Cluster Based Energy Routing System for

Wireless Sensor Networks

Ifrah Farrukh Khan and Muhammad Younus Javed Department of Computer Engineering, National University of Sciences & Technology (NUST), College of Electrical

and Mechanical Engineering, Rawalpindi, Pakistan.

Email: {ifrahkhan, myjaved }@ceme.nust.edu.pk

Abstract—Wireless Sensor Network has become part of

everyday life. Due to very small size of sensor nodes and

their limited battery power a lot of work has been done in

the area of energy management. Many routing protocols

have been developed for using battery power efficiently but

these protocols sacrifice QoS for this energy efficiency.

Energy Harvesting technologies have been proposed to

improve the lifetime of sensor nodes. Sensor nodes are

charged by using environmental sources such as light, wind,

vibration etc. These energy harvesting technologies have

been combined with energy transference methods for

transference of energy from one node to another. The new

area of research is multi hop energy transference and

algorithms for energy routing. This research paper is about

a cluster based architecture that can help in transporting

energy easily and efficiently from one node to another; an

algorithm has been designed for charging the nodes before

depleting their entire energy.

Index Terms—battery charging, energy efficiency, routing,

wireless sensor networks.

I. INTRODUCTION

Wireless Sensor Networks (WSNs) are composed of

tiny autonomous sensor nodes. These sensor nodes have

different sensing capabilities such as vibration sensing,

temperature sensing, light sensing etc. Sensor networks

are rapidly becoming part of everyday life. Main

applications of WSNs are military deployment, security

surveillance, patient information (Body area networks),

weather forecasting system etc. Due to the smaller size of

a sensor node it has limited processing capabilities, small

storage space and very limited battery power. Main

research area of WSN is energy efficiency. Many

researchers have developed different energy efficient

routing protocols, to increase the network life time [1]-[8].

Creation of routing holes [9], [10] show that energy

efficient routing is not adequate, so the new research area

i.e energy harvesting emerged. Many researchers have

proposed different methods of energy harvesting from the

sources such as wind, light, vibration, solar energy etc.

[11], [12], [13]. Energy harvesting only gives support to

the nodes present in energy rich areas and the nodes in

poor environmental conditions suffer from total energy

drainage. Energy transfer mechanism has been proposed

Manuscript received July 30, 2013; revised October 11, 2013

to provide energy to the neighbor nodes that are unable to

harvest energy for themselves [14]-[16]. Only one hop

energy transfer is not the complete solution because many

nodes farther from the transference node cannot survive.

Energy routing [17], [18] is the new research area that

can provide energy to all the nodes present in the network.

In this research paper a cluster based approach is

presented in which clusters are created on the basis of the

availability of transference node. A hybrid energy

transference technology is implemented i.e sunlight

reflection and magnetic resonance. Rest of the paper is

organized as follows, section 2 explains the available

energy transference techniques, section 3 is about the

research work done by other researchers, section 4 is

about the proposed system and the last section is about

the conclusions drawn and the planned future work.

II. ENERGY TRANSFERENCE TECHNIQUES

Energy can be easily transferred from one node to

another by using wires but in case of WSN it is not

suitable. In WSNs nodes are deployed in a random

topology and they can also be dropped using aircraft. The

nodes may be present in uneven places and at different

distances from each other. Hence wireless energy

transference is preferred in these kinds of networks.

Wireless energy transference is of different types such as

microwave, magnetic resonance, Laser/ LED light and

Reflected sunlight. All of these techniques have their pros

and cons.

Microwaves are the electromagnetic waves,

wavelength of these electromagnetic waves is between

0.01m and 3m. Frequency of these waves is between 30

GHz and 0.1 GHz. Microwaves can charge battery at

distances more than 2km and upto 80% charging

efficiency can be achieved. But due to safety hazards for

human life these waves are not used in most of the

scenarios.

Electromagnetic resonance is transference of energy

using coils. In this technique electromagnetic field is

created in one coil by passing electric current through it

while the second coil being affected by this

electromagnetic field produces induced current.

Electromagnetic resonance is safe for human life. Upto

90% charging efficiency can be achieved and the

effective distance is about 1 to 2 km. Reflected sunlight is

energy harvesting by using sunlight and transferring it to

International Journal of Materials Science and Engineering Vol. 1, No. 2 December 2013

©2013 Engineering and Technology Publishing 62doi: 10.12720/ijmse.1.2.62-66

the other node by using a reflecting surface such as

mirror. Efficiency of this technique is more than 90%

depending upon the distance from charging node. Energy

can be transferred to the node even present at distance

greater than 1km.Other technologies such as Laser/LED

light and thermoelectric have very low charging

efficiency. It is as low as 10%. In this research work two

most efficient techniques sunlight reflection and magnetic

resonance is used.

