International Journal of Advanced Engineering Science and Technological Research (IJAESTR) ISSN: 2321-1202, www.aestjournal.org @2015 All rights reserved
24
Analysis of Clustered algorithm for
Routing Protocols in protocols in Wireless sensor Networks
1Km Poonam, 2Shriniwas Singh
1
[email protected],
2[email protected]
1,2
Department of Computer Science Engineering SVSU, SITE, Meerut (U.P), India
Abstract- In the present scenario Energy efficiency of sensor nodes is a sizzling issue for wireless sensor networks. To realize this, networks have to be self- organizing, self healing, economical and energy- efficient. In the network area, there are various attempts to introduce energy awareness. Node clustering is used to reduce direct transmission to the base station, is one such attempt to control energy dissipation for sensor data gathering. The base station being located at variable distances from the nodes in the sensor field, each node actually dissipates a different amount of energy to transmit data to the same. The LEACH and PEGASIS protocols provide elegant solutions to this problem, but may not always result in optimal performance. In this paper we have proposed a novel data gathering protocol for enhancing the network lifetime by optimizing energy dissipation in the nodes. In our scheme each node only communicates with a close neighbour and takes turns in being the leader depending on its residual energy and location. This helps to rule out the unequal energy dissipation by the individual nodes of the network and results in superior performance as compared to LEACH and PEGASIS. With these results we analysed the routing performance of dissipated nodes which are at a large distance from the base station.
Keyword: Sensor Node, LEACH, PEGASIS, WSN Network, MATLAB etc
I. INTRODUCTION
Wireless sensor networks consist of sensor nodes that are randomly deployed in a large area, collecting important information from the sensor field. These sensor nodes have very limited energy resources and hence, the energy consuming operations such as data collection, transmission and reception, must be kept at a minimum. Moreover it is widely accepted that balancing the energy dissipation among the nodes of the network is a key factor for prolonging the network lifetime. Each sensor node is provided with transmit power control and Omni-directional antenna and therefore can vary the area of its coverage.
It has been observed in the Wireless Sensor Networks that the communication requires
ignificant amount of energy as compared to computations. The LEACH protocol presents an elegant solution to this energy utilization problem where nodes are randomly selected to collaborate to form small number of clusters and the cluster heads take turn in transmitting to the base station during a data gathering cycle. The PEGASIS protocol is a further improvement upon the LEACH protocol where a greedy chain of nodes is formed which take rounds in transmitting data to the base station.
The WSN-routing, for instance, uses multi-hop routing that is not based on the principle of fairness. The core operation of wireless sensor network is to collect and process data at the network nodes, and transmit the necessary data to the base station for further analysis and processing.
Currently there are several energy efficient communication models and protocols that are designed for specific applications, queries, and topologies. The routing algorithm proposed in this Paper is suitable for continuous monitoring of numerous widespread sensors, which are at a large distance from the base station.
A. Sensor Networks: A sensor node typically consists of four basic components: a sensing unit, a processing unit, a communication unit, and a power unit. The sensing unit usually consists of one or more sensors and analog-to-digital converters (ADCs). The sensors observe the physical phenomenon and generate analog signals based on the observed phenomenon. The ADCs convert the analog signals into digital signals, which are then fed to the processing unit. The processing unit usually consists of a microcontroller or microprocessor with memory which provides intelligent control to the sensor node. The communication unit consists of a short - range radio for performing data transmission and reception over a radio channel. The power unit consists of a battery for supplying power to drive all other components in the system.
International Journal of Advanced Engineering Science and Technological Research (IJAESTR) ISSN: 2321-1202, www.aestjournal.org @2015 All rights reserved
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Figure1: Sensor Node Architecture.
II. DATA AGGREGATION AND GATHERING IN WSN:
In wireless sensor network (WSN) security issue, Data confidentiality, integrity, and elimination of data redundancy are major requirements especially when the sensor network is deployed in a hostile environment. In that situation Data Aggregation technique helps to cope up with these issues. Data aggregation can reduce the number of data packets transmitted and the data conflict, thus raise the data accuracy and data collection efficiency through dealing with the redundant data in-network.
In order to conserve energy, a strategy to avoid redundant data and to aggregate data route must be used. This data centric approach may be better suited for wireless sensor network applications. In this approach, contents of data packets in route are examined by nodes on the path to perform some consolidation or elimination process to avoid redundancy due to data originating from different sources. To understand the practicability of usage of data aggregation, the context in which such techniques may be used needs to be analyzed. The type and dynamics of the data flowing in the wireless sensor network from the sources may be considered. There is no data redundancy as all sources send completely different data There is complete data redundancy as all.
