DiMo: Distributed Node Monitoring in Wireless
Sensor Networks
Andreas Meier†, Mehul Motani∗, Hu Siquan∗, and Simon Künzli‡
†Computer Engineering and Networks Lab, ETH Zurich, Switzerland
∗
Electrical & Computer Engineering, National University of Singapore, Singapore
‡Siemens Building Technologies, Zug, Switzerland
ABSTRACT
Safety-critical wireless sensor networks, such as a distributed
fire- or burglar-alarm system, require that all sensor nodes
are up and functional. If an event is triggered on a node,
this information must be forwarded immediately to the sink,
without setting up a route on demand or having to find an
alternate route in case of a node or link failure. Therefore,
failures of nodes must be known at all times and in case of
a detected failure, an immediate notification must be sent
to the network operator. There is usually a bounded time
limit, e.g., five minutes, for the system to report network or
node failure. This paper presents DiMo, a distributed and
scalable solution for monitoring the nodes and the topology,
along with a redundant topology for increased robustness.
Compared to existing solutions, which traditionally assume
a continuous data-flow from all nodes in the network, DiMo
observes the nodes and the topology locally. DiMo only
reports to the sink if a node is potentially failed, which
greatly reduces the message overhead and energy consump-
tion. DiMo timely reports failed nodes and minimizes the
false-positive rate and energy consumption compared with
other prominent solutions for node monitoring.
Categories and Subject Descriptors
C.2.2 [Network Protocols]: Wireless Sensor Network
General Terms
Algorithms, Design, Reliability, Performance
Keywords
Low power, Node monitoring, Topology monitoring, WSN
1.
INTRODUCTION
Driven by recent advances in low power platforms and
protocols, wireless sensor networks are being deployed to-
day to monitor the environment from wildlife habitats [1]
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to mission-critical fire-alarm systems [5]. There are, how-
ever, still some obstacles in the way for mass application
of wireless sensor networks. One of the key challenges is
the management of the wireless sensor network itself. With-
out a practical management system, WSN maintenance will
be very difficult for network administrators. Furthermore,
without a solid management plan, WSNs are not likely to
be accepted by industrial users.
One of the key points in the management of a WSN is the
health status monitoring of the network itself. Node failures
should be captured by the system and reported to adminis-
trators within a given delay constraint. Due to the resource
constraints of WSN nodes, traditional network management
protocols such as SNMP adopted by TCP/IP networks are
not suitable for sensor networks.
In this paper, we con-
sider a light-weight network management approach tailored
specifically for WSNs and their unique constraints.
Currently, WSN deployments can be categorized by their
application scenario: data-gathering applications and event-
detection applications. For data-gathering systems, health
status monitoring is quite straight forward. Monitoring in-
formation can be forwarded to the sink by specific health
status packets or embedded in the regular data packets. Ad-
ministrators can usually diagnose the network with a helper
program. NUCLEUS [6] is one of the network management
systems for data-gathering application of WSN. Since event-
detection deployments do not have regular traffic to send to
the sink, the solutions for data-gathering deployments are
not suitable. In this case, health status monitoring can be
quite challenging and has not been discussed explicitly in
the literature.
In an event-detection WSN, there is no periodic data trans-
fer, i.e., nodes maintain radio silence until there is an event
to report. While this is energy efficient, it does mean that
there is no possibility for the sink to decide whether the net-
work is still up and running (and waiting for an event to be
detected) or if some nodes in the network have failed and
are therefore silent. Furthermore, for certain military ap-
plications or safety-critical systems, the specifications may
include a hard time constraint for accomplishing the node
health status monitoring task.
In an event-detection WSN, the system maintains a net-
work topology that allows for forwarding of data to a sink
in the case of an event. Even though there is no regular
data transfer in the network, the network should always be
ready to forward a message to the sink immediately when-
ever necessary.
