社団法人 電子情報通信学会
THE INSTITUTE OF ELECTRONICS,
INFORMATION AND COMMUNICATION ENGINEERS

信学技報
TECHNICAL REPORT OF IEICE.

緊急情報の迅速かつ確実な転送のための無線センサネットワークアーキテクチャの設計と評価
河井 哲也†

若宮 直紀†

村田 正幸†† 大阪大学 大学院情報科学研究科〒 565–0871 大阪府吹田市山田丘 1–5
E-mail: †{t-kawai,wakamiya,murata}@ist.osaka-u.ac.jp
あらまし

社会インフラとして用いられる無線センサネットワークでは，緊急情報を他の情報と差別化し優先して伝達する必要がある．本稿では，様々な緊急事態に柔軟に対応するために，動作する領域や時間の異なる複数のメカニズムを統合するセンサネットワーク設計の方法論を提示する．また，この方法論にもとづき新たに設計したセンサネッ
トワークアーキテクチャを提案する．このアーキテクチャでは緊急情報は 3 つのクラスに分類され，緊急情報を優先させるために複数の単純で自己組織的かつ分散的なメカニズムが緊急事態の規模に応じて動作する．シミュレーショ
ンにより，このアーキテクチャは小規模および大規模な緊急事態下で緊急情報の到達率および遅延を向上させること
を示した．
キーワード

センサネットワーク，緊急情報，迅速性，信頼性

Design and Evaluation of a Wireless Sensor Network Architecture for
Fast and Reliable Transmission of Urgent Information
Tetsuya KAWAI† , Naoki WAKAMIYA† , and Masayuki MURATA†† Graduate School of Information Science and Technology, Osaka University
1–5, Yamadaoka, Suita, Osaka 565–0871 Japan
E-mail: †{t-kawai,wakamiya,murata}@ist.osaka-u.ac.jp
Abstract Wireless sensor networks used as a social infrastructure must be capable of diﬀerentiating and prioritizing transmission of urgent sensor information over other non-urgent information. In this paper, we show a network
design methodology where several mechanisms which function in diﬀerent spatial and temporal levels are integrated
to adapt to an emergency situation in a self-organizing and distributed manner. We also present a novel and simple
network architecture designed following the methodology. In this architecture, sensor information is classiﬁed into
three traﬃc classes and each node activates one or more of several simple, self-organizing, and fully-distributed
mechanisms in accordance with the scale of an emergency for fast and reliable transmission of urgent sensor information. Our simulation experiments showed that the architecture successfully improved the delivery ratio anddelay of the urgent sensor information under both a small scale and large scale emergencies.
Key words sensor networks, urgent information, fastness, reliability

1 Introduction

is collected to a BS through wireless communication among
sensor nodes [1].

Wireless Sensor Network (WSN) technology is expected to

WSN technology will be used for a wide variety of ap-

play an essential role for our society in the near future. A

plications, such as agricultural, health, environmental, and

WSN consists of a number of sensor nodes and a base sta-industrial purposes. Among them, a WSN used as a socialtion (BS). A node is equipped with a processing unit, a radio

infrastructure to make our life safe, secure, and comfortabletransceiver, and sensors. Nodes are deployed in a region tois one of the most promising. This sort of WSNs is supposedmonitor and environmental information detected by sensors

to carry various types of information, such as temperature,—1—

a battery and equipped with a variety of sensors. Although
the mechanisms proposed here work above the network layerand do not depend on any speciﬁc MAC or routing protocols,
we assume a contention-based MAC protocol and a multihop
routing protocol.
Each node observes the environment and reports obtained
sensor information to the BS at regular intervals, which isFig. 1 Examples of control mechanisms.

deﬁned by an application’s requirement. A way that sensor
information is transmitted to the BS depends on a routing

humidity, ﬁre alarm, intrusion warning, image, and sound.

mechanism or a data gathering mechanism employed. There

The urgent information, a ﬁre alarm for example, has tohave been a lot of excellent works on data gathering schemes

be transmitted through a WSN with higher reliability andwhich can be applied in normal situations, for example, [2].

lower latency than other non-urgent information. Since theOnce an emergency occurs, an appropriate series of actions

capacity of a wireless network is limited, a WSN must be

take place to deliver urgent information to the BS. For thecapable of diﬀerentiating and prioritizing packets depending

sake of scalability, there is no centralized control in our ar-on their urgency and importance of embedded sensor infor-chitecture and decisions are made by a node itself. Thosemation, which are deﬁned by an application. Furthermore,nodes which are not involved in the emergency keep theirin the event of a large emergency, such as an earthquake at-normal operation.

tack, a lot of nodes detect the emergency and send urgent
information at the same time. A WSN should mitigate aserious congestion caused by this simultaneous emission of alot of emergency packets, especially around a BS.

