Abstract— sensor nodes and cluster heads. A sensor

Abstract— The area of wireless sensor network (WSN) is
shrouded with the issues of routing protocol that has been witnessed in the
last few decade in the research community. One of the direct influences of
routing protocol is the performance of data aggregation in WSN. This paper
presents a novel routing protocol that performs a cost effective, reliable and
robust routing mechanism in wireless sensor network with its highly
compatibility with in-network data aggregation mechanism. A simulation study is
performed Matlab, where along with proposed routing algorithm, two more
conventional algorithm is chosen to perform the performance comparative
analysis. The performance parameters selected for simulation study shows that
the proposed routing protocol is robust against the conventional routing
protocol with respect to efficiency and data delivery phenomenon.
Keywords-component; In-network Data Aggregation, Routing Protocol, Wireless
Sensor Network I. INTRODUCTION A wireless sensor network consists of a sensor
nodes that has the perceptibility characteristics for some of the physical
attributes e.g. heat, motion, pressure, smoke, moisture etc 1. A sensor node
can be termed as very small devices which has very low computational capability
along with less availability of resources 2. Here resources mainly mean
energy, bandwidth, and memory. The prime operation that are performed by the
sensor node is called as data aggregation 3, which is a process where the
sensor node collects the physical information from the surrounding area and
forwards it to the base station. Normally, such forms of data aggregation
occurs using clustering. Each cluster consists of a specific number of sensor
nodes and cluster heads. A sensor node (which is also called as candidate
member node) is responsible for capturing the raw data from the surrounding
area to encapsulate certain events 4. This collected data are then forwarded
to cluster head by removing the redundancies. However, it doesn’t happen so
easily. In wireless sensor network, there are various forms of routing protocol
that are responsible for making the communication happens 567. These
routing protocols make the decision whether to forward the aggregated data to
the base station or to some other cluster head. The prime reason behind this is
to remove data redundancies in order to avoid overhead towards the base
station. These mechanisms indirectly conserve a significant amount of battery
lifetime of a sensor node. The biggest challenge is there are very few and
standardized energy-efficient routing protocol in wireless sensor network.
According to the theory, standard books, and standard research journals, the
frequently used standard routing protocol that has the supportability of energy
efficiency are LEACH, PEGASIS, TEEN, and APTEEN 8. These routing protocols
are said to significantly conserve energy on the defined test-bed. Although all
of them are has started it is based from efficient clustering, it significant
saves energy. Another interesting observation that we have made is that 98% of
the research papers that deals with energy-efficient routing considers LEACH,
whereas in reality there are many variants of LEACH as well as other energy
aware routing techniques. LEACH is the one of the standard hierarchical routing
protocol that was tested on Berkley nodes considering the 1st order radio
energy model 9 designed using core RF antenna circuitry system. This will
mean that LEACH is tested in real-time, whereas the original versions of other
routing protocols were basically tested from simulation viewpoint. Even the
recent variants of LEACH were also enhanced from soft computational viewpoint
and were validated only empirically and not experimentally. This is the prime
reason, why 98% of the research manuscript chooses to compare their energy
efficient techniques with LEACH. Due to such differences, many new algorithms
have been proposed for the routing problem in WSNs. These routing mechanisms
have taken into consideration the inherent features of WSNs along with the
application and architecture requirements. The problem definition of the
proposed system can be stated as-“It is quite a computationally challenging
task to design a framework that can ensure energy efficient and reliable
routing protocol that can accomplish superlative innetwork data aggregation in
wireless sensor network.” The discussed problem is basically intended to be
mitigated by the proposed system. This paper proposes an energy efficient
scheme called as REEDA that is essentially meant for controlling unwanted
energy dissipation for large scale wireless sensor network. The study has
focused on achieving energy efficient in-network data aggregation to show
better performance in network lifetime. Section II discusses about some of the
relevant literatures pertaining to energy efficient routing followed by
discussion on proposed system on Section III. Algorithm implementations are
discussed in Section IV while result outcomes were discussed in Section V.
Finally, summary of the paper is made in Section VI.

