Signal Strength based Service Discovery (S3D) in
Mobile Ad Hoc Networks
Jinshan Liu
Valérie Issarny
INRIA - Rocquencourt
Domaine de Voluceau, Rocquencourt, BP 105,
78153 Le Chesnay Cedex, France
Email:jinshan.liu@inria.fr
INRIA - Rocquencourt
Domaine de Voluceau, Rocquencourt, BP 105,
78153 Le Chesnay Cedex, France
Email:valerie.issarny@inria.fr
Abstract— Service discovery is an important part of realization
towards ubiquitous computing with self-configurable mobile ad
hoc networks. However, it is faced by the challenge of network
dynamics due to node mobility. In this paper, we propose a
signal strength based service discovery framework in mobile ad
hoc networks. Service discovery is carried out selectively along
reliable links by monitoring the qualities of wireless connections
to neighbor nodes. We show by simulation that, compared to
non-selective distributed service discovery, our approach achieves
higher service success ratio, less message overhead and shorter
service discovery latency.
I. I NTRODUCTION
Service discovery in Mobile Ad hoc NETworks (MANET)
is an important part of realization towards service access
anytime, anywhere. However, it poses greater challenges than
in traditional distributed systems, since service failure more
often results from disconnection due to host migration [1].
Therefore, transient wireless medium and device roaming
require a dedicated service discovery approach in MANET.
The basic criteria for evaluating a service discovery scheme
is the effectiveness with which: (1) service clients are able
to find a service provider and (2) the service provider will
successfully deliver the requested service. Since connections
can be broken with nodes moving, the most important factor
affecting service delivery success is the qualities of wireless
connections along the service path between service provider
and requester. Connectivity can be determined according to the
relative distance (e.g., Euclidean distance) of the geographical
positions of the nodes. For instance, physical proximity of
service providers to service seekers has been proposed as a
metric for service selection [2]. However, position acquiring
is luxury and comes at a price. From current state of the art,
one way to obtain (geographical) position is by using GPS.
But GPS is an extra hardware and suffers from poor signals
in indoor environments. Another way is by measuring relative
locations from fixed, position-known base stations [3]. But it
requires the coordination of multiple base stations (e.g., Angle
of Arrival [4]) and thus does not fit MANET.
Therefore, “geographical proximity” is an inefficient and
insufficient means for deriving connectivity. Two factors affecting link quality are signal strength (SS) and signal to noise
ratio (SNR). Thus, instead of trying to obtain geographical
locations of moving nodes, we resort to the measured wireless
connection metrics and their variation tendency to identify
reliable services in mobile ad hoc networks.
We propose in this paper a simple, yet efficient, service discovery framework, based on signal quality. To our knowledge,
our approach is the first effort to select mobile services in
MANET based on link quality. The rest of this paper is organized as follows. Section II examines the wireless communication background and surveys related work. Section III proposes
and elaborates our service discovery framework based on
signal quality. And then Section IV presents simulation-based
evaluation results. Finally, the paper finishes with concluding
remarks and future work.
II. BACKGROUND AND R ELATED W ORK
A. Background
The radio wave (signal) propagation is generally modeled
by the combination of large scale and small scale propagation
models [5]. Node movement over short distances (e.g., a few
wavelengths) may cause the received signal strength, which is
the sum of contributions of multiple components, to fluctuate
rapidly, giving rise to small scale fading. As the node moves
over longer distance, the local signal strength average (i.e.,
average of signals measured over a distance from 5λ to 40λ –
about from 0.6m to 5m for 802.11b) gradually decreases. We
call the distance locality distance and large-scale propagation
model estimates the average signal over the locality distance.
Large scale is of interest here because the aim of our
work is to capture the movement tendency of the nodes, and
thus to discover services that are less likely to fail because
of movement-induced network disconnection. However, small
scale fading makes it difficult to recognize the node’s moving
tendency and cannot be simply ignored. Small scale fading can
be modeled by Ricean fading (with line of sight) or Rayleigh
fading (with no line of sight) [5].
