A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Ramkumar, J.
- Preliminary Study on the Performance of Mallada boninensis (Okamoto) against Aleurocanthus woglumi Ashby on Citrus
Authors
1 Entomology Section, College of Agriculture, Nagpur, 440 001, Maharashtra, IN
Source
Journal of Biological Control, Vol 19, No 1 (2005), Pagination: 77-80Abstract
Mallada boninensis (Okamoto) eggs and first instar larvae were released thrice against Ateurocanthus woglumi Ashby at an interval of 15 days. Cumulative per cent reduction in the population of A. woglumi at the end of third release was recorded and the comparison was made between the treatments. The highest cumulative per cent reduction of 70.24 was recorded in malathion sprayed trees. Amongst various dosages/stages of M. boninensis, release of eggs @ 6 and 4 number/shoot recorded 36.72 and 34.65 per cent cumulative reduction in A. woglumi population and was found to be more effective than other dosages.Keywords
Ateurocanthus woglumi, Field Releases, Mallada boninensis.- MIAS Based on Kleinberg HITS Algorithm in E-Services
Authors
1 Anna University of Technology, Coimbatore, Tamilnadu, IN
Source
Programmable Device Circuits and Systems, Vol 3, No 6 (2011), Pagination: 263-268Abstract
E-services provides several applications and useful to the user, while using the web. Web is mainly a strongly connected environment and providers who provide the e-services must satisfy the user needs. When the user gives their instructions and executes the e-service, while the provider hides the complexity of the services to execute. Providers enrich the e-services by adapting multichannel provisioning (MIAS framework) and providers provide user to executing multi adaptive e-services with respect to orchestration. MIAS framework makes easy for the user to get the information.
In this paper, Kleinberg’s HITS Algorithm is used in multichannel adaptive framework to rank the Web Pages, fetch the information accurately for the users and measure the performance.
Keywords
Multi-Channel Adaptive Information Systems, Electronic Services, Kleinberg’s Hyperlink-Induced Topic Search (HITS) Algorithm.- RBAC Framework for Web Based Services in Work Flow Foundation
Authors
1 Computer Science and Engineering, Anna University of Technology, Coimbatore, Tamilnadu, IN
Source
Data Mining and Knowledge Engineering, Vol 3, No 7 (2011), Pagination: 392-397Abstract
In the business process, Work Flow foundation supports windows environment and control the order in which the pages shown. Then the individual Web pages services are integrated using Work Flow and invoke those services, knitting them together into a composite application. Based on role provisioning, the employees are allowed inside the organization and services are initiated. We used two protocols (Aggregate Zero Knowledge Proof Knowledge and Oblivious Commitment Protocols) to choose an appropriate person to a particular role. Enforcement point act as server to provide services to work and provide security based on Cryptographic algorithm and other xml language (authentication access).In this approach, alternate BPEL process is Workflow Foundation with RSA algorithm with 1024 bits is used. XACML is used to provide and strengthened the security level of the business organization. These changes provide flexibility and services in less expensive manner.
Keywords
Aggregate Zero Knowledge Proof Knowledge, Oblivious Commitment Based Envelope, Extensible Access Control Markup Language.- RBAC Framework Based on XACML Policy in WS-BPEL Process
Authors
1 M.E. Department of Computer Science and Engineering, Anna University of Technology, Coimbatore, IN
Source
Data Mining and Knowledge Engineering, Vol 3, No 1 (2011), Pagination: 33-38Abstract
In WS-BPEL process, Extensible Access Control Markup Language (XACML) is used as an authenticated tool to provide several services for an employee in an organization. There are several policies (XACML) used as an access control in Web Services. XACML policy as RBAC profile to support role based access controls policies. In an organization, there are several roles assigned to an employee based on their attributes. The attributes are used as an authenticating tool to assign the role and perform the task. The identity attributes are used for role provisioning policies to a particular employee i.e. social security number, date of birth, etc. are assigned as an identity attributes. In this aggregate zero knowledge proof knowledge (AgZKPK) and Oblivious commitment based envelope (OCBE) protocols are used during service (information) sharing between employees and to make it more flexible. This process may provide privacy to the user information and support multi-domain environment.Keywords
Aggregate Zero Knowledge Proof Knowledge, Pederson Commitment, Role Based Access Control, Security.- Improved Wolf Prey Inspired Protocol for Routing in Cognitive Radio Ad Hoc Networks
Authors
1 Department of Computer Science, VLB Janakiammal College of Arts and Science, Coimbatore, Tamil Nadu, IN
2 Department of Information Technology, Bharathiar University, Coimbatore, Tamil Nadu, IN
Source
International Journal of Computer Networks and Applications, Vol 7, No 5 (2020), Pagination: 126-136Abstract
Fifth Generation (5G) technology has started providing the brand new facilities to the mobile communication world. With its enhanced performance and scalability, it has attracted many domains. Routing overhead in 5G networks is increased rapidly because of the complexity present in the route discovery process, where optimization in routing. Poor routing becomes a sophisticated and dynamic challenge in the 5G network. Hence, there exists a need for finding the best route in an optimized manner. This paper proposes an Improved Wolf Prey Inspired Protocol (IWPIP) for finding the ideal route in the dynamic environment like 5G based cognitive radio ad-hoc network. IWPIP focuses on finding the ideal route based on the reliability of route, shorter distance, and shorter hops that minimize the consumption of energy to increase the network lifetime. Before sending the data packets, routes are evaluated using a fitness function. IWPIP's efficiency has been demonstrated through comprehensive simulation, which resulted in promising outcomes in terms of throughput, packet delivery and drop ratio, delay, and energy consumption.Keywords
Optimization, Routing, Bio-Inspired, Energy, Delay, Cognitive Radio Ad Hoc Networks, Wolf Prey Inspired Protocol.References
- F. Palmieri, "A Reliability and latency-aware routing framework for 5G transport infrastructures", Computer Networks, vol. 179, p. 107365, 2020. https://doi.org/10.1016/j.comnet.2020.107365
- J. Mu, "An improved AODV routing for the zigbee heterogeneous networks in 5G environment", Ad Hoc Networks, vol. 58, pp. 13-24, 2017. https://doi.org/10.1016/j.adhoc.2016.12.002
- Z. Li, Y. Hu, T. Hu and R. Ma, "PARS-SR: A scalable flow forwarding scheme based on Segment Routing for massive giant connections in 5G networks", Computer Communications, vol. 159, pp. 206-214, 2020. https://doi.org/10.1016/j.comcom.2020.05.014
- H. Riasudheen, K. Selvamani, S. Mukherjee and I. Divyasree, "An efficient energy-aware routing scheme for cloud-assisted MANETs in 5G", Ad Hoc Networks, vol. 97, p. 102021, 2020. https://doi.org/10.1016/j.adhoc.2019.102021
- A. Mesodiakaki, E. Zola, R. Santos and A. Kassler, "Optimal user association, backhaul routing and switching off in 5G heterogeneous networks with mesh millimeter wave backhaul links", Ad Hoc Networks, vol. 78, pp. 99-114, 2018. https://doi.org/10.1016/j.adhoc.2018.05.008
- M. Abolhasan, M. Abdollahi, W. Ni, A. Jamalipour, N. Shariati and J. Lipman, "A Routing Framework for Offloading Traffic From Cellular Networks to SDN-Based Multi-Hop Device-to-Device Networks", IEEE Transactions on Network and Service Management, vol. 15, no. 4, pp. 1516-1531, 2018. https://doi.org/10.1109/TNSM.2018.2875696
- H. Rastegarfar, T. Svensson and N. Peyghambarian, "Optical Layer Routing Influence on Software-Defined C-RAN Survivability", Journal of Optical Communications and Networking, vol. 10, no. 11, p. 866, 2018. https://doi.org/10.1364/JOCN.10.000866
- Martin, L. Dooley and K. Wong, "5G multi-layer routing strategies for TV white space secondary user access", IET Communications, vol. 13, no. 12, pp. 1801-1807, 2019. https://doi.org/10.1049/iet-com.2018.5848
- P. Yan, S. Choudhury, F. Al-Turjman and I. Al-Oqily, "An energy-efficient topology control algorithm for optimizing the lifetime of wireless ad-hoc IoT networks in 5G and B5G", Computer Communications, vol. 159, pp. 83-96, 2020. https://doi.org/10.1016/j.comcom.2020.05.010
- Z. Ma, B. Li, Z. Yan and M. Yang, "Remaining bandwidth based multipath routing in 5G millimeter wave self-backhauling network", Wireless Networks, vol. 25, no. 7, pp. 3839-3855, 2019. https://doi.org/10.1007/s11276-018-01919-y
- Z. Khan, P. Fan, F. Abbas, H. Chen and S. Fang, "Two-Level Cluster Based Routing Scheme for 5G V2X Communication", IEEE Access, vol. 7, pp. 16194-16205, 2019. https://doi.org/10.1109/ACCESS.2019.2892180
- J.Ramkumar and R.Vadivel, "Performance Modeling of Bio-Inspired Routing Protocols in Cognitive Radio Ad Hoc Network to Reduce End-to-End Delay", International Journal of Intelligent Engineering and Systems, Vol.12, No.1, pp. 221-231, 2019. https://doi.org/10.22266/ijies2019.0228.22
- X. Jin, R. Zhang, J. Sun and Y. Zhang, "TIGHT: A Geographic Routing Protocol for Cognitive Radio Mobile Ad Hoc Networks", IEEE Transactions on Wireless Communications, vol. 13, no. 8, pp. 4670-4681, 2014. https://doi.org/10.1109/TWC.2014.2320950
