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From energy conservation perspective in Wireless Sensor Networks (WSNs), clustering of sensor nodes is a challenging task. Clustering technique in routing protocols play a key role to prolong the stability period and lifetime of the network. In this paper, we propose and evaluate a new routing protocol for WSNs. Our protocol; Divide-and-Rule (DR) is based upon static clustering and dynamic Cluster Head (CH) selection technique. This technique selects fixed number of CHs in each round instead of probabilistic selection of CH. Simulation results show that DR protocol outperform its counterpart routing protocols.
Procedia Computer Science 19 ( 2013 ) 340 347
1877-0509 © 2013 The Authors. Published by Elsevier B.V.
Selection and peer-review under responsibility of Elhadi M. Shakshuki
doi: 10.1016/j.procs.2013.06.047
The 4th International Conference on Ambient Systems, Networks and Technologies
(ANT 2013)
Divide-and-Rule Scheme for Energy Ecient
Routing in Wireless Sensor Networks
2, N. Alrajeh3
1COMSATS Institute of Information Technology, Islamabad, Pakistan.
2Faculty of Engineering, Dalhousie University, Halifax, Canada.
3B.M.T., C.A.M.S, King Saud University, Riyadh, Saudi Arabia.
From energy conservation perspective in Wireless Sensor Networks (WSNs), clustering of sensor nodes is a challenging
task. Clustering technique in routing protocols play a key role to prolong the stability period and lifetime of the network.
In this paper, we propose and evaluate a new routing protocol for WSNs. Our protocol; Divide-and-Rule (DR) is based
upon static clustering and dynamic Cluster Head (CH) selection technique. This technique selects fixed number of
CHs in each round instead of probabilistic selection of CH. Simulation results show that DR protocol outperform its
counterpart routing protocols.
2011 Published by Elsevier Ltd.
Keywords: wireless sensor networks, WSN routing protocols, DR-Scheme
1. Background
Spatially dispersed wireless sensor nodes and one or more Base Stations (BSs) are embodied to form
WSN. Sensor nodes keep an eye on the physical or environmental conditions at dierent locations, and
communicate eciently with BS. Generally BS is power rich and nodes are equipped with low power.
Applications of WSNs are in security, trac management, environment monitoring, medical applications,
surveillance, etc.
Today’s research challenge in WSNs is coping with low power communication. Routing protocols in this
regard plays a key role in ecient energy utilization. In sending data from node to BS, selection of a specific
route, which tend to minimize the energy consumption is necessary. Old fashioned routing techniques are not
as energy ecient as present day clustering techniques. LEACH [1], LEACH-Centralized [2] and Multihop-
LEACH [3] are few of the earlier techniques of cluster based routing protocols for WSNs. Basically two
types of clustering techniques exist; static clustering and dynamic clustering. Clusters once established and
Email address:, (K.Latif)
URL: (K.Latif)
Available online at
© 2013 The Authors. Published by Elsevier B.V.
Selection and peer-review under responsibility of Elhadi M. Shakshuki
K.Latif et al. / Procedia Computer Science 19 ( 2013 ) 340 – 347
never be changed throughout network operation are known as static clusters, while clusters based on some
sort of network characteristics and are changing during network operation are known as dynamic clusters.
Proposed DR scheme is based on static clustering and minimum distance distance based CH selection.
Network area is logically divided into small regions (clusters). These regions are abbreviated as NCR1,
NCR2, NCR3, etc, as shown in figure 1. Nodes in each region select a CH except the region closest to the
BS, that is, region; R1. Nodes whose coordinates lie within the perimeter of R1, communicates directly
with BS. Selection of CHs in rest of the regions are based on reference point in each region; reference point
is the mid point of each region. Node closest to reference point is selected as CH first, then next closest
node and so on till least closest node. In each round only one CH is selected in each region furthermore, we
uses multi-hop technique for inter region communication to reduce communication distance. DR scheme
has the ability to select CH independent of random number and minimize communication distance to almost
less than or equal to reference distance. DR scheme uses hybrid theme of static clustering and dynamic CH
selection. This technique divides whole network area into 4(n1) Corner Regions (CRs) and 4(n1)+1Non
Corner Regions (NCRs). CHs are selected from NCRs only. Nodes of central region (NCR1) communicates
directly with BS while, nodes of CRs associate with adjacent side neighbour CH. DR scheme minimizes
communication distance, prolong stability period, enhances network lifetime, and increases throughput.
Now a day in many application, it is needed that sensor nodes are location aware. The Localization
problem is discussed in [4] and [5]. Location awareness of sensor nodes help in removing coverage holes
and movement of new sensor nodes in place of dead nodes. Energy holes in sensor networks also causes
depletion of network energy quickly. The analysis and modeling of energy hole of dierent routing protocols
are discussed in [6] and [7].
