A novel energy efficient routing algorithm for wireless sensor networks using sink mobility

International Journal of Wireless & Mobile Networks (IJWMN) Vol. 6, No. 6, December 2014
DOI : 10.5121/ijwmn.2014.6602 15
A NOVEL ENERGY EFFICIENT ROUTING
ALGORITHM FOR WIRELESS SENSOR NETWORKS
USING SINK MOBILITY
Deepa V. Jose1
and Dr.G.Sadashivappa2
1
Department of Computer Science, Christ University, Bangalore, India
2
Department of Telecommunication Engg, R.V College of Engineering, Bangalore,India
ABSTRACT
There are numerous applications for wireless sensor networks which are inevitable now a day in our daily
life. Majority of such applications which use wireless sensor networks will be in areas where the direct
human intervention is impossible. So the limited energy available in such sensors is a threat for prolonging
the life of the entire network. The need of energy efficiency in wireless sensor networks is a hot research
topic in which lot of new strategies for improvement in energy efficiency has been sought after. As
communication process consumes more energy, an energy efficient routing strategy can probably reduce
the energy consumption to a great extend. This paper gives an overview of the different routing techniques
in which mobile sinks are used to facilitate the routing process which can effectively reduce the energy use.
A new routing strategy with mobile sinks and a static sink is proposed and is compared based on the
matrices life time and average energy of the nodes with the existing Shortest Hop path (SH) algorithm. The
simulation results shows the proposed algorithm is more energy efficient than the existing one.
KEYWORDS
Energy Efficiency, Mobile Sinks, Routing Protocols, Sensor Nodes, Static Sinks, Wireless Sensor Networks.
1. INTRODUCTION
Wireless Sensor Networks (WSNs) usually consists of a large number of small sensor nodes
interconnected that offers the unique ability to monitor the physical world accurately. WSNs have
gained a lot of interest in the field of research nowadays due to its varied applications in diverse
fields that require surveillance and monitoring that have become inevitable in our daily life. But
the main drawback of such sensors are their resource constrained nature mainly in the power
backup capabilities of sensor nodes and as a result a number of issues have emerged out of which
energy efficiency is an important matter of concern.
WSN sensors are expected to be remotely deployed in unattended environments. The distributed
nature, dynamic topology and resource constraints of WSNs introduces very special requirements
in routing protocols that should be met as majority of the energy is usually utilized for routing.
The issues faced by sensor networks are related to routing, network failure due to energy holes or
energy drainage etc in which the problem of energy loss has to handled carefully as it can become
the cause for so many other issues. So improving the energy efficiency of sensor nodes is very
important. The most important feature of a routing protocol, in order to be efficient for WSNs, is
reduced energy consumption and their by the extension of the network’s lifetime. During the
recent years, many energy efficient routing protocols have been proposed for WSNs. But in
majority of them the mobility factor is not taken into consideration. In a WSN scenario there can

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be different types of mobility like the mobility of the nodes, sink mobility and the event or target
mobility. Literature have shown that energy can be saved in a high level if sink mobility is
achieved. In WSNs the criteria of designing routing etc depends on the application under
consideration. The sink mobility concept can be introduced in application like smart home
environment or medical applications like patient monitoring without many complications. But the
number of sinks to be introduced , the positioning of the sinks, the mobility or pathway of the
sink, it’s velocity, coverage area etc are the different factors which has to be determined with care
to have efficient results by attaining better energy utilization.
Wireless sensor Networks (WSNs) which consists of numerous tiny sensors, deployed randomly
is exploited in copious applications now a day. The small size of these sensors helps to get it
embedded in other devices very slickly which led to the use of sensors in variety of surveillance
and monitoring applications, health and medical fields, engineering and automation and so on.
The small size of the sensors is a boon as well as a hitch. Each sensor node will contain a sensing
unit, a processing unit, memory and a power source. Because of its small size, deployment is easy
but other processing, memory and power backup capacities have to be sacrificed. The block
diagram of the components of a sensor node will be as represented in Figure1.
Figure 1. Components of a sensor node
The location finding system and the mobilizer are optional components and will be avoided
usually as it can lead to the increase in size and cost of the entire system. The sensor ADC
converts the sensed analog signal to their digital form. There will be a small processor and a
memory for the processing and storage functions. The transceiver unit performs the transmission
and reception of the signals. The power source will be usually batteries. The batteries in the
wireless sensors are irreplaceable and of limited capacity.
Many energy harvesting techniques have been developed so as to frequently charge the sensors
but it requires an additional hardware resource which leads to the increase in the cost and size of
the sensor nodes which makes it difficult for many applications to adopt the use of WSNs. Above
all the application of WSNs will be in areas where the human intervention is difficult or usually
impossible. If anyone node dies off in the network, it can lead to the breakage of the entire
network and the application can be futile. So the only possible solution is to efficiently use the
available energy so as to extend the life time of the entire network.
The sensor nodes will be deployed randomly in an area where the sensing or monitoring has to
happen, called the sensor field. Some nodes can act as sensor as well router which will collect
data from all the nodes and passes on to the sink which is deployed outside the sensor filed. From
there the data can be accessed by the users using the different available technologies. A general
structure of the WSN is given in Figure 2.

