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BUFFER CAPACITY BASED NODE LIFE TIME ESTIMATION IN WIRELESS SENSOR NETWORK Kamalajith I Laktharia 1, G.R. Kanagachidambaresan2, R. Maheswar3, V. Mahima 4 and Ashraf Darwish 5 Department of CSE, Gujarat University, India1 Department of EEE & ECE, Sri Krishna College of Technology, Coimbatore, India2, 3&4 Professor, Faculty of Science, Helwan University, Egypt 5
[email protected] 1,
[email protected],
[email protected],
[email protected] 4and
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Abstract Lifetime improvement is the mandatory part of Wireless Sensor Network. The lifetime of the node is majorly influenced by the data sent, distance between nodes and residual energy of the node. The number of data transmitted cannot be limited; however the number of transmitter on and off energy dissipation could be minimized. The data transition cost and holding cost is derived with respect to data arrival rate. This article analysis the energy of dissipation of the node with respect to different arrival rate and buffer threshold value. The analysis is carried for different distance and different arrival rate. The data arrival rate to the node is considered as poisson in nature. The results prove that the threshold based model provides considerable amount of energy savage to the node, thereby can able to increase the network lifetime.
question of continuous monitoring. The node working in static closed monitoring does not cooperate self-harvesting. In such cases, manual replacement or routine recharging becomes necessary [8]. The network lifetime is mainly determined by the individual node lifetime [13-16].
Keywords: Wireless Sensor Network, network lifetime, threshold, energy
1. INTRODUCTION Wireless Sensor Network has become so inevitable for serving applications of monitoring events in the remote environment [1-6]. The number of battery replacement and charging (i.e.) human intervention can be limited by increasing the lifetime of the node. The lifetime of the node is majorly influenced by the distance between the transmitter and receiver, battery capacity, and number of data sent. Increasing the battery size would sacrifice the size of the node, making undue for monitoring. Thenumber of data transmitted cannot be sacrificed. The option of increasing the lifetime of the node is limited making researchers to seek a novel approach without sacrificing the constraint [7-12]. Figure 1 illustrates the typical Wireless Sensor Node with sensor, processing, transceiver and power unit. The lifetime of the node having selfharvesting unit does not need a battery replacement. However in such cases the availability of the power to harvest energy creates a
Figure 1 Wireless Sensor Node Architecture 2. Related works Many routing algorithms have been proposed to enhance the network lifetime [1-6]. The basic LEACH protocol addressed provides a random number based CH selection. However only few papers have considered the energy dissipated due to radio on and off. The energy dissipated due to radio on and off mainly depends on the MAC protocol we follow. The MAC protocol and their handshake properties are addressed in [19]. The number of handshake is minimized in [20] with respect to arrival rate. However the energy saving with individual node is not determined. The FSFT and TA-FSFT routing algorithm addressed in [2 & 4], provides a solution to energy saving. The energy consumed due to
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handshake signal is not broadly discussed in the papers so far. Limiting the number of handshakes by a queuing model provides additional energy saving reporting increased network lifetime.
(3) Where E[I] can be expressed as in equation 4. (4)
The rest of the paper is organized as follows. The proposed system is explained in Section 2. The results and discussion are given in Section 3 followed by conclusion.
3. PROPOSED SYSTEM
Equation 5 elucidates the relation of traffic intensity with idle condition probability (5) Calculating E[C] from equation 3 and 4. (6)
The node consumes high energy during transmission than receiving. Equation describes the second order radio model of the transceiving unit. ETX(k,d) = Eeleck + Efskd2; dd0 ERX(k) = Eeleck
Where
The number of cycles per unit time (Cy) is given in equation 7.
(1)
(7) (2)
Hence Cy is obtained from equation 7 and it is given as,
Where k : No. of bits, d : Distance, Eelec : Energy dissipated per bit to run the transmitter or the receiver circuit, Erx : Energy dissipated during receiving data Efs(pJ/(bit-m-2)), Emp(pJ/(bit-m -2)) :Energy dissipated per bit to run the transmit amplifier based on the distance between the transmitter and receiver. Transition cost and holding cost of the sensor node Terminologies Λ Arrival rate µ Service rate ρ Traffic Intensity (i.e) ratio of arrival rate to service rate. T Threshold ETX Energy consumption due to transmission of a packet from a sensor node to CH in joules ETR Energy consumption due to transitions and synchronization in joules E[T] Average energy consumption of a sensor node as a function of T in joules E[I] Average duration of time the sensor node is in Idle state E[C] Average duration of cycle Cy Number of cycles per unit time L Average number of packets PI Probability of sensor node to be in idle state. Probability that the sensor node in Idle state (PI) can be defined as the ratio of the average duration of time the sensor node is in Idle state to the average duration of cycle. Equation 3 illustrates the probability of node to be in idle condition.
