Understanding Packet Delivery Performance In Dense
Wireless Sensor Networks
∗
Jerry Zhao
Computer Science Department
University of Southern California
Los Angeles, CA 90089-0781
zhaoy@usc.edu
Ramesh Govindan
Computer Science Department
University of Southern California
Los Angeles, CA 90089-0781
ramesh@usc.edu
ABSTRACT
Wireless sensor networks promise fine-grain monitoring in
a wide variety of environments. Many of these environ-
ments (e.g., indoor environments or habitats) can be harsh
for wireless communication. From a networking perspec-
tive, the most basic aspect of wireless communication is the
packet delivery performance: the spatio-temporal charac-
teristics of packet loss, and its environmental dependence.
These factors will deeply impact the performance of data
acquisition from these networks.
In this paper, we report on a systematic medium-scale
(up to sixty nodes) measurement of packet delivery in three
different environments: an indoor office building, a habitat
with moderate foliage, and an open parking lot. Our findings
have interesting implications for the design and evaluation of
routing and medium-access protocols for sensor networks.
Categories and Subject Descriptors
C.2.1 [Network Architecture and Design]: Wireless
communication; C.4 [Performance of Systems]: Perfor-
mance attributes, Measurement techniques
General Terms
Measurement, Experimentation
Keywords
Low power radio, Packet loss, Performance measurement
1. INTRODUCTION
Wireless communication has the reputation of being no-
toriously unpredictable. The quality of wireless communica-
tion depends on the environment, the part of the frequency
∗
This work is supported in part by NSF grant CCR-0121778
for the Center for Embedded Systems.
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spectrum under use, the particular modulation schemes un-
der use, and possibly on the communicating devices them-
selves. Communication quality can vary dramatically over
time, and has been reputed to change with slight spatial
displacements. All of these are true to a greater degree for
ad-hoc (or infrastructure-less) communication than for wire-
less communication to a base station. Given this, and the
paucity of large-scale deployments, it is perhaps not surpris-
ing that there have been no medium to large-scale measure-
ments of ad-hoc wireless systems; one expects measurement
studies to reveal high variability in performance, and one
suspects that such studies will be non-representative.
Wireless sensor networks [5, 7] are predicted on ad-hoc
wireless communications. Perhaps more than other ad-hoc
wireless systems, these networks can expect highly variable
wireless communication. They will be deployed in harsh,
inaccessible, environments which, almost by definition will
exhibit significant multi-path communication. Many of the
current sensor platforms use low-power radios which do not
have enough frequency diversity to reject multi-path prop-
agation. Finally, these networks will be fairly densely de-
ployed (on the order of tens of nodes within communica-
tion range). Given the potential impact of these networks,
and despite the anecdotal evidence of variability in wireless
communication, we argue that it is imperative that we get
a quantitative understanding of wireless communication in
sensor networks, however imperfect.
Our paper is a first attempt at this. Using up to 60 Mica
motes, we systematically evaluate the most basic aspect of
wireless communication in a sensor network: packet delivery.
Particularly for energy-constrained networks, packet de-
livery performance is important, since that translates to net-
work lifetime. Sensor networks are predicated using low-
power RF transceivers in a multi-hop fashion. Multiple
short hops can be more energy-efficient than one single hop
over a long range link. Poor cumulative packet delivery per-
formance across multiple hops may degrade performance of
data transport and expend significant energy. Depending
on the kind of application, it might significantly undermine
application-level performance. Finally, understanding the
dynamic range of packet delivery performance (and the ex-
tent, and time-varying nature of this performance) is impor-
tant for evaluating almost all sensor network communication
protocols.
We study packet delivery performance at two layers of
the communication stack (Section 3). At the physical-layer
and in the absence of interfering transmissions, packet de-
livery performance is largely a function of the environment,
the particular physical layer coding scheme, and perhaps
individual receiver characteristics. We place a simple lin-
ear topology, with a single sender, in three different envi-
ronments: an office building, a local habitat, and an open
parking lot. For these three environments, we study the effi-
cacy of packet delivery under different transmit powers and
physical layer codings.
At the medium access layer, interfering transmissions con-
tribute to poor packet delivery performance. Many MAC
layers contain mechanisms, such as carrier sense and link-
layer retransmissions, to counteract these effects. We study
the efficacy of such mechanisms in our three environments
discussed above.
Our measurements (Sections 4 and 5) uncover a variety
of interesting phenomena. There are heavy tails in the dis-
tributions of packet loss, both at the physical layer and at
the MAC layer. In our indoor experiments at the physical
layer, for example, fully half of the links experienced more
than 10% packet loss, and a third more than 30%. At the
physical layer, this variability can be characterized by the
existence of a gray area within the communication range
of a node: receivers in this gray area are likely to experi-
ence choppy packet reception, and in some environments,
this gray area is almost a third of the communication range.
