time t, which is described as follows:
q
dij (t) = (vix − vjx )2 + (viy − vjy )2 .
(1)
In order to determine whether two nodes can communicate, we define the formula as follows:
(
1 when x 6 R,
Cp =
(2)
0 when otherwise,
where Cp represents the result of the judgment. If Cp is 1, it indicates that the two nodes can
communicate. Otherwise, if Cp is 0, two nodes can not perceive each other.
ij
We define the meeting duration time tij
md (s) of node vi and node vj . tmd (s) can be calculated
according to the formula as follows:
ij
ij
tij
md (s) = te (s) − ts (s),
(3)
where tij
s (s) represents the start time between two nodes vi and vj when they first come within
their transmission range, and tij
e (s) represents the end time between two nodes vi and vj when
they depart their transmission range. The start time and end time can be calculated according
to the formulas (4) and (5), respectively.
ij
tij
s (s) = min{di,j 6 R, t>te (s − 1)},
(4)
where tij
e (s − 1) represents the last depart time of nodes i and j.
ij
tij
e (s) = min{di,j > R, t>ts (s)}.
3
(5)
Delivery Prediction
Suppose there are four nodes A, B, C and D, and A wants to send messages to D. B and C are
all within A’s transmission range. In traditional WiFi communication, A can not broadcast the
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messages to B and C at the same time. So A can only use a transmission manner similar to the
“Three Handshakes” in internet. If A forwards messages to B firstly, then C can only choose to
wait, and vice versa. In Fig. 1 (a), A decides to send messages to B at time t1 . At time t2 when B
encounters D, and forwards messages to D directly (Fig. 1 (b)). This indicates that the process
of message forwarding is end. On the other hand, as shown in Fig. 1 (c), if A forwards messages
to C firstly, we can see that D will not receive the message in the next time (Fig. 1 (d)).
B
B
A
A
A
A
C
C
C
C
B
D
D
(a) t1
B
D
D
(b) t2
(c) t3
(d) t4
Fig. 1: Message delivery in opportunistic network based on WiFi
From Fig. 1 we can see that, due to the randomness of node mobility in opportunistic networks,
the message should be delivered by the node which has a greater probability of hit the target
node.
In this paper, we use the prediction of the future meeting between two nodes as the next hop
routing decision. In opportunistic networks, as mentioned in the system model of Section 2, each
node record the meeting duration time between itself and all the other nodes. Because the two
nodes may encounter many times, so the set of meeting duration time between node i and j can
be described as follows:
i,j
i,j
i,j
mdti,j = {mdti,j
1 , mdt2 , mdt3 , ..., mdtn },
(6)
where mdti,j represents total meeting duration time between node i and j, and an element of
the set mdti,j represents one meeting duration time between two nodes which can be calculated
by the formula (3).
When vi and vj encounter, they will all calculate the new meeting duration time with each
other, and add it into their set of meeting duration time. After that, if node vi needs to
send messages to the destination node vs , it will forward the messages to the node which is
in its transmission range and has a greater probability value of hitting the target node vs . The
prediction of probability value between node vi and vj can be calculated by the formula as follows:
n
i,j
mdt
4
1X
=
mdti,j
k .
n k=1
(7)
Buffer Management Strategy
Designing an efficient strategy of buffer management is also the main issue in opportunistic
networks. In critical scenarios, the network will generate a large number of data flows that may
overburden the limited bandwidth and buffer space of mobile nodes. But the current situation
D. Liu et al. / Journal of Information & Computational Science 12:2 (2015) 787–796
791
is that the traditional store-carry-and-f orward mechanism may easily make node’s buffer fully
used and drop some important messages. Therefore, it is necessary to design an efficient buffer
management strategy which can provide fewer drops of important messages and high delivery
probability.
