Design Methodology of a Wireless Sensor Network
Architecture for Urgent Information Transmission
Tetsuya Kawai, Naoki Wakamiya, and Masayuki Murata
Graduate School of Information Science and Technology
Osaka University, Japan{t-kawai, wakamiya, murata}@ist.osaka-u.ac.jp
ABSTRACT
Wireless sensor networks are expected to become an important social infrastructure which helps our life to be safe, secure, and comfortable. In this paper, we propose design
methodology of a network architecture for fast and reliable transmission of urgent information in wireless sensor
networks. In the methodology, several simple and fullydistributed control mechanisms which function in different
spatial and temporal levels are introduced to each node to offer differentiated transmission to packets in accordance with
their importance. We also show an example of a networkarchitecture designed following the methodology. Our practical experiments showed that the architecture successfully
improved the delivery ratio and delay of the urgent sensor
information.

Categories and Subject Descriptors
C.2.1 [Computer-Communication Networks]: Network
Architecture and Design

General Terms
Design, Performance, Implementation

Keywords
Sensor networks, Urgent information, Fastness, Reliability

1.

INTRODUCTION

Wireless Sensor Network (WSN) technology is expected
to play an essential role for our society in the near future. A
WSN consists of a number of sensor nodes and a base station(BS). A node is equipped with a processing unit, a radiotransceiver, and sensors. Nodes are deployed in a region
to monitor and then environmental information detected by
sensors is collected to a BS through wireless communication
among sensor nodes [1].
WSN technology will be used for a wide variety of applications, such as agricultural, health, environmental, andPermission to make digital or hard copies of all or part of this work forpersonal or classroom use is granted without fee provided that copies are
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WICON 2007, October 22-24, 2007, Austin, Texas, USA
c 2007 ACM 987-963-9799-04-2/07/10 ...$5.00.
Copyright °industrial purposes. Among them, a WSN used as a social
infrastructure to make our life safe, secure, and comfortable
is one of the most promising. This sort of WSNs is supposed
to carry various types of information, such as temperature,humidity, fire alarm, intrusion warning, seismic movement,
image, and sound. The urgent information, a fire alarm forexample, has to be transmitted through a WSN with higher
reliability and lower latency than other non-urgent information. Since the capacity of a wireless network is limited,a WSN must be capable of differentiating and prioritizing
packets depending on their urgency and importance of embedded sensor information, which are defined by an application. Furthermore, in the event of a large emergency, such as
an earthquake attack, a lot of nodes detect the emergency
and send urgent information at the same time. A WSN
should adopt a network-level mechanism to mitigate serious
congestion caused by this simultaneous packet emission.
There have been several proposals of QoS (Quality of Service) control [3, 19, 12] for WSNs. In ReInForM [4], eachnode stochastically relays received packets according to thedifferent forwarding probability, and the reliability of transmission is improved through a multipath and retransmission
mechanism. ESRT [15] regulates the emission rate at source
nodes with feedback from a BS to accomplish the desired
reliability. In [5], overheard packets are used for error correction in single-hop and multihop routing to improve thereliability. In [7], the authors propose a routing protocol tofind the best path in terms of delay. Felemban et al. [6]expands this idea to provide QoS differentiation both in reliability and in latency using a multipath technique. For
congestion mitigation in a WSN, CODA [17] combines hopby-hop backpressure and end-to-end feedback techniques. In[8], a prioritized MAC protocol is introduced in addition toa hop-by-hop traffic control. IFRC [14] is developed to realise adaptive fair and efficient rate allocation by sharing
information on the level of congestion among nodes.
The main contribution of this paper is proposal of design
methodology of a network architecture for transmission ofurgent sensor information. In comparison to the researchworks mentioned above, our approach is unique in that several simple mechanisms are incorporated above the network
layer. In order to adapt to the scale of an emergency ranging from a small event like a gas leakage to a catastrophic
event such as an earthquake attack, some simple mechanisms
are embedded in each node, instead of a monolithic andcomplicated mechanism which controls its overall behavior.
Those mechanisms work in different spatial and temporallevels and they autonomously and independently react toits surrounding situation locally observed. When a small
event happens, it is not necessary to involve all nodes to respond to the emergency. Instead, only nodes along the pathfrom the node which detects the event to a BS participate
in transmission of urgent information and adopt a hop-byhop retransmission mechanism for example. As the scale ofthe emergency grows over time, additional mechanisms comeinto effect and more nodes become involved in the urgent information transmission. On the other hand, in the event ofa large emergency, a lot of nodes detect the emergency at
the same time and send urgent information simultaneously
and independently from others. A WSN in this case immediately reacts as a whole to control serious congestion andensures fast and reliable transmission of urgent information.
We also show a network architecture called UMIUSI (aUtonomous Mechanisms Integrated for Urgent Sensor Information), which incorporates five simple mechanisms following the above methodology.
The rest of this paper is organized as follows. Section 2
proposes design methodology for fast and reliable transmission of urgent information and introduces our UMIUSI architecture. Evaluation of the architecture by practical experiments is presented in Section 3. Finally, we conclude
this paper in Section 4.