III. RELATED WORK

The issues, challenges and problems of wireless sensor

networks energy efficient routing has been studied by

various researchers.

A. Energy Efficient Routing Algorithms

Different energy efficient routing algorithms have been

proposed by researchers. These protocols can be

categorized as geographical, cluster based and

hierarchical routing protocols. Some geographical

protocols are discussed here such as Yu. et al. [4]

suggested a geographical information based protocol

named as GEAR Geographical and Energy Aware

Routing. This algorithm works in two steps, in first step it

forwards data to the selected region. In second step it

disseminates the data with in that region by using

recursive geographic forwarding algorithm. Depending

on the node density it divides the region into sub regions

and gives one copy of the packet to each region or uses

restrictive flooding in case of low density. This algorithm

also deals with the routing hole problem.

Another geographical information based energy aware

algorithm is EAGR i.e Energy Aware Greedy Routing. It

was proposed by Razia. et. al. [5]. This algorithm uses

location information. It combines energy level of the

nodes and average distance of the neighbors for selecting

hops for packets. This algorithm distributes the data-

forwarding load amongst all the nodes present in the

network that helps in increasing life of the network.

REAR, Reliable Energy Aware Routing was proposed

by Hassanein et. al. [7]. This algorithm provides energy

efficient routing as well as reliability of data delivery.

Three types of nodes have been used in this algorithm,

which are network Sink, Intermediate Nodes (IN) and

Target Source(TS). REAR works in four parts. First part

is the Service Path Discovery (SPD). Second part is

Backup Path Discovery(BPD). Third part is reliability of

transmission which is achieved by storing data at the

source node until acknowledgement is received. Fourth

part is release of reserved energy.

B. Energy Harvesting

Xiaofan Jiang et.al. [11] have proposed an energy

harvesting hardware called Prometheus, it is a system that

intelligently manages energy transfer for perpetual

operation without human intervention or servicing.

System is built upon two-stage buffer to prolong the life

time of the system hardware, that includes super-

capacitor and lithium rechargeable battery.

Chulsung Park et.al. [13] have designed a hardware

Ambimax that has the ability of harvesting energy from

different sources such as wind, light, heat and vibration.

This system has the ability to produce electricity with

minimum wastage of energy.

C. Energy Transference and Energy Routing

Energy harvesting is not sufficient in many cases such

as the nodes present in bright light will harvest energy for

themselves but other nodes in darker area or energy

deficient area suffer from energy depletion. This uneven

distribution of energy may result in poor performance of

the network. Affan A. Syed et.al. [14] propose an energy

transfer mechanism that consists of one motorized mirror

that can reflect the light by rotating or tilting the mirror.

The consumer or the target node indicates the charging by

turning on green LED light. They charged multiple nodes

on the basis of time slot allocated to each node. This

mechanism has been adopted by Adnan et.al. [16] and

after few enhancements they have proposed an energy

transfer method along with suggestions for proper

placement of transfer nodes as well as consumer nodes.

Energy routing is the next step towards energy efficiency.

Ting Zhu et.al. [17] proposed eShare which supports the

concept of energy sharing among multiple embedded

sensor devices by providing designs for energy routers(i.e.

energy storage and routing devices) and related energy

access and network protocols. Energy routers exchange

the energy sharing control information using their data

network while they share energy among connected

embedded sensor devices using their energy network.

They have used an array of ultra-capacitors as the main

component of an energy router. Mohamed K.Watfa et.al.

[18] have designed an energy routing protocol for

magnetic resonance energy transference, the authors have

also proposed the hardware to transfer magnetic energy

from one node to another. They have proved that energy

transfer efficiency at one hop is 60% while it becomes

20% at 8 hops. Magnetic resonance is effective only up to

1-3 m so it is good for indoor implementation only.

D. Clustering for Energy Routing

Wen Ouyang et.al. [19] have proposed optimal

partitioning methods for mobile charging machines, they

have proposed three methods to divide the available

region of wireless sensor network. The three proposed

methods are tier- based partition, sector-based partition

and the mixed partition.

