extending network lifetime using optimal … network lifetime using optimal transmission range in...
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Extending Network Lifetime using Optimal
Transmission Range in Wireless Sensor Networks
R. LOGAMBIGAI
Ramanujan Computing Center,
Anna University,
Chennai, India
K. THANIGAIVELU
Research Scholar,
Ramanujan Computing Center,
Anna University
Chennai, India
Dr. K. MURUGAN
Associate Professor,
Ramanujan Computing Center,
Anna University,
Chennai, India
Abstract— In wireless sensor networks (WSN), the energy hole
problem is the major factor which shorten the network lifetime.
While transmitting the data, a sensor node dissipates energy. The
energy consumption rate of sensor nodes differs with the
different transmission range distance. A sensor node with the
same transmission range dissipates more energy to forward the
data for the short distance and it results in higher energy
dissipation. In this paper, in order to find the best transmission
range which utilize the energy of the nodes efficiently and
prolongs the network lifetime, an Optimal Transmission Range
(OTR) algorithm is proposed. While transmission, a node
transmits the data to all the nodes within its transmission range
which results in unwanted energy consumption. In order to save
energy of the nodes, a region is constrained and the data are
transmitted to the nodes within the region. The random selection
of nodes to forward data will result in energy imbalance. To
balance the energy consumption, a maximum residual energy
node is selected in the region to transmit the data. Simulation
results show that the region constraint strategy with the OTR
outperforms same transmission range.
Keywords- Wireless sensor network, Transmission range,
Energy-hole, Network lifetime, Energy balance, Residual energy
I. INTRODUCTION
The advances in wireless communication and electronics over the last few years have enabled the development of networks of low-cost, low-power, multifunctional sensors that are small in size and able to sense, process data and communicate with each other [13]. Wireless Sensor Network (WSN) is comprised of sensor nodes with small form factors, a portable and limited energy supply to monitor physical conditions of an area of interest with broad applications, including disaster relief, environment control, bio-diversity mapping, etc. [13]. Due to the limitation of energy supply, the energy consumption of sensor network should be balanced wisely to prolong the network lifetime.
In many-to-one sensor networks, all sensor nodes generate and transmit data to a single sink. Since the sensor
nodes near the sink have to relay more data, it will deplete their energy much faster than other nodes which form the energy hole in a sensor network. It has been observed that the sensor nodes closer to the sink tend to die faster than those farther away from the sink, causing energy hole problem [3]. Consequently, a considerate amount of energy is wasted and the network lifetime ends prematurely. The most widely used model for analyzing energy hole problem is corona model. Authors in [9] present the model of concentric coronas to analyze energy hole problem. If we increase the number of nodes near the sink still it depletes their energy faster since it is only nodes which directly transmit the data to the sink. Here, we analyze the energy consumption of the nodes near the sink with non-uniform transmission range for the nodes in the network. The nodes which have maximum transmission range will transmit data directly to the sink instead of transmitting to the nodes near the corona. In order to save the energy of the nodes in the network, an Optimal Transmission Range (OTR) algorithm is proposed to find the better transmission range from the maximum transmission range of the nodes. To transmit the data with the better transmission range it will select only the maximum energy node with the closer distance. So, in order to balance the energy consumption of the nodes in the network, Region constraint strategy approach with the maximum residual energy selection scheme is proposed. This will balance the energy of the nodes in the network significantly. Performance metrics like network lifetime, throughput and energy dissipation are evaluated and compared the region constraint with OTR and with same transmission range.
The rest of the paper is organized as follows. In Section 2 related work are discussed. In Section 3, the proposed work is discussed with the network model and energy model. In Section 4, simulation scenario and metrics are discussed. Simulation results and analysis are provided in Section 5 and in section 6 the conclusions of this paper are drawn.
