1. for sensing the data. Figure 1.2



We Will Write a Custom Essay Specifically
For You For Only $13.90/page!

order now

Wireless sensor networks(WSNs) consists of large number of sensor nodes which are low power, small in size with limited energy and computational power 1.These sensor nodes can sense, measure, and gather information from the environment and transmit the sensed data to the base station. Smart sensor nodes are low power devices equipped with one or more sensors, a processor, memory, a power supply, a radio, and an actuator. Wireless sensor networks have several applications like process management, Area monitoring, Health care monitoring, Environmental/Earth sensing, Air pollution monitoring, Forest fire detection, Landslide detection, Water quality monitoring, Natural disaster prevention, Industrial monitoring, Machine health monitoring, Data logging, Water/Waste water monitoring, Structural Health Monitoring.


Wireless sensor network nodes are deployed over a large geographic area for sensing the data. Figure 1.2 shows a WSN having of sensor nodes and a sink. Wireless sensor network produces a large amount of data or information that is needed to be routed. These information passes through multiple hops to reach the sink.

Figure 1.2 Sensor Network Architecture
The architecture of node in wireless network has four components:
Sensing subsystem: It consisting of many sensors for acquiring data.

Processing subsystem: It consists of memory and a microcontroller for processing of data.

Radio subsystem: it is used for communication.

A power source.

Sensor node may include additional components, depending upon some specific applications:
To determine a position, a location finding system is used.

A mobilizer to change their location and configuration.


Energy harvesting sensor (EHS) nodes provide an attractive and green solution to the problem of limited battery life of wireless sensor networks (WSNs). Unlike a conventional node
that uses a non-rechargeable battery and dies once it runs out of energy, an EHS node can harvest energy from the environment like solar power, thermal energy, wind energy, salinity gradients, and kinetic energy and replenish its rechargeable battery.


Each EH-WSN node uses one or more energy harvesting devices to harvest
environmental energy. The EH-WSN is composed by different components as
it is shown in Figure 1.4. As we know, the energy needed for sensors should be electrical. So the first is to convert environmental energy to electrical one. Inside the node there are different components for sensing the area, collecting data and processing the data. So the part in this kind of nodes which is different when compared to WSN nodes, is the power part. Once the stored energy reach to a certain amount, the power supply for micro controller and transceiver will start to work. They will continue to work until they have energy, as soon as the energy finish, they stop and energy storage device start to save energy again.

Figure 1.4: Components of EH-WSN node 2
The other fact should be consider about EH-WSN node is that the available energy will be different in different nodes. So every node has its own harvesting rate, some nodes may have high energy and other may have low energy. Hence routing should be done in an energy efficient way. This issue rises the need for designing optimized energy routing algorithms


The flexibility, high sensing fidelity, low-cost and rapid deployment characteristics of sensor networks create many new and exciting application areas for remote sensing1. Applications include, but are not limited to, environmental monitoring, industrial machine monitoring, surveillance systems, and military target tracking. Each application differs in features and requirements. To support this diversity of applications, the development of new communication protocols, algorithms, designs, and services are needed 3. The different issues in wireless sensor networks are,
Energy efficiency criterion there is a need to discover different routing techniques to eliminate energy inefficiencies that may shorten the lifetime of the network.

Routing Protocols should incorporate multi-path design technique.

Fault tolerance is another desirable property for routing protocols. Routing protocols should be able to find a new path at the network layer even if some nodes fail or get blocked due to some environmental interference.

To maximize energy savings there is need to provide a flexible platform for performing routing and data management.

The routing protocol should exploit such redundancy to improve energy and bandwidth utilization.

Routing Protocols should take care of heterogeneous nature of the nodes i.e. each node will be different in terms of computation, communication and power 4.

To overcome the issues different routing algorithms have been designed.


Routing protocols for wireless sensor networks are responsible for maintaining the routes in the network which work under the limited resources available for sensor nodes. LEACH5 is one of the most popular hierarchical routing protocol for wireless sensor networks. It is completely distributed and it does not require global knowledge of network. LEACH uses single-hop routing where each node can transmit directly to the cluster-head and the sink. Therefore, it is not applicable in networks which are deployed in large regions. Furthermore, the idea of dynamic clustering brings extra overhead, e.g. cluster head changes, advertisements etc., which may cause the increase in energy consumption.

PEGASIS 6 outperforms LEACH by eliminating the overhead of dynamic cluster formation, minimizing the distance, non leader-nodes must transmit, limiting the number of transmissions and receival among all nodes, and using only one transmission to the BS per round. Nodes take turns to transmit the fused data to the BS to balance the energy depletion in the network and preserves robustness of the sensor web as nodes die at random locations. PEGASIS use multi-hop routing by forming chains and selection only one node to transmit to the base station instead of using multiple nodes. However, PEGASIS introduces excessive delay for distant node on the chain. As well as bottleneck can occur in presence of only single leader.