The gray area is also distinguished by significant variabil-
ity in packet reception over time. Relatively sophisticated
physical layer coding schemes are able to mask some of the
variability, but with a loss in bandwidth efficiency. At the
MAC layer, link-layer retransmissions are unable to reduce
the variability; packet losses at the MAC layer also exhibit
heavy tails. Moreover, the efficiency of the MAC layer is low:
50% to 80% of communication energy is wasted in overcom-
ing packet collisions and environmental effects. Finally, in
our harsher environments, nearly 10% of the links exhibit
asymmetric packet loss.
Taken together, this appears to paint a somewhat pes-
simistic picture of wireless communication for sensor net-
works. However, we contend that there might be a sim-
ple set of mechanisms that can greatly improve packet de-
livery in the environments that sensor networks are tar-
geted for. Such topology control
1
mechanisms would care-
fully (i.e., through measurement of actual performance) dis-
card poorly performing neighbors or neighbors to whom
asymmetric links exist. This represents a departure from
traditional lower-layer design, where decisions are made at
packet granularity (collision avoidance using RTS/CTS or
link-layer retransmissions). At least for static (non-mobile)
sensor networks, because pathological loss performance de-
pends upon spatial positioning (cf. our gray area), it is
meaningful to make decisions at the granularity of links to
neighbors.
2. RELATED WORK
There is very little work that has extensively evaluated
packet delivery performance on dense ad hoc wireless sen-
sors. Woo et al. [23] examine a packet loss trace between
1
Our use of this term is slightly different from its use in the
literature. Topology control in the ad-hoc context has meant
the adaptation of transmit powers to enable higher spatial
reuse [17], and some sensor networks work has used this
term to denote mechanisms that selectively turn off nodes
to reduce density and/or increase lifetime [24, 3, 2].
a pair of motes to construct packet loss models to evalu-
ate link quality estimators. Zhao et al. [26] describe results
from a measurement on a testbed of 26 motes and show the
existence of links with high packet loss and link asymmetry.
Most related to our work is that of Ganesan et al. [6], where
packet loss is studied on a large-scale (approximately 180
motes) testbed grid on an unobstructed parking lot. That
research also focuses on the loss and asymmetry of packet
delivery at both the link layer and the MAC layer. In this
paper, our study of packet delivery performance has more
control of the topology that allows us to more carefully ex-
amine spatial and temporal characteristics. Moreover, our
study examines packet delivery performance in harsher en-
vironments (indoor and habitat). We also examine different
physical-layer encoding schemes and a wider variety of per-
formance characteristics.
Measurements of infrastructure based wireless networks
have been studied in [13, 21]. However, those studies fo-
cus more on the patterns of user mobility and their impact
on traffic. Maltz et al. [15] describes a full scale testbed
constructed for studying ad-hoc routing protocols. More re-
cently, De Couto et al. [4] finds high variability in link qual-
ity, both on a wireless local network and a roof-top radio
frequency network. They argue the given such variability,
the widely accepted shortest path routing criterion is not
enough. This class of measurement work is clearly comple-
mentary to ours since it focuses on a different kind of radio
environment and different deployment densities than ours.
Finally, signal strength measurement has been used to
understand different aspects of radio propagation proper-
ties such as modeling path loss for in-door environment [20].
The SpotON system [8] measures signal strength from low
power radio transceivers to improve precision in localization
systems. Our measurements of signal strengths are comple-
mentary, designed to examine the efficacy of signal strength
estimation as an indication of link quality.
3. OVERVIEW, METRICS AND METHOD-
OLOGY
In this paper, we take a first step towards understand-
ing the performance of wireless communication in environ-
ments and at the densities that we expect sensor networks
to be deployed. The primary aspect of wireless communi-
cation performance of interest to us is packet delivery per-
formance. More precisely, our primary measure of perfor-
mance is packet loss rate (the fraction of packets that were
transmitted within a time window, but not received) or its
complement, the reception rate.
There are many, many factors that govern the packet de-
livery performance in a wireless communication system: the
environment, the network topology, the traffic patterns and,
by extension, the actual physical phenomena that trigger
node communication activity. It is difficult to isolate these
phenomena in order to study the impact of different factors
on packet delivery performance. Rather, we take a some-
what mechanistic view in this paper, and look at the packet
delivery performance at two different layers in the network-
ing stack: the physical layer and the medium-access layer.
We do this in a systematic fashion, in the sense that we
exert some control over network topology, traffic generation,
and the timing and duration of our experiments. Our exper-
iments are not entirely controlled, however, since our mea-
Figure 1: Experiments in an in-
door environment I
Figure 2: Experiments in a habitat
environment H
Figure 3: Illustration of node
placement in multi-hop experi-
ments I
surements are subject to external factors, such as vagaries
in the environment. This is deliberate, since (at least in
part), we wish to understand how environmental factors af-
fect communication.