II. RELATED WORK
In the recent research on wireless sensor network indicates that the energy hole problem affects the network lifetime. The energy of sensor nodes is also consumed unevenly in the network. X.Wu, G.Chen and S.K. Das [7] investigate the theoretical aspect of the non-uniform node distribution strategy to mitigate the energy hole problem. The authors said that balanced energy depletion in the network is possible if the number of nodes increases in geometric progression from the outer coronas to the inner ones. They proposed q-switch routing, a routing algorithm for the non-uniform node distribution strategy. Li and Mohapatra [11] study the uneven energy consumption problem in a large-scale wireless sensor network with many-to-one communication. The authors describe the energy hole in a ring model (like corona model). The authors present some approaches to solve the energy hole problem, including deployment assistance, traffic compression and aggregation. Olariu and Stojmenovic [9] discuss the relationship between the network lifetime and the width of each corona in concentric corona model. The authors prove that in order to minimize the total amount of energy spent on routing, all the coronas must have the same width. The problem of designing energy balanced routing scheme for wireless sensor networks is examined in [2]. H.Su and X.Zhang in [10] presented a mixed communication modes to balance the energy dissipation which prolongs the network lifetime. They discussed that sensor nodes can communicate with the cluster head in either single-hop or multi-hop mode. Authors in [10] presented an effective way for the energy hole problem. They used the direct transmission protocol and hop-by-hop transmission protocol to achieve energy-balanced consumption.
III. PROPOSED WORK
In this section, we describe the network model and energy model to investigate energy hole problem in WSN. In this paper, improved corona model [2] for sensor networks with different transmission range is used to analyze the energy hole problem.
A. Network Model
We assume that, sensor nodes are static and uniformly distributed in a circular field of radius rd with the single static base station being located at the centre of the network. Each sensor node has a non-rechargeable battery with the initial energy ε > 0. Sensor nodes in a corona have same transmission range. Sensor nodes in different corona have different transmission range. Sensors in a ith corona has a maximum transmission range txi. Sensor nodes use different radio power to transmit data to different ranges. We assume that each sensor node generates and sends l bits of data per unit time.
Definition (Corona lifetime) [11]. It is the ratio of the total initial energy in corona Ci and the energy consumption per unit time in corona Ci.
Definition (Network lifetime) [11]. It is the time interval from the very beginning of the network operation until the
instant at which the first sensor node depletes its energy. It is the shortest lifetime of a sensor node.
B. Energy Model
Each node is composed of three basic components: a sensing unit, a computing/processing unit and a wireless radio unit. Our energy model involves only the energy consumption of wireless radio which consumes energy for sending and receiving data. The radio model in [5] is used for analysis and simulations. The energy consumption rate for transmission of data over a distance d is given by
Etrans = (β1 + β2dα) l (1)
and the energy consumption rate for receiving data is given by
Erecv = β3l (2)
Here, l is the data rate of generating and sending data for sensor node, and α is 2 or 4, the term d
α accounts for path loss.
According to [5], some typical values for above parameters in current technologies are as follows:
β1 = 45 x 10-9
J/bit,
β2 = 10 x 10-12
J/bit/m2 (when α = 2) or
β2 = 0.001 x 10-12 J/bit/m4 (when α = 4),
β3 = 135 x 10-9
J/bit
C. Description of the Problem
Sensor nodes in a network use different levels of radio power to send their data to different transmission ranges. As in figure 1, we divide the maximum transmission range txi of sensor nodes of ith corona into ki levels. The unit length of transmission range denoted by d, satisfies the following equation,
d = txi / ki (3)
Figure 1. Transmission Range Levels
We partition the whole network region with radius R into m adjacent concentric parts called coronas (figure 2). The width of each corona is d and Ci denotes the ith corona. We denote m, the number of coronas by equation as follows :
m = R / d (4)
txi
4d
3d
2d
d
Figure 2. Corona Model
We assume that all nodes in the same corona have the same transmission range which is called the transmission range of the corona and the nodes in different corona have different transmission ranges. Let xi denote the transmission range level of the nodes in corona Ci (xi = 1, 2, . . . ki) and ki denote the maximum transmission range level for this corona. Let Ni
denotes the number of nodes in corona Ci.
The nodes in each corona transmit its generated data and also forward the data from the nodes in outer coronas. Assume each sensor node in Corona Ci generate and transmit L bits data per unit time.
