Designing a Sensor Network Architecture forTransmission of Urgent Sensor Information
Tetsuya Kawai, Naoki Wakamiya, and Masayuki Murata
Graduate School of Information Science and Technology
Osaka University, Osaka, Japan
Email: {t-kawai, wakamiya, murata}@ist.osaka-u.ac.jp

Abstract— In wireless sensor networks used as a social infrastructure, urgent information, such as a fire alarm, is needed to
be transmitted as fast and reliably as possible. In this paper,
we propose design methodology for a sensor network which
provides preferential control of urgent information over othernon-urgent information. In our methodology, several simple
mechanisms which function in different spatial and temporallevels are introduced on each node and they work autonomously
and independently as a reaction to the surrounding situation.
We also show an example of a network architecture designed
following the methodology. Our simulation experiments showedthat the architecture successfully improved the delivery ratio anddelay of the urgent sensor information.

I. I NTRODUCTION
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 basestation (BS). A node is equipped with a processing unit, aradio transceiver, 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, andindustrial 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.
There have been several proposals of QoS (Quality ofService) control for WSNs [2], [3]. ESRT [4] achieves thedesired reliability by regulating the packet emission rate atsource nodes with feedback from a BS. In [5], overheard
packets are used for error correction to improve the reliability
in both of single-hop and multihop communications. In [6],the authors propose a routing protocol to find the best path interms of delay. For congestion mitigation in a WSN, CODA [7]combines hop-by-hop backpressure and end-to-end feedback
techniques. In [8], a prioritized MAC protocol is introduced inaddition to a hop-by-hop traffic control. IFRC [9] is developed
to realise adaptive fair and efficient rate allocation by sharing
information on the level of congestion among nodes.
c1-4244-1557-8/07/$25.00 °2007
IEEE

The main contribution of this paper is proposal of design
methodology of a network architecture for transmission ofurgent sensor information. In comparison to the research works
mentioned above, our approach is unique in that several simple
mechanisms are incorporated above the network layer. In orderto adapt to the scale of an emergency ranging from a small
event like a gas leakage to a catastrophic event such as anearthquake attack, some simple mechanisms are embedded ineach node, instead of developing a monolithic and complicatedmechanism. Those mechanisms work in different spatial andtemporal levels and they autonomously and independently
react to its surrounding situation locally observed. When asmall 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 thesame time and send urgent information simultaneously andindependently from others. A WSN in this case immediately
reacts as a whole to control serious congestion for fast andreliable transmission of urgent information. In this paper, 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 II
proposes design methodology for fast and reliable transmissionof urgent information and Section III introduces our UMIUSI
architecture. Evaluation of the architecture by simulation experiments is presented in Section IV. Finally, we conclude thispaper in Section V.
II. D ESIGN M ETHODOLOGY
A. 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 numberof immobile sensor nodes. Sensor nodes are operated on abattery and equipped with a variety of sensors. Normally, eachnode observes its environment and reports obtained sensor

Fig. 1.

Examples of control mechanisms.