It is this urgency of data forwarding that
makes it undesirable to set up a routing table and neighbor
list after the event has been detected. The lack of regular
data transfer in the network also leads to difficulty in de-
tecting bad quality links, making it challenging to establish
and maintain a stable robust network topology.
While we have mentioned event-detection WSNs in gen-
eral, we accentuate that the distributed node monitoring
problem we are considering is inspired by a real-world ap-
plication: a distributed indoor wireless alarm system which
includes a sensor for detection of a specific alarm such as
fire (as studied in [5]). To illustrate the reporting require-
ments of such a system, we point out that regulatory speci-
fications require a fire to be reported to the control station
within 10 seconds and a node failure to be reported within
5 minutes [9]. This highlights the importance of the node-
monitoring problem.
In this paper, we present a solution for distributed node
monitoring called DiMo, which consists of two functions:
(i) Network topology maintenance, introduced in Section 2,
and (ii) Node health status monitoring, introduced in Sec-
tion 3. We compare DiMo to existing state-of-the-art node
monitoring solutions and evaluate DiMo via simulations in
Section 4.
1.1 Design Goals
DiMo is developed based on the following design goals:
• In safety critical event monitoring systems, the status
of the nodes needs to be monitored continuously, allow-
ing the detection and reporting of a failed node within
a certain failure detection time TD, e.g., TD = 5 min.
• If a node is reported failed, a costly on-site inspection
is required. This makes it of paramount interest to
decrease the false-positive rate, i.e., wrongly assuming
a node to have failed.
• In the case of an event, the latency in forwarding the
information to the sink is crucial, leaving no time to
set up a route on demand. We require the system to
maintain a topology at all times. In order to be robust
against possible link failures, the topology needs to
provide redundancy.
• To increase efficiency and minimize energy consump-
tion, the two tasks of topology maintenance (in par-
ticular monitoring of the links) and node monitoring
should be combined.
• Maximizing lifetime of the network does not necessar-
ily translate to minimizing the average energy con-
sumption in the network, but rather minimizing the
energy consumption of the node with the maximal load
in the network. In particular, the monitoring should
not significantly increase the load towards the sink.
• We assume that the event detection WSN has no reg-
ular data traffic, with possibly no messages for days,
weeks or even months. Hence we do not attempt to op-
timize routing or load balancing for regular data. We
also note that approaches like estimating links’ perfor-
mance based on the ongoing data flow are not possible
and do not take them into account.
• Wireless communications in sensor networks (especially
indoor deployments) is known for its erratic behav-
ior [2, 8], likely due to multi-path fading. We assume
such an environment with unreliable and unpredictable
communication links, and argue that message losses
must be taken into account.
1.2 Related Work
Nithya et al. discuss Sympathy in [3], a tool for detect-
ing and debugging failures in pre- and post-deployment sen-
sor networks, especially designed for data gathering appli-
cations. The nodes send periodic heartbeats to the sink
that combines this information with passively gathered data
to detect failures. For the failure detection, the sink re-
quires receiving at least one heartbeat from the node every
so called sweep interval, i.e., its lacking indicates a node fail-
ure. Direct-Heartbeat performs poorly in practice without
adaptation to wireless packet losses. To meet a desired false
positive rate, the rate of heartbeats has to be increased also
increasing the communication cost. NUCLEUS [6] follows
a very similar approach to Sympathy, providing a manage-
ment system to monitor the heath status of data-gathering
applications.
Rost et al. propose with Memento a failure detection sys-
tem that also requires nodes to periodically send heartbeats
to the so called observer node. Those heartbeats are not
directly forwarded to the sink node, but are aggregated in
form of a bitmask (i.e., bitwise OR operation). The ob-
server node is sweeping its bitmask every sweep interval and
will forward the bitmask with the node missing during the
next sweep interval if the node fails sending a heartbeat
in between. Hence the information of the missing node is
disseminated every sweep interval by one hop, eventually
arriving at the sink. Memento is not making use of ac-
knowledgements and proactively sends multiple heartbeats
every sweep interval, whereas this number is estimated based
on the link’s estimated worst-case performance and the tar-
geted false positive rate. Hence Memento and Sympathy
do both send several messages every sweep interval, most of
them being redundant.
