IEEE SENSORS JOURNAL, VOL. 11, NO. 11, NOVEMBER 2011

2861

A DTN-Based Sensor Data Gathering for
Agricultural Applications
Hideya Ochiai, Member, IEEE, Hiroki Ishizuka, Yuya Kawakami, and Hiroshi Esaki, Member, IEEE
(Invited Paper)

Abstract—This paper presents our ﬁeld experience in data collection from remote sensors. By letting tractors, farmers, and sensors have short-range radio communication devices with delay-disruption tolerant networking (DTN), we can collect data from thosesensors to our central database. Although, several implementations have been made with cellular phones or mesh networks inthe past, DTN-based systems for such applications are still under
explored. The main objective of this paper is to present our practical implementation and experiences in DTN-based data collection
from remote sensors. The software, which we have developed forthis research, has about 50 kbyte footprint, which is much smaller
than any other DTN implementation. We carried out an experiment with 39 DTN nodes at the University of Tokyo assuming an
agricultural scenario. They achieved 99.8% success rate for data
gathering with moderate latency, showing sufﬁcient usefulness indata granularity.
Index Terms—Delay-disruption tolerant networking (DTN), experiment, sensor data gathering, sensor networks.

I. INTRODUCTIONGRICULTURAL researchers and farmers deploy sensors
at their remote agricultural-ﬁelds to obtain the data of
temperature, humidity, soil moisture and so on. Automatic collection of those data greatly helps their analytical works. Currently, they are relying on network providers, for example, cellular phone network to achieve such automatic collection fromtheir remote sites, which is not feasible for most of the farmers
due to operational cost.
We have explored a delay-disruption tolerant networking(DTN)-based system that collects sensor data from remote sites
without relying on network providers. DTN, which stands fordelay (or disruption) tolerant network, was originally proposed
for interplanetary communication [4]. It is now widely acknowledged as a framework that can be applied to village-to-village
communications [7], vehicular communications ([10], [13]),A

Manuscript received January 31, 2011; revised September 21, 2011; accepted
September 23, 2011. Date of publication October 03, 2011; date of current
version October 26, 2011. This is an expanded paper from the IEEE SENSORS 2010 Conference and was published in its proceedings. The associate
editor coordinating the review of this paper and approving it for publication
was Dr. Thomas Kenny.
The authors are with the University of Tokyo, Tokyo, Japan 1138656(e-mail: jo2lxq@hongo.wide.ad.jp; isi@mcl.iis.u-tokyo.ac.jp; yuyarin@hongo.
wide.ad.jp; hiroshi@wide.ad.jp).
Color versions of one or more of the ﬁgures in this paper are available online
at http://ieeexplore.ieee.org.
Digital Object Identiﬁer 10.1109/JSEN.2011.2170562

and sensor networks ([12], [22]). Focusing on the application
to sensor networks, we contribute to the research community
by showing our implementation-based experiment.
Several approaches exist for collecting data from such remotesensors. However, we must keep in mind that sensors should
cover the agricultural ﬁelds sparsely but the number would become large because the ﬁeld itself is large. We must collect datafrom a number of sparsely deployed sensors. Thus, attaching acellular phone device or satellite communication device to every
sensor is not feasible for normal farmers because of the communication fee. Setting up wireless mesh network with ad-hoc
technologies (e.g., optimized link state routing protocol (OLSR)[6] or ad hoc on-demand distance vector (AODV) [16]) could be
used but it requires a huge number of relay nodes in the large
area (i.e., we must densely deploy them) so as to extend network
connectivity to sensors. The DTN approach, which uses the mobility of tractors and farmers, allows the collection of data fromthose sparsely deployed sensors.
We assume that sensors, vehicles (e.g., tractors, farmers) andthe homes of farmers have short-range radio communication devices. Farmers and researchers use their tractor or their foot tovisit their remote sites constantly: e.g., every day or every week.
The radio device on such vehicles contacts with sensors at the remote sites, and returns to their home position. By making use of
the ad hoc communication chances, they collect remote sensor
data to their data server.
This paper presents our ﬁeld experience on sensor data gathering conducted in the University of Tokyo assuming such an
agricultural scenario. We deployed ﬁve weather sensors sparsely
in the campus, and vehicular nodes collaboratively collected
data from them to our central database. Totally, we have introduced 39 DTN nodes for the experiment and they have achieved
99.8% success rate for data collection.
This paper is organized as follows. Section II describes thesystem overview for DTN-based data gathering from agricultural-ﬁelds. Section III presents the routing algorithms andmanagement mechanism for delivering data over the network.
Section IV shows the experiment and its result. Section V
provides discussion and related works. Finally, we conclude
this paper in Section VI.
II. DATA GATHERING WITH DTN FROM REMOTE SENSORS
Fig. 1 shows the overview of the data collection mechanism
from remote agricultural-ﬁelds. Here, we assume that thissystem is operated by a research laboratory or a farmer. Thereis a data server at their home position that archives the data