- R. Sahu, S. Sharma, M.A. Rizvi, "ZBLE: Zone Based Leader Election Energy Constrained AOMDV Routing Protocol", International Journal of Computer Networks and Applications, Vol. 6, no. 3, pp. 39-46, 2019. https://doi.org/10.22247/ijcna/2019/49643
- R. Yadav, R. Misra and D. Saini, "Energy aware cluster based routing protocol over distributed cognitive radio sensor network", Computer Communications, vol. 129, pp. 54-66, 2018. https://doi.org/10.1016/j.comcom.2018.07.020
- H. Salameh, S. Otoum, M. Aloqaily, R. Derbas, I. Ridhawi and Y. Jararweh, "Intelligent jamming-aware routing in multi-hop IoT-based opportunistic cognitive radio networks", Ad Hoc Networks, vol. 98, p. 102035, 2020. https://doi.org/10.1016/j.adhoc.2019.102035
- F. Tang, H. Zhang, L. Fu and X. Li, "Distributed Stable Routing with Adaptive Power Control for Multi-Flow and Multi-Hop Mobile Cognitive Networks," IEEE Transactions on Mobile Computing, vol. 18, no. 12, pp. 2829-2841, 2019. https://doi.org/10.1109/TMC.2018.2885762
- J. Singh and M. Rai, "CROP: Cognitive radio ROuting Protocol for link quality channel diverse cognitive networks", Journal of Network and Computer Applications, vol. 104, pp. 48-60, 2018. https://doi.org/10.1016/j.jnca.2017.12.014
- X. Tang, J. Zhou, S. Xiong, J. Wang and K. Zhou, "Geographic Segmented Opportunistic Routing in Cognitive Radio Ad Hoc Networks Using Network Coding," IEEE Access, vol. 6, pp. 62766-62783, 2018. https://doi.org/10.1109/ACCESS.2018.2875566
- J.Ramkumar and R.Vadivel, "Improved frog leap inspired protocol (IFLIP) – for routing in cognitive radio ad hoc networks (CRAHN)", World Journal of Engineering, vol. 15, no. 2, pp. 306-311, 2018. https://doi.org/10.1108/WJE-08-2017-0260
- J.Ramkumar and R.Vadivel, "CSIP—Cuckoo Search Inspired Protocol for Routing in Cognitive Radio Ad Hoc Networks", Advances in Intelligent Systems and Computing, Vol. 556, pp. 145-153, 2017. https://doi.org/10.1007/978-981-10-3874-7_14
- I. Akyildiz, W. Lee and K. Chowdhury, "CRAHNs: Cognitive radio ad hoc networks", Ad Hoc Networks, vol. 7, no. 5, pp. 810-836, 2009. https://doi.org/10.1016/j.adhoc.2009.01.001
- J.Ramkumar and R.Vadivel, "Intelligent Fish Swarm Inspired Protocol (IFSIP) For Dynamic Ideal Routing in Cognitive Radio Ad-Hoc Networks", International Journal of Computing and Digital Systems, Vol. 10, pp. 2-11. 2020. https://journal.uob.edu.bh:443/handle/123456789/3961
- R. Rahim, S. Murugan, S. Priya, S. Magesh and R. Manikandan, "Taylor Based Grey Wolf Optimization Algorithm (TGWOA) For Energy Aware Secure Routing Protocol", International Journal of Computer Networks and Applications, vol. 7, no. 4, p. 93, 2020. https://doi.org/10.22247/ijcna/2020/196041
- Query Aware Routing Protocol for Mobility Enabled Wireless Sensor Network
Authors
1 Department of Computer Science, Sankara College of Science and Commerce, Coimbatore, Tamil Nadu, IN
2 Department of Computing, Coimbatore Institute of Technology, Coimbatore, Tamil Nadu, IN
3 Department of Computer Science, VLB Janakiammal College of Arts and Science, Coimbatore, Tamil Nadu, IN
Source
International Journal of Computer Networks and Applications, Vol 8, No 3 (2021), Pagination: 258-267Abstract
Mobility Enabled Wireless Sensor Network (MEWSN) plays a significant role in different fields including environmental control, traffic control and healthcare. The performance of MEWSN is dependent not only on sensing but also on routing. Multiple research works are carried out by different researchers in the domain of routing in MEWSN, but still the performance of MEWSN gets lacked. Poor routing is the ischolar_main cause for the performance degradation of MEWSN. In this paper, a new routing protocol namely Query Aware Routing Protocol (QARP) is proposed to balance the load in MEWSN to prevent congestion and exhausted power utilization. Normal routing protocols either seek to match load or route, but both are considered in QARP. Also, identified routes are classified based on an enhanced relevant vector machine classification algorithm which assists in minimizing the delay and energy consumption. Using NS2, QARP is evaluated against previous routing protocols with standard performance metrics namely throughput, delay, packet delivery ratio and energy consumption. The packet delivery ratio achieved by QARP is 92.6%, where the existing routing protocols IFLIP and PARP has achieved 62.8% and 75.4% respectively.Keywords
WSN, MEWSN, Routing, Query, Load, Congestion.References
- S. Gudla and N. R. Kuda, “Learning automata based energy efficient and reliable data delivery routing mechanism in wireless sensor networks,” J. King Saud Univ. - Comput. Inf. Sci., 2021, doi: https://doi.org/10.1016/j.jksuci.2021.04.006.
- Y. Yigit, V. K. Akram, and O. Dagdeviren, “Breadth-first search tree integrated vertex cover algorithms for link monitoring and routing in wireless sensor networks,” Comput. Networks, vol. 194, p. 108144, 2021, doi: https://doi.org/10.1016/j.comnet.2021.108144.
- Y. Hong, D. Li, and Z. Chen, “Constructing virtual backbone with guaranteed routing cost in Wireless Sensor Networks,” Ad Hoc Networks, vol. 116, p. 102500, 2021, doi: https://doi.org/10.1016/j.adhoc.2021.102500.
- L. Jia, “Distributed energy balance routing algorithm for wireless sensor network based on multi-attribute decision-making,” Sustain. Energy Technol. Assessments, vol. 45, p. 101192, 2021, doi: https://doi.org/10.1016/j.seta.2021.101192.
- D. L. Reddy, P. C., and H. N. Suresh, “Merged glowworm swarm with ant colony optimization for energy efficient clustering and routing in Wireless Sensor Network,” Pervasive Mob. Comput., vol. 71, p. 101338, 2021, doi: https://doi.org/10.1016/j.pmcj.2021.101338.
- G. Thahniyath and M. Jayaprasad, “Secure and load balanced routing model for wireless sensor networks,” J. King Saud Univ. - Comput. Inf. Sci., 2020, doi: https://doi.org/10.1016/j.jksuci.2020.10.012.
- M. K. Singh, S. I. Amin, and A. Choudhary, “Genetic algorithm based sink mobility for energy efficient data routing in wireless sensor networks,” AEU - Int. J. Electron. Commun., vol. 131, p. 153605, 2021, doi: https://doi.org/10.1016/j.aeue.2021.153605.
- B. M. Sahoo, H. M. Pandey, and T. Amgoth, “GAPSO-H: A hybrid approach towards optimizing the cluster based routing in wireless sensor network,” Swarm Evol. Comput., vol. 60, p. 100772, 2021, doi: https://doi.org/10.1016/j.swevo.2020.100772.
- Y. U. Xiu-wu, Y. U. Hao, L. Yong, and X. Ren-rong, “A clustering routing algorithm based on wolf pack algorithm for heterogeneous wireless sensor networks,” Comput. Networks, vol. 167, p. 106994, 2020, doi: https://doi.org/10.1016/j.comnet.2019.106994.
- D. Mehta and S. Saxena, “MCH-EOR: Multi-objective Cluster Head Based Energy-aware Optimized Routing algorithm in Wireless Sensor Networks,” Sustain. Comput. Informatics Syst., vol. 28, p. 100406, 2020, doi: https://doi.org/10.1016/j.suscom.2020.100406.
- S. Prithi and S. Sumathi, “LD2FA-PSO: A novel Learning Dynamic Deterministic Finite Automata with PSO algorithm for secured energy efficient routing in Wireless Sensor Network,” Ad Hoc Networks, vol. 97, p. 102024, 2020, doi: https://doi.org/10.1016/j.adhoc.2019.102024.
- V. Mythili, A. Suresh, M. M. Devasagayam, and R. Dhanasekaran, “SEAT-DSR: Spatial and energy aware trusted dynamic distance source routing algorithm for secure data communications in wireless sensor networks,” Cogn. Syst. Res., vol. 58, pp. 143–155, 2019, doi: https://doi.org/10.1016/j.cogsys.2019.02.005.
- D. B.D. and F. Al-Turjman, “A hybrid secure routing and monitoring mechanism in IoT-based wireless sensor networks,” Ad Hoc Networks, vol. 97, p. 102022, 2020, doi: https://doi.org/10.1016/j.adhoc.2019.102022.
- M. Naghibi and H. Barati, “EGRPM: Energy efficient geographic routing protocol based on mobile sink in wireless sensor networks,” Sustain. Comput. Informatics Syst., vol. 25, p. 100377, 2020, doi: https://doi.org/10.1016/j.suscom.2020.100377.
- A. Mazinani, S. M. Mazinani, and M. Mirzaie, “FMCR-CT: An energy-efficient fuzzy multi cluster-based routing with a constant threshold in wireless sensor network,” Alexandria Eng. J., vol. 58, no. 1, pp. 127–141, 2019, doi: https://doi.org/10.1016/j.aej.2018.12.004.
- B. Abbache et al., “Dissimulation-based and load-balance-aware routing protocol for request and event oriented mobile wireless sensor networks,” AEU - Int. J. Electron. Commun., vol. 99, pp. 264–283, 2019, doi: https://doi.org/10.1016/j.aeue.2018.12.003.
- M. K, C. K, and S. C, “An energy efficient clustering scheme using multilevel routing for wireless sensor network,” Comput. Electr. Eng., vol. 69, pp. 642–652, 2018, doi: https://doi.org/10.1016/j.compeleceng.2017.10.007.
- C. Lin, Y. Sun, K. Wang, Z. Chen, B. Xu, and G. Wu, “Double warning thresholds for preemptive charging scheduling in Wireless Rechargeable Sensor Networks,” Comput. Networks, vol. 148, pp. 72–87, Jan. 2019, doi: 10.1016/j.comnet.2018.10.023.
- F. H. Awad, “Optimization of relay node deployment for multisource multipath routing in Wireless Multimedia Sensor Networks using Gaussian distribution,” Comput. Networks, vol. 145, pp. 96–106, 2018, doi: https://doi.org/10.1016/j.comnet.2018.08.021.
- L. Han, M. Zhou, W. Jia, Z. Dalil, and X. Xu, “Intrusion detection model of wireless sensor networks based on game theory and an autoregressive model,” Inf. Sci. (Ny)., vol. 476, pp. 491–504, Feb. 2019, doi: 10.1016/j.ins.2018.06.017.
- H. Huang, A. V. Savkin, M. Ding, and C. Huang, “Mobile robots in wireless sensor networks: A survey on tasks,” Comput. Networks, vol. 148, pp. 1–19, Jan. 2019, doi: 10.1016/j.comnet.2018.10.018.
- N. Ramluckun and V. Bassoo, “Energy-efficient chain-cluster based intelligent routing technique for Wireless Sensor Networks,” Appl. Comput. Informatics, 2020, doi: 10.1016/j.aci.2018.02.004.