A. Types of Data Gathering In WSNs
Direct Approach:
In direct approach all the nodes in a considered network or all transmitting information nodes are directly connected to the base station (BS), as shown in the figure below:
Figure: 2 Direct Approach
In Chain Based Approach
In Chain based Approach all the nodes in the given sensor network are connected in a chain format to each other, then the leader is selected, this leader is selected on the basis of its distance from the base station, that means the node which will be close to the base station will be allotted as head.
Figure: 3 Chain Based Approach
Clustered Approach
In Cluster based approach, the given network is broken or we can say it is divided in various clusters. In every cluster there are fixed number of nodes are present. Randomly any one of the node is selected as cluster-head (leader & CH) in every cluster which is directly connected to the base station. The main feature of the cluster-head is to gather useful information from the other nodes (CH), present in the considered cluster
Figure 4: Clustered Approach III. PROTOCOLS USED IN WSN
NETWORKS
A. LEACH
LEACH (Low Energy Adaptive Clustering Hierarchy) is a self-organizing, adaptive clustering- based protocol that uses randomized rotation of cluster-heads to evenly distribute the energy load among the sensor nodes in the network. LEACH based on two basic assumptions: (a) base station is fixed and located far away from the sensors, and (b) all nodes in the network are homogeneous and energy-constrained. The idea behind LEACH is to form clusters of the sensor nodes depending on the received signal strength and use local cluster heads as routers to route data to the base station. The LEACH protocol presents an elegant solution to his
International Journal of Advanced Engineering Science and Technological Research (IJAESTR) ISSN: 2321-1202, www.aestjournal.org @2015 All rights reserved
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energy utilization problem where nodes are randomly selected to collaborate to form small number of clusters and the cluster heads take turn in transmitting to the base station during a data gathering cycle. The goal of LEACH is to provide data aggregation for sensor networks while providing energy efficient communication that does not predictably deplete some nodes more than others.
LEACH is a hierarchical protocol in which most nodes transmit to cluster heads, and the cluster heads aggregate and compress the data and forward it to the base station. Each node uses a stochastic algorithm at each round to determine whether it will become a cluster head in this round. LEACH assumes that each node has a radio powerful enough to directly reach the base station or the nearest cluster head, but that using this radio at full power all the time would waste energy. Nodes that have been cluster heads cannot become cluster heads again for P rounds, where P is the desired percentage of cluster heads. Thereafter, each node has a 1/P probability of becoming a cluster head in each round. At the end of each round, each node that is not a cluster head selects the closest cluster head and joins that cluster. The cluster head then creates a schedule for each node in its cluster to transmit its data.
LEACH also uses CDMA so that each cluster uses a different set of CDMA codes, to minimize interference between clusters.
B. PEGASIS
The main idea in PEGASIS is for each node to receive from and transmit to close neighbours and take turns being the leader for transmission to the BS. This approach will distribute the energy load evenly among the sensor nodes in the network. We initially place the nodes randomly in the play field, and therefore, the ith node is at a random location.
The nodes will be organized to form a chain, which can either be accomplished by the sensor nodes themselves using a greedy algorithm starting from some node. Alternatively, the BS can compute this chain and broadcast it to all the sensor nodes. We used random 100-node networks for our simulations with similar parameters used in. We placed the BS at a far distance from all other nodes.
For a 50m x 50m plot, our BS is located at (25, 150) so that the BS is at least 100m from the closest sensor node. For constructing the chain, we assume that all nodes have global knowledge of the network and employ the greedy algorithm. We could have constructed a loop; however, to ensure that all nodes have close neighbours is difficult as this problem is similar to the travelling salesman problem.
For gathering data in each round, each node receives data from one neighbour, fuses with its own data, and transmits to the other neighbour on the chain. Note that node i will be in some random position j on the chain. Nodes take turns transmitting to the BS, and we will use node number i mod N (N represents the number of nodes) to transmit to the BS in round i. Thus, the leader in each round of communication will be at a random position on the chain, which is important for nodes to die at random locations. The idea in nodes dying at random places is to make the sensor network robust to failures. In a given round, we can use a simple control token passing approach initiated by the leader to start the data transmission from the ends of the chain. The cost is very small since the token size is very small. In Figure 5, node c2 is the leader and it will pass the token along the chain to node c0. Node c0 will pass its data towards node c2. After node c2 receives data from node c1, it will pass the token to node c4, and node c4 will pass its data towards node c2.
