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 3 October - December 2019

Research Paper

Culminate Coverage for Sensor Network through Bodacious-Instance Mechanism

Shahzad Ashraf*

College of Internet of Things Engineering, Hohai University, Changzhou, China.

Ashraf, S. (2019). Culminate Coverage for Sensor Network through Bodacious-Instance Mechanism. i-manager’s Journal on Wireless Communication Networks , 8(3), 1-9. https://doi.org/10.26634/jwcn.8.3.17310

Abstract

Due to unavoidable environmental factors, the wireless sensor networks are facing numerous tribulations regarding network coverage. This arose due to the uncouth deployment of the sensor nodes that influence the performance. To enhance the network coverage, a node deployment based Bodacious-instance Mechanism (BiM) has been proposed. Each instance describes a solution for the deployment of sensor nodes individually. Further variations of various parameters of BiM such as loudness, pulse emission rate, maximum frequency, grid points, and sensing radius has been explored and optimized values of these parameters are identified. The simulation results of node deployment based on tuned Bodacious-instance mechanism is also compared with BiM and fruit fly optimization algorithm (FOA) based node deployment in terms of mean coverage rate, computation time, and standard deviation. The coverage rate curve for various numbers of iterations and sensor nodes are also presented for tuned Bodacious-instance Mechanism, BiM, and FOA. The results demonstrate the effectiveness of tuned Bodacious-instance mechanism as it achieves more coverage rate than BiM and FOA.

References

[1]. Ashraf, S., Ahmed, T., Raza, A., & Naeem, H. (2020 a). Design of shrewd underwater routing synergy using porous energy shells. Smart Cities, 3(1), 74-92. https://doi.org/ 10.3390/smartcities3010005

[2]. Ashraf, S., Gao, M., Mingchen, Z., Ahmed, T., Raza, A., & Naeem, H. (2020). USPF: Underwater shrewd packet flooding mechanism through surrogate holding time Wireless Communications and Mobile Computing, 1–12. https://doi.org/10.1155/2020/9625974.

[3]. Ashraf, S., & Ahmed, T. (2020). Underwater Routing Protocols: Analysis of Intrepid Link Selection Mechanism, Challenges and Strategies. International Journal of Scientific Research in Computer Science, Engineering, 8(2), 1–9.https://doi.org/10.26438/ijsrcse/v8i2.19

[4]. Ashraf, S., Raza, A., Aslam, Z., Naeem, H., & Ahmed, T. (2020). Underwater resurrection routing synergy using astucious energy pods. Journal of Robotics and Control (JRC), 1(5), 173-184.

[5]. Ashraf, S., Ahmed, T., Saleem, S., & Aslam, Z. (2020). Diverging Mysterious in Green Supply Chain Management. Oriental Journal of Computer Science and Technology, 13(1), 22-28. http://doi.org/10.13005/ojcst13.01.02

[6]. Ashraf, S., Gao, M., Chen, Z., Kamran, S., & Raza, Z. (2017). Efficient node monitoring mechanism in WSN using contikimac protocol. International Journal of Advanced Computer Science and Applications, 8(11), 429-437.

[7]. Ashraf, S., & Ahmed, T. (2020). Challenging strategic trends in green supply chain management. International Journal of Engineering and Applied Sciences, 5(2), 71–74.https://doi.org/10.46565/jreas.2020.v05i02.006

[8]. Ashraf, S., Arfeen, Z. A., Khan, M. A., & Ahmed, T. (2014). SLM-OJ: Surrogate learning mechanism during outbreak juncture. International Journal for Modern Trends in Science and Technology, 6(5), 162–167. https://doi.org/10. 46501/IJMTST060525

[9]. Aoudia, F. A., Gautier, M., Magno, M., Berder, O., & Benini, L. (2016). A generic framework for modeling MAC protocols in wireless sensor networks. IEEE/ACM Transactions on Networking, 25(3), 1489-1500.

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[12]. Dervis, K. (2010). Artificial bee colony algorithm. Scholarpedia, 5(3), 6915.

