i-manager Publications

i-manager's Journal on Wireless Communication Networks (JWCN)

Current Issue Vol. 12 Issue 1

5G Wireless Communication Systems: Performance Analysis and Optimization using Massive MIMO Technology
Performance Optimization of 5G NR Technology for Industrial Automation
A Comparative Analysis of SDON and Traditional Optical Networks in Elastic Optical Environments
A Review of Techniques for Minimizing Energy Consumption and Enhancing Lifespan in Wireless Sensor Networks
NodeMCU and Wireless Sensor Networks in Agriculture: A Review of Environmental Parameter Sensing

Most Cited

Volume 8 Issue 2 July - September 2019

Research Paper

Comparitive Evaluation of Security Algorithms for Ad Hoc Wireless Networks in an Energy Constrained Environment

Rashid Husain* , Rabia Khan, Rajesh Kumar Tyagi*

* Department of Mathematics and Computer Science, Sule Lamido University, Kafin Hausa, Jigawa, Nigeria.

Master of Computer Application, Punjab Technical University, India.

* Department of Computer Science and Engineering, Amity University, Haryana, India.

Husain, R., Khan, R., and Tyagi, R. K. (2019). Comparitive Evaluation of Security Algorithms for Ad Hoc Wireless Networks in an Energy Constrained Environment. i-manager’s Journal on Wireless Communication Networks , 8(2), 1-7. https://doi.org/10.26634/jwcn.8.2.16788

Abstract

Ad hoc wireless networks are emerging as widely used technology in various domains. Among a diversity of application armed forces use it for sensing and tracking devices, environment monitoring, patient monitoring and tracking intelligent ambiance. Security is important because of its open availability and energy consumption. Hence, it is very challenging to save energy and enhanced security in an wireless sensor network. As the triggered packets promulgate through the network towards final data accession point, it will result in the energy consumption in a low powered network. Therefore the most important aspect of such networks is the energy of the nodes, which directly affects the lifetime of the entire networks. This paper assesses the impact of two secure algorithm such as Tiny Encryption Algorithm (TEA) and Data Encryption Standard (DES) security process on ad hoc wireless network. Hence, number of evaluations are undertaken in the simulator in order to accurately establish energy consumption for above mentioned algorithms. These two algorithms are implemented using Castalia Simulator and compared on parameters, such as available energy, maximum packet size, packet header overhead, frame overhead, routing cost, energy consumed per node, etc. The comparison between the security parameters are graphically plotted with detailed analysis.

References

[1]. Akyildiz, I. F., Su, W., Sankasubramaniam, Y., & Cayirci. E. (2002). Wireless sensor networks: A survey. Computer Networks, 38, 393–422.

[2]. Anand, M., Cronin, E., Sherr, M., Ives, Z. G., & Lee, I. (2006, July). Sensor network security: More interesting than you think. In Proceedings of the 1^st USENIX Workshop on Hot Topics in Security (HOTSEC’06). USENIX Association,USA. (pp.25-30).

[3]. Biryukov, A., & Cannière, C. D. (2005). Data encryption standard (DES). In Henk C. A. van Tilborg (ed.) Encyclopedia of Cryptography and Security. Springer. https://doi.org/10.1007/0-387-23483-7_94

[4]. Burman, S. (2007). Cryptography and security - future challenges and issues. In 15^th International Conference on Advanced Computing and Communications (ADCOM 2007) (pp.547 - 551). https://doi.org/10.1109/ ADCOM.2007.141

[5]. Gan, R., Xiao, Y., Shao, J., & Qin, J. (2019). An analysis on optimal attack schedule based on channel hopping scheme in cyber-physical systems. IEEE Transactions on Cybernetics. https://doi.org/10.1109/TCYB.2019. 2914144

[6]. Ganesan, P., Venugopalan, R., Peddabachagari, P., Dean, A., Mueller, F., & Sichitiu, M. (2003, September). Analyzing and modeling encryption overhead for sensor network nodes. In Proceedings of the 2^nd ACM International Conference on Wireless Sensor Networks and Applications (pp. 151-159). ACM. https://doi.org/10. 1145/941350.941372

[7]. Guimaraes, G., Souto, E., Sadok, D., & Kelner, J. (2004). The Impact of security mechanisms on nodes. In Proceedings of the VI Workshop on Adhoc Wire-less Comunication (WCSF), Fortaleza, Brazil.