H-HEED 7 has significant improvement in the life time in comparison with HEED protocol. H-HEED sense more effective data packets to the base station. Factors like noise, Physical obstacles and collision may affect the received power are ignored. When compared to multi-level H-HEED protocols the performs level is low in other level of HEED i.e., H-HEED and HEED protocol.

There are other routing distributed algorithms like DEBR AND ESCFR 8,9 which balance the energy consumption and extend the network lifetime. Although, the DEBR balances the Energy consumption very well, but a node may select a next hop node in opposite direction of the BS and the selected next hop node can do the same. Thus, it can unnecessarily increase the delay in transmitting the data to the BS. Another disadvantage of DEBR is that for balancing energy consumption it may select a node which has no next-hop node within its communication range and hence data packets cannot be reached to the BS.
A self-clustering method for heterogeneous network using Genetic Algorithm that optimizes the network life is proposed in 10. An energy efficient region-based hybrid routing protocol under distance, energy and load parameter have been proposed in 11, which minimizes the energy consumption of sensors in every round and further increases the throughput and lifetime of network. An overview of the design objectives of Wireless Sensor Networks is given in 12. And some of the design objectives are identified and compared with Routing protocols. Frequent modification on node placement requires more energy consumption. It is identified that the efficient energy utilization and optimal path can be achieved by hierarchical routing algorithms while comparing with other routing algorithms.

A new maximum lifetime routing algorithm based on uneven clustering, linear programming and fuzzy set theory is proposed in 13. The objective of these routing protocols is either minimizing the energy consumption or maximizing the network lifetime.


A new observation related to energy aware routing is the availability of the so-called energy scavengers which are devices able to harvest energy from ambient sources such as solar, wind, water etc 14. Harvesting energy for low-power devices like wireless sensors presents a new challenge as the energy harvesting device has to be comparable in size (i.e. small enough) with the sensors. There are complex tradeoffs to be considered when designing energy harvesting
circuits for WSNs arising from the interaction of various factors like the characteristics of the energy sources, energy storage device(s) used, power management functionality of the nodes and protocols, and the applications requirements. The change of power supply calls for a different version of network protocol for routing.


Energy replenishment in EH-WSN is uneven so data should be routed in efficient way which require efficient routing algorithms. Algorithms proposed in 15232 are extension of LEACH5 which extend LEACH and make it suitable for EH-WSNs. Solar-aware Wireless Sensor Network are proposed in1516. 15 utilizes solar power in wireless sensor networks and extend LEACH a well-known cluster-based protocol for sensor networks to become solar-aware. Voigt, et al. 16 designed two solar-aware routing protocols that preferably route packets via solar powered nodes and showed that the routing protocols provide significant energy savings.

A distributed framework for the sensor network to adaptively learn its energy environment was presented in 17. An example study of routing is showed that the proposed framework is able to utilize the extra knowledge about the environment to increase system lifetime. The optimal paths calculated in 1617 is based on each node having global knowledge of the whole network, which is usually inapplicable in WSNs. In 18, two geographic routing protocols are proposed in the presence of lossy wireless channel and energy renewable nodes. A forwarding node is selected based on its location, residual energy, and potential energy harvesting rate. A model to characterize the performance of multihop radio networks in the presence of energy constraints and design routing algorithms to optimally utilize the available energy is developed in19. The proposed algorithm is shown to achieve a competitive ratio that is asymptotically optimal with respect to the number of nodes in the network. A new threshold-based scheme is proposed to reduce the routing overhead while incurring only minimum performance degradation.

In 20 Battery estimation is presented to determine the current amount of charge units present in the battery , which can be used to determine optimized paths and control to maximize network lifetime. Three state-of-the-art routing algorithms for energy harvesting wireless sensor network are analyzed and compared in 21. Routing protocol taking into account three factors, transmission quality, energy consumption for data transfer and wasted energy is proposed in22 . When different weights are assigned to the three factors and a new route will be selected.
Energy Potential Function which is utilized to measure the node’s capability of energy harvesting is proposed23 , it extends LEACH to Energy Potential LEACH which is suitable for energy harvesting WSNs. Adaptive Energy-Harvesting Aware Clustering (AEHAC) routing protocol2 for perpetual-operated EH-WSNs. The protocol elects cluster head distributed and combines with energy harvesting rate and the node residual energy making it more efficient for networks which are completely powered by EH-WS nodes.
While EHS nodes are attracting considerable interest, several new challenges need to be overcome before they can be widely deployed. First, the energy harvesting process can be sporadic. Second, an EHS node needs additional circuitry to harvest, store, and to provide a regulated supply of the harnessed energy to its battery or supercapacitor 25. Hence, EH nodes are likely to be more expensive than conventional nodes, which come equipped with pre-charged, non-rechargeable batteries. Given the above challenges, hybrid WSNs, which comprise of a mixture of EHS nodes and conventional nodes, are likely. Up gradation of the legacy WSNs, in which conventional nodes are gradually replaced by EHS nodes, also naturally leads to hybrid WSNs. However, relatively less research has been done on hybrid WSNs. In the hybrid WSN considered in 26, the EH functionality is used only to relay information from a cluster head to the sink. A performance metric called k-outrage duration is introduced in 27 to compare hybrid and conventional WSNs. Analysis show that as the number of EHS nodes increases, the k-outrage duration increases more rapidly, while as the number of conventional nodes increases the k-outage duration begins to saturate.

