Buffer-Overflow and Noise-Handling Model: Guaranteeing Quality
of Service Routing for Wireless Multimedia Sensor Networks
Alanazi. Adwan , Elleithy .Khaled
Department of Computer Science and Engineering, University of Bridgeport, CT-USA
firstname.lastname@example.org ; email@example.com
Wireless Sensor Networks (WSNs) are the collection of sensor nodes that form a
momentary network without the support of any centralized administration or infrastructure.
In such a situation, it is mandatory for each sensor node to obtain the support of other sensor
nodes in order to advance the packet to its desired destination node, particularly to the sink
node or base station. One significant challenge in designing the wireless multimedia sensor
network is introducing an energy efficient routing protocol, which may transmit information
despite limited resources. Another significant problem is determining the resources of the
next hop node in advance. The routing protocols in existing literature mainly focus on
prolonging the network lifetime.
In this paper, we introduce the buffer-overflow distance-aware and noise-handling
(BODANH) model to guarantee the quality of service (QoS) for multipath routing over
wireless multimedia sensor networks. BODANH involves three components: buffer
allocation, distance measurement and signal-to-noise-ratio. This model prevents the loss of
data and avoids the congestion caused by buffer-overflow, identifies the node distance prior
to route discovery that helps determine the location and distance when node it is either
movable or immobile.
The performance of our model is compared to other QoS routing protocols.
Simulation results demonstrate that our model surpasses the other routing QoS routing
protocols in terms of throughput and the remaining live nodes in static and mobility
Keywords: Buffer overflow, routing, Quality of service, signal-to-noise-ratio,
wireless sensor networks
Due to the rapid advancement in emerging technologies particularly in micro
electro-mechanical systems, small scale energy devices, low power integrated digital circuits,
small scale energy supplies, microprocessors, and low power radios have provided the
platform for low cast, low energy, and multifunctional wireless sensor nodes that can
perceive and respond to deviations in physical phenomena. These sensor nodes are
equipped with tiny microprocessor, radio transceiver, small battery, and a set of transducers,
which are applied for obtaining information that redirect the vicissitudes in the surrounding
environment. Wireless sensor networks involve a number of tiny sensor nodes that coordinate
with each other to perform critical tasks (e.g. object tracking and environment monitoring,
etc.) and deliver the collected data to the sink node or base station. The areas of wireless
sensor network applications include healthcare, battlefield, surveillance, environmental
monitoring, detection of fire etc.,,. However, network density, limited node power,
severe bandwidth limitations, dynamicity of the topology and large-scale deployments have
caused many challenges in the management of WSNs. In addition, buffer overflow and noise
have also posed several challenges including congestion, data loss, performance dilapidation
and excess. Limited memory space causes buffer overflow and data packets start to drop. As
a result, retransmission is required for the lost data packets. Thus, additional energy is
consumed. Buffer detection is largely an open issue in WMSNs due to limited
computational capabilities and limited memory resources.
The sensor nodes handle low data volume in low data rate applications.
However, multimedia-driven applications are required to determine the status of a buffer
prior to sending the data to the next hop because sensor nodes may heavily be loaded due to
such applications, and buffer may start to overflow. In addition, buffer overflow invites the
congestion that may cause a reduction in network efficiency ,,. To handle the
congestion, it is important to determine the sufficient free buffer space prior to delivering the
data packets to next hop nodes. There are several approaches available in literature for
conventional networks. However, these approaches are too complicated to be introduced in
resource constrained WMSNs. Additionally, WMSNs vary in nature from wired network
because nodes in WMSN hold a single queue that is connected with a single transmitter.
Furthermore, the noise and distance of nodes are also more important for the discovery of the
path for guaranteeing the QoS provisioning .
Most approaches used to discover paths are based on the residual energy of the
node. These approaches are not workable in particular situations for example when the sensor
node is farther from the sink node and even holds the high residual energy; however, long
distance and noise weaken the signal strength. As a result the node does not receive all sent
packets,. Trade-offs are an efficient use of the buffer and energy of sensor nodes,
which are highly desirable when designing multi-path routing that guarantees the QoS
provision for WMSNs. This paper attempts to address the congestion and data overflow
caused by buffer limitations. Furthermore, we detect the noise and determine the distance
including the location of the node that helps in the discovery of an optimized path. The
contribution involves the BODANH mathematical model that improves the throughput and
extends the network life. The remainder of the paper is organized as follows:
Section II presents a proposed model called buffer-overflow, distance-aware and
noise handling. Section III describes simulation setup and performance study of the result.
Section IV concludes the paper.
I. BUFFER-OVERFLOW, DISTANCE AWARE AND NOISE HANDLING
Guaranteeing the QoS routing in wireless multimedia sensor networks is a highly
challenging problem due to scarce properties of the sensor node. Our aim is to present the
BODANH model in a manner that improves the throughput and prolongs the network
lifetime. Thus, we focus on detecting the capacity of buffer prior to sending the data packets
as well as determining the node distance and handling the noise. The BODANH model
includes the following features:
! Buffer allocation
! Distance measurement
! Signal-to-noise ratio
A. BUFFER ALLOCATION
Each sensor node measures all traffic flows passing
through each link , , Where is the
measurement of the new time interval, and is the number of
packets. Let us assume number of packets received by from
sensor node over the link during the time interval . Thus, the size of buffer measured
in new interval can be obtained as:
Where’ : Already existing packets in the buffer of sensor node.
