15 COMPARATIVE ANALYSIS OF ROUTING PROTOCOLS IN WSN
Nisha Verma
M. Tech Scholar, IMEC Sagar Mr. Sarvesh Rai Asstt. Prof. IMEC Sagar
Abstract - Many routing protocols on clustering structure have been proposed in recent years. In recent advances, achieving the energy efficiency, lifetime, deployment of nodes, fault tolerance, latency, in short high reliability and robustness have become the main research goals of wireless sensor network. Many routing protocols on clustering structure have been proposed in recent years based on heterogeneity. In this paper, a comparative study is carried out between various energy efficient routing protocols to improve the lifetime of wireless sensor networks. An analysis is done on the basis of simulated results.
Simulation is done in MATLAB environment. In this paper, a new technique is proposed which increases the life of the network in terms of half dead nodes, alive nodes and number of packets sent to the base station. A comparative study has been done with DEEC, DDEEC and EDEEC for wireless sensor networks. EDEEC is the proposed routing and clustering protocol in this thesis work. Simulation results show that proposed algorithm performs better as compared to others. The alive nodes in EDEEC sustains up to 10000 rounds for CH selection while in other protocols, the nodes died before 4000 rounds, hence it is concluded that EDEEC performs better in WSN. It is well known that better CH results the better performance of the communication system.
Keywords: Cluster Head Selection, Energy Efficiency, LEACH, Network Lifetime, Wireless Sensor Networks, DEEC, DDEEC and EDEEC.
1 INTRODUCTION
Wireless sensor networks is the network consisting of hundreds of compact and tiny sensor nodes which senses the physical environment in terms of temperature, humidity, light, sound, vibration, etc. These sensor nodes gather the data from the sensing field and send this information to the end user. These sensor nodes can be deployed on many applications. Current wireless sensor network is working on the problems of low-power communication, sensing, energy storage, and computation. Hierarchical- based routing is a cluster based routing in which high energy nodes are randomly selected for processing and sending data while low energy nodes are used for sensing and send information to the cluster heads. Clustering technique enables the sensor network to work more efficiently. It increases the energy consumption of the sensor network and hence the lifetime.
Fig. 1 WSN Architecture
For past several years, there is a need for a medium through which entire source of information can be transmitted from the place where it is needed and the received information should be precise to give an absolute and accurate picture of the real world.
Hence, a new category of networks has appeared and it is known as wireless sensor networks (WSNs). WSN contains large number of nodes, which collects the information from its surrounding environment. It can also describe as a network of sensing nodes with power facility. These nodes have capability to sense atmospheric or physical parameters such as humidity,
16 temperature, pressure from the
surroundings and send all collected information to the central node. These nodes work together to achieve a common goal. In WSN, sensor nodes are used for sensing, processing and communication and it can be either fixed or mobile. There are number of constraint which affect the performance of WSNs, these are energy, processing power, storage and transmission range.
Among these, energy is one of main constraint for WSNs. The energy of deployed sensor nodes gradually decreases in terms of data and distance. Lot of research work is going on to address the pitfalls of WSNs. In WSNs, sensors are densely deployed for collecting the data and information and it can be widely applicable to different domains such as agriculture, forests, coal mines, monitoring of rail tunnels, monitoring of solar photovoltaic cell in a grid, etc., and collect the data from all locations using a centralized Base Station (BS) with the help of cluster heads (CHs). In such networks, data is collected periodically by the BS.
Wireless sensor networks are generally deployed in remote areas like battle fields, disaster relief operations, biodiversity mapping etc. and the sensor nodes are generally battery powered devices. So it is difficult to recharge the battery at regular intervals. Hence key issues involved in wireless sensor networks are reducing energy consumption and extending network lifetime. Transmission of data is more energy consuming as compared to the processing of data. Many protocols have been proposed till now to make communication energy-effective.
Clustering is one of the key techniques followed in these protocols. LEACH a homogeneous protocol (because of the same initial energies of all nodes), elects cluster head based on a fixed probability assigned to each node and this probability decides after how many rounds a node can be again cluster head.