Given our goal of understanding packet delivery in sensor
networks, we employ a commonly-used sensor network plat-
form: the Mica mote [10], its RF Monolithics radio [19], and
the networking stack as implemented in TinyOS [9]. Clearly,
this is a moving target; as the platform continues to evolve,
the radio and the various protocols will continue to change.
We address this by not making fine distinctions that could
be invalidated by incremental improvements in the existing
platform, and by pointing out which of our observations are
likely to be affected by changes in technology.
In the following subsections, we discuss these experiments
in a bit more detail.
3.1 Packet Delivery at the Physical Layer
The physical layer of most wireless networking stacks has
two simple functions: framing and bit error detection or
correction. These two functions are affected by many differ-
ent factors. First, environmental characteristics can cause
multi-path signal reception, or signal attenuation. Second,
the spatial separation between sender and receiver can de-
termine the received signal strength. Finally, minor varia-
tions in receiver and sender circuitry or in battery levels can
adversely affect these functions of the physical layer.
To measure packet delivery at the physical layer, we use
the following general setup. We place approximately sixty
nodes in a chain topology. The precise pattern of node sep-
aration in this chain topology is discussed later. There is a
single sender: the node at the head of the chain sends out
a message periodically, and all other nodes receive. This
simple setup measures the impact of the environment and
the spatial separation between sender and receiver. It does
not measure individual receiver or sender diversity; in fact,
we are interested in the collective behavior or distributions
of performances. We show that these distributions are not
qualitatively affected by sender or receiver variations (we do
this by permuting the physical setup).
We then place this setup in, and take measurements from,
three different environments: an office building, a natural
habitat, and an empty parking lot. The first two have been
proposed as target environments for sensor networks, and
the last represents a relatively benign environment that pro-
vides some calibration and context for our results.
With this setup, we can study several interesting ques-
tions: How does packet loss vary across environments? What
is the spatial dependence on packet loss behavior? How
are environmental effects and spatial dependence masked
by different physical coding (error correction and detection)
schemes? Are there spatial correlations in packet delivery?
What are the temporal characteristics in packet delivery?
Note that even with such a carefully defined methodology,
we will have only obtained a few data points on packet deliv-
ery performance. We do not claim that our experiments or
our environments or the particular conditions under which
we conducted our experiments are “typical” in any way. But
we are fairly confident that our experimental conditions were
not pathological either; we repeated some of our experiments
at different times and did not observe any qualitative differ-
ences in our results. If anything, the actual behavior of these
environments is likely to be worse than that reported in the
paper, since we were careful to choose quiet times (e.g., late
nights in the indoor environment) for experimentation.
3.2 Packet Delivery at the MAC Layer
The medium-access layer has two functions
2
that impact
packet delivery performance: arbitrating access to the chan-
nel, and (optionally) some simple form of error detection.
In addition to factors that impact the physical layer, and
hence the performance of medium-access, two factors affect
the medium-access layer. First, the application workload
(and, in the case of sensor networks, the sensed environ-
ment) determines the traffic generated by nodes and hence
the efficacy of channel access. Second, the topology (or,
equivalently, the spatial relationship between nodes) affects
how many nodes might potentially contend for the channel
at a given point in time.
To understand packet delivery performance as observed
at the MAC layer, we use the following general setup. We
place sixty nodes in a somewhat ad-hoc fashion, but at den-
sities that we expect of sensor network deployments. Each
node periodically generates a message destined to one of its
neighbors; the periodicity of this message generation defines
an artificial workload. We then place this setup in three en-
vironments as before, and measure several aspects of packet
delivery performance.
The particular medium-access layer we choose is the de-
fault MAC that is implemented in TinyOS (henceforth called
2
Other functions, such as node addressing, are orthogonal
to the performance of packet delivery. Many traditional
medium-access layers are also interested in fairness, a sub-
ject we do not evaluate in this paper.
the TinyOS MAC). It incorporates a simple collision avoid-
ance scheme, and has a link-layer acknowledgment scheme
to which we added a retransmission mechanism that enables
us to study the efficacy of link-layer error recovery. Of this
MAC, we then ask the following questions: What is the over-
all packet delivery performance observed by the applications
upon MAC layer? Given such a density, what is capability of
the MAC layer deal with interference introduced by simul-
taneous transmission? What is the efficiency of the MAC
layer?
