Comparison of Heterogeneous Leach in Two ...

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Journal of Algorithms, Computer Network, and Security, Vol.1 No.1, January 2016 pISSN 2413-9513 | eISSN 2413-8126

Comparison of Heterogeneous Leach in Two Dimensional and Three Dimensional Wireless Sensor Networks Anisha Somani*1, Sheena Kohli2 and Partha Pratim Bhattacharya 3 1, 2, 3

College of Engineering and Technology,Mody University of Science and Technology, Lakshmangarh, INDIA 2 Email: {1any.somani22, sheena7kohli, 3hereispartha}@gmail.com

Abstract: Wireless Sensor Networks, being used for sensing, processing and communicating about the ambient conditions, find numerous applications in different areas. In most of the applications, two dimensional deployment of wireless sensor nodes is assumed, where height is considered negligible, like in terrestrial networks but in practical scenarios, three dimensional deployment is more feasible and realistic in ocean column monitoring, under-ground tunnels and other space applications. In this paper, comparison has been shown between the implementation of heterogeneous Low Energy Adaptive Clustering Hierarchical (LEACH) protocol in two dimensional plane and three dimensional space, considering three dimensional sensor node deployment as more practical to the real world. Keywords: 2D plane, 3D space, Base Station, Cluster Head, Cube, Dead node, Hexagon, Heterogeneous, Network lifetime.

1. Introduction In recent years, the potential use of Wireless Sensor Networks (WSN) has been increased rapidly in the applications like smart battlefield, healthcare, environmental monitoring, surveillance, traffic control etc. [1]. A Wireless Sensor Network consists of a set of small autonomous sensor devices called nodes, organized into a cooperative network. The sensors are used for sensing various ambient conditions, collecting and combining the data from the surroundings and finally sending the information to the Base Station (BS) or the central sink of the respective region. The sensors are constrained with limited resources like battery power, low cost, computing capability, low bandwidth and data rate. A WSN provides wide coverage in harsh and difficult environments that wasn’t possible with wired networks [2]. Denser levels of sensor node deployment, energy constraint, topology changes, limited communication capability and storage are some of the challenges faced by the WSNs. In the a network are distributed over a three dimensional space. In such cases, the area of interest needs to be studied as three dimensional space, being more representative, practical and equivalent to the real world [4].

2. Applications of Two Dimensional and Three Dimensional Wireless Sensor Networks [5] Wireless Sensor Networks find various applications in two dimensional (2D) areas. Some of the important ones are:

* Corresponding author

homogeneous sensor networks, all the sensor nodes are identical in terms of hardware capability and initial battery power. While in heterogeneous sensor networks, two or more types of sensor nodes with different capable resources are used. Such nodes are advance sensor nodes with more hardware capability and battery power. They may be used as Cluster Heads (CHs) in the network [3]. Further, the nodes are deployed depending upon the applications, where traditional wired communication is not feasible to be made available. Though the two dimensional design of wireless sensor networks is implicit and deliberated in most of the applications but it only gives limited results and observations. In such cases, considering the three dimensional design is more precise and applicable. The sensing of the environment in real world applications is in three dimensions only. Generally, it is assumed that all nodes of a network reside on a plane in two dimensional wireless sensor networks, where the third dimension is considered negligible in terrestrial sensor networks. This assumption is not always valid if a network is deployed in ocean or forests, where the nodes of  Radiation sensors are required in order to measure the amount of sunlight absorbed by the plants and trees.  Wireless Sensor Networks are used for tracking, smart battlefield surveillance, home automation and traffic control.  Long-term surveillance of chronically ill patients is done by Wireless Body Area Networks (WBAN). They are used for ambulatory monitoring of physical activities and healthcare [6].