Although recent developments in energy harvesting can be employed to replenish the power supply of sensor networks, energy management is still an important issue since the replenishing rates are typically small and the stochastic energy renewal process only provides a partial solution to the aggressive power demands of a large-scale network. Hence there is a need to develop routing algorithms which are energy efficient.


EHS are sporadic which makes energy management still a major issue. Therefore, reducing energy consumption of sensor nodes is very important. In order to be energy efficient, the algorithm is supposed to route the sensed data via other sensor nodes which are closer to the base station or on some energy efficient path. But this arrangement of data routing can lead the sensor nodes of the energy efficient path die quickly for frequently used as relay node. This uneven energy consumption dramatically reduces the network lifetime and decreases the coverage ratio.
To solve above problem there is need to route a network in such a way that the network remains balanced. In the proposed system we consider a hybrid network consisting both conventional WSN nodes and EH-WSN nodes. Sensor nodes select the next-hop relay nodes in such a way that it requires less energy and also takes care of balancing the energy consumption. The next hop relay node is selected by considering the cost function, which consists of three parameters residual energy, energy density and distance to receiver. A sensor node sj chooses its next hop relay node as sk by considering the cost function as in the following equation,
Cost(s j, sk) = EDensity(sk) × EResidual(sk)
dis(s j, sk)
Sensor node sj selects the node with maximum cost function as the next hop relay node. The above cost function efficiently addresses both the issues of energy balancing as well as energy consumption of the network. The first and second parameters (EDensity(sk) and EResidual(sk)) of the cost function, take care of the energy balancing of the network. The third parameter (dis(s j, sk)) tries to reduce the energy consumption of the network. Thereby, it helps the routing algorithm to be an efficient one.

For the EH-WSN nodes we assume that, once node energy falls below Ethreshold it replenishes itself immediately. But if a conventional WSN node energy falls below threshold value then network will be rerouted . EH-WSN nodes save the energy required in rerouting the network. This solution helps in efficient use of energy and also keeps the network balanced and hence helps in increasing the network lifetime.



We assume a hybrid model which consists of conventional WSN nodes and EH-WSN nodes where all the sensor nodes are deployed randomly and once they are deployed, they become stationary. All sensor nodes have same initial energy. Similar to 5, the data gathering operation is divided into rounds. In each round, all sensor nodes collect the local data and send it to the base station via other sensor nodes as a next hop relay node. First order radio model for energy is used as in 29 which considers the energy consumption in transmitting and receiving the data.


Routing is done by the sensor nodes by selecting the next-hop relay nodes in such a way that it requires less energy and also takes care of balancing the energy consumption. A sensor node may have many possible next-hop relay nodes within its communication range. However, in our approach only those neighbour sensor nodes are eligible to serve as a relay node which has lesser hop count. Now, a sensor node s j chooses the next hop relay node sk based on the cost function. We denote the cost function as Cost (sj,sk) which indicates the cost of selecting the sk as next hop relay node by the sj. To define the cost function, we consider the following parameters as shown in figure 4.1.

Energy density: The general principle behind energy balancing is that the data packets should move forward to the base station through the region of the network where sensor nodes have higher remaining energy. In order to achieve this objective we calculate energy density of a sensor node and the data packets are force to flow through the higher density region. Energy density is calculated as follows,
EDensity(sk)={EResidual(si), ?si ? Neighbor(sk) ? HC(si) ; HC(sk)} + EResidual(sk)

HC(si) is the hop count of si to the sink. The higher the EDensity(sk), the higher is the chance of getting selected as a relay node. Therefore, the cost function is proportional to the EDensity(sk).

Cost(s j, sk) ? EDensity(sk) ——-(1)
Residual energy: Although sk may have sufficient energy density due to comparably higher residual energy of its next-hop neighbors, it may have lesser remaining energy to serve as a relay node. In order to balance the energy consumption of the network, sk should have also sufficient residual energy. Higher the residual energy, the higher is the chance of getting selected as a relay node. Therefore, the cost function is proportional to the EResidual(sk). In other words,
Cost(s j, sk) ? EResidual(sk) ——- (2)
Distance to receiver: As s j consumes its energy due to long-haul transmission with sk, the distance between s j and sk should be lesser to reduce energy consumption of s j. The lesser the transmission distance, the higher is the chance of getting selected as a relay node. Therefore, the cost function is inversely proportional to the dis(s j, sk). In other words,
Cost(s j, sk) ? 1 ——- (3)
dis(s j, sk).