2.

PROPOSED METHODOLOGY AND ARCHITECTURE

2.1

System Overview

In this paper, we consider a WSN deployed in a building or a house to monitor and control a living and working
environment. A WSN consists of one BS and a number ofimmobile sensor nodes. The BS corresponds to a gateway
server or a home server with power supply and sends sensor
information to a monitoring station of a security company
or an administration center. Sensor nodes are operated ona battery and equipped with a variety of sensors. Normally,each node observes its environment and reports obtained
sensor information to the BS at regular intervals. A way
that sensor information is transferred to the BS depends
on a routing protocol or a data gathering mechanism employed. To save its power consumption, a sensor node sleeps
and wakes up in accordance with a sleep scheduling algorithm such as that in [2]. Once a node detects an emergency
or an unusual condition, it begins to emit packets containing urgent information at shorter intervals. On receiving
the urgent information, the BS reports the emergency tothe monitoring station through a regional network. Then,an acknowledgement is sent back to the BS. The ACK is
forwarded to the source node of the urgent information andthe node stops sending urgent information.
Although the methodology incorporates mechanisms above
the network layer and does not depend on any specific MAC
or routing protocols, we assume a contention-based MAC
protocol and a multihop routing protocol. A time divisionmultiple access (TDMA) protocol is also applicable, but weconsider that it has many practical problems to be solved
such as scheduling overhead and severe time synchronization
requirements. As for the network layer, a multihop schemewith limitation on the radio transmission energy is usually
preferred to avoid contention among wireless communicationand prolong the lifetime of batteries.

Figure 1: Examples of control mechanisms.

2.2

Design methodology

Since a large number of battery-driven nodes are deployed
in a WSN, energy efficiency, fault tolerance, and scalability
should be taken into account in designing a WSN architecture [1]. These factors need to be well considered also insuch a WSN as is assumed in the previous subsection. However, in the event of emergency, urgent information must betransmitted as fast and reliable as possible, thus reliability
and low latency are primary concerns. Therefore, we needa WSN architecture which satisfies requirements in both ofnormal and emergency conditions.
There have been a lot of excellent works on data gathering
schemes which can be applied in normal situations, for example, [9]. We take an approach to incorporate mechanisms
for urgent information transmission with any data gathering scheme well-designed for application-oriented communication. It means that a WSN operates on a data gathering
scheme in the normal situation. Once an emergency occurs,
an appropriate series of actions take place to deliver urgent
information to the BS. Those nodes which are not involvedin the emergency should keep their normal operation.
In summary, our design objectives of a WSN architecture
for transmission of urgent sensor information are:• High reliability and low latency. The reliability andlatency of transmission of urgent information are themost important issues. Urgent information should be
differentiated from other information and receive preferential controls according to their importance. We
consider that energy efficiency can be sacrificed to someextent for transmission of urgent information.
• Self-organizing and localized behavior. The type andscale of an emergency and the number of simultaneous
emergency events are unpredictable and dynamicallychange as time passes. A centralized architecture isinfeasible in an emergency due to variations of traffic pattern and the level of congestion. Therefore, weneed an architecture which is fully-distributed, selforganizing, and adaptive to dynamically changing conditions. As a consequence of localized reactions of eachsensor node to the surroundings and local interactions
among nodes, a globally-organized behavior of a WSN
against detected emergencies emerges as a whole.
• Simplicity. Since a node has limited computational capacity and a small amount of memory, mechanisms to
support fast and reliable transmission of urgent information must be simple enough. Simplicity also contributes to low energy consumption and less programming errors.