In [5], Strasser et al. propose a ring based (hop count) gos-
siping scheme that provides a latency bound for detecting
failed nodes. The approach is based on a bitmask aggre-
gation, being filled ring by ring based on a tight schedule
requiring a global clock. Due to the tight schedule, retrans-
missions are limited and contention/collisions likely, increas-
ing the number of false positives. The approach is similar
to Memento [4], i.e., it does not scale, but provides latency
bounds and uses the benefits of acknowledgements on the
link layer.
2. TOPOLOGY MAINTENANCE
Forwarding a detected event without any delay requires
maintaining a redundant topology that is robust against link
failures. The characteristics of such a redundant topology
are discussed subsequently.
The topology is based on so called relay nodes, a neighbor
that can provide one or more routes towards the sink with
a smaller cost metric than the node itself has. Loops are
inherently ruled out if packets are always forwarded to relay
nodes. For instance, in a simple tree topology, the parent is
the relay node and the cost metric is the hop count.
In order to provide redundancy, every node is connected
with at least two relay nodes, and is called redundantly con-
nected. Two neighboring nodes can be redundantly con-
nected by being each others relay, although having the same
cost metric, only if they are both connected to the sink.
This exception allows the nodes neighboring the sink to be
redundantly connected and avoids having a link to the sink
as a single point of failure.
network, all deployed nodes are (redundantly) connected.
In a (redundantly) connected
A node’s level L represents the minimal hop count to the
sink according to the level of its relay nodes; i.e., the relay
with the least hop count plus one. The level is infinity if the
node is not connected. The maximal hop count H to the
sink represents the longest path to the sink, i.e., if at every
hop the relay node with the highest maximal hop count is
chosen. If the node is redundantly connected, the node’s H
is the maximum hop count in the set of its relays plus one,
if not, the maximal hop count is infinity. If and only if all
nodes in the network have a finite maximal hop count, the
network is redundantly connected.
The topology management function aims to maintain a
redundantly connected network whenever possible. This
might not be possible for sparsely connected networks, where
some nodes might only have one neighbor and therefore can-
not be redundantly connected by definition. Sometimes it
would be possible to find alternative paths with a higher cost
metric, which in turn would largely increase the overhead for
topology maintenance (e.g., for avoiding loops).
For the cost metric, the tuple (L,H) is used. A node A
has the smaller cost metric than node B if
LA < LB ∨ (LA = LB ∧ HA < HB).
(1)
During the operation of the network, DiMo continuously
monitors the links (as described in Section 3), which allows
the detection of degrading links and allows triggering topol-
ogy adaptation. Due to DiMo’s redundant structure, the
node is still connected to the network, during this neighbor
search, and hence in the case of an event, can forward the
message without delay.
3. MONITORING ALGORITHM
This section describes the main contribution of this paper,
a distributed algorithm for topology, link and node monitor-
ing. From the underlying MAC protocol, it is required that
an acknowledged message transfer is supported.
3.1 Algorithm
A monitoring algorithm is required to detect failed nodes
within a given failure detection time TD (e.g., TD = 5 min).
A node failure can occur for example due to hardware fail-
ures, software errors or because a node runs out of energy.
Furthermore, an operational node that gets disconnected
from the network is also considered as failed.
The monitoring is done by so called observer nodes that
monitor whether the target node has checked in by sending
a heartbeat within a certain monitoring time. If not, the ob-
server sends a node missing message to the sink. The target
node is monitored by one observer at any time. If there are
multiple observer nodes available, they alternate amongst
themselves. For instance, if there are three observers, each
one observes the target node every third monitoring time.