[8]. Guimaraes, G., Souto, E., Sadok, D., & Kelner, J. (2005). Evaluation of security mechanisms in wireless sensor networks. Systems Communications (ICW'05, ICHSN'05, ICMCS'05, SENET'05), (pp. 428-433). IEEE. https://doi.org/10.1109/ ICW.2005.47

[9]. Healy, M., Newe, T., & Lewis, E. (2007, October). Resources Implications for Data Security in Wireless Sensor Network Nodes. In 2007 International Conference on Sensor Technologies and Applications (SENSORCOMM 2007) (pp. 170-175). IEEE. https://doi.org/10.1109/SENSOR COMM.2007.4394916

[10]. Heinzelman, W., R., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for ad-hoc wireless microsensor networks, In rd Proceedings 33 Hawaii International Conference on System Sciences (pp. 1-10).

[11]. Law, Y. W., & Havinga, P. J. M. (2005, December). How to secure a wireless sensor network. In 2005 International Conference on Intelligent Sensors, Sensor Networks and Information Processing (pp. 89-95). IEEE. https://doi.org/10.1109/ISSNIP.2005.1595561

[12]. Li, H., Shen, C., Zhao, Y., Sahin, G., Choi, H. A., & Shah, Y. (2019). High entropy secrecy generation from wireless CIR. Journal of Communications and Networks, 21(2), 177-191. https://doi.org/10.1109/JCN.2019.000 019

[13]. Liu, S., Gavrylyako, O. V., & Bradford, P. G. (2004). Implementing the TEA algorithm on sensors. In Proceedings 42^nd Annual Southeast Regional Conference, (pp. 64–69). https://doi.org/10.1145/9865 37.986553

[14]. Mini, R. A., Loureiro, A. A., & Nath, B. (2003, September). Prediction-based energy map for wireless sensor networks. In IFIP International Conference on Personal Wireless Communications (pp. 12-26). Springer. https://doi.org/10.1007/978-3-540-39867-7_2

[15]. Pathan, A. S. K.., Lee, H. W., & Hong, C. S. (2006, February). Security in wireless sensor networks: Issues and challenges. In 2006 8^thInternational Conference Advanced Communication Technology (Vol. 2, pp. 1043-1048). IEEE. https://doi.org/10.1109/ICACT.2006. 206151

[16]. Perrig, A., Szewczyk, R., Wen, V., Culler, D., & Tygar, J. D. (2001). SPINS: Security protocols for networks. In Proceedings of Seventh Annual International Conference on Mobile Computing and Networks, (pp. 1-11).

[17]. Xia, Z., Wang, X., Sun, X., & Wang, Q. (2016). A secure and dynamic Multi-Keyword Ranked Search Scheme over encrypted cloud data. IEEE Transactions on Parallel and Distributed Systems, 27(2), 340-352. https://doi.org/10.1109/TPDS.2015.2401003

[18]. Xiao, M., Wang, X., & Yang, G. (2006). Cross-layer design for the security of wireless sensor networks. Proceedings of the 6^th World Congress on Intelligent Control and Automation, Dalian, China, (pp.104-108).

[19]. Yang, H., & Wang, F. (2019). Wireless network intrusion detection based on improved convolutional neural network. IEEE Access, 7, 64366-64374. https:// doi.org/10. 1109/ACCESS.2019.2917299

[20]. Zhong, L. C., Rabaey, J. M., & Wolisz, A. (2004, June). An integrated data-link energy model for wireless sensor networks. In 2004 IEEE International Conference on Communications (Vol. 7, pp. 3777-3783). IEEE. https://doi. org/10.1109/ICC.2004.1313260

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Research Paper

Investigation of WSN Parameters for Realization of Quality of Service

Prashant V. Mane Deshmukh* , B. P. Ladgaonkar, S. K. Tilekar*

* Department of Electronics, Jayawantrao Sawant College of Commerce and Science, Hadapsar, Pune, Maharashtra, India.

Ganpatrao Arwade College of Commerce, Sangli, Maharashtra, India.

* Department of Electronics, Shankarrao Mohite Mahavidyalaya, Akluj, Maharashtra, India.