1530-437X/$26.00 © 2011 IEEE

2862

IEEE SENSORS JOURNAL, VOL. 11, NO. 11, NOVEMBER 2011

Timestamp: 2010-11-02T12:34:00+09:00
Temperature: 25.5
Humidity: 56.9
RainFall: 0.3

Fig. 1. By using the movement of vehicle, DTN allows data collection fromremote agricultural ﬁeld sensors.

This message is composed of header part and body part. In
the header part, it speciﬁes that the destination is 99 (their dataserver) and shows that it came from 1 (sensor #1). This message
still has 81250 second for delivery lifetime. In the body part,the message has timestamp and sensor readings. Though thiswas not the real format we used in the experiment (we usedbinary format in a UDP message), this kind of information was
contained in the message.
The sensor itself does not need to care the storage or retransmission of the message. The network manages the delivery withtaking care of storing and retransmitting by using the message
header information.
III. MESSAGE ROUTING TO PROPERLY GATHER DATA

Fig. 2. By setting higher potential to remote sensors and lower potential to dataserver, sensor data autonomously gathers at lower potential nodes, just as waterﬂows from higher places to lower places.

collected from their remote sites. Their tractor moves between
the home position and the remote sites. Each node (i.e., dataserver, vehicle and sensor) has a Wi-Fi interface, and the Wi-Fi
interfaces are working in “ad hoc” mode. Thus, when theycome into radio communication range, they ﬁnd each other andcan exchange data. For example, when the tractor arrives at the
remote sites, it gets the history record of the sensor data andsaves into its local storage. After return, it transfers the stored
data to the data server. Even though the delivery of data is not
real-time (because it is physically bound to the movement of
the vehicle), it is sufﬁcient for data analysis purposes.
We apply potential-based routing (PBR) approach to enable autonomous data transfer. Fig. 2 shows how PBR allows
autonomous data delivery from sensors to the data server bymaking use of the vehicle. In PBR, we introduce potential—ascalar value that represents a heuristic proximity from thedestination. We deﬁne a rule that a node sends messages tothe node that has the lowest potential among its neighbors. By
setting higher potential to the sensors and lower potential to
the server, the vehicle receives data from the sensors at the
remote sites and provides data to the server when returned. In
this way, sensor data gather at the data server by making use of
the movement of the vehicle.
Each sensor submits observed sensor readings in a message
form periodically to the network with specifying the destination
to be the data server. For example, the content of a message sent
by a sensor could be as follows:
Destination: 99
Source: 1
TTL: 81250