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Figure 2. Wireless sensor network
Energy efficiency in WSNs is a hot research area where lot of methods to improve the energy
efficiency is researched. There are different methods or techniques adopted to reduce the energy
consumption or to effectively use the available energy to the maximum extent possible. Some of
them are adopting different node deployment strategies, applying duty cycling concepts,
including the mobility of some nodes which can act as data collector and router, changing the role
of nodes accordingly etc. Each method will be appropriate for a specific application. According to
the literature the majority of the energy is consumed for communication rather than that of
processing and sensing. So the need of an energy efficient routing protocol is very important to
extend the network life time. The energy efficient routing protocols falls under the hierarchical
category where the clustering concept is used [1] [16] [17].
This paper proposes a new energy efficient routing algorithm which uses multiple mobile sinks
and duty cycling concepts to improve the energy efficiency. The paper is organized into different
sections. An overview of the related work in which the different papers related to mobile sinks
and their outcome is discussed along with the duty cycling concepts is given in Section 2
followed by Section 3 describing the proposed algorithm .Section 4 shows the comparison with
existing SH protocol with the simulation results based on the number of live nodes and the
average residual energy of the nodes after the simulation period. Conclusion and future
enhancements are mentioned in Section 5.
2. REVIEW OF LITERATURE
A mathematical model to find the best position of the sink in the network, which can reduce the
energy for data transmission between the source and the sink is described in [2] .They have used
the concept of cooperative routing scheme ,shortest path or direct routing to achieve energy
efficiency. In [3] the effect of sink mobility in prolonging the network life time is studied by
suggesting a framework and a mathematical model to check the mobility of the sink with the
delay constraints.
The authors in [4] say that when the sink moves along the area, the frequent location updates of it
will lead to higher consumption of energy by sensors. They propose an efficient algorithm named
IAR, an Intelligent Agent-based Routing protocol. They claim that their algorithm gives better
performance and this scheme effectively supports mobility of the sink without much trouble.

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Lino et al. in [5] focus on mobility supported applications and proposes a new algorithm called
MRLG- the Mobile-sink Routing that works better in large sensor networks in grid topology even
though it supports other topologies also. Assumptions made are the number of nodes will not
increase randomly, the sensor nodes will be static and the sink is freely movable. The authors
declare that results are compared with Collection Tree Protocol (CTP) and it outperforms CTP in
terms of delay, packet delivery ratio, end- and other routing overheads.
The authors in [6] consider the overload of frequent location updates due to sink mobility and
introduce a novel data gathering mechanism with mobile sinks. Simulation results show that this
method reduces the energy consumption and data loss considerably. Data aggregation mechanism
using mobile sink nodes in proposed in [7]. Here nodes are allowed to form as clusters using
Gravitational Search Algorithm and no Cluster Head election takes place but the role of Cluster
Heads is taken over by the introduced mobile sink nodes to perform data gathering, data
aggregation as well as communication operations which results in increasing the lifetime of the
sensor nodes. Simulations are carried out using Network Simulator with parameters energy
consumption and network lifetime.
In this paper [8] a new hierarchical routing protocol Layer Based Low Energy Adaptive
Clustering Hierarchy (LB-LEACH) is proposed to increase the lifetime of WSN in which layer
factor is considered as a cluster head selection parameter to improve the performance of cluster
head selection algorithm proposed in LEACH, EECHS and energy aware LEACH. For data
collection, a static sink and multiple mobile sinks is introduced. Authors claim LB-LEACH
performs better than LEACH, Energy aware LEACH and EECHS.
Yu Gu et al in their work [9] suggests a framework for analyzing the mobility of the sink. They
claim that simulations show the advantages of using a mobile sink and its influence of the factors
like delay and number of nodes. In [10] the authors aim to reduce the energy consumption in
multihop data retrieval and balance the energy among the entire sensor nodes which leads to
lifetime elongation. Here the cluster heads filter out the redundant data and passes to nodes with
residual energy close to mobile sinks. Simulation results show this method is far better than the
existing methods.
Duty cycling is an important way to reduce energy consumption. In order to efficiently use the
limited available energy of the sensor nodes a thorough study of duty-cycling concept combined
with data aggregation techniques is done in this paper [11]. The authors uses a specific method
using Shortest path tree for data aggregation which will be having uniform load and good latency
along with duty cycling. Simulations are done which proves the efficiency of their work.
In [12] the authors say that the immediate area surrounding the sink is the major site which limits
the network lifetime due to the heavy traffic-flow in that area. So their attempt is to improve the
energy efficiency of area around the sink which the authors claims with the duty cycling
introduced will improve life time to a better extend. In order to prove the efficacy of the approach
adopted, a detailed theoretical analysis and simulation results have also been provided.
The authors in [13] have studied the problem of minimizing the total transmission power for
reliable data dissemination in duty cycled WSNs in multicast or broadcast. They propose the
novel concept of Time-Reliability-Power (TRP) space as a general data structure for designing
data dissemination algorithms in WSNs. According to them through simulations are done which
evidently demonstrate the efficiency of their proposed algorithms. According to Jie Hao et al in
[14] sleep scheduling is a very common method to reduce energy consumption. In this article the
authors addresses the basic routing protocol design issues for duty cycled networks, classification

