Dynamic Slot Allocation for Improving Traffic Performance in Wireless Sensor Network

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 04 Issue: 11 | Nov -2017 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 1322
Dynamic Slot Allocation for Improving Traffic Performance in Wireless
Sensor Network
Arti Kamal1, Dr. Prabhat Patel2
1Research Scholar, Department of Electronics and Communication Engineering ,Jabalpur ,Madhya Pradesh, India
2Professor, Department of Electronics and Communication Engineering, Jabalpur, Madhya Pradesh, India
----------------------------------------------------------------------***--------------------------------------------------------------------
Abstract -Wireless sensor networks have recently received
increased attention for a broad array of applications such as
surveillance, environment monitoring, medical diagnostics,
and industrial control. A wireless sensor network (WSN)
contains numerous small sized sensor nodes that have
computation power. In WSNs serious incident data collected
by the sensor nodes are to be reliably delivered to the sink for
successful monitoring of an environment. The battery is the
main energy source in the wireless sensor network. For better
performance battery power should be sufficient. More energy
consumption reduces the lifetime of the wireless sensor
network. Also, if the channel utilization is more than
throughput of the wireless sensor network reduces. Thus,
wireless sensor networks medium access control (MAC)
protocols for energy efficiency comes at the cost of extra
packet delay and limited throughput, since a senderisallowed
to transmit in the short active periods, only. However, typical
applications, in addition to low rate periodic traffic, also
present burst traffic triggered upon event detections.
Therefore, there is an emerging need for a MAC protocol that
adapts its offered bandwidth to a dynamic traffic load, i.e.,
maintain low duty-cycle in light traffic conditionandschedule
more transmission opportunitieswhentraffic increasessothat
the energy is only used for carrying the application traffic
whenever needed. In this paper we propose a scheme which
reduces the traffic in the network using dynamic channel
allocation. The duty cycle of the network is also improved.
Key Words: Wireless Sensor Network, dynamic slot
allocation, MAC, CSMA, TDMA, Queue length.
1.INTRODUCTION
A wireless sensor network (WSN) [1] encompasses of
several lesser sized sensor nodes that have low working
out power. It also have communication ability and sensing
functionalities. Wireless sensornetworksestablisha specific
type of wireless data communicationnetworks.Everysensor
node can sense physical characteristics. WSNshavebeenthe
favorite choiceforthesucceedinggenerationmonitoring and
control systems. It can sense temperature, light, vibration,
humidity, electromagnetic strength or any other physical
characteristic, and communicate the sensed information [2]
to the other node through a series of numerous in-between
nodes that assist in forwarding the data. The design
challenges of WSN are limited energy capacity, sensor
locations [3], random and massive node disposition,
inadequate hardware devices, network system features and
data aggregation, unreliable environment, scalability, and
assorted recognizingapplicationnecessities.In WSNs,severe
happening data composed by the sensor nodes is to be
consistently distributed to the sink for efficacious observing
of an environment. The various applications of wireless
sensor network are smart grids and energy control systems,
industrial applications, transportations and logistics, smart
building for example indoor climate control, health care for
example medical health diagnostics and health monitoring,
precision agriculture,animal tracking,urbanterraintracking
and civil structure monitoring, entertainment, security and
surveillance.
The communication of WSN is not only effected by antenna
angle but also by weather conditions, obstacles etc. Further,
it is also depends on interference. Nodes in WSNs are
disposed to letdown due to hardware letdown, energy
reduction, communication link faults, mischievous attack,
and so on.
Nodes in sensor networks have very limited energy. The
main WSN objectives are less power consumption, better
channel utilization, less node cost, small node size,
scalability, security, fault tolerance, adaptability, QoS [4]
support and self configurability. Routing rules of wireless
sensor network naturally adjustthemselveswiththecurrent
environments which may vary with high mobility to low
mobility in extremes along with high bandwidth.
Routing in wireless network is different from simple adhoc
network. Wireless sensor network is infrastructure less.