The previous example (e.g., Fig. 2) is actually a simple case;only one vehicle is assumed and it is easy to set higher potential to sensors and lower potential to the server. However, theproblem becomes complex if the number of vehicles increases.
For example, in our experiment setting, a vehicle is assigned toa certain region and it cannot get out from the region. Other vehicles work in different regions. They have overlapped areas,
and they can exchange messages there. We must set appropriate
potential values to them so as to properly direct the messages totheir destinations.
We recognize that we can manually design and setup appropriate potential values for each node for the given deployment
or movement pattern. However, we here assume that they
determine such patterns in ad hoc manner and the patternschange day-by-day. Conﬁguration of potential values to, forexample, 30 nodes every day becomes a hard work. Thus, thenodes must develop proper potential values autonomously. If
we could allow autonomous development of potential values
at each node, the network can be applied to such a practicalscenario.
Inheriting the concept of potential-based routing, we generally proposed potential-based entropy adaptive routing (PEAR)
in our previous literature [14]. Thus, our ﬁeld study is one of theapplications of PEAR. In this section, we introduce the overview
of PEAR as a data routing framework for sensor data gathering.
However, because the main focus of this paper is not on the algorithms of PEAR but on the ﬁeld experiences on sensor data
gathering, please refer to the previous paper for more detail.
A. Potential-Based Routing (PBR)
As we described, we applied potential-based routing (PBR)
to sensor data gathering. We here provide the formal deﬁnitionof PBR.
Let
be a set of nodes andbe a set of neighbors.1 A node has potential values, each of whichof nodeis associated to a particular destination. We denote a potential
1In our deﬁnition,nbr(n) includes n itself: i.e., fng  nbr(n).

OCHIAI et al.: A DTN-BASED SENSOR DATA GATHERING FOR AGRICULTURAL APPLICATIONS

2863

value by: the potential of nodeassociated toat timeslot .
is the force on data
destinationfrom node toward neighbor , which we deﬁne as(1)Then, PBR forwards data according to the following rule.
,
if

(2)
Fig. 3. DTN nodes (UTMesh) for the experiment. We conﬁgured ﬁve nodes
for sensors and one node for database, used seven nodes as vehicular nodes anddeployed 26 static nodes (Fig. 5 illustrates the setting of the experiment).

else
(3)
Here, , a positive constant parameter, is the threshold of data
exceeds , the nexthop is such neighbortransfer. If
that gives maximum. If not, no nexthop is provided fordestination , which means that the data should be stored in thelocal buffer.
B. Potential-Field Construction
In order to develop potential values autonomously, we havedesigned the following potential-ﬁeld construction algorithm in
PEAR

Fig. 4. A DTN node is composed of Linux computer, storage device, Wi-Fi(IEEE802.11) interface, and rechargeable battery. With Wi-Fi in ad hoc mode,
DTN nodes can exchange data with neighbor nodes.

(4)
(5)
(6)
The potential of destination is always tied to 0 [(5)]. Other
potential values dynamically change depending on node-contact patterns. A potential normally grows by at every timeslot,
but decreases when the node has encountered such node thatgives lower potential [(4)]. As a whole, the destination has thelowest potential, the nodes near the destination have lower potential, and the distant nodes have higher potential. In this way,
the potential values autonomously develop as Fig. 2.
is a diffusion parameter that deﬁnes how rapidly potentialchanges propagate in the network. If it becomes larger, potential values decrease faster when the node has encountered lower
nodes, and dissemination of low-potential information becomes
faster.
C. Acyclic Message Propagation
We originally considered that forwarding data in the network
was the correct delivering scheme. Data forwarding means thata node removes data from its local buffer when forwarded to thenext node. In this data propagation scheme, the data possibly
return to the node which it has visited before. This is widely
known as loop, which only consumes network resources suchas radio transmissions, CPU, and data buffers.

We have introduced replica management algorithm into
PEAR, however, not only for avoiding such loop. The main
objective was to allow copy-based data transfer, which propagates data in the network not by forwarding but by copying.
It requires each node to have states to indicate which data arealready received. It has turned out that replica management
avoids forwarding loop because once a node gets data it does notreceive the same data again. Though copy-based data transfer
consumes network resources especially radio transmissions anddata buffers, the amount of sensor data in our application is notlarge and practically feasible (as our experiment demonstrates
in Section IV).
Actually, copy-based data transfer is effective. Spray and
Wait ([19], [20]) has shown that message replication in the
network reduces delivery latency and improves delivery successrate under random way point mobility models. Though it loads
on the network by radio transmissions and buffer usages, it
improves the availability (i.e., fault tolerance) and latency bytaking several delivery paths in parallel.
IV. IMPLEMENTATION AND DEPLOYMENT
A. Experiment Setting
We carried out an experiment with 39 DTN nodes from
UTMesh2 nodes (Fig. 3). We installed our experimental soft2UTMesh: wireless mesh networking testbed at the University of Tokyo