II. RELATED WORK This section presents the prior research
work that has been carried out in the area of energy-efficient routing and data
aggregation in wireless sensor network. Our prior work has reviewed some of the
existing issues in wireless sensor network along with different approaches of
energy conservations 10. Our recent work has introduced a technique called as
MLO i.e. Multi-Level Optimization that has significantly enhanced network
lifetime for large scale wireless sensor network 11. Jian et al. 12 have
developed an optimization technique based on bio-inspired algorithms. The
technique was mainly meant for enhancing the lifetime of radar sensors.
Enhanced version of ant colony optimization was used in the design of the
proposed study. The outcome of the proposed study was compared with LEACH
algorithm to find the technique better with respect to energy conservation.
Similar direction of the study was also carried out by Mao et al. 13, where
the authors have focused on developing energy efficient routing for
heterogeneous sensor network. The protocol design was carried out in GloMoSim
and is found to have slight improvement of existing routing techniques in those
times with respect to energy efficiency. Masazade et al. 14 have discussed a
technique that uses Cramer-Rao bound rule along with Monte Carlo mechanism for
solving localization problems. Interestingly, the entire focus of the study was
mainly to retain maximum energy of the nodes. Srivastava et al. 15 have
proposed a mechanism that can perform controlling of the transmitter node for
effective scheduling of the data packets. The design of the system model is
completely based on the control information using a unique feedback scheme that
gives the response of energy required to perform data aggregation. The outcome
of the study was found to be energy efficient however, it was done without
benchmarking. Wang et al. 16 have presented a both decentralized as well as
centralized scheme for scheduling the routing channels of the sensors considering
the mobility factor. The entire stress of the study was to achieve energy
efficiency in the mobility condition. The outcome of the study was evaluated
using number of alive nodes and energy mainly and it shows good minimization of
energy. The study carried out by Wang et al. 17 has addresses the ambiguity
factor in the energy dissipation for long term utility in energy harvesting of
wireless sensor network. The design was built using ZigBee module, MCU Atmega
microcontroller, Battery Voltage monitor, ADC etc.

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The uniqueness of this study is the discussion of power
expenditure profiling system that is found to quite helpful even in other
research work too. It assists to understand the relationship between energy
consumption and data transmission. Dziengel et al. 18 have presented another
unique study where a distributing surveillance method is presented with focus
on energy efficiency accomplishment. The technique is totally hardware based
approach. The outcome of the study basically shows the detection rate and
impact of data transmission over energy. Liu et al. 19 have discussed a
technique for addressing energy depletion in wireless sensor network. The
schema presented by the author has mainly three essential components i.e. i)
reward computation, ii) punishment computation, and iii) decision making as
shown in Fig.2. Figure 2 Schema presented by Liu et al. 19 The study towards
energy efficiency was also seen in the work of Rezai 20 and Jain et al. 21.
An analytical modelling is presented in these studies where Adhoc routing
protocols were seen to be used in wireless sensor network in order to ensure
energy efficiency. It is to be noted that Adhoc routing protocols were mainly
implemented in mobile Adhoc network. Most recently, Jorio et al. 22 have
presented a study that focuses jointly on clustering, routing, and energy
efficiency. The study has introduced a new hierarchical routing technique just
like LEACH in order to conserve energy while performing clustering. The study
has established an empirical relationship between clustering and energy
efficiency which are directly connected to each other. The study says if
efficient clustering is performed, energy is significantly conserved or else it
drains and drastically minimizes the network lifetime of wireless sensor
networks. Hence, it can be seen that there are massive archives in the
literatures that deals with energy efficiency. Each technique has its own
advantage factor as well as limiting factor. However, we find that less focus
is laid towards innetwork data aggregation energy efficiency while performing
routing. Hence, the presented paper is a continuation of the research work
where the emphasis is laid over attaining more energy conservation with some
robust benchmarking aspects in wireless sensor network.       