Etransi=NiL[β1 + β2(xid)α] xi = 1, 2, . . . ki (5)
The energy consumption of forwarding data from outer coronas to corona Ci includes the energy consumption for receiving and transmitting. Let Dreci denote the data received from outer coronas by the nodes in corona Ci per unit time.
Eforwardi=Dreci [β1 + β2(xid)α+β3] xi = 1, 2, . . . ki (6)
Let Ei denote the total energy consumption for corona Ci per unit time. Ei includes
Ei=Etrams i +Eforward i (7)
Let Ti denote the Corona lifetime of corona Ci. It satisfies:
Ti = εNi / Ei (8)
D. OTR Algorithm
In this section, the algorithm used to find the optimal transmission range for all coronas is explained. In order to prolong the network lifetime, we must find the optimal transmission range for all coronas. We propose OTR algorithm for sensor networks with non – uniform maximum transmission range. The OTR algorithm finds an optimum transmission range from the inner corona to outer corona.
For example, consider the number of coronas to be 5. Assume the maximum transmission range for corona 1, 2 to be 2d, 3 to be 3d and for 4 and 5 to be 4d. With the transmission range of each corona form the corona relationship table as shown in Figure 3. For example, the corona C1 is able to transmit data to the sink by using the transmission range d, and its next hop is corona C0 (sink node). For corona C3, it can choose the transmission ranges d, 2d, 3d to transmit data to the corona C2, C1, C0.
C1 C2 C3 C4 C5
d C0 C1 C2 C3 C4
2d C0 C1 C2 C3
3d C0 C1 C2
4d C0 C1
Figure 3. Corona Relationship
As in figure 4, the directed graph for 5 coronas has 5 vertices with the edges connecting according to the corona relationship. The vertex 1 to 5 indicates corona C1 to corona C5 and vertex 0 indicates the sink. The number of out edges of each vertex is equal to the maximum transmission range. For corona C5, the maximum transmission range is 4d. So the number of out edges for vertex 5 is 4.
Figure 4. Directed graph
Corona C1 directly transmits the data to the sink. If the T2 value from 2 to direct sink is maximum than T (2-1), then corona C2 directly transmits data to the sink. Then the transmission range for C2 is 2d. Suppose the minimum value path for 3 is 3-1-0, then T3 is calculated as the maximum of T(3-2-1-0), T(3-2-0) and T(3-1-0). if T(3-1-0) is maximum then transmission range for C3 is 2d. Similarly for C4 and C5 is calculated. If T4 is the maximum value of T(4-3-2-0) then the best transmission range for C4 is d and if T5 is the maximum value of T(5-3-2-0) then its transmission range is 2d.
The OTR algorithm is described as follows:
Step 1 Assume the maximum transmission range for each corona.
Step 2 Build the corona relationship table with the assumed maximum transmission range of all coronas.
Step 3 Construct weighted directed graph with corona ID as vertices and relationship between coronas as edges.
Step 4 Calculate Ei value for each corona with different transmission levels. These Ei values are treated as weights for the edge.
Step 5 Calculate the minimum Ei value path from each vertex to the destination. Calculate Ti value for minimum value path and for other out edges to the sink path. From these Ti values select the maximum Ti value.
5
3
4
1
2
0
Step 6 The next immediate vertex from the vertex i in the maximum Ti value path gives the transmission distance for the vertex i.
Step 7 Repeat the steps 5 & 6 for each vertex of the directed graph.
E. Region Constraint Strategy
In order to forward the data to the limited number of nodes, a region is selected based on some constraints. The node with the transmission range d transmits a data; it will received by all the nodes within its transmission range. In order to preserve the energy of the nodes, the node transmits data to the nodes in the certain region. In our proposed method, the region constraint strategy is used to select the region based on their transmission range distance. During network deployment, the transmission range for sensor nodes of each corona is set. Consider nodes of corona Ci have the transmission range 2d. Then region is formed for each node of corona Ci with the distance 2d. The constrained region consists of set of nodes to forward the data. If the region is found with no nodes, then region of distance d is found.