But it does not always select node with high energy as the cluster head. PEGASIS was proposed with the idea that if nodes form a chain from source to sink only one node in any given transmission time- frame will be transmitting to the base
station. But it added overhead as a sensor node required to know the energy status of its neighbours to route its data.
SEP is a heterogeneous protocol, considered two types of nodes- normal and advanced. Cluster head selection in SEP is done randomly on the basis of probability of each type (normal and advanced) of node as in does not support multi level heterogeneity of nodes and also the selection of cluster head is not dynamic; therefore the normal nodes will die first than the advanced nodes. DEEC proposed that all the nodes of the network use the initial energy and residual energy to specify the cluster head. DEEC embeds the factor of residual energy in the heterogeneous environment thus ensuring that always high energy nodes will have more chance to become cluster head than low energy nodes.
Several enhanced versions of DEEC were proposed such as DDEEC, EDEEC etc.
DDEEC introduces threshold residual energy and when energy level of advanced and normal nodes falls down to the limit of threshold residual energy then both type (normal and advanced) of nodes use same probability to become cluster head.
EDEEC was enhanced version of DEEC proposed to insert another node in the network (super node) with the existing normal and advanced nodes which increased the heterogeneity and lifetime as well. It has been evaluated in that DDEEC has low stability period, lifetime and throughput as compared to the EDEEC. So EDEEC act as motivating factor to work on and improve it further.
2 WORK STUDY
Work study of related work to the proposed technique helps in current research. Many researchers carried out so many experiments in the field of WSN to improve communication systems.
Some papers are discussed here.
Fazrial Amir Nugraha [1] et al performs a comparison between LEACH and DEEC protocols. The simulation for comparison is going to be executed to know network lifetime based on the parameters used which are an area size and the number of nodes. All parameters are going to be set in distinctive values and the nodes are spread randomly.
Based on scenarios simulated, DEEC
17 produces better performance than LEACH.
It has more around 50% network lifetime than LEACH. It can be marked that the number of rounds of DEEC created is bigger than the number of LEACH rounds. The total DEEC packets delivered to cluster-head from sender node are around 20% larger than LEACH packets delivered. In addition, for total packets sent to base station from cluster-head, DEEC packets sent has around 75%
greater than LEACH packets transmitted.
M. A. Rahmadhani et al [2]
explains that Low Energy Adaptive Clustering Hierarchy (LEACH) is one of clustering routing protocols on Wireless Sensor Network (WSN). LEACH algorithm is divided to setup phase and steady state phase. In a busy network, LEACH Routing has a high packet loss. To solve the problem, we need Delay Tolerant Network (DTN). DTN is an advanced architecture that allows communication in extreme conditions like a busy network. In this research, LEACH-WSN changes to optimize the network by adding DTN to LEACH-WSN over DTN. Simulation is performed to test the performance of LEACH-WSN over DTN based on changes in the number of node and buffer capacity. In changing the number of node, LEACH-WSN over DTN can improve performance by decreasing packet loss value by 50% of LEACH-WSN packet loss.
R. R. S. Sneha et al [3] explains that Wireless Sensor Network (WSN) is known to be a highly resource constrained class of network where energy consumption is one of the prime concerns. In this research, a cross layer design methodology was adopted to design an energy efficient routing protocol entitled
“Position Responsive Routing Protocol”
(PRRP). PRRP is designed to minimize energy consumed in each node by reducing the amount of time in which a sensor node is in an idle listening state and reducing the average communication distance over the network.
S. Cc, V Raychuodhury [4]
explains that the Delay Tolerant Networks (DTNs) have practical applications in various fields. DTNs have been studied in- depth by many researchers and multiple high quality survey papers have been produced which analyses DTN features, taxonomies, and applications. In recent
years, interest in DTN research has rekindled as there are several emerging network-based application domains that require delay tolerance support and thus can use DTN specific routing and data dissemination techniques. The objective of this survey is to help future researchers to identify DTN specific properties in the new applications and to apply appropriate routing protocols whenever necessary.