[13]. Erciyes University. (n.d.). Artificial Bee Colony (ABC) Algorithm Homepage. Erciyes University. https://abc.erc iyes.edu.tr

[14]. Franklin, J. E., & Urick, R. J. (1979). A binary detection model for at-sea sonar prediction. The Journal of the Acoustical Society of America, 66, S15. https://doi. org/10.1121/1.2017631

[15].Goyal, S., & Patterh, M. S. (2015, December). Flower pollination algorithm based localization of wireless sensor network. In 2015 2nd International Conference on Recent Advances in Engineering & Computational Sciences (RAECS) (pp. 1-5). IEEE. http://doi.org/10.1109/RAECS. 2015.7453299

[16]. Li, M., Du, X., Liu, X., & Li, C. (2019). Shortest path routing protocol based on the vertical angle for underwater acoustic networks. Journal of Sensors, 2019, 1-14. https:// doi.org/10.1155/2019/9145675

[17]. Marinaki, M., & Marinakis, Y. (2016). A glowworm swarm optimization algorithm for the vehicle routing problem with stochastic demands. Expert Systems with Applications, 46, 145-163. https://doi.org/10.1016/j.eswa. 2015.10.012

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[20]. Ren, Y., Li, J., Shi, S., Li, L., Wang, G., & Zhang, B. (2016). Congestion control in named data networking–a sur vey. Computer Communications, 86, 1-11. https://doi.org/10. 1016/j.comcom.2016.04.017

[21]. Stringer, H. (2007). Behavior of variable-length genetic algorithms under random selection. (Postgraduate Dessetation), University of Central Florida, Orlando, FL. 2004-2019.

[22]. Robinson, A. (2019, March 2). How to Calculate Euclidean Distance. Sciencing. https://sciencing.com /how-to-calculate-euclidean-distance-12751761.html

[23]. Zhang, J., Lei, Y., Chen, C., & Lin, F. (2016). Directional probability perceived nodes deployment based on particle swarm optimization. International Journal of Distributed Sensor Networks, 12(4), 2046392.

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Research Paper

Low Power and Area Efficient FFT Processor using Radix-24 Feed Forward Multipath Delay Commutator for Wireless Communication

C. Padma* , P. Jagadamba , P. Ramana Reddy *

, Department of Electronics and Communication Engineering, Jawaharlal Nehru Technological University, Ananthapuramu, Andhra Pradesh, India.

Department of Electronics and Communication Engineering, Srikalahasthi Institute of Technology, Srikalahasti, Andhra Pradesh, India.

Padma, C., Jagadamba, P., and Reddy, P. R. (2019). Low Power and Area Efficient FFT Processor using Radix-24 Feed Forward Multipath Delay Commutator for Wireless Communication. i-manager’s Journal on Wireless Communication Networks , 8(3), 10-17. https://doi.org/10.26634/jwcn.8.3.17115

Abstract

Radix-2^kdelay feed-back and radix-K delay commutator are the most well-known pipeline architecture for FFT design. The radix-2^k fast Fourier transform algorithm is employed to achieve low power at the same time a radix-2 FFT algorithm reduced number of twiddle factor multiplication it reduces the area. This article describes a novel radix-2⁴multiple delay commutators called 4 - path feed forward FFT architecture utilizing the advantages of the radix-2 algorithm, such as simple butterflies and less memory requirement. Therefore, it is more efficient in terms of hardware and higher throughput by implementing parallelism and multiple delay commutator. Combining benefits of both radix-2 algorithm and feed forward architecture, the proposed 4-path radix-2⁴ FFT processor achieves the lowest hardware requirements for multipliers which is 20% lesser and power consumption which reduces more than 30% when compared with dual path radix-2²and radix-2⁴ delay feedback FFT architecture. The entire FFT algorithms were implemented in Verilog hardware description language and synthesized with 90 nm Technology Libraries using Cadence RTL Compiler.

References

[1]. Birdsong, J. B., & Rummelt, N. I. (2016, September). The hexagonal fast Fourier transform. In 2016, IEEE International Conference on Image Processing (ICIP) (pp. 1809-1812). IEEE. https://doi.org/10.1109/ICIP.2016.7532670

[2]. Chen, Y., & Chen, H. (2013, April). A efficient design of a real-time FFT architecture based on FPGA. In IET International Radar Conference Proceedings. The Institution of Engineering & Technology. https://doi.org/10. 1049/cp.2013.0368

[3]. Chen, Y., Lin, Y. W., Tsao, Y. C., & Lee, C. Y. (2008), A 2.4- Gsample/s DVFS FFT processor for MIMO OFDM communication systems. IEEE Journal of Solid-State Circuits, 43(5), 1260-1273. https://doi.org/10.1109/JSSC. 2008.920320