2. Proposed Scheme
Localization problem is commonly addressed by many researchers. In localization, network field area is
logically divided into sub areas [4], [5]. This may helps in controlling the coverage hole. In our technique
we divide the network field into sub regions. The complete formation of region’s and detailed operation of
our scheme is discussed in this section.
2.1. Formation of Regions
In traditional cluster formation technique, CHs are elected on probabilistic bases and threshold calcu-
lated for each CH. Nodes then associate with each CH based upon received signal strength thus, forming a
cluster. In our protocol we divide entire network area into small logical regions. The division of the regions
is such that it reduces the communication distance between node to CH and CH to BS. Following two steps
describes formation of regions in detail.
In first step network is divided into nequal distant concentric squares. For simplicity, we take n=3
here therefore, network is divided into three equal distance concentric squares: Internal square(Is), Middle
square(Ms) and Outer square(Os). BS is located in the centre of network field therefore, its coordinates
are taken as reference point for formation of concentric squares. Division of network field into concentric
squares can be obtained from following equations.
Coordinates of top right corner of Is,Tr(Is).
Coordinates of bottom right corner of Is,Br(Is)
Coordinates of top left corner of Is,Tl(Is)
342 K.Latif et al. / Procedia Computer Science 19 ( 2013 ) 340 – 347
Coordinates of bottom left corner of Is,Bl(Is)
Where, dis the factor of distance from center of network to boundary of Is. value of dfor Msand Os
increases with a multiple of 2 and 3 respectively. If we have nnumber of concentric squares then we can
find the coordinates of nth square, Snfrom the following equations.
Tl(Sn)=(Cp(x)dn,Cp(y)+dn),and (7)
In second step we divide the area between two concentric squares into equal area quadrilaterals; latter
we name them as Corner Regions (CR) and Non Corner Regions (NCR). To divide area between Isand
Msinto four equal area quadrilaterals, we take the top right and bottom right corners of Isas the reference
points. Adding factor din the x-coordinate of top right and bottom right corner of Is, i.e., Tr(Is(x+d,y))
and Br(Is(x+d,y)), we get the co-ordinates of NCR2. Adding factor din the y-coordinate of top right
and top left corner of Is, i.e., Tr(Is(x,y+d)) and Tl(Is(x,y+d)), we get the co-ordinates of region NCR3.
Subtracting factor d, in the x-coordinate of top left and bottom left corner of Is, i.e., Tl(Is(xd,y)) and
Bl(Is(xd,y)), we get the co-ordinates of region NCR4. Subtracting factor d, in the y-coordinate of bottom
right and bottom left corner of Is, i.e., Br(Is(x,yd)) and Bl(Is(x,yd)), we get the co-ordinates of region
NCR5. Remaining areas left are the four CR, that is, CR2, CR3, CR4, CR5. Following the same sequence,
we can divide the area between Msand Osinto four equal area quadrilateral regions (NCR6, NCR7, NCR8,
NCR9) and corner regions (CR6, CR7, CR8, CR9), as shown in figure 1.
Fig. 1. Region’s formation
2.2. CH Selection
DR protocol considers multi-hop communication for inter-cluster communication. As we assume n=3
therefore, inter-cluster communication is performed at two levels, that is, at primary level and at secondary
level. Our CH selection follows, following approach.
K.Latif et al. / Procedia Computer Science 19 ( 2013 ) 340 – 347
2.2.1. Primary Level CH
Primary level CH selection follows the sequence; (i) nodes whose co-ordinates lie in (Is) are nearer to
BS therefore, they send data directly to BS, (ii) as clusters are static, therefore one CH is selected in each
NCR, (iii) mid point of each NCR is considered as reference point for selection of CH in that region, (iii)
nearest node from central reference point is selected as CH and, (iv) next nearest node from the reference
point is selected as CH for next round and so on.
2.2.2. Secondary Level CH.
Steps followed in selection of secondary level CHs are; (i) CHs in OSregions, send data to CHs of
exactly one level above adjacent region’s CH. These CHs are also known as secondary level CHs, (ii)
secondary level CHs aggregate their own cluster nodes data and, data of the primary level CH then, transmit
data to BS.
2.3. Protocol Operation
In setup phase BS divides the network field into small regions, on the bases of their co-ordinates. Is
nodes send data directly to BS. In each region one CH is selected per round. CHs of Osregions, select front
neighboring CHs of MSregions as their next hop CH. Nodes of CR selects, BS or neighbouring CHs as their
CH, based on minimum distance. If a tie occurs, for a node of CR, in selection of CH from its neighbouring
regions than, it is resolved by selecting the CH with greater residual energy.
In steady state phase each node send its data to CH in its allocated time slot. Primary level CHs send
aggregated data to their respective secondary level CHs. Secondary level CHs then, aggregate all collected
data and forward it to BS.
