Wireless Sensor Network, 2010, 2, 655-660
doi:10.4236/wsn.2010.29078 Published Online September 2010 (http://www.SciRP.org/journal/wsn)
L3SN: A Level-Based, Large-Scale, Longevous Sensor
Network System for Agriculture Information Monitoring*
Yongcai Wang, Yuexuan Wang, Xiao Qi, Liwen Xu, Jinbiao Chen, Guanyu Wang
Institute for Theoretical Computer Science, Tsinghua University, Beijing, P. R. China
[e-mail], [e-mail], [e-mail]
Received June 1, 2010; revised July 5, 2010; accepted August 17, 2010
Abstract
We developed L3SN, a scalable, longevous, adaptive, and internet accessible wireless sensor network system
for agriculture information monitoring, which is meticulously designed to meet the requirement of thousands
hectares coverage, years of time monitoring and the adverse environment. The system architecture, the agri-
culture sensor device, the mesh protocol, and the web-based information processing platform are introduced.
We also presented some implementation experience. The mesh protocol (LayerMesh) is highlighted, in
which “stair scheduling” and “distributed dynamic load-balancing” are proposed to response the scalability,
longevity and adaptivity requirements. We believe the design of L3SN is useful to many other large-scale,
longevous applications such as hydrologic monitoring, geological monitoring etc.
Keywords: Wireless Sensor Network, Agriculture Information Monitoring, Large-scale, Longevity,
Adaptivity
1. Introduction
Agriculture Informatization is an important area, related
to people life and national interest, is proposed to be
empowered by wireless sensor network technology. For
agriculture information monitoring, the water, soil condi-
tions, the crops, fruits conditions, as well as the condi-
tions of livestock are required to be monitored in
real-time by many spatially distributed wireless sensors.
These wireless sensors are battery or solar energy pow-
ered, equipped with wireless radio, storage unit, data
processing unit and various sensing units. They are de-
sired to be easily deployed into the large-scale farmland,
to self-organize to a functional distributed multi-hop
network via wireless communication, to work for years
of time to collect data, and to be self-maintenance
against the environmental dynamics of the four seasons.
If such systems are available, they can bring many
economic and social benefits. However, although the
wireless sensor network technology has been fast develop-
ed in the past ten years [1], it is still very challenging to
realize such an agriculture information monitoring sys-
tem. The difficulties come from three aspects.
1. Extremely large in scale. The farmland is often in
the scale of millions hectares. The complexity of network
organization and routing will turn to a qualitative change
when the network scale becomes very large.
2. Extremely long lifetime. Agriculture applications
need the network be functional for years of time, but the
scarce energy on the sensor node can hardly afford this,
especially when the network is large and the sensor has
many data to forward.
3. Adverse environment. The adverse weather will
challenge the durability of the hardware. The growth of
crops will block or affect the wireless links, causing the
network working in a highly dynamic radio environment.
Existing studies have presented some implementation
trails, but seldom results have been presented to thor-
oughly investigate and solve above challenges. In this
paper, we propose and demonstrate L3SN: A Level-
*PAPER CLASSIFICATION: Application System of Wireless Senso
r
N
etwor
k
This research is supported by the national 863 high tech R&D pro-
gram of the Ministry of Science and Technology of China under gran
t
N
o. 2006AA10Z216, the National Science Foundation of China under
grant No. 60604033, and the National Basic Research Program o
f
China Grant 2007CB807900, 2007CB807901.
Based, Large-Scale, Longevous Sensor Network for Ag-
riculture Information Monitoring. We focus on the de-
sign and implementation issues to show how the above
challenges are handled. Our main contributions are two

656 Y. C. WANG ET AL.
folds.
1. The design and development of the L3SN system,
including system architecture, interfaces, hardware, mesh
protocol, and the web-based information processing
platform.
2. The level-based mesh protocol, which is named as
LayerMesh is highlighted. We specially designed “stair
scheduling”, and “distributed dynamic load-balancing”
to handle the above challenges.
To our best knowledge, it is the first time that the fol-
lowing key points are developed and demonstrated for
the large scale sensor network.
1). Load-balancing problem in large scale network is
solved and implemented by a fully distributed algorithm.
2). The two-tiered architecture and the open interface
design provides infinite scalability.