Combining equations (1),(2) and (3), we obtain cost function
Cost(s j, sk) ? EDensity(sk) × EResidual(sk)
dis(s j, sk)
s j selects the sk having maximum cost value as relay node.

The sensor nodes which are in communication range of the sink (base station) send the data directly to the base station and don’t take part in the relay node selection algorithm. 4.2 shows the proposed algorithm.

Distance to receiver.

Residual Energy.

Energy Density.

Next Hop Relay Node Selection.

Figure 4.1:Next hop relay node selection.

1. Start
2.if(dis(sj, sink) ? dmax) then // here dmax is the maximum range of the node
4.Nj= Neighbour(sj)
5.max=-1.0 // max is the maximum cost
6.while(Nj ? Ø) do
7. Select a neighbour sk from Nj.

8. if( cost(sj, sk) > max)
10.max=cost(sj, sk)
11.end if
12.end while
13.end if

4.2 Algorithm for next hop relay node selection.

When residual energy of a sensor node falls below threshold value Ethreshold the node is checked weather it is an energy harvesting node or a normal wireless sensor node. If the low energy node is conventional WSN node then network will be rerouted or if the low energy node is EH-WSN then it is replenished to Einitial( i.e. to initial energy). EH-WSN nodes save the energy required in rerouting the network. 4.3 represents the algorithm for replenishing.

2.if (replenish==1) then
3. if(NODE_ENERGYk< Ethreshold)then
4. NODE_ENERGYk = Einitial
5.if (replenish==0) then
6.if(NODE_ENERGYk< Ethreshold)then
8.end if

4.3 Algorithm for replenishment.


The performance of proposed algorithm is compared with existing routing algorithm EEBR28 by evaluating with different parameters. The table below shows the simulation parameters.

Parameters Values
No. of Nodes 50
Einiti (initial energy of each node) 2000J
TX_ENERGY(energy required for transmitting a packet) 2J
RX_ENERGY(energy required for receiving a packet 2J
Dmax (communication range) 100m
Packet size 512bytes
Table 5.1 Simulation parameters

A round can be considered as total time to form a route, followed by data sensing and transmitting the data to the base station.

Figure 5.1 illustrate the lowest energy node in the network after several rounds. The x axis comparison graph indicates the number of rounds and the y axis indicates the lowest energy node in the network. Lowest energy node of PROPOSED SYSTEM has higher energy than the lowest energy node of EEBR even after 200 rounds as PROPOSED SYSTEM transmits data through energy efficient path. EEBR the node energy falls very quickly as it don’t have EHS nodes and no recharging of energy takes place the battery energy is the only source of energy.

Figure 5.1 Lowest energy node Vs Number of rounds.

Figure.5.2 Energy balancing Vs Number of rounds.

Figure 5.2 illustrate the nodes available for energy balancing after several rounds. The x axis comparison graph indicates the number of rounds and the y axis indicates the no. of nodes available for energy balancing. PROPOSED SYSTEM makes higher number of nodes available for energy balancing since it considers energy density and residual energy of nodes while selecting the route.

Figure 5.3 Residual energy Vs Number of rounds.

Figure 5.3 illustrate the residual energy of the network after several rounds. The x axis comparison graph indicates the number of rounds and the y axis indicates the residual energy in the network. Here lifetime parameter residual energy of the network in considered. PROPOSED SYSTEM has higher residual energy in the network even after 180 rounds. The residual energy of the network in PROPOSED SYSTEM doesn’t reduce quickly as it consists of EHS nodes in it and data is transmitted through energy efficient and energy balanced path. In EEBR the residual energy of the network falls very quickly after 36 rounds as it doesn’t have EHS nodes in it and the battery energy is the only source. EEBR has 1941J of energy after 36rounds and PROPOSED algorithm has same energy after 180rounds which shows that the PROPOSED SYSTEM increases the lifetime of the network.

From the above comparisons it is proved that PROPOSED SYSTEM works more efficiently than existing routing algorithm EEBR28 as it is a hybrid network which consists of EHS nodes and conventional WSN nodes and transmits data through energy efficient and balanced path. And hence increase the performance and the network lifetime.


The proposed algorithm considers that when a EH-WSN node’s energy falls below threshold it recharges itself immediately. But energy harvesting nodes are sporadic and the harvesting rate varies from node to node which should be considered. In future work sensor capabilities of harvesting energy from the environment and their capacities of storing energy will be taken into consideration and a routing algorithm will be designed . The algorithm could also be extended towards efficient utilization of sporadic and opportunistic energy availability in a performance aware manner.

1 I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci “Wireless sensor networks: A survey,” Computer Networks., vol. 38, pp. 393–422, Mar. 2002.