During signal transmission, the transmitter sends the signal
with certain transmission power. Subject to large-scale and
small-scale fading, the signal arrives at the receiver with certain power, along with the interference from background noise
and other parties who share the same band. Each receiver has a
receiver sensitivity, which is the power of the weakest signal
that can be reliably heard and demodulated by the receiver,
and a threshold Signal to Noise Ratio (SNR), defined as the
minimum received power difference between signal and noise
for the receiver to be able to receive successfully. Therefore,
signal transmission succeeds only if the received signals satisfy
both the receiver sensitivity and the SNR threshold. Both
SS and SNR of a received packet can be obtained from the
wireless card driver.
In order to observe the variation (i.e., both large-scale and
short-scale fading) of SS and SNR, we conduct experiments to
measure the two metrics, between two laptops equipped with
Lucent WaveLAN IEEE Silver cards, both of which implement
802.11b standard and have omni-directional antennas. The two
laptops are running on Linux (RedHat) with 2.4.26 kernel and
connecting with each other via a wireless link. The experiments are all carried out indoors in the day. The measurement
of signal and noise level was performed every 1 second for
a period of 50 seconds, by consecutively sending “HELLO”
message and measuring the signal and noise power from the
returned replies.
Variation of signal and noise power
-50
signal power
noise power
average signal power over locality distance
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-60
Power level (in dBm)
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-95
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0
10
20
30
40
50
Time
Fig. 1.
Signal and noise power when one node is moving
The experiment is carried out by having one of the laptop
move away from the other at human walking speed (Fig. 1).
It can be seen that the average signal strength over locality
distance (as shown with dotted line) clearly drops with longer
distance, in spite of fluctuation in a short distance. It proves
that node movement can be detected by observing the variation
tendency of average received SS over the locality distance.
After elaborating and observing SS and SNR in wireless
communication, we survey the related work on signal qualities
and service discovery.
B. Related Work
Existing wireline network oriented service discovery solutions, including Sun’s Jini, Microsoft’s UPnP as well as IETF’s
SLP [6], do not suit MANET well [7]. Although they do
not rely solely on centralized service registry (e.g., SLP and
UPnP), and service registry adopts time to live (e.g., Jini) to
counter node dynamics in MANET, they lack service selection
based on context information such as link quality.
More recent services (or resource) discovery in mobile
ad hoc networks have been proposed in [8]–[11]. Helal et
al. propose a service discovery and delivery protocol for ad
http://folk.uio.no/paalee/
hoc networks based on a distributed discovery manager [8],
which, however, does not distinguish reliable from unreliable
links in service discovery. Other solutions such as allia [9]
or GSD [10] are based on peer-to-peer caching of service
advertisements with service queries simply forwarded to peers,
or resource-rich peers if indicated so in service advertisements.
This might not be helpful because resource-rich terminals are
not surely reliably connected to the service seeker and steering
service queries preferably to rich terminals does not increase
chances of successful service discovery and delivery. Liu et
al. propose a distributed resource discovery in MANET using
dynamically generated directory agents [11], which provide
lookup service, and collect and predict QoS information.
However, the resource/service provider selection focuses on
metrics such as path delay, instead of path reliability.
Since communication in MANET is carried out via wireless
medium, we resort to signal strength (SS) and signal to noise
ratio (SNR) for server/service selection. SS and SNR are
widely used in WLAN access points deployment, as well
as positioning system to track mobile users or sensors [3].
These proposals utilize the mobile radio propagation model
to determine user location. Our approach does not use signal
strength to derive distance between two nodes. Instead, we
exploit the variation tendency of signal strength to detect the
relative movement between nodes, which is utilized to identify
leaving and thus unreliable service providers.
Signal strength has also been proposed as a metric for
selecting reliable routes in routing protocols for mobile ad
hoc networks [1], [12], [13]. The metrics used by [13] is the
average signal strength and route stability, while in [12], the
authors suggest a SNR-based neighbor selection for handling
unreliable links. These approaches utilize SS to indicate strong
or weak channels, without deriving any movement tendency.
With no link reliability information readily available from
the underlying layer, we resort to an inter-layer design by
exploiting the link quality of physical layer at application
layer, as detailed in the next section.