To satisfy the above requirements, we propose design methodology to combine several simple control mechanisms whichfunction in different temporal and spatial levels. In Fig. 1,
typical control mechanisms are arranged in accordance with
their temporal and spatial effect. In general, larger the spatial area where a mechanism influences is, longer the timerequired to achieve effective control is. In the methodology,
at least one mechanism is chosen for each of spatial levels. In
our UMIUSI architecture, those five mechanisms indicatedby gray circles are embedded in a sensor node.
When an emergency occurs, one or more mechanisms among
them start working autonomously and independently as areaction to the surrounding situation of a node. The collective behavior of these mechanisms offers appropriate preferential transmission of emergency packets. At the beginning of an event, quick-acting node-level and neighbor-levelmechanisms contribute to reliable and fast transmission until slower path-level mechanisms come into effect. As theevent develops and situation becomes more serious, additional mechanisms eventually become effective and networklevel control is conducted.
We should note here that it is not possible to transmit
all emergency packets with high reliability and low latency
because the capacity of a WSN is limited. Therefore, it isnecessary to classify sensor information into several classes
in accordance with the required QoS in terms of delay and reliability. Classification and prioritization can be determined
beforehand. Context-aware prioritization is also helpful toadapt to dynamically changing emergency conditions. Eachpacket has a field in its header to indicate its correspondingclass and packets in different classes are treated in a different
way in a WSN.

2.3

UMIUSI Architecture

As an example of a network architecture designed following our methodology, we propose the UMIUSI architecture.
We consider three classes of sensor information as one normal class and two emergency classes and prioritize emergency class information over normal class information. The
two types of urgent information are distinguished in more
important and less important.
• Normal Class. Any non-urgent information belongs tothis class. Normal class information is gathered to a
BS at regular intervals of tnorm . Without an emergency, the latency and reliability of normal class information should satisfy application’s requirements by
the adopted data gathering scheme. An application
can tolerate delay and loss of normal class information
under emergency conditions. Packets of this class arecalled normal packets.
• Important Class. This class is for urgent information,
but an application can tolerate loss and delay of important class information to some extent. Packets belonging to this class, called important packets, can be delayed or dropped depending on the level of congestionin an emergency. The interval of emission of important
packets timp < tnorm is determined by an application,
but could be regulated to be larger than tnorm to mitigate congestion.
• Critical Class. This class is for the most urgent andimportant information which requires highly reliable

Figure 2: The mechanisms leveraged in UMIUSI.
and fast transmission to the BS. Critical packets are
emitted by a node detecting an emergency at fixed regular intervals of tcri < tnorm , which is determined by anapplication. The total amount of critical class traffic
should not exceed the network capacity to guarantee ahigh delivery ratio and low delay of the required level.
Therefore, the number of sensor nodes for critical information should be limited at the deployment, or someof them should be categorized into the important class.
Following the methodology, we choose five mechanisms
for UMIUSI: priority queueing, rate control by local congestion detection, retransmission, assured corridor mechanism (ACM) [11], and rate control by backpressure. Thesemechanisms are simple and distributed, work independentlyof each other, and cover all the levels from node-level to
network-level. Figure 2 briefly summarizes how and where
they work. Although another combination with other mechanisms is also possible, we must carefully consider the relation among mechanisms. For example, we can also introduce in-network aggregation [13] for reducing the number ofpackets and thus suppressing congestion occurrence. However, adopting both hop-by-hop and end-to-end retransmission mechanisms is likely to be inefficient or redundant.
The detailed description of each mechanism is presented
in the following.