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Journal of Algorithms, Computer Network, and Security, Vol.1 No.1, January 2016 

Traffic and parking management to minimize emissions and avoid unnecessary journeys.  Sensors are deployed to help authorities and security forces to measure the levels of radiation in the affected zones or areas, without compromising the life of the workers [7].  Sensor nodes may also be used for biodiversity mapping and disaster relief operations. Wireless Sensor Networks also find numerous applications in three dimensional (3D) areas:  Wireless Sensor Networks used for habitat and environment monitoring, sensors deployed on the trees of different heights in a forest require 3D design.  Structural health monitoring of multi-storey buildings is more efficient in a 3D WSN system to sufficiently monitor the target region [8].  Sensor nodes deployed in the volcanic region used to collect weather and image data also prefer the 3D design.  For ocean column monitoring and disaster relief management, 3D sensor networks are useful in case of major disasters including earthquakes, storms, flood, fires etc. [9].  3D Wireless Sensor Network is used for monitoring railway tunnels, underground tunnels in the mines and caves in the forests [10].  Moreover, systems have been designed in which wireless chemical sensors are air-dropped to collect data about chemical plumes, spread over a certain area.

3. Literature Survey A routing protocol is used to decide the way routers communicate with each other and evaluating what path will be the best for a packet to travel. In Wireless Sensor Networks, routing is challenging because sensor nodes have limited resources, memory and computational power which makes it hard for the routing to cope with unpredictable and frequent topology changes, especially in a mobile environment. Routing protocols in WSN might differ depending on the application and network architecture [11]. Clustering is a key technique which reduces the data transmission to Base Station and increases the lifetime of the whole network. A sensor network can be made scalable by forming groups or clusters. Leader of the cluster is often referred to as the Cluster Head (CH), which is responsible for coordinating the data transmission activities of all sensors in its group. All sensors in a cluster communicate with a Cluster Head that acts as a coordinator for performing data fusion. Figure1 shows the basic picture of clustering. Cluster Heads in turn transmit the sensed data to the global sink. Clustering can be used as an energy-efficient communication protocol. Clustering localizes the routing setup within each cluster and therefore reduces the routing and topology maintenance overhead. Using clustering, the network appears organized, smaller and more stable. Various clustering algorithms have been specifically

designed for improving the scalability and imparting efficient communication in WSNs [11,12].

Base Station Cluster 1 CH1

CH2

Cluster2 Nod

Node

Nod

Node

Figure 1. Clustering in WSN LEACH (Low-Energy Adaptive Clustering Hierarchy) [13,14] is a kind of cluster-based routing protocols, which includes distributed cluster formation. LEACH randomly selects some sensor nodes as Cluster Heads and rotates this responsibility to evenly distribute the energy load among the sensors in the network. The operation of LEACH is done into two phases, the setup phase and the steady state phase. In setup phase, firstly the clusters are organized and CHs are selected among each group or cluster. Cluster heads change randomly over time in order to reduce the energy dissipation of nodes. This resolution is made by selecting a random number between 0 and 1 by a node. If the number is less than the following threshold value T (n), the node becomes a cluster head for the current round. The value of T (n) is computed using the following formula: 𝑃

𝑇 (𝑛 ) = {

1 𝑝

(1−𝑝∗(𝑟𝑚𝑜𝑑 ))

𝑖𝑓 𝑛 ∈ 𝐺 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒

}

(1)

0 where in equation (1), G is the set of nodes that are involved in the CH selection. In the steady state phase, the original data transfer from node to CH and then CH to the BS takes place as shown in Figure2. The important features of this protocol are:  It rotates the Cluster Heads in a randomized manner to achieve balanced energy dissipation and consumption.  Sensors have synchronized clocks for knowing the beginning of a new round.  Sensors do not need to know their location or distance information.  The idea of active clustering brings extra overhead, e.g. head changes, advertisements etc.