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 the mostOur approach is unique in a sense that several simple mech-

important issues. We consider that energy eﬃciency can be

anisms are incorporated above the network layer. It means

sacriﬁced to some extent for transmission of urgent informa-that other existing mechanisms on the MAC or network lay-tion.

ers can be incorporated with our architecture to improve the•Self-organizing and localized behavior. The type andreliability and latency of urgent information transmission.

scale of an emergency and the number of simultaneous emer-

Our architecture is fully decentralized. Each node activates

gency events are unpredictable and dynamically change as

one or more of the control mechanisms in accordance withtime passes. A centralized architecture is infeasible for thislocally observed conditions, and as a result, a series of appro-

dynamically changing condistions. Therefore, we need an

priate controls take place from locally to globally adapting

architecture which is fully-distributed, self-organizing, andto the scale of an emergency ranging from a small event likeadaptive. A globally-organized behavior of a WSN against

a gas leakage to a catastrophic event such as an earthquake

detected emergencies emerges as a consequence of localized

attack.

reactions of each sensor node.

The rest of this paper is organized as follows. Section 2 and•Simplicity. Since a node has limited computational

section 3 give the methodology for designing a network ar-capacity and a small amount of memory, mechanisms tochitecture for fast and reliable transmission of urgent sensor

support fast and reliable transmission of urgent information

information and the details of the proposed network architec-

must be simple enough.

ture respectively. The results of the simulation experiments

To satisfy the above requirements, a sensor node should

are shown in Section 4. We conclude this paper in Section 5.

have several simple control mechanisms (see Fig. 1), which
work in diﬀerent spatial and temporal levels, instead of ap-

2 Design Methodology

plying a single and complex mechanism to all types and scale

In this paper, we consider a WSN deployed in a building orof emergency. One or more mechanisms are activated ina house to monitor and control a living and working environ-response to the local conditions and emergency-dependent

ment. A WSN consists of one BS and a number of immobile

control emerges from local to the whole.

sensor nodes. The BS corresponds to a gateway server or a

Since the capacity of a WSN is limited, it is not possible tohome server with power supply and sends sensor informationtransmit all emergency packets with high reliability and low

to a monitoring station of a security company or an admin-latency. Therefore, it is necessary to classify sensor informa-

istration center if necessary. Sensor nodes are operated ontion into several classes in accordance with the required QoS
in terms of delay and reliability.
—2—

Fig. 3 An assured corridor.

3. 1 Assured corridor mechanism (ACM)
Fig. 2 The mechanisms leveraged in UMIUSI.

The main purpose of this mechanism is to avoid loss of
emergency packets caused by collisions with normal packets.

3 Proposal of Architecture

In addition, ACM contributes to avoiding delay caused bysleeping nodes.

We construct UMIUSI (aUtonomous Mechanisms Integrated for Urgent Sensor Information) architecture for transmission of urgent sensor information in a WSN following thedesign policy stated in the previous section. First, we consider three classes of sensor information as one normal classand two emergency classes and prioritize emergency class information over normal class information.
•

Normal Class. Any non-urgent information belongs

to this class. Normal class information is gathered to the
BS at regular intervals of tnorm . An application can toleratedelay and loss of normal class information under emergency
conditions. Packets of this class are called normal packets.
•

Important Class. This class is for urgent information,

but an application can tolerate loss and delay of importantclass information to some extent. Packets belonging to thisclass, called important packets, can be delayed or dropped
depending on the level of congestion in an emergency. Theinterval of emission of important packets timp < tnorm is determined by an application, but could be regulated to belarger than tnorm to mitigate congestion.
•

Critical Class. This class is for the most urgent andimportant information which requires highly reliable and fasttransmission to the BS. Critical packets are emitted by anode detecting an emergency at ﬁxed regular intervals oftcri < tnorm , which is determined by an application. The
total amount of critical class traﬃc should not exceed the
network capacity to guarantee a high 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 some of them should be categorized into theimportant class.
As stated in the previous section, mechanisms leveraged
in UMIUSI must be simple and work independently of otherschemes and protocols. In addition, a WSN should adapt
to dynamically changing emergency situations by adopting
control mechanisms working in diﬀerent time and topological levels, from fast and local to slow and global. From thesepoints of view, we incorporate following ﬁve mechanisms into