III. S IGNAL S TRENGTH BASED S ERVICE D ISCOVERY
(S3D)
As stated, because of signal strength fluctuation due to small
scale fading, the SS collected at one time can be in a large
range, without clear indication of the link quality. But if a
neighbor is moving further, the average of SS over locality
distance (e.g., 40λ) is decreasing [5]. Here, neighbors refer
to the nodes reachable in one hop. Therefore, by keeping
the history of recent consecutive beacons from neighbors, the
neighbor’s tendency to leave can be detected by observing
whether the average SS over consecutive locality distances is
decreasing. Neighbor’s departure or failure can be indicated
by absence of beacons from this neighbor for some threshold
length (e.g, three times the beacon interval in OLSR [14]).
In addition, a wireless link is possibly asymmetric, with
signal quality probably much different from two ends of a
link due to difference in transmission power, interference
extent and other factors. Hence, a wireless link is considered
reliable only when: (1) the signal qualities (i.e., SS and SNR)
from both ends are above receiver-specific threshold values,
(2) the average SS over consecutive locality distance is not
decreasing, and (3) the link is active (i.e., exchange of beacon
messages occurs recently enough). The neighbor on the other
end of a reliable link is named strongly connected neighbor
(SCN).
Our signal strength based service discovery (S3D) framework performs on-demand service discovery only along reliable links, so that the path between service requester and
provider is strongly connected. The underlying method of our
framework is that by regularly broadcasting beacon packets to
neighbors, each terminal derives the reliability of its neighbors
from the signal quality and its variation tendency. Then each
node can selectively send service queries only to strongly
connected neighbors (SCNs), which may in turn forward
service queries to their SCNs. In other words, the service
discovery and delivery are only carried out on reliable links
by avoiding those weak, obsolete and diminishing links.
Concretely, each node tracks the signal information of
wireless connection to its neighbors, by keeping a neighbor
table (NT), which contains a list of its (one-hop) neighbors.
Each entry contains a neighbor’s Id (NID), multiple samples of
SS and SNR from the node’s end, along with the timestamp of
the node’s signal measurement (SS, SNR, TS), average SS and
SNR from the neighbor’s end (NSS and NSNR), the timestamp
of the neighbor’s signal measurement (NTS), and the services
provided by the neighbor (PServices). Note that NT does not
incur large storage overhead because of the limited number of
one node’s one-hop neighbors.
service. If so, it requests the service from that SCN. If multiple
SCNs provide the service, further selection can be done either
by signal strength, link longevity, or other QoS metrics. If
no SCN provides the service, it multicasts a serv disc to
SCNs, which includes: (i) a unique sequence number; (ii) a
service path with the first address filled with its own address
and (iii) SCNs’ addresses along with their signal information.
To avoid unnecessary remulticasting, each node keeps a cache
of queries it has already handled. Note that service path in
serv disc contains the intermediate nodes the service query
has traversed to prevent looping.
On receiving a serv disc packet, a node (1) extracts
signal information of both ends, similar to serv beacon
handling; (2) checks whether it has already handled the query
from the sequence number. If it has, it discards the packet
and does nothing; (3) checks locally whether it (or one of
its SCNs) provides the service. If that is the case, it appends
its address (or its address and that SCN’s address) and sends
back a serv resp. (4) If neither it or any SCN provides that
service, appends its address to the end of the service path and
multicasts the serv disc to its SCNs that are not already
in the service path, with signal information of links to all the
destination neighbors’ piggybacked. Then it adds the query
sequence number into its handled service query cache.
C. Service Path Selection and Service Delivery
A. Beacon Packets
For every beacon interval, a node broadcasts to its (onehop) neighbors a UDP packet serv beacon, in which it
announces its provided services and informs the neighbors
about the average link qualities over the most recent locality
distance at its end. On receiving a serv beacon, a node
extracts SS and SNR of its end, and updates in NT the signal
quality of the link (i.e., both ends) to the beacon sender. In this
way, both nodes can obtain the link quality from both ends.