2864

IEEE SENSORS JOURNAL, VOL. 11, NO. 11, NOVEMBER 2011

Fig. 5. The experiment conﬁguration at the University of Tokyo. We deployed sensors (#1, . . ., #5), database, vehicular nodes (m1; . . . ; m7), and static nodes (1,. . ., 26). (a) We divided the campus into ﬁve regions, and vehicular nodes moved inside their assigned regions. We deployed several static nodes in the overlappedareas, intending that they help data propagation between the regions. (b) Database was setup at our laboratory, on the 10th ﬂoor of Engineering Building 2. We
deployed static nodes along with stairs to extend the network from (c) the ground level of the building. When vehicular nodes walk around 6, 7, and 8, the static
nodes absorb messages from the vehicular nodes and forward to the upper stairs.

ware for disruption tolerant networking into these nodes anddeployed into the campus of the University of Tokyo. The
software implemented PEAR with about 3000 lines in C sourcecode.3 The footprint was about 51 kbyte, which indicates that
it is runnable on embedded computers. It had 3072 entries fordata buffer in the experiment. We set 0.02 for , 0.2 for and
0 for [(2) and (4)].
Fig. 4 shows the conﬁguration of a DTN node. It has an embedded computer called Armadillo-220, which has 8 Mbyte forprogram memory and 32 MByte for working memory. It works
with ARM9 200 MHz CPU and Linux operating system. We
added an USB Wi-Fi (IEEE802.11) interface4 for ad-hoc communication with radio-range neighbors, and an USB storage toarchive the working logs. We used linux-2.6.12.3-a9-15 for its
kernel image. The DTN nodes were powered by battery during
the experiment.
We carried out the experiment in the following manner. As
Fig. 5, we divided the campus into ﬁve regions (A, B, C, D,
E). They had small overlapped areas where vehicular nodes cantransfer data to the other region. We deployed ﬁve weather sensors (#1, #2, #3, #4, #5) in each region respectively and assigned
to the regions as the ﬁgure. We
vehicular nodes
called for participation for the experiment from our laboratory
students, assigned each student a region and a DTN node, asked
them to be a vehicular node and to walk inside their working
regions. We deployed 8 static nodes (1, , 8) at the overlappedareas, intending that they help message transfer between the re3http://sourceforge.net/projects/pear/ﬁles/.
4GW-USMicroN, Planex Communications, Inc.

gions. Other static nodes (9, , 26) were deployed at the stairarea in Engineering Building 2 [Fig. 5(b)]. The data server was
deployed at the 10th ﬂoor, where our laboratory is located.
The weather sensors used in the experiment were Vaisala
WXT520. The sensor nodes observed the weather status(i.e., temperature, humidity, rainfall, wind speed and direction, air pressure) and sent them to the database every 30 swith 7200 s for delivery lifetime. Actually, each node had
an unique identiﬁer. For example, sensors were operated by. Vehicular nodes
were operated by. The node
ID of the data server was #099. Data transmission and routing
was based on those identiﬁers; i.e., sensors have sent data from#011, #012, #013, #014, #015 to #099. The naming rule above
such as sensor #1, sensor #2 and m1, which were different from
nodeIDs, was deﬁned just for readability.
The experiment was conducted for one day. But, we have
setup 80 min for core experiment time (13:50–15:10). We ﬁrst
setup static nodes and sensor nodes at the speciﬁed sites, thenvehicular nodes walked inside their own regions at the core time.
We studied the summarized network topology, potential-ﬁeld
construction, delivery pattern, delivery success rate, delivery latency, and the collected sensor readings.
B. Network Topology
Fig. 6 illustrates the summarized network topology madeduring the experiment duration. The boldness of links indicatesthe summary of contact-duration between the two nodes. The
vehicular nodes relayed data between static nodes and sensor

OCHIAI et al.: A DTN-BASED SENSOR DATA GATHERING FOR AGRICULTURAL APPLICATIONS

2865

Fig. 7. Dynamics of the potential-ﬁeld construction. After a while (in non-transitive phase), the closer nodes to the destination had lower potential values andthe distant nodes had higher potential values.