[4]. Chen, Y., Yi-zhuang, X., Chen, L., Chen, H., & Deng, Y. (2015, October), Design of a configurable fixed-point FFT processor. IET International Radar Conference, The Institution of Engineering & Technology. https://doi.org/10. 1049/cp.2015.1307

[5]. Heideman, M., Johnson, D., & Burrus, C. (1984), Gauss and the history of the fast Fourier transform. IEEE ASSP Magazine, 1(4), 14-21. https://doi.org/10.1109/MASSP. 1984.1162257

[6]. Jeon, D., Seok, M., Chakrabarti, C., Blaauw, D., & Sylvester, D. (2011), A super-pipelined energy efficient subthreshold 240 MS/s FFT core in 65 nm CMOS. IEEE Journal of Solid-State Circuits, 47(1), 23-34. https://doi.org/ 10.1109/JSSC.2011.2169311

[7]. Jiang, L. X., Liu, C. Y., & Zhang, P. (2012, October), A novel overall in-place in-order prime factor FFT algorithm. In 2012 5th International Congress on Image and Signal Processing (pp. 1500-1503). IEEE. https://doi.org/ 10.1109/CISP.2012.6469906

[8]. Mangaiyarkarasi, V., & Paul, C. K. C. (2014, July), Performance analysis between radix2, radix4, mixed radix4-2 and mixed radix8-2 FFT. In Second International Conference on Current Trends In Engineering and Technology-ICCTET 2014 (pp. 430-434). IEEE. https: //doi.org/10.1109/ICCTET.2014.6966332

[9]. Mittal, S., Khan, Z. A., & Srinivas, M. B. (2007, November), Area efficient high speed architecture of Bruun's FFT for software defined radio. In IEEE GLOBECOM 2007-IEEE Global Telecommunications Conference (pp. 3118-3122). IEEE. https://doi.org/10.1109/GLOCOM.2007. 590

[10]. Mohapatra, B. N., & Mohapatra, R. K. (2017, February), FFT and sparse FFT techniques and applications. In 2017 Fourteenth International Conference on Wireless and Optical Communications Networks (WOCN) (pp. 1-5). IEEE. https://doi.org/10.1109/WOCN. 2017.8065859

[11]. Mookherjee, S., DeBrunner, L., & DeBrunner, V. (2015, November), A low power radix-2 FFT accelerator for FPGA. In 2015 49th Asilomar Conference on Signals, Systems and Computers (pp. 447-451). IEEE. https://doi.org/10.1109/ ACSSC.2015.7421167

[12]. Padma, C., Jagadamba, P., & Reddy, P. R. (2018), A review on optimized FFT architectures for wireless communication system, Journal of Advanced Research in Dynamical and Control Systems, 10(14s), 1654-1662.

[13]. Padma, C., Jagadamba, P., & Reddy, P. R. (2020), Implementation of high Performance FFT architecture for DSP applications, International Journal of Advanced Science and Technology, 29(3s), 582-593.

[14]. Salehi, S. A., Amirfattahi, R., & Parhi, K. K. (2013), Pipelined architectures for real-valued FFT and Hermitiansymmetric IFFT with real datapaths. IEEE Transactions on Circuits and Systems II: Express Briefs, 60(8), 507-511. https://doi.org/10.1109/TCSII.2013.2268411

[15]. Wang, A., & Chandrakasan, A. (2005), A 180-mV subthreshold FFT processor using a minimum energy design methodology. IEEE Journal of Solid-State Circuits, 40(1), 310-319. https://doi.org/10.1109/JSSC.2004.837945

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Research Paper

Enforcing an Efficient Data Dissemination Protocol using VGEDD in Wireless Sensor Network

K. Papithasri * , V. A. Divya Bharathi**

* Department of Computer Science and Engineering, Muthayammal Engineering College, Rasipuram, Namakkal, India.