If sensor node is congested either due to bottleneck (heavy traffic) or full buffer,
then the buffer limit for each sensor node can be calculated as follows:
Where ‘ ’: Buffer limit, : The number of transmitted packets out of the
buffer, ‘ ’: The rate of packets transmitted in per second, ‘ ’ : The source of the data
, and’ : Buffer limit of ‘ ’ sensor node.
The sensor node forwards the packets that can be measured locally, if =1 then
‘s’ is the data source otherwise . The sensor node ’ advertises the buffer limit
‘ ’ to the sensor node ’ possibly by using piggybacking in the acknowledgement packet.
In response, the sensor node ’ applies a rate limit (actual rate on path) ‘ ’ that is
bounded by a rate limit. If the sensor node ’ itself is data source, it will assign the buffer to
node ’ as follows:
If the neighbor node attempts to enforce a buffer rate limit, it may casue congestion;
if the buffer capacity of the receiving node is full, then it administers rate limits. This process
is applied to the data sources. Finally, all the exaggerated data sources are able to adjust the
packets rates based on the allotted fair bandwidth. Note that only congested node administers
the rate limit that is updated periodically.
When the congestion state proceeds to sensor node ‘ ’ , the buffer rate limit is
stopped. This situation can occur by raising the buffer rate limits of sensor node ’ . The
Sensor node ’ is capable of identifying the situation of the congestion by detecting the
fullness of the buffer, and when that situation happens. The sensor nodes fix the buffer rate
limits to be ) and , rather than over-setting them. As a result, a sensor node
discontinues enforcing buffer rate limits once its congestion state is detached (buffer is
deflated) and the data rates at which the node accepts packets from the neighboring nodes are
lesser than the buffer rate limits.
B. DISTANCE MEASUREMENT
Based on the transmission rate of each sensor node in the sensing area of the
sensor network, the clustering process is initiated between clustering nodes and cluster head
nodes for determining the optimistic path. This process involves the messaging that holds the
information regarding the location of the sink node in wireless multimedia sensor
networks. In addition, all the sensor nodes detect their locations ‘₯’ from the sink node
based on the Euclidian distance:
Where : Distance of sensor node from sink node, ‘ ’ : Location of sink
node, location of sensor node after detecting the distance.
Our goal is to determine an optimized disjoint (primary) path and braided paths for
data communication. Thus, the sensor node that possesses the shortest distance ‘ ’
connects itself with the disjoint path. However, the sensor node that has extended distance
from the sink, joins the braided path. Our approach is applied with lower and higher
levels clusters in hierarchy. Let ‘ ’ be the distance between source node and sink node
and be the transmission rate and ’ be transmitted energy of sensor node that is
proportional to the received signal strength. Thus, transmitted power ‘ ’ of the node for
each cycle can be obtained as:
where ‘ ’: constant value that is considered as the requirement of signal strength, and
‘ ’ : distance loss factor. In this contribution, we only assume ideal MAC and only
interference is detected due to background that is set to be at the constant rate. Hence, the
received signal strength reduces the signal to noise ratio. Thus, the energy consumption for
sending one unit of data over the medium with distance ‘ can be obtained as:
In the wireless network, a major source of loss signal is attenuation.
Fundamentally, the transmission data rate increases then communication range decreases.
Thus, bit error rate is one of the important parameters that can be mapped into anticipated
signal-to-noise ratio (SNR) explained in the next section.
C. SIGNAL-TO-NOISE-RATIO (SNR)
If data transmission rate increases, then error rate also increases. In this situation,
transmitter requires higher SNR value to obtain the same bit error rate at the receiver
side. Thus, the relationship between SNR ‘Ŕ∆’ and transmitter power can be obtained as
Where channel attenuation, and Noise power. We can define noise power as
Noise power density, : Transmission rate, ᶕ : modulation pattern size,
Energy per bit , and Transmission symbol rate can be obtained as
Therefore, SNR is determined for background noise as:
II. SIMULATION SETUP AND PERFORMANCE ANALYSIS
In order to examine the performance of buffer-overflow the distance-aware and
noise-handling models, the wireless multimedia sensor network was created to cover the area
of 600 m x 600 m. The performance of BODANH is compared with other QoS routing
protocols: Mobicast, QoS and Energy Aware Multi-Path Routing Algorithm
(QEMPAR) and Cluster-based QoS aware routing protocol (CQARP) .The network
topology considered the following metrics:
• A Dynamic sink is set.
• Each node is initially assigned to the uniform energy.
• Each node senses the field at the different rates and is responsible for transmitting the
data to the sink node or base station.