3). The “stair scheduling” and “adaptive routing” pro-
vides novel solutions for energy efficient and adaptive
longtime maintenance.
With these key issues, we designed and developed the
L3SN system. The following sections of this paper are
organized as follows. In Section 2, related work will be
introduced. In Section3, the architecture and interfaces of
L3SN system will be presented. The LayerMesh protocol
will be introduced in Section 4. Some implementation
results and experience are discussed in Section 5. We
conclude the paper in Section 6.
2. Related Works
2.1. Agricultural Information Monitoring Using
Wireless Sensor Network
Wireless sensor network system for agriculture informa-
tion monitoring has attracted great research attentions.
Some preliminary experimental systems have been re-
ported. Jenna Burrell, et al., investigated different sensor
network configurations in vineyard application [2], and
summarized some design guidelines for agricultural
monitoring system. Murat Demirbas, et al. [3] deployed
a small scale sensor monitoring system in a green house
and drew suggestions that single-hop cluster should be a
better solution for easy-to-use and network longevity. A
mobile field data acquisition system was developed by
Gomide et al. [4] to collect data for crop management
and spatial-variability studies. Mahan and Wanjura [5]
cooperated with a private company to develop a wireless,
infrared thermometer system for infield data collection.
Cross-bow developed eKo system [6], which involves
solar-powered “eKo node” with zigbee radio and “eKo
view” for real-time data rendering. It uses Xmesh proto-
col to make the eKo system easily setup.
2.2. Large-Scale Longevious Sensor Network
Studies of Large-scale longevious sensor network can be
categorized into two classes: 1) application-oriented and
2) theoretical-oriented. In the first class, an early study of
WSNs for habitat monitoring is reported in [7]. In [8],
Werner-Allen et al deployed a WSN at Ecuador to moni-
toring the activity of an active volcano. But the system
scale is only 16 nodes. GreenOrbs [9] is reported as a
large-scale system for canopy closure estimates, which
had 120 sensors in 2009. However they used only the
built in low power listening mode to conserve energy,
which is not very energy efficient.
In theoretical aspect, many results can be found in the
literature. Li et al. [1] studied the message, energy and
time complexities for data collection, query and aggrega-
tion in large scale network. Yick et al. [10] provided an
extensive survey for the main results of this area. How-
ever, up to now, few results are found to thoroughly in-
vestigate and solve the scalability, longevity and envi-
ronment dynamics of agricultural wireless sensor net-
work.
3. L3SN System: Architecture and Interfaces
We designed and developed L3SN. The system architec-
ture and interfaces are introduced in this section.
3.1 System Architecture
L3SN is designed with the aim of open architecture and
standardized interfaces. Figure 1 shows the system ar-
chitecture and the standardized interfaces.
Particularly, the architecture of L3SN includes a Two-
tiered Sensor Network for data collection and an Infor-
mation Service Platform for data rendering and processing.
1) Two-tiered Sensor Network.
The sensor network in L3SN is composed by many
low-tier energy-limited nodes (LNs), and some high-tier
gateways. The LNs in low-tier capture environmental
data and report the raw data to the gateway. The gate-
ways organize the neighboring LNs into clusters and
work as the cluster head. It collects information in its
cluster, aggregates the information, and reports the re-
sults to the Internet. Through the Internet, the data cap-
tured by the wireless sensors are finally reported to the
service platform and utilized by end users.
2) Information Service Platform.
The information service platform contains data log-
ging daemon, database, and web server. It provides data
storage, data processing, data querying and other agri-
cultural information services to users.

Y. C. WANG ET AL.
657
Figure 1. System architecture and the standardized inter-
faces in L3SN system.
3.2 Standardized Interfaces
In order to easily integrate more kinds of agriculture
sensors into the system, and in order to standardize the
connection from the sensor network to the internet ser-
vice platform, we propose two standard interfaces in
L3SN:
1) Sensor-Wireless Interface
The “Sensor-Wireless Interface” defines mapping
functions, which transform the sensory data that is cap-
tured by the sensor devices to the meaningful data which
is easily interpreted by the LN nodes. For a sensor type
Si, which provide raw data X, the interface is defined as:
Si(X)={Y = f(X); (Ymin; Ymax); Yprecision} (1)
where f(X) is a mapping function which transforms the
raw data X to meaningful value Y. The variables (Ymin;
Ymax) characterize the measuring range of the sensor Si,
and Yprecision is the measurement accuracy. With this in-
terface, the raw sensory data of diverse format can be
transformed to the regularized format. Therefore, data
from different kinds of sensors can be easily interpreted
by the LN nodes.