- S. V. Manisekaran and R. Venkatesan, “An analysis of software-defined routing approach for wireless sensor networks,” Comput. Electr. Eng., vol. 56, pp. 456–467, Nov. 2016, doi: 10.1016/j.compeleceng.2016.06.017.
- A. Agrawal, V. Singh, S. Jain, and R. K. Gupta, “GCRP: Grid-cycle routing protocol for wireless sensor network with mobile sink,” AEU - Int. J. Electron. Commun., vol. 94, pp. 1–11, 2018, doi: https://doi.org/10.1016/j.aeue.2018.06.036.
- R. Simon Carbajo, E. Simon Carbajo, B. Basu, and C. Mc Goldrick, “Routing in wireless sensor networks for wind turbine monitoring,” Pervasive Mob. Comput., vol. 39, pp. 1–35, Aug. 2017, doi: 10.1016/j.pmcj.2017.04.007.
- A. E. Zonouz, L. Xing, V. M. Vokkarane, and Y. L. Sun, “Reliability-oriented single-path routing protocols in wireless sensor networks,” IEEE Sens. J., vol. 14, no. 11, pp. 4059–4068, Nov. 2014, doi: 10.1109/JSEN.2014.2332296.
- M. Zhao, J. Li, and Y. Yang, “A framework of joint mobile energy replenishment and data gathering in wireless rechargeable sensor networks,” IEEE Trans. Mob. Comput., vol. 13, no. 12, pp. 2689–2705, Dec. 2014, doi: 10.1109/TMC.2014.2307335.
- D. Sharma and A. P. Bhondekar, “Traffic and Energy Aware Routing for Heterogeneous Wireless Sensor Networks,” IEEE Commun. Lett., vol. 22, no. 8, pp. 1608–1611, Aug. 2018, doi: 10.1109/LCOMM.2018.2841911.
- Z. Sun, M. Wei, Z. Zhang, and G. Qu, “Secure Routing Protocol based on Multi-objective Ant-colony-optimization for wireless sensor networks,” Appl. Soft Comput. J., vol. 77, pp. 366–375, Apr. 2019, doi: 10.1016/j.asoc.2019.01.034.
- S. Al-Sodairi and R. Ouni, “Reliable and energy-efficient multi-hop LEACH-based clustering protocol for wireless sensor networks,” Sustain. Comput. Informatics Syst., vol. 20, pp. 1–13, Dec. 2018, doi: 10.1016/j.suscom.2018.08.007.
- J. Ramkumar and R. Vadivel, “Multi-Adaptive Routing Protocol for Internet of Things based Ad-hoc Networks,” Wirel. Pers. Commun., pp. 1–23, Apr. 2021, doi: 10.1007/s11277-021-08495-z.
- Lingaraj M and Prakash A, “Power Aware Routing Protocol (PARP) to Reduce Energy Consumption in Wireless Sensor Networks,” Int. J. Recent Technol. Eng., vol. 7, no. 5, pp. 380–385, 2019.
- J. Ramkumar and R. Vadivel, “Improved frog leap inspired protocol (IFLIP) – for routing in cognitive radio ad hoc networks (CRAHN),” World J. Eng., vol. 15, no. 2, pp. 306–311, 2018, doi: 10.1108/WJE-08-2017-0260.
- J. Ramkumar and R. Vadivel, “Bee inspired secured protocol for routing in cognitive radio ad hoc networks,” INDIAN J. Sci. Technol., vol. 13, no. 30, pp. 3059–3069, 2020, doi: 10.17485/IJST/v13i30.1152.
- R. Vadivel and J. Ramkumar, “QoS-Enabled Improved Cuckoo Search-Inspired Protocol (ICSIP) for IoT-Based Healthcare Applications,” pp. 109–121, 2019, doi: 10.4018/978-1-7998-1090-2.ch006.
- J. Ramkumar and R. Vadivel, “CSIP—cuckoo search inspired protocol for routing in cognitive radio ad hoc networks,” in Advances in Intelligent Systems and Computing, 2017, vol. 556, pp. 145–153, doi: 10.1007/978-981-10-3874-7_14.
- J. Ramkumar and R. Vadivel, “Meticulous elephant herding optimization based protocol for detecting intrusions in cognitive radio ad hoc networks,” Int. J. Emerg. Trends Eng. Res., vol. 8, no. 8, pp. 4549–4554, 2020, doi: 10.30534/ijeter/2020/82882020.
- J. Ramkumar and R. Vadivel, “Performance Modeling of Bio-Inspired Routing Protocols in Cognitive Radio Ad Hoc Network to Reduce End-to-End Delay,” Int. J. Intell. Eng. Syst., vol. 12, no. 1, pp. 221–231, 2019, doi: 10.22266/ijies2019.0228.22.
- T. N. Sugumar and N. R. Ramasamy, “mDesk: a scalable and reliable hypervisor framework for effective provisioning of resource and downtime reduction,” J. Supercomput., vol. 76, no. 2, pp. 1277–1292, Feb. 2020, doi: 10.1007/s11227-018-2662-5.
- Whale Optimization Routing Protocol for Minimizing Energy Consumption in Cognitive Radio Wireless Sensor Network
Authors
1 Department of Computer Science, VLB Janakiammal College of Arts and Science, Coimbatore, Tamil Nadu, IN
2 Department of Information Technology, Bharathiar University, Coimbatore, Tamil Nadu, IN
Source
International Journal of Computer Networks and Applications, Vol 8, No 4 (2021), Pagination: 455-464Abstract
Cognitive Radio Wireless Sensor Networks (CR-WSN) works on nodes that are dependent on batteries. A critical problem with CR-WSN is a lack of energy, especially in situations such as warfare where rapid and aggressive action is needed. The battery level of nodes degrades CR-WSN performance. Researchers face significant difficulty developing a routing protocol for CR-WSN, and that obstacle is posed by energy consumption to deliver a packet. A substantial number of nodes reside in CR-WSN. Every node in CR-WSN is constrained by battery. To minimize network cost, it should be feasible to have the routing protocol used for CR-WSN to be energy efficient. This paper proposes an optimization-based routing protocol, namely Whale Optimization Routing Protocol (WORP), for identifying the best route in CR-WSN to minimize the delay and lead to network efficiency. WORP draws inspiration from the behaviors of whales as they forage, similar to their hunting activity. By prioritizing residual energy and the total energy of the nodes in the route, WORP encourages energy-aware route selection. WORP is examined via simulation with NS2 against current routing protocols. Benchmark performance metrics are used to assess the effectiveness of WORP. Results make an indication that WORP has superior performance than current routing protocols in CR-WSN.Keywords
WSN, CR-WSN, Routing, Optimization, Delay, Energy Consumption.References
- K. Vijayan, G. Ramprabu, S. SelvakumaraSamy, and M. Rajeswari, “Cascading Model in Underwater Wireless Sensors using Routing Policy for State Transitions,” Microprocess. Microsyst., vol. 79, Article-id: 103298, 2020. https://doi.org/10.1016/j.micpro.2020.103298.
- P. Sethu Lakshmi and M. G. Jibukumar, “Performance Analysis of SWIPT in Multi-hop Wireless Sensor Networks,” in Procedia Computer Science, 2020, vol. 171, pp. 2157–2166, https://doi.org/10.1016/j.procs.2020.04.233.
- R. N. Yadav, R. Misra, and D. Saini, “Energy aware cluster based routing protocol over distributed cognitive radio sensor network,” Comput. Commun., vol. 129, pp. 54–66, 2018. https://doi.org/10.1016/j.comcom.2018.07.020.
- G. Jaber and R. Kacimi, “A collaborative caching strategy for content-centric enabled wireless sensor networks,” Comput. Commun., vol. 159, pp. 60–70, 2020. https://doi.org/10.1016/j.comcom.2020.05.018.
- B. Kabakulak, “Sensor and sink placement, scheduling and routing algorithms for connected coverage of wireless sensor networks,” Ad Hoc Networks, vol. 86, pp. 83–102, 2019. https://doi.org/10.1016/j.adhoc.2018.11.005.
- F. Niaz, M. Khalid, Z. Ullah, N. Aslam, M. Raza, and M. K. Priyan, “A bonded channel in cognitive wireless body area network based on IEEE 802.15.6 and internet of things,” Comput. Commun., vol. 150, pp. 131–143, 2020. https://doi.org/10.1016/j.comcom.2019.11.016.
- Z. Liu, M. Zhao, Y. Yuan, and X. Guan, “Sub channel and resource allocation in cognitive radio sensor network with wireless energy harvesting,” Comput. Networks, vol. 167, 2020. https://doi.org/10.1016/j.comnet.2019.107028.
- M. M. Hassani and R. Berangi, “A new congestion control mechanism for transport protocol of cognitive radio sensor networks,” AEU–Int. J. Electron. Commun., vol. 85, pp. 134–143, 2018. https://doi.org/10.1016/j.aeue.2017.12.026.
- C. Singhal and V. Patil, “HCR-WSN: Hybrid MIMO cognitive radio system for wireless sensor network,” Comput. Commun., vol. 169, pp. 11–25, 2021. https://doi.org/10.1016/j.comcom.2020.12.025.
- F. Al-Turjman, “Cognitive routing protocol for disaster-inspired Internet of Things,” Futur. Gener. Comput. Syst., vol. 92, pp. 1103–1115, 2019. https://doi.org/10.1016/j.future.2017.03.014.
- B. S. Awoyemi and B. T. Maharaj, “Quality of service provisioning through resource optimisation in heterogeneous cognitive radio sensor networks,” Comput. Commun., vol. 165, pp. 122–130, 2021. https://doi.org/10.1016/j.comcom.2020.11.006.
- V. Van Huynh, H. S. Nguyen, L. T. T. Hoc, T. S. Nguyen, and M. Voznak, “Optimization issues for data rate in energy harvesting relay-enabled cognitive sensor networks,” Comput. Networks, vol. 157, pp. 29–40, Jul. 2019. https://doi.org/10.1016/j.comnet.2019.04.012.
- A. S. Cacciapuoti, M. Caleffi, and L. Paura, “Reactive routing for mobile cognitive radio ad hoc networks,” Ad Hoc Networks, vol. 10, no. 5, pp. 803–815, 2012. Accessed: Feb. 28, 2021. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S1570870511000813.
- J. Lee and J. Lim, “Cognitive routing for multi-hop mobile cognitive radio ad hoc networks,” J. Commun. Networks, vol. 16, no. 2, pp. 155–161, 2014, doi: 10.1109/JCN.2014.000026.
