Energy Optimization in Zigbee using Prediction

based Shortest Path Routing Algorithm

Dhirendra Pratap Singh

Department of Electronics &

Communication Engineering National Institute of Technology

Hamirpur-l77 005, HP, India dhirendrapratap.nith@gmail.com

Yash Agrawal

Department of Electronics &

Communication Engineering National Institute of Technology

Hamirpur-l77 005, HP, India mr.yashagrawal@gmail.com

Surender Kumar Soni

Department of Electronics &

Communication Engineering National Institute of Technology

Hamirpur-l77 005, HP, India soni@nitham.ac.in

Abstract- Zigbee is a wireless standard that allows small amount

of data transfer to comparatively small range. It needs to confirm

802.15.4-2003 Low-Rate Wireless Personal Area Network

(WPAN) standard. In the present paper, Zigbee module along

with shortest path routing algorithm is defined. Prediction based

algorithm is defined. It helps in minimizing number of

transmissions. Along with it energy optimization is used. It helps

sink node to significantly save its energy. The simulations are

carried using MA TLAB.

Keywords-Wireless Sensor Networks; Data reduction approach;

GM(l,l) Model; Zigbee; IEEE 802.15.4 protocol.

I. INTRODUCTION

Zigbee is relatively new wireless standard. It is widely used in wireless monitoring and control applications for home and industrial environments which require lower data rate and longer battery life [1]. Zigbee standards are defIned by Zigbee Alliance and IEEE 802.l5.4. In Zigbee mesh network topology is used. Zigbee operates at 250kbps at 2.4GHz (global), 40kbps at 915MHz (America) and 20kbps at 868MHz (Europe) [2]. Shortest routing algorithm is used here [3-4]. It defmes the shortest path. It considers mediator router distance along with destination router distance for its calculation.

Prediction based algorithm is defined here. Transmitting section consists of sensing node and Zigbee routers. In this, sensing node senses the information signal and also predicts the future value. If the predicted value differs largely as compared to actual sensed data, then only it sends the data through Zigbee routers. If the predicted value is comparable to actual data, then it does not transmit data and hence numbers of transmissions are reduced. In receiver section also, monitoring device predicts the values. If it does not receive any data from Zigbee routers then it considers its predicted value as final value and if it receives information through Zigbee routers, then it updates the information coming through routers.

Here, Grey model is used for predicting the future values of time series data depending on some past given values of data [5]. Grey model is most widely used prediction model which can be utilized in many applications in which data is given in terms of time series.

978-1-4577-0748-3/12/$26.00 © 2012 IEEE

Presently, Grey models are being used in many applications such as industrial, fmancial, medical, transportation, geological, military, economic, meteorological etc. GM (1, 1) model is the basic model which can be denoted as "Grey model first order one variable". It has the advantage over other time series prediction models such that it is more accurate and less complex and hence widely used.

The paper is arranged as: Section I is the introduction. Section II defmes the routing algorithm in Zigbee. Section III describes the Prediction based approach for predicting the future values of the given time series of information data. The model used for prediction, known as GM (1, 1) is discussed in Section IV. Section V gives mathematics of GM (1, 1) model. Section VI gives Energy optimization approach. Simulations and results are given in section VII and Section VIII contains the conclusion.

II. ROUTING ALGORITHM

The routing algorithm defInes the path between source and destination node. Information is transmitted through routers. Zigbee uses mesh topology. It consists of routers arranged in mesh network. Every Zig bee mesh network consists of one coordinator. It is as shown in fIg. 1.

Monitpring device

Router

End device

Figure 1. Zigbee mesh network

R

R 01 dl

04 s2 d2 R

s5 03 d3

R R

Figure 2. Selection of routing path in Zigbee

Figure 2 defmes the routing path in Zigbee mesh network. Here information is to be send from node A to node B.

The shortest path routing algorithm is defmed as:

• Here suppose data is to be transmitted from node A to node B. Then source and destination addresses are taken.

• In the fIrst step, node A calculates the number of neighboring routers around it.

• Then it calculates its distance with all its neighboring routers viz. sl, s2, s3, s4, s5.