2) WSN-Internet Interface
The “WSN-Internet Interface” performs the task of
data aggregation. Since the gateway collects data from
all the LNs in its cluster, the data at the gateway is large
in amount and has some redundancy. In addition, the
message collected from a LN contains both the sensory
information and the routing information. The sensory
information is the agriculture data, and the routing in-
formation represents the multi-hop route that the mes-
sage has traveled. The data aggregation at the gateway is
performed in the following way:
1. Non-compressive aggregation to the sensory data.
The agriculture data from different sensor is accumulated
and forwarded to Internet in bulk for avoiding of infor-
mation loss.
2. Compressive aggregation to the routing data. The
routing information is mainly used for topology con-
struction and maintenance at the service platform. If all
the routing information from all the messages is trans-
mitted to the service platform, it will be very redundant.
A light weight compressive aggregation algorithm is
developed. If the received routing information at the
gateway is: R = [R1, R2, …, Rn], where n is the number
of sensors in the cluster. Ri is the route from the node i to
the gateway. We find that the routes of leaves are
enough to construct the routing tree, so the routes from
the intermediate node will be filtered out during the ag-
gregation. A white list based searching algorithm is de-
veloped to carry out data aggregation. The compression
rate is nl/n, where nl is the number of leaf nodes.
Once finishing the aggregation, the sensory informa-
tion and the routing information are formatted by the
Wireless-Internet interface into standardized message
types by adding preamble and ending marks, so that the
information platform can easily interpret the received
information.
Using the above two standardized interfaces, the L3SN
system can integrate different kinds of hardware, includ-
ing sensors and gateways. So user can focus on their ap-
plication-oriented development and hardware, and easily
access the wireless sensor network protocols and the
information platform of L3SN, which support the scal-
ability and easy-to-use features.
3.3. Mesh Protocol
For the two-tiered sensor network, sensors in the differ-
ent clusters use different radio channel and are assigned
different GroupIDs, so that sensors in one cluster will not
affect the sensors in the other clusters. A LayerMesh
protocol is meticulously designed within one cluster to
organize the large amount low-tier sensors to work in
energy efficient way, to smartly select multi-hop route
and to be adaptive to the environmental dynamics. The
key feature of LayerMesh is that routing and scheduling
are based on the level information.
The basic functions of the LayerMesh can be sepa-
rated into two phases:
1). Setup phase. When the sensors are initially de-
ployed, they have no knowledge about the neighbor sen-
sors and environment condition. All sensors are initially

658 Y. C. WANG ET AL.
active. The mesh protocol is designed to let the cluster
head broadcast self-organization messages periodically,
and let the LNs forward the messages. Via this CH
broadcasting and LN forwarding process, all the LNs
finish the tasks of:
1) time synchronization to the cluster head;
2) neighborhood survey;
3) local-level determination;
4) transmission route selection.
2) Runtime phase. After the setting up phase, LNs are
turned to online data collection status by receiving a
command broadcasted from the CH. Once entering this
status, each LN calculates and schedules its wakeup time
based on its local-level, and turns to sleep mode immedi-
ately. Each LN sleeps for most of time to save energy
and wakes up in the assigned slot to collect data and to
transmit data to its parents along the transmission route.
Its parents must have waked up to receive the message to
work in cooperation. Therefore, the tasks in working
phase include:
1) Energy efficient scheduling;
2) Cooperative multi-hop communication;
3) Longtime route maintenance.
To efficiently carry out above tasks to meet the re-
quirements of scalability, longevity and adaptivity, two
key innovative designs were proposed and developed in
LayerMesh.
3.3.1. Distributed Dynamic Load-Balancing
Load-balancing is one of the keys to deal with the scal-
ability and longevity challenges. Figure 2 illustrates the
load unbalance problem in multi-hop sensor network.