- L. Cheng, L. Zhong, X. Zhang, and J. Xing, “A staged adaptive firefly algorithm for UAV charging planning in wireless sensor networks,” Comput. Commun., vol. 161, pp. 132–141, 2020. https://doi.org/10.1016/j.comcom.2020.07.019.
- P. Feng, Y. Bai, J. Huang, W. Wang, Y. Gu, and S. Liu, “CogMOR-MAC: A cognitive multi-channel opportunistic reservation MAC for multi-UAVs ad hoc networks,” Comput. Commun., vol. 136, pp. 30–42, 2019. https://doi.org/10.1016/j.comcom.2019.01.010.
- S. Gopikrishnan, P. Priakanth, and G. Srivastava, “DEDC: Sustainable data communication for cognitive radio sensors in the Internet of Things,” Sustain. Comput. Informatics Syst., vol. 29, 2021. https://doi.org/10.1016/j.suscom.2020.100471.
- S. Aswale and V. R. Ghorpade, “Geographic Multipath Routing based on Triangle Link Quality Metric with Minimum Inter-path Interference for Wireless Multimedia Sensor Networks,” J. King Saud Univ.–Comput. Inf. Sci., vol. 33, no. 1, pp. 33–44, 2021. https://doi.org/10.1016/j.jksuci.2018.02.001.
- Y. U. Xiu-wu, Y. U. Hao, L. Yong, and X. Ren-rong, “A clustering routing algorithm based on wolf pack algorithm for heterogeneous wireless sensor networks,” Comput. Networks, vol. 167, p. 106994, 2020. https://doi.org/10.1016/j.comnet.2019.106994.
- C. Gu, M. Bradbury, J. Kirton, and A. Jhumka, “A decision theoretic framework for selecting source location privacy aware routing protocols in wireless sensor networks,” Futur. Gener. Comput. Syst., vol. 87, pp. 514–526, 2018. https://doi.org/10.1016/j.future.2018.01.046.
- I. Jemili, D. Ghrab, A. Belghith, and M. Mosbah, “Cross-layer adaptive multipath routing for multimedia Wireless Sensor Networks under duty cycle mode,” Ad Hoc Networks, vol. 109, p. 102292, 2020. https://doi.org/10.1016/j.adhoc.2020.102292.
- J. Ramkumar and R. Vadivel, “Performance Modeling of Bio-Inspired Routing Protocols in Cognitive Radio Ad Hoc Network to Reduce End-to-End Delay,” Int. J. Intell. Eng. Syst., vol. 12, no. 1, pp. 221–231, 2019. https://doi.org/10.22266/ijies2019.0228.22.
- J. Ramkumar and R. Vadivel, “Multi-Adaptive Routing Protocol for Internet of Things based Ad-hoc Networks,” Wirel. Pers. Commun., pp. 1–23,2021. https://doi.org/ 10.1007/s11277-021-08495-z.
- B. Abbache et al., “Dissimulation-based and load-balance-aware routing protocol for request and event oriented mobile wireless sensor networks,” AEU–Int. J. Electron. Commun., vol. 99, pp. 264–283, 2019. https://doi.org/10.1016/j.aeue.2018.12.003.
- M. K, C. K, and S. C, “An energy efficient clustering scheme using multilevel routing for wireless sensor network,” Comput. Electr. Eng., vol. 69, pp. 642–652, 2018. https://doi.org/10.1016/j.compeleceng.2017.10.007.
- F. H. Awad, “Optimization of relay node deployment for multisource multipath routing in Wireless Multimedia Sensor Networks using Gaussian distribution,” Comput. Networks, vol. 145, pp. 96–106, 2018. https://doi.org/10.1016/j.comnet.2018.08.021.
- R. Almesaeed and A. Jedidi, “Dynamic directional routing for mobile wireless sensor networks,” Ad Hoc Networks, vol. 110, p. 102301, 2021.https://doi.org/10.1016/j.adhoc.2020.102301.
- L. Rui, X. Wang, Y. Zhang, X. Wang, and X. Qiu, “A self-adaptive and fault-tolerant routing algorithm for wireless sensor networks in microgrids,” Futur. Gener. Comput. Syst., vol. 100, pp. 35–45, 2019. https://doi.org/10.1016/j.future.2019.04.024.
- A. Agrawal, V. Singh, S. Jain, and R. K. Gupta, “GCRP: Grid-cycle routing protocol for wireless sensor network with mobile sink,” AEU–Int. J. Electron. Commun., vol. 94, pp. 1–11, 2018. https://doi.org/10.1016/j.aeue.2018.06.036.
- K. Thangaramya, K. Kulothungan, R. Logambigai, M. Selvi, S. Ganapathy, and A. Kannan, “Energy aware cluster and neuro-fuzzy based routing algorithm for wireless sensor networks in IoT,” Comput. Networks, vol. 151, pp. 211–223, 2019. https://doi.org/10.1016/j.comnet.2019.01.024.
- R. Yarinezhad, “Reducing delay and prolonging the lifetime of wireless sensor network using efficient routing protocol based on mobile sink and virtual infrastructure,” Ad Hoc Networks, vol. 84, pp. 42–55, 2019. doi: https://doi.org/10.1016/j.adhoc.2018.09.016.
- J. Ramkumar and R. Vadivel, “CSIP—cuckoo search inspired protocol for routing in cognitive radio ad hoc networks,” in Advances in Intelligent Systems and Computing, vol. 556, pp. 145–153, 2017. https://doi.org/10.1007/978-981-10-3874-7_14.
- J. Ramkumar and R. Vadivel, “Bee inspired secured protocol for routing in cognitive radio ad hoc networks,” IIndianJpurnal of Science and Technology, vol. 13, no. 30, pp. 3059–3069, 2020. https://doi.org/10.17485/IJST/v13i30.1152.
- J. Ramkumar and R. Vadivel, “Meticulous elephant herding optimization based protocol for detecting intrusions in cognitive radio ad hoc networks,” Int. J. Emerg. Trends Eng. Res., vol. 8, no. 8, pp. 4549–4554, 2020. https://doi.org/10.30534/ijeter/2020/82882020.
- Dr.R.Vadivel and J.Ramkumar. “QoS-Enabled Improved Cuckoo Search-Inspired Protocol (ICSIP) for IoT-Based Healthcare Applications”, Incorporating the Internet of Things in Healthcare Applications and Wearable Devices, Chapter 6, Pages 109-121, 2020. https://doi.org/10.4018/978-1-7998-1090-2.ch006.
- J. Ramkumar and R. Vadivel, “Intelligent Fish Swarm Inspired Protocol (IFSIP) For Dynamic Ideal Routing in Cognitive Radio Ad-Hoc Networks,” Int. J. Comput. Digit. Syst., vol. 10, pp. 2–11, 2020, Accessed: Dec. 02, 2020. [Online]. Available: http://journals.uob.edu.bh.
- M. Tubishat, N. Idris, and M. Abushariah, “Explicit aspects extraction in sentiment analysis using optimal rules combination,” Futur. Gener. Comput. Syst., vol. 114, pp. 448–480, 2021. https://doi.org/10.1016/j.future.2020.08.019.
- M. Lingaraj, T. N. Sugumar, C. Stanly Felix and J. Ramkumar, "Query Aware Routing Protocol for Mobility Enabled Wireless Sensor Network", International Journal of Computer Networks and Applications (IJCNA), vol. 8, no.3, pp. 258-267, 2021. https://doi.org/10.22247/ijcna/2021/209192.
- S. Boopalan and S. Jayasankari, "Dolphin Swarm Inspired Protocol (DSIP) for Routing in Underwater Wireless Sensor Networks", International Journal of Computer Networks and Applications (IJCNA), vol. 8, no.1, pp. 44–53, 2021. https://doi.org/10.22247/ijcna/2021/207981.
- Constrained Cuckoo Search Optimization Based Protocol for Routing in Cloud Network
Authors
1 PG and Research Department of Computer Science, Dr. N.G.P. Arts and Science College, Coimbatore, Tamil Nadu, IN
2 Department of Information Technology, Bharathiar University, Coimbatore, Tamil Nadu, IN
3 Department of Computer Technology, Dr. N.G.P. Arts and Science College, Coimbatore, Tamil Nadu, IN
Source
International Journal of Computer Networks and Applications, Vol 8, No 6 (2021), Pagination: 795-803Abstract
Cloud Computing (CC) is the process of providing on-demand data to the user via the internet. In CC, users don't need to manage data storage and computational power actively. Finding the best route in a cloud network is entirely different from other general networks which it is due to high scalability. Protocols developed for other general networks will never suit or give better performance in cloud networks due to its scalability. This paper proposes a bio-inspired protocol for routing in a cloud network, namely Constrained Cuckoo Search Optimization-based Protocol (CCSOP). The routing strategy of CCSOP is inspired by the natural characteristics of the cuckoo bird towards finding a nest to lay its eggs. Levy Flight concept is applied with different constraints to enhance optimization performance towards finding the best route in a cloud network that minimizes energy consumption. CCSOP is evaluated in Greencloud using benchmark network performance metrics against the current routing protocols. The efficacy of CCSOP is evaluated using benchmark performance measures. CCSOP appears to outperform current cloud network routing protocols in terms of energy consumption.Keywords
Cuckoo, Cloud, Energy, Flight, Levy, Optimization, Routing, Scalability.References
- J. Ramkumar and R. Vadivel, “CSIP—cuckoo search inspired protocol for routing in cognitive radio ad hoc networks,” in Advances in Intelligent Systems and Computing, 2017, vol. 556, pp. 145–153, doi: 10.1007/978-981-10-3874-7_14.
- J. Ramkumar and R. Vadivel, “Meticulous elephant herding optimization based protocol for detecting intrusions in cognitive radio ad hoc networks,” Int. J. Emerg. Trends Eng. Res., vol. 8, no. 8, pp. 4549–4554, 2020, doi: 10.30534/ijeter/2020/82882020.
- J. Ramkumar and R. Vadivel, “Bee inspired secured protocol for routing in cognitive radio ad hoc networks,” INDIAN J. Sci. Technol., vol. 13, no. 30, pp. 3059–3069, 2020, doi: 10.17485/IJST/v13i30.1152.