• In the third step, all its neighboring routers calculate their distance with the destination router viz. dl, d2, d3. For better visibility of diagram, d4 and d5 are not shown.

• Then for each router, router's distance with source and destination as calculated in step II and III is summed up.

• Lastly, minimum distance from step fourth is taken and this is the shortest path between the source and destination node.

III. PREDICTION BASED DATA REDUCTION ApPROACH

In this approach, Routers and monitoring device will use same length prediction queues to store the predicted data values and same prediction model [11]. Data values used for predicting future values must be same for routers and monitoring device. Monitoring device will have 'n' numbers of prediction queues for 'n' routers. In each round, routers will sense the actual information data and will calculate the prediction value for corresponding round. Then router will calculate the prediction error between actual sensed value and the predicted value. If this error is less than the predefmed prediction error ethreshoJd denoted by 'ethreshoJd', router will not send this value to the monitoring node. But if the prediction error exceeds the predefIned prediction error threshold (ethreshoJd), router will update this value in its queue and will use it for further predictions. Now router will also send the copy of this value to the monitoring device. Monitoring device will update this value in its prediction queue of corresponding router and will also use this value for further predictions. This approach reduces a signifIcant number of data transmissions between routers and monitoring device. This reduced data transmissions will results in lesser energy consumption of router battery. In this way, we can enhance the lifetime of the batteries so that it can work for a long while.

IV. GREY PREDICTION MODEL

According to the system theory, any system can be defIned by color as:

(1) Black system: The system in which the information or the parameters are totally unknown.

(2) White system: The system in which the information or parameters are totally known.

(3) Grey system: The system in which some information or the parameters are known and some are unknown [7].

Grey models are most widely used predictive models for predicting the future values of a time series using some past values. GM(n,m) can be denoted as a grey model, where n is the order of the differential equation and m is the number of variables. GM (1,1) has the advantage over other predictive models used for predicting the future values in time series in terms of its computational effIciency and less complexity. GM(1,I) is pronounced as "Grey model fIrst order one variable". Presently, Grey (1,1) are being used in many applications such as industrial, fmancial, medical, transportation, geological, military, economic, meteorological etc [8].

V. MATHEMATICS OF GM(1,l) MODEL [5,6]

Let X(O) is the primitive data used for predictions and can be expressed as

Where N is the full length of data sequence.

To remove the randomness of the primitive data, it is subjected to the Accumulation Generating Operator (AGO) and the accumulated data can be expressed as

X(J) = [x(l)(1) x(J)(2) x(J)(3) ................. x(J)(r)] (2)

r

Where, X(1{t) = L x(1)(k) k=\ (3)

Then, x(O)(r) + a x(J)(r) = b is referred to as basic form of GM(1,I) model. Let Z(J) is the sequence generated from X(l) by adjacent neighbor mean and can be expressed as a sequence Z(J)=[z(J)(1) z(J)(2) z(J)(3) ...... z(J)(r)]. where z(\)(r) = alpha x(dr) + (I-alpha) x(l)(r-l) and for GM(1,I) model, alpha (weighing factor) is taken as 0.5.

The grey model parameters can be defmed as a (developing coeffIcient) and b (driving coeffIcient) and are calculated by Least square method using the equation given as

- Z (J ) (2) Where, B = -z(\) (3)

X(o)(2)

x(o)(3) and A=

x(o)(r)

(4)

Now, Whitenization (or image) equation of GM(1,l) can be

dx(l) expressed as -- + ax(1)= b

� 0) According to the Whitenization equation, the solution of

x(1)(r) at time (r+ 1) can be expressed as

Xp(l)(r+l) = [X(o)(1)-b/a]e-ar +b/ a (6)

To obtain the predicted value of the primitive data at time (r+ 1), the IAGO is used to establish the following grey model.