Deshmukh, P. V. M., Ladgaonkar, B. P., and Tilekar, S. K. (2019). Investigation of WSN Parameters for Realization of Quality of Service. i-manager’s Journal on Wireless Communication Networks , 8(2), 8-19. https://doi.org/10.26634/jwcn.8.2.16663

Abstract

In this paper, emphasizing on the Quality of Service (QoS), the parameters of the industrial Wireless Sensor Network have been investigated. Deploying an ubiquitous embedded technology, the smart sensor motes have been designed in which the standards of IEEE 1451 are realized. According to these standards, the Network Capable Application Processor (NCAP) plays a vital role on establishment of wireless communication, along with transduction and intelligent computing. The ZigBee device has been used to facilitate the sensor motes with smart communication module. The ZigBee devices are operating according to IEEE 802.15.4 with amended PHY and MAC layers. Thus, the sensor motes have been designed, wherein two standards IEEE 1451 and IEEE 802.15.4 are suitably confluenced. The base station, which is inherent part of WSN, is developed and utilized for the establishment of WSN in desired protocol. The parameters, such as Receiver Signal Strength Indicator (RSSI), Link Quality Indicator (LQI), Packet Reception Rate (PRR), Delay Time (DT) etc, being QoS parameters of the dedicated WSN are investigated for both indoor as well as outdoor environment. It was found that, the values of RSSI and LQI are measured for both indoor as well as outdoor environment, which are in the limit of agreement. Both RSSI and LQI decrease with increase in distance of mote from base station. The packet reception rate is estimated from the rate of packets stored in the data base. PRR is within the range from 96% to 100%, which suggest that the data loss is less than 4%. The latency expressed by measuring delay time is also very less. It was found that, the values of these QoS parameters are mostly sensitive to the infrastructural obstacles. From results of investigation, the WSN provides good QoS and hence ensure its suitability of industrial deployment.

References

[1]. Alhasanat, A. I., Matrouk, K. D., Alasha'ary, H. A., & Al- Qadi, Z. A. (2014). Connectivity-based data gathering with path-constrained mobile sink in wireless sensor networks. Wireless Sensor Network, 6(6), 118-128. https:// doi.org/10.4236/wsn.2014.66013

[2]. Chipde, N, U., & Raut, V, R., (2013). Industrial process parameter control using ethernet. International Journal of Science and Research, 2(5), 449-452.

[3]. Christin, D., Mogre, P. S., & Hollick, M. (2010). Survey on wireless sensor network technologies for industrial automation: The security and quality of service perspectives. Future Internet, 2 (2), 96-125. https://doi.org/10.3390/fi2020096

[4]. Deshmukh, P. V. M. (2018). Smart graphical user interface for wireless sensor network. i-manager's Journal on Software Engineering, 13(1), 1-8. https://doi.org/ 10.26634/jse.13.1.14844

[5]. Deshmukh, P. V. M., Ladgaonkar, B. P., Pathan, S. C., & Shaikh, S. S. (2013). Microcontroller Pic 18f4550 based wireless sensor node to monitor industrial environmental Parameters. International Journal of Advanced Research in Computer Science and Software Engineering, 3(10), 943- 950.

[6]. Deshmukh, P. V. M., Pathan, S. C., Chanvan, S. V., Tilekar, S. K., & Ladgaonkar, B. P. (2016). Wireless sensor network for monitoring of air pollution near industrial sector. International Journal of Advanced Research in Computer Science and Software Engineering, 6(6), 638-645.

[7]. Gungor, V. C., & Hancke, G. P. (2009). Industrial wireless sensor networks: Challenges, design principles, and technical approaches. IEEE Transactions on Industrial Electronics, 56(10), 4258-4265. https://doi.org/ 10.1109/TIE.2009.2015754

[8]. Hernandez, W. (2006). Improving the response of a load cell by using optimal filtering. Sensors, 6(7), 697- 711.https://doi.org/10.3390/s6070697

[9]. Lee, D. H. (2013). Fast notification architecture for wireless sensor networks. International Journal of Electronics, 100(3), 371-383. https://doi.org/10.1080/ 00207217.2012.713012

[10]. Nadimi, E. S., Søgaard, H. T., Bak, T., & Oudshoorn, F. W. (2008). ZigBee-based wireless sensor networks for monitoring animal presence and pasture time in a strip of new grass. Computers and Electronics in Agriculture, 61(2), 79-87. https://doi.org/10.1016/j.compag.2007. 09.010

[11]. Ngai, E. C., Lyu, M. R., & Liu, J. (2005). A real-time communication framework for wireless sensor-actuator networks. IEEE Aerospace Conference Paper, 1622(2), 1-9.

