Gateway based multi hop distributed energy efficient clustering protocol for heterogeneous wireless sensor networks

Gateway based multi hop distributed energy efficient clustering protocol for heterogeneous wireless sensor networks

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  International Journal ofUbiComp (IJU), Vol.6, No.4, October 2015 DOI:10.5121/iju.2015.6402 13 Gateway Based Multi-Hop Distributed Energy Efficient Clustering Protocol for Heterogeneous Wireless Sensor Networks Sheenam1 and Rupinder Kaur2 M. Tech Research Scholar1 , Assistant Professor2 1, 2 Department of Electronics and Communication Engineering, Punjabi University, Patiala, India ABSTRACT Wireless sensor network consists of application oriented and cheap micro-devices called sensors nodes having potential of connecting the physical world with virtual world by their sensing abilities. These sensor nodes are having restrained battery sources. Efficient energy management is current area of research in wireless sensor networks. Here we advice one such energy aware multi-hop protocol (G-DEEC) for two level heterogeneous networks. In G-DEEC, the Base Station is placed out of sensing area and rechargeable gateway nodes are placed inside field with other randomly deployed sensor nodes. Simulation shows the proposed protocol G-DEEC is better than single-hop DEEC in terms of number of half dead nodes, alive nodes and dead nodes; thereby showing improvement in network lifetime and stability. KEYWORDS Wireless sensor network, DEEC, lifetime, half dead nodes, alive nodes, stability, gateway. 1. INTRODUCTION Ongoing advancements in VLSI and MEMS (Micro-electro-mechanical Systems) lead to manufacture of sensor networks in large numbers. Sensors have many applications depending on the application they are being used for like forest fire detection in forests, enemy tracking in military areas; they are also used for monitoring temperature, pressure, intensity, chemical concentrations, tracking objects. Sensors are also useful in medical science for monitoring vital body functions [1]. Sensor nodes mainly consists of sensor with analog to digital converter (ADC) which sense the outer environment defending on application they are being used for; then the converter converts the sensed analog data to digital form as shown in figure 1. ADC forward the converted data to processor for processing of data and finally to trans-receiver antennas for sending to final Base station. Figure 1. The components of Sensor Node Sensor + ADC Processor + Storage Trans- Receiver POWER SUPPLY
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  International Journal ofUbiComp (IJU), Vol.6, No.4, October 2015 14 Sensors are randomly scattered in sensing field connected other sensor nodes and finally connected to base station. In conventional protocols like DT and MTE, data is transmitted directly from sensor nodes to base station (BS) and one node to neighboring node respectively [2]. So nodes away to BS die more often as compared to other nodes as they are require more energy in transmitting data in DT. Nodes near to BS are more probable to transfer data in MTE; so nodes near to BS die more often. Here comes the concept of clustering introduced in LEACH [3] in network field where a group of nodes are grouped together in one cluster with one Cluster head (CH). CH will aggregate the collected data and further transfer this data to BS. Wireless communication architecture as shown in figure 2 consists of nodes like A, B, C, D, E etc scattered in sensing field. If want to send data to BS; it follows multi-hop routing i.e. A send data to B and then to C and so on finally to BS. Figure 2. Architecture of multi-hop sensor network LEACH introduced clustering in homogeneous sensor networks where each sensor node is having same initial energy levels. Routing of data in sensor networks is done through many homogeneous and heterogeneous routing protocols. Homogeneous protocols include LEACH [3], PEGASIS [18], HEED [19], TEEN [16, 21], M-GEAR [23] whereas heterogeneous protocols include SEP [4], DEEC [5], DDEEC [6], E-DEEC [22], DEEC-WIN [12], SEARCH [14]. Homogeneous protocols have all nodes of same type in terms of size, shape and mode of energy supply whereas a heterogeneous protocol utilizes the energy discrimination of sensor nodes. Heterogeneous protocols are helpful in increasing the network lifetime compared to homogeneous protocols. As the sensor nodes are energy constrained in terms of battery power [17, 20] so here we introduce the gateway based multi-hop protocol where 9 gateway nodes are placed in sensor field in systematic manner so that the CH of each cluster formed is near to the gateway node. Deployment of gateway nodes reduces the transmission distance from sensor nodes to BS; thereby increasing the number of half dead nodes, alive nodes. Election of CH is done on basis of ratio of residual energy to average energy making the transmission more reliable. 2. RELATED WORK In [3], clustering was introduced for homogeneous sensor networks unlike conventional protocols like DT, MTE in order to increase the network lifetime of homogeneous sensor networks. Here the role of CH is rotated so that only one node doesn’t keep on draining it initial energy level. Simulation performed showed improvement in stability period of homogeneous sensor networks.