2.3.1

Assured corridor mechanism (ACM)

The main purpose of this mechanism is to avoid loss of
emergency packets caused by collisions with normal packets. In addition, ACM contributes to avoiding delay causedby sleeping nodes. ACM establishes an “assured corridor”
from a source node to a BS, in which emergency packets areprotected from normal packets.
An assured corridor consists of awake nodes, which is on
the path from a source node to a BS, and surrounding silent
nodes, which are in the range of radio signals of the awake
nodes. In normal operation, all nodes are in the NORMAL state and operate in accordance with a data gathering
scheme. Once a node detects an emergency, it moves to the
EMG SEND state and begins to periodically emit packets
labeled as critical or important. On receiving an emergency
packet for the first time, other node moves to either of the
SUPPRESSED or EMG FORWARD state. A node on thepath to the BS is responsible for forwarding emergency packets to the BS. Therefore, it moves to the EMG FORWARD
state, cancels its sleep schedule to keep awake, and immediately relays emergency packets it receives. A node which
receives an emergency packet but is not on the path moves
to the SUPPRESSED state. A node in the SUPPRESSED
state completely stops sending normal packets or decreases

Figure 3: The contribution of each mechanism in asmall scale event.
the sending rate of normal packets. Details of ACM withsimulation results can be found in [11].

2.3.2Retransmission

In order to recover a lost emergency packet while providing differentiated services, we introduce a prioritized scheduling algorithm to hop-by-hop retransmission. The algorithm
can be incorporated with most retransmission mechanisms
in either MAC layer or network layer, such as that in [16].
When a packet is lost, the first retransmission is scheduled
after a backoff. To prioritize retransmission of a criticalpacket, the backoff timer for a critical packet is set shorter
than that for an important packet, at 0.1 and 0.2 seconds
respectively in our experiments. If the first retransmission
fails, one or more trials are conducted by applying doubled
backoff, i.e., a binary backoff scheme, until retransmission
succeeds or a next-hop node goes to sleep.
An emergency packet is discarded at a node when it receives the next emergency packet originating at the same
source node. This is because that sensor data in the waiting
packet is obsoleted by the new data. It is also possible tomerge them and generate a new emergency packet.

2.3.3 Priority queueing
Each node has a priority queue for emergency packets,with which important packets are served only when there isno critical packet queued. This means that fast transmissionof critical packets is accomplished at the sacrifice of longer
transmission delay of important packets. Data aggregationsuch that proposed in [12] can be used to avoid this delay.
Transmission of normal packets at a node during an emergency is delayed until the node returns to normal operation.

2.3.4 Rate control by local congestion detection
To mitigate congestion as fast as possible by local control,
we introduce a rate control mechanism which is triggered bydetection of local congestion. In order to keep the reporting
rate of critical information at 1/tcri , the rate control is applied only to important class traffic. When a source node ofimportant packets detects congestion by, for example, monitoring packet reception rate, it increases the emission interval of important packets. Congestion detection can be doneby other methods, including observation of queue occupancy
and channel sampling such as proposed in [17, 8]. As a rate
control algorithm, a TCP-like AIMD (Additive Increase andMultiplicative Decrease) algorithm, such as that in [18], isempirically employed in our experiments.

2.3.5

Rate control by backpressure

In an event of large emergency such as an earthquake, evenif emission of normal packets is suppressed and source nodesof important packets regulate their emission rate, congestion
cannot be fully avoided around a node belonging to multiple

Figure 4: The contribution of each mechanism in alarge scale event.
paths and around the BS, where many emergency packets
concentrate on. We employ a backpressure mechanism for a
network-level traffic control in UMIUSI.
To suppress emission of important packets at their source
nodes, a backpressure message is sent back to source nodes
from a point of congestion by piggybacking on an emergency
packet. Details are as follows. When a node detects congestion, it sets an explicit congestion notification (ECN) bitin the header of important packets which it relays toward
the BS. By overhearing the packet, a node closer to thesource recognizes that congestion occurs in the path to the
BS. Then, it also sets an ECN bit of the following important
packet which it relays to the next-hop node. Consequently,
by means of overhearing, a congestion notification propagates to the source node. On receiving the notification, thesource node reduces the emission rate of important packets,and the congestion is mitigated. The node which detected
the congestion stops setting ECN bits once it confirms the
mitigation.