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 saveits power consumption, a sensor node sleeps and wakes up
in accordance with a sleep scheduling algorithm. Once a nodedetects an emergency or an unusual condition, it begins to emit
packets containing urgent information at shorter intervals.
Since the methodology incorporates mechanisms above the
network layer, any MAC protocols, routing protocols, data
gathering mechanisms, and sleep scheduling algorithms can beincorporated with a network architecture designed following
the methodology, with no or small modification. However, asa base of consideration, we assume a contention-based MAC
protocol and a multihop routing protocol in this paper. This
assumption does not spoil the applicability of the proposal,
because contention-based, e.g., CSMA/CA-type of MAC protocols are preferred for its simplicity and multihop routing
protocols are often used for energy saving and spatial reuseof wireless channels.
B. 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]. In addition, reliability and latency should be considered
to satisfy application requirements. Especially in an event ofemergency, reliability and latency of transmission of urgent
information are crucial.
For operations under normal conditions, a WSN adopts a
MAC protocol, a routing protocol, a data gathering scheme,
and a sleep scheduling algorithm, which fit to application
requirements. Once an emergency occurs, an appropriate seriesof actions should take place to deliver urgent information to a
BS with satisfactory high reliability and low latency. Control
should be local for a small-scale event keeping nodes which
are not involved in the emergency operating as usual. On theother hand, as an event grows or a large-scale event occurs, a
WSN should react as a whole for fast and reliable transmissionof urgent information.
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 mostimportant issues. Urgent information should be differ-entiated from other information and receive preferential
controls according to their importance. We consider thatenergy efficiency can be sacrificed to some extent fortransmission 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, we needan architecture which is fully-distributed, self-organizing,
and adaptive to dynamically changing conditions. As aconsequence of localized reactions of each sensor 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
which function in different temporal and spatial levels. In
Fig. 1, typical control mechanisms are arranged in accordancewith 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, atleast one mechanism is chosen for each of spatial levels.
The collective behavior of these mechanisms offers appropriate preferential transmission of emergency packets. At the
beginning of an event, quick-acting node-level and neighborlevel mechanisms contribute to fast and reliable transmission
until slower path-level mechanisms come into effect. As theevent develops and situation becomes more serious, additional
mechanisms eventually become effective and network-level
control is conducted. In our UMIUSI architecture, which will
be proposed in Section III, those five mechanisms indicatedby gray circles are embedded in a sensor node.
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 andreliability. 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.
III. UMIUSI A RCHITECTURE
As an example of a network architecture designed following
our methodology, we propose UMIUSI (aUtonomous Mechanisms Integrated for Urgent Sensor Information).

Fig. 3.
Fig. 2.

An assured corridor.

The mechanisms leveraged in UMIUSI.

A. Details of UMIUSI
We categorize sensor information into three classes as one
normal class and two emergency classes. Emergency class
information is prioritized over normal class information. The
two types of urgent information are distinguished in more
important and less important by the application layer.
• 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, reliability and latency of normal class information should satisfy
application requirements by an adopted data gathering
scheme. An application can tolerate delay and loss ofnormal 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 tothis class, called important packets, can be delayed or
dropped depending on the level of congestion under emergency condition. 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 andfast transmission to a 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 andlow 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 the important class. However, it is not a trivial task
and remains as one of future works.
Following the methodology, we choose five mechanisms for
UMIUSI: priority queueing, rate control by local congestion
detection, retransmission, assured corridor mechanism (ACM)[10], and rate control by backpressure. These mechanisms are
simple and distributed, work independently of each other, andcover all the levels from node-level to network-level. Figure 2
briefly summarizes how and where they work. Althoughanother combination with other mechanisms is also possible,
we must carefully consider the relation among mechanisms.

For example, we can also introduce in-network aggregation[11] for reducing the number of packets and thus suppressing
congestion occurrence. However, adopting both hop-by-hop
and end-to-end retransmission mechanisms is likely to beinefficient or redundant.
The detailed description of each mechanism is presented in
the following.
1) Assured corridor mechanism (ACM): The main purposeof this mechanism is to avoid loss of emergency packets, i.e.,
packets of critical or important class, caused by collisions
with normal packets. In addition, ACM contributes to avoiding
delay caused by sleeping nodes. ACM establishes an “assured
corridor” from a source node to a BS, in which emergency
packets are protected from normal packets. An example of anassured corridor is illustrated in Fig. 3.
An assured corridor consists of awake nodes, which is on thepath 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 nodedetects an emergency, it moves to the EMG SEND state andbegins to periodically emit critical or important packets. On
receiving an emergency packet for the first time, other nodes
move to either of the SUPPRESSED or EMG FORWARD
state. A node on the path to the BS is responsible for forwarding emergency packets to the BS. Therefore, it moves tothe 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 the sending rate of normal packets. When it
detects a new emergency by itself or it receives an emergency
packet as a relaying node, it also moves to either of the
EMG SEND or EMG FORWARD state. Details of ACM withsimulation results can be found in [10].
2) Retransmission: In order to recover a lost emergency
packet while providing differentiated services, we introduce aprioritized scheduling algorithm to hop-by-hop retransmission.
The algorithm can be incorporated with most retransmission
mechanisms in MAC or above layer, such as that in [12].
A node retransmits an emergency packet when it detects aloss. The hop-by-hop acknowledgement can be easily done by,for example, overhearing a packet sent by a next-hop node. If
the overheard packet does not contain the information that thenode sent, the packet is considered to be lost. In this case, theFig. 4.