III. PROPOSED SYSTEM The prime goal of the study is to
design a reliable and efficient routing protocol that can perform energy
efficient data aggregation considering in-network approach in wireless sensor
network. Therefore the proposed system is termed as REEDA i.e. routing with
Energy Efficiency in Data Aggregation. In order to accomplish the discussed
goal, following objectives were ascertained, • To perform an in-depth
investigation of the prior literatures that has attempted to solve the similar
issues in order to understand the effectiveness of the techniques used. • To
design a simulation test bed that can perform the simulation using the
cluster-based approach considering indicative simulation parameters. • To
design an election method of cluster head for performing in-network data
aggregation. • To apply shortest path algorithm for route generation. • To
implement an algorithm for maximizing the aggregation point and to use lesser
number of control packets to build the routing tree. The system architecture of
REEDA is shown in Fig.3. The design of the architecture uses a coworker node
that is responsible for identifying any significant events as well as transmits
the aggregated data to the controller node. The controller node is responsible
for gathering the aggregated data and forward to the base station. The design
of REEDA also consists of a relay node which represents the sensor node
transmitting the data to the base station. A Steiner tree is designed that
essentially performs mapping of the operational behavior of in-network data
aggregation along with some of the significant functionalities e.g. Designing
of Graph model, clusters design, generation of stabilized routes, and
rectification of unstabilized routes. The next section discusses about the
techniques applied for designing the proposed REEDA architecture. IV.
IMPLEMENTATION TECHNIQUES The implementation of the proposed study of REEDA is
carried out over Matlab. In order to accomplish the design requirements of REEDA,
following implementation steps were carried out: • Designing of Graph Model: A
spatial-based approach is applied for computing the distance from the base
station to all the sensor nodes. The base station forwards a control message
that is essentially consisted of node identifier and distance. A graph using
treebased approach is used that maps various hops to be formulated. The base
stations than forwards a control message with value 1 and with the lowest
energy. After receiving this message, the sensors reconfigure their value as 1.
This phenomenon happens in first event. However, in second event, the base
station increases the value to be 2 for the control message, which is also
updated by the sensor nodes receiving it. A hello message consisting of maximum
and minimum limit of each value is set and each sensor estimates its
Euclidean’s distance from the sink using received signal strength. Thereby a
specific graph model is formulated with hierarchical edges as well as vertices
to support the in-network data aggregation. • Cluster Design: After a
significant event is detected in the simulation area, all the corresponding
candidate as well as cluster head will actively participate in innetwork data
aggregation process. Exclusively for the generation of the initial event, the
selection of the cluster head is done on the basis that candidate sensor node
should be in the nearest distance from the base station. However, in case of
similar distance, the selection of the cluster head will be carried out on the basis
of the node bearing smallest identifier. In the ultimate cycle of the cluster
head selection process, the system will select only one candidate node to be
eligible as cluster head. The cluster head will now act as controller node
while the other nodes that are not selected as cluster head will act as
coworker node. The controller node will aggregate all the significant
information from the coworker node and will forward them to the base station.
The phenomenon significant reduces the work load of cluster and thereby
conserves significant amount of energy required in data aggregation in WSN
(Fig.4 and Fig.5). • Generation of Stabilized Routes: We define stabilized
routes are a route that is established between the nodes with sufficient
cut-off residual energy. The cut-off energy values can be different for
different applications and thereby we don’t discrete discuss about the cut-off
value. Our range is fixed around 0.3 Joule for cut-off energy value. The simple
algorithm is used for performing data transmission as exhibited in Fig.6. The
cluster head initiates formulating new communication channel for the purpose of
disseminating events. In this situation, the controller node transmits a
control message for generating a communication channel to its immediate edges
in the Fig.10 shows that LEACH has sharp fall of residual energy owing to the
centrality of the base station position. Intanagonwiwat et al. 23 approach
was found better than LEACH as it supports directed diffusion avoiding much
data redundancies and thereby conserves more power. But cumulatively, REEDA has
outperforms both Intanagonwiwat et al. 23 and LEACH. The prime issue of
Intanagonwiwat et al. 23 approach was that frequent dissemination of data for
every generation in the simulation area in order to update the other nodes.
This action required additional energy while performing routing and therefore
this charecteristics restricts the energy efficient performance of the
Intanagonwiwat et al. 23 approach. The proposed REEDA uses identification of
the unstabilized routes and then it performs rectification of unstabilized
routes and substitutes it by exploring more robust route. Moreover REEDA has no
dependency on the position of the base station making it more flexible to
support better energy efficient routing. The outcome of the study was
hypothetically compared with EHE-LEACH too 24, which has supportability of
energy efficiency for heterogeneous sensor network also. The outcome shown in
REEDA excels better in comparison to Intanagonwiwat et al. 23, LEACH 9,and
EHE-LEACH 24 with respect to throughput, energy efficiency, and processing
time. VI. CONCLUSION This paper discusses about an energy efficient routing
technique that is essentially focused on achieving better innetwork data
aggregation. The proposed system has introduced different type of
characteristic behavior of the sensor nodes in order to reduce the load of
cluster head during data aggregation in order to retain more amount of energy.
The proposed system is also capable of identifying the unstabilized routes and
replaces the unstabilized routes with more energy efficient routes. The
emphasis of the proposed study was purely on energy-efficiency, however, it is
equally important to understand the necessary impact of other parameters e.g.
bandwidth, QoS parameters e.g. propagation delay, etc on energy effectiveness
too. Our future work will be to further consider these parameters for
increasing the scope of outcomes on energy effectiveness. We are also planning
to implement a novel design of energy optimization considering the physical
level of the sensor network, which is quite challenging to achieve till date.
We will also investigate the possible applicability of energy efficiency on
existing security protocols frequently in used. As we strongly believe the
security protocols do have higher consumption of energy, hence, it is important
to ensure that our future work should address all these issues.



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