2.4

Discussion

By integrating the five mechanisms, UMIUSI adapts to avariety of emergencies. With a small emergency event, only
one or a few nodes would detect it. At the beginning of theevent, the retransmission mechanism becomes effective immediately since an emergency packet is not protected fromnormal packets until an assured corridor is established. Oncea corridor is established, the contribution of the retransmission mechanism becomes smaller as the number of packet
loss is reduced by ACM. The priority queueing mechanism
is not used since all emergency packets are likely to belong to
one class. In addition, rate control of important class traffic
by local congestion detection does not help much, since thenumber of nodes emitting important packets is small and thepossibility of congestion is expected to be small. If multiple
paths are established from a source node to a BS, there may
occur collisions among emergency packets traversing different paths. In such a case, the rate control mechanism bybackpressure is activated. Figure 3 is an intuitive sketch
to show how much each mechanism contributes to fast andreliable transmission of emergency packets in a small scale
event.
On the other hand, for a large scale emergency, many
nodes become a source of emergency packets. Figure 4 illustrates the degree of contribution of the mechanisms forthe case of large emergencies. Since most of nodes are involved in transmission of emergency packets as source nodes
or forwarding nodes, an assured corridor to suppress theemission of normal packet does not help much. On the contrary, mechanisms to mitigate congestion within a corridor
are effective. The priority queueing mechanism offers differentiated forwarding services to emergency packets in accor-

0.16

Table 1: The power consumption of nodes during
10 minutes. (mAh)
State
Node 1 Node 2 Node 3
NORMAL
8.786
8.814
8.759
EMG FORWARD
8.795
8.834
8.778
EMG SEND
8.795
8.836
8.779
SUPPRESSED
8.804
8.849
8.783

KA
ACM
ACM+RT

0.14

Per-hop loss rate

0.12
0.1
0.08
0.06
0.04
0.02

3.

0
0

100

150

200

Figure 5: The per-hop loss rate of emergency packets (small scale event).
0.05

KA
ACM
ACM+RT

0.045
0.04

PRACTICAL EXPERIMENTS

We implemented UMIUSI onto off-the-shelf sensor nodes
provided by OKI Electric Industries Co., Ltd. and conducted experiments using two testbeds. Testbed A consistedof 26 nodes including a BS which were put in a 10 m × 6 m
room. In Testbed B, 47 nodes were deployed over a floorof a building for feasibility demonstration. Before the practical experiments, we verified basic behavior of UMIUSI by
thorough simulation experiments, but results are not shownin this paper for space limitation.
In the experiments, IEEE 802.15.4 non-beacon mode was
employed for the MAC layer. The payload size of an emergency packets was 16 bytes including packet header witha class identifier, dummy sensor data, and a time stamp.
For the network layer, we adopted the synchronization-based
data gathering scheme [10] modified to ignore uni-directional
links. It employs a tree-based routing, and timing of packet
emission is the same among nodes of the same hop distancefrom the BS. In a normal state, all nodes adopt a sleep
schedule. Nodes on the same hop distance wake up at thesame time and receive packets from one-hop distant node.
Then, they send packets to next-hop nodes. Finally, after
overhearing packets emitted by the next-hop nodes, they goback to a sleep mode. In the experiments, we set the interval of normal packet emission tnorm at ten seconds, andthe offset between emissions of adjacent nodes at one second. Routes from nodes to the BS dynamically changed forvariations in radio environment.
For evaluation of transmission of emergency packets, wemade one (small scale event) or eight (large scale event)nodes become source nodes, i.e., with ACM, they moved
to the EMG SEND state. Each of them was scheduled toemit emergency packets at interval of temg = 0.5 seconds,
but the actual interval was about 0.58 seconds due to implementation constraints. Source nodes went back to normal
operation with the sleep schedule ten minutes later.
Table 1 summarises the amounts of power consumed byrandomly chosen three nodes in ten minutes for each of thefour states on Testbed A. It was measured by a digital power
meter (Yokogawa Electric WT210) attached to a DC input
of a node. We find that the difference in power consumption
among four states is relatively small compared to the con-