The contribution of each mechanism for a small-scale event.
Fig. 5.

first retransmission is scheduled after a backoff. To prioritize
retransmission of a critical packet, a backoff timer for a criticalpacket is set shorter than that for an important packet. If thefirst retransmission fails, one or more trials are conducted byapplying doubled backoff, i.e., a binary backoff scheme, until
retransmission succeeds or a next-hop node goes to sleep.
3) Priority queueing: Each node has a priority queue for
emergency packets, with which important packets are served
only when there is no critical packet queued. This means thatfast transmission of critical packets is accomplished at thesacrifice of longer transmission delay of important packets.
Transmission of normal packets at a node during an emergency
is delayed until the node returns to normal operation.
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 by detectionof local congestion. In order to keep the reporting rate ofcritical information at 1/tcri , the rate control is applied only
to important class traffic. When a source node of important
packets detects local congestion, it increases the emissioninterval of important packets. Congestion detection can bedone by, for example, observation of queue occupancy andchannel sampling such as proposed in [7], [8]. As a rate
control algorithm, a TCP-like AIMD (Additive Increase andMultiplicative Decrease) algorithm, such as that in [13], isempirically employed in our simulation experiments.
5) Rate control by backpressure: In an event of large
emergency such as an earthquake, even if emission of normal
packets is suppressed and source nodes of important packets
regulate their emission rate, congestion cannot be fully avoided
around a node belonging to multiple paths and around a BS,
where many emergency packets concentrate on. We employ abackpressure mechanism for a network-level traffic control in
UMIUSI. When a node detects congestion, it sets an explicit
congestion notification (ECN) bit in the header of important
packets which it relays toward a BS. By overhearing thepacket, a preceding node, i.e., a node closer to the source,
recognizes that congestion occurs in the path to the BS. Then,it also sets an ECN bit of the next important packet which
it relays to the next-hop node. Consequently, by means ofoverhearing, a congestion notification propagates to the source
node. On receiving the notification, the source node reduces
the emission rate of important packets, and the congestion is
mitigated.
B. Discussion
As stated above, UMIUSI is designed to be able to adapt toa variety of emergencies by combining the five autonomous

The contribution of each mechanism for a large-scale event.

mechanisms. Whereas there is neither a mechanism to identify
the type and scale of an emergency nor an explicit rule tochoose and coordinate mechanisms, and these mechanisms
work independently, resultant effects of the mechanisms working in different spatial and temporal levels give appropriate
traffic control as a whole regardless of the phase and scaleof an emergency. We choose the five mechanisms of UMIUSI
so that the mechanisms do not conflict with but complement
each other. For example, quick-acting node- or neighborlevel mechanisms complement slow path- or network-levelmechanisms at the initial phase of an event. Figures 4 and 5
are intuitive sketches to show how the mechanisms collaborate
by depicting the contribution of each mechanism to fast andreliable transmission of urgent information in a small-scaleand large-scale event respectively.
In a small-scale event, only one or a few nodes would detect
it. At the beginning of the event, the retransmission mechanism
becomes effective immediately since an emergency packet is
not protected from normal packets until an assured corridor
is established. Once a corridor is established, contribution of
the retransmission mechanism becomes smaller as the numberof 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 a small-scale event. In addition,
rate control of important class traffic by local congestion
detection does not help much, since the number of nodes
emitting important packets is small and the possibility ofcongestion 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 by backpressure is
activated. Figure 4 is an intuitive sketch to show how mucheach mechanism contributes to fast and reliable transmissionof emergency packets in a small-scale event.
In a large-scale event, since most of nodes are involvedin transmission of emergency packets as source nodes orforwarding nodes, an assured corridor to suppress the emissionof normal packet does not help much. On the contrary,mechanisms to mitigate congestion within a corridor are effective. The priority queueing mechanism offers differentiatedforwarding 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.
Afterwards, congestion among emergency packets traversing
different paths is solved by the backpressure mechanism.

