Cluster Based Energy Routing System for Wireless Sensor Networks

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sources such as wind, light, vibration, solar energy etc. ... All of these techniques have their pros .... sunlight on the solar energy harvesting panel of charging.
International Journal of Materials Science and Engineering Vol. 1, No. 2 December 2013

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

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.

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. 

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Index Terms—battery charging, energy efficiency, routing, wireless sensor networks.

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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

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

ENERGY TRANSFERENCE TECHNIQUES

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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.

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.

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.

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 dataforwarding 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.

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.

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

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 supercapacitor and lithium rechargeable battery.

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PROPOSED System

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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.

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.

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.

Figure 3. Solar energy transfer initialization

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 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.

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

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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.

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

APPENDIX A FLOW DIAGRAM

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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. J. Chen, R. Lin, Y. Li, and Y. Sun, “LQER: A link quality based routing for wireless sensor networks,” Sensors, vol. 8, pp. 10251038, 2008. 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. Q. Fang, J. Gao, and L. J. Guibas, “Locating and bypassing routing holes in sensor networks,” IEEE INFOCOM, 2004. 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. 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. 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.

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[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. 397398. [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 capacitordriven 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.

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[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.

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