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Journal of Algorithms, Computer Network, and Security, Vol.1 No.1, January 2016 Base Station

Nod e Nod

CH 1

Node

Nod e CH 2

Nod e

Figure3. Thus, to transmit a 1-bit message a distance d, the formula is [9]:

ETx (k, d) = { Node

Figure.2. Clustering in LEACH Protocol

4. Network and Energy Consumption Model In this paper, we have used the first order radio model for the network as shown in Figure3, which involves certain assumptions like [15, 16]:  In WSN, all sensors are randomly deployed in particular geographical area.  Sensor nodes are limited in power, computational and memory capacities.  The sensors communicate with each other or with the BS in the area they are deployed in.  The position of BS has been fixed in the network.  Being heterogeneous, the network has two types of nodes; advance nodes which have higher battery power and hardware capability as compared to the normal nodes.  All the sensor nodes collect the data from ambient environment and send the data to cluster head where data fusion and aggregation takes place before sending it to the Base Station.  Network lifetime is defined as the time span from the deployment to the time when the first sensor dies (or when the entire sensors die).  Compared with the power consumption of CPU and radio, the power consumption of sensor part is so small that it can be neglected.  The equations (2) and (3) are used to calculate transmission costs and receiving costs as shown in

kEelec + k ∈fs d2 , 𝑑 < d0 kEelec + k ∈fsmp d4 , 𝑑 ≥ d0

(2)

The electronics energy, Eelec, depends on the factors such as the digital coding, filtering and spreading of the signal. ε denotes the transmitter amplifier. For free space propagation, ε= εfs, when wavelength 2 is taken while for multipath space propagation, ε= εfsmp, wavelength is 4. do is the distance constant. For message reception, the equation will be:

ERx(d)=kEelec

(3)

Figure 3. Network and Energy Consumption Model

5. Implementation of Heterogeneous LEACH in Two Dimensional and Three Dimensional [4] Two Dimensional (2D) or bi-dimensional space is a geometric model of the planar projection of the physical universe, we live in. The two dimensions are commonly called length and width. Both directions lie in the similar plane shown in Figure4.

Journal of Algorithms, Computer Network, and Security, Vol.1 No.1, January 2016

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Figure 4. Bi-directional Cartesian Coordinate System

A Three Dimensional (3D) space is a geometric three parameter model of the physical universe as shown in Figure5. The three dimensions can be labelled by a combination of

three, chosen from the terms length, width, height, depth and breadth. Any three dimensions can be chosen, provided they do not lie in the same plane [17].

Figure 5. Three-directional Cartesian Coordinate System

5.1 Implementation of Heterogeneous LEACH in 2D The x and y coordinates are taken as the two dimensions, having 100 unit length at each side, with fixed number of 100

nodes deployed in the area of 100X100 with BS at (50,50) as shown in Figure6. For each node, x and y coordinates determine the position of that node in the network area.

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Journal of Algorithms, Computer Network, and Security, Vol.1 No.1, January 2016

Figure 6. LEACH in 2D Plane with Network Area of 100X100 with BS at (50,50) 5.2 Moving to 3D Implementation of Heterogeneous LEACH The 2D view of the network gives details of the nodes, deployed in the particular network area, but to a limited extent. There are certain applications where considering the network and the nodes from three dimensions is more practical and feasible, as stated in the foresaid part of the paper. The real world implementation and applications involve three dimensional views. Moreover, this three dimensional view gives more information and lets us to constitute a more efficient system of the wireless sensor networks. So, making the protocol analogous and closer to the real world, we have implemented the heterogeneous LEACH

protocol in the 3D space. For each node, the three coordinates i.e. (x, y, z) have been taken, for specifying its location in the network. The x, y and z coordinates are taken as the three dimensions, having 100 unit lengths at each side, with fixed number of 100 nodes deployed in the area of 100x100x100 with BS at (50, 50, 50) as shown in Figure7. For each node, x, y and z coordinates determine the position of that node in the network area. Secondly, we then changed the network area to 62x62x62 to increase the lifetime of batteries with BS at (31, 31, 31), computed according to the hexagon formula ((3√3/2) a2), to find out the coordinates has shown in Figure8.

Figure.7. LEACH in 3D Space with Network Area of 100x100x100 with BS at (50,50,50)

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Figure 8. LEACH in 3D Space with Network Area of 62x62x62 with BS at (31,31, 31)

6. Performance Evaluation All simulations have been implemented using MATLAB R2013a[18]. We have considered the network as a two dimensional network, as well as a three dimensional network. The performance of the heterogeneous LEACH protocol in 2D and 3D WSN has been evaluated the comparison of both is shown for a better pictorial understanding.