The observer node should not only check for the liveliness
of the nodes, but also for the links that are being used for
sending data packets to the sink in case of a detected event.
These two tasks are combined by selecting the relay nodes as
observers, greatly reducing the network load and maximiz-
ing the network lifetime. In order to ensure that all nodes
are up and running, every node is observed at all times.
The specified failure detection time TD is an upper bound
i.e., the interval within
for the monitoring interval TM ,
which the node has to send a heartbeat. Since failure detec-
tion time is measured at the sink, the detection of a missing
node at the relay needs to be forwarded, resulting in an ad-
ditional maximal delay TL. Furthermore, the heartbeat can
be delayed as well, either by message collisions or link fail-
ures. Hence the node should send the heartbeat before the
relay’s monitoring timer expires and leave room for retries
and clock drift within the time window TR. So the monitor-
ing interval has to be set to
TM ≤ TD − TL − TR
(2)
and the node has to ensure that it is being monitored every
TM by one of its observers.
The schedule of reporting to an observer is only defined
for the next monitoring time for each observer. Whenever
the node checks in, the next monitoring time is announced
with the same message. So for every heartbeat sent, the old
monitoring timer at the observer can be cancelled and a new
timer can be set according the new time.
Whenever, a node is newly observed or not being observed
by a particular observer, this is indicated to the sink. Hence
the sink is always aware of which nodes are being observed
in the network, and therefore always knows which nodes
are up and running. This registration scheme at the sink
is an optional feature of DiMo and depends on the user’s
requirements.
3.2 Packet Loss
Wireless communication always has to account for possi-
ble message losses. Sudden changes in the link quality are
always possible and even total link failures in the order of a
few seconds are not uncommon [2]. So the time TR for send-
ing retries should be sufficiently long to cover such blanks.
Though unlikely, it is possible that even after a duration
of TR, the heartbeat could not have been successfully for-
warded to the observer and thus was not acknowledged, in
spite of multiple retries.
The node has to assume that it will be reported miss-
ing at the sink, despite the fact it is still up and running.
Should the node be redundantly connected, a recovery mes-
sage is sent to the sink via another relay announcing be-
ing still alive. The sink receiving a recovery message and a
node-missing message concerning the same node can neglect
these messages as they cancel each other out. This recov-
ery scheme is optional, but minimizes the false positives by
orders of magnitudes as shown in Section 4.
3.3 Topology Changes
In the case of a new relay being announced from the topol-
ogy management, a heartbeat is sent to the new relay, mark-
ing it as an observer node. On the other hand, if a depre-
cated relay is announced, this relay might still be acting as
an observer, and the node has to check in as scheduled. How-
ever, no new monitor time is announced with the heartbeat,
which will release the deprecated relay of being an observer.
3.4 Queuing Policy
A monitoring buffer exclusively used for monitoring mes-
sages is introduced, having the messages queued according
to a priority level, in particular node-missing messages first.
Since the MAC protocol and routing engine usually have a
queuing buffer also, it must be ensured that only one single
monitoring message is being handled by the lower layers at
the time. Only if an ACK is received, the monitoring mes-
sage can be removed from the queue (if a NACK is received,
the message remains). DiMo only prioritizes between the
different types of monitoring messages and does not require
prioritized access to data traffic.
PRR
Sympathy (n=1)
Sympathy (n=2)
Memento
DiMo
80%
3.93e-2
1.55e-3
8.00e-3
3.15e-4
85%
1.68e-2
2.81e-4
3.38e-3
5.66e-5
90%
4.99e-3
2.50e-5
1.00e-3
4.99e-6
95%
6.25e-4
3.91e-7
1.25e-4
7.81e-8
4. EVALUATION
In literature, there are very few existing solutions for mon-
itoring the health of the wireless sensor network deployment
itself. DiMo is the first sensor network monitoring solution
specifically designed for event detection applications. How-
ever, the two prominent solutions of Sympathy [3] and Me-
mento [4] for monitoring general WSNs can also be tailored
for event gathering applications. We compare the three ap-
proaches by looking at the rate at which they generate false
positives, i.e., wrongly inferring that a live node has failed.