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  International Journal ofUbiComp (IJU), Vol.6, No.4, October 2015 15 In [4], clustering was used for heterogeneous sensor networks by proposing a new protocol SEP unlike LEACH, PEGASIS [18], HEED [19] etc. CH is chosen on basis of initial energy of sensor nodes. Two level heterogeneity is been considered i.e. normal nodes and advance nodes with additional energy. As advance nodes are having higher energy levels than normal nodes so they are more probable to become CH in each transmitting round. Simulation performed showed improvement in stability than LEACH. In [5], distributed energy efficient clustering (DEEC) was proposed for heterogeneous wireless sensor networks. CHs are chosen based on residual energy unlike SEP where they are chosen based on initial energy. Simulation performed showed improvement in stability and lifetime of network as compared to SEP. In [6], developed distributed energy efficient clustering (DDEEC) was proposed for heterogeneous wireless networks which balances the selection of CHs over entire sensor field. Here an improvement in CH selection as compared to DEEC is been done where advance nodes are equally probable of becoming CHs as normal nodes after energy depletion. Simulation performed showed improvement in stability period as compared to DEEC. In [7], balanced and centralized distributed energy efficient clustering (BCDEEC) for heterogeneous wireless sensor networks is been proposed which is an extension of DEEC. Simulation performed showed improvement in stability period as compared to DEEC and SEP. In [8], SEP was extended to three-level heterogeneity by considering normal nodes, intermediate nodes and advance nodes with different energy levels in protocol named SEP-E. Simulation performed showed improvement in terms of throughput and stability (First node dead) as compared to LEACH. In [9], heterogeneity aware hierarchical stable election protocol was proposed which tends to increase lifetime of Wireless sensor networks (WSNs) by reducing transmission cost between CH and BS. Two types of CHs are considered i.e. primary and secondary CHs. Secondary ones are chosen from primary ones. Simulation performed showed improvement in network lifetime as compared to LEACH, SEP and DEEC. In [10], Away cluster head scheme (ACH), was proposed foe heterogeneous wireless sensor networks which consider number of sensor nodes similar in all clusters so that energy dissipated is similar for all clusters and nodes are considered normal if distance from nearby CH is less than 12m, otherwise they are called as advance nodes. Simulation performed showed improvement in average residual energy as compared to LEACH and SEP. In [11], Cluster head re-election protocol (CRP), was proposed where re-election of CHs is done from elected ones on basis of residual energy. Simulation performed showed improvement in residual energy deviation as compared to LEACH and SEP. In [12], DEEC with Isolated Nodes (DEEC-WIN) was proposed where mechanism of nodes joining a cluster is improved. Some nodes near to BS communicate directly to BS. Simulation performed improvement as compared to DEEC.