The normal nodes are marked by circles, advance nodes by a +, Cluster Heads by filled circles and dead nodes by red dots. 6.1 Simulation Parameters The various parameters taken in the implementation of the protocol are given Table 1.

Table 1. Simulation Parameters S. No.

Parameters

Values

1

Network Area

100x100, 100x100x100, 62x62x62

2

BS position

(50,50), (50,50,50), (31,31,31)

3

Number of nodes

100

4

Cluster head probability

0.1

5

Initial Energy

0.5 J

6

Transmission and Reception Energy

50*0.000000001 J/bit

7

Dissipation Energy

10*0.000000000001 J/bit

8

Data Aggregation Energy

5*0.000000001 J/bit

9

Percentage of nodes that are advanced

0.1

10

Control message

500 bits

11

Data message

4000 bits

12

Simulation rounds

5000

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Journal of Algorithms, Computer Network, and Security, Vol.1 No.1, January 2016 6.2 Simulation Results The simulation results are taken for the three cases. First one being the original heterogeneous LEACH in 2D plane, with network area as 100 x 100, having BS at (50,50); second case is of LEACH in 3D space with keeping the same coordinates as that of first case 100 x 100 x 100, having BS at (50,50,50) and considering the network as a cube and the last case takes the same network area as that of first case , with network as hexagon, as a result changing the coordinates in three dimensional space 62 x 62 x 62 , having BS at (31,31,31). We have studied the effect on the network lifetime by analyzing the round numbers for the three cases on the basis of observing the round numbers in which the first node and the last node of the network dies for 5000 simulation round and the total number of nodes which die after 3000 simulation rounds.

A node dies in the network when it doesn’t have sufficient energy. The round number when the first advance & normal nodes die for both 2D and 3D network on each simulation run has been taken for the three different cases for the simulation round of 5000 as shown in Table2. The comparison shown in Figure9 depicts that when the heterogeneous LEACH in 3D is considered, taking the network area as cube the first node dies early as compared to simple heterogeneous LEACH in 2D but when we consider the third case, taking the network area same as the first case and changing its coordinates accordingly in the hexagon, the first node dies later because the sensor nodes in the hexagon network are closely deployed and due to this, there is reduction in energy consumption for transmission and reception.

6.2.1 First Node Dying Scenario For evaluating the network lifetime, we have observed the round numbers when any of the first node dies in the network. Table 2. Different Scenario when First Node Dies Sim. Run

1 2 3 4 5

Round No. when first advance node dies in 3D Network 1670 1760 1660 1547 1635

Round No. when first normal node dies in 3D Network 897 945 903 890 844

Round No. when first advance node dies in 2D Network 1881 1669 1962 1569 1841

Round No. when first normal node dies in 2D Network 952 947 982 991 965

Round No. when first advance node dies in 3D (hexagon) Network 1516 1563 1439 1654 1512

Round No. when first 3D normal node dies in 3D (hexagon) Network 969 955 993 998 979

Figure 9. Round Numbers v/s Different Heterogeneous LEACH Cases when the First Node Dies

Journal of Algorithms, Computer Network, and Security, Vol.1 No.1, January 2016 6.2.2 Total Number of Dead Nodes after 3000 Simulation Rounds on Varying Packet Size On varying packet size in each case, the number of nodes which die after 3000 simulation rounds is observed and is shown in Figure10. Table3 shows the result of the same. For the case first and third of heterogeneous LEACH 3D, the

results obtained are almost same but in the second case for LEACH 2D, the lesser number of nodes die after 3000 simulation rounds as compared to that of heterogeneous LEACH 3D. This parameter helps to analyze the amount of nodes getting snip. Figure11 and Figure12 show the total number of dead nodes at different packet size.