Figure 5: Token passing approaches The base station being located at variable distances from the nodes in the sensor field, each node actually dissipates a different amount of energy to transmit data to the same. The LEACH and PEGASIS protocols provide elegant solutions to this problem, but may not always result in optimal performance. In this paper we have proposed a novel data gathering protocol for enhancing the network lifetime by optimizing energy dissipation in the nodes. To achieve our design objective we have applied particle swarm optimization (PSO) with Simulated Annealing (SA) to form a Sub- optimal data gathering chain and devised a method for selecting an efficient leader for communicating to the base station. In our scheme each node only communicates with a close neighbour and takes turns in being the leader depending on its residual energy and location. This helps to rule out the unequal energy dissipation by the individual nodes of the network and results in superior performance as compared to LEACH and PEGASIS. Extensive computer simulations have been carried out which shows that significant improvement is over these schemes.
International Journal of Advanced Engineering Science and Technological Research (IJAESTR) ISSN: 2321-1202, www.aestjournal.org @2015 All rights reserved
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IV. ANALYSIS OF ENERGY EFFICIENT CLUSTERING ALGORITHM FOR DATA GATHERING IN WIRELESS SENSOR NETWORK
The cluster formation algorithm is designed to ensure that the expected number of clusters per round is np. The dissipated energy by the nodes can be analytically estimated using the computation and communication energy models. Each CH dissipates energy receiving signals from its members, aggregating the signals and transmitting the aggregate signal to the base station represented using the mp model (εmp). Thus, the energy spent by a CH node during a single round is:
ECH = E [Nv | N=n] (lEelec + l EDA) + l Eelec + l mpd4 (1)
Where l is the number of bits in each data message, d to BS is the average distance from a CH to the base station, and we assume lossy data aggregation with the energy for aggregation is EDA. As for each non-CH node, it only needs to transmit its data to the CH once during a round. Since the distance to the CH is small, the energy dissipation follows the Friss fs model (εfs). Thus, the energy used in each non-CH node is:
Enon-CH = lEelec + lεfsE2[Lv | N=n] (2)
This allows us to determine the energy spent in a cluster during each round as:
Ecluster=ECH +Enon-CH×E[Nv|N=n] (3)
Let C represent the total energy spent in the system, then:
E[C | N=n] = np Ecluster =
Nl [Eelec (3- 2p) + + EDA (1-p) + mpd4toBSP]
(4) Removing the conditioning on N yields:
E[C] = E[E[C | N = n]] = E[N] × Nl [Eelec (3- 2p) +
+ EDA (1-p) + mpd4toBSP]
= Al [[E (3- 2p) + + EDA (1-p) + mpd4toBSP]
Eelec + + EDA- mpd4toBSP = 0 (5)
popt (6)
The above simple analytical formula enables the computation of the CH probability with ease, which mainly depends on the sensor node density.
Another crucial metric of a sensor network is the system lifetime. Here, lifetime is defined as the time duration from the instant the network is deployed to the moment when the first sensor node runs out of energy. We can determine the average energy dissipated per sensor in each round of transmission. If each node initially has B joule of
battery energy, and there is only one transmission of sensed data to the CH in each round of t period, we could approximate lifetime, L in seconds through:
L = x t = (7) Based on this model, an analytical experimentation is performed to obtain a realistic total and average energy dissipated in a sensor network as well as its lifetime, against common network parameters.
V. TOTAL ENERGY SPENDS VERSUS CLUSTERHEAD (CH)
PROBABILITY
For Number of Nodes 1500:
The graph 1 represents the variation of the total energy spent by the network for different CH probabilities with number of nodes 1500. It is observed that the curve obtained form an inverted bell shape. The curve depicts a higher value of total energy dissipated in the network for very small CH probabilities.
As the CH probability is increased, more CHs are likely to be present with smaller cluster sizes. Even though average energy dissipated per cluster reduces, but as more nodes need to communicate directly with the base station at a higher transmission power, the overall energy dissipation goes higher. As expected, it is found that the energy dissipated in the network is minimal.
Graph 1 The variation of energy spent v/s cluster- head probability, when number of nodes (n) = 1500
VI. THE TOTAL ENERGY SPENDS AND THE NUMBER OF CLUSTER VERSUS THE SENSOR DENSITY
For Number of Nodes is 1500:
To see the impact of sensor density on the network energy consumption, we fixed the CH probability.
The total energy usage for different sensor density is depicted in graph 2, where the number of nodes is taken to be 1500.
International Journal of Advanced Engineering Science and Technological Research (IJAESTR) ISSN: 2321-1202, www.aestjournal.org @2015 All rights reserved
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However, the energy values are rather optimistic as we assume the wireless channel is error free. When the node density is increased, there are more nodes in the same region resulting in higher total energy usage.
