Refine your search
Collections
Co-Authors
Year
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
Sukanya, Vuppala
- A Novel All Members Group Search Optimization Based Data Acquisition in Cloud Assisted Wireless Sensor Network for Smart Farming
Abstract Views :101 |
PDF Views:2
Authors
Affiliations
1 Department of Computer Science and Engineering, University College of Engineering, Osmania University, Hyderabad, Telangana, IN
1 Department of Computer Science and Engineering, University College of Engineering, Osmania University, Hyderabad, Telangana, IN
Source
International Journal of Computer Networks and Applications, Vol 10, No 4 (2023), Pagination: 651-667Abstract
Recent times, the Wireless Sensor Networks (WSN) has played an important role in smart farming systems. However, WSN-enabled smart farming (SF) systems need reliable communication to minimize overhead, end-to-end delay, latency etc., Hence, this work introduces a 3-tiered framework based on the integration of WSN with the edge and cloud computing platforms to acquire, process and store useful soil data from agricultural lands. Initially, the sensors are deployed randomly throughout the network region to collect information regarding different types of soil components. The sensors are clustered based on distance using the Levy flight based K-means clustering algorithm to promote efficient communication. The Tasmanian devil optimization (TDO) algorithm is used to choose the cluster heads (CHs) based on the distance among the node and edge server, residual energy, and the number of neighbors. Then, the optimal paths to transmit the data are identified using the all members group search optimization (AMGSO) algorithm based on different parameters. Each edge server assesses the quality of the data (QoD) with respect to some data quality criteria after receiving the data from the edge server. Also, the load across the servers are balanced in order to overcome the overloading and under loading issues. The legitimate data that received higher scores in the QoD evaluation alone is sent to the cloud servers for archival. Using the ICRISAT dataset, the efficiency of the proposed work is evaluated using a number of indicators. The average improvement rate attained by the proposed model in terms of energy consumption is 40%, in terms of packet delivery ratio is 7%, in terms of network lifetime is 38%, and in terms of latency is 24% for a total of 250 nodes.Keywords
Smart Agriculture, Wireless Sensor Network, Edge Computing, Cloud Computing, Clustering, Routing, Data Quality Evaluation, Load Balancing.Full Text
References
- S. K. Dhillon, C. Madhu, D. Kaur, S. Singh, “A review on precision agriculture using wireless sensor networks incorporating energy forecast techniques.” Wireless Personal Communications vol. 113, no, 4, pp. 2569-2585, 2020.
- C. Venkataramanan, S. Ramalingam, A. Manikandan, “LWBA: Levy-walk bat algorithm based data prediction for precision agriculture in wireless sensor networks.” Journal of Intelligent & Fuzzy Systems Preprint pp. 1-14, 2021.
- A. Deroussi, I. Alihamidi, L. A. Charaf, A. A. Madi, A. Addaim, “Routing Protocols for WSN: A Survey Precision Agriculture Case Study.” InIEEE 2nd International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS) pp.1-7, 2020.
- V. G. Chavan, P. R. Patil, A. D. Potgantwar, P. R. Bhaladhare, “A Novel Intelligent Efficient and Dynamic Cluster Based Routing Protocol for IoT Assisted Precision Agriculture.” Journal Of Algebraic Statistics vol. 13, no. 3, pp. 4025-4037, 2022.
- J. Lloret, S. Sendra, L. Garcia, J. M. Jimenez, “A Wireless Sensor Network Deployment for Soil Moisture Monitoring in Precision Agriculture.” Sensors vol. 21, no. 21, pp. 7243, 2021.
- A. Chehri, H. Chaibi, R. Saadane, N. Hakem, M. Wahbi, “A framework of optimizing the deployment of IoT for precision agriculture industry.” Procedia Computer Science vol. 176, pp. 2414-2422, 2020.
- R. K. Singh, R. Berkvens, M. Weyn, “AgriFusion: An architecture for IoT and emerging technologies based on a precision agriculture survey.” IEEE Access vol. 9, pp. 136253-136283, 2021.
- N. N. Malik, W. Alosaimi, M. I. Uddin, B. Alouffi, H. Alyami, “Wireless sensor network applications in healthcare and precision agriculture.” Journal of Healthcare Engineering 2020, 8836613, 2020.
- B. N. Alhasnawi, B. H. Jasim, B. A. Issa, “Internet of things (IoT) for smart precision agriculture.” IJEEE vol. 16, pp. 28-38, 2020.
- D. K.Anguraj, V. N. Mandhala, D. Bhattacharyya, T. Kim, “Hybrid neural network classification for irrigation control in WSN based
- precision agriculture.” Journal of Ambient Intelligence and Humanized Computing pp. 1-12, 2021.
- J. Alejandrino, R. Concepcion, V. J. Almero, M. G. Palconit, A. Bandala, E. Dadios, “A hybrid data acquisition model using artificial intelligence and IoT messaging protocol for precision farming.” In IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM) pp. 1-6, 2020.