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of existing protocols based on different design criteria in this area, their working and the pros and
cons along with some future directions.
Lutz, K and Konig in [15] say about the hardware concepts related to energy reduction. This
paper also investigates about the different circuit design criteria including the duty cycling .This
is more or less an application specific idea but can be adopted to reduce energy usage. In [16] the
authors have addressed the problems of the cluster head related to uneven load and the
consequence related to this .In [17] a new routing strategy where multiple mobile sinks and
targets are involved is proposed with a comparison with the existing scheme.. A scheme to
conserve energy with sink mobility wherein the sink moves based on the energy of the cluster
head is proposed in [18].
The authors in [19] suggest methods to remove the overheads and additional wastage of energy
due to mobility of the sink and propose a solution for the triangular routing problem. They claims
that the proposed method proves to be efficient both by analytical and simulation techniques. In
[20] a mobile data collector based protocol is proposed and a comparison is done to prove its
efficacy. Using the ant colony algorithms an efficient routing strategy and the benefits of mobile
sinks is done in [21].
3. DESCRIPTION OF THE PROPOSED ALGORITHM
Among the different categories of existing routing protocols the hierarchical model is the one
which concentrates on energy efficiency. The protocols in this criterion use the clustering
concept. In clustering there will be different levels of clusters so that all the nodes need not take
part in the entire data transmission all the time. There will be a cluster head which will accept all
the nodes data under its leadership, aggregates it and send to the next higher level.
The proposed algorithm uses the clustering concept in which the sinks are made mobile. Mobility
of the sink node can be made randomly or based on a predetermined fashion. Clusters are formed
on a special strategy wherein the residual energy of the nodes is taken into consideration.
The maximum utilization of the energy is achieved by incorporating duty cycles among the
nodes. The nodes which are within the half range of a cluster head will be sharing the time slot
for transmission. During that particular time slot only the specific node will be active and rest of
the nodes will be in sleep mode thereby saving the energy. The data that is sensed by all the nodes
will be sent to the mobile sink which reduces the number of hops for transmission to the
destination sink which is made static. This also reduces the energy for transmission.
The sensor nodes are distributed randomly in the network. One sink is at fixed location. There are
multiple mobile sinks which are under the control of the static sink. The path of movement of
mobile sink is decided by the static sink. All the nodes in the network know the path to static sink.
Mobile sinks have bigger communication range and can communicate with static sink directly.
Initially the nodes send data to static sink.
The static sink receives data from the nodes and analyzes the behavior of nodes. It groups the
nodes based on the behavior and creates the cluster. The duty cycle is distributed to the nodes in
the cluster by the static sink and sent to the node. Based on the distribution of clusters, the static
sink does the path planning for the mobile sinks and instructs the mobile sink to travel the path.
Mobile sink travels in the path, stops at certain locations and collect data.