Wireless links are not reliable. All the routing protocols of
wireless sensor network require good energy. Wireless
sensor node may fail because of infrastructure. The wireless
sensor network protocols are location based protocols,
hierarchical protocols, data centric protocols, multipath
based protocols, QoS based protocols, mobility based
protocols, and heterogeneity basedprotocol.Afewprotocols
reported in literature are as follows: Location based - GAF,
TBF, SMECH, GeRaF, MECN [5], GEAR, Span, BVGF;
Hierarchical Protocols - APTEEN, LEACH, HEED, PEGASIS,
TEEN; Data-centric Protocols - Rumor Routing, ACQUIRE,
Quorum-Based Information Dissemination, SPIN, EAD,
Information-Directed Routing, HABID, GBR, EAR, IDR,
COUGAR, DD; Heterogeneity-based Protocols - CHR, CADR,
IDSQ; Multipath-based Protocols - Braided Multipath,
Sensor-Disjoint Multipath , N-to-1 Multipath Discovery;

Mobility-based Protocols - TTDD, SEAD [6], Dynamic Proxy
Tree-Base Data Dissemination, Joint Mobility and Routing,
Data MULES; and finally QoS-based protocols - SPEED,
Energy-aware routing, SAR.
The battery condition of WSN node is very important factor
for better communication.Thehardwareingoodconditionis
very necessary for WSN communication. To maintain a
sensor network running in a normal condition, many
applications such as time synchronization, reprogramming,
protocol update, etc are necessaryinflooding[8]manner.To
achieve availability, integrity and reliability routing rules
should be robust against malevolent attacks.
In this paper, we propose an algorithm which improves the
duty cycle, the network lifetime and reduces energy
consumption. The experimental outcomes depict the
improvement in the throughputofthesystemand endtoend
data transmission, reduction in the traffic of the wireless
sensor network due to dynamic channel allocation thereby
improving the overall system performance.
The rest of the paper is organized as follows. Section 2
provides a brief literature survey related to wireless sensor
network, and the routing protocols used in wireless sensor
network. Section 3 concentrates on the proposed work,
algorithm of the proposed work. Section 4 provides the
implementation and result analysis. Finally, Section 5
provides concluding remarks, limitation discussion.
2. LITERATURE SURVEY
Wireless sensor networks (WSNs) have been widely used in
many application areas such as infrastructure protection,
environment monitoring and habitat tracing. Because of
more energy consumption, the lifetime of the wireless
sensor network reduces. Also, if the channel utilization is
more than throughput of the wireless sensor network
reduces. Thus, wireless sensor networks medium access
control (MAC) protocols for energy efficiency comes at the
cost of extra packet delay and limited throughput, since a
sender is allowed to transmit in the short active periods,
only. Hence, to provide high throughput and short delay,
while still keeping low power consumption is still a research
challenge in current WSNs MAC protocols. However, typical
applications, in addition to low rate periodic traffic, also
present burst traffic triggered upon event detections.
Therefore, there is an emerging need fora MACprotocol that
adapts its offered bandwidth to a dynamic traffic load, i.e.,
maintain low duty-cycle in light traffic condition and
schedule more transmission opportunities when traffic
increases so that the energy is only used for carrying the
application traffic whenever needed. When the load
increases, the number of collisions and retransmissions
strongly degrade their bandwidth efficiency and generate
long delays.
Low duty-cycle is always used to improve the network
lifetime in WSN. However, its disadvantage is long delayand
low throughput if traffic is more. Authors in [9] proposed
hybrid CSMA/TDMA MAC protocol called iQueue-MAC for
dynamic and busy traffic. In this method if the traffic in a
system is low then the protocol uses a contention-based
CSMA mechanism that provides low delay with scattered
transmissions and if traffic is more and dynamic then it uses
a contention-free TDMA mechanism allocatingtransmission
slots. iQueue-MAC mitigates packet buffering and reduces
packet delay, combining the best of TDMA and CSMA. This
system can be used in both multi-channel andsinglechannel
mode. iQueue-MAC is able to effectively use multiple
channels, duplicating its throughput when compared to
single channel operation. Authors also proposed a
distributed sub-channel selectionalgorithmtoassignunique
sub-channels to routers to arrange theslottedtransmissions
in parallel.
For continuous transmission Wise-MAC [10] a contention-
based protocols, uses a ”more-bit” information in the data
packet header of data packets.RIMAC[11]Receiver-initiated
MAC uses beacon as the ACK transmission and next
forwarding for continuous transmission. RI-MAC and Wise-
MAC have low throughput at heavy load because of collision
between receiver and senders.
Z-MAC uses hybrid CSMA/TDMA procedure for static slot
allocation and reduces traffic overhead. In this mechanism
vacant slot can be used by others. Duetostaticslotallocation
the bandwidth is reduced. Strawman MAC [12] reduces the
contention by using extra Collision packets. The sender who
has sent the longest Collision packet wins the channel. But
the Collision packets introduce a considerable amount of
overheads to the system. RCMAC [13] improves RI-MAC,
that designates the nextsenderthroughACKpiggybackingto
reduce collision. However,howtoallocatebandwidthamong
senders is not specified.