** Department of Computer Science and Engineering, Muthayammal College of Engineering, Rasipuram, Namakkal, India.

Papithasri, K., and Bharathi, V. A. D. (2019). Enforcing an Efficient Data Dissemination Protocol using VGEDD in Wireless Sensor Network. i-manager’s Journal on Wireless Communication Networks , 8(3), 18-25. https://doi.org/10.26634/jwcn.8.3.17124

Abstract

In wireless sensor networks, there are many routing protocols proposed for data dissemination to mobile sinks and delivers the data to sink mobility has been considered as an excellent work to balance the nodes for the consumption of energy. For more benefits, the data dissemination to the mobile sink is a challenging task for the sensor nodes due to the active network topology pretentious by the sink motion. Nodes need to reconstruct their routes to the newest location of the sink for efficient data delivery which destabilizes the energy protection goal. A Virtual Grid-Based Efficient Data Dissemination (VGEDD) protocol is presented in this paper. It is a routing approach which minimizes the sensor node cost of reconstruction of routes. This approach maintains the best route nearby to the sink node's latest location. In this paper, a set of rules are proposed for communication that manages the routes reform method, thus it needs only a limited number of nodes to rearrange their data delivery routes. Performance analysis shows that Virtual Grid-Based Efficient Data Dissemination (VGEDD) protocol demonstrate improved network lifetime and reduced routes reform cost when compared with existing method. In this paper, we also analyse the developing techniques for lifetime improvement of mobile sink in Wireless Sensor Networks (WSN).

References

[1]. Hamida, E. B., & Chelius, G. (2008, May). A linebased data dissemination protocol for wireless sensor networks with mobile sink. In 2008 IEEE International Conference on Communications (pp. 2201-2205). IEEE. https://doi.org/10.1109/ICC.2008.420

[2]. Khan, M. I., Gansterer, W. N., & Haring, G. (2013). Static vs. mobile sink: The influence of basic parameters on energy efficiency in wireless sensor networks. Computer Communications, 36(9), 965-978. https://doi.org/ 10.1016/j.comcom.2012.10.010

[3]. Lin, C. J., Chou, P. L., & Chou, C. F. (2006, July). HCDD: hierarchical cluster-based data dissemination in wireless sensor networks with mobile sink. In Proceedings of the 2006 International Conference on Wireless Communications and Mobile Computing (pp. 1189- 1194). https://doi.org/10.1145/1143549.1143787

[4]. Luo, H., Ye, F., Cheng, J., Lu, S., & Zhang, L. (2005). TTDD: A two tier data dissemination model for large scale wireless sensor networks. Wireless Networks, 11(1), 161- 175.

[5]. Nazir, B., & Hasbullah, H. (2010, December). Mobile sink based routing protocol (MSRP) for prolonging network lifetime in clustered wireless sensor network. In 2010, International Conference on Computer Applications and Industrial Electronics (pp.624-629). IEEE. https://doi.org/10.1109/ICCAIE.2010.5735010

[6]. Tang, B., Wang, J., Geng, X., Zheng, Y., & Kim, J. U. (2012). A novel data retrieving mechanism in wireless sensor networks with path-limited mobile sink. International Journal of Grid and Distributed Computing, 5(3), 133-140.

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Research Paper

Efficient Data Collection for Mobile Wireless Sensor Network using Ant Based Routing

N. Karthigavani * , P. Sundaram , G. Sridevi *, V. Sowndarya ****

,, Department of Computer Science and Engineering, AVS Engineering College, Salem, Tamil Nadu, India.

Rajaji Polytechnic College, Salem, India.

Karthigavani, N., Sundaram, P., Sridevi, G., and Sowndarya, V. (2019). Efficient Data Collection for Mobile Wireless Sensor Network using Ant Based Routing. i-manager’s Journal on Wireless Communication Networks , 8(3), 26-37. https://doi.org/10.26634/jwcn.8.3.17236

Abstract

Mobility management in Mobile Wireless Sensor Networks (MWSNs) is a complex problem that must be taken into account. In MWSN, nodes move in and out of the network randomly. Hence, a path formed between two distant nodes is highly susceptible to changes due to unpredictable node movement. Also, due to the limited resources in WSN, the paths used for data transmission must be tested for the link quality and time consumed for data forwarding. In order to solve these issues, in this paper, an Ant based routing protocol with QoS effective data collection mechanism is proposed. In this protocol, the link quality and link delay are estimated for each pair of nodes. Link quality is estimated in terms of Packet Reception Rate (PRR), Received Signal Strength Indicator (RSSI) and Link Quality Index (LQI). A reliable path is chosen from the source to the destination based on the paths traversed by forward ants and backward ants. Then, if the link is found to be defective during data transmission, a link reinforcement technique is used to deliver the data packet at the destination successfully. The mobile robots collect the information with high data utility. In addition each mobile robot is equipped with multiple antennas and Space Division Multiple Access (SDMA) technique is then applied for effective data collection from multiple mobile robots. Simulation results show that the proposed routing protocol provides reliability by reducing the packet drop and end-to-end delay when compared to existing protocols.