An assured corridor consists of awake nodes, which is on
the path from the source node to the BS, and surrounding
silent nodes, which are in the range of the radio signals of
awake nodes (Fig. 3). All nodes in a WSN follow the statetransitions among four states:

NORMAL, EMG SEND,

EMG FORWARD, and SUPPRESSED. 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 a critical packet or
important packet. On receiving an emergency packet for theﬁrst time, other node moves to either of the SUPPRESSEDor EMG FORWARD state. A node on the path to the BS
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 nodein the SUPPRESSED state completely stops sending normal packets or decreases the sending rate of normal packets.
Details of ACM with simulation results can be found in [3].
3. 2 Retransmission
In order to recover a lost emergency packet and provide
diﬀerentiated services, we introduce a prioritized schedulingalgorithm of hop-by-hop retransmissions.
A node retransmits an emergency packet when it detects
a loss. The hop-by-hop acknowledgement can be easily doneby, for example, overhearing a packet sent by a next-hop
node. If the overheard packet does not contain the information that the node sent, the packet is considered to be lost.
An example of a retransmission schedule for the importantand critical class is shown in Fig. 4.
3. 3 Priority queueing
Each node has a priority queue for emergency packets, withwhich important packets are served only when there is no
critical packet queued. This means that fast transmissionof critical packets is accomplished at the sacriﬁce of longer
transmission delay of important packets.
Transmission of normal packets at a node in either the

UMIUSI (shaded circles in Fig. 1).
—3—

Fig. 4 An example schedule of retransmission of emergency packets. Double-headed arrows and stars show the possible duration for retransmission and averaged timings of packet
emission respectively.

Fig. 6 The contribution of each mechanism for a large scale event

and around the BS, where many emergency packets concentrate on. We adopt a mechanism in which a backpressure
message is sent back to source nodes from a point of congestion by piggybacking on an emergency packet to regulates
the emission rate of important packets.
When a node detects congestion, it sets an explicit conFig. 5 The contribution of each mechanism for a small scalegestion notiﬁcation (ECN) bit in the header of important
packets which it relays toward the BS. By overhearing theevent.

packet, a preceding node recognizes that congestion occurs
EMG SEND or EMG FORWARD state is delayed until thein the path to the BS. Then, it also sets an ECN bit ofnode moves to the NORMAL state.

the next important packet which it relays to the next-hop

3. 4 Rate control by local congestion detection

node. Consequently, By means of overhearing, a congestion

To mitigate congestion as fast as possible by local control,

notiﬁcation propagates to the source node. On receiving the

we introduce a rate control mechanism which is triggered by

notiﬁcation, the source node reduces the emission rate of im-detection of local congestion. In order to keep the report-

portant packets, and the congestion is mitigated.

ing rate of critical information at 1/tcri , the rate control is

3. 6 Discussion

applied only to important class traﬃc. In our simulation

By integrating the above mentioned ﬁve mechanisms,

experiments, a node detects local congestion by not receiv-

UMIUSI can adapt to a variety of emergencies. Now, let us

ing any acknowledgement from any of its next-hop nodes,

consider some example scenarios. With a small emergency

however, congestion detection can be done by other methods

event, a gas leakage warning for example, only one or a few

such as proposed in [4].

nodes would detect it. Among ﬁve mechanisms described in

As a rate control algorithm, we employ a TCP-like AIMD

Section 3, the priority queueing is not used since all emer-(Additive Increase and Multiplicative Decrease) algorithm,

gency packets are likely to belong to one class. In addition,such as that in [5], for its simplicity. When an EMG SEND

the rate control of important class traﬃc by local congestion

node conﬁrms delivery of an important packet to its next-

detection does not help much, since the number of nodes

hop node, its emission rate is increased by decreasing theemitting important packets is small and the possibility ofemission interval timp ascongestion is expected to be small. ACM is the most eﬀec-

timp ←

timp,
1 + αtimp

tive among ﬁve, because loss of emergency packets is mainly(1)caused by collisions with normal packets without an assured

where α (α > 0) is a constant. The upper bound of thecorridor. In addition, the retransmission is necessary, since

emission rate is determined by the application. When a nodean emergency packet is not protected from normal packets

detects congestion, its emission rate of important packets is

until a corridor is established. Figure 5 is an intuitive sketch

decreased by multiplying the parameter β (0 < β < 1), whichto show how much each mechanism contributes to fast andfurther corresponds to the following adjustment,

reliable transmission of emergency packets against time.

timp ← timp /β.