2 Jian Meng, Xuedan Zhang, Yuhan Dong, and Xiaokang Lin “Adaptive Energy-Harvesting Aware Clustering Routing Protocol for Wireless Sensor Networks”2012 7th International ICST Conference on Communications and Networking in China (CHINACOM)IEEE,pp.742-747,2012.

3 Jennifer Yick, Biswanath Mukherjee, Dipak Ghosal “Wireless sensor networks survey,” Computer Networks., vol. 52, pp. 2292–2330, 2008.

4 Gowrishankar.S , T.G.Basavaraju, Manjaiah D.H, Subir Kumar Sarkar Ghosal “Issues in wireless sensor networks ,” World Congress on Engineering., vol. 1, 2008.

5 Savita Lonare, Gayatri Wahane “A Survey on energy efficient routing protocols in wireless sensor network,” 4th ICCCNT,IEEE., 2013.

6 Stephanie Lindsey, Cauligi S. Raghavendra “PEGASIS: Power-Efficient Gathering in Sensor
Information Systems,” in Proc. IEEE Aerospace Conf., vol. 3, pp. 1125–1130, 2002.

7 Harneet Kaur, Ajay K.Sharma, “Hybrid Energy Efficient Distributed Protocol for
Heterogeneous Wireless Sensor Network,” International Journal of Computer Applications.,
Vol. 4, No.6, July 2010.

8 Chang-Soo Ok, Seokcheon Lee, Prasenjit Mitra, Soundar Kumara “Distributed energy balanced routing for wireless sensor networks” Computers and Industrial Engineering., vol. 57, pp. 125-135, 2009.

9 A. Liu, J. Ren, X. Li, Z. Chen, and X. S. Shen, “Design principles and improvement of cost function based energy aware routing algorithms for wireless sensor networks,” Computer Networks, vol. 56, no. 7, pp. 1951–1967, 2012.

10 Mohamed Elhoseny, Xiaohui Yuan, Zhengtao Yu, Cunli Mao, Hamdy K. El-Minir, and Alaa Mohamed Riad “Balancing Energy Consumption in Heterogeneous Wireless Sensor Networks using Genetic Algorithm,” IEEE Communications letters., 2014.

11 Sonam Maurya and A.K.Daniel “An Energy Efficient Routing Protocol Under Distance, Energy and Load Parameter for Heterogeneous Wireless Sensor Networks,” IEEE, International Conference on Information Technology., pp.161-166, 2014.

12 TamilSelviGnanasekaran, Sharmila Anand “Comparative Analysis on Routing Protocols in Wireless Sensor Networks,” IEEE, International conference on computer communication and informatics., 2014.

13 Jianhui Li, Guiping Liao, Fang Wang, Jinwei Li “Maximum Lifetime Routing Based on
Fuzzy Set Theory in Wireless Sensor Networks,” Academy publisher., pp. 2321-2328, 2013.

14 Winston K.G. Seah, Zhi Ang Eu, Hwee-Pink Tan,” Wireless Sensor Networks Powered by Ambient Energy Harvesting (WSN-HEAP) – Survey and challenges,” Wireless VITAE IEEE, pp. 1-5, 2009.

15 T. Voigt, A. Dunkels, J. Alonso, H. Ritter, and J. Schiller, “Solar-aware clustering in wireless sensor networks,” Computers and Communications, 2004. Proceedings. ISCC 2004. Ninth International Symposium, vol. 1, pp. 238 – 243, 2004.

16 T. Voigt, H. Ritter, and J. Schiller, “Utilizing Solar Power in Wireless Sensor Networks,” in Proc. IEEE LCN, 2003, pp. 416–422.

17 A. Kansal and M. B. Srivastava, “An environmental energy harvesting framework for sensor networks,” International symposium on Low power electronics and design, pp. 481–486, 2003.

18 K. Zeng, K. Ren, W. Lou, and P. J. Moran, “Energy Aware Efficient Geographic Routing in Lossy Wireless Sensor Networks with Environmental Energy Supply,” Wireless Networks, vol. 15, no. 1, pp. 39–51, 2009.

19 L. Lin, N. B. Shroff and R. Srikant, “Asymptotically optimal energy-aware routing for multihop wireless networks with renewable energy sources,” IEEE/ACM Transactions on Networking, vol. 15, pp. 1021-1034, 2007.

20 Torregoza, John Paul M.; Kong, In-Yeup; Hwang, Won-Joo , “Wireless Sensor Network Renewable Energy Source Life Estimation,” IEEE, First International Conference on Communications and Electronics ,2006.

21 David Hasenfratz, Andreas Meier, Clemens Moser, Jian-Jia Chen, and Lothar Thiele, ” Analysis, comparison, and optimization of routing protocols for energy harvesting wireless sensor networks,” In Sensor Networks, Ubiquitous, and Trustworthy Computing (SUTC), IEEE International Conference , pp. 19-26, 2010.