- M. Faheem, R. A. Butt, R. Ali, B. Raza, M. A. Ngadi, and V. C. Gungor, “CBI4.0: A Cross-layer Approach for Big Data Gathering for Active Monitoring and Maintenance in the Manufacturing Industry 4.0,” J. Ind. Inf. Integr., p. 100236, 2021, doi: https://doi.org/10.1016/j.jii.2021.100236.
- C. Y. Huang and Y. J. Chang, “An adaptively multi-attribute index framework for big IoT data,” Comput. Geosci., p. 104841, 2021, doi: https://doi.org/10.1016/j.cageo.2021.104841.
- J. Qu, “Research on mobile learning in a teaching information service system based on a big data driven environment,” Educ. Inf. Technol., pp. 1–19, Jun. 2021, doi: 10.1007/s10639-021-10614-z.
- R. Vadivel and J. Ramkumar, “QoS-Enabled Improved Cuckoo Search-Inspired Protocol (ICSIP) for IoT-Based Healthcare Applications,” pp. 109–121, 2019, doi: 10.4018/978-1-7998-1090-2.ch006.
- J. Ramkumar and R. Vadivel, “Multi-Adaptive Routing Protocol for Internet of Things based Ad-hoc Networks,” Wirel. Pers. Commun., pp. 1–23, Apr. 2021, doi: 10.1007/s11277-021-08495-z.
- J. Ramkumar and R. Vadivel, “Improved Wolf prey inspired protocol for routing in cognitive radio Ad Hoc networks,” Int. J. Comput. Networks Appl., vol. 7, no. 5, pp. 126–136, 2020, doi: 10.22247/ijcna/2020/202977.
- J. Ramkumar and R. Vadivel, “Performance modeling of bio-inspired routing protocols in Cognitive Radio Ad Hoc Network to reduce end-to-end delay,” Int. J. Intell. Eng. Syst., vol. 12, no. 1, pp. 221–231, 2019, doi: 10.22266/IJIES2019.0228.22.
- R. R. Hoy, “Quantitative skills in undergraduate neuroscience education in the age of big data,” Neurosci. Lett., p. 136074, 2021, doi: https://doi.org/10.1016/j.neulet.2021.136074.
- P. L. Martínez, R. Dintén, J. M. Drake, and M. Zorrilla, “A big data-centric architecture metamodel for Industry 4.0,” Futur. Gener. Comput. Syst., 2021, doi: https://doi.org/10.1016/j.future.2021.06.020.
- M. Rhahla, S. Allegue, and T. Abdellatif, “Guidelines for GDPR compliance in Big Data systems,” J. Inf. Secur. Appl., vol. 61, p. 102896, 2021, doi: https://doi.org/10.1016/j.jisa.2021.102896.
- A. Sevtsuk, R. Basu, X. Li, and R. Kalvo, “A big data approach to understanding pedestrian route choice preferences: Evidence from San Francisco,” Travel Behav. Soc., vol. 25, pp. 41–51, 2021, doi: https://doi.org/10.1016/j.tbs.2021.05.010.
- V. Keskar, J. Yadav, and A. Kumar, “Perspective of anomaly detection in big data for data quality improvement,” Mater. Today Proc., 2021, doi: https://doi.org/10.1016/j.matpr.2021.05.597.
- T. G. Kim and S. Yu, “Big Data Analysis of the Risk of Intracranial Hemorrhage in Korean Populations Taking Low-Dose Aspirin,” J. Stroke Cerebrovasc. Dis., vol. 30, no. 8, p. 105917, 2021, doi: https://doi.org/10.1016/j.jstrokecerebrovasdis.2021.105917.
- D. Balazka, D. Houtman, and B. Lepri, “How can big data shape the field of non-religion studies? And why does it matter?,” Patterns, vol. 2, no. 6, p. 100263, 2021, doi: https://doi.org/10.1016/j.patter.2021.100263.
- Y. Su and X. Wang, “Innovation of Agricultural Economic Management in the Process of Constructing Smart Agriculture by Big Data,” Sustain. Comput. Informatics Syst., p. 100579, 2021, doi: https://doi.org/10.1016/j.suscom.2021.100579.
- C. Wen, J. Yang, L. Gan, and Y. Pan, “Big data driven Internet of Things for credit evaluation and early warning in finance,” Futur. Gener. Comput. Syst., vol. 124, pp. 295–307, 2021, doi: https://doi.org/10.1016/j.future.2021.06.003.
- M. Nilashi et al., “Big social data and customer decision making in vegetarian restaurants: A combined machine learning method,” J. Retail. Consum. Serv., vol. 62, p. 102630, 2021, doi: https://doi.org/10.1016/j.jretconser.2021.102630.
- N. B. Long, H. Tran-Dang, and D. Kim, “Energy-Aware Real-Time Routing for Large-Scale Industrial Internet of Things,” IEEE Internet Things J., vol. 5, no. 3, pp. 2190–2199, 2018, doi: 10.1109/JIOT.2018.2827050.
- Y. Xu, Z. Yue, and L. Lv, “Clustering Routing Algorithm and Simulation of Internet of Things Perception Layer Based on Energy Balance,” IEEE Access, vol. 7, pp. 145667–145676, 2019, doi: 10.1109/ACCESS.2019.2944669.
- H. A. Omar, W. Zhuang, and L. Li, “Gateway Placement and Packet Routing for Multihop In-Vehicle Internet Access,” IEEE Trans. Emerg. Top. Comput., vol. 3, no. 3, pp. 335–351, 2015, doi: 10.1109/TETC.2015.2395077.
- Z. Ding, L. Shen, H. Chen, F. Yan, and N. Ansari, “Energy-Efficient Relay-Selection-Based Dynamic Routing Algorithm for IoT-Oriented Software-Defined WSNs,” IEEE Internet Things J., vol. 7, no. 9, pp. 9050–9065, 2020, doi: 10.1109/JIOT.2020.3002233.
- J. V. V Sobral, J. J. P. C. Rodrigues, R. A. L. Rabêlo, K. Saleem, and S. A. Kozlov, “Improving the Performance of LOADng Routing Protocol in Mobile IoT Scenarios,” IEEE Access, vol. 7, pp. 107032– 107046, 2019, doi: 10.1109/ACCESS.2019.2932718.
- T. Mick, R. Tourani, and S. Misra, “LASeR: Lightweight Authentication and Secured Routing for NDN IoT in Smart Cities,” IEEE Internet Things J., vol. 5, no. 2, pp. 755–764, 2018, doi: 10.1109/JIOT.2017.2725238.
- Q. Zhang, M. Jiang, Z. Feng, W. Li, W. Zhang, and M. Pan, “IoT Enabled UAV: Network Architecture and Routing Algorithm,” IEEE Internet Things J., vol. 6, no. 2, pp. 3727–3742, 2019, doi:10.1109/JIOT.2018.2890428.
- Z. Zhou, B. Yao, R. Xing, L. Shu, and S. Bu, “E-CARP: An Energy Efficient Routing Protocol for UWSNs in the Internet of Underwater Things,” IEEE Sens. J., vol. 16, no. 11, pp. 4072–4082, 2016, doi: 10.1109/JSEN.2015.2437904.
- C. Wang, L. Zhang, Z. Li, and C. Jiang, “SDCoR: Software Defined Cognitive Routing for Internet of Vehicles,” IEEE Internet Things J., vol. 5, no. 5, pp. 3513–3520, 2018, doi: 10.1109/JIOT.2018.2812210.
- K. Z. Ghafoor, L. Kong, D. B. Rawat, E. Hosseini, and A. S. Sadiq, “Quality of Service Aware Routing Protocol in Software-Defined Internet of Vehicles,” IEEE Internet Things J., vol. 6, no. 2, pp. 2817–2828, 2019, doi: 10.1109/JIOT.2018.2875482.
- W. Itani, C. Ghali, R. Bassil, A. Kayssi, and A. Chehab, “ServBGP: BGP-inspired autonomic service routing for multi-provider collaborative architectures in the cloud,” Futur. Gener. Comput. Syst., vol. 32, pp. 99–117, 2014, doi: https://doi.org/10.1016/j.future.2012.05.013.
- T. Baker, B. Al-Dawsari, H. Tawfik, D. Reid, and Y. Ngoko, “GreeDi: An energy efficient routing algorithm for big data on cloud,” Ad Hoc Networks, vol. 35, pp. 83–96, Dec. 2015, doi: https://doi.org/10.1016/j.adhoc.2015.06.008.
- S. Kaja, E. M. Shakshuki, and A. Yasar, “Long Short-Term Memory Approach for Routing Optimization in Cloud ACKnowledgement Scheme for Node Network,” Procedia Comput. Sci., vol. 184, pp. 461–468, 2021, doi: https://doi.org/10.1016/j.procs.2021.03.058.
- X. Peng and Y. Chang, “Energy-efficient routing technique for reliable data transmission under the background of big data for disaster region,” Comput. Intell., vol. 36, no. 4, 2020, doi: 10.1111/coin.12294.
- L. Zhao, Z. Bi, M. Lin, A. Hawbani, J. Shi, and Y. Guan, “An intelligent fuzzy-based routing scheme for software-defined vehicular networks,” Comput. Networks, vol. 187, p. 107837, Mar. 2021, doi: 10.1016/j.comnet.2021.107837.
- AFSORP: Adaptive Fish Swarm Optimization-Based Routing Protocol for Mobility Enabled Wireless Sensor Network
Authors
1 Department of Computer Science and Engineering, Annamalai University, Cuddalore, Tamil Nadu, IN
2 Department of Computer Science, Dr. N.G.P. Arts and Science College, Coimbatore, Tamil Nadu, IN
3 Department of Computer Science and Applications, Sankara College of Science and Commerce, Coimbatore, Tamil Nadu, IN
4 Department of Computer and Information Science, Annamalai University, Cuddalore, Tamil Nadu, IN
Source
International Journal of Computer Networks and Applications, Vol 10, No 1 (2023), Pagination: 119-129Abstract
Advances in information and communication technology and electronics have led to a surge in interest in mobility-enabled wireless sensor networks (MEWSN). These minuscule sensor nodes collect data, process it, and then transmit it via a radio frequency channel to a central station or sink. Most of the time, MEWSNs are placed in hazardous or difficult-to-access locations. To increase the lifespan of a network, available resources must be utilized as efficiently as possible. The whole network connection collapses if even one node loses power, rendering the deployment's goals moot. Therefore, much MEWSN research has focused on energy efficiency, with energy-efficient routing protocols being a key component. This paper proposes an Adaptive Fish Swarm Optimization-based Routing Protocol (AFSORP) for identifying the best route in MEWSN. AFSORP functions based on the natural characteristics of fish. The two most important steps in AFSORP are chasing and blocking, which respectively seek the optimal route and choose the appropriate route to send data from the source node to the destination node. Standard network performance measurements are used to assess AFSORP with the help of the GNS3 simulator. The results show that AFSORP performs better than the existing routing methods.Keywords
Routing, Mobility, WSN, MEWSN, Optimization, Fish, Energy.References
- F. R. Mughal et al., “A new Asymmetric Link Quality Routing protocol (ALQR) for heterogeneous WSNs,” Microprocess. Microsyst., vol. 93, p. 104617, 2022, doi: https://doi.org/10.1016/j.micpro.2022.104617.