50

Time (s)Per-hop delay (s)dance with their class. Rate control is first applied locally ata source node to mitigate local congestion among neighboring source nodes. Afterwards, congestion among emergency
packets traversing different paths is solved by the backpressure mechanism.
All of these reactions against different types of emergency
emerge as a consequence of autonomous and simple behaviorof nodes. There is neither a mechanism to identify the typeand scale of an emergency nor an explicit rule to choose andcoordinate mechanisms.

0.035
0.03
0.025
0.02
0.015
0.01
0.005
0
0

50

100
Time (s)150

200

Figure 6: The per-hop delay of emergency packets(small scale event).
sumed power. Therefore the active / sleep ratio in normal
operation would determine the lifetime of a node, although
we did not apply any sleep schedule in the experiments due
to implementation constraints. A node adopts three AAA
alkaline cells with serial connection. One cell has the capacity of about 1 Ah. Thus, if we apply a sleep schedule of theactive / sleep ratio of 1/600, i.e., being active for one secondin ten minutes, the lifetime of a node can be estimated as
11,400 hours (= 1.30 years). Once an emergency occurs anda node stays in either of EMG SEND, EMG FORWARD
or SUPPRESSED states for three minutes for example, it
shortens the lifetime of a node by 30 hours. Developing asleep schedule for these states is one of our future works.

3.1

Testbed A

In Testbed A, 25 sensor nodes were arranged in a 5×5 grid
topology with separation of one meter. A BS was put beside
the grid with one meter separation. The transmission power
was set to −27 dBm. The average delivery ratio of normal
packets over ten hours experiments was around 80 %.
In order to evaluate the effect of mechanisms comprising
UMIUSI, we compared five variants of combination of themechanisms. One is KA (keep awake), in which nodes in acorridor keep awake but neither suppression of normal packets nor other mechanisms is conducted. The second is ACM,
in which an assured corridor is established by suppressing
emission of normal packets. The third one is ACM+RT(retransmission), in which ACM and the retransmission are
applied. The fourth is ACM+RT+PQ (priority queueing),
in which the priority queueing is additionally applied. The

14

KA
ACM
ACM+RT, important
ACM+RT, critical
ACM+RT+PQ, important
ACM+RT+PQ, critical
FULL, important
FULL, critical

10Per-hop delay (s)Total throughput (packets/s)

0.2
12

8
6

ACM+RT+PQ, important
ACM+RT+PQ, critical
FULL, important
FULL, critical

4

0.15

0.1

0.05

2
0

0
0

50

100

150

200

0

50

Time (s)Figure 7: The total throughput of emergency packets (large scale event).
1

150

200

Figure 9: The per-hop delay of emergency packets(large scale event).
asp√
p = 1 − n 1 − Pn = 1 − n Q n .

0.1
Per-hop loss rate

100
Time (s)The per-hop delay d is defined as

0.01

d = Dn /n,KA
ACM
ACM+RT, important
ACM+RT, critical
ACM+RT+PQ, important
ACM+RT+PQ, critical
FULL, important
FULL, critical

0.001

1e-04

1e-05
0

50

100
Time (s)150

200

Figure 8: The per-hop loss rate of emergency packets (large scale event).