The prediction error can be computed as

e(r+ 1) = Ixp(o)(r+ 1 )-x(o)(r+ 1)1

Percentage prediction error can be calculated as

e(r+l)(%) = IXp(o)(r+ 1 )-x(o)(r+ 1)1

-'--------'-x 100 x(o)(r+ 1)

(7)

(8)

(9)

Mean absolute percentage error (MAPE) can be calculated as

N

Iei MAPE= �xl00

N

VI. ENERGY OPTIMIZATION

(10)

Lifetime of the Zigbee sensor node is a challenging factor which depends on its energy consumption. The communication part is the major cause of energy consumption which should be minimized so as to maximize the lifetime of sensor nodes. Hence prediction based approach can be used to reduce data transmissions as a result, significant amount of energy can be saved. Hence energy of the Zigbee sensor node can be optimized by this process.

SIMULATIONS & RESULTS

The simulations in this paper have been carried out using MATLAB.

The depleted energy of the sensor nodes in transmitting I bits over a distance 'D' between Zigbee sensor nodes and monitoring device for each round can be expressed as [10].

ETx (k,D) = ETx.elec (k) + ETx-amp (k,D)

ETx (k,D) = k Eelec + k €ampD4 for D>do

ETx (k,D) = k Eelec + k €rsD2 for D::Sdo

Where do = (€r/€amp)1!2

(11)

(12)

(13)

(14)

Where, Eelec represents power consumption in transmitting circuit or receiving circuit. €amp represents energy consumption

TABLE L TABLE OF PARAMETERS

S.No. Parameters used in Simulations

Parameters Values Units 1 Initial node energy 0.1 Joules

2 Data length 5000 Bits

3 Ee\ec 50 nJlbit

4 €am 0.0013 pJ/bit/m4

5 €r, 100 pJ/bit/m'

6 E.,e' 5 nJlbit

7 Window size 3

in the circuit to launch k-bits of information to 1 meter2 and €fs represents energy consumption in the circuit to launch k-bits of information to 1 meter2 in free space.

The analysis has been done for 75 rounds. Window size (data values used for prediction) is taken as '3'. The simulation has been carried out for different prediction error threshold (ethreshold) ' The number of transmissions and Mean absolute percentage error (MAPE) are calculated for different values of ethreshold·

Figure 3 shows the data transmission from router 'A' to monitoring device 'H' through the shortest path.

Zigbee select the shortest path for transmitting information data from Zigbee routers to monitoring device. In the figure 3, the router 'A' is transmitting information data to the monitoring device 'H' through C and E which is the shortest path between A and H.

Figure 4 shows the percentage prediction error 'e' for etllfeshold = 5%. In the paper, the simulation has been carried out for percentage prediction error 'e' given by the formula of 'e %' in Section III. If e % exceeds the ethreshold, router A has to transmit information data to the monitoring device.

The Mesh Network

{)

6

. 1;-1 ----:---;----:-----:-----7-----;--!

Figure 3. Routing in Mesh network

10 20

Prediction Error plot

30 40 Time

50 60 70 80

Figure 4. Percentage prediction error (e %) for e,h",hold =5%

TABLE II . TABLE OF NUMBER OF TRANSMISSIONS PERFORMED AND MAPE FOR DIFFERENT V ALUE OF THRESHOLD PREDICTION ERROR

S. No. Results (out of 75 rounds) ethmho/d (%) Number of transmissions MAPE

1 1 65 4.5374

2 2 55 4.5547

3 3 47 4.6220

4 4 40 4.6478

5 5 30 4.5643

6 6 26 4.6358

7 7 18 4.6402

8 8 17 4.9039

9 9 16 4.9662

10 10 II 5.3714

Table II shows the Mean absolute percentage error (MAPE) and number of transmissions for different values of elhreshold. The table also shows the total number of transmissions out of 75 rounds between router A and monitoring device H for

different values of etlueshold.

