A Field Experience on DTN-Based Sensor Data
Gathering in Agricultural Scenarios
Hideya Ochiai
The University of Tokyo/ NICT
jo2lxq@hongo.wide.ad.jp

Hiroki Ishizuka
The University of Tokyo
isi@mcl.iis.u-tokyo.ac.jp

Abstract—This paper describes our ﬁeld experience on datacollection from remote sensors. By letting tractors, farmers andsensors have short-range radio communication device with delaydisruption tolerant networking (DTN), we can collect data fromthose remote sensors to our central database. Although, several
implementations have been made by using PHS devices or meshnetwork in the past, DTN-based systems for such applications arestill under explored. The main contribution of this paper is topresent our practical implementation and experiences on DTNbased data collection from remote sensors. The software, which
we have developed for this research, is very small – only about3000 lines in C, which is much smaller than any other DTN
implementations. We carried out an experiment with 10 DTN
nodes in the University of Tokyo. They achieved 100% collection
with moderate delivery latency showing sufﬁcient usefulness indata granularity.
Index Terms—DTN, Application, Implementation, Sensor Networks

I. INTRODUCTION
Agricultural researchers and farmers deploy sensors at theirremote agricultural-ﬁelds to obtain the data of temperature,humidity, soil moisture and so on. Automatic collection of
those data greatly helps their analytical works and schedule
planning for their activity. Currently, they are relying on
network providers, for example, personal handy-phone system(PHS) to achieve such automatic collection from their remotesites, which is not feasible for most of the farmers because of
the operational cost reason.
We have explored a DTN-based system that collects sensor
data from remote sites without relying on network providers.
DTN, which stands for delay (or disruption) tolerant network,
was originally proposed for inter-planetary communication[1].
It is now widely acknowledged that we can apply theconcept to village-to-village communications[2], vehicularcommunications[5], [3] and sensor networks[4], [7]. Focusing
on the application of DTN to sensor networks, we contributeto the research community by showing our implementationbased experiment.
Several approaches exist for collecting data from such
remote sensors. 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. Thus,attaching a personal handy-phone system (PHS) device orsatellite communication device to every sensor is not feasible
for normal farmers because of the communication fee. Setting

Yuya Kawakami
The University of Tokyoyuyarin@hongo.wide.ad.jp

Hiroshi Esaki
The University of Tokyo/ NICT
hiroshi@wide.ad.jp

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

up wireless mesh network with ad-hoc technologies (e.g.,OLSR) could be used but it requires a huge number of relay
nodes in the large area so as to extend network connectivity tosensors. The DTN approach with using the mobility of tractors
and farmers allows sparse deployment of sensors in large area
and collects data along with the movement of daily works.
We assume that sensors, vehicles (e.g., tractors, farmers) andthe homes of farmers have short-range radio communication
device. Farmers and researchers use their tractor or their footto visit their remote sites constantly (e.g., everyday or everyweek). 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 collectremote sensor data to their data server.
This paper is organized as follows. Section 2 describes
the system overview for DTN-based data collection fromagricultural-ﬁelds. We show our experiments and the results
in section 3. In section 4, we provide a discussion for future
work. Section 5 provides the conclusion of this paper.
II. DTN-BASED DATA C OLLECTION FROM R EMOTE
AGRICULTURAL -F IELDS
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 datacollected 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 wiﬁ interface, and the wiﬁinterfaces are working in ”ad hoc” mode. Thus, when theyFig. 2. By setting higher potential to remote sensors and lower potential todata server, sensor data autonomously gathers at lower potential node, just as
water ﬂows from higher places to lower places.

come into radio communication range, they ﬁnd each otherand can exchange data. For example, when the tractor arrives
at the remote sites, it gets the history record of the sensor dataand saves into its local storage. After returned, it transfers thestored data to the data server. Even though the delivery ofdata 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 –a scalar 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 for the sensors and lower potential forthe server, the vehicle receives data from the sensors at theremote sites and provides data to the server when returned. In
this way, sensor data gathers at the data server by making useof 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 amessage sent by a sensor could be as follows:
Destination: 99
Source: 1
TTL: 81250

Fig. 3. DTN nodes for the experiment. People carry them by hand. They
implement potential-based entropy adaptive routing.