3. Energy Consumption Model
In this section, we develop a mathematical model, which describes how energy is consumed in dierent
regions of the network field. Basic energy consumption model is adopted from [8]. Equation 9 & 10 adopted
from [8] shows energy cost of transmission, TEenergy and reception, REnergy respectively for 1-bit of data over
distance Dmeters.
TEenergy =
Eelec +εfsD2if D<D0
Eelec +εamp D4,if DD0
REnergy =(Eelec) (10)
3.1. Energy Consumption in Is
Following equation calculates the area and energy consumption of Is.
From figure 1, each side of Isis 2din length and width therefore, Area of Is,A(Is):
Number of nodes in Is,N(Is):
where ρis the node density per unit area. Nodes of Istransmit data directly to BS therefore, their energy
consumption, ETx
Isis given by following equation.
Is=4ρd2TEnergy (13)
344 K.Latif et al. / Procedia Computer Science 19 ( 2013 ) 340 – 347
3.2. Energy Consumption in CRs
From figure 1, dis the reference distance for the formation of NCR. Therefore Area of CR, A(CR)is
given by:
Number of nodes, N(CR)inCR:
Nodes of CR may transmit data to BS or to neighbouring NCR’s CH, depending on the minimum distance
therefore, their energy consumption, ETx
CR for sending data to BS is given by equation:
CR =(1 P)ρd2TEnergy (16)
where Pis the probability of sending data to CH, and (1 P) is the probability of sending data to BS.
3.3. Energy Consumption in Ms
First we calculate energy consumption of normal nodes. Area of each NCR in Msis 2d2. There are four
NCRs and four CRs. Each CR node may associate with one of the NCR’s CH or send data directly to BS.
Energy consumption of normal nodes in Ms, per NCR, ETx
Ms/NCR is given by following equation.
Ms/NCR =(2ρd21)TEnergy (17)
Now we calculate energy consumption of CHs. There are total four CHs in four regions. Each CH con-
sumes energy in transmit (ETx
MsCH), aggregation (φ) and receive (ERx
MsCH ) process therefore, their energy
consumption is calculated individually.
Transmit Energy
MsCH =(2ρd2+Pρd2)TEnergy +φ(18)
Transmit energy of all CHs, ETx
Msall CH in region’s of Msis given by following equation :
Msall CH =(8ρd2+4Pρd2)TEnergy +4φ(19)
Receive Energy
MsCH =((2ρd21) +Pρd2)REnergy (20)
Receive energy of all CHs, ERx
Msall CH in regions’s of Msis given by following equation :
Msall CH =((8ρd24) +4Pρd2)REnergy (21)
Total energy consumed in region’s of Ms,ETot
Msis given by following equation :
Msnode +ETx
Msall CH +ERx
Msall CH (22)
3.4. Energy Consumption in Os
In DR protocol, the area of each NCR increases from inner to outer square. The area of NCR of outer
square region increases in the following fashion.
Length of one side of Ms’s , N CR =2d. Length of one side of Os’s, NC R =4dand so on. Width of all
the regions remains same that is d. Considering this value of length and width into account, area of each
NCR of the Oscan be calculated as 4d2. Taking the area into account we can calculate the total energy
consumption, ETot
Osof the Osfrom the following equation.
Osnode +ETx
Osall CH +ERx
Osall CH (23)
K.Latif et al. / Procedia Computer Science 19 ( 2013 ) 340 – 347
0 500 1000 1500 2000 2500 3000
No.of rounds(r)
No. of nodes allive
Fig. 2. Comparison: Rate of Alive Nodes
4. Performance Evaluation
4.1. Network Model
We evaluate our proposed DR scheme by comparing it with LEACH and LEACH-C. We assume a
network model of 100 nodes, with homogeneous initial energy of nodes, which are randomly deployed in
each region of 100 ×100m2network area. BS is assumed at the centre of network area. Interference eects
in wireless channels are ignored. For simulation purpose, we used MATLAB simulator and first order radio
model parameter’s are assumed, as shown in table 2.
Table 1. Radio parameters
Operation Energy Dissipated
Transmitter /Receiver Electronics Eelec=Etx=Erx=50nJ/bit
Data aggregation energy EDA=5nJ/bit/signal
Transmit amplifier (if d to BS<do) Efs=10pJ/bit/4m2
Transmit amplifier (if d to BS>do) Emp=0.0013pJ/bit/m4
4.2. Results
In this section, we evaluate our proposed protocol in terms of stability period, network life time and
throughput. Average results obtained after 50 times execution of our protocol.