4.1
Scheduling Policy
In order to build the proposed scheduling and drop policies, we introduced the following message
properties in Table 1. For one node, F T is introduced in order to record the forwarding times
of message in the network, and it is one part of node attributes. Suppose that after node A
forwarding message i to node B, F Ti of node B is inherited from node A, then F Tb,i ← F Ta,i ,
and F Ta,i ← F Ta,i + 1. If node C and B come within their transmission range, and C does not
have message i, they will update F Tb,i and F Tc,i as follows: F Tc,i ← F Tb,i , F Tb,i ← F Tb,i + 1.
Table 1: Message properties notation
Properties
Description
T T Li
Initial Time To Live for message i
T Ei
Time of Entering queue for message i
RTi
Remaining Time To Live for message i
Ti = T T Li - RTi
Elapsed Time for message i
F Ti
Forwarding Times of message i
Scheduling policy reassigns the priorities for each message and determines which messages
should be transmitted when two nodes need to exchange messages. Our scheduling policy depends
a lot on the incoming traffic queue, which considers the priority class of a message and its F T , RT
and T E. The attribute of each message is represented as: M essagei (P Li , F Ti , RTi , T Ei ), where
P Li denotes the priority class of a message i. We define that a priority with a smaller number has
a higher priority than a priority with a larger number. In the process of scheduling, the message
with the highest priority in incoming message queue is scheduled first. If two messages have the
same priority level, then the message with smaller F T will be scheduled first. If the P L and
F T of two messages are the same, the scheduling policy will sequentially compare the RT and
T E of the messages, and then determine which one should be firstly scheduled. In summary, the
scheduling order is P L >F T >RT >T E.
As shown in Fig. 2, suppose there are 8 messages in incoming message queue of one node. In
traditional scheduling, all the messages will be scheduled following by F IF O (First Input First
Output). In our proposed scheduling policy, message 8 with the higher P L and smallest F T is
scheduled first. Then message 7 is scheduled subsequently. As message 1’s T E is shorter than
that of message 4, message 1 is scheduled after message 7. The remaining messages are also
scheduled according to this policy. So the dispatching order of 8 messages is: 8 → 7 → 1 → 4 →
3 → 6 → 2 → 5.
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(PL, FT, RT, TE)
Receiving message
Forwarding message
1 (1, 12, 1800, 5)
2 (2, 15, 3000, 12)
3 (2, 5, 1800, 13)
4 (1, 12, 1800, 10)
5 (2, 15, 3000, 16)
6 (2, 8, 2200, 1)
7 (1, 12, 1000, 8)
8 (1, 6, 1000, 3)
Incoming message queue
8 (1, 6, 1000, 3)
7 (1, 12, 1000, 8)
1 (1, 12, 1800, 5)
4 (1, 12, 1800, 10)
3 (2, 5, 1800, 13)
6 (2, 8, 2200, 1)
2 (2, 15, 3000, 12)
5 (2, 15, 3000, 16)
Transmitting queue
Fig. 2: An example of use of the scheduling policy
4.2
New Drop Policy
Drop policy decides which messages should be dropped when a node exhausts all available storage.
Under a disaster scenario, the traditional store-carry-and-f orward mechanism may easily make
one node’s buffer fully used, so the node needs to drop some messages in its buffer. We propose
a new drop policy which is based on the optimal drop policy [5], and can optimize the average
delivery delay for Delay Tolerant Networks. The optimal drop policy aims at minimizing the
average delivery delay of all messages, which uses the following utility for each message i:
mi (Ti ) ´
1 ³
,
(8)
1−
L−1
ni Ti 2 λ
where L is the number of nodes in opportunistic networks. mi (Ti ) and ni (Ti ) are the number of
nodes (excluding source) that have seen message i since its creation until elapsed time Ti and
number of copies of message i in the network after elapsed time Ti , respectively. λ is the meeting
rate between two nodes under the given mobility model,
λ=
1
,
E[U ]
(9)
where E[U ] is the average meeting time. In this paper, we use the Random W aypoint (RW P )
mobility model [6]. So λ can be calculated by the following formula:
E[U ]rwp =
1
N
(T + T stop ),
pm verwp + 2(1 − pm ) 2KL
(10)
where N , K, verwp , L, T and T stop represent the size of network area, the transmission range,
the average node speed, the expected epoch length, the expected epoch duration and the average
pause time after an epoch, respectively.