[12]. Rasin, Z., & Abdullah, M. R. (2009). Water quality monitoring system using zigbee based wireless sensor network. International Journal of Engineering and Technology, 9(10), 24-28.

[13]. Rorato, O., Bertoldo, S., Lucianaz, C., Allegretti, M., & Notarpietro, R. (2013). An ad-hoc low cost wireless sensor network for smart gas metering. Wireless Sensor Network, 5(3), 61-66. https:// doi.org/10.4236/ wsn.2013.53008

[14]. Somani, N. A., & Patel, Y. (2012). Zigbee: A low power wireless technology for industrial applications. International Journal of Control Theory and Computer Modelling (IJCTCM), 2(3), 27-33. https://doi.org/10.5121/ ijctcm.2012.2303

[15]. Suhonen, J., Hämäläinen, T. D., & Hännikäinen, M. (2010). Class of service support layer for wireless mesh networks. International Journal of Communications, Network and System Sciences, 3(2), 140-151.

[16]. Xu, G., Shen, W., & Wang, X. (2014). Applications of wireless sensor networks in marine environment monitoring: A survey. Sensors, 14(9), 16932-16954. https://doi.org/10.3390/s140916932

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Research Paper

Channel Parameters Analysis Using Cognitive Radio Wireless Sensor Network (CR-WSN) for Wireless Communication

Dr.R. Porselvi * , M. Murugan, P. S. Meena Sri*, V. Pradeepa****

* Research Scholar, Department of Electronics and Communication Engineering, SRM Valliammai Engineering College, Chennai, Tamil Nadu, India & Senior Assistant Professor, Tagore Engineering College, Chennai, Tamil Nadu, India.

Department of Electronics and Communication Engineering, SRM Valliammai Engineering College, Chennai, Tamil Nadu, India.

*-**** Department of Electronics and Communication Engineering, Tagore Engineering College, Chennai, Tamil Nadu, India.

Porselvi, R., Murugan, M., Sri, P. S. M., and Pradeepa, V. (2019). Channel Parameters Analysis Using Cognitive Radio Wireless Sensor Network (CR-WSN) for Wireless Communication. i-manager’s Journal on Wireless Communication Networks , 8(2), 20-28. https://doi.org/10.26634/jwcn.8.2.16796

Abstract

Wireless communication ensures well-organized spectrum utilization. It uses various resources allocation such as channel, frequency, bandwidth, etc., to make communication swiftly and easier. Cognitive Radio (CR) is an emerging technology that deals with the challenges to facilitate a feasible access to the spectrum. Hence, data losses and delay are the main issues in the Cognitive Radio Network (CRN). Many protocols and algorithms have been proposed, although the issues have not yet been completely addressed. Cognitive radio allows utilizing unused spectrum, so we have to check if it uses channel estimation techniques. Cognitive Radio Wireless Sensor Network (CR-WSN) coupled with cooperative communication minimizing the time delay for users. In this work, spectrum sensing and spectrum sharing is used to solve the problem of resource allocation and to ensure better management. Another method used in CR to improving wireless communication is Road Side Units (RSUs), which have limited coverage area and high deployment cost. The opportunistic encounters between mobile nodes increases the data transfer rate by carrier sensing and chunk scheduling using hand over process. Our objective is to control the data loss and increase the signal strength. A new approach is proposed in mobile node through carry and forward transfers to minimize the time delay and it is implemented by NS2 tool.

References

[1]. Chen, K. C., Kung, L. H., Shiung, D., Prasad, R., & Chen, S. (2007, December). Self-organizing terminal architecture for cognitive radio networks. In the 10^th International Symposium on Wireless Personal Multimedia Communications (pp. 926-931).

[2]. Chen, W. H., Lin, W. R., Tsao, H. C., & Lin, C. (2016). Probabilistic power allocation for cognitive radio networks with outage constraints and one-bit side information. IEEE Transactions on Signal Processing, 64(4), 867-881. https://doi.org/10.1109/TSP.2015.2494852

[3]. Cordeiro, C., Challapali, K., Birru, D., & Shankar, S. (2005, November). IEEE 802.22: The first worldwide wireless standard based on cognitive radios. In 1^st IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005. (pp. 328-337). IEEE https://doi.org/10.1109/DYSPAN.2005. 1542649.