CoSenS [14], a collecting then sending burst protocol was
proposed to provide traffic adaptation. It dynamically
adjusts the duration of its data collectingperiodaccordingto
the estimated traffic load. The traffic estimation algorithmis
based on the weighted exponential average (similar to that
used for RTT estimation in TCP protocol). ContikiMAC [15]
efficiently integrates several unique techniques of other
WSNs MACs, such as burst forwarding, phase-lock, and data
packet strobe. However, ContikiMAC is mostly designed to
handle low rate packets, it has no specific mechanism to
handle burst traffic loads.
WirelessHart [16], ISA100 and the new IEEE802.15.4e
standard are currently the most popular wireless solutions
for industrial applications. These standards utilize Time
Slotted Channel Hopping (TSCH) technique to provide
deterministic transmissions and robustness. However,
currently, they lack link scheduling algorithms which are
crucial for assigning slots/frequency resources in WSNs.

Compared to existing solutions, iQueue-MAC mitigates
contention and retransmission by shifting intensivesenders
into the TDMA slots period. The senders’ queue-length
information is piggybacked on data packet, so that the time
slots are assigned right upon queuing detection. The crux of
iQueue-MAC is an efficient closed loop control mechanism
that uses nodes’ queue-length as the measured output and
uses adaptive time slots assignment as the control input to
mitigate packets queuing. In fact, prior to iQueue-MAC, the
similar idea emerged in the FTT (Flexible Time-Triggered)
paradigm which is originallyproposedforCAN andEthernet,
however, iQueue-MAC makes it more suitable for WSNs.
The different methods provided for dynamic slot allocation
[17] comes at the cost of limited throughput, and additional
packet delay. The sender is allowed to transmit in the short
active periods, only with less energy efficiency. Thus, under
high traffic load, the absence of collisions makes them very
efficient supporting high throughput.However,iftheoffered
bandwidth does not match exactly the communication
requirements, either bandwidth will bewastedifnodeshave
nothing to transmit or queues will build up if nodes have
more to transmit than what fits in the allocatedslots,leading
to long delays.
How to make available short delay, high throughputandless
power consumption is main problemand researchchallenge
in existing WSNs protocols. The different low duty-cycle
protocols deliver low energy effectiveness under the
assumption that the system has long-standing low rate
intervallic traffic. Nevertheless, typical applications, in
addition to low rate interrupted traffic, also require
contemporary burst traffic generated upon occurrence
detections, e.g., target detection. Consequently, there is a
developing need for a protocol that get usedtoitsobtainable
bandwidth to a dynamic data traffic load, i.e., conserve little
duty-cycle in low traffic condition and provide more
transmission prospects when data traffic increases,andthat
the energy is only applied for carrying the network traffic on
every occasion needed. The methods may have more traffic
overhead and more energy consumption.
3. PROPOSED ALGORITHM
Keeping in view the above we propose an algorithm which
caters for the requirements to improve the WSNs lifetime
and throughput. The steps in the proposed algorithm are as
follows:
Step1. Network initialization stage: Node chooses neighbors
unique ID and broadcasts beacons for network
establishment. Any node receiving beacons from different
coordinators at the same moment will be tagged as the
alternative gateway node, and the one with the nearest
neighbor will be considered as the gateway by coordinators.
All coordinators determine the position of their own and
neighbors using gateway.
Step2. Nodes access stage: After nodesaccesstothenetwork,
they apply for the allocation time according to the step 3.
Step3. Nodes mobility tracking and prediction: When the
position information indicates that the node has already
moved to another network, the current associated network
will inform the target node to reserve time slots for the
coming node.
Step4. Nodes mobility support:Ifthereare bufferedpacketsin
the former associated network when nodes enter intoa new
network, the former coordinator would forward those
packets to the new one.
Proposed Algorithm
1) Initialization step: minq=0.20 * queue size
Maxq=0.80 * queue size, Warn=queue size/2
Threshold value setup
2) Each node checks its congestion statues by using
average queue length, channel utilization ration and
residual energy. Compute average queue length.
The frequency of data packet is decided according to
congestion status
If frequency is high then
Ok incoming traffic is low, no action is taken
Else if frequency is medium, congested and
neighboring nodes perform data transmission
then
Traffic is medium, low amount of data can be
transmitted
Else if frequency is low, high congestion in network
then
Traffic is high, alternate best path is
dynamically established and data can be
transmitted
End if
3) Test data packet distribution ratio of the system
If data packet distribution ratio drop to the given
threshold then
Starting source node arbitrarily pick the
supportive address of any one node
neighbor
Send request to the nod
If anyone node reply from other path except
neighbor node then take the reverse locating
program and direct check data packets
Else
Goto End
End if
End
The first step is initialization step. In this step, all the
initialization parameters are set. The minimum queue is set
to 20% of queue size. Maximum queue issetto80%ofqueue
size. Warning value is set to half of the queue size. Threshold
value is set to .75.