0.05

KA
ACM
ACM+RT

Total throughput (packets/s)

Loss rate

0.04

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

60

0.03

0.02

0.01

50
40
30
20
10

0

Fig. 6.

300 320 340 360 380 400 420 440 460 480 500

0300 320 340 360 380 400 420 440 460 480 500

Time (s)Time (s)The loss rate of emergency packets (small-scale event).

Fig. 7.

The total throughput of important packets (large-scale event).
1

IV. S IMULATION E XPERIMENTS
0.1

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 thetransmission range of radio signals is set to 2.5 m.
We employ the synchronization-based data gathering
scheme [14] for the underlying data gathering mechanism.
It employs a broadcast-based routing, and timing of packet
emission is the same among nodes of the same hop distancefrom a BS. In a normal state, all nodes adopt a sleep schedule.
Nodes on the same hop distance wake up at the same time andreceive 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 go back to a sleep mode.
In the simulation experiments, we set the interval of normal
packet emission tnorm at ten seconds, and the offset between
emissions of adjacent nodes at one second. It means that anode is awake for two seconds in a data gathering interval often seconds.
Each simulation experiment lasts for 500 seconds including
300 seconds for initialization without any emergency. One(small-scale event) or 32 (large-scale event) nodes are randomly chosen after the initialization, and they detect an eventat randomly chosen time within following 10 seconds. They
wait for next-hop nodes to wake up, then start transmitting
emergency packets at intervals of 0.5 seconds. The nodes stayin the emergency state for 180 seconds and then go back to thenormal state. The same experiment is repeated for 100 times
with different node layouts. For the retransmission schedule,
the backoff for a critical packet ranges from 0.1 to 0.2 seconds
after the first emission and that for an important packet isfrom 0.25 to 0.3 seconds. In the experiments for a large-scale
event, four of the 32 emergency nodes are of the critical classand others are of the important class. Congestion detection
is done by not receiving any acknowledgement from any of
its next-hop nodes, and the parameters for additive increase
and multiplicative decrease in AIMD rate control are set at

Loss rate

A. Simulation model
0.01

0.001

1e-04

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

1e-05
300 320 340 360 380 400 420 440 460 480 500
Time (s)Fig. 8.

The loss rate of emergency packets (large-scale event).