- A. Jana, A. Roy, “Remotely Data Acquisition In Precision Agriculture: A Secure Way Of Data Transmission.” Int. Journal of Sci & Tech Research vol.9,no.3,pp.874-878,2020.
- H. K. D. Sarma, “Data Aggregation in Internet of Things aiming at Precision Agriculture.” In IEEE International Symposium on Smart Electronic Systems (iSES)(Formerly iNiS) pp. 271-276, 2021.
- K. Ghosh, S. Sharma, ‘Fermat Point-Based Wireless Sensor Networks: A Default Choice for Measuring and Reporting Farm Parameters in Precision Agriculture.” In Smart Agriculture Automation Using Advanced Technologies pp. 141-150, 2021. Springer, Singapore,
- A. C. Tagarakis, D. Kateris, R. Berruto, D. Bochtis, “Low-cost wireless sensing system for precision agriculture applications in orchards.” Applied Sciences vol. 11, no. 13, pp. 5858, 2021.
- F. Khelifi, “Monitoring system based in wireless sensor network for precision agriculture.” In Internet of Things (IoT) pp. 461-472, 2020. Springer, Cham,
- C. Li, D. Chen, C. Xie, Y. Tang, “Algorithm for wireless sensor networks in ginseng field in precision agriculture.” Plos one vol. 17, no. 2, pp. e0263401, 2022.
- J. Agarkhed, P. Y. Dattatraya, S. Patil, “Precision agriculture with cluster‐based optimal routing in wireless sensor network.” International Journal of Communication Systems vol. 34, no. 10, pp. e4800, 2021.
- V. Pandiyaraju, R. Logambigai, S. Ganapathy, A. Kannan, “An energy efficient routing algorithm for WSNs using intelligent fuzzy rules in precision agriculture.” Wireless Personal Communications vol. 112, no. 1, pp. 243-259, 2020.
- K. N. Qureshi, M. U. Bashir, J. Lloret, A. Leon, “Optimized cluster-based dynamic energy-aware routing protocol for wireless sensor networks in agriculture precision.” Journal of sensors 2020.
- Y.D. Yao, X. Li, Y.P. Cui, J.J. Wang, C. Wang, “Energy-efficient routing protocol based on multi-threshold segmentation in wireless sensors networks for precision agriculture.” IEEE Sensors Journal vol. 22, no.7, pp. 6216-6231, 2022.
- H.B. Mahajan, A.A. Junnarkar, M. Tiwari, T. Tiwari, M. Upadhyaya, “LCIPA: Lightweight clustering protocol for industry 4.0 enabled precision agriculture.” Microprocessors and Microsystems vol. 94, pp. 104633, 2022.
- A. Kumar, B.S. Dhaliwal, D. Singh, “CL‐HPWSR: Cross‐layer‐based energy efficient cluster head selection using hybrid particle swarm wild horse optimizer and stable routing in IoT‐enabled smart farming applications.” Transactions on Emerging Telecommunications Technologies vol. 34, no. 3, pp. e4725, 2023.
- K. M. Awan, A. Ali, F. Aadil, K. N. Qureshi, “Energy efficient cluster based routing algorithm for wireless sensors networks.” In IEEE International Conference on Advancements in Computational Sciences (ICACS) 1-7, 2018.
- J. Shen, A. Wang, C. Wang, P. CK Hung, C.F. Lai, “An efficient centroid-based routing protocol for energy management in WSN-assisted IoT.” Ieee Access vol. 5, pp. 18469-18479, 2017
- K. Haseeb, I. Ud Din, A. Almogren, N. Islam, “An energy efficient and secure IoT-based WSN framework: An application to smart agriculture.” Sensors vol. 20, no. 7, pp. 2081, 2020.
- F. A. Zeidabadi, S. A. Doumari, M. Dehghani, Z. Montazeri, P. Trojovsky, G. Dhiman, “AMBO: all members-based optimizer for solving optimization problems.” CMC-Comput. Mater. Contin vol. 70, pp. 2905-2921, 2022.
- S. He, Q. H. Wu, J. R. Saunders, “Group search optimizer: an optimization algorithm inspired by animal searching behavior.” IEEE
- transactions on evolutionary computation vol. 13, no. 5, pp. 973-990, 2009.
- L. Abualigah, “Group search optimizer: a nature-inspired meta-heuristic optimization algorithm with its results, variants, and applications.” Neural Computing and Applications vol. 33, no. 7, pp. 2949-2972, 2021.
- J. Zhang, X. Li, X. Zhang, Y. Xue, G. Srivastava, W. Dou, “Service offloading oriented edge server placement in smart farming.” Software: Practice and Experience vol. 51, no. 12, pp. 2540-2557, 2021.