False positives tell us something about the monitoring pro-
tocol since they normally result from packet losses during
monitoring. It is crucial to prevent false positives since for
every node that is reported missing, a costly on-site inspec-
tion is required.
DiMo uses the relay nodes for observation. Hence a pos-
sible event message and the regular heartbeats both use the
same path, except that the latter is a one hop message only.
The false positive probability thus determines the reliability
of forwarding an event.
We point out that there are other performance metrics
which might be of interest for evaluation.
In addition to
false positives, we have looked at latency, message overhead,
and energy consumption. We present the evaluation of false
positives below.
4.1 Analysis of False Positives
In the following analysis, we assume r heartbeats in one
sweep for Memento, whereas DiMo and Sympathy allow
sending up to r − 1 retransmissions in the case of unac-
knowledged messages. To compare the performance of the
false positive rate, we assume the same sweep interval for
three protocols which means that Memento’s and Sympa-
thy’s sweep interval is equal to DiMo’s monitoring interval.
In the analysis we assume all three protocols having the same
packet-loss probability pl for each hop.
For Sympathy, a false positive for a node occurs when
the heartbeat from the node does not arrive at the sink in
a sweep interval, assuming r − 1 retries on every hop. So
a node will generate false positive with a possibility (1 −
(1 − pr
l )d)n, where d is the hop count to the sink and n the
numbers of heartbeats per sweep. In Memento, the bitmask
representing all nodes assumes them failed by default after
the bitmap is reset at the beginning of each sweep interval.
If a node doesn’t report to its parent successfully, i.e., if all
the r heartbeats are lost in a sweep interval, a false positive
r. In DiMo the node is
will occur with a probability of pl
reported missing if it fails to check in at the observer having
In this case, a recovery message is
a probability of pl
triggered. Consider the case that the recovery message is not
kept in the monitoring queue like the node-missing messages,
but dropped after r attempts, the false positive rate results
in pl
r(1 − (1 − pl
r.
r)d).
Table 1 illustrates the false positive rates for the three
protocols ranging the packet reception rate (PRR) between
80% and 95%. For this example the observed node is in
a five-hop distance (d = 5) from the sink and a common
Table 1: False positive rates for a node with hop
count 5 and 3 transmissions under different packet
success rates.
number of r = 3 attempts for forwarding a message is as-
sumed. Sympathy clearly suffers from a high packet loss,
but its performance can be increased greatly sending two
heartbeats every sweep interval (n = 2). This however dou-
bles the message load in the network, which is especially
substantial as the messages are not aggregated, resulting in
a largely increased load and energy consumption for nodes
next to the sink. Comparing DiMo with Memento, we ob-
serve the paramount impact of the redundant relay on the
false positive rate. DiMo offers a mechanism here that is not
supported in Sympathy or Memento as it allows sending up
to r− 1 retries for the observer and redundant relay. Due to
this redundancy, the message can also be forwarded in the
case of a total blackout of one link, a feature both Memento
and Sympathy are lacking.
4.2 Simulation
For evaluation purposes we have implemented DiMo in
Castalia 1.3, a state of the art WSN simulator based on the
OMNet++ platform. Castalia allows evaluating DiMo with
a realistic wireless channel (based on the empirical findings
of Zuniga et al. [8]) and radio model but also captures effects
like the nodes’ clock drift. Packet collisions are calculated
based on the signal to interference ratio (SIR) and the radio
model features transition times between the radio’s states
(e.g., sending after a carrier sense will be delayed). Speck-
MAC [7], a packet based version of B-MAC, with acknowl-
edgements and a low-power listening interval of 100 ms is
used on the link layer. The characteristics of the Chipcon
CC2420 are used to model the radio.