Fig. 4. An experiment scenario in the University of Tokyo. We deployedsensors, database and mobile nodes into three regions (A, B and C).

message still has 81250 second for delivery lifetime. In thebody part, the message has timestamp and sensor readings.
Though this is not the real format we used in the experiment(we used binary format in a UDP message), this kind of
information was contained in the message.
The sensor application program itself does not need to care
the storage or transfer of the message. The network manages
the delivery with taking care of storing and transferring byusing the message header information.
III. I MPLEMENTATION AND D EPLOYMENT
A. Experiment Setting

Timestamp: 2010-11-02T12:34:00+09:00
Temperature: 25.5
Humidity: 56.9
RainFall: 0.3
This message is composed of header part and body part.
In the header part, it speciﬁes that the destination is 99 (theirdata server) and shows that it came from 1 (sensor #1). ThisWe assembled 11 DTN nodes (as Fig. 3) and installed thesoftware we developed for disruption tolerant networking1 .
The software implements potential-based entropy adaptive
routing (PEAR)[6] with about 3000 lines in C source code.
We carried out experiments in the campus of the University ofTokyo. As Fig. 4, we allocated three regions (A, B and C) with
1 http://sourceforge.net/projects/pear/ﬁles/Fig. 5. Dataﬂow from the sensors (#1, #2) to the database (#99). Red arrows illustrate delivery of data from sensor #1, and blue arrows from sensor #2.
The number shown along with an arrow indicates how many DTN messages were transmitted between the two edges.

small overlapped areas. The nodes were numbered as #1, #2,#3, ..., #10, #99. Both #1 and #2 had a weather sensor and sent
100-bytes message to #99 in every ten second. #99 was thegateway node for a data server that archives the history recordof sensor readings. The nodes from #3 to #10 were assigned toworkers (i.e., farmers) who moved in their working region. We
deployed sensor #1 into region A and sensor #2 into region B.
#99 was deployed into region C. #3 and #4 moved in B. #5,#6 and #7 moved in C. #8, #9 and #10 moved in A. We set
2400 second for message TTL. The experiment was conducted
for 60 minutes, and we studied the delivery pattern, rate andlatency and the collected sensor readings.
B. Experiment Result

Fig. 6. Distribution of delivery latency from the sensors (#1, #2) to the
database (#99).

Fig. 5 shows the message delivery pattern from sensor#1 and #2 to #99. Red arrows illustrate the transmissionof messages came from sensor #1, and blue arrows fromsensor #2. The number along with an arrow shows how
many messages were transmitted at the same timeslot. Here,#9 is not shown because it was malfunctioning during theexperiment and thus, we removed from the graph. Fig. 6
shows the spectrum of delivery latency. Data were collectedwith moderate latency about 10 minutes to 30 minutes, effectively using the movement of people (we admit that thesedistributions certainly depended on physical movement of thenodes). Finally, all the data were gathered at the database.
Fig. 7 is the sequence of the collected temperature. We canread that it has collected data in sufﬁcient granularity for
agricultural application usages. Actually, there was no loss.
All the messages have been delivered.

[4] X. Li, W. Shu, M. Li, H. Huang, and M.-Y. Wu. DTN routing in vehicularsensor networks. In IEEE Globecom, 2008.
[5] S. M., X. Li, D. Li, M.-Y. Wu, M. Li, and W. Shu. Routing in large-scalebuses ad hoc networks. In IEEE WCNC, 2008.
[6] H. Ochiai and H. Esaki. Mobility entropy and message routing incommunity-structured delay tolerant networks. In ACM AINTEC, pages
93–102, 2008.
[7] Y. Wang and H. Wu. Delay/fault-tolerant mobile sensor network (DFTMSN): a new paradigm for parvasive information gathering. IEEETransactions on mobile computing, 6(9), sep 2007.

Fig. 7. Collected temperature from sensor #1 and #2. There was no loss
during data delivery. It provided sufﬁcient granularity.

IV. D ISCUSSION
Compared to PHS-based sensor data gathering, DTN-based
system has much longer delay in delivering sensor data fromremote sites. However, farmers can install their own DTNbased sensor data gathering system using the equipments they
have: e.g., tractors. If the application allows the delay that thenetwork has, the DTN approach would be practically usefulfor their applications.
We have used PEAR as an implementation of DTN in ourexperiment. The period of the experiment is shorter than theexpected real deployment. We have also assumed that thepowers of nodes are always turned on. However, in more
realistic situation, the power of them may fail, but messages
are still need to be delivered to the destination. In that, we stillneed to explore and develop the implementation that assumes
such situations.
V. C ONCLUSION
In this paper, we have presented our practical study onDTN-based sensor data gathering for agricultural-ﬁeld sensors.
To get sensor data automatically from remote sensors, PHS
devices with network providers could be used. However, DTNbased approach also allows data gathering by making use of
the movement of tractors or farmers.
We have carried out an experiment with PEAR, a DTN
implementation, in the campus of the University of Tokyo.
We conﬁrmed that messages from sensors steadily transferredto the data server. In our experiment settings, all the messages
were delivered in 10 minutes to 30 minutes delay.
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