When buffer overflow happens, the optimal drop policy drops the message with the smallest
value calculated by formula (8). But mi (Ti ) and ni (Ti ) in formula (8) are also global variables
which each nodes can hardly get. In order to facilitate the calculation, we try to replace both
mi (Ti ) and ni (Ti ) with F Ti . Then we can get an approximate result of drop value as follows:
F Ti ´
1 ³
1
−
.
(11)
L−1
Ti 2 λ
D. Liu et al. / Journal of Information & Computational Science 12:2 (2015) 787–796
793
Otherwise, taking into account the importance of different messages in a real disaster scenario,
for example, messages of the staff emergency is more important than messages of the extent
of damaged houses, we divide the messages into two priority classes: 1 presents an emergency
message, 2 presents a normal message, and class 1’s level is higher than class 2’s level. So formula
(11) with priority classes can be described as follows:
Di =
1
1 ³
F Ti ´
,
+α
1
−
P Li
L−1
F Ti 2 λ
(12)
where Di is the drop value of message i, message with smallest Di in node’s buffer will be dropped
first, P Li = 1 or 2 represents the priority classes of message i, and α is a control parameter.
5
Experiment Configurations and Results
To evaluate our proposed opportunistic networks routing protocol based on WiFi, we illustrate
the effectiveness of ONRWF by comparing it with the traditional WiFi and our proposed SVSS
in previous work. We will also analyze the effects of network with the three performance metrics:
delivery ratio, average energy consumption and average queue length.
5.1
Experiment Configurations
• Mobility model and topology. To describe the characteristics of the movement of nodes in
mobile environment, mobility models are defined and studied as a critical part in the research of
wireless networks. However, a majority of mobility models are commonly defined by hypothesis
and they can not reflect how mobile nodes really move. In order to reflect the relatively movement
of hosts in opportunistic networks, we attempt to get truthful data in real life situations. The
scenarios include dormitories, university library and buildings, where the students equipped with
smart phones can move optionally in accordance with the Random Waypoint (RWP) mobility
model. The topology includes 50 students equipped with smart phones randomly placed in an
area of 2000 m by 2000 m of Shaanxi Normal University (SNNU). The students move at 0-1.5 m/s
speed optionally according to the RWP mobility model during the period of 20 minutes, and the
pause interval is 10 seconds. In our evaluation, we perform experiments on an Android testbed.
All the smart phones install the ONRWF protocol that we implemented and use Android (2.3)
as their operating systems. Considering the gap between the theoretical transmission speed and
the actual transmission speed of WiFi, we set the message size to 100 KB.
• Experiment settings. We use two different real scenarios to verify the ONRWF and compare
with conventional WiFi and SVSS in transmission performance. One is “multi-source with the
same priority messages” which has 6 smart phones only with the same priority messages. The
other one is “multi-source with multi-priority messages” where there are 3 smart phones with
priority 1 messages and 3 smart phones with priority 2 messages. With the elapse of the time,
messages spread in the network, and this experiment is based on the two different real scenarios
and aims at getting high spread performance in size per unit time.
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D. Liu et al. / Journal of Information & Computational Science 12:2 (2015) 787–796
5.2
Experiment Results
By analyzing the following performance metrics of opportunistic networks, our research attempts
to evaluate the routing performance between WiFi, SVSS and ONRWF.