The load of node A is six times of the load of node B,
caused by the multi-hop load accumulation. Node A will
die much quickly than the node B, although they are in
the same level. This problem is general and serious when
the network scale is large. Previous research has proved
that the load-balance tree construction problem with de-
terministic link is NP-complete [11].
Our solution is to propose a distributed dynamic
load-balancing algorithm. In this algorithm, the links are
no longer deterministic, but probabilistic. At time t, a
node i updates the traffic assignment probabilities to its
parent candidates based on the following neighborhood
information:
1. L(t) = [Lt
k,1,Lt k,2,…,Lt k,n],the current loads of parent
candidates.
2. P(t) = [Pt
1,Pt 2,…,Pt n],the traffic assignment prob-
ability from i to its parent candidates at time t. where n is
the number of parent candidates. k means the level num-
ber. We have developed an algorithm using Equation 2
to update the transmission probabilities of the node i. Its
convergence and optimality in load balance has been
proved [12].
AB
1
1
3
1
6
7
1Load accumulation
in multi-hop
sensor network
 Every LN generates
load 1
 The load of node A
is six times of the load
of node B, because of
load accumulation and
unbalance
1
 The number besides
the LN means load
Figure 2. Load unbalance problem caused by load accumu-
lation in multi-hop sensor network.
, 1
,
1
tt
iki t
intt
j
kj
j
PL
P
PL




(2)
Before transmission, the node i generates a random
number x for route selection. It transmits data to parent
candidate j if:
1
1
11
jj
t
c
cc
1t
c
P
xP






(3)
3.3.2. Stair Scheduling
We have developed a level-based stair scheduling
scheme to carry out energy efficient data collection and
cooperative multi-hop communication. The key idea is to
schedule the sensors based on their levels. As shown in
Figure 3, sensors in each level spend most of time in
sleeping. The sensor in level k will wake up one slot ear-
lier than the sensors in level k-1. After waking up, each
sensor will be active for only three time slots:
1) R-Slot, each sensor in level k listens to channel to
receive data from its children (in level k + 1)
2) T-Slot, the sensor transmits the locally collected
data and the forwarding data to its parents (in level k-1).
3) Syn-Slot, the sensor listen to the channel to receive
the message from its parent and do time synchronization.
From the network point of view, the active slots of the
sensors form a stair like scheduling scheme. The key
point for stair scheduling implementation is the multi-
hop time synchronization. In setup phase of LayerMesh,
we use PulseSync[13] MAC stamping method to do
multi-hop time synchronization. In stair scheduling, we
also use a Syn-Slot to do online time-synchronization. In
our experiments, the time synchronization error within
10 hops is less than 1ms. So the stair scheduling can be
efficiently implemented. More details of collision avoid-
ance and implementation of stair scheduling can be re-
ferred to [14].
4. L3SN System Development
We developed prototypes of the L3SN system, including
the LNs, the gateways, and the information service plat-

Y. C. WANG ET AL.
659
TSyn
RTSyn
RTSyn
RT Syn
Hop n
Hop n-1
Hop n-2
Hop 1
sleep
sleep
sleep
sleep
sleep sleep
TSyn
RTSyn
RTSyn
RT Syn
Hop n
Hop n-1
Hop n-2
Hop 1
sleep
sleep
sleep
sleep
sleep sleep
Figure 3. Level-based Stair Scheduling for energy efficient
data collection and cooperative multi-hop communication.
form, and we have developed and tested a L3SN system
with 60 LNs and one gateway.
4.1. LN Node
Our LN is shown in Figure 4. It is composed by:
1) A solar panel for energy recharge, which can pro-
vide up to 200mw/hour energy supply in a sunny day.
2) Waterproof shell and four waterproof interfaces for
easily plugging in of different agriculture sensors. The
four interfaces are RS232, I2C, RS485, Analog respec-
tively.
3) A RF230 radio companied with 12dB antenna. The
measured maximum transmission range is 300m, and the
data rate is 250k bps.
4) The speed of MCU is up to 16MHz when power
supply is enough. The MCU has 128K ROM, 8K RAM,
and 10bit ADC.
5) We use air-temperature, air-humidity, soil-tem-
perature (RS485), soil-humidity (RS485), light, and CO2
sensors which can be easily plugged into the LN.