References

[1]. Alayev, Y., Chen, F., Hou, Y., Johnson, M. P., Bar-Noy, A., La Porta, T. F., & Leung, K. K. (2014). Throughput maximization in mobile WSN scheduling with power control and rate selection. IEEE Transactions on Wireless Communications, 13(7), 4066- 4079. https://doi.org/10.1109/TWC.2014.2315196

[2]. Awwad, S. A., Ng, C. K., Noordin, N. K., & Rasid, M. F. A. (2011). Cluster based routing protocol for mobile nodes in wireless sensor network. Wireless Personal Communications, 61(2), 251- 281. https://doi.org/10.1007/s11277-010-0022-8

[3]. Ba, P. D., Niang, I., & Gueye, B. (2014). An optimized and power savings protocol for mobility energy-aware in wireless sensor networks. Telecommunication Systems, 55(2), 271-280. https://doi.org/10.1007/s11235-013-9780-4

[4]. Bijarbooneh, F. H., Flener, P., Ngai, E., & Pearson, J. (2013, June). Optimising quality of information in data collection for mobile sensor networks. In 2013, IEEE/ACM 21st International Symposium on Quality of Service (IWQoS) (pp. 1-10). IEEE. https://doi.org/10.1109/IWQoS. 2013. 6550277

[5]. Karim, L., & Nasser, N. (2012). Reliable location-aware routing protocol for mobile wireless sensor network. IET communications, 6(14), 2149-2158. https://doi.org/10.1049/iet-com.2011.0696

[6]. Koucheryavy, A., & Salim, A. (2010, February). Prediction-based clustering algorithm for mobile wireless sensor networks. In 2010, The 12th International Conference on Advanced Communication Technology (ICACT) (Vol. 2, pp. 1209-1215). IEEE.

[7]. Le, D. V., Oh, H., & Yoon, S. (2013). RoCoMAR: robots' controllable mobility aided routing and relay architecture for mobile sensor networks. Sensors, 13(7), 8695-8721. https://doi.org/10.3390/s130708695

[8]. Li, K., & Hua, K. A. (2013, December). Mobility-assisted distributed sensor clustering for energy-efficient wireless sensor networks. In 2013, IEEE Global Communications Conference (GLOBECOM) (pp. 316-321). IEEE. https://doi.org/10.1109/ GLOCOM.2013.6831090

[9]. Peng, L., & Xu, J. B. (2009, December). ECDGA: An energyefficient cluster-based data gathering algorithm for mobile wireless sensor networks. In 2009, International Conference on Computational Intelligence and Software Engineering (pp. 1- 4). IEEE. https://doi.org/10.1109/CISE.2009. 5366773

[10]. Rondinone, M., Ansari, J., Riihijärvi, J., & Mähönen, P. (2008, April). Designing a reliable and stable link quality metric for wireless sensor networks. In Proceedings of the workshop on Real-world wireless sensor networks (pp. 6-10). https://doi.org/ 10.1145/1435473.1435476

[11]. Sara, G. S., Kalaiarasi, R., Pari, N. S., & Sridharan, D. (2010). Energy Efficient Clustering And Routing in Mobile Wireless Sensor Network. International Journal of Wireless & Mobile Networks, 2(4), 106-114.

[12]. Xiong, Y. P., Niu, J. W., Ma, J., & Sun, L. M. (2010). Efficient data delivery in mobile sensor networks. Journal of Communication and Computer, 7(5), 23-29.

[13]. Yoon, S., Soysal, O., Demirbas, M., & Qiao, C. (2008, June). Coordinated locomotion of mobile sensor networks. In 2008, 5th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (pp. 126-134). IEEE. https://doi.org/10.1109/SAHCN.2008.25

[14]. Zhang, X., He, J., & Wei, Q. (2010, July). Energy-efficient routing for mobility scenarios in wireless sensor networks. In Proceedings of the 3rd International Symposium on Electronic Commerce and Security Workshops. (pp. 80-83). Cuangzhou.

[15]. Zhao, M., Ma, M., & Yang, Y. (2010). Efficient data gathering with mobile collectors and space-division multiple access technique in wireless sensor networks. IEEE Transactions on computers, 60(3), 400-417. https://doi. org/10.1109/ TC.2010.140

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Research Paper

Optimized MRMC Technique in Wireless Mesh Networks

Navreet Kaur* , Jatinder Singh Saini **

*-** Baba Banda Singh Bahadur Engineering College, Fatehgarh Sahib, Punjab, India.