- R. Kumar, S. Shekhar, H. Garg, M. Kumar, B. Sharma, and S. Kumar, “EESR: Energy efficient sector-based routing protocol for reliable data communication in UWSNs,” Comput. Commun., vol. 192, pp. 268–278, 2022, doi: https://doi.org/10.1016/j.comcom.2022.06.011.
- H. Li, S. Wang, Q. Chen, M. Gong, and L. Chen, “IPSMT: Multi-objective optimization of multipath transmission strategy based on improved immune particle swarm algorithm in wireless sensor networks,” Appl. Soft Comput., vol. 121, p. 108705, 2022, doi: https://doi.org/10.1016/j.asoc.2022.108705.
- Z. Guo and H. Chen, “A reinforcement learning-based sleep scheduling algorithm for cooperative computing in event-driven wireless sensor networks,” Ad Hoc Networks, vol. 130, p. 102837, 2022, doi: https://doi.org/10.1016/j.adhoc.2022.102837.
- S. Mavinkattimath and R. Khanai, “A low power and high-speed hardware accelerator for Wireless Body Sensor Network (WBSN),” Mater. Today Proc., 2022, doi: https://doi.org/10.1016/j.matpr.2022.06.013.
- J. Ramkumar and R. Vadivel, “Performance Modeling of Bio-Inspired Routing Protocols in Cognitive Radio Ad Hoc Network to Reduce End-to-End Delay,” Int. J. Intell. Eng. Syst., vol. 12, no. 1, pp. 221–231, 2019, doi: 10.22266/ijies2019.0228.22.
- J. Ramkumar and R. Vadivel, “Multi-Adaptive Routing Protocol for Internet of Things based Ad-hoc Networks,” Wirel. Pers. Commun., vol. 120, no. 2, pp. 887–909, Apr. 2021, doi: 10.1007/s11277-021-08495-z.
- R. Jaganathan and R. Vadivel, “Intelligent Fish Swarm Inspired Protocol (IFSIP) for Dynamic Ideal Routing in Cognitive Radio Ad-Hoc Networks,” Int. J. Comput. Digit. Syst., vol. 10, no. 1, pp. 1063–1074, 2021, doi: 10.12785/ijcds/100196.
- A. Behura and M. R. Kabat, “Chapter 13 - Optimization-based energy-efficient routing scheme for wireless body area network,” in Cognitive Data Science in Sustainable Computing, S. Mishra, H. K. Tripathy, P. K. Mallick, A. K. Sangaiah, and G.-S. B. T.-C. B. D. I. with a M. A. Chae, Eds. Academic Press, 2022, pp. 279–303. doi: https://doi.org/10.1016/B978-0-323-85117-6.00016-9.
- M. F. Carsancakli, M. A. Al Imran, H. U. Yildiz, A. Kara, and B. Tavli, “Reliability of linear WSNs: A complementary overview and analysis of impact of cascaded failures on network lifetime,” Ad Hoc Networks, vol. 131, p. 102839, 2022, doi: https://doi.org/10.1016/j.adhoc.2022.102839.
- V. Kavitha and K. Ganapathy, “Galactic swarm optimized convolute network and cluster head elected energy-efficient routing protocol in WSN,” Sustain. Energy Technol. Assessments, vol. 52, p. 102154, 2022, doi: https://doi.org/10.1016/j.seta.2022.102154.
- A. Sundar Raj and M. Chinnadurai, “Energy efficient routing algorithm in wireless body area networks for smart wearable patches,” Comput. Commun., vol. 153, pp. 85–94, 2020, doi: https://doi.org/10.1016/j.comcom.2020.01.069.
- A. S. Toor and A. K. Jain, “Energy Aware Cluster Based Multi-hop Energy Efficient Routing Protocol using Multiple Mobile Nodes (MEACBM) in Wireless Sensor Networks,” AEU - Int. J. Electron. Commun., vol. 102, pp. 41–53, 2019, doi: https://doi.org/10.1016/j.aeue.2019.02.006.
- J. E. Z. Gbadouissa, A. A. A. Ari, C. Titouna, A. M. Gueroui, and O. Thiare, “HGC: HyperGraph based Clustering scheme for power aware wireless sensor networks,” Futur. Gener. Comput. Syst., vol. 105, pp. 175–183, Apr. 2020, doi: https://doi.org/10.1016/j.future.2019.11.043.
- X. Fu, H. Yao, and Y. Yang, “Exploring the invulnerability of wireless sensor networks against cascading failures,” Inf. Sci. (Ny)., vol. 491, pp. 289–305, 2019, doi: https://doi.org/10.1016/j.ins.2019.04.004.
- T. Nath and M. Azharuddin, “Application of wireless sensor networks for Rhino protection against poachers in Kaziranga National Park,” AEU - Int. J. Electron. Commun., vol. 111, p. 152882, Nov. 2019, doi: 10.1016/J.AEUE.2019.152882.
- A. Bereketli, M. Tümçakır, and B. Yeni, “P-AUV: Position aware routing and medium access for ad hoc AUV networks,” J. Netw. Comput. Appl., vol. 125, pp. 146–154, Jan. 2019, doi: 10.1016/J.JNCA.2018.10.014.
- D. Adhikari, D. Datta, and R. Datta, “Impact of BER in fragmentation-aware routing and spectrum assignment in elastic optical networks,” Comput. Networks, vol. 172, p. 107167, May 2020, doi: 10.1016/J.COMNET.2020.107167.
- J. Liu et al., “QMR:Q-learning based Multi-objective optimization Routing protocol for Flying Ad Hoc Networks,” Comput. Commun., vol. 150, pp. 304–316, 2020, doi: https://doi.org/10.1016/j.comcom.2019.11.011.
- H. Zemrane, Y. Baddi, and A. Hasbi, “Mobile AdHoc networks for Intelligent Transportation System: Comparative Analysis of the Routing protocols,” Procedia Comput. Sci., vol. 160, pp. 758–765, 2019, doi: https://doi.org/10.1016/j.procs.2019.11.014.
- J. Wang, H. Zhang, X. Tang, and Z. Li, “Delay-tolerant routing and message scheduling for CR-VANETs,” Futur. Gener. Comput. Syst., vol. 110, pp. 291–309, 2020, doi: https://doi.org/10.1016/j.future.2020.04.026.
- P. Chithaluru, R. Tiwari, and K. Kumar, “AREOR–Adaptive ranking based energy efficient opportunistic routing scheme in Wireless Sensor Network,” Comput. Networks, vol. 162, p. 106863, 2019, doi: https://doi.org/10.1016/j.comnet.2019.106863.
- Lingaraj M and Prakash A, “Power Aware Routing Protocol (PARP) to Reduce Energy Consumption in Wireless Sensor Networks,” Int. J. Recent Technol. Eng., vol. 7, no. 5, pp. 380–385, Jan. 2019, Accessed: Apr. 07, 2021. [Online]. Available: https://www.ijrte.org/wp-content/uploads/papers/v7i5/E1969017519.pdf
- F. Al-Salti, N. Alzeidi, K. Day, and A. Touzene, “An efficient and reliable grid-based routing protocol for UWSNs by exploiting minimum hop count,” Comput. Networks, vol. 162, p. 106869, Oct. 2019, doi: 10.1016/J.COMNET.2019.106869.
- K. Patil, M. Jafri, D. Fiems, and A. Marin, “Stochastic modeling of depth based routing in underwater sensor networks,” Ad Hoc Networks, vol. 89, pp. 132–141, 2019, doi: https://doi.org/10.1016/j.adhoc.2019.03.009.
- B. Chakraborty, S. Verma, and K. P. Singh, “Temporal Differential Privacy in Wireless Sensor Networks,” J. Netw. Comput. Appl., vol. 155, p. 102548, 2020, doi: https://doi.org/10.1016/j.jnca.2020.102548.
- Minimizing Energy Consumption in Vehicular Sensor Networks Using Relentless Particle Swarm Optimization Routing
Authors
1 Department of Computer Science, Skyline University, NG
2 Department of Computer Science, Dr. N.G.P. Arts and Science College, Tamil Nadu, IN
3 Department of Computer Science and Applications, Sankara College of Science and Commerce, Tamil Nadu, IN
4 Department of Computer Science and Engineering, Annamalai University, Tamil Nadu, IN
5 Department of Computer and Information Science, Annamalai University, Tamil Nadu, IN
Source
International Journal of Computer Networks and Applications, Vol 10, No 2 (2023), Pagination: 217-230Abstract
Increasing traffic issues, particularly in highly populated nations, have prompted recent interest in Vehicular Sensor Networks (VSNETs) from academics in several fields. Accident rates continue to rise, highlighting the need for a highly functional Smart Transport System (STS). Improvements to the STS should not be spread thin across the board but should concentrate on improving traffic flow, maintaining system reliability, and decreasing vehicle carbon dioxide and methane emissions. Current routing protocols for VSNETs consider various scenarios and approaches to provide safe and effective vehicle-to-infrastructure communication. The reliability of vehicle connections during data transmission has not been well explored. This paper proposes a Relentless Particle Swarm Optimization based Routing Protocol (RPSORP) for VSNET to use vehicle kinematics and mobility to identify vehicle location, send routing information packets to road-side devices, and choose the most reliable path for travel. RPSORP optimizes local and global search to minimize energy consumption in VSNET. The RPSORP is evaluated in the GNS3 simulator using Throughput, Packet Delivery, Delay, and Energy Consumption metrics. RPSORP has superior performance than state-of-the-art routing protocols.Keywords
VSNET, Routing, Swarming, PSO, Local-Search, Global-Search.References
- H. Khelifi, S. Luo, B. Nour, H. Moungla, S. H. Ahmed, and M. Guizani, “A blockchain-based architecture for secure vehicular Named Data Networks,” Comput. Electr. Eng., vol. 86, p. 106715, 2020, doi: 10.1016/j.compeleceng.2020.106715.