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To create cluster, node with similar behavior and within certain coverage area is grouped into one
cluster. The behaviour of node is modelled using ARIMA and the model is compared to measure
the similarity of node behaviour. The mobile sink stops at certain locations in the path and sends a
broadcast packet to neighbourhood. Nodes receiving this broadcast packet will construct the
route. The data it has will be sending to nearest mobile sink. Mobile sink will do the aggregation
and send the aggregated packet to static sink.
The proposed algorithm is compared with the shortest hop path algorithm. The SH algorithm
seems to be energy efficient since it takes the shortest hop to reach the destination. Here only the
shortest hop is taken into consideration. But the energy level of the nodes is also a major factor to
be addressed. The main drawback of this is the continuous use of the nodes in the shortest hop
path to the destination which leads to the fast energy drainage of those nodes which will
ultimately lead to the network failure. This can occur even when there are nodes with sufficient
energy for data transmission which are never used at all. Since we are using the mobile sinks this
problem is avoided and the number of hops is also reduced as the sink is mobile there by reducing
the energy to a great extends.
4. SIMULATION RESULTS
The proposed algorithm is compared with the SH algorithm. The simulations are carried out using
MATLAB. The simulation parameters are given in Table 1. The simulations are done based on
the assumption that the mobile sinks are rechargeable and do not have any failure during the
entire session. They will be able to move and reach each cluster head to collect the information.
Table 1.Simulation parameters
No of Nodes 100
Area of simulation 100*100 m
Range of Sensor 10 m
Initial Energy at sensor 50 Joules
Transmission Energy .5 Joules
Receiving Energy .2 Joules
No of mobile sink 4
No of static sink 1
Deployment Model Random
The initial node deployment in the designated area according to the parameters given in Table.1 is
represented in Figure 3. Simulation is done by changing the parameters to prove the efficacy of
the proposed algorithm. Simulations exhibited that the Mobile Sink Assisted routing , the

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proposed one, is better than the SH routing to a great extend in terms of number of live nodes, the
average energy of the nodes and the packet delivery ratio within the simulation time allocated.
Figure 3. Initial node deployment
The cluster head selection scenario is represented in Figure 4.The formation of the cluster is done
following the LEACH protocol. Based on the probabilistic function followed in LEACH the
cluster heads are selected. They in turn broadcast this message to their neighbours. Based on the
received signal strength each node forms a part of the cluster. After this stage the sinks are made
mobile to different directions where they will collect the data from the respective cluster heads
when they reach near each cluster head. The mobile sinks aggregates all the information and
passes on to the static sink from where the user can access the information.
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Figure 4. Clustered sensor network

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The simulation results based on the parameters number of live nodes, the average energy of each
node and the packet delivery ratio for the proposed mobile sink assisted algorithm and SH
algorithm is given below in Figure 5, Figure 6 and Figure 7 respectively.
Figure 5. Life time
From the above figure it is evident that the number of live nodes drops to zero before the expiry
of the simulation time in the case of SH. But in the case of mobile sink assisted almost double the
nodes are alive during the simulation period which shows that the proposed algorithm have
shown more than 50% improvement based on the number of live nodes i.e. the life time of the
network. Figure 6 represents the average energy of the sensor nodes.

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Figure 6. Average energy
It is evident that the proposed algorithm gives more than 10% improvement in the average energy
level of the nodes compared to SH which in turn will lead to a better lifetime. Same way in the
case of packet delivery ratio also a huge difference in the two approaches is clearly evident from
the simulation result.
Figure 7. Packet delivery ratio
5. CONCLUSION
We are aware that energy efficiency is always a critical concern in wireless sensor networks even
though lot of work related to this exists. Mobility of the nodes is a major issue in this field as the
mobility can be of that of the sensor nodes or of the sinks. The proposed mobile sink assisted
algorithm makes the sinks mobile so as to reduce the number of hops to the destination thereby
reducing the energy consumption. The duty cycling introduced makes only specific nodes alive
and all others to sleep mode which in turn will reduce the energy consumption. The proposed
algorithm proves to have better life time compared to SH which is evident from the simulation
results. The average energy of the nodes also is higher compared to SH. So it can be concluded
that the proposed algorithm gives better life time and hence efficient than SH.
The future enhancement of this algorithm which is underway is to change the mobility structure
of the mobile sinks along with the cluster formation strategy and comparing the results for other
matrices like delay and throughput .Further the proposed mobile sink assisted algorithm will be
compared with the existing energy efficient routing protocols to sturdily emphasize the efficiency
of the proposed one.
6. ACKNOWLEDGMENTS

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I sincerely thank Dr. G Sadashivappa for the inspiration rendered throughout. He is always
dynamic and helpful enough to share his valuable expertise, knowledge and experience to make
this research work a fruitful one. I thank my family and friends for their constant support and
motivation.
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Authors
Ms.Deepa V. Jose is currently pursuing Ph.D. in Computer Science from Christ University
Bangalore. Her research interests are in the field of Wireless sensor networks and
Cryptography.
Dr.G Sadashivappa have more than twenty five years of experience in teaching research and
industry. His research interests include Fibre optic communication, Signal Processing and
Applications.

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