On the other hand, for large scale emergency, such as an(2)earthquake, many nodes become a source of emergency pack-3. 5 Rate control by backpressure

ets. Figure 6 illustrates the degree of contribution of the

In an event of a larg emergency such as an earthquake, themechanisms for the case of large emergencies. Since mostrate control with local congestion detection cannot fully mit-of nodes are involved in transmission of emergency pack-igate congestion around a node belonging to multiple paths

ets as source nodes in the EMG SEND state or forwarding—4—0.0450.04
0.035

Fig. 7 The synchronization-based data gathering scheme.

delay (s)

0.03
0.025
0.02
0.015
0.05

loss rate

0.04

KA
KA+SP
KA+SP+RT

0.01
0.005

KA
KA+SP
KA+SP+RT0
300 320 340 360 380 400 420 440 460 480 500
time (s)0.03

Fig. 9 The delay of emergency packets (small emergency).
0.02
0.01
0
300 320 340 360 380 400 420 440 460 480 500
time (s)Fig. 8 The loss rate of emergency packets (small emergency).

300 seconds for initialization without any emergency. To simulate a small scale and large scale emergency, one or 32 nodes
respectively are randomly chosen after the initial 300 seconds, and they detect an event at randomly chosen timewithin following 10 seconds respectively. They wait for next-

nodes in the EMG FORWARD state, an assured corridor
to suppress the emission 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 accordance with their class. Rate control is ﬁrst
applied locally at a source node to mitigate local congestion among neighboring source nodes. After this, congestion
among emergency packets traversing diﬀerent paths is solvedby 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.

4 Evaluation of Architecture
We implemented UMIUSI for the ns-2 network simulator
package and conducted extensive simulation experiments. In
all of the simulation experiments, 200 sensor nodes are uniformly and randomly distributed in a 20 m × 20 m twodimensional region with a BS at its center. IEEE 802.15.4
non-beacon mode is used as the MAC protocol and the transmission range of radio signals is set to 2.5 m.
We employ the synchronization-based data gathering
scheme [6] for the underlying data gathering mechanism. In
the synchronization-based data gathering scheme, the number of hops is maintained as a level value at each node andtiming of packet emission is the same among nodes of thesame level, see Fig. 7. In our simulation experiments, thedata gathering cycle tnorm and oﬀset coeﬃcient δ are set at
10 seconds and 0.1.
Each simulation experiment lasts for 500 seconds including

hop nodes to wake up, then starts transmitting emergency
packets with intervals of 0.5 seconds. For a large scale emergency, four out of 32 EMG SEND nodes are of the criticalclass and the others are of the important class. The nodes
stay in the EMG SEND state for 180 seconds and then getback to the normal state. The same experiment is repeatedfor 100 times with diﬀerent node layouts. The retransmission schedule of emergency packets is the same as in Fig. 4.
For the AIMD rate control, the parameter α in Eq.(1) is 0.05
and β in Eq.(2) is 0.5 taken from [5].
In order to evaluate the eﬀect of mechanisms comprising UMIUSI, we compared ﬁve variants of integration of
the mechanisms. One is KA (keep awake), in which nodes
in a corridor keep awake but neither suppression of normal packets nor other mechanisms is conducted. The second is called KA+SP (suppression) and equivalent to ACM,
in which an assured corridor is established by suppressing
emission of normal packets. Other three are KA+SP+RT,
KA+SP+RT+PQ, and KA+SP+RT+PQ+RC, where RT,
PQ, and RC denote the retransmission, priority queueing,
and rate control mechanisms respectively.
4. 1 Small scale event
For experiments of a small scale event, KA+SP+RT+PQ
and KA+SP+RT+PQ+RC show the identical results as
those of KA+SP+RT, since there is no important class trafﬁc. Therefore, the results of these two variants are not shownin the ﬁgures.
The loss rate of emergency packets is shown in Fig 8. The
loss rate here is deﬁned as the ratio of emergency packets
which did not arrive at the BS out of all emergency packets
emitted from a source node. The horizontal axis corresponds
to the simulation time. In KA, the loss rate rises since collisions with normal packets are unavoidable. On the other
hand, in KA+SP, although the initial value is the same as
—5—

0.3

1

0.25
0.2

0.01
0.001
1e-04

KA
KA+SP
KA+SP+RT, important
KA+SP+RT, critical
KA+SP+RT+PQ, important
KA+SP+RT+PQ, critical
KA+SP+RT+PQ+RC, important
KA+SP+RT+PQ+RC, critical