Fig. 6. The summarized network topology made in the experiment. The boldness of links indicates the summary of contact-time duration between the two
nodes. The vehicular nodes relayed static nodes and sensor nodes in their regions. As a whole, all the nodes were connected into a single network.

nodes in their regions. As a whole, all the nodes were connected
into a single network. The data transmitted from sensors were
copied via these links (when available) and ﬁnally arrived at
the database.
The static nodes deployed in the building tightly connectedwith each other. Vehicular nodes (m5, m6, m7) sometimes visited the entrance of the building (around static nodes 6, 7, 8). m7
visited sensor #1, m5 and m6 visited sensor #3 and static node
(3 and 4). In the same way, m1 bridged data from sensor #4 tosensor #3 or static 1 (in this case, sensor #3 was working as arelay node for sensor #4).
C. Potential-Field Construction
Fig. 7 illustrates how potential values have changed during
the experiment. This diagram shows the potentials of sensor
nodes (#3, #4), vehicular nodes (m1, m5, m6) and static nodes(1, 6, 20). At 13:50, vehicular nodes departed from the entranceof the building to their working regions. The potential values
started to increase after the departure. In the transitive phase,the vehicular nodes contacted sensor #3, static 1, and sensor #4,
which had been working alone. The potentials of those nodes
were high because they had not contacted any nodes in the previous several hours, but after the contacts those potentials decreased. After vehicular nodes walked inside their regions manytimes, the network went into a nontransitive phase. The potential
values change dynamically according to the movement of vehicles. However, this diagram tells that, in the long run, the closer
nodes to the destination had lower potential values (static 20 and
6) and the distant nodes had higher potential values (sensor #4,
static 1, and m1).

D. Delivery Pattern, Success Rate, and Latency
Fig. 8 illustrates the data delivery pattern in the networkfrom the sensors #1, #2, #3, #4, #5 to the database. Eacharrow shows data transmissions between the nodes. The
boldness indicates how many data were sent at the sametime (bolder arrows sent larger number of data elements).
The color identiﬁes the source of the data. Each color corresponds as follows:.
This diagram tells the following dataﬂow.
• Vehicles took data from sensors and sent mostly to relay
nodes (including sensor #3). For example, vehicle m7 took
data from sensor #1 around 14:38 and sent to static 6 and 7
around 14:44. Vehicle m1 took data from sensor #4 around
14:35 and copied them to sensor #3 (relay node) around14:41.
• Static nodes deployed in the ﬁeld relayed data between theregions. For example, static 4 received data from sensor #2
and #5 via vehicle m3 (Region B) around 14:38 and copied
them to vehicle m6 (Region C) around 14:43.
• Data were sometimes copied between vehicles directly:
e.g., from m3 to m5, from m7 to m5, and from m5 to m6.
• Static nodes deployed in the building (static 6, , 26) relayed data to the upper stairs.
• Finally, the data reached the database.
Fig. 9 shows delivery success rate and delivery latency fromeach sensor to the database. This analysis was made in the following manner. First, we focused on the data sent between 13:30
and 14:30, then calculated the latency for each data reached at
the database before 15:10. The experiment ended at 15:10 when
they remained still in their own region. We got this diagram bysummarizing them into delivery success rate. If some data are
missing, the success rate does not reach 1.
Data were collected with moderate latency about 10–75 min,effectively using the movement of vehicles—these distributions
depended on the physical movement. Finally, almost all the data
were gathered at the database. The network achieved 100% delivery from sensor #2, #3, #4, #5. The delivery rate from sensor#1 was 99.2%. The vehicle who took a role in Region A did
not visit sensor #1 until 14:37 though the experiment begun at
13:50. This failure of the movement probably missed a small
piece of data from sensor #1. Anyway, totally, the network has
achieved 99.8% delivery success rate.