After sending a serv disc, a client may receive multiple
replies with different service paths. Therefore, a client-side
service path selection scheme needs to be in place to select the
best candidate. Service path selection is followed by service
delivery, which is carried out along the selected service path.
From the client’s perspective, service response time (T) is
the sum of service discovery time, service selection time and
service delivery time.
Service path selection can be based on metrics of: hop
count, geographical proximity, round trip latency [15], among
others. We select the path with the least service discovery time,
by always choosing the first service discovery reply because:
(1) It is simple and requires no service path selection time; (2)
Geographical proximity requires extra hardware like GPS and
is inefficient; (3) Smallest hop count approach incurs waiting
time for all responses to arrive and does not necessary lead to
shorter latency [16]; and (4) The measured service discovery
time, which is the round trip time between the service seeker
and a SCN of the service host1 , serves a decent estimation
because it is a “probing” value – the path has experienced the
shortest delay and is probably shorter and less congested than
later arriving service responses. It is surely possible that the
traffic along the selected service path becomes heavier after
the service discovery or the traffic along another path turns
lighter which makes it a better choice, but we argue that it is
unavoidable price paid for prediction.
B. Service Discovery
When a node looks up for a service, it first looks into its
Neighbor Table (NT) to see if one of its SCNs provides the
1 There might be very small difference since service discovery also involves
service and neighbor lookup, but it is negligible because those information is
stored locally.
NID SS [0..n] SN R[0..n] T S [0..n] NSS NSNR NTS PServices
To monitor link qualities, discover and deliver services, the
following five types of packets are defined and used in our
framework:
• serv beacon is for exchanging signal and service
information among neighbors.
• serv disc is a discovery request for service.
• serv resp is a response message for service discovery.
• serv data carries the data from the service client to
service provider during the service.
• serv ack contains the returned service results.
After receiving the first serv resp, the service seeker
initializes the service using the embedded service path.
IV. P ERFORMANCE E VALUATION
In this section, we present the simulation results of our proposed approach. Our main objective is to investigate whether
our solution for service discovery and selection improves
service reliability, and whether the solution exhibits satisfactory performance in terms of message overhead and service
discovery latency.
A. Performance Metrics
From the user’s perspective, the first concern is whether
the service request is served successfully. A failure can result
from two reasons: (i) Failure to find a service which satisfies
its request; (ii) Success in finding the service, but failed
service delivery because of broken connection attributed to
node mobility (as this is the dominant reason for service
failure). We use service delivery success ratio, which is the
number of successful service deliveries to the total number of
service invocations (i.e., after successful service discovery),
to quantify the service reliability. The second concern for
the user is the experienced service discovery latency, which
equals the length of the interval between when the user issues
a service discovery request and when a service discovery
reply is received. To evaluate a service discovery scheme, it is
also necessary to investigate its message overhead. Therefore,
overall, we are interested in the performance metrics of our
framework as follows: (1) service delivery success ratio; (2)
total number of sent messages for each service discovery; and
(3) service discovery latency.
Because it takes time for the random waypoint model to
reach a stable distribution of mobile nodes [19], the initial
part of the simulation is not representative of the reality and
is discarded. At the end of the simulation, because the service
lasts some time and the service client needs some time to wait
for arrival of acknowledgments, no queries are sent at the end
of the simulation. Therefore, we choose to skip the first 200
seconds of the simulation result. From 200s to 450s, for each
second, there is a node sending service discovery in a roundrobin way. Each simulation setting is executed 30 times, thus
there are totally 250 × 30 = 7500 service discovery attempts
for each setting and the average performance is presented.
Multicast is simulated by specifying in the packet header a
list of destination addresses. A node does not handle (except
checking if it is one of the destinations) the packet if its address
is not in the destination list.
For the sake of comparison, we also simulate a distributed
service discovery framework without beacons (e.g., the distributed query-based architecture in [20]). For simplicity of
presentation, we name it DIST. Like S3D, DIST implements
a limited-scope flooding approach for on-demand service
discovery, but it does not distinguish strongly connected neighbors from weakly connected neighbors. Nodes in DIST also
cache handled service queries to avoid unnecessary rebroadcasting, and traversed nodes are included in the packet header
to avoid looping.