Table 3. Total Number of Dead Nodes after 3000 Simulation Rounds on Varying Packet Size Packet Size

2000 2500 3000 3500 4000

Total Number of dead node in 3D Network 90 93 97 98 99

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Total Number of dead node in 2D Network 88 90 94 95 97

Total Number of dead node in 3D (hexagon) Network 84 92 96 97 98

Figure 10. Total Number of Dead Nodes after 3000 Simulation Rounds v/s Variation in Packet Size

Figure 11. Total Number of Dead Nodes after 3000 Simulation Rounds v/s Packet Size at 2000

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Journal of Algorithms, Computer Network, and Security, Vol.1 No.1, January 2016

Figure 12. Total Number of Dead Nodes after 3000 Simulation Rounds v/s Packet Size at 4000

6.2.3 Round Number when the Last Node Dies At a certain point, the whole network becomes dead, when all its nodes have insufficient energy. We have noted the round number when the last node of the network dies in Table 4. The lifespan of a network can be calculated from this parameter.

The result shows that for the first case of heterogeneous LEACH 3D of network area according to the cube, the sensor nodes die early as compared to the simple heterogeneous LEACH 2D while for the third case of heterogeneous LEACH 3D of network area according to the hexagon, nodes die later as compared to the first and the second case. The results are shown in Figure13.

Table 4. Round Number when Last Advance and Normal Node Dies Sim. Run

1 2 3 4 5

Round No. when last advance node dies in 3D Network 3600 3675 3655 2963 3632

Round No. when last normal node dies in 3D Network 1370 1456 1448 1414 1365

Round No. when last advance node dies in 2D Network 3017 4425 3283 3474 4351

Round No. when last normal node dies in 2D Network 1526 1644 1494 1485 1638

Round No. when last advance node dies in 3D (hexagon) Network 4881 3097 3859 3524 3900

Round No. when last normal node dies in 3D (hexagon) Network 1703 1759 1579 1572 1661

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Figure 13. Round Number v/s Different Heterogeneous LEACH Cases for Last Node Dead

7. Conclusion Recently, three dimensional Wireless Sensor Networks have shown a great popularity due to their large applications like ocean column monitoring, disaster management, habitat surveying, underground observing, etc. In this paper, we have implemented the heterogeneous LEACH protocol in 3D sensor network, where third parameter of height is considered along with length and width and compared its result with the heterogeneous LEACH in 2D network. The effect of network energy is observed on varying the size of packets being transmitted in the network and changing the area of the network as cube and hexagon. The lifetime of the network falls when the distance between the nodes decreases due to change in the network area. Though, considering three dimensions is closer to the real world applications, it consumes more energy. Further, work may be done on optimizing the energy dissipation of heterogeneous 3D networks.

5.

6.

7.

8.

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Authors’ Profile Anisha Somani received her B.Tech. degree in Electronic and Communication Engineering in 2014 from Rajasthan Technical University, Kota. She is pursuing her M.Tech degree from College of Engineering and Technology, Mody University of Science and Technology, Lakshmangarh in the field of Wireless Communication Technology. Sheena Kohli is currently working as Assistant Professor in Department of Computer Science and Engineering at Mody University of Science and Technology, Lakshmangarh, Rajasthan, India. She has received her B.Tech degree in Information Technology from Rajasthan Technical University, in 2010. She completed her M.Tech in Information Technology from Banasthali University, Rajasthan, India, in 2012. Her research interests include wireless sensor networks and underwater acoustic sensor networks Partha Pratim Bhattacharya was born in India on January 3, 1971. He has 19 years of experience in teaching and research. He served many reputed educational institutes in India in various positions starting from Lecturer to Professor and Principal. At present, he is working as Professor in Department of Electronics and Communication Engineering in the College of Engineering and Technology, Mody University of Science and Technology, Lakshmangarh, Rajasthan, India. He worked on Microwave devices and systems and mobile cellular communication systems. He has published a good number of papers in refereed journals and conferences. His present research interest includes wireless communication. Dr. Bhattacharya is a member of The Institution of Electronics and Telecommunication Engineers, India and The Institution of Engineers, India. He is the recipient of Young Scientist Award from International Union of Radio Science in 2005. He is working as the chief editor, editorial board member and reviewer in many reputed journals.