. c o

:� � c

b o

� E = z

Number of Transmission vs ethreshold

ethreshold(%)

70 60 50 40 30 20 10

2 3 6 7 8 9 10 4 5 6 7 8 9 W

___ No. of transmissions 65 55 47 40 30 26 18 17 16 11

Figure 5. Number of transmission Vs e,h",hold plot

MAPE vs ethreshold

12

10

8 "' Q. 6 «

. --:;: • • • • • • • • 4

2

0 1 2 3 4 5 6 7 8 9 10

I ethreshold(%) 1 2 3 4 5 6 7 8 9 10 I ....... MAPE 4.537 4.555 4.622 4.648 4.564 4.636 4.64 �.904 4.966 5.371

Figure 6. MAPE Vs e,h",hold plot

Figure 5 shows the number of transmission vs ethreshold plot. It can be analyzed that number of transmissions are gradually decreasing with the increase of ethreshold value.

Figure 6 shows MAPE Vs ethreshold plot. From the figure, it can be analyzed that the mean absolute percentage error remains almost constant with the increase in ethreshold value.

Figure 7 shows the comparison between actual data value present in A VQsensor and the predicted value present in PVQsensor.lf the error between the actual value and predicted value comes greater than elhreshold, then the actual value is updated at the place of predicted value and used for further predictions.

Figure 8 gives the residual energy plot which shows the rate of energy depletion in direct method is greater than the prediction based approach.

The rate of energy consumption depends on number of transmissions which depends on the accuracy of prediction model used.

Predicted value and AVa compaison plot 3S '-

--�--�--�--�--�--r=�==�==�

Rounds

Figure 7. Plot between actual value and predicted value of data

0.1 �:::::::::-�-�-��-�--r;::=�==:::;r

0.09

0.08

� w

g 0.07 ·c ... � 0: 0.06

0.05

0.04L-���-�-�-�-�-�-�-�--' o � � � W 100 1� � 1� 1W 200

Number of rounds

Figure 8. Residual Energy Plot

CONCLUSION

Shortest routing algorithm for Zigbee module has been explained. Prediction based approach has been used for Zigbee so as to maximize life-time of the sensor and routing nodes. Grey model has been used for the prediction approach. It is shown that energy consumption is less in prediction based approach as compared to the direct transmission of data between sensor and monitoring node at the receiver. This would result in longer battery life of Zigbee nodes.

REFERENCES

[I] IEEE std.802.15.4, "Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specification for Low-Rate Wireless Peronal Area Network," October 1, 2003.

[2] L. Shizhuang, L. Jingyu, F. Yanjun, "Zigbee Based Wireless Sensor Networks and Its Applications in Industrial," IEEE International Conference on Automation and Logistics, pp.1979 -1983, 2007.

[3] B. Yan-li, C. Qiao-lin, W. Fang, "Improved Zigbee Network Routing Algorithm," Engineering and applications of computer, 2009.

[4] S. Zheng, Z. Xiao-Guang, R. Dianxu Ruan, P. Hui Li, Xun, "A Routing Protocol based on Flooding and AODV in the Zigbee Network," Intelligent Systems and Applications, pp.I-4, May 2009.

[5] Sifeng Liu, Yi Lin," Grey Systems Theory and Applications," Springer, 2010.

[6] Erdal Kayacan, Baris Ulutas, Okyay Kaynak, "Grey system theory­based models in time series prediction," Expert Systems with Applications, vol. 37, pp.1784-1789, 2010.

[7] Guiyi Wei, Yun Ling, Binfeng Guo, Bin Xiao, Athanasios V. Vasilakos, "Prediction-based data aggregation in WSNs: Combining grey model and Kalman filter," Computer Communication, vo1.34(6), pp. 793-802, 2011.

[8] Ujjwal Kumar, V.K. Jain, "Time series models (Grey-Markov, Grey Model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India," Energy, vol. 35, pp.1709-1716, 2010.

[9] Jamal N. AI-Karaki, Ahmed E. Kamal, "Routing Techniques in Wireless Sensor Networks: A Survey," IEEE Wireless Communications, 2004.

[10] Wendi B. Heinzelman, Anantha P. Chandrakasan and Hari Balakrishnan, "An Application-Specific Protocol Architecture for Wireless Microsensor Networks," IEEE Transactions on Wireless Communications, voU, no.4, October 2002.

[I I] Giuseppe Anastasi, Marco Conti, Mario Di Francesco, Andrea Passarella, "Energy Conservation in Wireless Sensor Networks: a Survey".