22 Rong Cui, Zhaowei Qu, Sixing Yin,” Energy-efficient Routing Protocol For Energy Harvesting Wireless Sensor Network, “15th IEEE International Conference on Communication Technology (ICCT) ,pp. 500-504,2013.

23 Meng Xiao, Xuedan Zhang, Yuhan Dong, ” An Effective Routing Protocol for Energy
Harvesting Wireless Sensor Networks,” IEEE Wireless Communications and Networking Conference (WCNC),pp. 2080-2084, 2013.

24 Zahid Kausar, A.S.M. Reza, Ahmed Wasif Saleh, Mashad Uddin Ra, ” Energizing wireless sensor networks by energy harvesting systems: Scopes, challenges and approaches,” Renewable and Sustainable Energy Reviews Volume 38 , pp. 973-989, 2014.

25 S. Sudevalayam and P. Kulkarni, “Energy harvesting sensor nodes: Survey
and implications,” IEEE Commun. Surveys Tuts., vol. 13, pp. 443–461, quarter 2011.

26 P. Zhang, G. Xiao, and H.-P. Tan, “A preliminary study on lifetime maximization
in clustered wireless sensor networks with energy harvesting nodes,” in Proc. ICICS, pp. 1–5, Dec. 2011.

27 Shilpa Rao and Neelesh B. Mehta, “Hybrid Energy Harvesting Wireless Systems:
Performance Evaluation and Benchmarking,” IEEE Wireless Communication and Networking Conference, pp. 4244-4249,2013.

28 Deepika Singh, Pratyay Kuila, Prasanta K. Jana, “A Distributed Energy Efficient and Energy Balanced Routing Algorithm for Wireless Sensor Networks,” ICACCI,IEEE , pp.1657-1663, 2014.

29 Heydar, Mehdi, Dehgan, Mohsan, “BN-LEACH: An Improvement on LEACH Protocol
using Bayesian Networks for Energy Consumption Reduction in Wireless Sensor Networks,”
IST,IEEE, pp. 1138-1143,2014.


The objectives of the study are as under:
• To study the impact of leverages on ROA (Return On Asset) and EPS (Earning Per Share).
a) To study the impact of operational leverage on ROA & EPS.
b) To study the impact of combined leverage on ROA &EPS.
c) To study the impact of financial leverage on ROA & EPS.
• To ascertain the effect of adjustments in those leverages on return to equity of share holders.
• To assess the impact on profitability.

We Will Write a Custom Essay Specifically
For You For Only $13.90/page!

order now

• To analyze the overall profitability of the firm.
• To know the financial position or growth of the firm.
• We need to calculate capital structure.
• To understand the debt effect of the firm.
• To understand the meaning, significance and limitation of the leverage analysis.
• To calculate financial, operating, combined leverages of the organization.
• To know how debt and profitability are related.
• Positives and negatives effects on profitability.
The information for the study has been obtained from two sources viz., Primary and Secondary sources. The primary data are obtained from the discussions with the company executives and employees. Personal observation also helped in extracting the data. Interviews have been conducted with the company officials after taking prior permission from them. The secondary data are obtained from the company reports, records, manuals and bulletins also business magazines, text books and web sources have been used in obtaining the relevant data
And using various statistical tools to analyze and get the desired output which will be a fruitful result for the project I have used CORRELATION, REGRESSION, And DESCRIPTIVE STATISTICS and found out the relation between the dependent and independent variables.
Secondary data studies whole company records and company’s balance sheet in which the project work has been done. In addition, a number of reference books, journals and report were also used to formulate the theoretical model for the study. And some information was also drawn from the websites. The data is collected from the following sources:
• Financial statements referred from annual report of Srujana Elite Engineering Solutions Pvt Ltd.
• The data is collected from NSIC.
• Various books preferred about leverages.
The data is collected directly from the company. Primary data is the first hand information that is collected during the period of research. Primary data has been collected through discussions held with the staffs in the accounts department. Some types of information were gathered through oral conversations with the cashier.

The project report entitled “Debt Effect on Profitability” in Srujana Elite Engineering Solutions Pvt Ltd, Hyderabad, is organized in five chapters.

1. The First chapter gives the theoretical aspects of capital structure and financial leverage and contains objectives, methodology, frame work and limitations of the study.
2. The Second chapter comprises the profile of engineering industry in India and the profile of NSIC and Srujana Elite Engineering Solutions Pvt Ltd.
3. The Third chapter deals with review of literature.
4. The Fourth chapter carries the analysis of the debt effect on profitability.
5. The Last chapter concludes the study with findings and some suggestive measures for better performance.


• The present study is confined to Srujana Elite Engineering Solutions Pvt Ltd.
• The major limitation of the project under study was time. Since it was to be completed within a short period of time, a comprehensive study could not be made.
• The analysis was restricted to the data available in the balance sheet of Srujana Elite Engineering Solutions Pvt Ltd from the year 2012-2017.