• The sensor nodes are 10% to 60% mobiles.
• Each sensor node involves the homogenous capabilities with the same communication
capacity and computing resources.
• The location of sensor nodes is determined in advance.
The aforesaid network topology is suitable for several applications WSNs, such as
home monitoring, reconnaissance, biomedical applications, airport surveillance, fire
detection, home automation, agriculture and animal monitoring. The real application of this
introduced model is airport surveillance where the sensor nodes are either static or mobile,
which are used for monitoring the travelers and staff members. The simulation was
conducted by using network simulator-2. The scenario consists of 400 homogenous
sensor nodes with initial energy 4 joules. The base station is located at point (0, 1100). The
packets size is 256 bytes. Initial energy of node is set 4.5 joules. The rest of parameters are
explained in table 1.
Table 1: Simulation parameters and its corresponding values
Size of network
600 × 600 square
Number of nodes
Number of frames
Distance from the
base station to the
center of WSN
Initial node energy
Size of Packets
Sensing Range of
10%, 20%, 40%
Based on simulation, we are interested in the following metrics.
! Throughput with stationary nodes
! Throughput with and different nodes
! Remaining alive nodes with mobility
A. Throughput with stationary nodes
Throughput is an average-mean of successfully delivered data packets. Figure 8
shows the throughput performance of the model based on stationary nodes. We observe that
once simulation time increases then throughput performance starts dropping, but BODANH
is not highly affected as compared to other routing protocols; QEMPAR, Mobicast and
CQARP. After completion of simulation time, BODANH reduces only 2Kb/sec throughput
while other competing protocols reduce from 12.5 to 17.75 Kb/sec. Based on the obtained
result, we prove that our model is effective when nodes are stationary.
Figure 1. Throughput with static nodes
A. Throughput with different mobility ratios
The mobility affects throughput performance. The throughput performance of the
network reduces when the ratio of mobile sensor nodes (mobility of nodes) start to increase.
We show in Figures 2-4 that mobility affects the performance of all competing protocols;
however, the throughput of BODANH is still higher than other QEMPAR, Mobicast and
CQARP routing protocols. In fact, higher mobility ratio causes lower packet delivery ratio.
We also observe that a drop in transmission of the packets causes the retransmission of the
packets. As a result, additional energy is consumed for sending the lost packets.
Figure 2. Throughput with 10% mobile nodes
Figure 3. Throughput with 20% mobile nodes
Figure 4. Throughput with 30% mobile nodes
B. Remaining alive nodes with stationary nodes
We describe the number of remaining live nodes in Figure 5 after performing some
simulation rounds (Environment sensing rounds) using stationary nodes. We observe that
once simulation rounds increase then the energy of the nodes depletes. As a result, the nodes
start to die.
Figure 5. Alive remaining node VS sensing routs with static nodes
BODANH outperforms QEMPAR, Mobicast and CQARP. At the end of 135
simulation rounds, BODANH has remaining 483 alive nodes whereas other protocols have
remaining 450 alive nodes. Simulation results demonstrate that BODANH loses 3.4% nodes,
but competing protocols lose 10% nodes.
C. Remaining alive nodes with mobility
The mobility affects the performance of the network, but performance can be
improved using an effective model. In Figures 6-8, we show the behavior of the network in
our proposed BODANH and other competing QEMPAR, Mobicast and CQARP routing
Figure 6. Alive remaining node VS sensing routs with 10% mobile sensor nodes
We use 10%, 20%, 30%, 40% and 50% mobile sensor nodes and measure how many
nodes survive after completion of sensing rounds. We observe that with the increase of
mobile sensor nodes, the network starts to lose the nodes. This situation gets worse with
higher number of mobile sensor nodes. All the participating protocols are affected. However,
BODANH outperforms to other competing routing protocols. We demonstrate that
BODANH improves the network lifetime despite of mobile sensor nodes.
Figure 7. Alive remaining node VS sensing routs with 20% mobile sensor nodes
Figure 8. Alive remaining node VS sensing routs with 30% mobile sensor nodes
This paper introduces a buffer-overflow distance-aware and noise-handling model to
guarantee the QoS provisioning for wireless multimedia sensor networks. This BODANH
model creates a reliable discovery route based on buffer allocation, distance measurement
and signal-to-noise-ratio. These features of model reduce congestion, improve the throughout
and extend the network lifetime. Tradeoff is between mobility and network lifetime and
throughput. The performance of BODANH has been compared with other routing protocols,
which are QEMPAR, Mobicast and CQARP in terms of throughput and number of remaining
live nodes. To validate the effectiveness of a model, we have used ns2 to simulate an airport
surveillance system. Based on the simulated results, BODANH outperforms the other
participating routing protocols. BODANH obtains 11.4% throughput and 6.8% to 19.6%
network lifetime in the static and mobility scenarios. The outcome validates that the
BODANH model is a better choice for improving the network lifetime and guaranteeing QoS
provision. In future, BODANH model will be extended by incorporating more features in
order to validate other QoS metrics.
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