3 PROPOSED WORK
In this paper EDEEC protocol is proposed.
EDEEC is enhanced version of DEEC protocol. The prime purpose of the proposed model is to dissipate the energy consumption. EDEEC is compared with DEEC and DDEEC. Different simulations using the MATLAB program has been done. Network performance in terms of life span of the nodes of the network, energy consumption of the system and number of packets received by BS has been checked. All the parameters used in programming the three methods are identical. The results show that the proposed model prolongs the network lifetime and is better than the other three clustering protocols in terms of all. In WSN selection of CH is basic phenomenon, but there are so many routing protocols are there to accomplish this task. For better performance of the heterogeneous WSN EDEEC is employed here. The algorithm shown below explains the steps used in the simulation of the proposed model. Algorithm for the proposed simulation is as follows:
1. Define the network size.
2. Define total number of nodes.
3. Define initial energy of the nodes.
4. Initialise round to select CH.
5. Calculate the probability to be a CH.
6. Select best node as CH.
7. Cluster member choose nearest CH to join.
8. Calculate energy consumption in selection of CH.
9. Calculate no. of alive nodes, dead nodes and data transfer rate.
10. Draw the curve to check performance of each protocol.
4 RESULT DISCUSSION
Simulation of the proposed model is done in MATLAB. Simulated results are shown in figure 2, figure 3 and figure 4. Figure 2
18 shows the number of dead nodes versus
rounds curve. Here rounds stands for number of trials to select a cluster head.
Better cluster head gives better performance in communication system.
More rounds give a better cluster head.
Cluster head is a link between individual node and base station. It is very clear from fig 2 that in EDEEC protocol nodes sustains up to 10000 rounds.
Fig. 2 Number of dead nodes v/s rounds plot
Fig. 3 number of alive nodes v/s rounds plot
Fig 3 is a reciprocal representation of fig 2 and it also reflects the same thing that EDEEC is better in energy consumption.
The DEEC and DDEEC protocols are inferior then EDEEC.
Fig. 4 number of packets sent to the BS v/s rounds plot
Figure 4 shows the rate of data transfer.
For DEEC protocol data transfer rate is better than DDEEC but lesser than EDEEC. For EDEEC data transfer rate is higher than DEEC and DDEEC protocol.
5 CONCLUSION
From the comparative study of the existing research work, it has been concluded that there are various energy efficient protocols for WSNs which consists of fixed and mobile sensor nodes that improve the cluster head selection approach to extend the lifetime of WSNs.
Hence, the issue of energy consumption will be addressed and investigated further by using the proposed methodology, to increase the performance of the WSNs in terms of reducing energy consumption and also average latency, by improving lifetime of sensor nodes, and by reducing bandwidth consumption. Similarly based on this formulation, the optimum clustering technique can further be developed to extend the lifetime of WSNs that significantly reduce the number of rounds for selecting optimal CH. Routing protocols plays a very significant part to produce interruption less and efficient communication between source and destination nodes. The performance, service and reliability of a network mostly depend on the selection of good routing protocol. In this thesis EDEEC (Enhanced Distributed Energy Efficient Clustering) protocol is proposed which improves stability and energy efficient property of the heterogeneous wireless sensor network and hence increases the lifetime of the network. Simulation results show that EDEEC performs better as compared to other protocols (DEEC and DDEEC) in heterogeneous environment for wireless sensor networks. The alive nodes in EDEEC sustains up to 10000 rounds for CH selection while in other protocols, the nodes died before 5000 rounds, hence it is concluded that EDEEC better in performance.
REFERENCES
1. Fazrial Amir Nugraha, Dodi Wisaksono, Siti Amatullah Karimah, “The comparative analysis between LEACH and DEEC based on the number of nodes and the range of coverage area”, IEEE 2019.
2. M. A. Rahmadhani, L. V. Yovita, Ratna Mayasari, “Energy consumption and Packet
19 loss analysis of LEACH Routing protocol on
WSN over DTN”, IEEE 2018.