Capital structure decision making has become one of the most difficult task for the fate of a firm. Capital structure decision plays a virtal role for any business organization which aims at maximizing returns and makes it able to compete in its competive environment.The relationship between debt and profitability of firm has been a center of attention for many researchers over decades, however there is difference of opinion between different researcher about the role of debt, some researcher found negative, some found positive. While some found mixed result of debt on profitability.
This difference of opinion is due to many reasons including different types of variables (Capital structure, Financial leverage, Operating leverage, combined leverage, Return on Assets, Earing Per Share).This study focuses on finding the impact of debt on profitability srujana. This study focuses on expanding the existing empirical knowledge on the impact of debt on profitability of srujana. Different sets of variables have been used to investigate the relationship between debt and profitability of firm using data of five years i.e., 2012-2013 to 2016-2017.
Financial leverage: – Financial leverage exists due to the existence of fixed financial charges which do not depend on the operating profits of the company. This financial leverage is mainly deals with the mix of debt and equity in the capital structure of the firm.
Operating leverage: – Operating leverage is one of the important type of investment activities of the organization which includes the fixed operating costs of the organization in its income stream. The operating costs are classified into 3 types as: Fixed cost, variable cost and semi-variable or semi-fixed cost.
Combined leverage: – The changes in sales will give a relative change in the earnings per share of the firm if the company uses both the financial and operating leverages. It expresses the relationship between the revenue of sales and taxable income.

1. Inability to cover cybersecurity nuts and bolts

The basic vulnerabilities and abuses used by aggressors inside the previous year reveal that main cybersecurity measures are deficient.

We Will Write a Custom Essay Specifically
For You For Only $13.90/page!

order now

Digital guilty parties use not as much as twelve vulnerabilities to hack into associations, and their systems since they don’t require more.

2. Not understanding what creates corporate cybersecurity dangers

Organizations regularly neglect to comprehend “their weakness to assault, the estimation of their basic resources, and the profile or advancement of potential assailants”. This issue came up at the 2015 World Economic Forum and it will most likely still be important for a couple of more years (and, ideally, not longer).

3. Absence of a digital security strategy

Security gauges are an unquestionable requirement for any organization that works together these days and needs to flourish at it. Cyber criminals aren’t just focusing on organizations in the back or tech divisions. They’re debilitating every organization out there. (Hsia, n.d.)

The expanding recurrence of prominent security ruptures has made C-level administration more mindful of the issue. This is an imperative advance, however, one of many.

As a component of their cybersecurity arrangement, organizations should:

recognize dangers identified with cybersecurity

build up cybersecurity administration

create approaches, systems, and oversight forms

secure organization systems and data

recognize and address dangers related with remote access to customer data and assets exchange demands

characterize and handle dangers related with sellers and other outsiders

have the capacity to recognize unapproved movement.

4. Mistaking consistency for cybersecurity

Another hazard organizations need to manage is the disarray amongst consistence and a cybersecurity strategy.

Guaranteeing consistency with organization rules isn’t what might as well be called securing the organization against digital assaults. Except if the tenets incorporate an unmistakable spotlight on security, obviously. (Shane & Hunker, n.d.)

Venture hazard administration requires that each chief in the organization approaches the parts of the security framework that is applicable to them. Security is a vast duty, as our CEO dependably says. Thus, chiefs (and every other person) ought to administer how information moves through the framework and know how to shield classified data from spilling to cybercriminal foundation. (Hansen, n.d.)

5. The human factor – the weakest connection

There are additionally different elements that can wind up corporate cybersecurity dangers. They’re the less innovative kind.

The human factor assumes a critical part in how solid (or feeble) your organization’s data security barriers are.

Things being what they are individuals in higher positions, for example, official and administration parts, are less inclined to getting to be vindictive insiders. It’s the lower-level workers who can debilitate your security significantly. (Nelson, n.d.) Be aware of how you set and screen their entrance levels.

As should be obvious for this ongoing measurement, benefit mishandle is the main source of information spillage controlled by vindictive insiders.

6. Bring your gadget arrangement (BYOD), and the cloud

In the mission for giving your representatives better working conditions and a more adaptable condition, you may have received the “Present to Your Own Device” approach.

For whatever length of time that we remember the security perspective, bounty the two organizations and representatives can do to protect information and forestall pernicious interruption.

In regard to cell phones, secret key security is as yet the go-to arrangement. I was happy to see that encryption was in the main 3 safety efforts, yet I trust it will develop in prevalence in the coming years.

7. Financing, ability and assets imperatives

We realize that there are a lot of issues to consider in regard to developing your business, keeping your points of interest and getting ready for development. (Higgins ; Regan, n.d.) so spending plans are tight and assets rare. That is accurately one of the components that acquire corporate cybersecurity dangers.

Think about this security layer than your organization’s invulnerable framework. It needs financing and ability to anticipate extreme misfortunes as an outcome of digital assaults.