• Delivery ratio. The delivery ratio is the ratio of the number of successfully delivered
messages to the number of all the generated messages, reflecting the degree of successfully received
messages in networks. Fig. 3 (a) and (b) respectively show the delivery ratio performance under
“multi-source with the same priority messages” and “multi-source with multi-priority messages”
background cases. From the experiment results, it is obvious that the ONRWF shows a good
performance in delivery ratio. In Fig. 3 (a) we can see, the delivery ratio of SVSS and ONRWF
are much higher than WiFi. The main reason is that the SVSS and ONRWF can provide more
powerful broken-point continuingly-transferring mechanism to support for third party, by which
more smart phones can receive the data from source phones. The details can be found in our
previous work [8]. Fig. 3 (b) shows the delivery ratio performance of priority 1 message under
“multi-source with multi-priority messages” background case. Due to the proposed scheduling
policy which schedules high priority messages preferentially, the delivery ratio performance of
ONRWF achieves the highest delivery ratio, even much higher than WiFi and SVSS.
0.6
0.5
0.7
WiFi
SVSS
ONRWF
Delivery ratio (%)
Delivery ratio (%)
0.7
0.4
0.3
0.2
0.1
0
240
360
480
600 720 840
Times (s)
(a)
960
1080 1200
0.6
0.5
WiFi
SVSS
ONRWF
0.4
0.3
0.2
0.1
0
240
360
480
600 720 840
Times (s)
(b)
960
1080 1200
Fig. 3: Comparison chart of delivery ratio under (a) “multi-source with the same priority messages” and
(b) “multi-source with multi-priority messages” scenarios
• Average energy consumption. We observe from Fig. 4 (a) and (b) that the ONRWF has
less battery consumption compared to WiFi and SVSS at a given time, e.g., 1200 seconds. Before
the experiment, all 50 smart phones are fully charged. During the experiment, the battery states
are recorded by the batteryM anager class. If we look at the battery levels when the message
transmission is completed, we see that the battery consumption levels of WiFi, SVSS and ONRWF
are similar. But ONRWF can bring lower energy consumption in mobile environment. The
main reason is that ONRWF can provide more powerful broken-point continuingly-transferring
mechanism to support for third party, just like SVSS. Moreover, the efficient routing mechanism
and buffer management strategy of ONRWF can bring lower message loss rate, thereby increase
the life cycle of the network.
• Average queue length. Fig. 5 shows the average queue length performance under “multisource with multi-priority messages” at 840 seconds during the experiment. We observe that the
ONRWF provides a good data reception performance of messages with high priority. The longer
queue length of high priority messages in buffer indicates that network has better storage and
transmission performance. We provide excellent buffer management strategy of ONRWF, which
400
350
300
Energy consumption (mAh)
Energy consumption (mAh)
D. Liu et al. / Journal of Information & Computational Science 12:2 (2015) 787–796
WiFi
SVSS
ONRWF
250
200
150
100
50
0
120 240 360 480 600 720 840
Times (s)
(a)
960 1080 1200
400
350
300
795
WiFi
SVSS
ONRWF
250
200
150
100
50
0
120 240 360 480 600 720 840
Times (s)
(b)
960 1080 1200
Fig. 4: Comparison chart of average energy consumption under (a) “multi-source with the same priority
Data in transmitting quue (kb)
messages” and (b) “multi-source with multi-priority messages” scenarios
180000
160000
140000
120000
100000
80000
60000
40000
20000
0
Class 2 message
Class 1 message
WiFi
SVSS
Routing mechanism
ONRWF
Fig. 5: The average queue length performance under “multi-source with multi-priority messages” at 840
seconds during the experiment
can significantly improve the data reception performance in mobile environment.
6
Conclusions
In this paper, we designed, implemented and evaluated a novel routing protocol based on WiFi for
opportunistic networks, namely ONRWF, which aims to make up for the lack of bandwidth and
buffer space. We proposed the formula to calculate the prediction of the future meeting between
two nodes as the next hop routing decision, in which the average prediction of probability value
is used to maximize the delivery probability. In order to improve the efficiency of resourceconstrained node’s buffer space, we proposed the buffer management strategy which contains
scheduling policy and drop policy. The proposed scheduling policy aims to improve message
delivery of high priority, while the proposed drop policy focuses on improving the efficiency of
buffer space. Finally, we conduct the experiment in the real world to verify the effectiveness of
the proposed routing protocol.
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