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  International Journal ofUbiComp (IJU), Vol.6, No.4, October 2015 16 In [13], Energy efficient two level distributed clustering (EE-TDLC) scheme, was proposed to save transmission cost by considering secondary level CHs after primary CHs. Simulation performed showed improvement in number of CHs and improvement in stability period. 3. SYSTEM MODEL OF PROPOSED PROTOCOL (G-DEEC) 3.1 Basic assumptions of proposed protocol G-DEEC 1. BS is placed out of network field which is stationary after deployment. 2. Bs is location aware and aware about location of other sensor nodes 3. Each sensor node is provided with different identity in network area called as sensor ID (Identifier). 4. Location of gateway nodes is been decided before deployment such that each CH formed is near to gateway node. 5. Gateway nodes are rechargeable and stationary after placement. 6. Transmission distance is reduced between CHs and BS with aid of gateway nodes. 7. Sensor nodes are uniformly distributed over network field. 8. Two level heterogeneous nodes are deployed i.e. normal nodes and advance nodes. Figure 3. Network model of proposed G-DEEC ○ Normal nodes ◊ Advanced nodes × Base Station * Gateway nodes Here we used first order radio dissipation model as used in LEACH [3] as shown in figure 4. This model describes the amount of energy dissipated in transmitting, receiving and fusing data from sensor nodes to Base station. Energy dissipated by transmitter is higher than receiver. The energy consumption in transmitting l- bit data packet at distance d is given as ( ) { (1)
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  International Journal ofUbiComp (IJU), Vol.6, No.4, October 2015 17 Figure 4. Radio dissipation model Where is amount of energy dissipated per bit to run electronic circuit. is free space path loss and is multi path attenuation constant depending on model. Here d is distance between two interacting ends and is threshold distance calculated as √ (2) 3.2 THE G-DEEC PROTOCOL In this research we proposed a heterogeneity aware multi-hop energy efficient clustered protocol for WSNs. G-DEEC (Gateway based multi-hop DEEC), where gateway are stationary battery rechargeable sensor nodes which are deployed at center of boundaries, corners and middle of sensor field so that the CH from each cluster is each approachable to gateway node. Data transmission in G-DEEC follows multi-hop data transmission i.e. CHs collect and aggregate all of data coming from sensor node members of their cluster and forward this data for further processing to gateway nodes; whose function is to process the collected data from CHs and pass this data to BS. Gateway nodes reduce traffic problems and increase the rate of data transmission. As number of gateways are deployed in network field, increases reliability of network from failures of gateway node as if one gateway node is blocked or damaged in particular case; another nearby located gateway node can do the network operations. Data may follow DT (Direct Transmission) in case they are near to BS or near to gateway node. This protocol considers two types of sensor nodes in terms of energy levels i.e. normal nodes and m fraction of advance nodes. Total energy = Energy of normal nodes + Energy of advance nodes = n (1-m) +nm (1+ ) = n (1+m ) (3) where n is total number of nodes and is initial energy of normal nodes and m is fraction of advance nodes having extra energy than normal nodes. CHs are elected based on ratio of residual energy and average energy of network as in DEEC [5]. Different election probabilities are considered for normal nodes and advance nodes as: Transmitter TX Amplifier Receiver L- bit (l, d) ( ) L-bit d
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  International Journal ofUbiComp (IJU), Vol.6, No.4, October 2015 18 (4) ( ) (5) Where is residual energy and is ideal average energy that each node should own to keep network alive to longer extent. ( ) (6) Where r is current round and R is the total number of processing rounds. After election of CHs per round, a single CH is selected per round based on random number generated and comparing this number with threshold value ( ) for normal nodes and ( ) for advance nodes as in equation below: ( ) { * ( )+ } (7) ( ) { [ ( )] } (8) Where r is round number of current processing and g’ is set of normal nodes not been cluster heads in last ⁄ rounds and g’’ is set of advance nodes not been cluster heads in last ⁄ rounds. 4. PERFORMANCE EVALUATION 4.1 Simulation settings In order to improve the performance of G-DEEC, we simulated our proposed G-DEEC protocol in MATLAB. We consider a 100m*100m wireless sensor network field comprised of 100 nodes deployed uniformly over it with some pre-planned Gateway nodes. BS is deployed 25m away from top sensor area boundary. BS is stationary at its place and gateway nodes are also non- movable but rechargeable to increase the number of alive nodes so as to increase the network lifetime. To assess the performance of proposed G-DEEC with DEEC, we ignore the interference in transmission channels. We consider packet size of 4000 bits. 9 Gateway Nodes are located at predefined distance covering the network field. Data can be transmitted directly to BS in case they are near to BS than the CH or gateway node. Simulation parameters are given in Table 1. 4.2 Performance metrics 4.2.1 Network lifetime- This is defined as time till network is operational and calculated from number of half dead nodes, dead nodes, alive nodes. 4.2.1.1 Half Dead nodes (HDN) - These are defined as nodes which are partially dead and partially alive and still perform network operations. They need to high in number to increase network lifetime. Total HDN comprised of NHDN (Normal half dead nodes) and AHDN (Advance half dead nodes).
