Volume 13 , Issue 1 , PP: 49-58, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Hamza M. Ridha Al-Khafaji 1 * , Refed Adnan Jaleel 2
Doi: https://doi.org/10.54216/FPA.130104
Wireless Sensor Network (WSN) is one of the most significant contributors to the Internet of Things (IoT), and it plays a significant role in the lives of individuals. There are three main problems in the design of traditional WSN based-IoT. First problem about data; the WSN transmits a huge volume of data to the IoT for processing. The second problem is the energy; since sensor nodes rely on their limited battery, conserving energy is crucial, and the third problem about efficiency of transmission. This paper presents new WSN based IoT framework that integrate important techniques to solve these problems; To increase the effectiveness of data processing and storing, the intelligent Adaptive Boosting stochastic algorithm is applied. IEEE 802.15.4e time slotted channel hopping (TSCH) protocol is used because it has the benefits such as collision-free transmission and multi-hop transmission. Data reduction at the Gateway (GW) level of the network is achieved through spatial correlation between sensors with the goal of conserving energy. Principle idea of this new framework is to identify the advantages of integrating the important techniques; intelligent Adaptive Boosting Stochastic diffusion search algorithm, TSCH, and Special correlation model. As a result, the proposed framework can thereby satisfy the need for a long battery life of low-rate applications and at the same time, the need for high throughput for high-rate uses also for testing it in achieved efficient classification of data, the important performance measures are used.
Internet of things , wireless sensor network , time synchronized channel hopping , spatial correlation model , adaptive boosting stochastic algorithm
[1] Kuo, Y. W., Li, C. L., Jhang, J. H., & Lin, S. (2018). Design of a wireless sensor network-based IoT platform for wide area and heterogeneous applications. IEEE Sensors Journal, 18(12), 5187-5197.
[2] Maivizhi, R., & Yogesh, P. (2021). Q-learning based routing for in-network aggregation in wireless sensor networks. Wireless Networks, 27(3), 2231-2250.
[3] Ramezanifar, H., Ghazvini, M., & Shojaei, M. (2021). A new data aggregation approach for WSNs based on open pits mining. Wireless Networks, 27(1), 41-53.
[4] Idrees, A. K., & Al-Qurabat, A. K. M. (2021). Energy-efficient data transmission and aggregation protocol in periodic sensor networks based fog computing. Journal of Network and Systems Management, 29(1), 1-24.
[5] Younan, M., Houssein, E. H., Elhoseny, M., & Ali, A. E. M. (2021). Performance analysis for similarity data fusion model for enabling time series indexing in internet of things applications. PeerJ Computer Science, 7, e500.
[6] Jarah, N. B. (2020). Technique Pair of node to provide Power in WSNs. Karbala International Journal of Modern Science, 6(2), 2.
[7] Xu, G., Shi, Y., Sun, X., & Shen, W. (2019). Internet of things in marine environment monitoring: A review. Sensors, 19(7), 1711.
[8] A.K.M. Al-Qurabat, A.K. Idrees, Data gathering and aggregation with selective transmission technique to optimize the lifetime of Internet of Things networks, Int. J. Commun. Syst. 33 (2020), e4408.
[9] Suganya, E., & Rajan, C. (2020). An AdaBoost-modified classifier using stochastic diffusion search model for data optimization in Internet of Things. Soft Computing, 24(14), 10455-10465.
[10] Sandhu SK, Kumar A (2017) Hybrid meta-heuristics based scheduling technique for cloud computing environment. Int J Adv Res Comput Sci 8(5):1457–1465
[11] M Allayla, N., NazarIbraheem, F., Adnan Jaleel, R.: Enabling imageoptimisation and artificial intelligence technologies forbetter internet of things framework to predict COVID.IET Netw. 1–11 (2022). https://doi.org/10.1049/ntw2.12052M ALLAYLA ET AL. - 11
[12] A. K. M. Al-Qurabat, and A. K. Idrees,“Energy-efficient adaptive distributed data collection method for periodic sensor networks,” International Journal of Internet Technology and Secured Transactions, vol. 8, no. 3, pp. 297-335, 2018.