Kaur, N., and Saini, J. S. (2019). Optimized MRMC Technique in Wireless Mesh Networks. i-manager’s Journal on Wireless Communication Networks , 8(3), 38-45. https://doi.org/10.26634/jwcn.8.3.17140

Abstract

A wireless Mesh network comprises of three fundamental components: hubs, portals, and software. The spatially distributed measurement hubs interface with sensors to monitor assets or their environment. Right now the improved Multiple Radios and Multiple Channels (MRMC) protocol in Wireless Mesh Networks (WMNs) is implemented based on Advanced Ant Colony Optimization (AACO) optimization scheme. AACO is utilized to give a corresponding way to every link in case of its failure. Right now mutation operator is used and the new mutation rate is created by the self-versatile methodology. The proposed approach assists to reduce the load and drops in network so utilizing the proposed methodology the Quality of Service (QOS) parameters, for example, packet delivery ratio, throughput, overheads, average end-to-end delay, average energy consumption are improved as shown in result. The improvement of 16% is shown between the existing and proposed approach in above defined features.

References

[1]. Al-Nakhala, N., Riley, R., & Elfouly, T. (2015). Distributed algorithms in wireless sensor networks: An approach for applying binary consensus in a real testbed. Computer Networks, 79, 30-38. https://doi.org/ 10.1016/j.comnet.2014.12.011

[2]. Chamberland, S. (2005). Point of presence design in Internet protocol networks with performance guarantees. Computers & Operations Research, 32(12), 3247-3264. https://doi.org/10.1016/j.cor.2004.05.010

[3]. Chaudhry, A. U., Ahmad, N., & Hafez, R. H. (2012). Improving throughput and fairness by improved channel assignment using topology control based on power control for multi-radio multi-channel wireless mesh networks. EURASIP Journal on Wireless Communications and Networking, 2012(1), 155. https://doi.org/10.1186/ 1687-1499-2012-155

[4]. Diarrassouba, I., Lourimi, A., Mahjoub, A. R., & Youssef, H. (2013). Hose workload based exact algorithm for the optimal design of virtual private networks. Computer Networks, 57(14), 2766-2774. https://doi.org /10.1016/j.comnet.2013.06.009

[5]. Goścień, R., Walkowiak, K., & Klinkowski, M. (2015). Tabu search algorithm for routing, modulation and spectrum allocation in elastic optical network with anycast and unicast traffic. Computer Networks, 79, 148- 165. https://doi.org/10.1016/j.comnet.2014.12.004

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[8]. Hsu, T. H., & Wu, J. S. (2008). An application-specific duty cycle adjustment MAC protocol for energy conserving over wireless sensor networks. Computer Communications, 31(17), 4081-4088. https://doi.org/10. 1016/j.comcom.2008.08.010

[9]. Kim, J., & Park, K. H. (2009). An energy-efficient, transport-controlled MAC protocol for wireless sensor networks. Computer Networks, 53(11), 1879-1902. https: //doi.org/10.1016/j.comnet.2009.03.002

[10]. Liu, Y. Y., Hong, J., & Yue, G. X. (2006). Routing protocol with optimal location of aggregation point in wireless sensor networks. The Journal of China Universities of Posts and Telecommunications, 13(1), 1-5. https://doi. org/10.1016/S1005-8885(07)60070-3

[11]. Ma, X. X., & Qin, Z. G. (2008). Partition and multipath transmission: An encryption-free reputation sharing protocol in Gnutella-like peer-to-peer network. Computer communications, 31(14), 3059-3063. https://doi.org/10. 1016/j.comcom.2008.05.037

[12]. Magurawalage, C. M. S., Yang, K., Hu, L., & Zhang, J. (2014). Energy-efficient and network-aware offloading algorithm for mobile cloud computing. Computer Networks, 74, 22-33. https://doi.org/10.1016/j.comnet. 2014.06.020

[13]. Mahmood, M. A., Seah, W. K., & Welch, I. (2015). Reliability in wireless sensor networks: A survey and challenges ahead. Computer Networks, 79, 166-187. https://doi.org/10.1016/j.comnet.2014.12.016