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  International Journal ofUbiComp (IJU), Vol.6, No.4, October 2015 19 4.2.1.2 Dead nodes (DN) - These are nodes with no energy left for processing. They need to be low in number for increasing network lifetime. Total DN comprised of NDN (Normal dead nodes) and ADN (Advance dead nodes). 4.2.1.3 Alive nodes (AN) - These are defined as nodes having some left energy after processing so these nodes can still be part of network operations. They need to be high in number to increase network lifetime. Total AN comprised of NAN (Normal alive nodes) and AAN (Advance alive nodes). 4.2.2 First node dead (FND) - This is defined as time interval between start of processing rounds till first node becoming dead. Late the round of dying first node arrives, higher the stability of network. 4.2.3 Remaining energy- This is defined as leftover energy per processing round. Higher this energy increases the network lifetime as network will be operational for much longer time. 4.2.4 Packet transmission- This is defined as amount of data exchanged between CHs and BS. The rate of data transmission from sensor nodes to CH and further to BS via CH; gateway or directly is considered. 4.3 Simulation results and Analysis 4.3.1 Network lifetime: Following in this section we are analyzing the network lifetime in terms of HDN, DN, AN. 4.3.1.1 Analysis of HDN (Half Dead Nodes) - In figure 5, Total number of half dead nodes in DEEC and proposed DEEC is shown. The number is zero in DEEC which is coming out to be 60 in proposed G-DEEC at 2000 iteration showing 60% overall improvement in half dead nodes. Figure 6 shows number of normal half dead nodes in DEEC and proposed G-DEEC. As m= .2 means 80 normal nodes and 20 advance nodes initially. Normal nodes becoming half dead are zero in DEEC at 2000 iteration and 60 in G-DEEC showing 75% overall improvement in normal half dead nodes. Figure 7 shows number of advance half dead nodes in DEEC and G-DEEC respectively. There are 20 advance nodes initially which reduced to zero at 2000 iteration in both DEEC and G-DEEC showing zero improvement in number of advance half dead nodes. Figure 5. Total number of HDN
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  International Journal ofUbiComp (IJU), Vol.6, No.4, October 2015 20 Figure 6. Number of NHDN Figure 7. Number of AHDN 4.3.1.2 Analysis of DN (Dead Nodes) – Figure 8 shows number of dead nodes in DEEC and G- DEEC. At 2000 iteration the numbers of dead nodes are 80 in DEEC and 17 in G-DEEC. This high amount of reduction in number of dead nodes shows 63% improvement in lifetime. Figure 9 shows the number of normal dead nodes. Normal nodes were 80 initially which are maintained to be still 80 in DEEC showing no improvement and reduced to 17 in G-DEEC showing 78.75 % improvement in lifetime. Reduction in number of dead nodes increases network lifetime.
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  International Journal ofUbiComp (IJU), Vol.6, No.4, October 2015 21 Figure 8. Total number of DN Figure 9. Number of NDN Figure 10 shows number of advance dead nodes in DEEC and G-DEEC. Initial there were 20 advance nodes which are reduced to zero in both DEEC and G-DEEC showing zero overall improvement.
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  International Journal ofUbiComp (IJU), Vol.6, No.4, October 2015 22 Figure 10. Number of AND 4.3.1.3 Analysis of AN (Alive Nodes) - Figure 11 shows number of total alive nodes in both DEEC and G-DEEC. The number is 20 in DEEC and 83 in G-DEEC. This rise of alive node number increases lifetime by 63%. Figure 12 shows the number of normal alive nodes which are initially 80. The number is 0 in DEEC and 60 in G-DEEC showing 75% improvement. Figure 11. Number of total AN
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  International Journal ofUbiComp (IJU), Vol.6, No.4, October 2015 23 Figure 12. Number of NAN Figure 13 shows number of advance alive nodes in DEEC and G-DEEC. The number is 20 initially which is still 20 in both protocols showing no improvement. Figure 13. Number of AAN 4.3.2 Analysis of First Dead Node (Stability) - Figure 14 shows bar graph of the time interval till first node dies. The round at which first node died in DEEC is 1242 and 1369 in G-DEEC showing 6.35% increase in stability.