Graph 2 Total energy spent when the numbers of nodes were taken to be 1500.
VII. ENERGY CONSUMPTION
The energy consumed for specific number of frames and different head-sizes is compared in this section. Figure 6 shows graphs that illustrate the variation in the energy consumed per node with respect to the number of clusters and network diameter. The x-axis and y-axis represent the number of clusters and the energy consumed in one round, respectively.
Figure 6: Energy consumed per round with respect to number of clusters.
When the number of clusters are below the optimum range, for example 10, the data collecting sensor nodes have to send data to the distant cluster heads. On the other hand, when the numbers of clusters are greater than optimum range, there will be more transmissions to the distant base station.
Figure 7: Energy consumed per round with respect to head-set size and network diameter.
The results of reduced energy consumption, as illustrated in Figure 6 and Figure 7, show that using a head-set of sensor nodes is more desirable than a single cluster head. Moreover, this protocol provides a more systematic approach of reducing the energy consumption. If more nodes are added in LEACH, all the nodes are treated alike and these extra nodes will also be used in collecting the sensor data. However, in our approach, the number of sensor nodes for data collection remains unchanged and the number of control and management nodes can be adjusted.
CONCLUSION
As energy-awareness is highly critical in the design of sensor networks, we proposed the Low Energy Adaptive Clustering Hierarchy (LEACH) that does not require location information a priori. The objective of LEACH is to minimize the total energy dissipated by using non monitored rotating cluster head election. LEACH is also able to control a cluster’s diameter based on the message LEACH and approximate its nodes’ distance to cluster heads using the message timestamp, which could be used to create a collision-free transmission schedule. An
Analytical model of this algorithm is derived based on the results from stochastic geometry to determine a realistic energy dissipation and network lifetime patterns. It was demonstrated that there is an optimal probability, which could easily be determined from the given expression and pre- configured into the nodes, to achieve an overall energy efficient operation. It was also found that there is a decreasing improvement on network lifetime, when more nodes are deployed within the same region.
REFERENCES
[1] Soheil ghiasi, ankur shrivastav, Xiaojian Yang, and Majid Sarrafzadeh “Optimal
International Journal of Advanced Engineering Science and Technological Research (IJAESTR) ISSN: 2321-1202, www.aestjournal.org @2015 All rights reserved
29
Energ Aware clustering in Sensor Networks”
ISSN 1424-8220.
[2] Nauman Israr and Irfan Awan “Muitlhop Clustering Algorithm for Load Balancing in Wireless Sensor Network” I.J of SIMULATION vol. 8 No.1.
[3] Niels Reijers and Koen Lagendeon
“Efficient code Distribution in Wireless sensor network” WSNA’03 ACM 1-58113- 764-8/03/0009
[4] Gaurav Gupta and Mohamed Younis
“Performance Evaluation of Load-Balanced Clustering of wireless sensor networks”
IEEE 0-7803-7661-7/03. 2003
[5] Su Ping “Delay measurement tome synchronization for wireless sensor networks” IRB-TR-03-013. June 2003 [6] Seema Bandoopadhyay and Edward J.Coyle
“An Energy efficient hierarchical clustering algorithm for Wireless sensor networks”
IEEE 0-7803-7753-2/03. 2003
[7] Yang Yu, Bhaskar Krishnamachari and viktor K. Prasanna “Energy-Latency Tradeoff for data gathering in wireless sensor network” IEEE 0-7803-8356-7/04.
2004
[8] Srisankar S. Kunniyur and Santosh S.
Venkatesh “Network Devolution and the Growth of Sensory Lacunae in sensor networks” WEBCOM, 2004.
[9] Dr. Garimella Ramamurthy, Vasanth Iyer and V Bhawani Radhika, “Level Controlled Clustering In Wireless Sensor Networks”.
3rd International Conference on Sensing Technology, 2008
[10] Jukka Kohonen “Data gathering in sensor Network” WEBCOM, 2004.
[11] Sung Hyun Son, Mung Chiang, Sanjeev R.
Kulkarni, Stuart C. Schwartz “Clustering in distributed incremental estimation in Wireless sensor networks. DAAD19-00-1- 0466
[12] Bhaskar Krishnamachari “Modelling Data gathering in wireless sensor networks”
Springer, pp. 572-591. 2005
[13] Canfeng Chan, Jian Ma and Ke Yu
“Designing energy effiecent wireless sensor networks with mobile sinks”. ACM 1- 59593-343-3/06/0011. 2006.