(8) Now, the average energy consumption of an sensor node E(T) can be expressed as , (9)
4. RESULTS AND DISCUSSION The model is simulated in Matlab with the following prelims. Figure 2 illustrates the network architecture for simulating the model.
Figure 2 Network model Simulation Parameters Parameters
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Value
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Eelec 50nJ/bit Efs 10pJ /bit-m2 Initial Energy 0.5 Joule Probability of becoming a Cluster Head 0.1 Normal Data message size 2000 bytes Header bytes 50 bytes Operation Time(sec) I (mA) Initialize Radio 350e-6 6 Turn on radio 1.5e-3 1 Switch to transmit mode 250e-6 15 Transmit 1 byte 416e-6 20 Figure 3 illustrates the arrival rate of the node following poisson distribution.
Figure 4 Queue threshold (T) vs average energy consumption (mJ) showing minimum energy consumption at optimal threshold Figure 5 shows that the probability of packet loss decreases as T increases for mean arrival rate per node of 5 and 10 packets/sec. From Figure 5, it is observed that, for higher value of T, the buffer gets more free space when the packets are transmitted when compared with transmitting packets when lesser value of T is reached. It is also observed that the packet loss is almost zero.
Figure 3 Poisson distribution (λ) The average energy consumption of a node is determined for various values of T and it is shown in Figure 4. From Figure 4, it is inferred that, as T increases, the average energy consumption per node decreases and then increases and minimum energy is consumed for the optimal threshold. The average energy consumption per node decreases as T increases because, as T increases, the average number of cycles decreases resulting in less energy consumption. The average energy consumption per node increases as T increases because, with increase in T, the average number of cycles decreases but increases the average number of packets in the buffer resulting in more energy consumption. From Figure 4, it is also clear that the minimum energy is consumed for the optimal threshold value (T*) = 8. Figure 4 illustrates the node lifetime with different arrival rate. The node possess high lifetime maximum lifetime on certain threshold.
Figure 5 Queue threshold (T) vs probability of packet loss Figure 6 illustrates the average number of cycles by node with respect to different arrival rate. From Figure 6, it is inferred that the average number of cycles reduces as the value of T increases. Here, the transitions from idle state to active state and vice versa in a sensor node is less when the queue threshold is high because the time taken for the buffer to be filled with threshold number of packets for high value of T is more when compared to that for a low value of T. Hence the average number of cycles is reduced when T increases. Also from Figure 6, the average number of cycles increases as the mean arrival rate per node in a cluster
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increases because when the arrival rate increases, the buffer is filled with threshold number of packets quickly thus resulting in increased number of transitions. This phenomenon is shown in Figure 6 which clearly shows that the average number of cycles is increased when the mean arrival rate per node in a cluster increases.
state has significant effect in minimizing the average energy consumption of the sensor node. The results clearly indicate that the average energy consumption per node can be reduced to a large extent by selecting an optimal threshold value. It is also concluded that the probability of packet loss decreases as queue threshold increases. The results also show that the analytical results present an excellent matching with simulation results validating the accuracy of this approach.
ACKNOWLEDGEMENT This work was partially supported by Electronics Sector Skills Council of India sponsored Centre of Excellence in VLSI and Embedded System Design, Sri Krishna College of Technology, Coimbatore, India.
REFERENCES
Figure 6 Queue threshold (T) vs average number of cycles Figure 7 illustrates the energy saving with different buffer capacity. Figure 7 shows the energy consumption savings (%) for different values of T. By assuming T = 4, T* = 8 and T = 12 and mean arrival rate per node = 5 packets/sec, the energy consumption savings (%) is determined and it is found to be 71%, 79% and 78% respectively when compared to no threshold condition. It is inferred that the (%) saving in energy consumption is maximum for the optimal threshold value (T*) = 8.
Figure 7 Queue threshold (T) vs energy consumption savings (%)
CONCLUSION Results obtained show that the introduction of queue threshold to make the sensor node to switch from idle state to active
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