[4]. Holland, O., Attar, A., Olaziregi, N., Sattari, N., & Aghvami, A. H. (2006, September). A universal resource awareness channel for cognitive radio. In 2006 IEEE 17^th International Symposium on Personal, Indoor and Mobile Radio Communications (pp. 1-5). IEEE. https://doi.org/ 10.1109/PIMRC.2006.254338

[5]. Hung, S. C., Hung, S. Y., Wu, E. H. K., & Chen, G. H. (2006, September). A decentralized CR system algorithm for cognitive borrowing scheme from primary users. In 2006 IEEE 17^th International Symposium on Personal, Indoor and Mobile Radio Communications (pp. 1-5). IEEE. https://doi.org/10.1109/PIMRC.2006.254337

[6]. Kang, X., Zhang, R., Liang, Y. C., & Garg, H. K. (2011). Optimal power allocation strategies for fading cognitive radio channels with primary user outage constraint. IEEE Journal on Selected Areas in Communications, 29(2), 374-383. https://doi.org/10.1109/JSAC.2011.110210

[7]. Kolodzy, P. (2003, March). Spectrum policy task force: findings and recommendations. In International Symposium on Advanced Radio Technologies (ISART) (Vol. 5, No. 12, pp. 459-465).

[8]. Li, L., Zhou, X., Xu, H., Li, G. Y., Wang, D., & Soong, A. (2010). Simplified relay selection and power allocation in cooperative cognitive radio systems. IEEE Transactions on Wireless Communications, 10(1), 33-36. https://doi.org/ 10.1109/TWC.2010.101810.100311

[9]. Manolakos, A., Noam, Y., & Goldsmith, A. J. (2015). Null space learning in cooperative MIMO cellular networks using interference feedback. IEEE Transactions on Wi reless Communications, 14 (7), 3961-3977. https://doi.org/10.1109/TWC.2015.2415483

[10]. Mansukhani, J., Ray, P., & Varshney, P. K. (2016). Coupled detection and estimation based censored spectrum sharing in cognitive radio networks. IEEE Transactions on Wireless Communications, 15(6), 4206- 4217. https://doi.org/10.1109/TWC.2016.2536731

[11]. Noam, Y., & Goldsmith, A. J. (2013). Blind null-space learning for MIMO underlay cognitive radio with primary user interference adaptation. IEEE Transactions on Wireless Communications, 12 (4), 1722-1734. https://doi.org/10.1109/TWC.2013.021213.120643

[12]. Rezki, Z., & Alouini, M. S. (2012). Ergodic capacity of cognitive radio under imper fect channel-state information. IEEE Transactions on Vehicular Technology, 61(5), 2108-2119. https://doi.org/10.1109/TVT.2012. 2195042

[13]. Wang, L. C., Wang, C. W., Lu, Y. C., & Liu, C. M. (2007, March). A concurrent transmission MAC protocol for enhancing throughout and avoiding spectrum sensing in cognitive radio. In 2007 IEEE Wireless Communications and Networking Conference (pp. 121-126). IEEE.

[14]. Wang, Q., Ren, K., Ning, P., & Hu, S. (2015). Jamming-resistant multiradio multichannel opportunistic spectrum access in cognitive radio networks. IEEE Transactions on Vehicular Technology, 65(10), 8331- 8344. https://doi.org/10.1109/TVT.2015.2511071

[15]. Wu, Q., Wu, D., Xu, Y., & Wang, J. (2014). Demandaware multichannel opportunistic spectrum access: A local interaction game approach with reduced information exchange. IEEE Transactions on Vehicular Technology, 64(10), 4899-4904. https://doi.org/10.1109/ TVT.2014.2369484

[16]. Zhang, R., Gao, F., & Liang, Y. C. (2010). Cognitive beamforming made practical: Effective interference channel and learning-throughput tradeoff. IEEE Transactions on Communications, 58(2), 706-718. https://doi.org/10.1109/TCOMM.2010.02.080476

[17]. Zhang, L., Xiao, M., Wu, G., Zhao, G., Liang, Y. C., & Li, S. (2016). Proactive cross-channel gain estimation for spectrum sharing in cognitive radio. IEEE Journal on Selected Areas in Communications, 34(10), 2776-2790. https://doi.org/10.1109/JSAC.2016.2605298