[14] Xiuli Ren and Haibin Yu “Security mechanisms for wireless sensor networks”
IJCSNS International journal of computer Science and network Security, VOL.6 No 3, March 2006.
[15] Ossama Younis, Marwan Krunz, and Srinivasna Ramasubhrmanian “Node clustering in wireless sensor networks:
Recent developments and deployment Challenges”.IEEE 0890-8044/06. 2006 [16] Primoz Skraba, qQing Fang, An Nguyen and
Leonidas Guibas “sweeps over wireless sensor networks”. ACM 1-59593-334- 4/06/0004. 2006.
[17] Yao-Chung, Zhi-Sheng Lin and Jiann-Liang chen “Cluster Based Self-Organisation for Wireless Sensor Network”. IEEE 0-7803- 9459-3/06. 2006
[18] John S. Stankovic “wireless sensor networks”. IEEE personal Comm. Magzine.
2006
[19] Ioan Raicu “ Routing algorithm for wireless sensor networks” ACM MOBICOM 2002 [20] Kayhan Erciyes, Orhan Dagdeviren, Deniz
Cokuslu and Deniz Ozsoyeller “Graph Theoretical clustering Algorithm in mobile Ad-Hoc networks and Wireless sensor networks” APPL. COMPUT. Math 6(2007), no.2 pp.162-180.
[21] Takehiro Furuta, Mihiro Saski, Fumio Ishizaki, Atsuo Suzuki and Hajime Miyazawa “A new clustering algorithm using facility location theory for wireless sensor networks”. NANZAN-TR-2006-04.
[22] Yang-Fa Huang, Ching-Mu Chen, Tsair- Rong Chen, Jong-Shin Chen, Neng-Chung Wang “performance of energy efficient relaying for cluster-based wireless sensor networks”. Communication of IIMA, VOL.7- ISSUE-3. 2007
[23] Dali Wei, Shaun Kaplan and Anthony Chan
“Energy efficient Clustering Algorithm for wireless sensor networks”. IEEE, 978-1- 4244-2052-0/-8. 2008
[24] Dr. Garimella Rama murthy, Vasanth Iyer, V. Bhavani Radhika, “Level controlled clustering in wireless sensor networks”.
IEEE 978-1-4244-2177-0/08.2008
[25] Francisco J. Claudius, rico Radeke, Dimitri Marandin, Petia Todorova and Slobodanka tomic, “Performance study of reconfiguration algorithm in cluster-tree
International Journal of Advanced Engineering Science and Technological Research (IJAESTR) ISSN: 2321-1202, www.aestjournal.org @2015 All rights reserved
30
topologies for wireless sensor networks”.
IEEE, 1-4244-1144-0/07. 2007
[26] Ameer Ahmed Abbasi and Mohamed Younis, “A survey on clustering algorithm for wireless sensor networks”.
ScienceDirecT COMPUTER
COMMUNICATION 30 (2826-2841). 2007.
[27] C Behrens, O Bishoff, M. Lueders, and R.
Laur, “Energy Efficient topology control for wireless sensor networks using online battery monitoring”. Radio Sci, 5,205-208, 2007.
[28] Feilong TANG, Minyi Guo.Minglu Li,Cho- Li Wang and Mianxiong Dong, “Secure Routing for wireless mesh sensor networks in pervasive environments”. ”.
INTERNATIONAL JOURNAL OF
INTELLIGENT CONTROL AND
SYSTEM, VOL-12, NO-4. 2007
[29] Xiaomie Cao and Guihai Chen, “ROSS:
Resource oriented security solution for heterogeneous clustered sensor networks”.INTERNATIONAL JOURNAL OF INTELLIGENT CONTROL AND SYSTEM, VOL-12, NO-4 317-324. 2007 [30] Quijun Gu and Jawad Drissi, “Localised
Broadcast Authentication in Large Sensor Networks”. INTERNATIONAL JOURNAL OF INTELLIGENT CONTROL AND SYSTEM, VOL-12, NO-4 341-350. 2007 [31] Lin Shen and Xiangquan SHI, “A dynamic
cluster-based key management protocol in
wireless sensor networks”.
INTERNATIONAL JOURNAL OF
INTELLIGENT CONTROL AND
SYSTEM, VOL-13, NO-2 146-151. 2008 [32] Michiel Horsman, Mihai Marin-Perian,
Pierre Jansen and Paul Havinga, “A Simulation Framework for Evaluating Complete Reprogramming Solutions in Wireless Sensor Networks”.IEEE 978-1- 4244-1653-0/08. 2008.