1e-05
300 320 340 360 380 400 420 440 460 480 500
time (s)Fig. 10 The loss rate of emergency packets (large emergency).

delay (s)loss rate

0.1

0.15
0.1
0.05
0
300 320 340 360 380 400 420 440 460 480 500
time (s)Fig. 11 The delay of emergency packets (large emergency). The
legends are the same as in Fig. 10.

that of KA, the loss rate drops gradually in about 20 seconds.
This corresponds to time required to establish an assured cor-

retransmission and backoﬀ become small due to reduction ofridor from a source to the BS, which further depends on theimportant class traﬃc.

hop distance of the source node and the sleep schedule of thesynchronization-based data gathering scheme. With retrans-

5 Conclusion

mission, i.e., KA+SP+RT, there is no packet loss. However,Urgent sensor information is needed to be transmitted

the total number of emergency packets emitted is larger thanpreferentially in a WSN used as a social infrastructure.

that of KA+SP by 15 % and this increase leads to additional

In this paper, we presented a network architecture called

energy expenditure.

UMIUSI designed for fast and reliable transmission of urgent

As for the end-to-end delay (Fig. 9), which is time needed

information in wireless sensor networks. Sensor information

to deliver an emergency packet from the source node to theis categorized into three traﬃc classes. In order to prioritize

BS, suppression of normal packets in KA+SP contributes totransmission of the critical class, ﬁve simple mechanisms, i.e.,slight reduction of the delay by shorter backoﬀ in MAC layer

assured corridor mechanism, retransmission, priority queue-

than KA. However, retransmission introduces additional de-ing, rate control by local congestion detection, and rate con-lay in waiting for retransmission and the resultant delay oftrol by backpressure, collaborate consistently. We veriﬁed

KA+SP+RT becomes larger than the case of KA.

that the architecture successfully improved the delivery ra-

4. 2 Large scale event

tio and the delay of emergency packets independently of the

The loss rate of emergency packets in a large emergency

scale of emergency through simulation experiments.

with 32 EMG SEND nodes is shown in Fig. 10. By com-

References

paring results of KA and KA+SP, it can be seen that sup-[1] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam andE. Cayirci: “Wireless sensor networks: a survey”, Computer
Networks, 38, 4, pp. 393–422 (2002).
[2] C. Intanagonwiwat, R. Govindan and D. Estrin: “Directed
diﬀusion: a scalable and robust communication paradigm
for sensor networks”, Proceedings of the 6th annual international conference on Mobile computing and networking(MobiCom 2000), Boston, Massachusetts, United States,
pp. 56–67 (2000).
[3] T. Kawai, N. Wakamiya and M. Murata: “A fast and reliable transmission mechanism of urgent information in sensor networks”, Proceedings of the 3rd International Conference on Networked Sensing Systems (INSS 2006), Chicago,
Illinois, USA (2006).
[4] B. Hull, K. Jamieson and H. Balakrishnan: “Mitigatingcongestion in wireless sensor networks”, Proceedings of the
2nd international conference on Embedded networked sensor systems (SenSys 2004), Baltimore, Maryland, USA, pp.
134–147 (2004).
[5] A. Woo and D. E. Culler: “A transmission control scheme
for media access in sensor networks”, Proceedings of the
7th annual international conference on Mobile computing
and networking (MobiCom 2001), Rome, Italy, pp. 221–235(2001).
[6] N. Wakamiya and M. Murata: “Synchronization-based data
gathering scheme for sensor networks”, IEICE Transactions
on Communications, E88-B, 3, pp. 873–881 (2005).

pression of emission of normal packets has little eﬀect ina large scale event, since most of packet loss is caused bycollisions among emergency packets. By introducing the retransmission mechanism, the loss rate of critical class traﬃc
is further decreased than important class traﬃc due to retransmission scheduling. With the rate control mechanisms,
i.e., KA+SP+RT+PQ+RC, the loss rate of the critical classis reduced to about 0.25 %, while that of KA+SP+RT+PQ
is around 2 %.
Figure 11 shows the delay of emergency packets in a large
emergency. The delay is almost the same between KA andKA+SP and they are the smallest among the ﬁve variants
since there is no retransmission involved. In KA+SP+RT,
the delay rises in the ﬁrst 20 seconds for both classes, reﬂecting retransmissions due to collisions among emergency
packets. The scheduled retransmission contributes to smaller
delay of critical class traﬃc than that of important class trafﬁc. The rate control mechanisms further decrease the delayof critical class traﬃc to about 7 ms, since the number of—6—