[18]. Zhang, L., Zhao, G., Wu, G., & Chen, Z. (2012, December). Proactive channel gain estimation for coexistence between cognitive and primary users. In 2012 IEEE Global Communications Conference (GLOBECOM) (pp. 1410-1415). IEEE. https://doi.org/10. 1109/GLOCOM.2012.6503311

[19]. Zhu, X., Shen, L., & Yum, T. S. P. (2007). Analysis of cognitive radio spectrum access with optimal channel reservation. IEEE Communications Letters, 11(4), 304- 306. https://doi.org/10.1109/LCOM.2007.348282

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Research Paper

Design and Development of Microstrip Resonator for Wireless Applications

Kanniyappan N.* , Amali C.**

* Department of Electronics and Communications Engineering, Jerusalem College of Engineering, Chennai, Tamil Nadu, India.

** Department of Electronics and Communications Engineering, SRM Valliammai Engineering College, Chennai, Tamil Nadu, India.

Kanniyappan, N., and Amali, C. (2019). Design and Development of Microstrip Resonator for Wireless Applications. i-manager’s Journal on Wireless Communication Networks , 8(2), 29-33. https://doi.org/10.26634/jwcn.8.2.16781

Abstract

The performance of microwave communication is mitigated due to the existence of interference signals from other wireless communication systems. The major interferences are caused at IEEE802.lla/b Wireless Local Area Network (WLAN) system from other wireless terrestrial networks. In this paper, the design of an L-shaped resonator for wireless applications is proposed. The proposed method is designed by the L-shaped resonator at 2.45 GHz in Industrial Scientific and Medical (ISM) band. This method has been extended to include the design and result discussions of the L-shaped resonator. Methods of increasing bandwidth such as changing L-shaped resonator width and cascading stages of filters are presented. The filters have been simulated using Advanced Design System (ADS), Electromagnetic Design System (EMDS), and implemented on FR4 substrate.

References

[1]. BalaSenthilMurugan, L., Raja, S. A. A., Chakravarthy, S. D., & Kanniyappan, N. (2012). Design and implementation of a microstrip band-stop filter for microwave applications. Procedia Engineering, 38, 1346-1351. https://doi.org/10.1016/j.proeng.2012. 06.166

[2]. Garcia-Garcia, J., Martin, F., Falcone, F., Bonache, J., Gil, I., Lopetegi, T.,Laso, M. A. G., Sorolla, M., & Marques, R. (2004). Spurious passband suppression in microstrip coupled line band pass filters by means of split ring resonators. IEEE Microwave and Wireless Components Letters, 14(9), 416-418. https://doi.org/10.1109/LMWC. 2004.832066

[3]. Gupta, A., & Gangwar, R. K. (2018). Dual-Band circularly polarized aperture coupled rectangular dielectric resonator antenna for wireless applications. IEEE Access, 6, 11388-11396. https://doi.org/10.1109/ ACCESS.2018.2791417

[4]. Hong, J. S. G., & Lancaster, M. J. (2001). Microstrip filters for RF/Microwave applications, Wiley- Interscience.

[5]. Jachowski, D. R. (2004, June). Passive enhancement of resonator Q in microwave notch filters. In 2004 IEEE MTTS International Microwave Symposium Digest (Vol. 3, pp. 1315-1318). IEEE. https://doi.org/10.1109/MWSYM.2004. 1338808

[6]. Jha, A. K., Delmonte, N., Lamecki, A., Mrozowski, M., & Bozzi, M. (2019). Design of microwave-based angular displacement sensor. IEEE Microwave and Wireless Components Letters, 29(4), 306-308. https://doi.org/ 10.1109/LMWC.2019.2899490

[7]. Kazerooni, M., Rad, G. R., & Cheldavi, A. (2009, March). Behavior study of simultaneously Defected Microstrip and Ground Structure (DMGS) in planar circuits. In PIERS proceedings (Vol. 1, No. 1214, pp. 895-900).

[8]. Kornprobst, J., Wang, K., Hamberger, G., & Eibert, T. F. (2017). A mm-wave patch antenna with broad bandwidth and a wide angular range. IEEE Transactions on Antennas and Propagation, 65(8), 4293-4298. https://doi.org/10.1109/TAP.2017.2710261

[9]. Marques, R., Martin, F., & Sorolla, M. (2007). Metamaterials with negative parameters, Wiley- Interscience.