IV. PROPOSED System

The proposed system has the capability of recharging

battery by using solar as well as magnetic induction

transference methods. These two transference methods

have the highest charging efficiency as discussed in

section 2, that is why these methods have been opted for

the new charging system. The proposed system has two

types of transference nodes, one is solar energy reflecting

node and the second one is magnetic resonance charging

node. Solar energy reflecting node is a fixed node that is

placed in the bright light from where it can absorb energy

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©2013 Engineering and Technology Publishing 63

for charging itself as well as reflecting energy to the

nodes present in darker areas. Magnetic induction

transference node can be any node with in wireless sensor

network. It will serve as backup charging node for those

nodes which are farther from solar energy reflecting node,

or the nodes present in areas where solar light cannot be

reflected.

A. Structure of Solar Energy Transference Nodes [16]

Adnan Iqbal et.al. have designed a motorized setting

that consists of two servo motors for pan and tilt

operation. A mirror has been mounted on these servo

motors for reflecting sunlight on energy scarce nodes.

The pan and tilt operation is important for focusing the

sunlight on the solar energy harvesting panel of charging

node. Structure of solar energy transfer node is shown in

Fig. 1.

B. Structure of Magnetic Resonance Transference

Nodes [18]

Mohamed K. Watfa et.al. have proposed a magnetic

energy transference node. It consists of coil coupled with

rechargeable battery. Battery acts as load when it is being

charged and works as source when charging other nodes.

Shown in Fig. 2.

Figure 1. Solar energy transference node. source: [16]

Figure 2. Magnetic energy transference node. source[18]

C. Energy Routing Process.

First of all the network is initialized and all the nodes

attach themselves with an energy transference node. All

the nodes have the capability to either charge themselves

with solar energy or with magnetic resonance. Cluster of

nodes is created depending on the transference capability

of solar light reflecting node. Nodes present in the

effective diameter of this node are considered as one

cluster. In each cluster there are three types of nodes 1.

Charging through direct sunlight, 2. Getting reflected

Sunlight and 3. Getting charged from magnetic induction.

Figure 3. Solar energy transfer initialization

Figure 4. Magnetic resonance energy transfer initialization

The first type of node is present in the area where

direct sunlight is available so the node is charging itself

directly from the available energy. A threshold energy

level is assigned to these nodes, in case of unavailability

of sunlight these nodes can associate themselves to the

magnetic induction charging nodes when their energy

gets depleted to the level of their threshold. When the

node reaches threshold level it sends association request

to the available magnetic energy serving node. The

serving node sends acknowledgement back to the

requesting node and adds it to the list of nodes to be

charged.

The second type of nodes is present in sunlight

deficient areas or the areas where light intensity is not

adequate for charging battery. These nodes send

association request to the sunlight energy transference

node, in response the serving node sends back

acknowledgement and adds it to the list of nodes. As

shown in Fig. 3. The transference node assigns equal

timeslots to the nodes present in the list and charges their

batteries in round robin fashion.

The third type of nodes is those which are neither in

direct sunlight nor in a state to charge their batteries using

sunlight energy transference node. This type of nodes

sends request to the advertised magnetic induction

transference node. The serving node that is present at less

than 8 hops accepts the request and send back

acknowledgement to the requesting node. Shown in Fig.

4. And Appendix A shows the complete flow of energy

routing process.

International Journal of Materials Science and Engineering Vol. 1, No. 2 December 2013

©2013 Engineering and Technology Publishing 64

Sensor Nodes

EZ430-RF2500-SHE – MSP430 nodes developed by

Texas Instruments. These nodes have solar energy

harvesting panel and also provide extra input for another

energy harvester.

V. CONCLUSION AND FUTURE WORK

Working of wireless sensor network can be improved by providing charging nodes to the energy deficient nodes of the network. Sunlight is the most powerful source of energy for charging the battery of the sensor nodes. But few nodes cannot be charged by using this

source of energy so the next most suitable option is magnetic resonance. High charging efficiency can be attained by using the combination of these two techniques. Life time of wireless sensor network can be considerably improved. Implementation of simulation of this proposed system using a suitable network simulator is in progress and results will be published as soon as they are produced. Implementation of this system on real sensor network is in future consideration.

APPENDIX A FLOW DIAGRAM

REFERENCES

[1] K. Akkaya and M. Younis, “A Survey on routing protocols for wireless sensor networks,” Elsevier Journal of Ad Hoc Networks.

vol. 3 i3. pp. 325-349.