The sensor-wireless interface converts the voltage val-
ues from sensors to meaningful temperature and humid-
ity data. For example, the scope of voltage reading of the
humidity sensor is 0- 1500mv; the measurement accu-
racy is 3%, and its transformation function is y = f(v) =
0.05071(v -106.9), therefore the interface of the humidity
sensor is:
S(v) = {0.05071* (v-106:9); {0; 1500}; 3%} (4)
4.2. Gateway
The gateway is composed by a sink sensor, a serial con-
verter and a GPRS DTU [14]. The composition of the
gateway is shown in Figure 4. The sink sensor use the
same hardware as the LN, it is connected to GPRS DTU.
The GPRS DTU provides transparent data forwarding
from the sink sensor to the Internet. It supports standard
TCP/IP protocol, and can be always online by heartbeat
signal. The WSN-Internet interface is applied to the sink
sensor, as explained in Subsection 3.2.
4.3. Weather Station
We have also developed weather station to monitor the
weather information including wind speed, temperature,
light etc. The weather station is built based on Davis’s
product and we
4.4 Information Service Platform
We developed the information service platform. The
platform consists of a data collection daemon, a data
base and a web server.
1) The daemon processes data reception from the
GPRS DTUs, and stores the received data into the data-
base.
2) We build the database using mySQL[14]. It main-
tains the sheets of LNs and gateways to store the histori-
cal and the real-time data.
3) The web server is implemented by JavaScript and
Tomcat. It uses the Google Earth API to render the to-
pology information of the sensor network, and supports
graphical rendering of the real-time and historical data of
the LNs and the gateways. The web server also supports
various query forms for spatial and historical data analy-
sis.
The snapshots of the web pages are shown in Figure 5
and Figure 6.
5. Conclusions
We have design and developed L3SN, a level-based,
large-scale, longevous sensor network for precision ag-
riculture information monitoring. It is composed by a
two-tiered sensor network and an information service
platform. In the low tier of the sensor network, a large
amount of energy-limited sensor nodes (LNs) are de-
ployed to capture and report information of their desig-
nated vicinity. In the high tier of the sensor network,
some powerful GPRS gateways organize the LNs to
form clusters and report the aggregated information to
the Internet. The information service platform is de-
Solar panel
Antenna
Waterproof shell
RS485
I
2
C
Analog
RS232
Figure 4. The prototype of LN node and the gateway.

Y. C. WANG ET AL.
660
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Figure 5. The prototype of LN node and the gateway.
[6] “EKo Wireless Sensor Vineyard, Crop and Environment
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[7] A. Mainwaring, J. Polastre, R. Szewczyk, D. Culler and J.
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Figure 6. The prototype of LN node and the gateway.
[10] X. Y. Li, Y. J. Wang and Y. Wang, “Complexity of Data
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signed to log, render and analyze the temporal and spatial
agriculture information to provide value-created ser-
vices. We have presented the key design issues of L3SN,
including the system architecture, two standardized in-
terfaces and the mesh protocol. Distributed dynamic load
balancing and stair scheduling and adaptive routing are
proposed to handle the large scale, longevous and adap-
tivity requirements.
[11] C. Buragohain and D. Agrawal, et al. “Power Aware
Routing for Sensor Databases,” Proceedings of Interna-
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[12] Y. C. Wang, Y. X. Wang and X. Qi, “Optimal Distributed
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In future work, we will 1) further improve stair sched-
uling to enhance the energy efficiency performance. The
scheduling scheme will be made adaptive to the load of
the sensors. So that redundant time slots can be saved to
improve energy efficiency. 2) In the second stage, we
plan to deploy more 300 LN nodes to Huantai City,
Shandong Province to do larger scale field test.
[13] C. Lenzen, P. Sommer and R. Wattenhofer, “Optimal
Clock Synchronization in Networks,” Proceedings of
Conference on Embedded Networked Sensor Systems,
Zurich, 2009, pp. 225-238.
[14] Y. X. Wang, Y. C. Wang, X. Qi and L. W. Xu. “OPAIMS:
Open Architecture Precision Agriculture Information
Monitoring System,” ACM CASES’09, Paris, October
2009, pp.233-239.
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