- O. S. Al-Heety, Z. Zakaria, M. Ismail, M. M. Shakir, S. Alani, and H. Alsariera, “A Comprehensive Survey: Benefits, Services, Recent Works, Challenges, Security, and Use Cases for SDN-VANET,” IEEE Access, vol. 8, pp. 91028–91047, 2020, doi: 10.1109/ACCESS.2020.2992580.
- M. A. Hossain et al., “Multi-Objective Harris Hawks Optimization Algorithm Based 2-Hop Routing Algorithm for CR-VANET,” IEEE Access, vol. 9, pp. 58230–58242, 2021, doi: 10.1109/ACCESS.2021.3072922.
- M. Naderi, F. Zargari, and M. Ghanbari, “Adaptive beacon broadcast in opportunistic routing for VANETs,” Ad Hoc Networks, vol. 86, pp. 119–130, 2019, doi: 10.1016/j.adhoc.2018.11.011.
- M. Lingaraj and A. Prakash, “Power aware routing protocol (PARP) to reduce energy consumption in wireless sensor networks,” Int. J. Recent Technol. Eng., vol. 7, no. 5, pp. 380–385, Jan. 2019, Accessed: Apr. 07, 2021. [Online]. Available: https://www.ijrte.org/wpcontent/uploads/papers/v7i5/E1969017519.pdf
- T. N. Sugumar and N. R. Ramasamy, “mDesk: a scalable and reliable hypervisor framework for effective provisioning of resource and downtime reduction,” J. Supercomput., vol. 76, no. 2, pp. 1277–1292, Feb. 2020, doi: 10.1007/s11227-018-2662-5.
- A. J. Kadhim and S. A. H. Seno, “Energy-efficient multicast routing protocol based on SDN and fog computing for vehicular networks,” Ad Hoc Networks, vol. 84, pp. 68–81, 2019, doi: 10.1016/j.adhoc.2018.09.018.
- L. Yao, J. Wang, X. Wang, A. Chen, and Y. Wang, “V2X Routing in a VANET Based on the Hidden Markov Model,” IEEE Trans. Intell. Transp. Syst., vol. 19, no. 3, pp. 889–899, 2018, doi: 10.1109/TITS.2017.2706756.
- K. A. Awan, I. Ud Din, A. Almogren, M. Guizani, and S. Khan, “StabTrust-A Stable and Centralized Trust-Based Clustering Mechanism for IoT Enabled Vehicular Ad-Hoc Networks,” IEEE Access, vol. 8, pp. 21159–21177, 2020, doi: 10.1109/ACCESS.2020.2968948.
- R. Yarinezhad, “Reducing delay and prolonging the lifetime of wireless sensor network using efficient routing protocol based on mobile sink and virtual infrastructure,” Ad Hoc Networks, vol. 84, pp. 42–55, Mar. 2019, doi: https://doi.org/10.1016/j.adhoc.2018.09.016.
- D. BD and F. Al-Turjman, “A hybrid secure routing and monitoring mechanism in IoT-based wireless sensor networks,” Ad Hoc Networks, vol. 97, p. 102022, 2020, doi: https://doi.org/10.1016/j.adhoc.2019.102022.
- S. Maurya, V. K. Jain, and D. R. Chowdhury, “Delay aware energy efficient reliable routing for data transmission in heterogeneous mobile sink wireless sensor network,” J. Netw. Comput. Appl., vol. 144, pp. 118–137, 2019, doi: https://doi.org/10.1016/j.jnca.2019.06.012.
- S. Jain, K. K. Pattanaik, and A. Shukla, “QWRP: Query-driven virtual wheel based routing protocol for wireless sensor networks with mobile sink,” J. Netw. Comput. Appl., vol. 147, p. 102430, 2019, doi: https://doi.org/10.1016/j.jnca.2019.102430.
- P. Srinivasa Ragavan and K. Ramasamy, “Software defined networking approach based efficient routing in multi-hop and relay surveillance using Lion Optimization algorithm,” Comput. Commun., vol. 150, pp. 764–770, 2020, doi: 10.1016/j.comcom.2019.11.033.
- Z. Sun, M. Wei, Z. Zhang, and G. Qu, “Secure Routing Protocol based on Multi-objective Ant-colony-optimization for wireless sensor networks,” Appl. Soft Comput. J., vol. 77, pp. 366–375, Apr. 2019, doi: 10.1016/j.asoc.2019.01.034.
- W. Qi, Q. Song, X. Kong, and L. Guo, “A traffic-differentiated routing algorithm in Flying Ad Hoc Sensor Networks with SDN cluster controllers,” J. Franklin Inst., vol. 356, no. 2, pp. 766–790, 2019, doi: 10.1016/j.jfranklin.2017.11.012.
- F. Al-Turjman, “Cognitive routing protocol for disaster-inspired Internet of Things,” Futur. Gener. Comput. Syst., vol. 92, pp. 1103– 1115, Mar. 2019, doi: 10.1016/j.future.2017.03.014.
- R. W. L. Coutinho, A. Boukerche, and A. A. F. Loureiro, “A novel opportunistic power controlled routing protocol for internet of underwater things,” Comput. Commun., vol. 150, pp. 72–82, Jan. 2020, doi: 10.1016/j.comcom.2019.10.020.
- R. Yarinezhad and S. N. Hashemi, “Solving the load balanced clustering and routing problems in WSNs with an fpt-approximation algorithm and a grid structure,” Pervasive Mob. Comput., vol. 58, p. 101033, 2019, doi: 10.1016/j.pmcj.2019.101033.
- M. Vigenesh and R. Santhosh, “An efficient stream region sink position analysis model for routing attack detection in mobile ad hoc networks,” Comput. Electr. Eng., vol. 74, pp. 273–280, 2019, doi: 10.1016/j.compeleceng.2019.02.005.
- K. N. Qureshi, S. Din, G. Jeon, and F. Piccialli, “Link quality and energy utilization based preferable next hop selection routing for wireless body area networks,” Comput. Commun., vol. 149, pp. 382– 392, 2020, doi: 10.1016/j.comcom.2019.10.030.
- S. Rashidibajgan and R. Doss, “Privacy-preserving history-based routing in Opportunistic Networks,” Comput. Secur., vol. 84, pp. 244– 255, 2019, doi: 10.1016/j.cose.2019.03.020.
- E. P. M. Câmara Júnior, L. F. M. Vieira, and M. A. M. Vieira, “CAPTAIN: A data collection algorithm for underwater optical-acoustic sensor networks,” Comput. Networks, vol. 171, p. 107145, Apr. 2020, doi: 10.1016/j.comnet.2020.107145.
- J. Ramkumar and R. Vadivel, “Multi-Adaptive Routing Protocol for Internet of Things based Ad-hoc Networks,” Wirel. Pers. Commun., vol. 120, no. 2, pp. 887–909, Apr. 2021, doi: 10.1007/s11277-021- 08495-z.
- R. Jaganathan and R. Vadivel, “Intelligent Fish Swarm Inspired Protocol (IFSIP) for Dynamic Ideal Routing in Cognitive Radio Ad-Hoc Networks,” Int. J. Comput. Digit. Syst., vol. 10, no. 1, pp. 1063–1074, 2021, doi: 10.12785/ijcds/100196.
- J. Xu et al., “Data transmission method for sensor devices in internet of things based on multivariate analysis,” Meas. J. Int. Meas. Confed., vol. 157, p. 107536, Jun. 2020, doi: 10.1016/j.measurement.2020.107536.
- G. Han, M. Xu, Y. He, J. Jiang, J. A. Ansere, and W. Zhang, “A dynamic ring-based routing scheme for source location privacy in wireless sensor networks,” Inf. Sci. (Ny)., vol. 504, pp. 308–323, 2019, doi: https://doi.org/10.1016/j.ins.2019.07.028.
- Use of Resnet Modelling for Tig Weld Feature Digitization And Correlation – A Technique for AI Based Welding System
Authors
1 University of Cape Town, ZA
2 Indian Institute of Technology, Kanpur, IN
Source
Manufacturing Technology Today, Vol 22, No 1 (2023), Pagination: 25-32Abstract
TIG Welding is being practiced in the manufacturing industry and it demands highly skilled labour. Artificial Intelligence (AI) is developing rapidly as researchers are constantly finding new ways in which intelligent machines can add value to their industry. An AI-based welding system stands to add value by increasing production rates, improving safety, and decreasing the human input required. Weld monitoring is a key activity in the TIG welding process and successful use of AI system will enable failure prediction and the proactive corrective actions. The aim of this project is to explore, test, and compare ResNet modelling based machine learning algorithms and examine their ability to monitor welds. In this project the weld monitoring process includes collecting images of weld joint for weld feature digitization. Also, the study enables predicting whether the weld shows good quality, contamination, burn through, misalignment, lack of fusion, or lack of penetration through a ResNet modelling based image analysis.Keywords
ResNet Modelling, TIG Welding, Image Analysis, AI Based Weld System.References
- Bacioiu, D., Melton, G., Papaelias, M., Shaw, R. (2019). Automated defect classification of Aluminium 5083 TIG welding using HDR camera and neural networks. Journal of Manufacturing Processes, 45, 603-613. 10.1016/j.jmapro.2019. 07.020
- Das, D., Pratihar, D., Roy, G., Pal, A. (2017). Phenomenological model-based study on electron beam welding process, and input-output modelling using neural networks trained by back-propagation algorithm, genetic algorithms, particle swarm optimization algorithm and bat algorithm. Applied Intelligence, 48(9), 2698-2718. 10.1007/s10489- 017-1101-2
- Fande, A. W., Taiwade, R. V., Raut, L. (2022). Development of activated tungsten inert gas welding and its current status: A review. Materials and Manufacturing Processes, 37(8), 841-876. 10.1080/10426914.2022.2039695
- Gyasi, E., Handroos, H., Kah, P. (2019). Survey on artificial intelligence (AI) applied in welding: A future scenario of the influence of AI on technological, economic, educational and social changes. Procedia Manufacturing, 38, 702-714. 10.1016/ j. promfg.2020.01.095
- Kesse, M., Buah, E., Handroos, H., Ayetor, G. (2020). Development of an artificial intelligence powered TIG welding algorithm for the prediction of bead geometry for TIG welding processes using hybrid deep learning, Metals, 10(4), 451, 2020. 10.3390/met10040451
- Plato.stanford.edu, (2022) Fuzzy Logic (Stanford Encyclopaedia of Philosophy), Plato.stanford.edu, 2022 Available: https://plato.stanford.edu/ entries/ logic-fuzzy/
- Xia, C., Pan, Z., Fei, Z., Zhang, S., Li, H. (2020). Vision based defects detection for Keyhole TIG welding using deep learning with visual explanation. Journal of Manufacturing Processes, 56, 845-855. 10.1016/j.jmapro.2020.05.033
- Influence of process variables on surface roughness of 316L stainless steel parts fabricated via selective laser melting process
Authors
1 National Institute of Technology Delhi, IN
2 CSIR–National Physical Laboratory, New Delhi, IN
3 Indian Institute of Technology Kanpur, Kanpur, IN
Source
Manufacturing Technology Today, Vol 22, No 1 (2023), Pagination: 33-38Abstract
Selective laser melting process (SLM) is a metal additive manufacturing technique with excellent design freedom and feasibility. In SLM, a high-energy source is used to melt powder particles into a pattern of successive layers. However, the major challenge associated with the SLM process is that the parts have a high surface roughness (Ra) compared to forming, machining, and rolling processes. In this paper, the core parameters, including scan speed, hatch distance, laser power, and energy density effects discussed as the roughness parameters. The experimental runs were designed based on Taguchi L9 orthogonal array. The results displayed that Ra of samples was largely affected by laser power as compared to scanning speed and hatching spacing. The Ra of samples achieved less at high energy density. In contrast to other surface finishing operations, the polished sample showed the average Ra value of 0.049 μm manufactured at an energy density of 58.83 J/mm3.Keywords
Selective Laser Melting, Process Parameter, Energy Density, 316L SS, Surface Roughness.References
- AlMangour, B., Grzesiak, D., & Yang, J. M. (2017). In-situ formation of novel TiC-particle-reinforced 316L stainless steel bulk-form composites by selective laser melting. Journal of Alloys and Compounds, 706, 409-418.