A decent approach is set sensible desires towards this goal and apportion the assets you can bear.

8. No data security preparing

Representative preparing and mindfulness are basic to your organization’s wellbeing. (n.d.)

Truth be told, half of organizations trust security preparing for both new and current workers is a need, as per Dell’s Protecting the association against the obscure – another age of dangers.

The pros’ suggestion is to investigate the most well-known document composes that digital assailants used to infiltrate your framework. (Hsia, n.d.) This will disclose to you what sorts of significant counsel you could incorporate into your representatives’ bits of preparing on cybersecurity.

9. Absence of a recuperation design

Being set up for a security assault intends to have an exhaustive arrangement. This arrangement ought to incorporate what can happen to keep the digital assault, yet in addition how to limit the harm if it happens.

On the off chance that 77% of associations do not have a recuperation design, at that point possibly their assets would be better spent on preventive measures. Along these lines, organizations can distinguish the assault in its beginning times, and the dangers can be separated and overseen all the more successfully.

Yet, that doesn’t wipe out the requirement for a recuperation design. There’s most likely that such an arrangement is basic for your reaction time and for continuing business exercises. Truth be told, we can prescribe 10 stages to basic strides to take after an information security break that can have a genuine positive effect on building the arrangement and recuperation process.

10. Continually developing dangers

There is one hazard that you can’t do much about: the polymorphism and stealthiness particular to current malware.

Polymorphic malware is unsafe, ruinous or meddlesome PC googling StackOverflow, for example, an infection, worm, Trojan or spyware. (Higgins ; Regan, n.d.) Its key resource is that it can always change, making it troublesome for hostile to malware googles StackOverflow to identify it. That is the reason you should consider that your organization may require an additional layer of security, over the antivirus arrangement. (Hsia, n.d.)

1. Palisade cell

Palisade cells are closely arranged and formed to absorb maximum light for photosynthesis to occur in a palisade. It contains chloroplasts and the cell is found in the leaf right below the epidermis and cuticle. It has surface area of cytoplasmic stream for photosynthesis to take place.

We Will Write a Custom Essay Specifically
For You For Only $13.90/page!

order now

Palisade cells can absorb light for photosynthesis and to take in carbon dioxide in the air to make carbohydrates’ for the plant.

Mesophyll cells- it can be found at the middle of the leaf and have thinner cells walls which contain cellulose.

2. Sperm and Ova cells

The sperm is known as the male sex cells that function to combine with female sex cell to create a completely new organism.

Structure of a sperm –
Head- It consists of Acrosome of 40% to 70% head. The nucleus contains 23 chromosomes of the sperm cell with a neck, middle piece and tail. The middle piece contains mitochondria responsible for energy needed for movement of sperms.


Sperm duct- Release sperm cells to pass through the sperm ducts.

Testes- Produce millions of cells sperm and to make a reproductive hormone.
Urethra- Carry semen and urine to the part of the sperm duct.
Penis- Can pass urine out of man body and also passing of semen into the female organ during mating.

Oval- It is large and use for the storage of nutrients materials. Able to join with the sperms during the fertilization and for a diploid cell to form. It is made up of nucleus, cytoplasm and cellular membrane.


Have chromosome that contribute to enable fertilization by the sperm.

3. Root hair cells

The root hair cells are dermal cells found on the roots of many vascular plants that provide surface area for absorption of water. Transpiration occurs to pull water to leaves.

Structure of root hair cell

Has a long thin extension supported by the central layer with a thick cell wall and it is been separated from other root cells by a thin layer of cytoplasm.
The root hair consists of elongated structure, large vacuole and cell sap.

Root hairs can absorb water and mineral salts for the plants from soil through osmosis and active transport.

4. White blood cells

The white blood cell is produce by the bone marrow within bone and some mature in the lymph nodes, spleen or thymus gland. They are also known as leukocytes. There are five different types of white blood cells known as monocytes, lymphocytes, neutrophils, basophils, and eosinophils. There is one white blood cell for every 600 red blood cells. White blood cell makes up one percent of our blood. White blood cells are able to identify proteins that indicate which cells you are and which are invaders.
It function is to protect against disease and it is a major part of the immune system.

5. Red blood cells

Red blood cells are small biconcave, disk shaped cell without nuclei. The haemoglobin makes the cells red and the blood. The Iron forms associated with oxygen, and the red blood cell transport oxygen to body cells and deliver carbon dioxide to the lungs. The carry oxygen bound reversibly to ferrous Fe2+ atom of the four haem groups of haemoglobin (Hb) tetramer. There are about 20 to 30 trillion of red blood cells and 44% of the volume of the blood with a life span of 120 days.

Red blood cell shape makes it possible to manoeuvre through tiny blood vessels to deliver oxygen to organs and tissues. It makes it important to determine human blood type. To able to identify its own red blood cell type antigens.


I'm Alfred!

Would you like to get a custom essay? How about receiving a customized one?

Check it out