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  International Journal ofUbiComp (IJU), Vol.6, No.4, October 2015 24 Figure 14. First Node Dead 4.3.3 Analysis of Remaining Energy- Figure 15 shows the network remaining energy per round. The energy remained is 0 joules in DEEC at 1500 iteration and 0.05 joules in G-DEEC showing 10% increase in remaining energy as initial energy was 0.5 joules. The network parameters considered in research are given in Table 1. Figure 15. Remaining Energy
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  International Journal ofUbiComp (IJU), Vol.6, No.4, October 2015 25 Table 1. Network parameters Parameters Definition Values n Population of sensor nodes 100 Initial energy of sensor nodes 0.5J Optimal Probability 0.1 Transmitter / Receiver electronics energy 50nJ/ bit EDA Data aggregation energy 5nJ/bit/signal Energy consumed by transmit amplifier at shorter distance 10pJ/bit/m2 Energy consumed by transmit amplifier at longer distance 0.0013pJ/bit/m4 Threshold distance 70m m Fraction of advance nodes 0.2 α Fraction of extra energy 3 4.3.4 Analysis of packets transmission: Figure 16 shows the count of packet transmission from to CH. At 2000 iteration, 80 packets are sent to CH in G-DEEC whereas the number is 20 in DEEC. Each packet is of 4000 bit length. Figure 16. Packets transmission to CH Figure 17 shows the packets transmission to Base station. The number is 3 in DEEC and 13 in G- DEEC.
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  International Journal ofUbiComp (IJU), Vol.6, No.4, October 2015 26 Figure 17. Packets transmission to BS Table 2. Simulation Results Performance metric DEEC G-DEEC Overall Improvement (%) Number of HDN (Total) 0 60 60 Number of NHDN 0 60 75 Number of AHDN 0 0 0 Number of DN (Total) 80 17 63 Number of NDN 80 17 78.75 Number of AND 0 0 0 Number of AN (Total) 20 83 63 Number of NAN 0 60 75 Number of AAN 20 20 0 FND (Stability) 1242 1369 6.35 Remaining Energy (joules) 0 0.05 10
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  International Journal ofUbiComp (IJU), Vol.6, No.4, October 2015 27 5. CONCLUSIONS AND FUTURE SCOPE In this research we proposed a multi-hop DEEC in order to increase the number of alive nodes and half dead nodes and decrement in dead nodes. Simulation performed in MATLAB shows 60%, 63%, 63% improvement in number of half dead nodes, Dead nodes and alive nodes resp. in return giving 62% on an average improvement in lifetime, 6.35% improvement in stability and 10% in remaining energy. This work can further be extended to improvement in threshold levels and CH selection criteria. REFERENCES [1] Q. Wang and I. Balasingham, (2010) “Wireless sensor networks: Application centric design”, Journal of Communications, vol. 2, no.5, pp. 134-149. [2] J. Yick, B. Mukherjee and D. Ghosal, (2008) "Wireless sensor network survey", Journal of Computer Networks, vol. 52, no.2, pp. 2292-2330. [3] W.R. Heinzelman, A. Chandrakasan and H. Balakrishnan, (2000) “Energy-efficient communication protocol for wireless microsensor networks”, In Proc. International Conference on System Sciences, pp. 4-7, Maui, Hawaii. [4] G. Smaragdakis, I. Matta and A. Bestavros, (2004) “SEP: A Stable Election Protocol for Clustered Heterogeneous Wireless Sensor Networks”, In Proc. of IEEE International Workshop on SANPA, pp. 1–11, Boston. [5] L. Qing, Q. Zhu and M. Wang, (2006) ”Design of a distributed energy-efficient clustering algorithm for heterogeneous wireless sensor networks”, Journal of Computer Communications, vol. 29, no.4, pp. 2230-2237. [6] B. Elbhiri, S. Rachid, S. El Fkihi and D. Aboutajdine, “Developed distributed energy-efficient clustering (DDEEC) for heterogeneous wireless sensor networks”, In proc. IEEE 5th International Symposium on I/V Communications and Mobile Network, pp. 1-4.,Rabat, 2010. [7] S.B. Alla, A. Ezzati, A. Mouhsen, A.B. Hssane and M.L. Hasnaoui, (2011) ”Balance and centralized energy efficient clustering for heterogeneous wireless sensor networks”, In proc. IEEE 3rd International conference on next generation networks and services, pp .39-44, Hammamet. [8] M.M. Islam, M.A. Matin, and T.K Mondol, (2012) “Extended stable election protocol for three-level hierarchical clustered heterogeneous WSN”, In Proc. of IET Conference on Wireless Sensor Systems, pp. 1-4, London, England. [9] A.A. Khan, N. Javaid, U. Qasim, Z. Lu and Z. A. Khan, (2012) “HSEP: Heterogeneity-Aware Hierarchical Stable Election Protocol for WSNs”, In Proc. IEEE International Conference on Broadband and Wireless Computing, Communication and Applications, pp. 373-378, Victoria, Canada. [10] N. Javaid, M. Waseem, Z. A. Khan, U. Qasim, K. Latif and A. Javaid, (2013) ”ACH: Away cluster heads scheme for energy efficient clustering protocols in WSNs”, In proc. IEEE Saudi International Conference on Electronics, Communications and Photonics, pp.1-4, Fira. [11] R. Tandon and S. Nandi, (2013) ”CRP: Cluster head Re-election protocol for heterogeneous wireless sensor networks”, In proc. IEEE 5th International conference on communication systems and networks, pp. 1-10, Bangalore. [12] Z. Kaili, Z. Lihua, Wang Xu, (2013) ”An Improved DEEC Algorithm with Isolated nodes”, In proc. International conference on Information and Communication Technologies IETICT, pp. 228-232, Beijing. [13] M. Kaur, A. Jain and A.K. Goel, (2014) ”Energy efficient two level distributed clustering scheme to prolong stability period of wireless sensor network”, In proc. IEEE International Conference on Advances in Computing, Communication and Informatics, pp. 68-73, New Delhi, India. [14] M.Y. Wang, J. Ding, W.P. Chen and W.Q. Guan, (2015)”SEARCH: A stochastic election approach for heterogeneous wireless sensor networks”, IEEE Communication letters, vol.9, no.3, pp. 443-446.