last one, FULL employs all of the mechanisms explained in
Section 2.3. For the variants with RT, the first retransmission was scheduled at 0.1 seconds after the first transmission
for the critical class and 0.2 seconds for the important class,respectively. A binary backoff scheme was applied to following retransmissions. In FULL, local congestion detection
was done by monitoring packet reception rate at each node.
When a node received more than 20 packets in recent two
seconds, it considered that the wireless channel was highly
loaded. We observed that the number of packet losses roseup sharply when the traffic exceeded this threshold in preliminary experiments. For the AIMD rate control, the parameters for multiplicative decrease and additive increase
were 0.5 and 0.05 packets/s respectively taken from [18].
Since the hop distance from a node to the BS dynamically changed during the experiments, we employ the perhop loss rate and per-hop delay as evaluation metrics. Letting n the hop distance from a source node to the BS andpk (k = 1, 2, · · · , n) the per-hop loss rate in transmission of
emergency packets at k-th hop, the loss rate Pn observed at
the BS is given by
Pn = 1 − Q n = 1 −nY(1 − pi )i=1where Qn is the delivery ratio observed at the BS. In theexperiments, packet losses were detected at the BS using
a sequence number in the header of an emergency packet
to obtain Pn . Then, assuming pk is identical for all hops
(p1 = p2 = · · · = pn = p), the per-hop loss rate p is definedwhere Dn is time taken from emission of an emergency
packet at a source node to reception of the packet at the
BS. Note that, since only a clock on the hundred milliseconds scale was provided for the application layer, time error
in Dn observed was 100 ms at maximum.

3.1.1

Small Scale Event

In the scenario of a small scale event, one critical classnode became a source node of critical packets and went backto normal operation ten minutes later. We conducted experiments twice for each of randomly chosen eight nodes.
Figure 5 shows the per-hop loss rate of emergency packets
averaged over the 16 experiments. In these experiments,
suppression of normal packets became effective immediately
after a node began emitting critical packets, since the hop
distance to the BS was so small, 1.6 on average, that it
did not take long to establish an assured corridor. With
retransmission, the per-hop loss rate was further improved
to be less than 1 %.
However, retransmission led to increase of the per-hop delay (Fig. 6). Even if collision was mostly avoided by the
suppression, packet losses frequently occurred (see ACM in
Fig. 5) due to random channel errors. Therefore, a numberof retransmissions were needed to recover those lost packets,
which resulted in increase of the delay.

3.1.2

Large Scale Event

For the experiments of a large scale event, we consideredeight sets of seven important class nodes and one criticalclass node. These eight nodes were moved to the EMG SEND
state in about ten seconds and went back to the NORMAL
state about ten minutes later. We conducted experiments
twice for each of the eight sets.
First, the total throughput of the critical and importantclass averaged over the experiments is illustrated in Fig 7.
Here the total throughput is defined as the number of emergency packets received by the BS per second. Little difference was observed between the total throughput of ACM+RT
and that of ACM+RT+PQ for both classes, thus results of
ACM+RT are not shown in Fig. 7. With the rate control

Total throughput (packets/s)

3
2.5
2
1.5
1
0.5
importantcritical

0
0

100

200

300

400

500

Time (s)Figure 11: The total throughput of emergency packets in Testbed B.

Figure 10: A small scale event in Testbed B. The
thick lines represent concrete walls and steel doors.
The rectangles with thin lines represent steel desks.
mechanisms, we can see that the total throughput of the important class decreased around 1.5 packets/s in 30 seconds
while that without the rate control in ACM+RT+PQ was
kept high at 11.5 packets/s.
The per-hop loss rate of emergency packets is illustrated
in Fig. 8. Suppression of normal packets had little effect onreliability in a large emergency as can be seen in comparison between KA and ACM. Adding the priority queueing
mechanism to ACM+RT was also not much helpful in thisexperiment settings, because paths of important and criticalclass traffic seldom overlapped with each other. In FULL,
the per-hop loss rate of the critical class gradually decreased
as the emission rate of important class was regulated by therate control mechanisms. Since an important class packet
had more chances to be retransmitted than a critical classpacket due to the prolonged interval of emission by rate control, the per-hop loss rate of the important class was smaller
than that of the critical class.
Figure 9 shows the per-hop delay of emergency packets.
In FULL, the per-hop delay of the critical class gradually decreased in the first 30 seconds as the important class traffic
was regulated by the rate control mechanisms. The emissioninterval of important class packets was prolonged by the rate
control mechanisms, thus waiting time to be retransmitted
at an EMG FORWARD node could be as long as a few seconds. Such occasional large delay caused the large variationin the per-hop delay of the important class in FULL.