[10]. Nizam, M. M., Soh, P. J., & Suhaizal, A. H. M. (2009, November). Design of a modified L-shaped bandstop filter for UWB applications. In 2009 SBMO/IEEE MTT-S International Microwave and Optoelectronics Conference (IMOC) (pp. 177-182). IEEE. https://doi.org/ 10.1109/IMOC.2009.5427606

[11]. Tirado-Mendez, J. A., Jardon-Aguilar, H., Andrade- Gonzalez, E. A., & Reyes-Ayala, M. (2007). A simplified method to reduce dimensions of planar passive circuits using defected ground and defected microstrip structures. Microwave Journal, 50(11), 58-70.

[12]. Wang, C. J., & Lin, C. S. (2008). Compact DGS resonator with improvement of Q-factor. Electronics Letters, 44(15), 908-910. https://doi.org/10.1049/el: 20081417

[13]. Zhang, Y., Deng, J. Y., Li, M. J., Sun, D., & Guo, L. X. (2019). A MIMO dielectric resonator antenna with improved isolation for 5G mm-wave applications. IEEE Antennas and Wireless Propagation Letters, 18(4), 747- 751. https://doi.org/10.1109/LAWP.2019.2901961

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Research Paper

Energy Efficient Pollution Monitoring System using Wireless Sensor Networks

S. Anandamurugan*

Department of Information Technology, Kongu Engineering College, Erode, Tamil Nadu, India.

Anandamurugan, S. (2019). Energy Efficient Pollution Monitoring System using Wireless Sensor Networks. i-manager’s Journal on Wireless Communication Networks , 8(2), 34-39. https://doi.org/10.26634/jwcn.8.2.16644

Abstract

Pollution is escalated by developments that typically occurs when countries develop through rising towns, upward traffic, high-speed economic activity, industrialization, and excessive levels of energy employment. The supplementary opening of the metropolitan areas to people, land use patterns and trade development have led to the complicated threat of air pollution. Air pollution is a key factors for the constituents in the environment and leads to global warming and acid rains. To arrest such dangerous imbalances in nature, air pollution monitoring system is of utmost significance. Wireless Sensor Networks (WSNs) constitute tremendous technology solutions that can identify, determine, and assemble data from the real world and convey the sensed data to the user, based on a quantity of local estimation process. These networks are conform with the physical environment, which is premeditated and at elevated resolutions adding value and sum of real-world data and information for applications analogous to pollution monitoring. One of the purposes behind the sensors is that if commercialized, they would caution people to be more rational every day while dealing with air pollution. Users can keep away from areas where the levels have been cruelly elevated, and can be imagined to put pressure on local powers to address the issue. Also, data gathered using multiple sensors across a region could provide the public with much supplementary information for being careful and present precise air worth reports than is currently possible.

References

[1]. Al-Ali, A. R., Zualkernan, I., & Aloul, F. (2010). A mobile GPRS-sensors array for air pollution monitoring. IEEE Sensors Journal, 10(10), 1666-1671. https://doi.org/ 10.1109/JSEN.2010.2045890

[2]. Bernard, S., & Kazmin, A. (2018). Dirty air: how India became the most polluted country on earth. Financial Times. Retrieved from https://ig.ft.com/india-pollution/

[3]. Jelicic, V., & Magno, M. (2013). Multimodal wireless sensor network for energy efficient monitoring of gases. IEEE Sensors Journal, 13(1), 328-338.

[4]. Khedo, K., & Mungur, A. (2010). Wireless sensor network based pollution monitoring. Journal on Wireless Networks, 12(8), 256-270.

[5]. Oliveira, L., & Rodrigues, J. (2011). Wireless sensor networks: A study of environment. Journal on Wireless Communications, 6(2), 143-151.

[6]. Rastogi, S., & Rao, S. A. G. (2016). A real time GPRS and web based system for monitoring air pollutants using mobile data acquisition unit. International Journal for Scientific Research & Development, 4(7), 39-43.