[2] K. Akkaya and M. Younis, “Energy and QOS aware routing in wireless sensor networks,” Source Cluster Computing Archive, vol.

8, no. 2-3, pp. 179 – 188, 2005. [3] V. Rahunathan, C. Schurgers, S. Park, and Mani B. Srivastava,

“Energy aware wireless sensor networks,” Dept of Elec Eng, Uni

of California, L. A, 2004, pp. 1-17. [4] Y. Yu, D. Estrin, and R. Govindan, “Geographical and energy

aware routing: A recursive data dissemination algorithm for wireless sensor networks,” UCLA-CSD, Technical Report TR- 01-

0023, May 2001.

[5] R. Haider and Dr. M. Younas Javed, “EAGR: Energy aware greedy routing in sensor networks,” Future Generation

Communication and Networking, vol. 2, pp. 344349-344349, Dec.

2007.

[6] B. Karp and H. Kung, “Gpsr: Greedy perimeter stateless routing

for wireless networks,” in ACM MOBICOM, Boston, August 2000.

[7] H. Hassanein and J. Luo, “Reliable energy aware routing in

wireless sensor networks,” in Proc. Second IEEE Workshop on Dependability and Security in Sensor Networks and Systems, 2006,

pp. 54-64. [8] J. Chen, R. Lin, Y. Li, and Y. Sun, “LQER: A link quality based

routing for wireless sensor networks,” Sensors, vol. 8, pp. 1025-

1038, 2008. [9] N. Ahmed, Salil S. Kanhere, and S Jha, “The holes problem in

wireless sensor networks a survey,” SIGMOBILE Mob. Comp. Comm. Rev., vol. 9, no. 2, pp. 4-18, 2005.

[10] Q. Fang, J. Gao, and L. J. Guibas, “Locating and bypassing

routing holes in sensor networks,” IEEE INFOCOM, 2004. [11] X. F. Jiang, J. Polastre, and D. Culler. “Perpetual environmentally

powered sensor networks,” in Proc. 4th International Symposium on Information Processing in Sensor Networks, Piscataway, IEEE

Press, NJ, USA, 2005, pp. 65.

[12] A. Kansal and M. B. Srivastava, “An environmental energy harvesting framework for sensor networks,” in Proc. 2003

International Symposium on Low Power Electronics and Design,

Seoul, Korea, ACMPress, 2003, pp. 481-486.

[13] C. Park and P. H. Chou, “AmbiMax: Autonomous energy

harvesting platform for multi-supply wireless sensor nodes,” IEEE SECON, vol. 1, pp. 168-177, September 2006.

International Journal of Materials Science and Engineering Vol. 1, No. 2 December 2013

©2013 Engineering and Technology Publishing 65

[14] A. A. Syed, Y. Cho, and J. Heidemann, “Demo abstract: Energy transference for sensor nets,” in Proc. 8th ACM SenSys

Conference, Zurich, Switzerland, ACM, November 2010, pp. 397-

398. [15] I. Talzi, A. Hasler, S. Gruber, and C. Tschudin, “Perma sense:

Investigating perma frost with a wsn in the swiss alps,” in Proc. 4th Workshop on Embedded Networked Sensors, ACM, New York,

USA, 2007, pp. 8-12.

[16] A. Iqbal and S. A. Khayam, “Reliable data delivery in wireless sensor and ad hoc networks,” PhD Thesis.

[17] T. Zhu, Y. Gu, T. He, and Z. L. Zhang, “eShare: A capacitor-driven energy storage and sharing network for long-term

operation,” SenSys’ 10, Zurich, Switzerland, November 3-5, 2010.

[18] M. K. Watfa, H. A. Hassanieh, and S. Salmen, “The Road to immortal sensor nodes,” in Proc. ISSNIP 2008, Sydney, Australia,

15-18 December, 2008.

[19] W. Ouyang, C. W. Yu, C. M. Huang, and T. H. Peng, “Optimum partition for distant charging in wireless sensor networks,” in Proc.

MSN’11, Beijing, China, 16-18 December, 2011.

Ifrah F. Khan is Ph.D student at the National University of Science and Technology, CEME, Rawalpindi, Pakistan. She completed her MS in

Software Egineering from NUST in 2009. Her area of research is

wireless sensor networks.

Muhammad Y. Javed is working as Dean CEME, at National University of Science and Technology, Rawalpindi.

International Journal of Materials Science and Engineering Vol. 1, No. 2 December 2013

©2013 Engineering and Technology Publishing 66