3. R. R. S. Sneha and D. Nithya “Wireless sensor Network: A survey”, IJARSCCES, vol. 2, no. 2, pp. 92-95, 2016.
4. S. Cc, V Raychuodhury, G. Marphiya, A.
Singla, “A survey of Routing and data dissemination in Delay Tolerance Network”, JNCA, vol. 6, pp. 128-146, 2016.
5. X. Liu, “A typical Hierarchical Routing protocol for Wireless Sensor Network: A review”, IEEE Sens. J., vol. 15, no. 10, pp.
5372-5353, 2015.
6. G. Xu, W. Shen and X. Wang, “Applications of Wireless Sensor Networks in marine environment monitoring: A survey”, Sensors, vol. 14, no. 9, pp. 16932-16954, 2014.
7. M. Toulabi, S. Javadi, I. Azad and T.
Branch, “A survey on routing protocols in WSN”, JWSN, 2013, pp. 1-3, 2013.
8. Zang Z, Qi JD, Cao YJ. “A robust routing protocol in wireless sensor networks”. In:
IET International Conference on Wireless Sensor Network. China: IET; 2010. pp.
276-279.
9. Ehsan S, Hamdaoui B. “A survey on energy-efficient routing techniques with QoS assurances for wireless multimedia sensor networks”. IEEE Communications Surveys
& Tutorials. 2012;14(2), PP. 265-278.
10. O. Younis, S. Fahmy, "HEED: A hybrid, energy efficient, distributed clustering approach for ad-hoc sensor networks", IEEE Transactions on Mobile Computing vol. 3, no 4, pp 660-669, 2004.
11. L. Qing, Q. Zhu, M. Wang, “Design of a distributed energy-efficient clustering algorithm for heterogeneous wireless sensor networks". ELSEVIER, Computer Communications 29, 2006, pp 2230- 2237.
12. Faheem and V. C. Gungor, „„Energy efficient and QoS-aware routing protocol for wireless sensor network-based smart grid applications in the context of industry 4.0,‟‟
Appl. Soft Comput., vol. 68, pp. 910–922, Jul. 2018.
13. A. S. M. Hosen, G. H. Cho, and I. H. Ra,
„„An eccentricity based data routing protocol with uniform node distribution in 3D WSN,‟‟ Sensors, vol. 17, no. 9, p. 2137, 2017.
14. M. Al-Shalabi, M. Anbar, T. C. Wan, and A.
Khasawneh, „„Variants of the low-energy adaptive clustering hierarchy protocol:
Survey, issues and challenges,‟‟
Electronics, vol. 7, no. 8, p. 136, 2018.
15. R. E. Mohamed, A. I. Saleh, M.
Abdelrazzak, and A. S. Samra, „„Survey on wireless sensor network applications and energy efficient routing pro- tocols,‟‟
Wireless Pers. Commun., vol. 101, no. 2, pp. 1019–1055, 2018.
16. S. Dutt, S. Agrawal, and R. Vig, „„Cluster- head restricted energy efficient protocol (CREEP) for routing in heterogeneous wireless sensor networks,‟‟ Wireless Pers.
Commun., vol. 100, no. 4, pp. 1477–1497, 2018.
17. D. Wu, J. He, H. Wang, C. Wang, and R.
Wang, „„A hierarchical packet forwarding mechanism for energy harvesting wireless sensor networks,‟‟ IEEE Commun. Mag., vol. 53, no. 8, pp. 92–98, Aug. 2015.
18. G Yin, G Yang, W Yang, B Zhang, W Jin.
“An energy-efficient routing algorithm for wireless”. In. International Conference on Internet Computing in Science and Engineering (ICICSE‟08), IEEE, China;
2008.
19. Zang Z, Qi JD, Cao YJ. “A robust routing protocol in wireless sensor networks”. In:
IET International Conference on Wireless Sensor Network. China: IET; 2010. pp.
276-279
20. Ehsan S, Hamdaoui B. “A survey on energy-efficient routing techniques with QoS assurances for wireless multimedia sensor networks”. IEEE Communications Surveys & Tutorials. 2012; 14(2), PP. 265- 278.