More than our physical layer experiments, there are many
caveats to be aware of in our medium-access layer experi-
ments. First, the TinyOS MAC is quite simplistic in that
it does not include virtual carrier sense mechanisms like
RTS and CTS for hidden-terminal mitigation. Our con-
clusions are somewhat limited by this; we intend to address
this shortcoming by evaluating against S-MAC [25] when
a stable implementation becomes available. We note, how-
ever, that many deployments of sensor networks using the
TinyOS MAC are already under way; our performance mea-
surements can give some understanding of behavior observed
in the field in these and other deployments planned for the
near future. As we discuss later, our results also give some
insight into the design of future MAC layers for sensor net-
works. Second, we investigate one topology (or one node
density) that we expect to be somewhat typical. We have
no experimental data to justify this, but in our indoor office
deployment, our network size corresponds to roughly one
node per office.
Despite these caveats, we believe that there are many
lessons to be leaned from our experiments, as we discuss
in Sections 4 and 5.
3.3 Instrumentation
Before we discuss the experiments, we discuss our ex-
perimental platform and the experimental instrumentation.
This, together with a description of the actual experiments
in later sections, should help convince the reader both of
the logistical difficulty of conducting any kind of systematic
study in these networks, as well as the care we have taken
(to the extent possible).
We use Mica motes [10] in this study as the experimental
platform. It is widely available and has been used in wire-
less sensor network research. Each Mica mote has a 4MHz
Atmel processor(128K EEPROM and 4KB RAM), 512KB
flash memory, and an ASK (amplitude shift keying) low
power 433 Mhz radio [19]. We installed an omni-directional
whip antenna to replace the built-in trace antenna on motes.
In our experiments, the radio has a nominal throughput
of 20Kbps. The low-level radio interface also supports the
measurement of received signal strength, in a manner we de-
scribe later. Finally, the Micas come with an event-driven
operating system called TinyOS [9]. TinyOS’s networking
stack includes a default physical layer that supports single-
error correction and double bit error detection (SECDED)
capabilities. On top of this, its default MAC layer imple-
ments a simple CSMA/CA scheme, together with link-layer
acknowledgments.
To simplify experimental control and data collection, we
used or wrote several pieces of instrumentation and exper-
imentation software. The first such software module is a
simple traffic generator. Driven by a clock which has an
accuracy of one millisecond, the traffic generator repeatedly
sends out packets tagged with a sequence number. The ex-
act periodicity depends on the experiment. A second mod-
ule allows us to upload experimental parameters (such as
packet sending rate, experiment duration) wirelessly to all
motes within the radio range. We do this using a laptop
connected to a mote’s interface board. To store information
about received data, we use the logger component built into
TinyOS. At the end of an experimental run, we collect the
motes and download the data from the logger to a central
database.
In order to study the impact of more physical layers than
the default one that comes with TinyOS, we implemented
two schemes: a simple 4-bit/6-bit (or 4b6b) coding and a
Manchester coding. We describe these schemes in greater
detail in a later section. In order to study the correlation
between packet loss and signal strength, we implemented a
careful signal strength measurement module, whose details
we reveal later. The TinyOS MAC delivers an acknowledg-
ment; we added an optional re-transmission mechanism to
understand the efficacy of link-layer ARQ (Automatic Re-
peat Request) to the MAC layer. Finally, in some cases, we
used randomized intervals for packet generation based upon
a precomputed set of random numbers. This allowed us
to use different distributions (e.g., exponential) for packet
generation times than that allowed by the uniform random
number generator available on the motes.
One of practical challenges in our experiments was to ef-
ficiently reprogram so many motes and download log con-
tent from them. We reduced the frequency of programming
motes by parameterizing the experiments as much as possi-
ble. In addition, we implemented a simple negotiation pro-
tocol such that reprogramming and downloading is as simple
as ”plug-and-play” on multiple programming boards on mul-
tiple PCs. Together with audible feedback of the download
progress, this reduces human intervention as much as possi-
ble and expedites the process of conducting experiments.
4. PACKET DELIVERY AT THE PHYSICAL
LAYER
Our first set of experiments analyzes packet delivery per-
formance at the physical layer. In this section, we discuss
the methodology we use for our experiments, and then de-
scribe various aspects of packet delivery performance at the
physical layer.
4.1 Detailed Methodology
The topology for this set of experiments consisted of ap-
proximately 60 motes, most of which were placed in a line
at 0.5m apart. Guided by results from preliminary experi-
ments, we intentionally removed some nodes from near the
transmitter and placed more nodes at a finer granularity
(0.25m apart) close to the edge of the communication range,
giving us finer resolution in that region. Our node placement
was therefore slightly non-uniform, and we are careful to ac-
count for this in our analysis. Finally, because we conducted
experiments over several days, we were careful to mark node
positions so that nodes could be precisely placed.
The traffic pattern for this experiment consisted of the
node at one end of the line transmitting one packet per sec-
ond, each with a monotonically increasing sequence number.
All the other nodes merely received packets and recorded re-
ceived packets in local storage. In order to purely measure