F. Maximum Residual Energy Node Selection
For transmission of data, the random selection of nodes in a constrained region results in unbalanced energy consumption in each corona. In order to balance the energy of each corona, a maximum residual energy node is selected for transmission. For each node of Corona Ci, constrained region contains set of nodes. By broadcasting the query, the energy level of each node in the region is known to the node. Then from this information the maximum residual energy is selected for transmission. Periodically, the maximum residual energy node is chosen from the set of nodes in the region for transmission.
IV. SIMULATION ENVIRONMENT
The proposed work was evaluated using NS-2[14]. Sensor nodes are scattered across an area of 250x250m. All the sensor nodes have the initial energy (ε) of 5J. The maximum radio transmission range of sensor nodes is 200m. The number of transmission levels (k) is 4. Simulation parameters are shown in Table 2.
TABLE I. SIMULATION PARAMETERS
PARAMETER VALUE
Initial Energy of each node (ε) 5J
Radio-Propagation Model Two Ray Ground
Simulation Area 250 x 250 m
Maximal Transmission Range (tx) 200m
Number of Transmission Levels (k) 4
Length of unit data (L) 19.2x103 bits/sec
Number of Nodes per corona 20 to 50 (variable)
Number of Corona 5 to 10 (variable)
Packet Size 512 bytes
Tx Power Dissipation 0.00360w
Rx Power Dissipation 0.00295w
Idle Power Dissipation 0.0035w
V. SIMULATION RESULTS AND ANALYSIS
We compare the region constraint strategy using OTR algorithm with the same transmission range by increasing the number of nodes in a corona. The experimental results show that the OTR algorithm performs better than the other for all the performance metrics including network lifetime, throughput and energy dissipation of a node. Figure 5 shows the network lifetime with two approaches. It is seen that the network lifetime of the OTR algorithm increases as the number of nodes in the network increases.
Figure 5. Network Lifetime
Figure 6 shows the energy dissipation at network lifetime. It is observed that using OTR algorithm the nodes dissipates more energy than the other one, since its network lifetime is increasing. Since the sensor nodes with same transmission range have minimum network lifetime, the energy dissipation is low. In OTR, using equation (8) the Ti values are found which minimize the energy consumption by finding the maximum optimal transmission range for each corona thereby increases the network lifetime.
Figure 6. Energy Dissipation
Figure 7 shows the throughput of OTR algorithm and the same transmission range. It is observed that the percentage of throughput decreases. As the number of nodes per corona increases, more number of packets will be transmitted in the network leading to congestion at the sink thereby reducing the throughput. In the higher number of nodes we can see the variation in throughput. Number of nodes upto 40 in both the approaches the throughput is decreasing. But from number of nodes greater than 40 the throughput of OTR is higher than other.
We also conducted simulation with increasing the network radius. The number of coronas varies from 5 to 10.
Figure 7. Throughput
Figure 8. Network Lifetime
Figure 9. Energy Dissipation
Figure 10. Throughput
Figure 8 to Figure 10 shows the performance of OTR algorithm and the same transmission range for network lifetime, throughput and energy dissipation at network lifetime for different network radius.
From figure 8, it is observed that the network lifetime decreases as the network radius increases in both approaches. This is because the data traffic is increasing while the radius is increasing, especially from the inner coronas. From figure 9, we observe that the total initial energy is increasing while the network radius is increasing. So the residual energy of the sensor nodes is slowly increasing.
In figure 10, we note that the throughput is decreases as the network radius increases. This is due to the number of packets increases which leads to congestion, affects the network throughput. Upto number of corona 6 the performance of both OTR and the other are same. For number of coronas greater than 7 the performance of OTR is better than the same transmission range.
VI. CONCLUSION
In a typical WSN scenario, the nodes nearer to the sink dissipate more energy and nodes in other coronas have to forward the data which results in imbalance of energy consumption. The OTR algorithm performs better by finding the best transmission range of the nodes to save the energy than the same transmission range for all the nodes. By using the best transmission range for the nodes and maximum energy node selection with region constraint prolongs the network lifetime. Simulation results and analysis show that OTR algorithm gives better performance than region constraint strategy. It is further planned to study the energy consumption and network lifetime with different node distributions in the network.
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