2866

IEEE SENSORS JOURNAL, VOL. 11, NO. 11, NOVEMBER 2011

Fig. 8. Dataﬂow from the sensors (#1, #2, #3, #4, #5) to the database. An arrow shows data transmission between the nodes. Bolder arrows sent larger number ofdata at the same time. The color corresponds to the source of the data ﬂows as (#1; #2; #3; #4; #5) =(red; yellow; green; blue; violet).

E. Application
Fig. 10 shows the sequence of the collected temperature.
We can read that the network has collected data with sufﬁcient
time-granularity at least for agricultural use cases. Actually,
there were almost no loss (see Fig. 9). Almost all the data weregathered at the database by effectively using the movement ofvehicles.
According to the deployed location, the temperature patterns
were quite different. The temperature of sensor #2, which was
deployed under the sunlight, rapidly increased before 13:55 anddecreased after that (because the sunlight was blocked off byclouds). The result indicates that the interval of readings anddelivery success rate were sufﬁcient for tracking this kind ofchanges.

V. DISCUSSION AND RELATED WORK
We have demonstrated with real implementation that theapplication of DTN allows sensor data gathering with sufﬁcient
success rate. We organized the network for data collection fromremote sensors without buying PHS or cellular phones. Instead,
we used mobile nodes assuming that farmers and tractors
move around in their agricultural-ﬁelds. In this work, we havestudied the performance of PEAR—a data routing scheme forsuch network. It efﬁciently used the pattern of mobile nodes
and achieved 99.8% success rate. The footprint of PEAR was
51 kbyte, thus, we could implement them into an embedded
computer.
Sensor data gathering with the application of DTN was identiﬁed, for example, by DFT-MSN [22] and vehicular sensor networks [12]. Ren et al. [17] proposed a hybrid approach with

OCHIAI et al.: A DTN-BASED SENSOR DATA GATHERING FOR AGRICULTURAL APPLICATIONS

Fig. 9. Delivery rate and delivery latency from each sensor to the database.
Finally, the network achieved 100% delivery from all the sensors except #1.
The delivery rate from sensor #1 was 99.2%. The pattern of the latency actuallydepends on the physical movement of vehicles.

2867

Other ﬁeld-experiments related to sensors were ZebraNet [9],Alan Mainwaring et al. [21] and LUSTER [18]. ZebraNet presented a network and system design for wildlife tracking. Theydeveloped some prototype systems and planned to deploy 30
nodes to Mpala Research Centre, Kenya. Alan et al. carried out
ﬁeld-experiments on habitat and environmental monitoring with
Mica Motes. They have shown 28% success rate in multihop
cases without DTN. LUSTER deployed static wireless sensor
nodes to Hog Island. They applied DTN for improving reliability of communication.
In the context of sensor data gathering, some researchers
demonstrate the use of portable sensors: e.g., people-centric
sensing [5], BikeNet [8], and vehicular sensing platforms [11].
They say that they can create a map (e.g., CO2 map) withsmaller number of portable sensors with the trace of the node
movement. The difference is that we have assumed physicallyﬁxed sensors in this paper.
An earlier version of this paper [15] has provided a smallerscale experiment with 11 nodes, which can be called as a preliminary experiment. In this paper, we carried out a larger-scale
experiment with 39 nodes. Both experiments have shown great
delivery success rate and sufﬁcient usefulness.
VI. CONCLUSION

Fig. 10. Collected temperature from sensor #1, . . ., #5. The experimentachieved almost 100% delivery (see Fig. 9) and provided sufﬁcient time-granularity in the gathered data.

cellular phones. However, their research targets were theoretical improvements of, for example, success rate and power consumption with only simulation-based experiments. Implementation work or feasibility study (i.e., proof-of-concept) were left
untouched.
Though their targets were not primarily on sensor data gathering, UMass DieselNet allowed the study of message routing
on bus-based DTNs (e.g., Xiaolan et al. [23], MaxProp [3], and
RAPID [1]). They used WiFi attached small computers such as
DTN-throwboxes [2] for their research. However, we found thatmost of their work were measurement of contacts and simulation of message routing. Only the paper on RAPID [1] presented
the deployment of their DTN routing protocol. But, the scale
was not large: i.e., each bus generated four packets (1 k-byte)
per hour to every other bus and they achieved 88% success rate.
We recognize that we cannot simply compare their work withours because the assumed application and the network environment are different. However, sensors in our work generated data
every 30 s, and our network achieved 99.8% success rate.