The simulations are performed for a network containing 80
nodes, arranged in a grid with a small Gaussian distributed
displacement, representing an event detection system where
nodes are usually not randomly deployed but rather evenly
spread over the observed area. 500 different topologies were
analyzed. The topology management results in a redun-
dantly connected network with up to 5 levels L and a max-
imum hop count H of 6 to 8.
A false positive is triggered if the node fails to check in,
which is primarily due to packet errors and losses on the
wireless channel. In order to understand false positives, we
set the available link’s packet reception rate (PRR) to 0.8,
allowing us to see the effects of the retransmission scheme.
Furthermore, this fixed PRR also allows a comparison with
the results of the previous section’s analysis and is shown
in Figure 1(a). The plot shows on the one hand side the
monitoring based on a tree structure that is comparable to
the performance of Memento, i.e., without DiMo’s possibil-
ity of sending a recovery message using an alternate relay.
On the other hand side, the plot shows the false positive rate
of DiMo. The plot clearly shows the advantage of DiMo’s
redundancy, yet allowing sending twice as many heartbeats
than the tree approach. This might not seem necessarily fair
at first; however, in a real deployment it is always possible
(a) Varying number of retries; PRR = 0.8.
(b) Varying link quality.
Figure 1: False positives: DiMo achieves the targeted false positive rate of 1e-7, also representing the
reliability for successfully forwarding an event.
that a link fails completely, allowing DiMo to still forward
the heartbeat. The simulation and the analysis show a slight
offset in the performance, which is explained by a simulation
artifact of the SpeckMAC implementation that occurs when
the receiver’s wake-up time coincides with the start time of
a packet. This rare case allows receiving not only one but
two packets out of the stream, which artificially increases
the link quality by about three percent.
The nodes are observed every TM = 4 min, resulting in
being monitored 1.3e5 times a year. A false positive rate of
1e-6 would result in having a particular node being wrongly
reported failed every 7.7 years. Therefore, for a 77-node net-
work, a false positive rate of 1e-7 would result in one false
alarm a year, being the targeted false-positive threshold for
the monitoring system. DiMo achieves this rate by setting
the numbers of retries for both the heartbeat and the recov-
ery message to four. Hence the guard time TR for sending
the retries need to be set sufficiently long to accommodate
up to ten messages and back-off times.
The impact of the link quality on DiMo’s performance is
shown in Figure 1(b). The tree topology shows a similar
performance than DiMo, if the same number of messages
is sent. However, it does not show the benefit in the case
of a sudden link failure, allowing DiMo to recover immedi-
ately. Additionally, the surprising fact that false positives
are not going to zero for perfect link quality is explained by
collisions. This is also the reason why DiMo’s curve for two
retries flattens for higher link qualities. Hence, leaving room
for retries is as important as choosing good quality links.
5. CONCLUSION
In this paper, we presented DiMo, a distributed algorithm
for node and topology monitoring, especially designed for
use with event-triggered wireless sensor networks. As a de-
tailed comparative study with two other well-known moni-
toring algorithm shows, DiMo is the only one to reach the
design target of having a maximum error reporting delay
of 5 minutes while keeping the false positive rate and the
energy consumption competitive.
The proposed algorithm can easily be implemented and
also be enhanced with a topology management mechanism
to provide a robust mechanism for WSNs. This enables its
use in the area of safety-critical wireless sensor networks.
Acknowledgment
The work presented in this paper was supported by CTI
grant number 8222.1 and the National Competence Center
in Research on Mobile Information and Communication Sys-
tems (NCCR-MICS), a center supported by the Swiss Na-
tional Science Foundation under grant number 5005-67322.
This work was also supported in part by phase II of the
Embedded and Hybrid System program (EHS-II) funded by
the Agency for Science, Technology and Research (A*STAR)
under grant 052-118-0054 (NUS WBS: R-263-000-376-305).
The authors thank Matthias Woehrle for revising a draft
version of this paper.
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