0.05 packets/s and 0.5 respectively.
B. Results and Discussion
In order to evaluate the effect of mechanisms comprising
UMIUSI, we compared five variants of integration 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 additionallysuppressing 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 additionallyapplied. The last one, FULL, employs all of the mechanisms
explained in Section III-A.
1) Small-scale event: The loss rate of emergency packets
is shown in Fig. 6. The loss rate here is defined as the ratioof emergency packets which did not arrive at a BS out of
all emergency packets emitted from a source node. The horizontal axis corresponds to the simulation time. The results of
ACM+RT+PQ and FULL are identical to those of ACM+RT,
since there is no important class traffic. Therefore, the resultsof these two variants are not shown in Fig. 6. In the case ofKA, keeping intermediate nodes awake without suppressionof normal packets, the loss rate rises since collisions withnormal packets are unavoidable. On the other hand, in ACM,
although the initial rate is the same as that of KA, the loss ratedrops gradually in about 30 seconds. This corresponds to timerequired to establish an assured corridor from a source to theBS, which further depends on the hop distance of the source
node and the sleep schedule of the synchronization-based data
gathering scheme. With retransmission, i.e., ACM+RT, there
is no packet loss. However, the total number of emergency
packets emitted is larger than that of ACM by 15 % and thisincrease leads to additional energy expenditure.
While the results are not shown because of space limitation,
the end-to-end delay with suppression of normal packets in
ACM (38.3 ms) is slightly smaller than that in KA (38.5 ms)
due to shorter backoff in MAC layer. However, retransmission
introduces additional delay in waiting for retransmission andthe resultant delay of ACM+RT (41.0 ms) becomes larger thanthat of KA.
2) Large-scale event: The total throughput of critical andimportant class averaged over 100 experiments is illustrated inFig. 7. Here the total throughput is defined as the number of
emergency packets received by a BS per second. Note thatthe plots of the critical class in ACM+RT+PQ and FULL
are overlapped to seem like a single plot. With rate control
mechanism (FULL), the total throughput of the important classdrops gradually in about 40 seconds to be around 5 packets/s,
while that without rate control is 50 packets/s. No difference inthe total throughput can be seen in the critical class between
with and without the rate control. This is because the total
traffic of the critical class is 8 packets/s and, as shown later,
the loss rate of critical packets is as low as around 2 %even without the rate control. Similarly, little difference wasobserved between the total throughput of ACM+RT and that of
ACM+RT+PQ for both classes, thus the results of ACM+RT
are not shown in Fig. 7. After 490 seconds, EMG SEND nodes
go back to the NORMAL state, thus the throughput drops.
The loss rate of emergency packets is shown in Fig. 8.
By comparing results of KA and ACM, it can be seen thatsuppression of emission of normal packets has little effect ina large-scale event as conjectured in Fig. 5, since most of
packet loss is caused by collisions among emergency packets.
By introducing the retransmission mechanism, the loss rateof critical class traffic is further decreased than importantclass traffic due to retransmission scheduling. With the rate
control mechanisms, i.e., FULL, the loss rate of the criticalclass is reduced to about 0.25 %, while that of ACM+RT+PQ
is around 2 %. Considering this together with the results ofFig. 7, we can say that we sacrifice 90 % of the throughput of
the important class in order to make the loss rate of the criticalclass one-eighth. The AIMD parameters affect this trade off.
The average end-to-end delay is almost the same at 40.8 ms
in KA and ACM and they are the smallest among the five variants since there is no retransmission involved. In ACM+RT, thedelay rises up in the first 20 seconds to be 147 ms and 198 ms
for the critical and important class respectively, reflecting more
frequent retransmissions due to collisions among emergency
packets. The scheduled retransmission contributes to smaller
delay of critical class traffic than that of important class traffic.
With the rate control mechanisms, the delay of the criticalclass gradually decreases to be 67.8 ms, since the number ofretransmission and backoff become smaller as the rate control

mechanisms come into effect to regulate important class traffic.
These observations in the simulation experiments are consistent with the discussion in Section III-B.
V. C ONCLUSION
In this paper, we presented the methodology for designinga WSN architecture for fast and reliable transmission ofurgent information, where several simple and fully-distributed
mechanisms working in different spatial and temporal levels are incorporated. We designed a network architecture,
called UMIUSI, following the methodology. We verified that
UMIUSI successfully improved the delivery ratio and the delay of emergency packets regardless of the scale of emergency.
ACKNOWLEDGMENT
This research was supported by “Special Coordination
Funds for Promoting Science and Technology: Yuragi Project”
and a Grant-in-Aid for Scientific Research (A)(2) 16200003
of the Ministry of Education, Culture, Sports, Science andTechnology in Japan.
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