In this paper, we have presented our practical study on DTNbased sensor data gathering for agricultural-ﬁeld sensors. We
recognize that cellular and mobile phones could be also used fordata collection from remote sensors. However, we demonstrated
that DTN-based approach can develop data gathering networkby making use of the facilities that farmers or agricultural researchers have: e.g., tractors.
We have carried out an experiment with 39 DTN nodes andPEAR, a DTN implementation, on the campus of the University of Tokyo assuming an agricultural scenario. We conﬁrmedthat data from sensors were transferred to the data server witheffectively using the movement of vehicles. In our experimentsettings, almost all the data (99.8%) were delivered in 10–75
min delay.
REFERENCES[1] A. Balasubramanian, B. N. Levine, and A. Venkataramani, “Replication routing in DTNs: A resource allocation approach,” IEEE/ACM
Trans. Networking, vol. 18, no. 2, pp. 596–609, Apr. 2010.
[2] N. Banerjee, M. D. Corner, and B. N. Levine, “Design and ﬁeld experimentation of an energy-efﬁcient architecture for DTN throwboxes,”
IEEE/ACM Trans. Networking, vol. 18, no. 2, pp. 554–567, Apr. 2010.
[3] J. Burgess, B. Gallagher, D. Jensen, and B. N. Levine, “MaxProp:
Routing for vehicle-based disruption-tolerant networks,” in Proc. IEEE
INFOCOM, 2006, pp. 1–11.
[4] S. Burleigh, A. Hooke, L. Torgerson, K. Fall, V. Cerf, B. Durst, K.
Scott, and H. Weiss, “Delay-tolerant networking: An approach to interplanetary internet,” IEEE Commun. Mag., vol. 41, no. 6, pp. 128–136,
Jun. 2003.
[5] A. T. Campbell, S. B. Eisenman, N. D. Lane, E. Miluzzo, R. A. Peterson, H. Lu, X. Zheng, M. Musolesi, K. Fodor, and G.-S. Ahn, “Therise of people-centric sensing,” IEEE Internet Computing: Mesh Networking, pp. 30–39, Jul. 2008.
[6] T. Clausen and P. Jacquet, “RFC3626: Optimized link state routing
protocol (OLSR),” Oct. 2003.
[7] D. Demmer and K. Fall, “DTLSR: Delay tolerant routing for developing regions,” in Proc. ACM NSDR, 2007.