- Aqilah, D. N., Farazila, Y., Suleiman, D. Y., Amirah, M. A. N., & Izzati, W. B. W. N. (2018). Effects of process parameters on the surface roughness of stainless steel 316L parts produced by selective laser melting. Journal of Testing and Evaluation, 46(4), 1673-1683.
- Brytan, Z. (2017). Comparison of vacuum sintered and selective laser melted steel AISI 316L. Archives of Metallurgy and Materials, 62.
- Calignano, F., Manfredi, D., Ambrosio, E. P., Iuliano, L., & Fino, P. (2013). Influence of process parameters on surface roughness of aluminum parts produced by DMLS. International Journal of Advanced Manufacturing Technology, 67(9), 2743-2751.
- Cherry, J. A., Davies, H. M., Mehmood, S., Lavery, N. P., Brown, S. G. R., & Sienz, J. (2015). Investigation into the effect of process parameters on microstructural and physical properties of 316L stainless steel parts by selective laser melting. International Journal of Advanced Manufacturing Technology, 76(5), 869-879.
- Ghorbani, J., Li, J., & Srivastava, A. K. (2020). Application of optimized laser surface re-melting process on selective laser melted 316L stainless steel inclined parts. Journal of Manufacturing Processes, 56, 726-734.
- Kurzynowski, T., Gruber, K., Stopyra, W., Kuźnicka, B., & Chlebus, E. (2018). Correlation between process parameters, microstructure and properties of 316 L stainless steel processed by selective laser melting. Materials Science and Engineering: A, 718, 64-73.
- Pant, M., Nagdeve, L., Kumar, H., & Moona, G. (2022). A contemporary investigation of metal additive manufacturing techniques. Sādhanā, 47(1), 1-19.
- Prashanth, K. G., Scudino, S., Maity, T., Das, J., & Eckert, J. (2017). Is the energy density a reliable parameter for materials synthesis by selective laser melting. Materials Research Letters, 5(6), 386-390.
- Song, B., Dong, S., Zhang, B., Liao, H., & Coddet, C. (2012). Effects of processing parameters on microstructure and mechanical property of selective laser melted Ti6Al4V. Materials & Design, 35, 120-125.
- Strano, G., Hao, L., Everson, R. M., & Evans, K. E. (2013). Surface roughness analysis, modelling and prediction in selective laser melting. Journal of Materials Processing Technology, 213(4), 589-597.
- Sun, Z., Tan, X., Tor, S. B., & Chua, C. K. (2018). Simultaneously enhanced strength and ductility for 3D-printed stainless steel 316L by selective laser melting. NPG Asia Materials, 10(4), 127-136.
- Thijs, L., Verhaeghe, F., Craeghs, T., Van Humbeeck, J., & Kruth, J. P. (2010). A study of the microstructural evolution during selective laser melting of Ti–6Al–4V. Acta materialia, 58(9), 3303-3312.
- Wang, D., Liu, Y., Yang, Y., & Xiao, D. (2016). Theoretical and experimental study on surface roughness of 316L stainless steel metal parts obtained through selective laser melting. Rapid Prototyping Journal. 22(4), 706-716.
- Yakout, M., Elbestawi, M. A., & Veldhuis, S. C. (2018). On the characterization of stainless steel 316L parts produced by selective laser melting. International Journal of Advanced Manufacturing Technology, 95(5), 1953-1974.
- Numerical and experimental study of micro-convex dimple developed by laser additive manufacturing for surface applications
Authors
1 Indian Institute of Technology Kanpur, IN
Source
Manufacturing Technology Today, Vol 22, No 1 (2023), Pagination: 45-50Abstract
Surface texturing using laser is one such technique exhaustively used for enhancing the surface properties of the components. In this work, a 2D FEM is built to simulate the thermo-fluidic phenomena of surface texturing in the preplaced IN718 powder. Transient heat transfer and fluid flow were used to predict the temperature and velocity fields. Experiments are conducted to develop micro-convex dimple texture on the surface, which usually enhances the surface hydrophobicity and tribological properties. The experimental and numerical results are in good agreement and reveal that with increase in the number of pulses, the height of the micro-convex dimples decreases.Keywords
Additive Manufacturing, Convex Dimple, Texture, Simulation, Melt Pool Oscillations.References
- Bayat, M., Thanki, A., Mohanty, S., Witvrouw, A., Yang, S., Thorborg, J., Tiedje, N. S., & Hattel, J. H. (2019). Keyhole-induced porosities in Laser-based powder bed fusion (L-PBF) of Ti6Al4V: High-fidelity modelling and experimental validation. Additive Manufacturing, 30(July), 100835. https://doi.org/10.1016/j.addma.2019.100835
- Dinda, G. P., Dasgupta, A. K., & Mazumder, J. (2012). Texture control during laser deposition of nickel-based superalloy. Scripta Materialia, 67(5), 503-506.
- Etsion, I. (2005). State of the art in laser surface texturing. Journal of Tribology, 127(1), 248-253.
- Gan, Z., Yu, G., He, X., & Li, S. (2017). Numerical simulation of thermal behavior and multicomponent mass transfer in direct laser deposition of Co-base alloy on steel. International Journal of Heat and Mass Transfer, 104, 28-38.
- Hirano, K., Fabbro, R., & Muller, M. (2011). Experimental determination of temperature threshold for melt surface deformation during laser interaction on iron at atmospheric pressure. Journal of Physics D: Applied Physics, 44(43). https://doi.org/10.1088/0022-3727/44/ 43/435402
- Jiao, L., Chua, Z. Y., Moon, S. K., Song, J., Bi, G., & Zheng, H. (2018). Femtosecond laser produced hydrophobic hierarchical structures on additive manufacturing parts. Nanomaterials, 8(8), 601.
- Knapp, G. L., Raghavan, N., Plotkowski, A., & Debroy, T. (2019). Experiments and simulations on solidification microstructure for Inconel 718 in powder bed fusion electron beam additive manufacturing. Additive Manufacturing, 25, 511-521.
- Mandal, V., Sharma, S., Singh, S. S., & Ramkumar, J. (2022). Laser surface texturing in powder bed fusion: numerical simulation and experimental characterization. Metals and Materials International, 28(1), 181-196.
- Mandal, V., Tripathi, P., Kumar, A., Singh, S. S., & Ramkumar, J. (2022). A study on selective laser melting (SLM) of TiC and B4C reinforced IN718 metal matrix composites (MMCs). Journal of Alloys and Compounds, 901, 163527.
- Mandal, V., Tripathi, P., Sharma, S., Jayabalan, B., Mukherjee, S., Singh, S. S., & Ramkumar, J. (2023). Fabrication of ex-situ TiN reinforced IN718 composites using laser powder bed fusion (L-PBF): Experimental characterization and high-fidelity numerical simulations. Ceramics International.
- Otero, N., Romero, P., Gonzalez, A., & Scano, A. (2012). Surface texturing with laser micro cladding to improve tribological properties. Journal of Laser Micro/Nanoengineering, 7(2).
- Romero, P., Otero, N., González, A., García, G., & Scano, A. (2011). Additive generation of surface microstructures for fluid-dynamic applications by using single-mode fibre laser assisted microcladding. Physics Procedia, 12, 268-277.
- Sarker, A., Tran, N., Rifai, A., Elambasseril, J., Brandt, M., Williams, R., Leary, M., & Fox, K. (2018). Angle defines attachment: Switching the biological response to titanium interfaces by modifying the inclination angle during selective laser melting. Materials & Design, 154, 326-339.
- Sharma, S., Mandal, V., Ramakrishna, S. A., & Ramkumar, J. (2019). Numerical simulation of melt pool oscillations and protuberance in pulsed laser micro melting of SS304 for surface texturing applications. Journal of Manufacturing Processes, 39, 282-294.
- Simonelli, M., Tse, Y. Y., & Tuck, C. (2014). On the texture formation of selective laser melted Ti-6Al-4V. Metallurgical and Materials Transactions A, 45(6), 2863-2872.
- Wang, M., Wu, Y., Lu, S., Chen, T., Zhao, Y., Chen, H., & Tang, Z. (2016). Fabrication and characterization of selective laser melting printed Ti–6Al–4V alloys subjected to heat treatment for customized implants design. Progress in Natural Science: Materials International, 26(6), 671-677.
- Wei, H. L., Mazumder, J., & DebRoy, T. (2015). Evolution of solidification texture during additive manufacturing. Scientific Reports, 5(1), 1-7.
- Zhou, X., Li, K., Zhang, D., Liu, X., Ma, J., Liu, W., & Shen, Z. (2015). Textures formed in a CoCrMo alloy by selective laser melting. Journal of Alloys and Compounds, 631, 153-164.