C. Service Delivery Success Ratio
Service success/failure with S3D and DIST (out of 250 requests)
200
S3D success
DIST success
S3D failure
DIST failure
180
B. Simulation Environment
140
Number
We evaluate our solution with ns-2 with CMU wireless
extensions [17]. Our simulated network consists of 50 mobile
nodes in an area of from 250m × 250m to 610m × 610m
(increasing by 40m for each simulation setting), with each
node having a transmission range of 100m with a Ricean
propagation model [18]. We use the Random Waypoint mobility model with each node moving at walking speed, i.e.,
between 0.5m/s and 2m/s, with pause time of 0, i.e., nodes
are always in motion. The Distributed Coordination Function
(DCF) of IEEE 802.11 protocol is used as the MAC layer
protocol. Each wireless channel has bandwidth of 2 Mbps. We
do not use any routing protocol, and multi-hop transmission
is done by intermediate nodes’ forwarding at the application
layer.
We assume that each node can host up to 10 services, out
of 50 service types. Each node randomly demands a service
that it does not host, and every service is served as a Constant
Bit Rate (CBR) streaming application of 56 Kbps over 10
seconds. The service discovery range is 5 hops. Beacons are
sent every 2 seconds, and locality distance is set to 5 meters,
i.e., about 40 λ for IEEE 802.11b. The whole simulation lasts
500 seconds.
160
120
100
80
60
40
250
300
350
400
450
500
550
600
Area
Fig. 2. Service success and failure number of S3D and DIST (out of 250
queries)
The performance of S3D and DIST with respect to service
success and failed number is compared in Fig. 2. It can be observed that S3D outperforms DIST by having more successful
service delivery and much less failed service delivery for all
areas, attributed to the link quality based path selection. And
it is easy to derive the service delivery success ratio from the
graph, and after calculation, it can be found that S3D always
has an advantage of at least 10% over DIST in terms of service
delivery success ratio.
D. Message Overhead
link quality aware service discovery as shown in the results of
evaluation.
Future research will focus on investigating the effect of
enlarging the range of service advertisement, i.e., service
advertisement is propagated to multi-hop peers. Recently there
has been approaches of directory based service discovery in
MANET [11], [21], [22]. Future work will incorporate these
approaches into S3D to improve scalability for employment
in large mobile ad hoc networks.
Average Message Overhead
200
Average Message Overhead with S3D
Average Message Overhead with DIST
180
160
Message number
140
120
100
80
ACKNOWLEDGEMENT
60
This research is partially supported by the European IST
UBISEC Project (IST-2002-506926).
40
20
0
250
300
Fig. 3.
350
400
450
Simulation area
500
550
600
Message overhead of S3D and DIST
Message overhead of each service discovery framework
measures the number of sent packets, incurred by service
discovery. For S3D, the overhead includes the packets of
serv beacon, serv disc and serv resp, while the
overhead for DIST only includes the later two. As shown in
Fig. 3, S3D incurs less message overhead because of selective
multicast instead of blind broadcast. The earned advantage
clearly surpasses the overhead brought by periodic beacons.
E. Service Discovery Latency
Service Discovery Latency
0.14
S3D Service Discovery Latency
DIST Service Discovery Latency
0.12
Second
0.1
0.08
0.06
0.04
0.02
0
250
Fig. 4.
300
350
400
450
Simulation area
500
550
600
Service Discovery Latency of S3D and DIST
Service discovery latency for S3D and DIST are presented
in Fig. 4. S3D clearly outperforms DIST, because each node
caches the service information of its strong connected neighbors, and is likely to answer the query one hop earlier. The gain
evidently surpasses the overhead brought by beacon packets.
V. C ONCLUDING R EMARKS AND F UTURE W ORK
Service discovery and selection in mobile ad hoc networks
pose unique challenges because of possible service failures
resulting from node mobility. In this paper, we propose a
signal strength based service discovery (S3D) framework as a
solution by steering the service discovery over reliable links.
Our approach improves service delivery success ratio based on
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