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  International Journal ofUbiComp (IJU), Vol.6, No.4, October 2015 28 [15] J. Ibriq and I. Mahgoub, (2004) “Cluster-based routing in wireless sensor networks: issues and challenges”, In proc. IEEE International Symposium on Performance Evaluation of Computation and Telecommunication System, pp. 25-29, San Jose, California. [16] M. Omari, N. Tiouririne and D. Dahmila, (2012) “Simulation of the TEEN and the SPIN protocols in a wireless sensor network environment”, Journal of Telecommunication, vol. 3, no. 5, pp. 9-15. [17] F.A. Aderohunmu, (2010) “Energy Management Techniques in Wireless Sensor Networks: Protocol Design and Evaluation”, M.S. thesis, Otago University, New Zealand. [18] S. Lindsey, C.S. Raghavendra, (2002)”PEGASIS: Power-Efficient Gathering in Sensor Information Systems” IEEE Proceedings on Aerospace, vol. 3, no.2, pp.1-6. [19] O. Younis, S. Fahmy, (2004) ” HEED: A hybrid, energy efficient, distributed clustering approach for ad hoc sensor networks” IEEE Transactions on Mobile Computing, vol. 3, no. 4, pp. 660-669. [20] B.M. Lee, H.M. Hsieh, J.T. Wang, K. L. Hsiao, (2014)” Note for LC Oscillator Power Management for Wireless Network Sensors Technique, Energy and Power Engineering Science, pp. 17-21. [21] A. Manjeshwar and D.P Agrawal, (2001) “TEEN: A routing protocol for enhanced efficiency in wireless sensor networks”, In proc. IEEE International Parallel and Distributed Processing Symposium, pp. 2009 -2015, San Francisco, California. [22] P. Saini and A. K. Sharma, (2010) “E-DEEC- Enhanced distributed energy efficient clustering scheme for heterogeneous WSN”, In proc. IEEE 1st International Conference on Parallel, Distributed and Grid Computing, pp. 205-210, Solan. [23] Q. Nadeem, M.B. Rasheed, N. Javaid, Z.A Khan, Y. Maqsood and A. Din, (2013) “M-GEAR: Gateway-Based Energy-Aware Multi-Hop Routing Protocol for WSNs”, In proc. IEEE International Conference on Broadband, Wireless Computing, Communication and Applications, pp. 164-169, Compiegne, France. Authors SHEENAM received her Bachelor’s of Technology in Electronics and Communication Engineering from SVIET, Banur affiliated to Punjab Technical University, Jalandhar, Punjab, India in 2012, and currently doing final semester of Masters of Technology in Electronics and Communication Engineering from Punjabi University, Patiala, Punjab, India. She yet has published one paper on wireless sensor networks. Her research interests include energy management in wireless sensor networks etc. RUPINDER KAUR is currently working as Assistant professor at Punjabi University, Patiala, Punjab, India. She received her Masters of Technology in Electronics and Communication Engineering from Guru Nanak Dev University, Amritsar, Punjab, India in 2012. She has 3 years of teaching experience. Her research interests include wireless communication etc.