[13] A. K. Idrees, and A. K. M. Al-Qurabat, “Distributed Adaptive Data Collection Protocol for Improving Lifetime in Periodic Sensor Networks,” IAENG International Journal of Computer Science, vol. 44, no. 3, 2017.
[14] A. K. M. Al-Qurabat, and A. K. Idrees, “Distributed data aggregation and selective forwarding protocol for improving lifetime of wireless sensor networks,” Journal of Engineering and Applied Sciences, vol. 13, pp. 4644–4653, 2018.
[15] N. Mahakalkar, R. Pethe, ‘‘Review of Routing Protocol in a Wireless Sensor Network for an IOT Application,” 2018 3rd International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India, 2018, pp. 21-25, doi: 10.1109/CESYS.2018.8723935
[16] M. Priyanga, S. Leones Sherwin Vimalraj, J. Lydia, ‘‘Energy Aware Multiuser & Multi-hop Hierarchical –Based Routing Protocol for Energy Management in WSN-Assisted IoT,” 2018 3rd International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India, 2018, pp. 701-705, doi: 10.1109/CESYS.2018.8724073
[17] R. Prakash, P. Kansal, V. K. Kakar, ‘‘Optimized Hybrid Clustered Protocol for IoT Heterogeneous Wireless Sensor Networks,” 2019 IEEE Conference on Information and Communication Technology, Allahabad, India, 2019, pp. 1-6, doi: 10.1109/CICT48419.2019.9066258
[18] Al-Rifaie MM, Joyce D, Shergill S, Bishop M (2015) Investigating stochastic diffusion search in data clustering. In: 2015 SAI intelligent systems conference (IntelliSys). IEEE, pp 187–194
[19] Chen J, Hu K, Wang Q, Sun Y, Shi Z, He S (2017) Narrowband internet of things: implementations and applications. IEEE Internet of Things J 4(6):2309–2314
[20] Mahdavinejad MS, Rezvan M, Barekatain M, Adibi P, Barnaghi P, Sheth AP (2018) Machine learning for Internet of Things data analysis: a survey. Digit Commun Netw 4(3):161–175
[21] Darabian H, Dehghantanha A, Hashemi S, Homayoun S, Choo KKR (2019) An opcode-based technique for polymorphic Internet of Things malware detection. Concurr Comput Pract Exp. https:// doi.org/10.1002/cpe.5173
[22] Kaur P, Kumar R, Kumar M (2019) A healthcare monitoring system using random forest and internet of things (IoT). Multimed Tools Appl. https://doi.org/10.1007/s11042-019-7327-8
[23] Alves RCA, Margi CB (2016) IEEE 802.15.4e TSCH mode performance analysis. In: IEEE 13th international conference on mobile ad hoc and sensor systems (MASS), pp 361–362. https://doi.org/ 10.1109/MASS.2016.054.
[24] Sciancalepore S, Vuˇcini´c M, Piro G, Boggia G, Watteyne T (2017) Link-layer security in TSCH networks: effect on slot duration. Trans Emerg Telecommun Technol 28(1):e3089.
[25] Abdulzahra, S. A. (2021). Energy Conservation Approach of Wireless Sensor Networks for IoT Applications. Karbala International Journal of Modern Science, 7(4).
[26] S.A. Abdulzahra, A.K.M. Al-Qurabat, A.K. Idrees, Data reduction based on compression technique for big data in IoT, in: 2020 Int. Conf. Emerg. Smart Comput. Informatics, ESCI, 2020, pp. 103e108, https://doi.org/10.1109/ESCI48226.2020. 9167636.
[27] Suganya, E., & Rajan, C. (2020). An AdaBoost-modified classifier using stochastic diffusion search model for data optimization in Internet of Things. Soft Computing, 24(14), 10455-10465.