In next step, each node checksitscongestionstatuesbyusing
average queue length, channel utilizationrationandresidual
energy. It also compute the average queue length. The
frequency of data packet is decided according to congestion
status. If frequency is high then congestion is up to the mark
i.e. incoming traffic is low, no action is taken. Otherwise if
frequency is medium, congested and neighboring nodes
perform data transmission then traffic is medium, low
amount of data can be transmitted. Else if frequency is low,
high congestion in network then traffic ishigh,alternatebest
bath is dynamically established and data can betransmitted.
The next step is to test data packet distribution ratio of the
system If data packet distribution ratio drop to the given
threshold then starting source node arbitrarily pick the
supportive address of any one node neighbor. Send request
to the node If anyone node reply from other path except
neighbor node then take the reverse locating program and
direct check data packets otherwise data canbetransmitted.
4. IMPLEMENTATION AND RESULT ANALYSIS
We used Network Simulator 2 simulator software for
implementation of proposed algorithm. We also usedC/C++
and TCL language for implementation. We performed our
experiment in Intel i3 4.0 GHz machine with 2GB RAM.
Figure 1: Channel searching at 72.9 second
Figure 1 represents the channel searching at 72.9 second.
The different nodes aresearchingpathfordata transmission.
We used 50 nodes for simulation of algorithm. Nodes
mobility is also introduced to check the performance of the
network.Forthisimplementation,network parameters,such
as Dimension, Number of nodes, traffic, transmission rate,
Routing protocol, transmission range, sensitivity,
transmission power etc., are used. Transmission range is
specified as 300m. Movement model is used as random
waypoint. Simulation durationissetas90s.Traffic typeisset
as constant bit rate. Radio range is set as 250m. Data pay
load is set as 512 bytes. The NAM is used for animation in
implementation. Tcl script language with Object-oriented
extensions.
Figure 2 shows the throughput analysis of the proposed
system. The throughput is improvedduetodynamicchannel
allocation.
Figure: 2 Throughput Analysis
The congestion aims to drop the data packets or to hold the
resources such that the communication is affected. The
packets forwarding capacity of scheme is increased with
period of time. As compared to all the reported related
works the proposed work has the minimum packets drop
ratio.
Table 1: Duty cycle
Our Method IQueue CoSens
17.16% 17.930% 27.472%
Table 1 shows the average dutycycleofnodesinexperiment.
As seen from the table our method has efficientperformance
in peak traffic time. The proposed method achieves 0.77%
and 9.872% decrease in duty cycle as compare to iQueue
method and CoSens methods, respectively. Also it takes
around 5 ms of less time for data communication in the
network. Due to small duty cycle, energy consumption of
nodes decreases which, in turn, increases the lifetime of the
network. Further, if we already know the traffic situation in
the network we can decrease the power consumption of the
network. In our work, we have checked the traffic situation
using queue length status of the node. We also checked the
packet dropped of the node so the system will discard the
route information from the route status of the node. In the
simulation, it was observed that the dynamic channel
allocation method allocates better channel for data
communication and thus improve the lifetime of the
network.

5. CONCLUSIONS
A wireless sensor network encompasses of several lesser
sized sensor nodes that have low working out power.
WSNs have been the favorite choice for the succeeding
generation monitoring and control systems. Wirelesssensor
networks have recently received increased attention for a
broad array of applications such as surveillance,
environment monitoring,medical diagnostics,andindustrial
control. The greatest noteworthy advantage of sensor
networks is that they increased the computation ability to
physical atmosphere where human beings cannot reach.
How to provide high throughput and short delay, while still
keeping low power consumption is still a research challenge
in current WSNs MAC protocols. Low cycle duty cycle is
always used to improve the network lifetime in WSN. Its
disadvantage is long delay and low throughput if traffic is
more. If the channel utilization is more than throughput of
the wireless sensor network reduces. We proposed an
algorithm which improves the duty cycle and lifetime of the
wireless sensor network. The experimental outcomes
represented the throughput of the system and end to end
data transmission improved. The traffic of the wireless
sensor network reduced due to dynamic channel allocation
and overall system performance increased.
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