[7]. Sensor node. (n.d). Wikipedia. Retrieved from https://en.wikipedia. org/wiki/Sensor_node

i-manager's Journal on Wireless Communication Networks (JWCN)

Current Issue Vol. 12 Issue 1

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Volume 8 Issue 2 July - September 2019

Comparitive Evaluation of Security Algorithms for Ad Hoc Wireless Networks in an Energy Constrained Environment

Rashid Husain* , Rabia Khan**, Rajesh Kumar Tyagi***

* Department of Mathematics and Computer Science, Sule Lamido University, Kafin Hausa, Jigawa, Nigeria. ** Master of Computer Application, Punjab Technical University, India. *** Department of Computer Science and Engineering, Amity University, Haryana, India.

Husain, R., Khan, R., and Tyagi, R. K. (2019). Comparitive Evaluation of Security Algorithms for Ad Hoc Wireless Networks in an Energy Constrained Environment. i-manager’s Journal on Wireless Communication Networks , 8(2), 1-7. https://doi.org/10.26634/jwcn.8.2.16788

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Investigation of WSN Parameters for Realization of Quality of Service

Prashant V. Mane Deshmukh* , B. P. Ladgaonkar**, S. K. Tilekar***

* Department of Electronics, Jayawantrao Sawant College of Commerce and Science, Hadapsar, Pune, Maharashtra, India. ** Ganpatrao Arwade College of Commerce, Sangli, Maharashtra, India. *** Department of Electronics, Shankarrao Mohite Mahavidyalaya, Akluj, Maharashtra, India.

Deshmukh, P. V. M., Ladgaonkar, B. P., and Tilekar, S. K. (2019). Investigation of WSN Parameters for Realization of Quality of Service. i-manager’s Journal on Wireless Communication Networks , 8(2), 8-19. https://doi.org/10.26634/jwcn.8.2.16663

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Channel Parameters Analysis Using Cognitive Radio Wireless Sensor Network (CR-WSN) for Wireless Communication

Dr.R. Porselvi * , M. Murugan**, P. S. Meena Sri***, V. Pradeepa****

Porselvi, R., Murugan, M., Sri, P. S. M., and Pradeepa, V. (2019). Channel Parameters Analysis Using Cognitive Radio Wireless Sensor Network (CR-WSN) for Wireless Communication. i-manager’s Journal on Wireless Communication Networks , 8(2), 20-28. https://doi.org/10.26634/jwcn.8.2.16796

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Design and Development of Microstrip Resonator for Wireless Applications

Kanniyappan N.* , Amali C.**

* Department of Electronics and Communications Engineering, Jerusalem College of Engineering, Chennai, Tamil Nadu, India. ** Department of Electronics and Communications Engineering, SRM Valliammai Engineering College, Chennai, Tamil Nadu, India.

Kanniyappan, N., and Amali, C. (2019). Design and Development of Microstrip Resonator for Wireless Applications. i-manager’s Journal on Wireless Communication Networks , 8(2), 29-33. https://doi.org/10.26634/jwcn.8.2.16781

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Energy Efficient Pollution Monitoring System using Wireless Sensor Networks

S. Anandamurugan*

Department of Information Technology, Kongu Engineering College, Erode, Tamil Nadu, India.

Anandamurugan, S. (2019). Energy Efficient Pollution Monitoring System using Wireless Sensor Networks. i-manager’s Journal on Wireless Communication Networks , 8(2), 34-39. https://doi.org/10.26634/jwcn.8.2.16644

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Rashid Husain* , Rabia Khan, Rajesh Kumar Tyagi*

* Department of Mathematics and Computer Science, Sule Lamido University, Kafin Hausa, Jigawa, Nigeria.

Master of Computer Application, Punjab Technical University, India.

* Department of Computer Science and Engineering, Amity University, Haryana, India.

Prashant V. Mane Deshmukh* , B. P. Ladgaonkar, S. K. Tilekar*

* Department of Electronics, Jayawantrao Sawant College of Commerce and Science, Hadapsar, Pune, Maharashtra, India.

Ganpatrao Arwade College of Commerce, Sangli, Maharashtra, India.

* Department of Electronics, Shankarrao Mohite Mahavidyalaya, Akluj, Maharashtra, India.

Dr.R. Porselvi * , M. Murugan, P. S. Meena Sri*, V. Pradeepa****

* Department of Electronics and Communications Engineering, Jerusalem College of Engineering, Chennai, Tamil Nadu, India.

** Department of Electronics and Communications Engineering, SRM Valliammai Engineering College, Chennai, Tamil Nadu, India.