[28] Ali, N. G., Abed, S. D., Shaban, F. A. J., Tongkachok, K., Ray, S., & Jaleel, R. A. (2021). Hybrid of K-Means and partitioning around medoids for predicting COVID-19 cases: Iraq case study. Periodicals of Engineering and Natural Sciences (PEN), 9(4), 569-579.
[29] IEEE Standard for Local and Metropolitan Area Networks—Part. 15.4: Low-Rate Wireless Personal Area Networks (LR-WPANs) Amendament1: MAC Sublayer, IEEE Standard 802.15.4e, Apr. 2012.
[30] Vogli, E., Ribezzo, G., Grieco, L. A., & Boggia, G. (2015, March). Fast join and synchronization schema in the IEEE 802.15. 4e MAC. In 2015 IEEE Wireless Communications and Networking Conference Workshops (WCNCW) (pp. 85-90). IEEE.
[31] Val, I., Arriola, A., Cruces, C., Torrego, R., Gomez, E., & Arizkorreta, X. (2015, May). Time-synchronized Wireless Sensor Network for structural health monitoring applications in railway environments. In 2015 IEEE World Conference on Factory Communication Systems (WFCS) (pp. 1-9). IEEE.
[32] Zhu, D., Bendlin, R., Akoum, S., Ghosh, A., & Heath, R. W. (2019). Directional frame timing synchronization in wideband millimeter-wave systems with low-resolution ADCs. IEEE Transactions on Wireless Communications, 18(11), 5350-5366.
[33] Feng, D. C., Liu, Z. T., Wang, X. D., Jiang, Z. M., & Liang, S. X. (2020). Failure mode classification and bearing capacity prediction for reinforced concrete columns based on ensemble machine learning algorithm. Advanced Engineering Informatics, 45, 101126.
[34] Al-Rifaie, M. M., & Bishop, J. M. (2013). Stochastic diffusion search review. Paladyn, Journal of Behavioral Robotics, 4(3), 155-173.
[35] Abed, A. S., Hassan, H. F., Aldulaimi, M. H., Zahra, M. M. A., & Jaleel, R. A. (2022, April). An Effective Framework for Enhancing Performance of Internet of Things using Ant Colony Meta-Heuristic and Machine Learning Algorithms. In 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) (pp. 2498-2502). IEEE.
[36] Mosa, A. M., Hamed, E. A., Hussein, Z., & Jaleel, R. A. (2022, April). Improved Smart Forecasting Model to Combat Coronavirus using Machine Learning. In 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) (pp. 1953-1957). IEEE.
[37] Fadhil, Z. M., & Jaleel, R. A. (2022, April). Multiple Efficient Data Mining Algorithms with Genetic Selection for Prediction of SARS-CoV2. In 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) (pp. 2016-2020). IEEE.
[38] Harb, H., Makhoul, A., Laiymani, D., Bazzi, O., & Jaber, A. (2015). An analysis of variance-based methods for data aggregation in periodic sensor networks. In Transactions on large-scale data-and knowledge-centered systems XXII (pp. 165-183). Springer, Berlin, Heidelberg.
[39] Bahi, J. M., Makhoul, A., & Medlej, M. (2014). A two tiers data aggregation scheme for periodic sensor networks. Adhoc & Sensor Wireless Networks, 21(1).
[40] Harb, H., Makhoul, A., Couturier, R., & Medlej, M. (2015, June). ATP: An aggregation and transmission protocol for conserving energy in periodic sensor networks. In 2015 IEEE 24th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (pp. 134-139). IEEE.
[41] Nadweh, R., " On the Fusion of Neural Networks and Fuzzy Logic, Membership Functions and Weights", Galoitica Journal Of Mathematical Structures and Applications, Vol 7, 2023.
[42] Charchekhandra, B., " The Reading and Analyzing Of The Brain Electrical Signals To Execute a Control Command and Move an Automatic Arm", Pure Mathematics for Theoretical Computer Science, Vol 1, 2023.
[43] Al Basheer, O., " On Some Novel Simplex Linear Codes Defined Over The Algebraic Ring F2+vF2 ", Neoma Journal Of Mathematics and Computer Science, 2023.