[14]. Quintero, A., & Castro, H. (2007). A location routing protocol based on smart antennas for ad hoc networks. Journal of Network and Computer Applications, 30(2), 614-636.https://doi.org/10.1016/j.jnca.2006.01.004

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[17]. Wang, P., Cai, Y., Huang, J., & Xu, X. (2006). A hierarchical multicast protocol in mobile IPv6 networks. Computer communications, 30(1), 144-152. https://doi. org/10.1016/j.comcom.2006.08.004

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

Current Issue Vol. 12 Issue 1

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Most Cited

Volume 8 Issue 3 October - December 2019

Culminate Coverage for Sensor Network through Bodacious-Instance Mechanism

Shahzad Ashraf*

College of Internet of Things Engineering, Hohai University, Changzhou, China.

Ashraf, S. (2019). Culminate Coverage for Sensor Network through Bodacious-Instance Mechanism. i-manager’s Journal on Wireless Communication Networks , 8(3), 1-9. https://doi.org/10.26634/jwcn.8.3.17310

Abstract

References

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Low Power and Area Efficient FFT Processor using Radix-24 Feed Forward Multipath Delay Commutator for Wireless Communication

C. Padma* , P. Jagadamba **, P. Ramana Reddy ***

*,*** Department of Electronics and Communication Engineering, Jawaharlal Nehru Technological University, Ananthapuramu, Andhra Pradesh, India. ** Department of Electronics and Communication Engineering, Srikalahasthi Institute of Technology, Srikalahasti, Andhra Pradesh, India.

Padma, C., Jagadamba, P., and Reddy, P. R. (2019). Low Power and Area Efficient FFT Processor using Radix-24 Feed Forward Multipath Delay Commutator for Wireless Communication. i-manager’s Journal on Wireless Communication Networks , 8(3), 10-17. https://doi.org/10.26634/jwcn.8.3.17115

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Enforcing an Efficient Data Dissemination Protocol using VGEDD in Wireless Sensor Network

K. Papithasri * , V. A. Divya Bharathi**

* Department of Computer Science and Engineering, Muthayammal Engineering College, Rasipuram, Namakkal, India. ** Department of Computer Science and Engineering, Muthayammal College of Engineering, Rasipuram, Namakkal, India.

Papithasri, K., and Bharathi, V. A. D. (2019). Enforcing an Efficient Data Dissemination Protocol using VGEDD in Wireless Sensor Network. i-manager’s Journal on Wireless Communication Networks , 8(3), 18-25. https://doi.org/10.26634/jwcn.8.3.17124

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Efficient Data Collection for Mobile Wireless Sensor Network using Ant Based Routing

N. Karthigavani * , P. Sundaram **, G. Sridevi ***, V. Sowndarya ****

*,***,**** Department of Computer Science and Engineering, AVS Engineering College, Salem, Tamil Nadu, India. ** Rajaji Polytechnic College, Salem, India.

Karthigavani, N., Sundaram, P., Sridevi, G., and Sowndarya, V. (2019). Efficient Data Collection for Mobile Wireless Sensor Network using Ant Based Routing. i-manager’s Journal on Wireless Communication Networks , 8(3), 26-37. https://doi.org/10.26634/jwcn.8.3.17236

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Optimized MRMC Technique in Wireless Mesh Networks

Navreet Kaur* , Jatinder Singh Saini **

*-** Baba Banda Singh Bahadur Engineering College, Fatehgarh Sahib, Punjab, India.

Kaur, N., and Saini, J. S. (2019). Optimized MRMC Technique in Wireless Mesh Networks. i-manager’s Journal on Wireless Communication Networks , 8(3), 38-45. https://doi.org/10.26634/jwcn.8.3.17140

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C. Padma* , P. Jagadamba , P. Ramana Reddy *

, Department of Electronics and Communication Engineering, Jawaharlal Nehru Technological University, Ananthapuramu, Andhra Pradesh, India.

Department of Electronics and Communication Engineering, Srikalahasthi Institute of Technology, Srikalahasti, Andhra Pradesh, India.

* Department of Computer Science and Engineering, Muthayammal Engineering College, Rasipuram, Namakkal, India.

** Department of Computer Science and Engineering, Muthayammal College of Engineering, Rasipuram, Namakkal, India.

N. Karthigavani * , P. Sundaram , G. Sridevi *, V. Sowndarya ****

,, Department of Computer Science and Engineering, AVS Engineering College, Salem, Tamil Nadu, India.

Rajaji Polytechnic College, Salem, India.