2868[8] S. Eisenman, E. Miluzzo, N. Lane, R. Peterson, G. S. Ahn, and A. T.
Campbell, “BikeNet: A mobile sensing system for cyclist experience
mapping,” ACM Trans. Sensor Networking, vol. 6, no. 1, pp. 1–39, Dec.
2009.
[9] P. Juang, H. Oki, Y. Wang, M. Martonosi, L.-S. Peh, and D. Rubenstein, “Energy-efﬁcient computing for wildlife tracking: Design tradeoffs and early experiences with ZebraNet,” in Proc. ACM SIGOPS,
2002, pp. 96–107.
[10] F. Laurent and G.-C. Felipe, “Using delay tolerant networks for car2car
communications,” in Proc. IEEE Ind. Electron. Symp., Jun. 2007, pp.
2573–2578.
[11] U. Lee, E. Magistretti, M. Gerla, P. Bellavista, and A. Corradi, “Dissemination and harvesting of urban data using vehicular sensing platforms,” IEEE Trans. Veh. Technol., vol. 58, no. 2, pp. 882–901, Feb.
2009.
[12] X. Li, W. Shu, M. Li, H. Huang, and M.-Y. Wu, “DTN routing in vehicular sensor networks,” in Proc. IEEE Globecom, 2008, pp. 1–5.
[13] S. M. , X. Li, D. Li, M.-Y. Wu, M. Li, and W. Shu, “Routing inlarge-scale buses ad hoc networks,” in Proc. IEEE WCNC, 2008, pp.
2711–2716.
[14] H. Ochiai and H. Esaki, “Mobility entropy and message routing in community-structured delay tolerant networks,” in Proc. ACM AINTEC,
2008, pp. 93–102.
[15] H. Ochiai, H. Ishizuka, Y. Kawakami, and H. Esaki, “A ﬁeld experience on DTN-based sensor data gathering in agricultural scenarios,” inProc. IEEE Sensors, 2010, pp. 955–958.
[16] C. Perkins, E. Belding-Royer, and S. Das, “RFC3561: Ad hoc on-demand distance vector (AODV) routing,” Jul. 2003.
[17] B. Ren, J. Ma, and C. Chen, “The hybrid mobile wireless sensor networks for data gathering,” in Proc. ACM IWCMC, 2006.
[18] L. Selavo, A. Wood, Q. Cao, T. Sookoor, H. Liu, A. Srinivasan, Y.
Wu, W. Kang, J. Stankovic, D. Young, and J. Porter, “LUSTER: Wireless sensor network for environmental research,” in Proc. ACM SenSys,
2007.
[19] T. Spyropoulos, K. Psounis, and C. S. Raghavendra, “Spray and Wait:
An efﬁcient routing scheme for intermittently connected mobile networks,” in Proc. ACM SIGCOMM, 2005, pp. 252–259.
[20] T. Spyropoulos, K. Psounis, and C. S. Raghavendra, “Efﬁcient routing
in intermittently connected mobile networks: The multiple-copy case,”
IEEE/ACM Trans. Networking, vol. 16, no. 1, pp. 77–90, Feb. 2008.
[21] R. Szewczyk, A. Mainwaring, J. Polastre, J. Anderson, and D. Culler,“An analysis of a large scale habitat monitoring application,” in Proc.
ACM SenSys, 2004.
[22] Y. Wang and H. Wu, “Delay/fault-tolerant mobile sensor network(DFT-MSN): A new paradigm for pervasive information gathering,”
IEEE Trans. Mobile Computing, vol. 6, no. 9, pp. 1021–1034, Sep.
2007.
[23] X. Zhang, J. Kurose, B. N. Levine, D. Towsley, and H. Zhang, “Studyof a bus-based disruption-tolerant network: Mobility modeling and impact on routing,” in Proc. ACM MobiCom, 2007.

IEEE SENSORS JOURNAL, VOL. 11, NO. 11, NOVEMBER 2011

Hideya Ochiai received the B.E. degree and M.E. degree in information and communication engineeringfrom the University of Tokyo, Tokyo, Japan, in 2006
and 2008, respectively. Currently, he is working towards the Ph.D. degree at the University of Tokyo.
He is also a Researcher with the National Instituteof Information and Communications Technology(NICT), Japan. His research interests cover wide-area
sensor networking, delay tolerant networking, andbuilding automation systems.

Hiroki Ishizuka received the B.E. and M.E. degrees
from Tokyo Denki University, Tokyo, Japan, in 2006
and 2008, respectively. Currently, he is working towards the Ph.D. degree in information and communication engineering at the University of Tokyo.
His research interests cover sensor networking,application of android phones and participatory
sensing.

Yuya Kawakami received the B.E. degree in information and communication engineering fromthe University of Tokyo, Tokyo, Japan, in 2009.
Currently, he is working towards the M.S. degree atthe University of Tokyo.
His research interests cover visualization of
dataﬂow in delay tolerant networks, content delivery
networks, and interdomain routing.

Hiroshi Esaki received the Ph.D. degree from the
University of Tokyo, Tokyo, Japan, in 1998.
In 1987, he joined the Research and Development
Center, Toshiba Corporation. From 1990 to 1991,
he was with the Applied Research Laboratory,
Bell-Core Inc., NJ, as a Residential Researcher.
From 1994 to 1996, he was with the Center for
Telecommunication Research, Columbia University,
New York. Since 1998, he has served as a Professor
at the University of Tokyo, and as a board memberof WIDE Project. Currently, he is the Executive
Director of IPv6 promotion council, Vice President of JPNIC, IPv6 Forum
Fellow, and Director of WIDE Project.