346 K.Latif et al. / Procedia Computer Science 19 ( 2013 ) 340 – 347
Fig. 3. Comparison: Rate of CHs per round
4.3. Stability Period
Here we evaluate the performance of DR in terms of stability period by comparing it with LEACH and
LEACH-C. Figure 2 shows that, our proposed protocol carry out maximum rounds till the death of first
node. DR perform 28.63% better than LEACH and 12.31% better than LEACH-C. The reason is straight
forward; distant nodes of CRs are not enforced to associate with CH. Nodes of CRs may associate either
with minimum distant CH or minimum distant BS. Thus, DR minimize communication distance. DR selects
optimal number of CHs.
4.4. Number of CHs per Round
Figure 3 shows number of CHs formed in each round are fixed. Which shows that near to optimum
number is achieved and load is balanced throughout the network operation time, a step towards ecient
energy utilization.
5. Conclusion and Future Work
In this paper we have proposed a new clustering techniques for ad-hoc WSNs. DR Scheme uses static
clustering and minimum distance based CH selection. We have used a two level hierarchy for inter cluster
communication. The beauty of our technique is the formation of square and rectangular regions, which di-
vides the network field into small regions, as a result the communication distance for intra cluster and inter
cluster reduces. However CR nodes associate with CH or BS depending on the minimum distance. Our
proposed DR scheme uses a hybrid approach of static clustering and dynamic CH selection. In MATLAB
simulation we compared our results with LEACH and LEACH-C. Characteristics of achieving optimum
number of CHs in each round and hierarchical inter CHs communication of our technique provided better
results than its counterparts, in terms of stability period, network life time, area coverage and throughput.
However, Large network area and greater number of nodes decrease DR eciency in terms of energy con-
sumption. Another drawback arises when Cluster members associate with CH of its own region even if CH
of other region is at a shorter distance. In future we would like to compensate deficiencies explained in this
section and implementation of DR in clustering protocols like Threshold sensitive energy ecient sensor
network protocol [9], stable election protocol [10], distributed energy ecient clustering [11], etc.
0500 1000 1500 2000 2500 3000
No.of rounds(r)
No. of CHs
K.Latif et al. / Procedia Computer Science 19 ( 2013 ) 340 – 347
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... These standard parameters are treated as constant values of literals used in the program. The energy dissipated by the associated Cluster Head [22] is ...
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Energy conservation is one of the most important design considerations for battery powered wireless sensors networks (WSNET). Energy constraint in WSNETs limits the total amount of sensed data (data capacity) received by the sink. The data capacity of WSNETs is significantly affected by deployment of sensors and the sink. A major issue, which has not been adequately addressed so far, is the question of how node deployment governs the data capacity and how to improve the total data capacity of WSNETs by using non-uniform sensor deployment strategies. In this paper, we discuss this problem by analyzing the commonly used static model of sensors networks. In the static model, we find that after the lifetime of a sensor network is over, there is a great amount of energy left unused, which can be up to 90% of the total initial energy. This energy waste implies that the potential data capacity can be much larger than the capacity achieved in the static model. To increase the total data capacity, we propose two strategies: a non-uniform energy distribution model and a new routing protocol with a mobile sink. For large and dense WSNETs, both of these strategies can increase the total data capacity by an order of magnitude.
Conference Paper
Wireless distributed microsensor systems will enable the reliable monitoring of a variety of environments for both civil and military applications. In this paper, we look at communication protocols, which can have significant impact on the overall energy dissipation of these networks. Based on our findings that the conventional protocols of direct transmission, minimum-transmission-energy, multi-hop routing, and static clustering may not be optimal for sensor networks, we propose LEACH (Low-Energy Adaptive Clustering Hierarchy), a clustering-based protocol that utilizes randomized rotation of local cluster based station (cluster-heads) to evenly distribute the energy load among the sensors in the network. LEACH uses localized coordination to enable scalability and robustness for dynamic networks, and incorporates data fusion into the routing protocol to reduce the amount of information that must be transmitted to the base station. Simulations show the LEACH can achieve as much as a factor of 8 reduction in energy dissipation compared with conventional outing protocols. In addition, LEACH is able to distribute energy dissipation evenly throughout the sensors, doubling the useful system lifetime for the networks we simulated.
The clustering Algorithm is a kind of key technique used to reduce energy consumption. It can increase the scalability and lifetime of the network. Energy-efficient clustering protocols should be designed for the characteristic of heterogeneous wireless sensor networks. We propose and evaluate a new distributed energy-efficient clustering scheme for heterogeneous wireless sensor networks, which is called DEEC. In DEEC, the cluster-heads are elected by a probability based on the ratio between residual energy of each node and the average energy of the network. The epochs of being cluster-heads for nodes are different according to their initial and residual energy. The nodes with high initial and residual energy will have more chances to be the cluster-heads than the nodes with low energy. Finally, the simulation results show that DEEC achieves longer lifetime and more effective messages than current important clustering protocols in heterogeneous environments.