3.2

Testbed B

The purpose of the experiments in Testbed B is to verify
feasibility of UMIUSI in practical settings. In Testbed B, 46
sensor nodes and a BS were deployed on a floor of several
rooms and a hallway in a concrete building, as illustrated

Figure 12: A snapshot in the large scale event inTestbed B (t = 400 s).
in Fig. 10. All of the five mechanisms of UMIUSI (FULL)
were used, and parameters were the same as in Testbed A
other than the transmission power of −7 dBm.

3.2.1

Small Scale Event

Figure 10 shows a snapshot where Node 34 detected an
emergency. Node 34 reached the BS via Node 2 by a path established by the synchronization-based data gathering scheme.
Therefore, Node 2 was an EMG FORWARD node in thesnapshot. Nodes indicated by dark circles could hear radio
signals of Nodes 2 and 34 and moved to the SUPPRESSED
state. Due to shadowing and fading, the geographical proximity does not necessarily correspond to neighbor relation.
The average per-hop loss rate over eight experiments setting
Nodes 4, 7, 16, 25, 30, 34, 41, and 46 an EMG SEND node
was 0.37%. The average hop distance to the BS was 2.75.

3.2.2

Large Scale Event

Next we considered a scenario where a small scale emergency grew to become large, such as a fire. In this scenario,
Node 33 first detected an event, moved to the EMG SEND
state, and began sending important packets at time t = 0.
At t = 80 seconds, Node 4 of the critical class next detected
the event, followed by Nodes 22 and 36 at t = 240 seconds
and Nodes 6, 13, 20, and 31 at t = 340 seconds. These six
nodes were of the important class.
When Nodes 4 and 33 were in the EMG SEND state,
emergency packets were directly transmitted to the BS. As

shown in Fig. 11 the average throughput was about 1.7 packets/s for both nodes. After Nodes 22 and 36 detected theevent at t = 240 seconds, local congestion occurred andthe three EMG SEND nodes of the important class, i.e.,
Nodes 33, 22, and 36, reduced the emission rate in order tomitigate congestion. The total throughput of the importantclass was controlled around 2.3 packets/s. At t = 340 seconds, other four nodes newly began to emit important packets and this caused congestion around Node 2. To reduce theimportant class traffic at its source, Node 2 sent a backpressure to Node 20 (Fig. 12). As a result, the total throughputof the important class was kept about the same level, andthere was no loss of critical class packets throughout thisten minutes experiment. The loss rate of important classpackets was 0.6 %.
As shown in this experiment, traffic control developed
from path-level to network-level adapting to the growing
scale of emergency without any centralized control in UMIUSI
architecture.

4.

CONCLUSION

Urgent sensor information is needed to be transmitted
preferentially in a WSN used as a social infrastructure. In
this paper, we presented the methodology for designing a
WSN architecture for fast and reliable transmission of urgent information. We consider that several simple and fullydistributed mechanisms working in different spatial and temporal levels should be incorporated onto a node so thatthe collective control of these mechanisms offers preferential
transmission of urgent information adapting to the scale ofemergency.
We also showed a network architecture, called UMIUSI,
designed following the methodology. In this architecture,sensor information is categorized into three traffic classes
and five simple mechanisms collaborate consistently. We
verified through practical experiments that the architecture
successfully improved the delivery ratio and the delay of
emergency packets regardless of the scale of emergency.

5.

ACKNOWLEDGEMENTS

This research was supported by “Special Coordination Funds
for Promoting Science and Technology: Yuragi Project” anda Grant-in-Aid for Scientific Research (A)(2) 16200003 of the
Ministry of Education, Culture, Sports, Science and Technology in Japan.

6.

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